We recommend this as a first step for the new gun owner, those who have never had formal training, or for those who have not trained with a handgun in a long time.
This course provides the very foundation of knowledge and skill necessary to safely handle handguns in a comfortable learning environment.
This informational course covers many topics including the purchase, storage, transportation, carry and cleaning of a firearm.
Duration: 1 day / 4 hours (3 hrs in classroom / 1 hrs at the range).
Applications of the Handgun (Self defense, sports, hunting, etc).
Please wear long pants with pockets, closed toe shoes (no flip flops or sandals), and a crew neck (T-shirt) or button-up shirt (no low-cut v-neck), also a cap or hat with a brim. Bring water to stay hydrated during the range time.
"My husband, son and I took the CHL class together and it was wonderful. We learned so much information and it was presented in a way that we could remember it.
~by R.B., S.B. and E.R.


text:
Q: Google Calendar list events method not returning any events The Events: list is not returning all the events.
I see a lot of events on the calendar, but when I call Events: list, I get only some of them.
It's not a matter of maxResults being too low. I have it set to return 2500, the max. I get 1251 or so, I deleted about 24 via the API, and now it returns 1227.  However there are a bunch on the calendar that aren't coming back.
It's not a matter of the page token either. I'm looping to see if somehow the rest are on the next pageToken list, but when I call getNextPageToken after the first page, it's null.
The optional parameters I'm using is:
    $optParams = array(
        'maxResults' => 2500,
        'singleEvents' => TRUE,
        'pageToken' => $pageToken,
    );

If I set singleEvents to be false, or remove it, Google responds with an error.

singleEvents  boolean Whether to expand recurring events into instances
  and only return single one-off events and instances of recurring
  events, but not the underlying recurring events themselves. Optional.
  The default is False.

This seemed to start happening about when Google changed the GMail name to G suites.
My code is below: (when I var_dump $calendarList, I get only 1 calendar.  I used similar code to add the calendar events in the first place.)
$calendarList  = $service->calendarList->listCalendarList();
$calendarListEntry = $service->calendarList->get('primary');

$continue = 1;
echo "reading the calendar for duplicate appointments<br>";
while ($continue == 1) {
    $optParams = array(
        'maxResults' => 2500,
        'singleEvents' => TRUE,
        'pageToken' => $pageToken,
    );

    $events = $service->events->listEvents($calendarListEntry->id, $optParams);
    foreach ($events->getItems() as $event) {
        //process events
    }
    $pageToken = $events->getNextPageToken();
    if ($pageToken == null) { continue = 0; }
}

Edit:  weirdly, when I lowered the maxResults to 250, I got a few that hadn't shown up in the result originally.  But I'm still not getting all of them.
Edit2: forgot to include the call to read the next page token.
Edit 3: Weirdly, although I'm passing in 'singleEvents' => TRUE, I'm also getting events that are marked to repeat.

A: With pagination
$events = $service->events->listEvents('primary');

while(true) {
  foreach ($events->getItems() as $event) {
    echo $event->getSummary();
  }
  $pageToken = $events->getNextPageToken();
  if ($pageToken) {
    $optParams = array('pageToken' => $pageToken);
    $events = $service->events->listEvents('primary', $optParams);
  } else {
    break;
  }
}

redpajama_set_name: RedPajamaStackExchange


NCIt Definition: An X-linked inherited bleeding disorder caused by deficiency of the coagulation factor IX.
GARD Definition: Hemophilia B is a bleeding disorder that slows the blood clotting process. People with this condition experience prolonged bleeding or oozing following an injury or surgery. In severe cases of hemophilia, heavy bleeding occurs after minor trauma or even in the absence of injury. Serious complications can result from bleeding into the joints, muscles, brain, or other internal organs. Milder forms may not become apparent until abnormal bleeding occurs following surgery or a serious injury. People with an unusual form of hemophilia B, known as hemophilia B Leyden, experience episodes of excessive bleeding in childhood but have few bleeding problems after puberty. Hemophilia B is inherited in an X-linked recessive pattern and is caused by mutations in the F9 gene. - this information is from GARD/ORDR/NCATS.
NICHD Definition: An X-linked recessive deficiency of coagulation factor IX characterized by a tendency to bleed. Hemophilia B occurs in approximately 1 in 20,000 live male births.
CSP Definition: deficiency of blood coagulation factor IX inherited as an X-linked disorder; clinical features resemble those in hemophilia A, but patients present with fewer symptoms.


We believe in the future of the islands. Cable and Wireless, your local Internet Service Provider (ISP) has been in TCI for over 100 years and plans to continue developing the Internet technology for TCI with the belief that the Internet holds the future for communications.
The TCI Mall has been a success since its inception in 1997 and has become an integral part of our local Internet product.
By living and working in Turks and Caicos Islands, we respond faster to the needs of our customers, ensuring fresh content on the TCI Mall web sites.
We intend to continue developing a superior Internet Virtual Mall, to enhance the marketing efforts already put forth by the government, individuals and the business sector.
We focus our attention on quality and accuracy.
You can compare our work to other similar malls and see the difference.
The grouping of all the Turks and Caicos company web sites together in a community Mall is a powerful marketing tool for the Islands. Your business can only benefit from this collective effort to put the Turks and Caicos Islands on the map.
We are ranked high on the lists of all the major search engines. Our status with the Search Engines is continually monitored to ensure the TCI Internet Mall is notably visible on the most popular search engines.
More than one domain identifies our site. The TCI Mall is found under these various domain names: http://www.tcimall.tc/, www.tcimall.com, www.turksandcaicos.tc and www.provo.com. This gives an additional boost to the coverage of TCI and your site's exposure via the Turks and Caicos Gateway page.


We have had very full, very festive days as of late.
Do you feel ready for 2015? What holiday foods rocked your world this year? Does a new year excite or daunt you?
I always look forward to the new year :) January/February are usually busy months for me!
Wishing you a wonderful, healthy and successful 2015!
Thanks Duni! Looking forward to seeing what 2015 has to bring!
Stunning photos! Hope you have a wonderful 2015!
Thanks, Carrie. You have a happy New Year too!
Those are lovely images ! Ready for 2015 yes ! Ready for tomorrow... eeeh. Food this holiday that I really enjoyed was Bajan pone ! Yum. Hope you enjoyed the holidays, it looks as though you did !
Mmmmm...pone! My fave is the cassava pone: coconut, raisins, cinnamon ...so delish! Thanks for stopping by!
Great photo memories and I also love Oliver's painting. I have a recipe on my blog that I posted today. Happy New Year to you.


text:
using System.Xml;
using TableauAPI.RESTHelpers;

namespace TableauAPI.ServerData
{
    /// <summary>
    /// Base class for information common to Workbooks and Data Sources, so we don't have lots of redundant code
    /// </summary>
    public abstract class SiteDocumentBase : IHasProjectId, ITagSetInfo, IHasSiteItemId
    {
        /// <summary>
        /// Document ID
        /// </summary>
        public readonly string Id;
        /// <summary>
        /// Document Name
        /// </summary>
        public readonly string Name;

        /// <summary>
        /// Project ID
        /// </summary>
        public readonly string ProjectId;

        /// <summary>
        /// Project Name
        /// </summary>
        public readonly string ProjectName;

        /// <summary>
        /// Owner ID
        /// </summary>
        public readonly string OwnerId;

        /// <summary>
        /// Tags
        /// </summary>
        public readonly SiteTagsSet TagsSet;

        /// <summary>
        /// Any developer/diagnostic notes we want to indicate
        /// </summary>
        public readonly string DeveloperNotes;

        /// <summary>
        /// XML Namespace manager for the document
        /// </summary>
        protected readonly XmlNamespaceManager NamespaceManager;

        /// <summary>
        /// Assists with the assistance of constructing an document which returns SiteDocumentBase
        /// </summary>
        /// <param name="xmlNode"></param>
        protected SiteDocumentBase(XmlNode xmlNode)
        {
            Name = xmlNode.Attributes?["name"].Value;
            Id = xmlNode.Attributes?["id"].Value;

            //Note: [2015-10-28] Datasources presently don't return this information
            //        this.ContentUrl = xmlNode.Attributes["contentUrl"].Value;

            //Namespace for XPath queries
            NamespaceManager = XmlHelper.CreateTableauXmlNamespaceManager("iwsOnline");

            //Get the project attributes
            var projectNode = xmlNode.SelectSingleNode("iwsOnline:project", NamespaceManager);
            if (projectNode != null)
            {
                ProjectId = projectNode.Attributes?["id"].Value;
                ProjectName = projectNode.Attributes?["name"].Value;
            }

            //Get the owner attributes
            var ownerNode = xmlNode.SelectSingleNode("iwsOnline:owner", NamespaceManager);
            if (ownerNode != null)
            {
                OwnerId = ownerNode.Attributes?["id"].Value;
            }

            //See if there are tags
            var tagsNode = xmlNode.SelectSingleNode("iwsOnline:tags", NamespaceManager);
            if (tagsNode != null)
            {
                TagsSet = new SiteTagsSet(tagsNode);
            }
        }

        /// <summary>
        /// Space delimited list of tags
        /// </summary>
        public string TagSetText
        {
            get
            {
                var tagSet = TagsSet;
                if (tagSet == null) return "";
                return tagSet.TagSetText;
            }
        }

        string IHasProjectId.ProjectId
        {
            get { return ProjectId; }
        }

        /// <summary>
        /// true if the document is tagged with the provided tag; false otherwise.
        /// </summary>
        /// <param name="tagText"></param>
        /// <returns></returns>
        public bool IsTaggedWith(string tagText)
        {
            var tagSet = TagsSet;
            if (tagSet == null)
            {
                return false;
            }
            return TagsSet.IsTaggedWith(tagText);
        }

        string IHasSiteItemId.Id => Id;
    }
}

redpajama_set_name: RedPajamaGithub


Many people have been giving Zija (Moringa Oleifera) to their four legged friends; including cats, dogs, horses and many different exotics.
Normally the powder form is best. However there are many dogs and cats that love to drink the liquid!
One packet of Zija mix is good for up to 250 pounds of body weight, so amounts are determined, as with people, by how much your pet weighs.
You can either mix with water or sprinkle over dry food. If you have an animal that is sick, you can safely increase their normal amount by giving it to them twice a day until they recover.
For horses we recommend 2 packets of mix a day, one with each meal. Horses love it and will lick it right out of a dry bucket!
On days when they are competing it is appropriate to give them an extra packet. Their performance is greatly increased without the use of harmful drugs.
Remember that this is simply an organic whole food and cannot be overdosed.
<---- This is what the Moringa Tree contains all naturally.
Zija is overflowing with cell-ready nutrients, antioxidants, and vital proteins. Zija is the first - and only - company to channel Moringa's dramatic nutritional properties into a refreshing, nourishing beverage for everyday use. Zija is 100% natural and is the only product that contains the fruit, leaf and seed of the Moringa plant. Most importantly, it is a raw, organic, whole food product; 100% bioavailable, efficacious and enzymatically alive.


