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Governor Lifts Some COVID-19 Restrictions on Restaurants, Houses of Worship
Published February 1, 2021 • Updated on February 1, 2021 at 5:56 pm
Houses of worship can now hold up to 50% capacity with no limit and the curfew for restaurants has been extended as the COVID-19 positivity rate and hospitalizations continue to trend downward.
For the first time in months, both the positivity rate and the number of hospitalizations have dipped.
Previously, places of worship have had to cut down on their capacity limits. In phase 2.1 reopening, houses of worship could have up to 50% capacity indoors, but that capacity was capped at 100 people with masks and social distancing required.
Now, that 50% capacity stays the same but without any limitations on the number of people.
Governor Ned Lamont said masks and social distancing are still required.
As for restaurants and other businesses, the curfew in place is now extended to 11 p.m. The curfew was previously 10 p.m.
"We appreciate that the governor and his team continue to work with our industry with a focus on reopening Connecticut's economy. This is another step in that direction, and the extra hour will be a benefit to restaurants across the state," Executive Director of the CT Restaurant Association, Scott Dolch, said in a statement.
At the same time, it's important that we take further steps in the weeks ahead, including fully lifting this curfew just as neighboring Massachusetts and Rhode Island have done. Connecticut can continue to be a leader in fighting COVID while also being mindful of our economic recovery," he continued.
These changes will become effective within the next 24 hours, state officials said during a news conference on Monday.
New COVID-19 Numbers
The state's COVID-19 positivity rate remains steady at 3.86% and 73 more people have died of the virus over the weekend.
Connecticut's coronavirus death toll is now 7,119.
Get the latest news on COVID-19 delivered to you. Click here to sign up for our daily coronavirus newsletter.
Of over 101,000 tests performed, 3,931 came back positive. A total of 253,954 state residents have tested positive for the virus since it emerged.
On Friday, Connecticut's COVID-19 test positive rate was down to 3.64%.
Hospitalizations have decreased by 73 over the past three days. The are now 912 people with the virus in state hospitals.
The governor said a little over 10,000 vaccination appointments have to be rescheduled because of Monday's storm.
The snowstorm is impacting coronavirus vaccine clinics in Connecticut.
Vaccine Update
A total of 425,144 doses of the vaccine have been administered in the state.
Of those doses, 338,859 were first doses and 86,285 were second doses.
So far, 45% of people 75 and over have received their first dose.
COVID-19 vaccination update in Connecticut:
➡338,859 1st doses administered
➡86,285 2nd doses administered
➡TOTAL: 425,144 doses administered
So far, 45% of the state's 75+ population has received their 1st dose.
Connecticut remains #3 nationally in vaccine administration. pic.twitter.com/mPN6kBZ887
— Governor Ned Lamont (@GovNedLamont) February 1, 2021
Lamont said there are 4,000 medical reserve corp volunteers helping vaccinate Connecticut residents.
Connecticut remains number 3 nationally in terms of vaccine administration.


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RAF Supports NATO Allies Across Europe
RAF fighter jets, Surveillance and Refuelling aircraft have been in action across Europe this week in support of NATO allies.
Missions included escorting US strategic bombers and scrambling twice to monitor Russian planes operating around the Baltic Nations airspace.
During the early morning of 15 June, Typhoons from RAF Coningsby met up with two USAF B-52 bombers off the North of Scotland as they arrived from their base in the USA to conduct a long-range strategic training mission in the annual NATO BALTOPs exercise in the Baltic region.
Royal Air Force Typhoons of XI(F) Squadron from RAF Coningsby
The Typhoons welcomed the B-52s as they entered UK airspace and then escorted them across the North Sea as they were refuelled by USAF KC-135 tankers from RAF Mildenhall. The Typhoons from XI(F) Sqn were supported by a Voyager tanker from RAF Brize Norton, with coordination being provided by RAF Sentry and Sentinel aircraft from RAF Waddington.
As the B-52s from the 5th Bomb Wing, Minot Air Force Base, North Dakota left Danish airspace, they conducted additional training with French Mirage 2000 and then RAF Typhoon jets from the Baltic Air Policing mission as they flew over Latvia and Estonia.
"NATO remains the best guarantor of our collective security and we continue to stand alongside our allies in the Baltics. That is why, despite the ongoing pandemic, the RAF's participation in this multinational exercise is vital for enhancing Allied capabilities and demonstrating our combined strength."
Defence Secretary
The following day Typhoons from the RAF Lossiemouth based 6 Squadron deployed in Lithuania, switched from the BALTOPS exercise to intercept a SU-24 Fencer E and two SU-35 Flanker M Russian aircraft operating off the Baltic coast. This was followed on the 18 June by another Typhoon scramble to deter and intercept a Russian IL-20 COOT A intelligence gathering aircraft over the Baltic Sea.
Wing Commander Stu Gwinnett, the 135 Expeditionary Air Wing commander that is carrying out the NATO Air Policing mission said:
"These missions have highlighted the flexibility of the NATO Baltic Air Policing mission; whether it's working with the US Bomber Task Force or reacting to live Baltic airspace incursions, we are ready to react. This is a testament to the flexibility, training and professionalism of the deployed RAF personnel here in Lithuania and our ability to work with our NATO partners."
Reflecting on the air activity this week Air Chief Marshal Mike Wigston, the Chief of the Air Staff said:
"In a week that has been a painful reminder of the dangers of what we do, we have also demonstrated air and space power on a global scale, operating at range, at speed, and precisely. Royal Air Force command and control, surveillance, refuelling and fighter aircraft working alongside our NATO allies, patrolling our skies and protecting our shared freedom."

