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I was searching for a Property and found this listing (MLS® #98724772). Please send me more information regarding 1038 Dunnigan Street, Twin Falls, ID, 83301. Thank you!
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How well do you use the space inside your buildings?
Posted on Thursday, September 24th, 2015 at 12:26 pm.
Posted on Thursday, September 24th, 2015 at 12:20 pm.
Heartbeat commissioned Community Ventures to undertake a primary care assessment of the premises of its members practice and to develop specific plans for a primary care hub to deliver a new profile of services in the area.
Posted on Thursday, September 24th, 2015 at 12:18 pm.


text:
The Elie Wiesel Foundation for Humanity and LRN Announce Student Winners of the 2020 Prize in Ethics Essay Contest
Tuesday, 10. November 2020 16:00
NEW YORK, Nov. 10, 2020 (GLOBE NEWSWIRE) -- Alexandra Lang, a senior at Northwestern University, has won the 2020 Prize in Ethics Essay Contest presented by The Elie Wiesel Foundation for Humanity, LRN Corporation and LRN Founder and Chairman Dov Seidman.
In "'Any Fraction of Infinity': Aging, Illness, and the Right to Continue," Lang, a philosophy major and psychology minor, explores the ethical and moral complexities and the self-reflection that occur at the end of life through her experiences providing support to an elderly hospice patient.
"By writing a page of Emelia's story, I hope to provide some shadow of the longevity she longed for, and a tribute to the strength and dignity of a woman who, irrespective of age and prognosis, had the courage to express her unapologetic renunciation of the eventuality of death," Lang writes in the piece.
The Prize in Ethics Essay Contest, established in 1989 by Professor Elie Wiesel and his wife, Marion Wiesel, is an annual competition that challenges college students to address the urgent and complex ethical issues that confront the modern world. Since its inception, thousands of young people have written essays for consideration. After Professor Wiesel's death in 2016, The Elie Wiesel Foundation and LRN, a global ethics and compliance education and technology company, have continued the Prize in honor of Professor Wiesel's legacy and memory.
"We are proud to partner with the Elie Wiesel Foundation for Humanity in awarding the annual ethics prize. We join with Marion Wiesel in celebrating this remarkable group of student winners, who so thoughtfully considered ethical issues they have encountered, and, as Professor Wiesel would so often say, think higher and feel deeper. Seeing a new generation of moral leaders rising to meet the challenges before us is a source of hope for the future," said Seidman.
Second prize in this year's contest was awarded to Christina Ge of Brown University for the essay, "Blank Childhoods," which looks at how the lives and experiences of the author's ancestors have affected her current engagement with ethics and politics.
Aditya Sharma of Columbia University received third prize for "Of Permits and Peoples," which explores the complex link between citizenship and belonging through the following ethical dilemma: "If I had the chance to replace my current passport with one that granted me visa-free entry into most countries, would I do it?"
Honorable mention was awarded to Devin Kinsella of University of Northwestern-St. Paul for "Towards Vice or Virtue: An Ethical Inquiry into Suffering," which explores the author's pursuit to better understand suffering following the deep grief felt after his wife's miscarriage. The winning essays can be found on the Foundation's website: http://eliewieselfoundation.org/prize-ethics/winners/.
About The Elie Wiesel Prize in Ethics Essay Contest
The Elie Wiesel Foundation Prize in Ethics Essay Contest encourages students to write thought-provoking personal essays that raise questions, single out issues and offer rational arguments for ethical action. The contest is open to undergraduate full-time Juniors and Seniors who are registered at accredited four-year colleges or universities in the United States. All submissions to the essay contest are judged anonymously. Winning essays present intensely personal stories, originality, imagination, and clear articulation and convey genuine grappling with an ethical dilemma. For suggested essay topics and more information, visit https://www.eliewieselfoundation.org/prizeinethics.aspx.
About The Elie Wiesel Foundation for Humanity
Elie Wiesel and his wife, Marion, established The Elie Wiesel Foundation for Humanity soon after he was awarded the 1986 Nobel Prize for Peace. The Foundation's mission, rooted in the memory of the Holocaust, is to combat indifference, intolerance and injustice through international dialogue and youth-focused programs that promote acceptance, understanding and equality. For more information: www.eliewieselfoundation.org, "like" us on Facebook, or follow @eliewieselfdn on Twitter.
About LRN
LRN's mission is to inspire principled performance. Since 1994, LRN has helped over 25 million people at more than 700 companies worldwide simultaneously navigate complex legal and regulatory environments and make ethical decisions, and has also helped hundreds of companies foster ethical, responsible, and inclusive cultures. LRN's combination of practical tools, education, and strategic advice helps companies translate their values into concrete corporate practices and leadership behaviors that create sustainable competitive advantage. In partnership with LRN, companies need not choose between living principles and maximizing profits, or between enhancing reputation and growing revenue: all are a product of principled performance. As a global company, LRN works with organizations in more than 100 countries.
For additional information on LRN, visit www.LRN.com.
Michael-Jon Romano
Sommerfield Communications
Michael-Jon@Sommerfield.com