text:
Protecting People, Property and Wildlife
1-800-CRITTER
New York Critters
Birds at Airports
House Mice
Norway Rats
House Finches
Flying Squirrels
Non-Venomous Snakes
Venomous Snakes
Voles
New York Magpies
Magpies are members of the corvid family, which also includes ravens, crows and jays. They are easily distinguished from other birds by their size and striking black and white color pattern. They have unusually long tails (at least half of their body length) and short, rounded wings. The feathers of the tail and wings are iridescent, reflecting a bronzy-green to purple. They have white bellies and shoulder patches and their wings flash white in flight. Like other corvids, they are very vocal, even boisterous. Typical calls include a whining " maag " and a series of loud, harsh "chuck" notes. Where magpies are not harassed, they can be extremely bold. If hunted or harassed, though, they become elusive and secretive.
Two distinct species are found in North America , the black-billed and yellow-billed magpies. They are easily separated by bill color, as their names imply, and by geographic location. Black-billed magpies average 19 inches (47 cm) in length and 1/2 pound (225 g) in weight. They have black beaks and no eye patches. Yellow-billed magpies are somewhat smaller (17 inches [42 cm]) and weigh slightly less than 1/2 pound (225 g). Their bills and bare skin patches behind their eyes are bright yellow.
Magpies are found in western North America. Ranges of the two species do not overlap. Black-billed magpies are found from coastal and central Alaska to Saskatchewan, south to Texas, and west to central California, east of the Sierra-Cascade range. They migrate in winter to lower elevations, and in northern parts of their range, south to areas within their breeding range. Occasionally they wander to areas further east and south of their normal range.
Yellow-billed magpies are residents of the Sacramento and San Joaquin valleys of central California and range south to Santa Barbara County. They do not wander outside of their normal range as often as black-billed magpies, but they have been found in extreme northern California.
Magpies are associated with the dry, cool climatic regions of North America. They are typically found close to water in relatively open areas with scattered trees and thickets. The black-billed magpie inhabits foothills, ranch and farm shelterbelts, sagebrush, streamside thickets, parks, and in Alaska, coastal areas. The yellow-billed magpie inhabits farmlands, stream groves and areas with scattered oaks or tall trees.
Magpies, like other corvids , are intelligent birds. They learn quickly and seem to sense danger. They are boisterous and curious, but shy and secretive in the presence of danger. They mimic calls of other birds and can learn to imitate some human words. They have readily adapted to the presence of humans and have taken advantage of new food sources provided.
Magpies are gregarious and form loose flocks throughout the year. Pairs stay together yearlong, but mates are replaced rapidly if one is lost.
Nest building typically begins in early March for black-billed magpies and earlier for yellow-billed magpies. Black-billed magpies build large nests, sometimes 48 inches (125 cm) high by 40 inches (100 cm) wide, made of sticks in low bushes or in trees usually within 25 feet (7.5 m) from the ground. The nest chamber is a cup lined with grass and mud, and normally enclosed by a canopy of sticks. Two entrances are common. Yellow-billed magpies build similar nests, but theirs often resemble mistletoe clumps, which are common in trees where they nest. Magpie nests are usually found in small colonies. Magpies nest once a year, but will re-nest if their first attempt fails. Other species of birds and mammals often use magpie nests after they have been abandoned.
Black-billed magpies lay 6 to 9 eggs, whereas yellow-billed magpies lay 5 to 8. Incubation normally starts in April, except further north where it may begin as late as mid-June. The incubation period is 16 to 18 days and young are able to fly 3 to 4 weeks after hatching.
Magpies are not swift fliers. They elude predators and danger by flitting in and out of trees or diving into heavy cover. They usually stay near cover, but often forage in open areas on the ground. Like other corvids, magpies walk with a strut and hop quickly when rushed.
Damage Identification
Magpies cause a variety of problems, especially where their numbers are high. Most problems occur in localized areas where loose colonies have concentrated in close proximity to humans.
Magpies can cause substantial damage locally to crops such as almonds, cherries, corn, walnuts, melons, grapes, peaches, wheat, figs and milo . Their damage is greatest in areas where insects and wild mast are relatively unavailable.
Magpies are often found near livestock where they feed on dung-and carrion-associated insects. They also forage for ticks and other insects on the backs of domestic animals. Perhaps the most notorious magpie behavior is the picking of open wounds and scabs on the backs of livestock. If they find an open wound, such as that from a new brand, they may pick at it until they create a much larger wound. The wound may eventually become infected and, in some instances, may kill the animal. Magpies, like ravens, may peck the eyes out of newborn or sick livestock.
Magpies rob wild bird and poultry nests of eggs and hatchlings. Typically, that does not affect wild bird populations except in local areas where limited habitat makes nests easy to find. They can be very destructive to poultry, however, especially during the nesting season when magpie parents are gathering food for their young.
Magpie roosts can be a nuisance because of excessive noise and the odor associated with droppings. During winter, magpies may congregate in loose colonies and form nightly roosts of hundreds after they have migrated southward and to lower elevations. They typically roost in dense thickets or trees.
Magpies are protected as migratory non-game birds under the Federal Migratory Bird Treaty Act. Under the Federal Codes of Regulation (CFR 50, 21.43) it is stated, however, that "a Federal permit shall not be required to control . . . magpies, when found committing or about to commit depredations upon ornamental or shade trees, agricultural crops, livestock, or wildlife, or when concentrated in such numbers as to constitute a health hazard or other nuisance. . . ." Most state or local regulations are similar, but consult authorities before taking any magpies.
Damage Prevention and Control Methods
Keep young poultry, poultry nests, and vulnerable livestock in covered fenced pens. Netting can be used to protect small areas and valuable crops.
Habitat Modification
Remove nests of offending magpies that are raiding poultry farms. Remove low brush and roost trees in areas where damage is excessive.
Frightening
A frightening program using pyrotechnics, scarecrows, and propane cannons in conjunction with human presence is effective for magpies in most damage situations, especially for roosts and crops.
None are registered.
Toxicants
Modified Australian crow and circular-funnel traps can be used to help protect heavily damaged crops from a large local population. Proper care of traps and decoy birds is necessary. Use No. 0 and 1 padded-jaw pole traps to take a few offending individuals. Check local, state, and federal laws before trapping.
The above information was adapted from PREVENTION AND CONTROL OF WILDLIFE DAMAGE with permission of the editors, Scott E. Hygnstrom, Robert M. Timm, and Gary E. Larson (Cooperative Extension Division, Institute of Agriculture and Natural Resources University of Nebraska-Lincoln, United States Department of Agriculture Animal and Plant Health Inspection Service, Animal Damage Control, Great Plains Agricultural Council Wildlife Committee).
Our local New York offices offer Magpies services in the following cities: Airmont, Altmar, Amawalk, Ardsley, Armonk, Auburn, Aurora, Babylon, Baldwin Place, Baldwinsville, Barneveld, Bayville, Beacon, Bedford, Bedford Hills, Bohemia, Boonville, Brentwood, Brewster, Briarcliff Manor, Bridgewater, Bronxville, Bronxville, Buchanan, Camden, Camillus, Canandaigua, Carmel, Cato, Cayuga Heights, Cazenovia, Centereach, Central Square, Chappaqua, Chester, Chestnut Ridge, Cicero, Clay, Clayville, Cleveland, Clinton, Cold Spring, Commack, Coram, Cornwall, Cornwall-on-hudson, Cortland, Cortlandt Manor, Cross River, Croton-on-hudson, Deer Park, Dewitt, Dix Hills, Dobbs Ferry, Dryden, East Hampton, East Islip, East Syracuse, Eastchester, Elbridge, Elmsford, Fabius, Fair Haven, Farmingville, Fayetteville, Fishkill, Florida, Freeville, Fulton, Garden City, Garrison, Gates, Geneva, Glen Cove, Goshen, Grand View-on-hudson, Greece, Greenwood Lake, Groton, Hannibal, Happague, Harriman, Harrison, Hastings-on-hudson, Haverstraw, Hicksville, Highland Falls, Hillburn, Holbrook, Holland Patent, Homer, Huntington, Hyde Park, Interlaken, Irondequiot, Irvington, Ithaca, Jamesville, Jefferson Valley, Jordan, Kaser, Katonah, Kirvas Joel, Lacona, Lafayette, Lagrangeville, Lansing, Larchmont, Liverpool, Lodi, Long Island, Mahopac, Mamaroneck, Manlius, Manorville, Marathon, Marcellus, Maybook, Mcgraw, Medford, Meridian, Mexico, Middletown, Millbrook, Millerton, Millwood, Minoa, Mohegan Lake, Monroe, Montebello, Montgomery, Montrose, Moravia, Mount Kisco, Mount Vernon, Nanuet, Nelsonville, New City, New Hartford, New Hempstead, New Rochelle, New Square, New Windsor, New York Mills, Newburgh, North Port, North Salem, North Syracuse, Nyack, Oneida Castle, Oriskany, Oriskany Falls, Ossining, Oswego, Otisville, Ovid, Parish, Patchogue, Pawling, Pearl River, Peekskill, Pelham, Pelham Manor, Penfield, Phoenix, Piermont, Pittsford, Pleasantville, Pomona, Pond Ridge, Ponkonkoma, Port Byron, Port Chester, Port Jefferson, Port Jervis, Poughkeepsie, Prospect, Pulaski, Purchase, Purdys, Ramapo, Red Hook, Remsen, Rhinebeck, Riverhead, Rochester, Rome, Rye, Rye Brook, Salt Point, Sandy Creek, Scarsdale, Sea Cliffe, Seneca Falls, Sherrill, Skaneateles, Sleepy Hollow, Sloatsburg, Smithtown, Sodus, Solvay, Somers, South Blooming Grove, South Nyack, South Salem, Spring Valley, Stony Point, Suffern, Sugar Loaf, Sylvan Beach, Syracuse, Tarrytown, Thornwood, Tivoli, Trumansburg, Tuckahoe, Tully, Tuxedo Park, Union Springs, Unionville, Upper Nyack, Utica, Valhalla, Verbank, Vernon, Verplanck, Waccabuc, Walden, Wappingers Falls, Warwick, Washingtonville, Waterloo, Waterville, Weedsport, Wesley Hills, West Hampton, West Haverstraw, West Islip, White Plains, Whitesboro, Woodbury, Yaphank, Yonkers, Yorkville
New York Wildlife Services
Squirrel Control & Squirrel Removal
Raccoon Removal & Control
Bat Control & Bat Removal
Mice Control, Mice Removal
Wildlife Control and Removal
Animal Odor Removal
Pigeon Control
For your local Wildlife Control Expert, call 1-800-CRITTER.