redpajama_set_name: RedPajamaCommonCrawl


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PDF2 PDF |Add To My Favorites | Version: 08/25/14 - Chaptered 08/13/14 - Enrolled 06/03/14 - Amended Senate 05/28/14 - Amended Senate 01/06/14 - Introduced
AB-1438 Sex offenders: certificates of rehabilitation.(2013-2014)
An act to amend Sections 290.5, 3000.1, and 4852.01 of the Penal Code, relating to sex offenders.
[ Approved by Governor August 25, 2014. Filed with Secretary of State August 25, 2014. ]
AB 1438, Linder. Sex offenders: certificates of rehabilitation.
(1) Existing law requires a person who has been convicted of specified sex offenses to register with local law enforcement authorities as a sex offender. Existing law authorizes a person convicted of a felony or a misdemeanor violation of certain sex offenses, the accusatory pleading of which has been dismissed, to file a petition for a certificate of rehabilitation and pardon provided that the petitioner has not been incarcerated since the dismissal of the accusatory pleading, is not on probation for the commission of another felony, and presents satisfactory evidence of 5 years residence in this state prior to the filing of the petition. Under existing law, a petition for a certificate of rehabilitation and pardon does not apply to a person who, among other specified crimes, has been convicted of willfully and lewdly committing any lewd and lascivious act upon the body of a child who is under the age of 14. Under existing law, a person convicted of certain sex offenses is not, upon obtaining a certificate of rehabilitation, relieved of his or her duty to register as a sex offender.
This bill, in addition, would make the provisions for obtaining a certificate of rehabilitation inapplicable to a person who is convicted of engaging in sexual intercourse, sodomy, oral copulation, or sexual penetration with a child who is 10 years of age or younger and would provide that such a person who has obtained a certificate of rehabilitation is not relieved of his or her duty to register as a sex offender. The bill would also make the provisions for obtaining a certificate of rehabilitation inapplicable to a person who is convicted of aggravated sexual assault of a child.
Because certain sex offenders would be precluded from being relieved of the duty to register with local officials, the bill would impose a state-mandated local program.
(2) Existing law requires the period of parole for an inmate sentenced to a life term for aggravated sexual assault of a child and for engaging in sexual intercourse, sodomy, oral copulation, or sexual penetration with a child who is 10 years of age or younger to be for the remainder of the inmate's life.
This bill would instead impose lifetime parole on a person convicted of either of the above offenses.
(3) The California Constitution requires the state to reimburse local agencies and school districts for certain costs mandated by the state. Statutory provisions establish procedures for making that reimbursement.
This bill would provide that no reimbursement is required by this act for a specified reason.
Vote: MAJORITY Appropriation: NO Fiscal Committee: YES Local Program: YES
Section 290.5 of the Penal Code is amended to read:
290.5.
(a) (1) A person required to register under Section 290 for an offense not listed in paragraph (2), upon obtaining a certificate of rehabilitation under Chapter 3.5 (commencing with Section 4852.01) of Title 6 of Part 3, shall be relieved of any further duty to register under Section 290 if he or she is not in custody, on parole, or on probation.
(2) A person required to register under Section 290, upon obtaining a certificate of rehabilitation under Chapter 3.5 (commencing with Section 4852.01) of Title 6 of Part 3, shall not be relieved of the duty to register under Section 290, or of the duty to register under Section 290 for any offense subject to that section of which he or she is convicted in the future, if his or her conviction is for one of the following offenses:
(A) Section 207 or 209 committed with the intent to violate Section 261, 286, 288, 288a, or 289.
(B) Section 220, except assault to commit mayhem.
(C) Section 243.4, provided that the offense is a felony.
(D) Paragraph (1), (2), (3), (4), or (6) of subdivision (a) of Section 261.
(E) Section 264.1.
(F) Section 266, provided that the offense is a felony.
(G) Section 266c, provided that the offense is a felony.
(H) Section 266j.
(I) Section 267.
(J) Section 269.
(K) Paragraph (1) of subdivision (b) of Section 286, provided that the offense is a felony.
(L) Paragraph (2) of subdivision (b) of, or subdivision (c), (d), (f), (g), (i), (j), or (k) of, Section 286.
(M) Section 288.
(N) Paragraph (1) of subdivision (b) of Section 288a, provided that the offense is a felony.
(O) Paragraph (2) of subdivision (b) of, or subdivision (c), (d), (f), (g), (i), (j), or (k) of, Section 288a.
(P) Section 288.5.
(Q) Section 288.7.
(R) Subdivision (a), (b), (d), (e), (f), (g), or (h) of Section 289, provided that the offense is a felony.
(S) Subdivision (i) or (j) of Section 289.
(T) Section 647.6.
(U) The attempted commission of any of the offenses specified in this paragraph.
(V) The statutory predecessor of any of the offenses specified in this paragraph.
(W) Any offense which, if committed or attempted in this state, would have been punishable as one or more of the offenses specified in this paragraph.
(b) (1) Except as provided in paragraphs (2) and (3), a person described in paragraph (2) of subdivision (a) shall not be relieved of the duty to register until that person has obtained a full pardon as provided in Chapter 1 (commencing with Section 4800) or Chapter 3 (commencing with Section 4850) of Title 6 of Part 3.
(2) This subdivision does not apply to misdemeanor violations of Section 647.6.
(3) The court, upon granting a petition for a certificate of rehabilitation pursuant to Chapter 3.5 (commencing with Section 4852.01) of Title 6 of Part 3, if the petition was granted prior to January 1, 1998, may relieve a person of the duty to register under Section 290 for a violation of Section 288 or 288.5, provided that the person was granted probation pursuant to subdivision (d) of Section 1203.066, has complied with the provisions of Section 290 for a continuous period of at least 10 years immediately preceding the filing of the petition, and has not been convicted of a felony during that period.
Section 3000.1 of the Penal Code is amended to read:
(a) (1) In the case of any inmate sentenced under Section 1168 for any offense of first or second degree murder with a maximum term of life imprisonment, the period of parole, if parole is granted, shall be the remainder of the inmate's life.
(2) Notwithstanding any other law, in the case of any inmate sentenced to a life term under subdivision (b) of Section 209, if that offense was committed with the intent to commit a specified sexual offense, Section 269 or 288.7, subdivision (c) of Section 667.51, Section 667.71 in which one or more of the victims of the offense was a child under 14 years of age, or subdivision (j), (l), or (m) of Section 667.61, the period of parole, if parole is granted, shall be the remainder of the inmate's life.
(b) Notwithstanding any other law, when any person referred to in paragraph (1) of subdivision (a) has been released on parole from the state prison, and has been on parole continuously for seven years in the case of any person imprisoned for first degree murder, and five years in the case of any person imprisoned for second degree murder, since release from confinement, the board shall, within 30 days, discharge that person from parole, unless the board, for good cause, determines that the person will be retained on parole. The board shall make a written record of its determination and transmit a copy of it to the parolee.
(c) In the event of a retention on parole pursuant to subdivision (b), the parolee shall be entitled to a review by the board each year thereafter.
(d) There shall be a hearing as provided in Sections 3041.5 and 3041.7 within 12 months of the date of any revocation of parole of a person referred to in subdivision (a) to consider the release of the inmate on parole and, notwithstanding paragraph (3) of subdivision (b) of Section 3041.5, there shall be annual parole consideration hearings thereafter, unless the person is released or otherwise ineligible for parole release. The panel or board shall release the person within one year of the date of the revocation unless it determines that the circumstances and gravity of the parole violation are such that consideration of the public safety requires a more lengthy period of incarceration or unless there is a new prison commitment following a conviction.
(e) The provisions of Section 3042 shall not apply to any hearing held pursuant to this section.
Section 4852.01 of the Penal Code is amended to read:
(a) Any person convicted of a felony who has been released from a state prison or other state penal institution or agency in California, whether discharged on completion of the term for which he or she was sentenced or released on parole prior to May 13, 1943, who has not been incarcerated in a state prison or other state penal institution or agency since his or her release, and who presents satisfactory evidence of a three-year residence in this state immediately prior to the filing of the petition for a certificate of rehabilitation and pardon provided for by this chapter, may file the petition pursuant to the provisions of this chapter.
(b) Any person convicted of a felony who, on May 13, 1943, was confined in a state prison or other institution or agency to which he or she was committed and any person convicted of a felony after that date who is committed to a state prison or other institution or agency may file a petition for a certificate of rehabilitation and pardon pursuant to the provisions of this chapter.
(c) Any person convicted of a felony or any person who is convicted of a misdemeanor violation of any sex offense specified in Section 290, the accusatory pleading of which has been dismissed pursuant to Section 1203.4, may file a petition for certificate of rehabilitation and pardon pursuant to the provisions of this chapter if the petitioner has not been incarcerated in any prison, jail, detention facility, or other penal institution or agency since the dismissal of the accusatory pleading and is not on probation for the commission of any other felony, and the petitioner presents satisfactory evidence of five years residence in this state prior to the filing of the petition.
(d) This chapter shall not apply to persons serving a mandatory life parole, persons committed under death sentences, persons convicted of a violation of Section 269, subdivision (c) of Section 286, Section 288, subdivision (c) of Section 288a, Section 288.5, Section 288.7, or subdivision (j) of Section 289, or persons in military service.
(e) Notwithstanding any other law, the Governor has the right to pardon a person convicted of a violation of Section 269, subdivision (c) of Section 286, Section 288, subdivision (c) of Section 288a, Section 288.5, Section 288.7, or subdivision (j) of Section 289, if there are extraordinary circumstances.
No reimbursement is required by this act pursuant to Section 6 of Article XIII B of the California Constitution because the only costs that may be incurred by a local agency or school district will be incurred because this act creates a new crime or infraction, eliminates a crime or infraction, or changes the penalty for a crime or infraction, within the meaning of Section 17556 of the Government Code, or changes the definition of a crime within the meaning of Section 6 of Article XIII B of the California Constitution.