redpajama_set_name: RedPajamaCommonCrawl


Permission/dress code forms are due back to Sharon in the office by April 12th.
They are available from Mrs. Arnett or the office.
Senior pictures for slideshow - due to Mrs. Arnett by April 26th.
10-15 pictures of you with friends through the years.
Senior pictures for the yearbook if you don't want the ones taken here at school should be emailed to Mrs. Arnett by May 3rd.
To receive important updates from Ms Shope about senior information click on the link and follow the instructions.
October 3-17 - Butter Braids Fundraiser.
November 5 - Deadline to let Ms Shope know you are going on senior trip. The trip will cost approx. $550.00.
January 7-22 - Little Casesar's Pizza Fundraiser.
Febrruary 15 - Senior Recognition between JV and Varsity Basketball Games.
April 13-17 - Senior Trip to Washington DC.
If you need information regarding any of these events please contact MS Shope.


text:
Voices from
Norwich University News Edition: Voices on Peace and War
A time for domestic reconciliation
Academic study of reconciliation in the last three decades has focused on converting the process of enmity to amity in virtually every corner of the world, from Europe to Africa, Southeast and Northeast Asia to Latin America.
Missing from this list is the United States. Given the current fragility of the American polity and society, coupled with the hope and optimism of the Biden administration, now might be the time for Reconciliation Studies to focus on the United States, in general, and its subjugation of Black people, in particular. Can we inwardly apply some of the lessons learned from outside the United States?
Many international efforts at reconciliation have been mired in the first phase of reconciliation (Japan-South Korea). Others are traversing the stony path with interim achievements (South Africa). A minority of cases is far along in the journey (Germany-Israel). In attempting to reframe the narrative of conflict to one of peace in the first phase of these international processes, four components appear critical:
Offering acknowledgment of what happened in the past
Seeking the unvarnished truth
Recognizing the centrality of remembrance
Articulating the goals of healing and trust building
This work must be done by both officials and the broader public; it must be institutionalized, visible, and inter-generational.
Today, the U.S. is riven by four interconnected crises: political, racial, economic and health-related. They present a monumental set of challenges for government and society; the necessity of reconciliation is apparent in all four. There are signs that efforts are underway to confront the challenges.
On the political and racial front, most recently, a bill was introduced in the House to create a 1/6 Commission to seek the truth about the attack on the Capitol by Trump loyalists and its antecedents. Already in December 2020, U.S. Sen. Cory Booker, D-N.J., introduced a bill to establish a Commission on Truth, Racial Healing, and Transformation to examine systematic racism and police violence. Earlier, in April 2019, Booker introduced a bill to craft a Commission to Study and Develop Reparation Proposals for African Americans that would investigate the history of slavery and discrimination from 1619 to the present. In these actions, he was moving forward the earlier work in Congress with House Resolution 40 by U.S. Reps. Sheila Jackson Lee, D-Texas, and John Conyers, D-Mich. When Jackson Lee introduced H.R. 40 in January 2019, 142 representatives signed on; only two supported a similar effort in 2014. The creation of such commissions and their work could provide an acknowledgment of racial discrimination and exclusion in all segments of social, economic and political life.