redpajama_set_name: RedPajamaCommonCrawl


The screener results (exactly 250 stocks) suggested that this market is extremely weak. Only 36 stocks were in uptrends, while 210 were in downtrends. Only TSU, AFSI, HFC, IACI, and hrBN had strong uptrends (more then 69) and were not more than halfway towards a trend reversal.
I chose AFSI for the periodic purchase, bypassing TSU because it is too similar to CHL and bypassing SHG because it is more than halfway towards a trend reversal.


text:
Q: Best way to load images in a Adapter I am currently having a little problem with my application. It is a music player and is working perfectly, except for one annoyance. When I run my SongAdapter, it will load a lot of views with the songs that are present on the device. However, this is way too heavy to load on the Main Thread, therefore I tried running it in the background. However, this isn't fast enough.
Main question:
Is there any fast and reliable way to load the images into the views.
Here is the code of my SongAdapter.java:
public class SongAdapter extends BaseAdapter {

    //song list and layout
    private ArrayList<Song> songs;
    private LayoutInflater songInf;

    //constructor
    public SongAdapter(Context c, ArrayList<Song> theSongs){
        songs=theSongs;
        songInf=LayoutInflater.from(c);
    }

    @Override
    public int getCount() {
        return songs.size();
    }

    @Override
    public Object getItem(int arg0) {
        return null;
    }

    @Override
    public long getItemId(int arg0) {
        return 0;
    }

    @Override
    public View getView(int position, View convertView, ViewGroup parent) {

        //map to song layout
        RelativeLayout songLay = (RelativeLayout) songInf.inflate(R.layout.song_list_item, parent, false);

        //get title and artist views
        TextView songView               = (TextView)songLay.findViewById(R.id.titleListTextView);
        TextView artistView             = (TextView)songLay.findViewById(R.id.artistListTextView);
        RelativeLayout relativeLayout   = (RelativeLayout)songLay.findViewById(R.id.layoutSelector);
        RoundedImageView albumView      = (RoundedImageView)songLay.findViewById(R.id.albumListImageView);

        //get song using position
        Song currSong = songs.get(position);

        //get title and artist strings
        albumView.setImageBitmap( getAlbumart(currSong, parent.getContext()) );
        songView.setText(currSong.getTitle());
        artistView.setText(currSong.getArtist());

        relativeLayout.setTag( currSong.getID());

        return songLay;
    }

    public Bitmap getAlbumart(Song currentSong, Context context ) {
        Bitmap bm = BitmapFactory.decodeResource(context.getResources(), R.drawable.album_default);

        long albumId = (long) currentSong.getAlbumId();

        try {
            final Uri sArtworkUri = Uri.parse("content://media/external/audio/albumart");

            Uri uri = ContentUris.withAppendedId(sArtworkUri, albumId);

            ParcelFileDescriptor pfd = context.getContentResolver().openFileDescriptor(uri, "r");

            if (pfd != null) {
                FileDescriptor fd = pfd.getFileDescriptor();
                bm = BitmapFactory.decodeFileDescriptor(fd);
            }
        } catch (Exception e) {
            Log.d("Var log", "Error:" + e);
        }
        return bm;
    }

}

Thanks for the help.

A: Universal Image Loader aims to provide a powerful, flexible and highly customizable instrument for image loading, caching and displaying. It provides a lot of configuration options and good control over the image loading and caching process.
It works for me, hope it will solve your problem.

A: aside of using Picasso or any other library to load bitmaps.
below is a code same to recycle views, just replace the setImageBitmap() with the one you are usign now (Picasso or whatever)
@Override
public View getView(int position, View convertView, ViewGroup parent) {
    ViewHodler holder = null;

    if(convertView == null){
        holder = new ViewHodler();
        convertView = songInf.inflate(R.layout.song_list_item, parent, false);
        holder.songView = (TextView)convertView.findViewById(R.id.titleListTextView);
        holder.artistView = (TextView)convertView.findViewById(R.id.artistListTextView);
        holder.albumView = (RoundedImageView)convertView.findViewById(R.id.albumListImageView);
        holder.relativeLayout   = (RelativeLayout)convertView.findViewById(R.id.layoutSelector);
        convertView.setTag(holder);
    }else{
        holder =(ViewHodler) convertView.getTag();
    }

    //get song using position
    Song currSong = songs.get(position);

    //get title and artist strings
    holder.albumView.setImageBitmap( getAlbumart(currSong, parent.getContext()) ); // replace getAlbumart() with the new way you are using
    holder.songView.setText(currSong.getTitle());
    holder.artistView.setText(currSong.getArtist());

    holder.relativeLayout.setTag( currSong.getID());

    return convertView;
}

class ViewHodler{
    TextView songView;
    TextView artistView;
    RelativeLayout relativeLayout;
    RoundedImageView albumView;
}

PS: getting RelativeLayout i mean this R.id.layoutSelector
if it's a big view it will load more memory.


MIUI 8 is simple and functional mobile operating system from Xiaomi. Today, we talk about 15 tips how to increase battery life Xiaomi smartphone. They will help your smartphone to work much longer on a single charge.
1. Disable push notifications from unused applications.
Today almost all applications send their push notifications. As we know, push notifications strongly consume battery power. If you want to make your smartphone work longer, we suggest to disable notifications from unused or rarely used applications.
Go to Settings> Notifications> Applications and turn off unnecessary apps.
Modern smartphones can be used as GPS-navigators. But you do not always use GPS, so you should disable it to increase battery life Xiaomi smartphone.
When you don't need GPS, just turn it off, and you smartphone will last longer.
3. Energy saving mode in MIUI 8.
Saving mode allows your smartphone to use battery power more efficiently. This function is located in the built-in application "Security".
Here you can activate the energy-saving mode, as well as configure some applications in manual mode.
Leave important applications, such as messages, instant messengers, email, and others, so that they continue to work and send you notifications of events.
4. Adjust the brightness of your display.
It's not a secret that screens of modern smartphones consume the most energy. The default mode in MIUI 8 is automatic brightness control. A special sensor analyzes the level of illumination and adjusts the brightness of the screen for maximum comfort. It is undoubtedly convenient, but also costly for your battery.
We suggest you turn off automatic brightness adjustment and manually set the minimum comfortable reading level for the display backlight. So you will be able to significantly increase battery life Xiaomi smartphone.
5. Disable Vibrate on Touch.
Some users like feeling of tactile return from the touch of the screen in the form of vibration. But if our goal is to use the battery as efficiently as possible – it's better to disable this feature in MIUI 8.
Go to Settings> Sounds & Vibration> Vibrate on Touch and turn off the toggle switch.
Another strongly consumed energy element of any Xiaomi smartphone is Wi-Fi. We recommend to use Wi-Fi only when it's really necessary. The fact is that when Wi-Fi module is active, it constantly looks for available networks and of course spends on it the resources of the battery.
You can disable Scanning always available in Wi-Fi settings menu.
Another great feature of MIUI 8 is the synchronization of your photos, contacts, notes and other things in Mi Cloud. If you have a lot of new data to upload to the cloud service, it's better to disable automatic synchronization with Mi Cloud. So you will be able to increase battery life Xiaomi smartphone.
8. Vibration with Push Notifications.
Another moment associated with vibration, which can be turned off and thereby keep the battery charge. When you receive new Push notification, the screen of your Xiaomi smartphone lights up, and the phone itself vibrates.
If you want to disable vibration, go to Settings> Notifications and Status Bar > Apps Notifications. Turn off vibration from all apps or some of them, and the battery will last longer.
Android smartphones can update applications themselves, without user intervention. It is done with an active Wi-Fi or 3G/LTE connection. This process consume battery, since several applications download a capacious update, and then install it.
You should disable auto-update apps on Google Play and Mi App Store.
10. Display Activity Time (Sleep).
If you notice, display goes off after 1-2 minutes, if you don't use the smartphone. But you can reduce shutdown time to 30 or even 15 seconds. Go to Settings – Lock Screen and Password – Sleep.
It is a simple lifehack that increase battery life Xiaomi smartphone.
MIUI supports both static wallpaper and dynamic ones. Using standard or third-party wallpapers for your smartphone, you can also save enough of your battery's resources, which will have a positive effect on battery life.
12. Update to the latest version of MIUI OS.
Developers MIUI improve firmware every day. Using the latest version of the software guarantees not only the safety of your personal data, but also the correct, harmonious operation of all smartphone systems and batteries in particular.
13. Activate the airplane mode at a poor signal level.
In your city there may be places where the quality of communication leaves much to be desired. If you stay in such weak coverage areas for a long time, activate the airplane mode. The trick is that with a weak signal, the smartphone spends a lot of battery charging on network search, which negatively affects on autonomy.
14. Disable 3G/LTE if you are not using Internet.
In order to increase battery life Xiaomi smartphone, you can disable not only all of the above, but also the Internet. Go to Settings, and disable mobile data (2G/3G/LTE).
Another tip that will help to save the charge of your smartphone is system animation. MIUI OS always use animation: clicking on a shortcut, minimizing applications, working in task manager.
You can go to Battery and Performance settings, and disable system animations. MIUI will look easier without effects, but the phone will work longer from one charge.
So, use these tips to increase battery life Xiaomi smartphone.


Breastfeeding is a tender act that strengthens the bond between the mother and her child. As you've probably heard many times before, breastfeeding is simply the best feeding method a mother could choose for her baby. The reason that breastfeeding is so superior to any other method is that it is a natural and pure process, perfectly planned by Mother Nature. Breastmilk offers to the newborn all the vitamins and proteins needed. It is quickly digested without causing any discomfort to the infant.
However, lots of babies need supplements in their nutrition even though they are breastfed. This is a very common issue many parents come across, and the obvious solution here is Gerber Good Start Gentle! This Good Start Infant Formula is ideal for breastfed babies.
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Gerber Good Start Gentle Formula is the perfect supplementing formula, ideal for breastfeeding mothers. It is recommended for babies from birth to twelve months of age. It offers a special blend of probiotics and DHA that are crucial components that babies usually receive from breast milk. Moreover, this Gerber baby formula does not include any genetically modified ingredients, which makes the Gerber baby formula even healthier for your baby.
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So apart from the fact that this Gerber formula is as pure as possible with no trace of genetically engineered substances, it is also a product coming from a well-known, trustworthy company. That might not sound so important to you at first, but "the name", the brand of your baby's formula is a significant factor when it comes to picking one. "But, why?" you might ask. It is pretty simple really. Gerber is an expert when it comes to baby nutrition and more specifically baby formulas. Dealing with good companies also means dealing with good services. Gerber's support group is available all day/every day in case the customers have any questions or any issue for which they'd need assistance. Their "Customer Care" support group offers complimentary services like consultation by Gerber's internationally certified lactation consultant or by Gerber's dietitians. Last but not least, Gerber good start formula comes with a full instructions package so that you won't have to worry about anything. The instructions include "step by step" guides, warnings, any sort of information that is vital for your child's health and well-being. The guide to making the formula is unbelievably easy (2 ounces of water to 1 scoop of Gerber formula).
Why is Gerber good start ideal for breastfed babies and babies with stomach issues?
Good start infant formula is perfect for infants with sensitive tummies or infants that are used to being breastfed simply because it is extra gentle to the stomach. This Gerber baby formula is not demanding for the newborn's digestive system, so that makes the whole process a lot easier for the baby's body. Digestion is quick, and the formula often comforts babies with stomach aches. Furthermore, Gerber good start includes Comfort Protein, perfect for tiny bellies. The digestion becomes a piece of cake, and your baby is relieved and happy, which at the end of the day, is the only thing that matters!
Is Gerber good start formula good enough to completely replace breastfeeding?
Women nowadays are so busy that breastfeeding might seem like an impossible task. Well with the Gerber Good Start Gentle Formula you don't need to worry about anything. The particular formula can perfectly replace the breastmilk and because of its comfort proteins, the baby receives every necessary substance for healthy development.
Note: You can actually use Gerber Good Start Formula to supplement a toddler's nutrition as well. Just add it in his/her milk.
What about the smell and taste of Gerber Baby Formula?
The taste of Good Start Infant Formula can be kind of metallic because of the mineral supplements it includes, although it is said to be significantly sweeter than other formulas. It also smells a lot better according to consumers who compared that formula to other "popular" ones.
Lastly, a vast percentage of the parents who've tried Gerber baby formula agree that this formula was instantly taken by their babies. It was the easiest to digest, and it caused no difference to the baby's stools. Even the most "hard to please" babies reacted great to that formula, despite the fact that they had rejected tons of other formulas in the past. It has a reasonable price and is easily purchased online or at your local store.
This is a widely admitted "fantastic" formula for all the reasons listed above, but mainly because babies tend to prefer it and like it the most. It is healthy, full of nutritious treasures and has helped parents all around the world find what they desperately needed. Ideal for sensitive tummies, it is smooth and gentle comforting every baby who suffers from constipation, stomach aches or any other digestive issue.