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Review: Shadows: Awakening – Breathing life into the dungeon crawlers
November 1, 2018 November 1, 2018 / Dustin Murphy
With a release in just the past couple of months, Shadows: Awakening finds itself seated within a genre filled with popular games in a similar fashion. But the question is, since its establishment in 2014 release as Shadows: Awakening – Book I with a remastering featuring the never-before-released Book II. But does it hit all the right strides? Find out in our review.
+Challenging puzzles and level designs that require the use of both the Devourer and the puppet
+Combat is deep, meaningful and rich with unique encounters
+Not one level or map feels the same due to the designs made for the each of them
+Lore is extremely deep, putting it right on par with games such as Pillars of Eternity and Diablo
-The difficulty scaling can be surprising and off-putting at first and will be welcoming to those that seek a challenge.
-Load screens can take away from the overall level of immersion
As a fan of games such as Diablo, Sacred and Pillars of Eternity, I'd almost found myself a bit surprised by the silent release of Shadows: Awakening when it released back in August. Now, here we are, almost three months since the games official release on PC, PlayStation 4 and Xbox One.
The surprising part about this games release? I've found that very few people have actually heard of Shadows: Awakening. After all, the isometric dungeon-crawling genre has once more seen a resurgence in recent years. Among those games comes Shadows: Awakening, a game that is deep, accessible, and provides a moderate challenge as an isometric action RPG.
But you might be wondering, how can I call this a dungeon crawling isometric RPG that fits in with the likes of games such as Vikings: Wolves of Midgard, Diablo, Sacred, Pillars of Eternity and that nifty little series Torchlight without acknowledging the many variants that there are? Well, there's a reason that I mention each of these games, a reason that I even bring them into light when it comes to turn-based strategy RPGs to loot-grind based dungeon divers.
There seems to be a lot of inspiration from other games in the RPG genre
Because of how Shadows: Awakening is handled, you may take note of how each of these games is implemented within a single title. In one aspect, you have the loot-based focus that Diablo is established as. One aspect is that it has a deep and meaningful story, its design adds an extra layer of depth alongside a robust set of gameplay mechanics much akin to what Pillars of Eternity when you begin digging deeper into the game itself.
Characters themselves are fleshed out, there are books filled with lore and dialogue options spread across the game much like Sacred and Diablo both have to offer. Even leveling your character's stats is something similar to that of the genre Diablo helped pave the way for. Even with lore and design elements aside, the gameplay itself is deep, is meaningful and it's one that will try even the most experienced of RPG fans alike.
The major change from any of the aforementioned titles is an extra layer of mechanics: You can switch between multiple characters with a press of a button. You can switch between each of the puppets as you progress through the game, allowing you to have four puppets (champions) at a time while also maintaining the Shadow you actually are, the one controlling said puppets. Two of them being the primary characters that you will use throughout your adventure.
Character creation wasn't needed to help drive Shadows: Awakening's amazing experience
Unlike some of the game's I mentioned earlier; namely Torchlight and Sacred; you won't get to design your character in any way. Each character has a set appearance, a set voice, but different armors that you can equip. Along with their own unique armor designs, each character also has their own abilities, lore, base skills, and name that has already been decided for the player.
When the game starts up you will have several important instances play out before you. You will be taking on the role of Devourer, the shadow controlling the puppets, who is a powerful demon that's been summoned by a powerful mage by the name of Krenze. For all intents and purposes, neither like each other and both have a bone to pick with one another.
After your brief introduction to both characters, some basic gameplay element tutorials, you will be tossed instantly into the mess of things, a place where both Krenze and Devourer will have to work together in order to stop a threat much larger than the problems they face from taking over. In the opening seconds, Devourer is tasked with picking one of three characters as a primary puppet he will control through possession.
Once it's time to choose a character, you will be given the chance to choose from Jasker the hunter, Kalig the warrior, and Evia the mage. Once selected – I opted for Evia – your story gets underway and Shadows: Awakening instantly begins to show off how tightly knitted every characters lore is within the world around them. Instantly Evia and Devourer began to discuss what events caused her to be brought back from the dead and what kind of threats are aimed to consume the world.
But due to our lack of wanting to point out spoilers, let's move on and discuss the game itself.
Exploring the world is exciting, challenging, and astonishing
Much like the games that came before it, Shadows: Awakening lays everything out before you. There's a Journal that allows you to track your main story missions, side quests, and your lore incase you get lost. Alongside the Journal you are even given a mini-map in the lower right of the screen, which you will want to pay plenty of attention to as you explore.
Due to how massive the world is, how many sections of the game require you to transition between a puppet and Devourer, it's worth noting the layout of a dungeon or a zone you are in. You will sometimes need to dart off in the form of Devourer and others as a puppet in order to access rooms that would otherwise be inaccessible to one another.
Just like any good RPG of this kind, the mini-map and full-sized map are also filled with Icons on the map which indicate your points of interest such as Cenotaphs (fast travel points), vendors or even a Sanctuary where you can swap out your puppets over the course of the 50+ hour story.
As a side note, you may want to know that once you pick your core character such as Evia, you can't choose another and you are going to need to replay the game in order to experience the story of the others, putting the game itself at 100+ hours to complete. For fans of Diablo or Sacred, you should be no stranger to one such experience and should be rather familiar with this task as it adds to the replayability of isometric RPGs.
Even the dungeons themselves are a delight to experience next to each of the characters. Due to the "fog of war" being present, you will find yourself finding new things each time through. You'll find that you may have missed a treasure in the shadow realm while finding others in the physical and vise versa your first time through.
I found myself even missing out on the chance to encounter a rare boss or two, missing out on the chance to obtain some rare loot due to their spawn. While this was uncommon, it does happen and it's a flustering experience when you go running past the chance to obtain some of the game's harder to obtain loot. Among the exploring for mini-bosses loot, etc.
You will complete puzzles based on finding items, defeating bosses and even doing touch plate based navigation through a grid. The earliest example of this is a grid that's 8 plates by 8 plates. If you misstep on one, you will find your character taking a decent amount of fire damage. In order to complete the puzzle and find your path, you will need to find the path by switching to Devourer before switching back to your puppet and navigating the puzzle in that very fashion.
But this isn't the only mechanic that actually uses the shadow realm to its best. Navigating to sanctuaries to swap out your puppets, finding Shadow Traders who sell and buy Devourer's equipment are present throughout the game. This extra layer of depth expands upon previous experiences and helps bring the lands of the Heretic Kingdom to life.
There is plenty to see, plenty to explore and itemization isn't a problem – but enemies are
Much like any RPG on the market, Shadows: Awakening brings a number of challenges to life from the very beginning. Some include the fact that enemies will have immunities from the very start. Because of this, you may find it best to keep some of your old equipment while making sure to utilize your newer gear to the best you can and even keeping a spare weapon set or two in your arsenal.
Because of this approach, you will sometimes find that elements on your weapons can be more problematic than they are helpful. While there are dozens upon dozens of pieces of equipment from armor pieces to cloaks, boots, rings, etc., there are still things you have to worry about. Some bosses may be immune to multitudes of elements, making it so you will need to use both Devourer and your puppet in order to take them on.
You will even find that, while perks and skills are available via leveling, that you will still need to enhance your weapons through various Essences that you will find scattered through the regions. Some will increase protections, others will increase your damage, and even items such as legendary items will offer a robust set of bonuses for you to use – something similar to that of Diablo and how the game handles item rarities and their bonuses.
Unfortunately, I found myself having to alternate between my puppet(s) and Devourer in order to complete some of the encounters I had. Early on, however, it's more unforgiving and brings the difficulty to an entirely new degree of encounters from the very start. Shadows: Awakening didn't mind throwing out the challenge they had to offer all those years ago and continued doing so through and through.
On the PlayStation 4 it was hard not to take note of minimal load times, beautiful graphics and the density of animations within the characters, particle effects for spells and the ambient density of things such as fog, water and sunshafts from local lighting within the areas I'd explored in my adventures through Shadows: Awakening.
Among them came the buttery smooth framerates that never seemed to dip down below their targeted performance ratios, which seems to be somewhere around 30fps regardless of being on a PlayStation 4 Pro or not. There were moments, very few at that, where a minor dip would happen during chaotic battles with multiple foes in both the physical and shadow planes.
On a side note, it's not often this happens and it shouldn't be considered a major detriment to the overall experience. It's not common you will be overwhelmed by a dozen or more foes on the screen or unimaginable of traps being activated at once. Rather, the game does take a mild approach to the Diablo formula and finds a comfortable place between Diablo and Pillars of Eternity while rewarding the player for their efforts all-the-while.
Whether it's visual or audio-design based, the ambient features within Shadows: Awakening are quite pleasing, enjoyable and really bring home the level of immersion players will have. The only downside, the load screens are plenty when you transition from one zone to another. You'll find yourself facing these load screens rather often as you bump between heading to town and then off to another area within the zone you are in.
After a while, it does become a bit tedious and will chip away at the amounted interest players may have in the game, but it is something that can be addressed with later updates.
The Conclusion – it's time to Awaken
When said and done, Shadows: Awakening is an excellent adventure for those looking for one such genre. Offering the depth of Pillars of Eternity where story matters and even the atmosphere of Sacred while giving forth the need of loot-based rewards equivalent to that of the Diablo series.
While the game does express an increased level of difficulty and design, it's one that can come off as foreboding from the very start. Fights aren't easy, learning the basics of the game such as leveling, itemization, and classes can be a challenge. Maps are large, multi-tiered thanks to both the shadow and physical planes, but underneath it all, Shadows: Awakening is one of the strongest titles out there on the market.
Platforms: PC, PlayStation 4 and Xbox One
Version Reviewed: PlayStation 4
Developer: Games Farm
Publisher: Kalypso Media
Cost:$49.99
It's rich in lore, design elements and quirky gameplay mechanics that you may not see anywhere else. For fans of games like Diablo, Torchlight, and Pillars of Eternity style games, this is going to be a must-have as its replayability, its stories and gameplay elements are nothing short of captivating. It'll be one to keep you busy for days on end and will surely keep you busy for many days to come.
Kalypso Media, PC, PS4, Shadows: Awakening, Xbox One
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Aleksandr Legkov
Name Full News Original
Aleksandr Gennadyevich•Legkov
Aleksandr•Legkov
Александр Геннадьевич•Легков
7 May 1983 in Krasnoarmeysk, Moskva (RUS)
Dynamo Khanty-Mansiysk, Khanty-Mansiysk (RUS)
2006 Winter Olympics Cross Country Skiing (Skiing) RUS Aleksandr Legkov
15 kilometres, Men (Olympic) =15
30 kilometres Skiathlon, Men (Olympic) 4
4 × 10 kilometres Relay, Men (Olympic) Russian Federation 8
50 kilometres, Men (Olympic) 1 Gold
4 × 10 kilometres Relay, Men (Olympic) Russian Federation 2 Silver
Listed in Olympians Who Won a Medal at the World Nordic Ski Championships (0–1–1 2007 Sapporo CCS silver: 4×10 km relay; 2013 Val di Fiemme CCS bronze: 4×10 km relay)
Listed in Doping Irregularities at the Olympics (2014 Sochi – 30km skiathlon (11), 50km (1), 4x10 relay (2) – Involved in scheme involving switched samples)
Curling had been a medal sport at the 1924 Olympics, but did not re-gain that status until the 1998 Nagano Olympics. In between, it was a demonstration sport three times (1932, 1988 and 1992).


text:
\section{Introduction}
Named entity recognition (NER) is a fundamental task in NLP. It aims to identify and classify entities in a text into predefined types. It is an essential tool for information retrieval, question answering, \cite{banerjee2019information} and text summarization tasks \cite{patil2016survey,li2020survey}. However, except for some resource-rich monolingual languages, NER annotated data for most other languages are still very limited \cite{kruengkrai2020improving}. Moreover, it is usually time-consuming to annotate such data, particularly for low-resource languages such as multilingual and code-mixed \cite{liu2021mulda}. Therefore, transfer learning and leveraging the other datasets for multilingual and code-mixed NER has attracted growing interest recently, especially with the influx of deep learning methods.

This paper presents the system description for named entity recognition on the code-mixed dataset. Code-mixing is defined as using two or more languages in a single sentence or utterance \cite{dowlagar2021offlangone}. The use of code-mixed language is prevalent in most multilingual societies. Due to linguistic complexity arising from mixing two languages, the processing of code-mixed sentences is a challenging task \cite{bali2014borrowing}. So, the models that are trained on monolingual and multilingual datasets typically fail to handle code-mixed inputs \cite{khanuja2020new}. Therefore, to encourage research on code-mixing, the speech and NLP communities are organizing several shared tasks. The shared tasks have concentrated on language identification, POS-tagging, sentiment analysis, hate speech detection, and several datasets exist for these as well. Similarly, SEMEVAL 2022's Task 11 sub-task 13 was devoted to identifying named entities in code-mixed languages \cite{multiconer-report}. This task aims to classify the given tokens in the code-mixed sentences as persons, corporation, location, and others. An example is shown in Table \ref{tab:example1}.

\begin{table}[]
\begin{tabular}{|l|llll|}
\hline
\textbf{Sentence} & \textit{hameM} & this & magic & moment \\
\hline
\textbf{Languge} & Hi & En & En & En \\
\hline
\textbf{NER tags} & O & B-CW & I-CW & I-CW \\
\hline
\end{tabular}
\caption{An example of the code-mixed NER annotated sentence. The Hindi words are converted from utf to wx format and are italicized}
\label{tab:example1}
\end{table}

\begin{table*}[]
\begin{tabular}{llllllllll}
\hline
\textit{sIriyala} & \textit{naMbara} & \textit{xvArA} & kleding & in & de & oudheid & \textit{kI} & \textit{pahacAna} & \textit{kareM} \\
O & O & O & B-PROD & I-PROD & I-PROD & I-PROD & O & O & O \\
\hline
what & city & is & dig & me & out & in? &  &  &  \\
O & 0 & O & B-CW & I-CW & I-CW & O &  &  &  \\
\hline
\textit{AmAra} & das & testament & \textit{mUlya} & \textit{kawa?} &  &  &  &  &  \\
0 & B-CW & I-CW & O & O &  &  &  &  & \\
\hline
\end{tabular}
\caption{An example of the code-mixed NER annotated sentence. The multilingual words are converted from utf to wx format and are italicized}
\label{tab:example2}
\end{table*}

The lack of annotated data is a crucial issue for code-mixed datasets. Lack of data poses a problem of data overfitting and poor entity recognition. The language models trained on such low resource datasets cannot generalize the training data, thus performing low on the test datasets. Several previous studies have used monolingual data as training signals for transfer learning, and these data can also be used in the form of pre-training. Thus, we used a similar approach of including the multilingual data along with the code-mixed dataset.