Accompanying these institutional efforts, political leaders have used the language of reconciliation. House Speaker Nancy Pelosi, D-Calif., has insisted that to govern one needs "truth" and "trust." The day before the Inauguration, then-President-elect Joe Biden stated at the Lincoln Memorial ceremony to commemorate the 400,000 (at the time) Americans lost to COVID-19 that "to heal, we must remember."
While the U.S. Congress debates whether to create truth commissions, local governments in the cities of Boston, New York, Philadelphia and San Francisco, and in the state of Maryland, have created such entities. The mostly white city council of liberal Asheville, North Carolina, voted in July 2020 to apologize for slavery and to offer financial support for Black homeownership and businesses.
Public participation in all of these efforts is crucial. Other ongoing societal activities include discussions of race relations and discrimination in churches, synagogues and mosques. Book clubs also present an opportunity for these discussions. The Legacy Museum in Montgomery, Alabama, which records the history of lynching, provides a further example. Literature has also played a role, for example in the writings of Ibram Kendi, Isabel Wilkerson and Ta-Nehisi Coates. The Black Lives Matter movement has demonstrated the power of protest to raise awareness and change culture and stereotypes. The New York Times 1619 project, which seeks to include the contribution of Blacks to the American historical narrative, brought a Pulitzer Prize to Nikole Hannah-Jones, its founder. It has also engendered much debate about the course of American history.
Regarding the massive challenge of the many-layered economic crisis, President Biden and his vice president, Kamala Harris, have prioritized on their agenda the $1.9 trillion economic relief and stimulus bill, that, among other things, would provide financial payments to low-income Americans and increase the minimum wage. While the bill is being fiercely negotiated in Congress, Biden has issued executive orders to support families in need financially and to increase the minimum wage for some workers.
The overarching crisis facing the new administration, the multiple threats of COVID-19, is being met in a variety of ways. On the basis of a 200-page plan and mindful of the disproportionate number of victims among Black and Brown Americans, the government has focused on a new public health campaign, on increasing the manufacturing, supply and distribution of the COVID-19 vaccine, and on enlarging access to health care via changes in the Affordable Care Act.
These are a few indications of the beginning of a reconciliation process that will be long, never-ending, arduous, and nonlinear, if we are to glean lessons from international cases. Such a process is necessary for repairing the fabric of American society and saving American democracy.
— Lily Gardner Feldman is a senior fellow at the American Institute for Contemporary German Studies at Johns Hopkins University.
By Lily Gardner Feldman
Category: Voices on Peace and War
Voices-Peace-War
The 2021 Edition of the Journal of Peace and War Studies (JPWS)
The 2021 issue of the Journal of Peace and War Studies gives special focus to the theme, "Preparing Military Leaders to Effectively Resolve 21st Century Security Challenges." Its publication and theme coincide with the 2021 International Symposium of Military Academies (ISOMA), which took place October 4-8, 2021, at Norwich University.
Read online or download the 2021 JPWS Journal