text:
Traveling Exhibit Honors Chicago's Koko Taylor and Other Women of the Blues
Angel Idowu | June 28, 2021 8:23 pm
They say a picture is worth a thousand words, and that's exactly what Lynn Orman Weiss' traveling exhibition does, sharing through photos how women have contributed to one of the most influential music genres.
"Chicago is, I would like to say, the portal — a significant city for women of the blues," said Lynn Orman Weiss, curator of "Women of the Blues: A Coast to Coast Collection."
This collection of historic snapshots was six years in the making, and features more than 100 women, including Koko Taylor.
Koko Taylor (Credit: Nunu)
Holle Thee Maxwell (Credit: Peter Hurley)
Donna Herula (Credit: Karen Murphy)
Big City Blues covers featuring Koko Taylor and Marie Dixon, Willie Dixon's wife. (Credit: Robert Whithall)
Anne Harris (Credit: Roman Sobus)
Tia Gouttebel (Credit: Lola Reynaerts)
"Koko Taylor or 'the Chicago Queen of the Blues' played such a significant role in inspiring the next generation and myself," Orman Weiss said.
Taylor's daughter Joyce Taylor Threatt (aka "Cookie") agrees, and says her mother, who grew up in the church and started as a gospel singer, remained true to herself and her faith.
"Koko Taylor was an original, traditional blues singer from her heart. It's what she loved and lived for, but not how she lived," Taylor Threatt said.
The exhibition also features up-and-coming artists like Melody Angel. Inspired by Taylor and other featured artists, Angel says learning this history is imperative for her development as an artist.
"Not only are you honoring the genre, but you're honoring the people who created it," Angel said. "For me, learning about my history and music history, my self-esteem was built up knowing that I came from a resilient group of people. Everything in American music starts with the blues. There is no genre that hasn't been influenced so if you want to be great at any genre, you need to look into the blues."
Lynn Orman Weiss (Credit: Robert Subar)
From Chicago to St. Louis to Memphis, this exhibit not only celebrates artists who've laid the foundation for the genre, but the photographers documenting these musical celebrations across the country.
"Normally on this day, we'd be elbow to elbow, lens to lens in the pit at Blues Fest," Orman Weiss said. "I wanted to give a voice to the photographers, because I'm one of them, and everyone catches the essence of the performer differently. It becomes this beautiful narrative on who they are."
The exhibit "Women of the Blues" just finished its run at the Delmark Records Gallery and will be heading to Bronzeville this fall.
Follow Angel Idowu on Twitter: @angelidowu3
Angel Idowu is the JCS Fund of the DuPage Foundation Arts Correspondent.
Koko Taylor
Joyce Taylor Threatt
Lynn Orman Weiss
DuPage Foundation
A Hospital Photographer Turns Her Camera on Chicago's Artist Community
Long-Lost Photos of Chicago Artists Resurface in 'Encuentros,' an Exhibit at the Poetry Foundation
Daguerreian Society Aims to Preserve Oldest Photography Form
Chicago Photographer Captured Communal Magic of City Festivals
Picturing Music History with Photographer Paul Natkin
A Chicago Native Wants You to Know Birding is for Everyone


text:
\section{Introduction}
The document summarization task requires several complex language abilities: understanding a long document, discriminating what is relevant, and writing a short synthesis. Over the last few years, advances in deep learning applied to NLP have contributed to the rising popularity of this task among the research community \cite{see2017get, kryscinski2018improving, scialom-etal-2019-answers}.
As with other NLP tasks, the great majority of available datasets for summarization  are in English, and thus most research efforts focus on the English language. The lack of multilingual data is partially countered by the application of transfer learning techniques enabled by the availability of pre-trained multilingual language models. This approach has recently established itself as the \emph{de-facto} paradigm in NLP \cite{guzman2019flores}.

Under this paradigm, for encoder/decoder tasks, a language model can first be pre-trained on a large corpus of texts in multiple languages. Then, the model is fine-tuned in one or more \emph{pivot} languages for which the task-specific data are available. At inference, it can still be applied to the different languages seen during the pre-training.
Because of the dominance of English for large scale corpora, English naturally established itself as a pivot for other languages. The availability of multilingual pre-trained models, such as BERT multilingual (M-BERT), allows to build
models for target languages different from training data.
However, previous works reported a significant performance gap between English and the target language, e.g. for classification \cite{conneau2018xnli} and Question Answering \cite{lewis2019mlqa} tasks. A similar approach has been recently proposed for summarization \cite{chi2019cross} obtaining, again, a lower performance than for English.

For specific NLP tasks, recent research efforts have produced evaluation datasets in several target languages, allowing to evaluate the progress of the field in zero-shot scenarios. Nonetheless, those approaches are still bound to using training data in a \emph{pivot} language for which a large amount of annotated data is available, usually English.
This prevents investigating, for instance, whether a given model is as fitted for a specific language as for any other.  Answers to such research questions represent valuable information to improve model performance for low-resource languages.

In this work, we aim to fill this gap for the automatic summarization task by proposing a large-scale MultiLingual SUMmarization (MLSUM) dataset. The dataset is built from online news outlets, and contains over 1.5M article-summary pairs in 5 languages: French, German, Spanish, Russian, and Turkish, which complement an already established  summarization dataset in English.

The contributions of this paper can be summarized as follows:
\begin{enumerate}
    \item We release the first large-scale multilingual summarization dataset;
    \item We provide strong baselines from multilingual abstractive text generation models;
    \item We report a comparative cross-lingual analysis of the results obtained by different approaches.
\end{enumerate}

\section{Related Work}

\subsection{Multilingual Text Summarization}

Over the last two decades, several research works have focused on multilingual text summarization. \citet{radev2002evaluation} developed MEAD, a multi-document summarizer that works for both English and Chinese. \citet{litvak2010new} proposed to improve multilingual summarization using a genetic algorithm. A community-driven initiative, MultiLing \cite{giannakopoulos2015multiling}, benchmarked summarization systems on multilingual data. While the MultiLing benchmark covers 40 languages, it provides relatively few examples (10k in the 2019 release). Most proposed approaches, so far, have used an extractive approach given the lack of a multilingual corpus to train abstractive models \cite{duan2019zero}.

More recently, with the rapid progress in automatic translation and text generation, abstractive methods for multilingual summarization have been developed. \citet{ouyang2019robust} proposed to learn summarization models for three low-resource languages (Somali, Swahili, and Tagalog), by using an automated translation of the New York Times dataset..
Although this showed only slight improvements over a baseline which considers translated outputs of an English summarizer, results remain still far from human performance. Summarization models from translated data usually under-perform, as translation biases add to the difficulty of summarization.

Following the recent trend of using multi-lingual pre-trained models for NLP tasks, such as Multilingual
BERT (M-BERT) \cite{pires2019multilingual}\footnote{\url{https://github.com/google-research/bert/blob/master/multilingual.md}} or XLM \cite{lample2019cross}, \citet{chi2019cross} proposed to fine-tune the models for summarization on English training data. The assumption is that the summarization skills learned from English data can transfer to other languages on which the model has been pre-trained. However a significant performance gap between English and the target language is observed following this process. This emphasizes the crucial need of multilingual training data for summarization.

\subsection{Existing Multilingual Datasets}
\label{Existing_Multilingual_datasets}

The research community has produced several multilingual datasets for tasks other than summarization. We report two recent efforts below, noting that both \emph{i)} rely on human translations, and \emph{ii)} only provide evaluation data.

\paragraph{The Cross-Lingual NLI Corpus}
The SNLI corpus \cite{bowman2015large} is a large scale dataset for natural language inference (NLI). It is composed of a collection of 570k human-written English sentence pairs, associated with their label, entailment, contradiction, or neutral. The Multi-Genre Natural Language Inference (MultiNLI) corpus is an extension of SNLI, comparable in size, but including a more diverse range of text.
\citet{conneau2018xnli} introduced the Cross-Lingual NLI Corpus (XNLI) to evaluate transfer learning from English to other languages: based on MultiNLI, a collection of 5,000 test and 2,500 dev pairs were translated by humans in 15 languages.

\paragraph{MLQA}
Given a paragraph and a question, the Question Answering (QA) task consists in providing the correct answer. Large scale datasets such as \cite{rajpurkar2016squad,choi2018quac,trischler2016newsqa} have driven fast progress.\footnote{For instance, see the SQuAD leaderboard:  \url{rajpurkar.github.io/SQuAD-explorer/}} However, these datasets are only in English. To assess how well models perform on other languages, \citet{lewis2019mlqa} recently proposed MLQA, an evaluation dataset for cross-lingual extractive QA composed of 5K QA instances in 7 languages.

\paragraph{XTREME}
The Cross-lingual TRansfer Evaluation of Multilingual Encoders benchmark covers 40 languages over 9 tasks. The summarization task is not included in the benchmark.

\paragraph{XGLUE}
In order to train and evaluate their performance across a diverse set of cross-lingual tasks, \citet{liang2020xglue}  recently released XGLUE, covering both Natural Language Understanding and Generation scenarios. While no summarization task is included, it comprises a News Title Generation task: the data is crawled from a commercial news website and provided in form of article-title pairs for 5 languages (German, English, French, Spanish and Russian).