We used the multilingual pre-trained BERT model as our model for code-mixed NER. The model uses code mixed training data along with the multilingual training and mulilingual validation data.

We have analyzed that Bi-LSTM with CRF models has shown an improved accuracy on the token classification tasks such as POS tagging, language identification, and NER. The ensemble of BERT or XLM-RoBERTa with Bi-LSTM and CRF would have shown a further improvement in the code-mixed NER. Also, using language identification as a downstream task with the current method might have improved the NER's accuracy.

The paper is organized as follows. Section \ref{rw} provides related work on Named Entity Recognition on CM social media text. Section \ref{data} provides information on the task and examples of datasets. Section \ref{work} describes the proposed work. Section \ref{experimental_setup} presents the experimental setup and Section \ref{results} project the performance of the model. Section \ref{conclude} concludes our work.

\section{Related Work}
\label{rw}
Code-mixed NER has attracted a lot of attention in the NLP community this decade. This section lists the latest works on code-mixed named entity recognition.

\citet{priyadharshini2020named,winata2019learning} generated multilingual meta representations from pre-trained monolingual word embeddings. The model learned to construct the best word representation by mixing multiple sources without explicit language identification.

\citet{aguilar2019named} presented a shared task on named entity recognition in the CALCS workshop. The language pairs used were English-Spanish (ENG-SPA) and Modern Standard Arabic Egyptian (MSA-EGY). They used Twitter data and nine entity types to establish a new dataset for code-switched NER benchmarks. The participating teams used LSTM, CNN, CRF, and word representations to recognize named entities.

\citet{singh2018named} We presented a new Code-Mixed Hinglish corpus for NER. Different machine learning classification algorithms with word, character, and lexical features are used to establish baselines. The algorithms used were Decision tree, Long Short-Term Memory (LSTM), and Conditional Random Field (CRF).

\cite{meng2021gemnet,fetahu2021gazetteer} presented a novel CM NER model. They proposed a gated architecture that enhances existing multilingual Transformers by dynamically infusing multilingual knowledge bases, a.k.a gazetteers. The evaluation of code-mixed queries shows that this approach efficiently utilizes gazetteers to recognize entities in code-mixed queries with an F1=68\%, an absolute improvement of +31\% over a non-gazetteer baseline.

\cite{meng2021gemnet} mentioned that including Gazetteer features could cause models to overuse or underuse them, leading to poor generalization. They proposed a new approach for gazetteer knowledge integration by including Context in Gazetteer Representation using encoder and Mixture-of-Experts gating network models. These models overcome the feature overuse issue by learning to conditionally combine the context and gazetteer features instead of assigning them fixed weights.

\section{Task Setup}
\label{data}
The shared task detects semantically ambiguous and complex entities in short and low-context settings. Complex NEs, like the titles of creative works (movie/book/song/software names), are not simple nouns. Usually, they take imperative clauses, or they often resemble typical syntactic constituents. Such NEs are harder to recognize \cite{ashwini2014targetable}. Syntactic parsing of such complex noun phrases is hard, and most NER systems fail to identify them. Inside–outside–beginning (IOB) format \cite{ramshaw1999text} is used for annotating entities. A few examples of complex NEs and ambiguous NEs from the code-mixed dataset are given in Table \ref{tab:example2}.

So the MultiCoNER shared task encourages the models to handle such complex NEs. A huge dataset \cite{multiconer-data} is released for this task \cite{multiconer-report}. The languages focused on in this shared task are: English, Spanish, Dutch, Russian, Turkish, Korean, Farsi, German, Chinese, Hindi, and Bangla. The shared task also offered an additional track with code-mixed and multilingual datasets. In this paper, we will be concentrating on the code-mixed dataset.

\section{System Overview}
\label{work}
We finetuned the pre-trained multilingual BERT model by using the multilingual training and validation datasets for code-mixed named entity recognition. We found that the training data is insufficient for the deep learning language model to identify the named entities in the validation data correctly. The data scarcity of low-resource languages has been a significant challenge for building NLP systems since they require a large amount of data to learn a robust model. We observed that the multilingual NER training data is similar to the code-mixed dataset. Also, it is relatively large when compared to the code-mixed dataset. In our approach, the multilingual training and validation data is combined with the code-mixed training dataset. Using the combined dataset, we finetune the deep neural network model. Our method thus attempts to learn language-agnostic features by using the combined multilingual and code-mixed dataset. This finetuned model can be used to infer named entity information at a token level on a code-mixed low resource language.

We used the pre-trained mBERT \cite{devlin2018bert} model for code-mixed NER. mBERT is a transformer-based self multi-headed attention model that is pre-trained on a massive collection of multilingual data and can be finetuned for our NER task. As the model is pre-trained on a large corpus, the semantic and syntactic information is well modeled and can be directly finetuned for a specific task. BERT is a bi-directional transformer model \cite{vaswani2017attention}. It analyzes the meaning of a term depending on its context given on both sides. The transformer part in the BERT works like an attention mechanism capable of learning the contextual relationships between the terms in a sentence.

\section{Experimental Setup}
\label{experimental_setup}
The section presents the baselines, hyper-parameter settings, and analysis of observed results.

\subsection{Baselines}

The baselines used for the proposed work is:

\paragraph{Conditional random field (CRF) \cite{lafferty2001conditional}} CRF is a statistical model and is a well-known approach for handling NER problems. The CRF model considers the neighboring samples by modeling the prediction as a graphical model. It assumes that the tag for the present word (denoted as $y_{i}$) is dependent on the tag of its previous/next word (denoted as $y_{i-1}$ or $y_{i+1}$).

\paragraph{MultiCoNER baseline \cite{multiconer-report}} The XLM-RoBERTa base with CRF model is used as a baseline for NER.

\paragraph{Pre-trained multilingual BERT (mBERT) \cite{devlin2018bert}} A pre-trained multilingual BERT model with token classification without leveraging the multilingual data is used as a baseline.

\subsection{Hyperparameters and libraries}

For developing our model, the neural network library used is PyTorch, and the pre-trained multilingual BERT model (\textit{bert-base-multilingual-cased}) and XLM-ROBERTa base model (\textit{xlm-roberta-base}) is obtained from the hugging face-transformers library and is finetuned for the code-mixed NER task. The model is implemented in Kaggle Notebook with GPU processing.

The batch size of the datasets is kept as 64. The maximum length of the sentence from the training data is considered during the input data encoding/padding. Due to subword tokenization, we used the first token for predicting the tag. The optimizer used is weighted Adam with the learning rate of 2e-5 and epsilon value equal to 1e-5. The dropout is set to 0.1. The loss function used is a cross-entropy loss that is inbuilt into the transformer's BERT model. The number of epochs used for training the model is 30. The training is stopped when there is no change in validation accuracy for more than four epochs.

\begin{table*}[]
\centering
\begin{tabular}{|l|ll|ll|}
\hline
\textbf{Model} & \multicolumn{4}{c|}{\textbf{F1-score}} \\ \cline{2-5}
 & \multicolumn{2}{l|}{\textbf{w/o multilingual data}} & \multicolumn{2}{l|}{\textbf{with multilingual data}} \\ \cline{2-5}
 & \textbf{valid data} & \textbf{test data} & \textbf{valid data} & \textbf{test data} \\ \hline
CRF & 0.565 & 0.561 & 0.560 & 0.556 \\
mBERT & 0.627 & 0.612 &\textbf{0.716} & \textbf{0.707} \\
MultiCoNER baseline & 0.651 & 0.645 & 0.725 & 0.719 \\
\hline
\end{tabular}
\caption{The performance of the models on the code-mixed dataset with and without including multilingual data. Our submission for the given task is highlighted.}
\label{tab:result}
\end{table*}

\begin{figure}[h]
\includegraphics[width = \columnwidth]{cm_ner_initial.png}
\caption{Confusion matrix of CM-NER baseline}
\label{cm1}
\end{figure}

\begin{figure}[h]
\includegraphics[width = \columnwidth]{cm_ner_final.png}
\caption{Confusion matrix of CM-NER by leveraging multilingual data}
\label{cm2}
\end{figure}

The CRF is obtained from pytorch-crfsuite library\footnote{https://github.com/scrapinghub/python-crfsuite}. The previous word and its tag, the next word, and its tag is used as the features to predict the tag of the current word.