text:
Top secret military alliance uncovered
Research /
Interpreting Objects, Texts & Contexts /
Was a white super-state in Southern Africa possible? Was Ian Smith's disgraced pariah state of Rhodesia always doomed to failure? Could we be living in a world where Nelson Mandela had never been released? What if apartheid had continued in South Africa?
New research by Filipe Ribeiro de Meneses, Professor of History at Maynooth University, offers a glimpse into a top-secret military agreement that could have changed the course of world history. Together with Dr Robert McNamara from the University of Ulster, De Meneses, who specialises in Portuguese history, has uncovered evidence of ALCORA, a covert alliance between Portugal, South Africa, and Rhodesia (now Zimbabwe), which was aimed at propping up the apartheid regimes and institutional racism of both African states.
ALCORA began in 1970 and lasted until 1974, when Portugal's dictatorship was overthrown and the country began a slow journey towards democracy. The alliance was so secret that only senior military officers, defence ministers, and Prime Ministers knew of its existence. To this day, it remains unknown as to why the name "ALCORA" was chosen, or what it means.
At the time, Portugal was the last remaining colonial power in Europe. It hovered in a luminal state, wavering between an all-out commitment to its remaining colonies in Africa (including Mozambique and Angola), or repudiating colonialism and throwing its lot with the European Economic Community. As a colonising power, it defied the will of the fledgling European Community. The Netherlands, Ireland and the countries of Scandinavia were among the most vocal critics of Portugal.
While de Meneses became aware of ALCORA during his research for the biography of Salazar, the Portuguese dictator, it was not until 2010 that he collaborated with Robert McNamara to look for evidence of this secretive organisation. This was 30 years after the end of white-ruled ruled and the birth of Zimbabwe; it also marked the declassification of confidential military files. His work is part of a wider research project, "Defying the winds of change: Portugal, Rhodesia and South Africa 1961 – 1980", funded by the Irish Research Council.
From late 1973, ALCORA was headquartered in Pretoria, South Africa. All along, it was a means for the three countries to suppress the growing independence movements in Southern Africa – movements advocating the "dangerous" idea of "one person, one vote" and racial equality. This was an evolving military alliance with significant political and economic consequences.
Until now, nobody knew the extent of the collaboration between the three countries. The work of de Meneses and McNamara lifts the lid, showing that ALCORA was much more than a loose alliance. There were tiers of councils and committees dealing with practical military issues such as intelligence gathering, communications, photographic reconnaissance and mapping. There were efforts to create a common manual which every officer could carry in his pocket to deal with counterinsurgency warfare. ALCORA produced studies on the military capabilities of surrounding countries, including their airpower and their airfields.
In addition to these military contacts, the Polícia Internacional e de Defesa do Estado (PIDE), the secret police that backed the Portugese dictatorship, first began meeting with the South African and Rhodesian regimes in the 1950s. "These meetings intensified to the point that there was a bi-annual conference between the three intelligence agencies, held in a different location every year," de Meneses explains. "They came to be seen as social occasions: they often had a programme and might involve trips to restaurants and bars."
At the meetings, they discussed the common threat: the idea that everybody should have a vote. By working with Portugal, South Africa hoped to suppress Angola, Mozambique's and Rhodesia's independence movements, keeping African nationalism at bay. "There were claims of an 'unholy alliance' between the countries made as far back as the early 1960s, but even the loudest detractors never knew they were working at this level and at this scale, with such ambitious plans," says de Meneses. "It is interesting that, even in recent memoirs and interviews given by people who were involved in ALCORA, none of them ever mentioned it. It has been kept remarkably quiet.
"Portugal was particularly keen to ensure the truth did not come out. Even the Truth and Reconciliation Commission of South Africa did not seem aware of ALCORA. France, Spain, Britain, and the United States, for all their intelligence resources, don't seem to have known about the spy agency either. This begs further questions, and we hope the answers will emerge."
ALCORA continued to grow until its sudden collapse in 1974, when frustrated army officers in Lisbon grew tired and impatient of the autocratic regime and overthrew Salazar's successor, Marcelo Caetano during the bloodless "Carnation Revolution." While the Portuguese military's top brass had focused on interactions with Rhodesia and South Africa, they failed to notice the collapsing morale of their own officers, de Meneses points out.
"We are left wondering: if the Carnation Revolution had not taken place, how much closer would these three countries have become? Angola, Mozambique, Namibia, South Africa and Rhodesia were combined, a ring of incredibly rich and productive land with gold, diamonds and oil. The alliance was always intended to be open-ended. How far would they have gone to defend this territory and its resources? It is a story of 'what if', but it is an intriguing question."
In the years that followed the collapse of the Portuguese dictatorship and ALCORA, Portugal rapidly abandoned its African colonies. The South Africans and the Rhodesians stumbled from one policy to another. "They had bet heavily on ALCORA and now they didn't know what to do. There was division: some argued for detente with other African states, while others pushed for more forceful lines of defence and intervention; the latter argument led to South Africa invading Angola in 1975, which in turn invited a Cuban reaction."
This intervention failed and South Africa withdrew. Shortly after this, it became clear to the black population that the military was not invincible, and the Soweto Uprisings in South Africa heralded the beginning of a wave of popular anti-apartheid protests that eventually sapped the will of the racist state to continue.
In the long-term, ALCORA prolonged Portugal's colonial wars and the idea of a white-run Rhodesia, and poisoned the region's politics. "Was there always going to be democracy in southern Africa?" asks de Meneses. "The birth and accidental death of ALCORA suggest that things could have been very different for the region, for Portugal, and for Europe."