\subsection{Existing Summarization datasets}
\label{Existing_Summarization_datasets}

We describe here the main available corpora for text summarization.

\paragraph{Document Understanding Conference}
Several small and high-quality summarization datasets in English  \cite{harman2004effects, dang2006duc} have been produced in the context of the Document Understanding Conference (DUC).\footnote{\url{http://duc.nist.gov/}}
They are built by associating newswire articles with corresponding human summaries. A distinctive feature of the DUC datasets is the availability of multiple reference summaries: this is a valuable characteristic since, as found by \citet{rankel-etal-2013-decade}, the correlation between qualitative and automatic metrics, such as ROUGE \cite{lin2004rouge}, decreases significantly when only a single reference is given. However, due to the small number of training data available, DUC datasets are often used in a domain adaptation setup for models first trained on larger datasets such as Gigaword, CNN/DM \cite{nallapati2016abstractive, see2017get} or with unsupervised methods \cite{dorr2003hedge, mihalcea2004textrank, barrios2016variations}.

\paragraph{Gigaword}
Again using newswire as source data, the english Gigaword \cite{napoles2012annotated, rush2015neural, chopra2016abstractive} corpus is characterized by its large size and the high diversity in terms of sources. Since the samples are not associated with human summaries, prior works on summarization have trained models to generate the headlines of an article, given its incipit, which induces various  biases for learning models.

\paragraph{New York Times Corpus}
This large corpus for summarization consists of hundreds of thousand of articles from The New York Times\cite{sandhaus2008new}, spanning over 20 years. The articles are paired with summaries written by library scientists.
Although \cite{grusky2018newsroom} found indications of bias towards extractive approaches, several research efforts have used this dataset for summarization \cite{hong2014improving,durrett2016learning,paulus2017deep}.

\paragraph{CNN / Daily Mail}
One of the most commonly used dataset for summarization \cite{nallapati2016abstractive, see2017get, paulus2017deep, dong2019unified}, although originally built for Question Answering tasks \cite{hermann2015teaching}. It consists of English articles from the CNN and The Daily Mail associated with bullet point highlights from the article.
When used for summarization, the bullet points are typically concatenated into a single summary.

\paragraph{NEWSROOM}
Composed of 1.3M articles \cite{grusky2018newsroom}, and featuring high diversity in terms of publishers, the summaries associated with English news articles were extracted from the Web pages metadata: they were originally written to be used in search engines and social media.

\begin{comment}
\paragraph{Wikipedia}
Deviating from the use of newswire as source, \citet{liu2018generating} proposed to use the lead section of Wikipedia articles as  multi-document summarization references.

With two millions articles, and a far larger input text than other summarization datasets, authors consider the lead section  the authors suggested a two-stages approach:
\begin{enumerate}
    \item retrieve a few important sentences via extractive methods;
    \item use those sentences as input to a Transformer \cite{vaswani2017attention} in order to generate an abstractive summary.
\end{enumerate}
\end{comment}

\paragraph{BigPatent}
\citet{sharma2019bigpatent} collected 1.3 million U.S. patent documents, across several technological areas, using the Google Patents Public Datasets. The patents abstracts are used as target summaries.

\paragraph{LCSTS}
The Large Scale Chinese Short Text Summarization Dataset \cite{hu2015lcsts} is built from 2 million short texts from the Sina Weibo microblogging platform. They are paired with summaries given by the author of each text. The dataset includes 10k summaries which were manually scored by human for their relevance.

\section{MLSUM}
As described above, the vast majority of summarization datasets are in English. For Arabic, there exist the Essex Arabic Summaries Corpus (EASC) \cite{el2010using} and KALIMAT \cite{el2013kalimat}; those comprise circa 1k and 20k samples, respectively. \citet{pontes2018new} proposed a corpus of few hundred samples for Spanish, Portuguese and French summaries. To our knowledge, the only large-scale non-English summarization dataset is the Chinese LCSTS \cite{hu2015lcsts}.
With the increasing interest for cross-lingual models, the NLP community have recently released multilingual evaluation datasets, targeting classification (XNLI) and  QA \cite{lewis2019mlqa} tasks, as described in \ref{Existing_Multilingual_datasets}, though still no large-scale dataset is avaulable for document summarization.

To fill this gap we introduce MLSUM, the first large scale multilingual summarization corpus.
Our corpus provides more than 1.5 millions articles in French (FR), German (DE), Spanish (ES), Turkish (TR), and Russian (RU). Being similarly built from news articles, and  providing a similar amount of training samples per language (except for Russian), as the previously mentioned CNN/Daily Mail, it can effectively serve as a multilingual extension of the CNN/Daily Mail dataset.

In the following, we first describe the methodology used to build the corpus. We then report the corpus statistics and finally interpret the performances of baselines and state-of-the-art models.

\subsection{Collecting the Corpus}
The CNN/Daily Mail (CNN/DM) dataset (see Section \ref{Existing_Summarization_datasets}) is arguably the most used large-scale dataset for summarization. Following the same methodology, we consider news articles as the text input, and their paired \emph{highlights/description} as the summary. For each language, we selected an online newspaper which met the following requirements:
\begin{enumerate}
\item Being a \emph{generalist} newspaper: ensuring that a broad range of topics is represented for each language allows to minimize the risk of training topic-specific models, a fact which would hinder comparative cross-lingual analyses of the models.
\item Having a large number of articles in their public online archive.
\item Providing human written highlights/summaries for the articles that can be extracted from the HTML code of the web page.
\end{enumerate}

After a careful preliminary exploration, we selected the online version of the following newspapers:
\begin{itemize}
    \item Le Monde\footnote{\url{www.lemonde.fr}} (French)
    \item S{\"u}ddeutsche Zeitung\footnote{\url{www.sueddeutsche.de}} (German)
    \item El Pais\footnote{\url{www.elpais.com}} (Spanish)
    \item Moskovskij Komsomolets\footnote{\url{www.mk.ru}} (Russian)
    \item Internet Haber\footnote{\url{www.internethaber.com}} (Turkish)
\end{itemize}

For each outlet, we crawled archived articles from 2010 to 2019. We applied one simple filter: all the articles shorter than 50 words or summaries shorter than 10 words are discarded, so as to avoid articles containing mostly audiovisual content. Each article was archived on the Wayback Machine,\footnote{\url{web.archive.org}, using \url{https://github.com/agude/wayback-machine-archiver}} allowing interested research to re-build or extend MLSUM.
We distribute the dataset as a list of immutable snapshot URLs of the articles, along with the accompanying corpus-construction code,\footnote{\url{https://github.com/recitalAI/MLSUM}} allowing to replicate the parsing and preprocessing procedures we employed.
This is due to legal reasons: the content of the articles is copyrighted and redistribution might be seen as infringing of publishing rights. Nonetheless, we make available, upon request, an exact copy of the dataset used in this work. A similar approach has been adopted for several dataset releases in the recent past, such as Question Answering Corpus \cite{nips15_hermann} or XSUM \cite{xsum-emnlp}.

Further, we provide recommended train/validation/test splits following a chronological ordering based on the articles' publication dates. In our experiments below, we train/evaluate the models on the training/test splits obtained in this manner. Specifically, we use: data from 2010 to 2018, included, for training; data for 2019 (\texttildelow10\% of the dataset) for validation (up to May 2019) and test (May-December 2019). While this choice is arguably more challenging, due to the possible emergence of new topics over time, we consider it as the realistic scenario a successful summarization system should be able to deal with. Incidentally, this also bring the advantage of excluding most cases of leakage across languages: it prevents a model, for instance, from seeing a training sample describing an important event in one language, and then being submitted for inference a similar article in another language, published around the same time and dealing with the same event.

\subsection{Dataset Statistics}

\begin{table*}[!ht]
    \centering
    \begin{tabular}{l|l|l|l|l|l||r}
         &  FR & DE	& ES & RU & TR & EN\\
         \hline
Dataset size & 424,763 & 242,982 & 290,645 & 27,063 & 273,617 & 311,971\\
Training set size & 392,876 & 220,887 & 266,367 & 25,556 & 249,277 & 287,096 \\
\hline
Mean article length & 632.39 & 570.6 & 800.50 & 959.4 & 309.18 & 790.24\\
Mean summary length & 29.5 & 30.36 & 20.71 & 14.57 & 22.88 & 55.56\\
Compression Ratio    & 21.4 & 18.8 & 38.7 & 65.8 & 13.5 & 14.2 \\
Novelty (1-gram) &15.21&14.96&15.34&30.74&28.90&9.45\\
\hline
Total Vocabulary Size & 1,245,987 & 1,721,322 & 1,257,920 & 649,304 & 1,419,228 & 875,572\\
Occurring 10+ times & 233,253 & 240,202 & 229,033 & 115,144 & 248,714 & 184,095\\

    \end{tabular}
    \caption{Statistics for the different languages. \emph{EN} refers to CNN/Daily Mail and is reported for comparison purposes. Article and summary lengths are computed in words. Compression ratio is computed as the ratio between article and summary length. Novelty is the percentage of words in the summary that were not in the paired article. Total Vocabulary is the total number of different words and Occurring 10+, the total number of words occurring 10+ times.  }
    \label{tab:table_description_dataset}
\end{table*}
We report statistics for each language in MLSUM in Table~\ref{tab:table_description_dataset}, including those computed on the CNN/Daily Mail dataset (English) for quick comparison. MLSUM provides a comparable amount of data for all languages, with the exception of Russian with ten times less training samples. Important characteristics for summarization datasets are the length of articles and summaries, the vocabulary size, and a proxy for abstractiveness, namely the percentage of novel n-grams between the article and its human summary. From Table~\ref{tab:table_description_dataset}, we observe that Russian summaries are the shortest as well as the most abstractive.

Coupled with the significantly lower amount of articles available from its online source, the task can be seen as more challenging for Russian than for the other languages in MLSUM. Conversely, similar characteristics are shared among other languages, for instance French and German.

\subsection{Topic Shift}
With the exception of Turkish, the article URLs in MLSUM allow to identify a category for a given article. In Figure~\ref{fig:topic_stackplot} we show the shift over categories among time. In particular, we plot the 6 most frequent categories per language.

\begin{figure*}[!ht]
    \centering
    \includegraphics[width=.49\linewidth]{IMG/topic_stackplot_de.pdf}
    \includegraphics[width=.49\linewidth]{IMG/topic_stackplot_es.pdf}
    \includegraphics[width=.49\linewidth]{IMG/topic_stackplot_fr.pdf}
    \includegraphics[width=.49\linewidth]{IMG/topic_stackplot_ru.pdf}
    \caption{Distribution of topics for German (top-left), Spanish (top-right), French (bottom-left) and Russian (bottom-right), grouped per year. The shaded area for 2019 highlights validation and test data.}
    \label{fig:topic_stackplot}
\end{figure*}

\section{Models}
We experimented on MLSUM with the established models and baselines described below.
Those include supervised and unsupervised methods, extractive and abstractive models. For all the experiments, we train models on a per-language basis. We used the recommended hyperparameters for all languages, in order to facilitate assessing the robustness of the models. We also tried to train one model with all the languages mixed together, but we did not see any significant difference of performance.