\section{Results and Analysis}
\label{results}
Table \ref{tab:result} presents the f1-score of the models on the Dravidian code-mixed dataset. From the above results, it is clear that our system, i.e., leveraging the multilingual NER data in a low-resource code-mixed setting, improves the NER task compared to the baseline models. The CRF model didn't perform well on the given NER task, as this statistical model does not capture the semantics of the tokens. Even the CRF with multilingual data performed poorly on this task compared to the baseline NN models. It shows the importance of capturing semantical, syntactic, and contextual information while building the NER model on these complex datasets.

Our submission, the pre-trained mBERT by leveraging the multilingual dataset, performed better than the MultiCoNER baseline by 6\%. Even the MultiCoNER baseline with the multilingual dataset performed better than our submission.

The confusion matrices with and without multilingual data of our submission on the code-mixed NER validation dataset are shown in the Figures \ref{cm1} and \ref{cm2}. By using confusion matrices, we observed that the multilingual data given in Figure \ref{cm2} helped better identify the  CW, PROD, CROP, and LOC entities when compared to the baseline model.

\section{Conclusion and future work}
\label{conclude}
In this paper, we addressed the shared task on named entity recognition for the code-mixed dataset. As the code-mixed data is a low resource language and there are no pre-trained models, we leveraged the multilingual dataset for training the NER model. The model used for testing our method is the pre-trained multilingual BERT. We finetuned the pre-trained mBERT for the code-mixed NER task by using the code-mixed training data and multilingual training and validation datasets.

The use of meta embeddings for dealing with code-mixed datasets has recently attracted a lot of attention. It might be possible that meta embedding-based NER will work better on this code-mixed dataset. Unlike the social media data where code-mixed sentences/words are written in Roman script, the native script is used for each word, so the language identification will work better on this dataset. Using Language identification or POS tagging as a downstream task for NER on this dataset will help in improving the code-mixed NER.

\section{Introduction}
Named entity recognition (NER) is a fundamental task in NLP. It aims to identify and classify entities in a text into predefined types. It is an essential tool for information retrieval, question answering, \cite{banerjee2019information} and text summarization tasks \cite{patil2016survey,li2020survey}. However, except for some resource-rich monolingual languages, NER annotated data for most other languages are still very limited \cite{kruengkrai2020improving}. Moreover, it is usually time-consuming to annotate such data, particularly for low-resource languages such as multilingual and code-mixed \cite{liu2021mulda}. Therefore, transfer learning and leveraging the other datasets for multilingual and code-mixed NER has attracted growing interest recently, especially with the influx of deep learning methods.

This paper presents the system description for named entity recognition on the code-mixed dataset. Code-mixing is defined as using two or more languages in a single sentence or utterance \cite{dowlagar2021offlangone}. The use of code-mixed language is prevalent in most multilingual societies. Due to linguistic complexity arising from mixing two languages, the processing of code-mixed sentences is a challenging task \cite{bali2014borrowing}. So, the models that are trained on monolingual and multilingual datasets typically fail to handle code-mixed inputs \cite{khanuja2020new}. Therefore, to encourage research on code-mixing, the speech and NLP communities are organizing several shared tasks. The shared tasks have concentrated on language identification, POS-tagging, sentiment analysis, hate speech detection, and several datasets exist for these as well. Similarly, SEMEVAL 2022's Task 11 sub-task 13 was devoted to identifying named entities in code-mixed languages \cite{multiconer-report}. This task aims to classify the given tokens in the code-mixed sentences as persons, corporation, location, and others. An example is shown in Table \ref{tab:example1}.

\begin{table}[]
\begin{tabular}{|l|llll|}
\hline
\textbf{Sentence} & \textit{hameM} & this & magic & moment \\
\hline
\textbf{Languge} & Hi & En & En & En \\
\hline
\textbf{NER tags} & O & B-CW & I-CW & I-CW \\
\hline
\end{tabular}
\caption{An example of the code-mixed NER annotated sentence. The Hindi words are converted from utf to wx format and are italicized}
\label{tab:example1}
\end{table}

\begin{table*}[]
\begin{tabular}{llllllllll}
\hline
\textit{sIriyala} & \textit{naMbara} & \textit{xvArA} & kleding & in & de & oudheid & \textit{kI} & \textit{pahacAna} & \textit{kareM} \\
O & O & O & B-PROD & I-PROD & I-PROD & I-PROD & O & O & O \\
\hline
what & city & is & dig & me & out & in? &  &  &  \\
O & 0 & O & B-CW & I-CW & I-CW & O &  &  &  \\
\hline
\textit{AmAra} & das & testament & \textit{mUlya} & \textit{kawa?} &  &  &  &  &  \\
0 & B-CW & I-CW & O & O &  &  &  &  & \\
\hline
\end{tabular}
\caption{An example of the code-mixed NER annotated sentence. The multilingual words are converted from utf to wx format and are italicized}
\label{tab:example2}
\end{table*}

The lack of annotated data is a crucial issue for code-mixed datasets. Lack of data poses a problem of data overfitting and poor entity recognition. The language models trained on such low resource datasets cannot generalize the training data, thus performing low on the test datasets. Several previous studies have used monolingual data as training signals for transfer learning, and these data can also be used in the form of pre-training. Thus, we used a similar approach of including the multilingual data along with the code-mixed dataset.

We used the multilingual pre-trained BERT model as our model for code-mixed NER. The model uses code mixed training data along with the multilingual training and mulilingual validation data.

We have analyzed that Bi-LSTM with CRF models has shown an improved accuracy on the token classification tasks such as POS tagging, language identification, and NER. The ensemble of BERT or XLM-RoBERTa with Bi-LSTM and CRF would have shown a further improvement in the code-mixed NER. Also, using language identification as a downstream task with the current method might have improved the NER's accuracy.

The paper is organized as follows. Section \ref{rw} provides related work on Named Entity Recognition on CM social media text. Section \ref{data} provides information on the task and examples of datasets. Section \ref{work} describes the proposed work. Section \ref{experimental_setup} presents the experimental setup and Section \ref{results} project the performance of the model. Section \ref{conclude} concludes our work.

\section{Related Work}
\label{rw}
Code-mixed NER has attracted a lot of attention in the NLP community this decade. This section lists the latest works on code-mixed named entity recognition.

\citet{priyadharshini2020named,winata2019learning} generated multilingual meta representations from pre-trained monolingual word embeddings. The model learned to construct the best word representation by mixing multiple sources without explicit language identification.

\citet{aguilar2019named} presented a shared task on named entity recognition in the CALCS workshop. The language pairs used were English-Spanish (ENG-SPA) and Modern Standard Arabic Egyptian (MSA-EGY). They used Twitter data and nine entity types to establish a new dataset for code-switched NER benchmarks. The participating teams used LSTM, CNN, CRF, and word representations to recognize named entities.

\citet{singh2018named} We presented a new Code-Mixed Hinglish corpus for NER. Different machine learning classification algorithms with word, character, and lexical features are used to establish baselines. The algorithms used were Decision tree, Long Short-Term Memory (LSTM), and Conditional Random Field (CRF).

\cite{meng2021gemnet,fetahu2021gazetteer} presented a novel CM NER model. They proposed a gated architecture that enhances existing multilingual Transformers by dynamically infusing multilingual knowledge bases, a.k.a gazetteers. The evaluation of code-mixed queries shows that this approach efficiently utilizes gazetteers to recognize entities in code-mixed queries with an F1=68\%, an absolute improvement of +31\% over a non-gazetteer baseline.

\cite{meng2021gemnet} mentioned that including Gazetteer features could cause models to overuse or underuse them, leading to poor generalization. They proposed a new approach for gazetteer knowledge integration by including Context in Gazetteer Representation using encoder and Mixture-of-Experts gating network models. These models overcome the feature overuse issue by learning to conditionally combine the context and gazetteer features instead of assigning them fixed weights.

\section{Task Setup}
\label{data}
The shared task detects semantically ambiguous and complex entities in short and low-context settings. Complex NEs, like the titles of creative works (movie/book/song/software names), are not simple nouns. Usually, they take imperative clauses, or they often resemble typical syntactic constituents. Such NEs are harder to recognize \cite{ashwini2014targetable}. Syntactic parsing of such complex noun phrases is hard, and most NER systems fail to identify them. Inside–outside–beginning (IOB) format \cite{ramshaw1999text} is used for annotating entities. A few examples of complex NEs and ambiguous NEs from the code-mixed dataset are given in Table \ref{tab:example2}.

So the MultiCoNER shared task encourages the models to handle such complex NEs. A huge dataset \cite{multiconer-data} is released for this task \cite{multiconer-report}. The languages focused on in this shared task are: English, Spanish, Dutch, Russian, Turkish, Korean, Farsi, German, Chinese, Hindi, and Bangla. The shared task also offered an additional track with code-mixed and multilingual datasets. In this paper, we will be concentrating on the code-mixed dataset.

\section{System Overview}
\label{work}
We finetuned the pre-trained multilingual BERT model by using the multilingual training and validation datasets for code-mixed named entity recognition. We found that the training data is insufficient for the deep learning language model to identify the named entities in the validation data correctly. The data scarcity of low-resource languages has been a significant challenge for building NLP systems since they require a large amount of data to learn a robust model. We observed that the multilingual NER training data is similar to the code-mixed dataset. Also, it is relatively large when compared to the code-mixed dataset. In our approach, the multilingual training and validation data is combined with the code-mixed training dataset. Using the combined dataset, we finetune the deep neural network model. Our method thus attempts to learn language-agnostic features by using the combined multilingual and code-mixed dataset. This finetuned model can be used to infer named entity information at a token level on a code-mixed low resource language.