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My dear friend Sarah celebrates her birthday tonight, and as I can't be there I drew her favourite ever-so-pouting Haikyuu character, Tobio.


Over four decades, China has repeatedly proved the predictions of naysayers to be wrong and weathered one international crisis after another!
Now, many African countries are using China's experience to establish their own economic zones, which are set to bring in the development that the African continent has dreamed of.
Looking ahead, China and US trade relations, whatever the twists and turns, will ultimately return to the track of win-win cooperation and stable growth, benefiting the two nations and contributing to the world's economic growth as well.
It won high recognition from the leaders at the summit and attracted wide attention from the international community.


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text:
Q: Converting .bmp to .png using powershell causes memory leak Intro
I am trying to convert '.bmp' files to '.png' files. I found a piece of code earlier that does this using PowerShell. What happens is with every image converted the increment increased in my RAM usage is roughly equal to the size of an image. This increases to the max RAM I have and slows down my PC.
Question
Do I need to release the loaded images from RAM, and if so, how would I do this?
Code
The code I am using is from here: https://code.adonline.id.au/change-image-formats-powershell/

Code used:
$usb1 = 'U:'
$usb2 = 'F:'
$usb3 = 'H:'
$usb4 = 'J:'
$usb5 = 'K:'
$usb6 = 'I:'

$usblist = $usb1,$usb2,$usb3,$usb4,$usb5,$usb6

foreach($usbs in  $usblist){
$usb = $usbs
############################# convert ###################################
function ConvertImage{
    param ([string]$path)
        foreach($path in Get-ChildItem -Directory $usb -Recurse){
Write-Host $path
            #Load required assemblies and get object reference
    [Reflection.Assembly]::LoadWithPartialName("System.Windows.Forms") | Out-Null
        foreach($file in (ls "$path\*.bmp")){
            $convertfile = new-object System.Drawing.Bitmap($file.Fullname)
            $newfilname = ($file.Fullname -replace '([^.]).bmp','$1') + ".png"
            $convertfile.Save($newfilname, "png")
            $file.Fullname
    }
 }
};ConvertImage -path $args[0]
}

PS. I would prefer to keep using PowerShell, as I use this for further processing the data.

A: A System.Drawing.Bitmap is disposable. Dispose it after use. Also, there is no need to re-define functions or re-load assemblies multiple times.
[Reflection.Assembly]::LoadWithPartialName("System.Windows.Forms") | Out-Null

$paths = 'U:', 'F:', 'H:', 'J:', 'K:', 'I:'

foreach ($path in $paths) {
    Get-ChildItem -File "$path\*.bmp" -Recurse | ForEach-Object {
        $bitmap = [System.Drawing.Bitmap]::new($_.FullName)
        $newname = $_.FullName -replace '.bmp$','.png'
        $bitmap.Save($newname, "png")
        $bitmap.Dispose()
    }
}

redpajama_set_name: RedPajamaStackExchange


Melbourne-based songstress MYAMI returns with another cracker, 'I Oh I'.
Like with 'Soldier', this track has been produced by Wayfarer//.
MYAMI still cites influences from old school soul singers with 'I Oh I' and this can be see from the trap style drums sneaking in at times, but essentially 'I Oh I' is a slightly more modern and edgier take on the traditional R&B.
"The confusion of growing up as a competitive teenager and trying to figure out who you are amongst the jungle of voices telling you who to be. The song is a flash back to look at the person I was, growing up in the often toxic surroundings of a teenage girls life".


text:
Counselling cells must in schools, colleges: Altaf Bukhari
By Guest on 8:56 pm August 22, 2017 No Comment
Minister for Education, Syed Mohammad Altaf Bukhari today said establishment of counseling cells in educational institutions is imperative for students' guidance and the government is keen to establish such centres at an earliest.
He said setting up of counseling cells would lead to massive participation of students in education reforms. Through this setup, the students can provide their suggestions and opinions in framing curriculum which will lead to more value-based education, he added.
The Minister made these remarks while interacting with students at Kothi Bagh Higher Secondary School during a face to face interactive programme 'Ru-ba-Ru' which was organized by Directorate of School Education, Kashmir.
Altaf Bukhari said that career counseling camps will also be organized which would help in creating an atmosphere of understanding between the students and the school/college administration and ultimately lead to the betterment of the education system.
Secretary School Education, Farooq Ahmad Shah, Director School Education (Kashmir), Dr G N Itoo, Deputy Commissioner, Srinagar, Syed Abid Rasheed, CEO Srinagar, Farooq Ahmad Dar, Besides Principals, Zonal officers and other concerned officials of Education Department were present on the occasion.
During the interaction, the Minister answered scores of questions from students of various schools of district Srinagar who attended the programme.
The students put forward their questions and issues like shortage of teachers, need to introduce more subjects, improvement in infrastructure, establishment of counselling cells among other issues.
Bukhari listened to the students patiently and assured them that the Government is trying its level best to address these issues. He appealed the students to utilize their precious time in studies to reach the greatest levels in their lives.
On the demand of the students, Bukhari announced reservation of 10% quota for Urdu teachers from the 2400 posts of teachers which will be recruited through Service Selection Board.
Speaking on the occasion, Secretary School Education, Farooq Ahmad Shah said that quality education is the foundation of a strong nation and the school education is the basis for education system.
Later, the Minister distributed 40 All Terrain Bikes (ATB's) to the meritorious male-students of 10th, 11th and 12th standards from district Srinagar at the Kothi Bagh Higher Secondary School. The Minister also interacted with the staff of the school and discussed various issues regarding upgradation of school infrastructure and other related issues.
Counselling cells must in schools, colleges: Altaf Bukhari added by Guest on 8:56 pm August 22, 2017