\subsection{Extractive summarization models}
\label{sec:models}

\paragraph{Oracle}
Extracts the sentences, within the input text, that maximise a given metric (in our experiments, ROUGE-L) given the reference summary.
It is an indication of the maximum one could achieve with extractive summarization. In this work, we rely on the implementation of \citet{narayan-etal-2018-ranking}.

\paragraph{Random}
In order to elaborate and compare the performances of the different models across languages, it is useful to include an unbiased model as a point of reference. To that purpose, we define a simple random extractive model that randomly extracts $N$ words from the source document, with $N$ fixed as the average length of the summary.

\paragraph{Lead-3}
Simply selects the three first sentences from the input text.  \citet{sharma2019bigpatent}, among others, showed that this is a robust baseline for several summarization datasets such as CNN/DM, NYT and BIGPATENT.

\paragraph{TextRank}
An unsupervised algorithm proposed by \citet{mihalcea2004textrank}. It consists in computing the co-similarities between all the sentences in the input text. Then, the most central to the document are extracted and considered as the summary. We used the implementation provided by \citet{DBLP:journals/corr/BarriosLAW16}.

\subsection{Abstractive summarization models}
Most of the models for abstractive summarization are neural sequence to sequence models \cite{sutskever2014sequence}, composed of an encoder that encodes the input text and a decoder that generates the summary.

\paragraph{Pointer-Generator}
\label{par:Pointer-Generator}
\citet{see2017get} proposed the addition of the copy mechanism \cite{vinyals2015pointer} on top of a sequence to sequence LSTM model. This mechanism allows to efficiently copy out-of-vocabulary tokens, leveraging attention \cite{bahdanau2014neural} over the input. We used the publicly available OpenNMT implementation\footnote{\url{opennmt.net/OpenNMT-py/Summarization.html}} with the default hyper-parameters. However, to avoid biases, we limited the preprocessing as much as possible and did not use any sentence separators, as recommended for CNN/DM. This explains the relatively lower reported ROUGE, compared to the model with the full preprocessing.

\paragraph{M-BERT}
Encoder-decoder Transformer  architectures are a very popular choice for text generation.
Recent research efforts have adapted large pretrained self-attention based models for text generation \cite{peters2018deep,radford2018improving,devlin2019bert}.

In particular, \citet{liu2019text} added a randomly initialized decoder on top of BERT. Avoiding the use of a decoder, \citet{dong2019unified} proposed to instead add a decoder-like mask during the pre-training to \emph{unify} the language models for both encoding and generating. Both these approaches achieved SOTA results for summarization.
In this paper, we only report results obtained following \citet{dong2019unified}, as in preliminary experiments we observed that a simple multilingual BERT (M-BERT), with no modification, obtained comparable performance on the summarization task.

\begin{table*}[!htb]
    \centering
    \begin{tabular}{l|r|r|r|r|r||r}
         &FR&DE&ES&RU&TR&EN\\
         \hline
Oracle&37.69&52.3&35.78&29.80&45.78&53.6\\
Random&11.88&10.22&12.63&6.7&11.29&11.23\\
TextRank&12.61&13.26&9.5&3.28&21.5&28.61\\
Lead\_3&19.69&33.09&13.7&5.94&28.9&35.2\\
Pointer-Generator&23.58&35.08&17.67&5.71&32.59&33.32\\
M-BERT&25.09&42.01&20.44&9.48&32.94&35.41\\
    \end{tabular}

    \begin{tabular}{l|r|r|r|r|r||r}
         &FR&DE&ES&RU&TR&EN\\
         \hline
Oracle&24.73&31.67&26.45&20.32&26.42&29.99\\
Random&7.54&6.67&6.48&2.5&6.29&10.56\\
TextRank&10.77&13.01&11.14&3.79&14.36&20.37\\
Lead\_3&12.62&23.85&10.26&5.77&20.24&21.16\\
Pointer-Generator&14.07&24.41&13.17&5.69&19.78&20.78\\
M-BERT&15.07&26.47&14.92&6.77&26.26&22.16\\
    \end{tabular}
    \caption{ROUGE-L (top) and METEOR (bottom) results obtainedby the models described in \ref{sec:models} on the different proposed datasets .}
    \label{tab:results}
\end{table*}

\section{Evaluation Metrics}
\label{sec:evaluation_metrics}
\paragraph{ROUGE}
Arguably the most often reported set of metrics in summarization tasks, the  Recall-Oriented Understudy for Gisting Evaluation \cite{lin2004rouge} computes the number of n-grams similar between the evaluated summary and the human reference summary.

\paragraph{METEOR}
The Metric for Evaluation of Translation with Explicit ORdering \cite{banerjee2005meteor} was designed for the evaluation of machine translation output. It is based on the harmonic mean of unigram precision and recall, with recall weighted higher than precision. METEOR is often reported in summarization papers \cite{see2017get,dong2019unified} in addition to ROUGE.

\paragraph{Novelty}
Because of their use of copy mechanisms, some abstractive models have been reported to rely too much on extraction \cite{see2017get,kryscinski2018improving}. Hence, it became a common practice to report the percentage of novel n-grams produced within the generated summaries.

\paragraph{Neural Metrics}
Several approaches based on neural models have been recently proposed. Recent works \cite{eyal2019question,scialom-etal-2019-answers} have proposed to evaluate summaries with QA based methods: the rationale is that a good summary should answer the most relevant questions about the article. Further, \citet{kryscinski2019evaluating} proposed a discriminator trained to measure the factualness of the summary. While \citet{bohm-etal-2019-better} learned a metric from human annotation. All these models were only trained on English datasets, preventing us to report them in this paper.
The availability of MLSUM will enable future works to build such metrics in a multilingual fashion.

\begin{figure*}[!htb]
    \centering
    \includegraphics[width=.32\linewidth]{IMG/novelty_percentage_fr.pdf}
    \includegraphics[width=.32\linewidth]{IMG/novelty_percentage_de.pdf}
    \includegraphics[width=.32\linewidth]{IMG/novelty_percentage_es.pdf}
    \includegraphics[width=.32\linewidth]{IMG/novelty_percentage_ru.pdf}
    \includegraphics[width=.32\linewidth]{IMG/novelty_percentage_tu.pdf}
    \includegraphics[width=.32\linewidth]{IMG/novelty_percentage_en.pdf}
    \caption{Percentage of novel n-grams for different abstractive models (neural and human), for the 6 datasets.}
    \label{fig:novelty}
\end{figure*}

\section{Results and Discussion}

The results presented below allow us to compare the models across languages, and investigate or hypothesize where their performance variations may come from. We can distinguish the following factors to explain differences in the results:
\begin{enumerate}
    \item Differences in the data, independently from the language, such as the structure of the article, the abstractiveness of the summaries, or the quantity of data;
    \item Differences due to the language itself -- either due to metric biases (e.g. due to a different morphological type) or to biases inherent to the model.
\end{enumerate}

While the first fold of differences have more to do with domain adaptation, the second fold motivates further the development of multilingual datasets, since they are the only mean to study such phenomenon.

Turning to the observed results, we report in Table~\ref{tab:results} the ROUGE-L and METEOR scores obtained by each model for all languages.
We note that the overall order of systems (for each language) is preserved when using either metric (modulo some swaps between Lead\_3 and Pointer Generator, but with relatively close scores).

\paragraph{Russian, the low-resource language in MLSUM}

For all experimental setups, the performance on Russian is comparatively low.

This can be explained by at least two factors.
First, the corpus is the most abstractive (see Table~\ref{tab:table_description_dataset}, limiting the performance figures obtained for the extractive models (Random, LEAD-3, and Oracle).
Second, one order of magnitude less training data is available for Russian than for the other MLSUM languages, a fact which can explain the impressive improvement of performance (+66\% in terms of ROUGE-L, see Table~\ref{tab:results}) between a \emph{not pretrained} model (Pointer Generator) and a \emph{pretrained} model (M-BERT).

\subsection{How abstractive are the models?}
We report the novelty (i.e. the percentage of novel words in the summary) in Figure \ref{fig:novelty}. As previous works reported \cite{see2017get}, pointer-generator networks are poorly abstractive, relying too much on their copy mechanism.
It is particularly true for Russian: the lack of data probably makes it easier to learn to copy than to cope with natural language generation.
As expected, pretrained language models such as M-BERT are consistently more abstractive, and by a large margin, since they are exposed to other texts during pretraining.

\subsection{Model Biases toward Languages}

\paragraph{Consistency among ROUGE scores}

The Random model obtains comparable ROUGE-L scores across all the languages, except for Russian. This can be explained by the aforementioned Russian corpus characteristics: highest novelty, shortest summaries, and longest input documents (see Table~\ref{tab:table_description_dataset}).

Thus, in the following, for pair-wise language-based comparisons we focus only on scores obtained, by the different models, on French, German, Spanish, and Turkish -- since we cannot draw meaningful interpretations over Russian as compared to other languages.

\paragraph{Abstractiveness of the datasets}

The Oracle performance can be considered as the upper limit for an extractive model since it extracts the sentences that provide the best ROUGE-L. We can observe that while being similar for English and German, and to some extent Turkish, the Oracle performance is lower for French or Spanish.

However, as described in figure \ref{tab:table_description_dataset}, the percentage of novel words are similar for German (14.96), French (15.21) and Spanish (15.34). This may indicate that the relevant information to extract from the article is more spread among sentences for Spanish and French than for German. This is confirmed with the results of Lead-3: German and English have a much higher ROUGE-L -- 35.20 and 33.09 -- than French or Spanish -- 19.69 and 13.70.

\paragraph{The case of TextRank}

\begin{table}[!htb]
    \centering
    \begin{tabular}{l|c|c}
         &T/P &B/P\\
         \hline
FR&0.53&1.06\\
DE&0.37&1.20\\
ES&0.53&1.15\\
RU&0.57&1.65\\
TR&0.65&1.01\\
CNN/DM (EN)&1.10&1.06\\
CNN/DM (EN full preprocessing)&0.85&-\\
DUC (EN)&1.21&-\\
NEWSROOM (EN)&1.10&-\\
    \end{tabular}
    \caption{Ratios of Rouge-L: T/P is the ratio of TextRank to Pointer-Generator and B/P is the ratio of M-BERT to Pointer-Generator. The results for CNN/DM-full preprocessing, DUC and NEWSROOM datasets are those reported in Table 2 of \citet{grusky2018newsroom} (Pointer-C in their paper is our Pointer-Generator).}
    \label{tab:ratios}
\end{table}
The TextRank performance varies widely across the different languages, regardless Oracle. It is particularly surprising to see the low performance on German whereas, for this language, Lead-3 has a comparatively higher performance.
On the other hand, the performance on English is remarkably high: the ROUGE-L is 33\% higher than for Turkish, 126\% higher than for French and 200\% higher than for Spanish. We suspect that the TextRank parameters might actually overfit English.