We used the pre-trained mBERT \cite{devlin2018bert} model for code-mixed NER. mBERT is a transformer-based self multi-headed attention model that is pre-trained on a massive collection of multilingual data and can be finetuned for our NER task. As the model is pre-trained on a large corpus, the semantic and syntactic information is well modeled and can be directly finetuned for a specific task. BERT is a bi-directional transformer model \cite{vaswani2017attention}. It analyzes the meaning of a term depending on its context given on both sides. The transformer part in the BERT works like an attention mechanism capable of learning the contextual relationships between the terms in a sentence.

\section{Experimental Setup}
\label{experimental_setup}
The section presents the baselines, hyper-parameter settings, and analysis of observed results.

\subsection{Baselines}

The baselines used for the proposed work is:

\paragraph{Conditional random field (CRF) \cite{lafferty2001conditional}} CRF is a statistical model and is a well-known approach for handling NER problems. The CRF model considers the neighboring samples by modeling the prediction as a graphical model. It assumes that the tag for the present word (denoted as $y_{i}$) is dependent on the tag of its previous/next word (denoted as $y_{i-1}$ or $y_{i+1}$).

\paragraph{MultiCoNER baseline \cite{multiconer-report}} The XLM-RoBERTa base with CRF model is used as a baseline for NER.

\paragraph{Pre-trained multilingual BERT (mBERT) \cite{devlin2018bert}} A pre-trained multilingual BERT model with token classification without leveraging the multilingual data is used as a baseline.

\subsection{Hyperparameters and libraries}

For developing our model, the neural network library used is PyTorch, and the pre-trained multilingual BERT model (\textit{bert-base-multilingual-cased}) and XLM-ROBERTa base model (\textit{xlm-roberta-base}) is obtained from the hugging face-transformers library and is finetuned for the code-mixed NER task. The model is implemented in Kaggle Notebook with GPU processing.

The batch size of the datasets is kept as 64. The maximum length of the sentence from the training data is considered during the input data encoding/padding. Due to subword tokenization, we used the first token for predicting the tag. The optimizer used is weighted Adam with the learning rate of 2e-5 and epsilon value equal to 1e-5. The dropout is set to 0.1. The loss function used is a cross-entropy loss that is inbuilt into the transformer's BERT model. The number of epochs used for training the model is 30. The training is stopped when there is no change in validation accuracy for more than four epochs.

\begin{table*}[]
\centering
\begin{tabular}{|l|ll|ll|}
\hline
\textbf{Model} & \multicolumn{4}{c|}{\textbf{F1-score}} \\ \cline{2-5}
 & \multicolumn{2}{l|}{\textbf{w/o multilingual data}} & \multicolumn{2}{l|}{\textbf{with multilingual data}} \\ \cline{2-5}
 & \textbf{valid data} & \textbf{test data} & \textbf{valid data} & \textbf{test data} \\ \hline
CRF & 0.565 & 0.561 & 0.560 & 0.556 \\
mBERT & 0.627 & 0.612 &\textbf{0.716} & \textbf{0.707} \\
MultiCoNER baseline & 0.651 & 0.645 & 0.725 & 0.719 \\
\hline
\end{tabular}
\caption{The performance of the models on the code-mixed dataset with and without including multilingual data. Our submission for the given task is highlighted.}
\label{tab:result}
\end{table*}

\begin{figure}[h]
\includegraphics[width = \columnwidth]{cm_ner_initial.png}
\caption{Confusion matrix of CM-NER baseline}
\label{cm1}
\end{figure}

\begin{figure}[h]
\includegraphics[width = \columnwidth]{cm_ner_final.png}
\caption{Confusion matrix of CM-NER by leveraging multilingual data}
\label{cm2}
\end{figure}

The CRF is obtained from pytorch-crfsuite library\footnote{https://github.com/scrapinghub/python-crfsuite}. The previous word and its tag, the next word, and its tag is used as the features to predict the tag of the current word.

\section{Results and Analysis}
\label{results}
Table \ref{tab:result} presents the f1-score of the models on the Dravidian code-mixed dataset. From the above results, it is clear that our system, i.e., leveraging the multilingual NER data in a low-resource code-mixed setting, improves the NER task compared to the baseline models. The CRF model didn't perform well on the given NER task, as this statistical model does not capture the semantics of the tokens. Even the CRF with multilingual data performed poorly on this task compared to the baseline NN models. It shows the importance of capturing semantical, syntactic, and contextual information while building the NER model on these complex datasets.

Our submission, the pre-trained mBERT by leveraging the multilingual dataset, performed better than the MultiCoNER baseline by 6\%. Even the MultiCoNER baseline with the multilingual dataset performed better than our submission.

The confusion matrices with and without multilingual data of our submission on the code-mixed NER validation dataset are shown in the Figures \ref{cm1} and \ref{cm2}. By using confusion matrices, we observed that the multilingual data given in Figure \ref{cm2} helped better identify the  CW, PROD, CROP, and LOC entities when compared to the baseline model.

\section{Conclusion and future work}
\label{conclude}
In this paper, we addressed the shared task on named entity recognition for the code-mixed dataset. As the code-mixed data is a low resource language and there are no pre-trained models, we leveraged the multilingual dataset for training the NER model. The model used for testing our method is the pre-trained multilingual BERT. We finetuned the pre-trained mBERT for the code-mixed NER task by using the code-mixed training data and multilingual training and validation datasets.

The use of meta embeddings for dealing with code-mixed datasets has recently attracted a lot of attention. It might be possible that meta embedding-based NER will work better on this code-mixed dataset. Unlike the social media data where code-mixed sentences/words are written in Roman script, the native script is used for each word, so the language identification will work better on this dataset. Using Language identification or POS tagging as a downstream task for NER on this dataset will help in improving the code-mixed NER.