In Table \ref{tab:ratios}, we report the performance ratio between TextRank and Pointer Generator on our corpus, as well as on CNN/DM and two other English corpora (DUC and NewsRoom). TextRank has a performance close to the Pointer Generator on English corpora (ratio between 0.85 to 1.21) but not in other languages (ratio between 0.37 to 0.65).

This suggests that this model, despite its generic and unsupervised nature, might be highly biased towards English.

\paragraph{The benefits of pretraining}
We hypothesize that the closer an unsupervised model performance to its maximum limit, the less improvement would come from pretraining. In Figure~\ref{fig:delta_models}, we plot the improvement rate from TextRank to Oracle, against that of Pointer-Generator to M-BERT.

Looking at the correlation emerging from the plot, the hypothesis appears to hold true for all languages, including Russian -- not plotted for scaling reasons ($x=808;y=40$), with the exception of English.
This exception is probably due to the aforementioned bias of TextRank towards the English language.

\begin{figure}
    \includegraphics[width=\linewidth]{IMG/delta_models.pdf}
    \caption{Improvement rates from TextRank to Oracle (in abscissa) against rates from  Pointer Generator to M-BERT (in ordinate).}
    \label{fig:delta_models}
\end{figure}

\paragraph{Pointer Generator and M-BERT}

Finally, we observe in our results that M-BERT always outperforms the Pointer Generator. However, the ratio is not homogeneous across the different languages as reported in Table~\ref{tab:ratios}. In particular, the improvement for  German is much more important than the one for French. Interestingly, this observation is in line with the results reported for Machine Translation: the Transformer \cite{vaswani2017attention} outperforms significantly ConvS2S \cite{gehring2017convolutional} for English to German but obtains comparable results for English to French -- see Table 2 in \citet{vaswani2017attention}.

Neither model is pretrained, nor based on LSTM \cite{hochreiter1997long}, and they both use BPE tokenization \cite{shibata1999byte}. Therefore, the main difference is represented by the self-attention mechanism introduced in the Transformer, while ConvS2S used only source to target attention. We thus hypothesise that self-attention plays an important role for German but has a limited impact for French. This could find an explanation in the morphology of the two languages:
in statistical parsing, \citet{tsarfaty2010statistical} considered German to be very sensitive to word order, due to its rich morphology, as opposed to French. Among other reasons, the flexibility of its syntactic ordering is mentioned. This corroborates the hypothesis that self-attention might help preserving information for languages with higher degrees of word order freedom.

\subsection{Possible derivative usages of MLSUM}

\paragraph{Multilingual Question Answering}
Originally, CNN/DM was a Question Answering dataset \cite{hermann2015teaching}. The hypothesis is that the information in the summary is also contained in the pair article. Hence, questions can be generated from the summary sentences by masking the Named Entities contained therein.

The masked entities represent the answers, and thus a masked question should be answerable given the source article. So far, no multilingual \emph{training} dataset has been proposed for Question Answering.

This methodology could be thus applied on MLSUM as a first step toward a large-scale multilingual Question Answering corpus. Incidentally, this would also allow progressing towards multilingual Question Generation, a crucial component to employ the neural summarization metrics mentioned in Section~\ref{sec:evaluation_metrics}.

\paragraph{News Title Generation}
While the release of MLSUM hereby described covers only article-summary pairs, the archived news articles also include the corresponding titles. The accompanying code for parsing the articles allows to easily retrieve the titles and thus use them for News Title Generation.

\paragraph{Topic detection}
A topic/category can be associated with each article/summary pair, by simply parsing the corresponding URL.
A natural application of this data for summarization would be for template based summarization \cite{perez2019generating}, using it as additional features. However, it can also be a useful multilingual resource for topic detection.

\section{Conclusion}
We presented MLSUM, the first large-scale MultiLingual SUMmarization dataset, comprising over 1.5M article/summary pairs in French, German, Russian, Spanish, and Turkish.
We detailed its construction, and its complementary nature to the CNN/DM summarization dataset for English. We reported extensive preliminary experiments, highlighting biases observed in existing summarization models as well as analyzing and investigating the relative performances across languages of state-of-the-art approaches.
In future work, we plan to add other languages including Arabic and Hindi, and to investigate the adaptation of neural metrics to multilingual summarization.

redpajama_set_name: RedPajamaArXiv


Top Vegetarian Lip Balms in India – Keeping your lips healthy and plump is easy when you get hold of perfect vegetarian lip balm for you. Despite being inundated with lip balms of varied shapes, sizes, colors and flavors these days, if you are at the sea as to which lip balm to eventually opt for, you have landed at the right spot. Recent years have stood testimony to the introduction and sprawl of a handful of Indian brands that thrive more on what they add and not on how to market it. While some of you may have heard of them in passing, most of us haven't. But if you wish to know if there exist lip balms that not only are chemical-free but also cent per cent vegetarian, scroll down and check out these Top Vegetarian Lip Balms in India.
"BON for everyone," the Auroville-based BON Organics' aim is crystal clear- to provide quality organic product to everyone at a reasonable price. "That's how it's meant to be," says its friendly CEO- Ms. Nishantainidevi Chandrasekaran. Proudly a USDA certified organic brand, their products' range encompasses shampoos (my favorite), conditioners, gels, cold-processed soaps and eatables like- palm sugar & chocolate, my fixation with the brand is fuelled by its 100% handmade all natural & organic Lip Staining Balm- the Red Lip Stain. Its a 100% vegetarian lip balm that stains your lips in a sultry red hue with a mesmeric matte finish for a considerable time period- 8 hours in my case. Incredulous, isn't it?
Made up of Shea Butter (Vitellariaparadoxa), Cocoa Butter (Theobroma cacao), BON Pure Aloe Vera Gel (Aloe barbadensis), Coconut Oil (Cocos nucifera), Sweet Almond Oil (Prunusdulcis), Cruelty Free Bees- wax (Certified), Essential Oil of Vanilla blended with Essential Oils, peppered lovingly with saffron, the composition is bound to sweep you off your feet. Everything about this lip stain is not only likeable but addictive.
A staying power over 8 hours (stays overnight as is the case with me), it imparts a gorgeous bright tint which by the time 8 hours come to a close lightens but still looks appealing. The moisturisation ability is creditable too. While it may take time to heal chapped lips particularly of those who are addicted to lip sticks, if you have normal/slightly dry lips, 2-3 applications elicit well-hydrated lips. In all, if you haven't stained your lips with BON's lip stains yet, you my friend have been depriving them off oodles of goodness & gorgeousness!
Price of BON Organics Lip Staining Balm- the Red Lip Stain : Rs.400/- for 8 g/0.3 fl. Oz.
Organic & Flirty lip care-so goes their tagline. For conscious consumers who emphasise the composition of a product, Goa-based Island Kiss is a delight. Their chemical free and 100% vegetarian lip balms are currently available in five tempting variants. Pampering your pucker with three bewitching flavors last summer, the new launches this year namely- The Black Rose & Grenade Rouge as well as the Flamingo Pink & Peonies Lip Moisturiser truly, aptly and rightfully justify the time taken to launch them.
With a noteworthy vegetarian composition, the Flamingo Pink & Peonies Lip Moisturiser brings an element of coquettishness to your appearance courtesy its perky light pink hue and sweet aroma. A subtle sheen imparted, it is neither greasy nor oily despite the presence of butters and oils in abundance. An actual raspberry-like stain (you can use on your cheeks too) that is 100% edible, it has no lead & chemicals. You can lick it (it's edible) but I'd suggest pampering your luscious lips with it unless hunger pangs are driving you nuts! Unleash your feminine hue with it. Lip sticks do not stand a chance in front of it.At any cost.
There efficacy of Hydra's products is best manifested by the fact that all the customers of this all-natural, cruelty-free and vegetarian brand sing paeans in its admiration. Be it its chemical-free face cream or a luscious body wash, its soothing lip balm or a lavish body butter, its owner/founder Ms. Ambika Goel ensures that if something is offered from Hydra- her brand, it is nothing short of spectacular. From the packaging to the performance, each aspect is looked into conscientiously by Ambika who suggests products based on one's skin type, condition, problems, etc. Ergo, if you are baffled, wondering which product would be your savior all you need to do is drop a line to her.
Talking about their vegetarian lip balm, the product that caught my fancy was Hydra's Organic Lip Balm. It is made up of cocoa butter, almond oil, olive oil, lavender essential oil & jojoba oil. Nothing more than these go into the tub of this product. With a staying power of 4+ hours, this tint-less lip balm leaves chapped lips intensely moisturized by the end of 4 hours. Severely chapped lips may take a fortnight to heal majorly but they will heal. If daubed heavily, it lasts throughout the night easily. A simple no-nonsense vegetarian balm, grab one to heal your inanimate lips.
Quirky name? I concur. Far more quirky are the entrepreneurs who helm the Bengaluru-based Cucumber Organics. Run by the amiable Priya & Zaheer who have a whale of their time answering all queries pertaining to their alluring products as well as general skin care queries as much as you may like throwing them, while keeping monotonous language out of their tête-à-tête with you. Touching base with them is akin to talking to old pal post a significant number of years!
All of their products are not only free from noxious chemicals but also vegetarian in composition. Talking about vegetarian lip balm, the one that caught my fancy was the Orange Lip Balm for the simple reason that it does what it is meant to- hydrate. Intensely. The staying power being 2-3 hours, it has a smooth texture which when applied doesn't feel greasy. Making no bones about the composition of any of their products (the range is slowly but surely increasing) which is indeed creditable, the lip balm is not only for women but also for men to indulge in. If you were whether the price paid is justified, the answer is in affirmation.
Mrs. Roopa Honnaswamy's Bengaluru-based Nature's Emporium cannot be turned a blind eye at all while enlisting the most drool-worthy vegetarian lip balms in India. The brand offers lip butters in four variants at present namely in mojito, mandarin, lavender and grapefruit lip balm. All of which are free from deleterious chemicals and also cent per cent vegetarian in composition. Priced equally, the variant that has to be credited is the Lavender Lip Balm.
The clean, floral and earthy scent of Lavender essential oil is what makes it extra soothing. Lavender is a great scent for relaxing the senses. Made up of Shea Butter, Sweet Almond Oil, Beeswax, and Lavender Essential Oil, each ingredient lends its bounties (and no side effects) to your lips, healing dry and chapped lips to the core. The only flea in the ointment happens to be the transitory nature of the balm that is, it stays for a short time period on one's lips. If this aspect is ameliorated, this would make for a value-for-money product.
If quality has a synonym, it would be SkinCafe! Based out of Pune as well as Mumbai, a limited team of zealous and passionate innovators is behind this eclectic brand whose range encompasses lip balms, tints/stains, scrubs and face masks (smoothies to say)-all of which are free from nasties like alcohol, petrolatum, silicones, coal tar dyes and the ilk. Soon about to launch matte lip balms for men (they care, don't they?), their products boast of a commendable composition.
Coming to their vegetarian lip balms, what deserves a special shout-out is the Special Edition Spiced Pumpkin Latte Lip Butter. The lip balms are made up of USDA Certified organic ingredients, unrefined butters, pure essential oils with the option of substitution of Beeswax with Candelilla Wax for vegans (at no extra cost). The Spiced Pumpkin Latte Lip Butter enwraps your lips in an impenetrable shield of hydration for 3 hours, leaving behind a soft sheen. Cent per cent vegetarian, thanks to its tube packaging, it can be readily carted while travelling. A brand worth investing in!
Dr. Mohit Bhatia's eclectic brand- Fuschia based out of New Delhi cannot be turned a blind eye to. Handmade, natural and chemical-free products being the brand's USP, they offer chemical free and totally vegetarian lip balms. With their lip balms presently available in a number of flavors namely- choco, alphonso mango, cherry, pomegranate, peach plus, etc. what caught my eye were two variants which infused not only oodles of moisturization but also subtle delightful tints.
The Cherry Lip Balm in particular brings together the bounties of Shea Butter & Kokum Butter combined with Vitamin E. Free from Parabens, Petroleum Base, Artificial Tints and Synthetic perfumes, it is not only cruelty-free but also vegetarian in composition. Shea butter, natural beeswax, vitamins and natural oils protect and nourish lips, leaving them kissable soft.
The other variant is the Choco Butter Lip Balm that boasts of a similar composition but vastly different in terms of the fragrance elicited. For all the chocolate fanatics out there, smearing this on your lips may compel you to lick with each passing second but if you resist the urge to do so, you may end up with tangibly moisturised lips.
Can't get of Aroma Essentials? Well, you have to blame Mrs. Madhurima Ramakrishna- the owner/founder/master craftswoman behind this prolific brand. Constantly brewing with ideas on which beauty product to whip next, Aroma Essentials' vegetarian lip balms – offered both in tub/pot form as well as Chapstick (stick form) work wonders to rescue your lips from inclement winters. Mostly tint-less, they do not cater to just women but also men.
What deserves a special mention is the Rosemary Mint Lip Balm that will lull your olfactory senses to a different- ethereal and serene place the moment its tub is opened. Mint's presence ensures that if you have smeared it during scorching summers, your lips are gingered up with a cooling sensation.
For those fond of ChapSticks, the Orange-Grapefruit variant solves the purpose. Packed in a twisty tube, it bears a subtle pink tinge. It is made with unrefined butters, cold-pressed carrier oils, food grade colour and pure essential oils- all of which benefit your lips not only in the short run but also in the long run. Apt for use by everyone, all of Aroma Essentials lip products are vegetarian. This rule of thumb is well applicable to their lip balms.