redpajama_set_name: RedPajamaArXiv


text:
Ambushed by Schumer: Surprise Medical Billing Bill Bites The Dust
Being ambushed by "surprise billing" is often a legal form of patient abuse, immorally and outrageously administered by the powerful to the powerless. The thoughtfully developed and widely supported bipartisan Murray-Alexander bill would have helped a lot.
How depressing it was to read that it was the Minority Leader of the Senate, Chuck Schumer, tightly tied to the concentrated powerful medical institutions, who scotched this example of rare cooperation in giving succor to the oppressed.
Is Senator Schumer's "wait 'til next year" really an appropriate expression of empathy for the harm that he has just done, allowing once again the swampy adage, them that has is them that gets?
Despicable.
I have the honor of having my article to this effect posted on Benj Demott's interesting FirstoftheMonth.org. Here is the link http://www.firstofthemonth.org/surprise-medical-billing-outrage-and-betrayal-thy-name-is-schumer/
And here is the text:
Surprise Medical Billing: Outrage and Betrayal (Thy Name is Schumer)
December 23, 2019 by Budd Shenkin
Congress can rarely ameliorate even patent outrages. We know this. One person's outrage is another person's profit, and the profiteer usually has more cash and more leverage than the outragee. But, it looked like one medical outrage was actually about to be solved, at least partially, by this highly partisan Congress, and highly partisan administration.
The outrageous problem they were on the verge of solving is so-called "surprise medical bills." I've posted about it here. The surprise bills come when you dutifully report to a hospital that is in-network to you, or in the case of my son who had a tree fall on him in the middle of the night, a state-assigned trauma center, and the hospital is paid by your insurance as it should be. But then it turns out that the ER doc, or the anesthesiologist, or the radiologist, or the trauma surgeon, or the pathologist – in short, all those specialties that you need for your case, are not in the network. They didn't need to be, because what's in it for them? The hospitals join the insurance network because they have to, to ensure that patients come to them. But once you're there, the docs don't have to compete with anyone for your business, you're their red meat. So they remain "out of network," and they send you a huge bill that your insurance will only pay part of. That's the surprise. It's not like it's a birthday surprise, it's more like legal robbery. And it can amount to a very large amount of money.
This has been a problem for a very long time, but somehow it has come to a head and, along with the scandal of how expensive medicines have become, it made its way onto the congressional agenda. And, notwithstanding the terribly partisan environment, progress was made with Patty Murray (Dem) and Lamar Alexander (GOP) coming up with a plan whereby the docs' permissible bills would be related to the average in-network bills for their services. The plan had wide support.
Until it didn't. The fix was set to be included in the recently passed appropriation bill, until at the last minute, the very last minute, it was dropped. There was opposition from those to whom the outrage was a source of profit. That would be many large medical centers, and notably, the large private equity groups who have bought groups like ER docs, put them together nationwide, had each one of the docs drop out of all networks, and raise their standard charges by as much as nearly 100%. One person's outrage is another person's – or corporation's – profit.
I've been waiting to see who succumbed to the pressure, and the bribes of contributions to campaign funds. I wasn't sure, but here is the story in the WaPo: https://www.washingtonpost.com/business/economy/congress-showers-health-care-industry-with-multi-billion-victory-after-wagging-finger-at-it-for-much-of-2019/2019/12/19/9422aa6a-2028-11ea-9146-6c3a3ab1be6c_story.html
Turns out a major villain of the piece is our old friend, the ever-ingratiating Chuck Schumer. "A bipartisan push to curb the practice of surprise medical billing was delayed until next year, with Senate Minority Leader Charles E. Schumer (D-N.Y.) working behind the scenes to raise objections to the package, according to three people familiar with the talks who spoke on the condition of anonymity to share details of private negotiations."
I might have known. He who makes his points with glasses on end of nose, convincing the podium of his case as he reads what has been prepared for him, occasionally "with expression," occasionally looking up and looking around…He who takes every cue and every dollar available from our Bibi supporters at AIPAC. The article says that Chuck has deep ties to the large medical institutions of New York City. As Gomer says, surprise, surprise, surprise. They're making the money from surprise billing – through the docs – and they're supporting those docs as the docs support them. The public? "Next year" we'll consider it. Right. But meanwhile big time capitalistic monopolistic government-owning big medicine is getting everything it wants.
Every study of the problems of health care organization points to primary care as needing the most bolstering, and excess profits of concentration centering on large medical centers and specialty groups as the problem. Doesn't matter to Chuck, it seems.
I'm just seething. More fleecing of the American ordinary person by large corporations in concert with paid off politicians.
They're not all like that. Elizabeth Warren recounts how she found out what it meant to be "at the table" when she sat down with Barney Frank as they put together Dodd-Frank. She wanted something, Barney looked around the table, saw no objection, so he told her, "Ok – you've got that." Chuck is the definition of the table. No matter the wide support, the bipartisanship, the outrage. Let's get some money for the DCCC or whatever, let's keep those relationships with the monopolists going, we're at the table.
Don't watch them making sausages, they say. There are lots of trade-offs to be made. Don't concentrate just on your one issue, take a broad view.
That may be wise. But to me, concerned as I am about the health care system, this schmear looks like an unholy payoff to monopolists for legal bribes.
Addendum: This article about medical bills and insurers is relevant. Fraud is involved here, but being "out of network" is key. And notice how little insurance surveillance matters.
Second addendum: This article shows that certain hospital based specialties especially prone to surprise billings - especially diagnostic radiology, anesthesiology, emergency medicine, and pathology -- have especially high standard charges. https://www.brookings.edu/blog/usc-brookings-schaeffer-on-health-policy/2019/12/05/provider-charges-relative-to-medicare-rates-2012-2017/
Third addendum (1/7/2020): This article shows how legal challenges can help individuals contest surprise bills. Now all we need is a national legal service that advertises on TV -- call this number and we'll help you contest your surprise bills! https://www.statnews.com/2020/01/06/3-legal-challenges-derail-surprise-medical-bills/
Presidential Pardon - A Long Article, and an Oped
Back in February I wrote a blog post suggesting that the power of the Presidential pardon has run into problems of abuse, and needs to be reformed. I suggested requiring each pardon to be co-signed by the Speaker of the House. Here's that post:
http://buddshenkin.blogspot.com/2019/02/proposal-presidential-pardon-reform.html.
Then I acquired an excellent co-author, my friend of 30 years David Levine, who is Raymond Sullivan Professor of Law at UC Hastings College of the Law in San Francisco. Together we wrote a 6,000 word article advocating this proposal, which was published in the Hastings Constitutional Law Quarterly:
https://repository.uchastings.edu/cgi/viewcontent.cgi?article=2099&context=hastings_constitutional_law_quaterly
And now we have reduced those 6,000 words to a succinct 650 words oped, which has just been published in the East Bay Times:
https://www.eastbaytimes.com/2019/12/15/opinion-reform-presidential-pardon-after-ford-bush-clinton-trump-abuses/.
Frankly, it's a thrill for David and me to see our work make it to both legal and popular publications. Beyond that, maybe it's even possible that the idea will make it to the stage of formal consideration when post-Trump reforms are being entertained. That would not only be a thrill for us, but a source of great satisfaction for helping to support the idea of America, which animates both of us.
Here is the text of the oped:
A few weeks ago, President Trump pardoned convicted or accused war criminals over the profound objection of his most senior military officials, one of whom, the Secretary of the Navy, resigned in protest.
Obviously, this is not the only presidential power the President has used in an innovative and personal manner, rather than as originally intended and customarily exercised. In each case, he is forcing the country to ask itself – is this a power that should be retained, be modified, or be further protected?
We believe that the answers to these questions in the case of the power of the pardon are yes, yes, and yes. And the way to do it, we think, is to modify it by passing a constitutional amendment to require an additional signature for each pardon. That signature would come from the highest ranking independently elected constitutional official in the line of succession, the Speaker of the House.
We inherited the power to pardon from the English monarchy. Alexander Hamilton explained in Federalist 74 that sometimes mercy from on high is necessary to improve justice, and sometimes to secure civil harmony by forgiving and forgetting. Examples of the latter are Washington's pardoning members of the Whiskey Rebellion, Lincoln's and Johnson's pardoning of Civil War soldiers, and Carter's pardoning of Vietnam draft resisters.
Presidents largely fulfilled Hamilton's goals until Ford's controversial pardon of Nixon after Watergate, to end "our long national nightmare." Then in 1992, taking the advice of his Attorney General, William Barr, George H. W. Bush pardoned his friends and colleagues, the Iran-Contra conspirators, which cut short the investigation and shielded him from inquiry into his own possible complicity.
In the waning hours of his presidency in 2000, Bill Clinton pardoned 177 miscreants, over 60 of whom had not been vetted by the Justice Department's Office of Pardons, including fugitive arms dealer Marc Rich and his wife Hillary's brother. George W. Bush commuted the sentence of Scooter Libby, preserving Libby's right to use the Fifth Amendment to constrain any further investigations.
And now we have President Trump, who, in addition to the war criminal cases, has also pardoned racial profiler Sheriff Joe Arpaio from his criminal contempt of court conviction and right wing provocateur Dinesh D'Souza for illegal Republican campaign contributions. Most ominously, Trump has dangled pardons before potential witnesses against him, has claimed he can pardon himself, and few doubt he will pardon his family and other close associates if it becomes necessary.
Clearly, although Trump's excesses are the most extreme, Hamilton's hopes for the pardon have been thwarted too often since Watergate.
What to do? We should not repeal the pardon power, because its value has been proved over centuries. We don't think it would work to add specific restrictions, such as forbidding preemptive pardons, or pardons of close associates and family; such restrictions could never be complete and could be evaded. Much more protective, we think, would be the co-signature requirement.
If this requirement had been in force, would Speaker Carl Albert have co-signed for the Nixon pardon, Tom Foley for Iran-Contra, Dennis Hastert for Rich and for Libby, Paul Ryan for Arpaio, or Nancy Pelosi for the war criminals, and for what may come? We can't know, of course, but bringing a second high official into the process would increase the odds of screening out many questionable pardons.
Constitutional amendments are notoriously and rightfully hard to enact. We believe, however, that our proposal would not have strong entrenched interests against it, and if worse is still to come from Mr. Trump, the urgency of reform might carry the day.
If unreasonable and self-serving pardons were just a momentary lapse with this president, we could hope that traditional norms would reappear with the next. Given the post-Watergate trend, however, we should require another signature to secure Hamilton's vision.
Ambushed by Schumer: Surprise Medical Billing Bill...


Faculty of Agriculture was founded on November 10 1960, under auspices Universitas Kotapraja Malang. Since July 11 1961, by the decree of the president of Republic of Indonesia number 258/K/1961, Universitas Kotapraja Malang has been changed into Universitas Brawijaya.
Faculty of Agriculture, University of Brawijaya had state status by the decree of Minister PTIP number 92 August 1 1962 and July 1 1962 Faculty of Agriculture was under auspices Airlangga University Surabaya. Since January 5 1963, University of Brawijaya was given state status by the decree of Miniter PTIP number 1 year 1963. Faculty of Agriculture which was previously under auspices Airlangga University was given back to University of Brawijaya.
Until 1974, Faculty of Agriculture had two departments which were agricultural engineering and agricultural economy. In 1975, education system had improvement which was applying credit system. In the same year, Faculty of Agriculture opened four departments to improve and adjust to the need demand. They were Agronomy Department, Agricultural Economy Department, Plant Protection Department and Soil Science Department.
In 1978, the education system changed significantly. The change was to begin Bachelor of Agriculture education system with 144 credits within 4 years. Faculty of Agriculture also changed the name of departments. The department which changed the name were Agricultural Cultivation Department (previously Agronomy), Agricultural Economy Department, Pest and Plant Disease Department (previously Plant Protection), Soil Science Department, and Agricultural Technology Department.
Diploma III Agriculture and Plantation Program was founded in 1984. On March 17 1990, the directorate general of higher education issued the decree 14/Dikti/Kep/1990 about the opening of Diploma III Plant Production which combined Dimploma III Plantation and Diploma III Agriculture. Diploma III Agribusiness in faculty of Agriculture, University of Brawijaya was founded based on the decree of directorate general of higher education no.230/DIKTVKep/1999, date May 18 1999. Diploma III of Faculty of Agriculture manages currently three study programs which are Horticultural Plant production (previously Plant Protection), Agribusiness, and Landscape Architecture.
Since 1995, the admission of undergraduate freshmen, Faculty of Agriculture, University of Brawijaya was directly to the study program and it will be directed to respective department in the fifth semester. In 1996, according to rector's instruction University of Brawijaya, faculty of Agriculture opened Undergraduate extension program for all study programs. In 1997, Faculty of Agriculture opened new study program which was Plant Breeding study program based on the decree of directorate general of higher education no.78/DIKTi/Kep/1997.
Based on decree of Ministry of Education and Culture No. 0124/O/1998 date January 26 1998, the Agricultural Technology department officially became Agricultural Technology Faculty.
Until October 2010, Faculty of Agriculture had 141 permanent lecturers comprising 29 professors, 70 doctors, 65 masters, and 6 bachelors of Agriculture and other fields. It also had 84 non-permanent lecturers comprising 11 doctors, 43 masters, and 16 bachelors. The number of staffs faculty was 96 people (80 people were civil servant), while 16 people were honorary staffs.


Once used as a woodlot to fuel the furnaces at the West Barnstable Brick Company, the 86 acre Makepeace sanctuary located in Mashpee is primarily a continuous pitch pine and oak species forest. The land was donated to Orenda by the heirs of Mr. Makepeace, known as "The Cranberry King" for his thriving cranberry growing business.
The 50 acre adjacent Mercy Lowe land was purchased in 2001 at auction. Many generous donors and funders made this acquisition possible.
Six distinct forest types have been identified; all are variations of the pitch pine/oak forest. Other tree species include: red maple, American holly, cherry, American beech, pignut hickory, bigtooth aspen and sassafras. Other vegetation includes: hawthorne, hazelnut, northern arrowwood, witherod and other viburnums, lowbush blueberry, inkberry and other shrubs, as well as pink ladyslippers.
The habitat supports state rare species Eastern box turtle and Pine Barrens Buckmoth.
Makepeace Sanctuary donated by heirs of William Makepeace.
Mercy Lowe land purchased at auction.
Directions: Access by foot only—from the northeast gate of the Carl Monge Sanctuary (opposite Hane Road on Lovells Lane) proceed southwest down Simon Lowe Road (also called Bookers Road) to Grafton Pocknet Road. Then walk north along the dirt road which becomes Bearses Road. Follow road north to Great Hay Road which is gated and comes in from the right. This junction is the northern end of the Makepeace property along Bearses Road. Time? About 45 minutes and yes, it's a hike!
By car, take Quashnet Road off of Great Neck Road north and proceed westward to the locked gate, and park. Quashnet then becomes Wm. Mingo Road which heads due west and joins Grafton Pocknet Road. Grafton Pocket Road runs north and becomes Bearses Road.
Beginning at the junction of Great Hay and Bearses Road the Makepeace property runs on both sides of the dirt road southward for 1,700 feet. At that point, it joins the Mercy Lowe property which also runs on both sides of the road southward for 552 feet.
This entry was posted in Makepeace & Mercy Lowe Sanctuaries, Our Sanctuaries. Bookmark the permalink.


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Choosing a font isn't always difficult, but it can be more time-consuming than necessary. That's because most apps don't let you view fonts side-by-side, so if you want to compare your options, your only choice is to manually make the change. And then, of course, you have to remember exactly what your previous options looked like, and what those fonts were called. A font manager like FontViewOK (free) can make this process a whole lot simpler.
FontViewOK lets you see how your text will look in 213 different fonts, but doesn't let you narrow the list to compare just two or three options.
This free app lets you view your text in a variety of fonts, side-by-side, so you can see which one best fits your needs. All of FontViewOK's options are clearly labeled, so you don't have to worry about remembering any font names.
FontViewOK is a small executable that you run whenever you'd like to use the app--no installation needed. That means it's portable: you can load the file on a USB drive and use it on any Windows-based PC. Its creator, SoftwareOK, boasts that the app is so easy to use that you won't need a help file...so they haven't included one. I'll agree that the app is easy to use: you simply enter your text in the form at the top of the app, and FontViewOK automatically shows you how it will look in 213 different fonts.
But I'd still like a help file, as the app does have a few limitations that could be explained more clearly. For one, you can't search for a certain font; you have to find its name in the drop-down list provided at the top of FontViewOK's window. If you're browsing through 213 options, finding a specific font can take some time, but, then, so can browsing through all of the available fonts in an application like Microsoft Word. Also, I wish the app let you view just two or three font options at once; as it stands now, you can only scroll through all of the fonts available to see how they look. While this gives you plenty of options to choose from, the result can be overwhelming. It would be nice if you could eliminate the options you know you don't want.
These quibbles are minor, though. If you're creating a visual document, such as an invitation or a newsletter, FontViewOK can make it easy to find the best font for the job.


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What is animal research?
Numbers of animals
Understanding animal experiments
A to Z of animals
The 3Rs and Animal Wefare
Why do animal research?
For human health
Human health timeline
For animal health
Forty reasons why we need animals in research
UK animal research regulation
Cosmetic testing
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Research & medical benefits
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Understanding Animal Research > News > Research & medical benefits > Smallpox
Posted by: Mia Rozenbaum
Category: Research & medical benefits
The pre-Victorian historical description of smallpox written by De la Condamine in 1754 states "Every tenth death was due to smallpox, and one fourth of mankind were either killed by it, or crippled or disfigured for life. The disease was a river that everyone had to cross. " (1)
Smallpox was a deadly disease caused by the variola virus that was present before Christ in certain parts of the world. It was one of the world's most devastating diseases known to humanity. It reached Europe certainly by the sixth century - Bishop Gregory of Tours mention in 582 an epidemic with vesicular eruption which began with sickness, fever and back pain. The fever abated with the copious eruption of hard, white vesicles which were very painful. (2, 4)
Against this background, it is not hard to understand the search for a prevention or cure; in this case the development of the technique of inoculation (or variolation). This was the deliberate placing of pustular matter, collected during a mild epidemic, into an incision in a healthy child. This practice stemmed from the clinical observation that one attack of smallpox conferred protection against the disease. (2)
Jenner's experiments, published in 1798, were the first step towards the eventual eradication of smallpox and obviated the need for variolation. Jenner, like others was intrigued that individuals who came in contact with the comparatively rare disease of cowpox were immune to smallpox and wouldn't develop symptoms, even when they were inoculated with the virus. " Vaccination" with cowpox resulted in immunity to smallpox. Vaccination, even today is the only prevention used against the virus – no specific treatment is available. (3)
Initially vaccination was usually effected by the use of "humanised" lymph – lymph from the pustules of those previously vaccinated. However, the occasional accidental transmission of syphilis and the lack of sufficient vaccine material to combat an epidemic led to the use of animals to prepare the vaccine. Horses, mules, goats and rabbits were all used at one time or another to generate the virus. However in the 1970's, calves became the standard method to prepare the vaccine, due to the facility with which the supply could be multiplied at relatively short notice to deal with even the most serious epidemic. The potency of the prepared virus was tested by the rapidity with which it could produce lesions in rabbits, it was also usually tested on mice to ensure the absence of tetanus bacilli.
The new vaccine was accompanied with a striking and rapid fall of smallpox mortality. In the first half of the 20th century, Europe and North America gradually became smallpox free due to extensive vaccination and containment measures. The smallpox virus was declared eradicated in 1980 following a global immunization campaign led by the World Health Organization. The last known natural case was in Somalia in 1977. Since then, the only known cases were caused by a laboratory accident in 1978 in Birmingham, England, which killed one person and caused a limited outbreak. Today, except for laboratory stockpiles, the variola virus has been eliminated. (5)
De la Condamine (1759) Mémoire sur l'inoculation de la petite vérole.Histoire et Mémoires de l'Académie Royale des Sciences (read at the public session of April 24, 1754)
Jack Botting, ed. Regina Botting, Animals and Medicine: The Contribution of Animal Experiments to the Control of Disease. Cambridge, UK: Open Book Publishers, 2015. http://dx.doi.org/10.11647/OBP.0055
http://www.bt.cdc.gov/agent/smallpox/overview/disease-facts.asp
http://www.bt.cdc.gov/agent/smallpox/
http://www.who.int/csr/disease/smallpox/en/
cow,
rabbit,
smallpox,
Last edited: 16 September 2015 12:17
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Topless Woman In McDonald's Rampage Blames It On Bipolar Disorder
April 10, 2014 8:47 am - NewsBehavingBadly.com
Sandra Suarez, 41,the woman who was seen toplessly ransacking a Florida McDonald's, says she's bipolar.
Footage posted to the website LiveLeak.com shows the woman's three-minute outburst inside a McDonald's restaurant at 66th Street and 102nd Avenue in Pinellas Park.
The video shows the woman — clad only in skimpy undies — emptying the contents of a cabinet onto the floor, knocking over registers and equipment, and helping herself to some soft-serve ice cream. Voices off-camera, apparently employees, chuckle and provide commentary as the surveillance footage rolls.
The LiveLeak website says the incident took place over the weekend, leading to some initial confusion with local law enforcement agencies about whether it actually happened in the area. But Pinellas Park police now confirm it happened back on March 24…
Suarez was taken to Northside Hospital as a precaution, but police said doctors determined she wasn't under the influence of drugs or alcohol.
She was charged with criminal mischief and resisting arrest, then was released on $7,000 bond.
Reached by FOX 13's Steve Nichols on Tuesday, the mother of two blamed the tirade on bipolar disorder and she does not remember much of it.


Those who intend to make it in their careers need to get into one of the many trade school programs. These programs shall help you develop skills that shall be useful not only in securing a job position but in coping well and excelling when given the opportunity. You get access to these opportunities immediately after training.
There used to be a time when those who went through the standard college schedule were assured of a job. But of late, this is not the case. Many degree holders are job hunting. It is no longer a guarantee getting a job, when you have the once-coveted degree. This is because degree coursework no longer produces people ready to work unsupervised.
Employers have neither the budget nor the patience to start training a person they just hired. They prefer if they came in ready to work. This gives those with experience an edge. The next preferred lot is those from trade schools. It is, therefore, more preferable for young people to go to trade school and not a conventional college. The earlier wish for most people to join college is quickly fading away. They, in turn, save so much money.
If you are from a rich home, you can afford college. For those with modest means, college is no longer an option. Student loans have also become quite expensive. Trade schools also have the advantage of affordability.
Trade school programs are also not as long as college degree ones. Most do not last further than two years. The student will also save on most of the auxiliary expenses. Their expenses shall never exceed those of a college degree pursuant.
This time shall be spent gathering relevant skills to be applied in the job market, once training is done. They offer job-specific skills. Their coursework is detailed and thorough. Nothing they teach is to pass time, or miss application. There shall be no need for an employer to start training a newbie afresh. This makes their career progression an even more assured journey.
So much has changed in the job market. More and more employers are turning down degree holders who do not further their skills. Employers wish to see real-world skills. This is what should compel any young person to join trade school. There are plenty of such schools when you search for them on the internet. An example is the Intercoast Colleges. On their website, you shall find many options that will set you up for a career life. You shall also look through the Intercoast Reviews page, to see what other students have to say. This will be helpful in your decision making.