Grace DePledge is a studio potter who finds joy in creating hand-made pots that communicate vitality, elegance, and strength. Her functional porcelain work is known for carved patterned surfaces that reference nature.
Grace grew up surrounded by vineyards, rivers, and the mountains of California, where she developed a love for nature and artistic expression at an early age. She holds a bachelor's degree in liberal arts, and spent many professional years as a creative director, cultivating skills in graphic design, typography, photography, and writing, and using those elements to craft meaningful messages. Having transferred her creative energy to clay, she now seeks to craft meaningful pots—finding both a connection with her roots in design, as well as tremendous inspiration in exploring form and incising pattern on the dimensional porcelain surface.
Believing that experiencing beauty is essential to life, Grace values investing her work with human touch, enduring quality, and fine craftsmanship. Her pots are exhibited regularly and live happily in the homes of those who appreciate her graceful aesthetic.
Copyright 2017 Grace DePledge. All rights reserved.


text:
Patient dies in outbreak at hospital
Megan Henry
mhenry@dispatch.com
A patient died Sunday after an outbreak of Legionnaires' disease at the new Mount Carmel Grove City hospital.
"My entire team and I are deeply saddened by the death announced tonight by Mount Carmel Grove City, and our thoughts and prayers go out to the family," Ohio Department of Health Director Amy Acton said in a news release.
"We share concern for all impacted by this outbreak. The Ohio Department of Health continues to work closely with Franklin County Public Health and the Mount Carmel (Health) System as we work to ensure patient safety."
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Seven patients have been diagnosed with the disease. The patient in the first case stayed at the new $361 million hospital from April 29 through May 7, and five other patients who contracted the disease stayed sometime between May 8 and May 20. The seventh case was identified late Friday.
The hospital has not identified the patient who died.
"Out of respect for the family's privacy and in keeping with patient privacy laws, we are not discussing the specifics and complexities of patient information," said Dr. Richard Streck, chief clinical operations officer for the Mount Carmel Health System, in a news release. "We can say that it's too early to determine the final cause of death. For most people, the risk of developing Legionnaires' disease is low; however, individuals with chronic, underlying medical conditions are at increased risk."
Streck said the hospital is "working with county and state health officials to identify the source of the bacteria. We've taken several steps to protect our patients, staff and visitors, including implementing extensive water restrictions. We are running additional tests on water sources throughout Mount Carmel Grove City, and our entire water supply is undergoing supplemental disinfection. We're confident that we can safely maintain full services of the hospital."
Streck advised anyone who has been hospitalized and developed a cough, muscle aches, headaches, a fever or shortness of breath to contact their primary care physician.
Legionnaires' disease kills about 10% of those diagnosed in the general population, but that rate can rise to 1 in 4 people when the outbreak is at a nursing home or hospital.
Legionella bacteria can be found naturally in water but can become a health concern by spreading into building water systems and places such as shower stalls, faucets and drinking fountains.
Outbreaks most commonly occur in buildings with "complex water systems," including hotels, hospitals and cruise ships, where bacteria can spread in cooling towers, hot water tanks and plumbing systems, according to the federal Centers for Disease Control and Prevention.
@megankhenry


A couple of years ago, I took on a challenge to experience what it would be like to go without breakfast for four days. It would be so easy, I thought. I can totally do this! But by the fourth day, I was miserable, grumpy and just plain hangry. Yet, a part of me was relieved to know that I would have a nice, hearty breakfast to enjoy once the challenge was over. Unfortunately, 1 in 5 children in Canada go hungry every morning. In fact, according to the Breakfast Club of Canada 1 in 5 children is at risk of starting the school day on an empty stomach due to a lack of access to nutritious food. As a teacher, this is incredibly disheartening. It is no surprise that kids who enjoy a balanced breakfast generally have more energy and perform better in school. I see it all the time. Conversely, I have also noticed how much a student can struggle to maintain focus or grasp a new concept when all they can think about is how much their little tummy is grumbling.
The good news is that, for the past 20 years, Danone Canada has partnered with the Breakfast Club of Canada to help ensure that a healthy breakfast is accessible to the ones who need it the most. Thanks to this all-important breakfast program, children are given access to balanced meals, with healthy essential foods such as yogurt, in an environment that allows their self-esteem to flourish. In a recent survey by Leger and Danone Canada, 83% of Quebecers eat breakfast in the morning but 77% of Quebec families identified toast, coffee and cereals or oatmeal as the most popular breakfast items. I can definitely relate because these popular breakfast foods are easy to prepare during the morning rush before driving the kids to school and heading off to work. However, these foods are missing the essential protein that gives our body an energy boost while helping us to feel fuller, longer. This is why I love having yogurt on hand. Not only does it add a protein boost, it is a good source of calcium, vitamin B-2, B-12, potassium, and magnesium.
Another reason I love having yogurt readily available is that it is easy to prepare a balanced breakfast based on yogurt. One of my go-to recipes is the yogurt parfait. It is super simple to prepare and, with my favourite Activia vanilla yogurt, it is incredibly tasty, too.
Wash and dry the berries. Peel and slice the banana. Place the yogurt, fruit, granola and any additional toppings in the middle of the table with some bowls or dessert cups and spoons, and let your kids create their own layered yogurt parfait.
I am glad that Danone has partnered with the Breakfast Club. A healthy breakfast is so important.
I like to support companies that give back to charities and I will be purchasing more Danone products after reading your review about their contributions to the Breakfast Club of Canada, thanks!
This looks like a very healthy breakfast!
I can't imagine going without breakfast; so great that Danone Canada has partnered with Breakfast Club of Canada to provide breakfast to kids who need it most.
This is one healthy dish that I would totally love, yummmm sounds so good!!
DANON Activia is my favourite yogurt, so good for you.
I love Danone Activia yogurt and I have one every morning with some fruit.I should try adding some chia seeds or some flax or granola.It's also awesome to learn that they have partnered with the Breakfast Club.That's great!
Danone Activia is my favorite yogurt that I have every morning with some fruit.I should try it with some flax seed, chia seeds or granola.It's also awesome to learn that they have partnered with the Breakfast Club.That's great!
There are some great ideas here! Thank you for sharing.


Laughter Yoga Workshops guide people to health, happiness and a connected community. Funding for Brixton workshops comes from a Council partnership with the NHS – and the time is right for new projects to apply!
Laughter Yoga's mission is to inspire people to achieve healthy minds, bodies and souls through Laughter Yoga and Mindfulness workshops. Zeenat runs workshops in Coldharbour funded through a grant from the Lambeth Wellbeing Fund – part of 'Project Smith', a Lambeth Council and NHS programme.
"I started these workshops because of my own mental health and family struggles. I personally learnt the benefits of Laughter Yoga and Mindfulness and want to guide people to overcome their own issues. I am confident I can inspire people to find what makes them happy, and just go for it!
Zeenat's motivation is to "make a real difference to the lives in the Coldharbour community in a fun and easy way! It's amazing! One parent told me that she re-learnt how to laugh after not being able to for a long time because of the stresses of life".
Zeenat is applying to the Lambeth Wellbeing Fund again for a new project for mothers and babies, and has applied for more workshops at Longfield Hall. And the time is here for more new projects to apply for funding.
For more information on Zeenat's workshops email her at info@zeelol.com or call 07957 624030.
For more information on the Lambeth Wellbeing Fund see their information pages, email Dave Goslyn or call 07540 095 696.


The Panther track team had great weather for opening day at home vs. Scituate and Middleboro on Tuesday and didn't disappoint spectators by securing two big victories. The wins put the Panthers at 3-0 on the season. Blake Hickey won four events while turning out particularly impressive performances in the hurdles and triple jump, Nate Stephansky and Chris Ryan tossed in two wins of their own and Paul Duffey, Luke Laubacher, and Ryan Henderson all had great days continuing to post great numbers. Jacob Roumanos had an outstanding day in the shot put throwing 43' 1" as well. The team now prepares for the Haverhill Invitational on Saturday.