CrazySexyFit: Project Fear Less: Adopt kittens! Yes, plural!
I have been telling people (and myself) that I'm totally going to get a cat...as soon as I'm settled into my new place, and as soon as I have a little money saved up, and as soon as I get back from vacation, and on and on. But no more. These are my kittens, Pamina on the left up there and Tamino in the foreground (Tam and Mina for short. Bonus points if you get the reference; if not, scroll down).
Why is this scary? Well, first of all, because I am now solely responsible for two little furry lives other than my own. If I try to fill my day with a lot of outside-the-home stuff to do, that means I'm kept busy (and hopefully happy) but they're left alone here. (I still work 15-hour days on occasion, which is part of the reason I got two.) I can't just up and travel if I have a weekend free - I need to make sure that they'll have food, water and a clean litter-box. It's not one of those actions people think of as huge and life-changing, but the truth is that as I intend to keep them, that decision will affect, to some degree, where I live; where, when and for how long I travel; what I do with my spare time; how I decorate my home and a zillion other things I'm sure to find out.
Another reason this took some guts: it cost money. Not a small amount. I have a nice chunk saved up from working so much over the summer and I'm not completely strapped right now, although income has lessened considerably for the school year, but I always get really nervous when it comes to spending largish amounts of money, by which I mean anything over about $75 at one time (unless it's on food, because I take what I eat very seriously and I know how to use things to their fullest potential). So it takes a couple of deep breaths before I can drop cash on things like decent shoes, for example - I flopped and limped around in not one but TWO different pairs (for work and daily wear) of completely worn-out, pieces-falling-off shoes for months before I bit the bullet and bought new ones. I'm nervous about a lot of money issues when I really don't need to be - I know how to make and follow a budget, I have multiple sources of income and I have enough meat and fat stockpiled right now to not buy any more food for at LEAST a month, probably more. If I spent $10 on a few heads of cabbage and made sauerkraut, I could even eat meals resembling normal people food. But I digress.
PS: The story behind the kittens' names is this: we called my grandfather "Papageno," after a character from Mozart's opera "The Magic Flute." Tamino and Pamina are the male and female lead in that opera. I think he would approve.


text:
\section{Introduction}
\vspace{-5pt}

Navigation is a critical task in building intelligent agents. Navigation tasks can be expressed in many forms, for example, point goal tasks involve navigating to specific coordinates and semantic navigation involves finding the path to a specific scene or object. Irrespective of the task, a core problem for navigation in unknown environments is exploration, \textit{i.e.}, how to efficiently visit as much of the environment. This is useful for maximizing the coverage to give the best chance of finding the target in unknown environments or for efficiently pre-mapping environments on a limited time-budget.

Recent work from \cite{chen2019learning} has used end-to-end
learning to tackle this problem.
Their motivation is three-fold: \textit{a)} learning
provides flexibility to the choice of input modalities
(classical systems rely on observing geometry through the use
of specialized sensors, while learning systems can infer
geometry directly from RGB images), \textit{b)} use of learning
can improve robustness to errors in explicit state estimation,
and \textit{c)} learning can effectively leverage structural
regularities of the real world, leading to more efficient behavior in
previously unseen environments. This lead to their design
of an end-to-end trained neural network-based policy that
processed raw sensory observations to directly output
actions that the agent should execute.

While the use of learning for exploration is well-motivated, casting
the exploration problem as an end-to-end learning problem has its
own drawbacks. Learning about mapping, state-estimation, and path-planning
purely from data in an end-to-end manner can be prohibitively
expensive. Consequently, past end-to-end learning work for exploration
from \cite{chen2019learning} relies on the use of imitation learning
and many millions of frames of experience, but still performs worse than
classical methods that don't require any training at all.

This motivates our work. In this paper, we investigate alternate
formulations of employing learning for exploration that retains the advantages that learning has to offer, but doesn't suffer
from the drawbacks of full-blown end-to-end learning.
Our key conceptual insight is that use of learning for
leveraging structural regularities of indoor environments,
robustness to state-estimation errors,
and flexibility with respect to input modalities, happens at different time scales
and can thus be factored out.
This motivates the use of learning in a modular and hierarchical fashion inside of what one may call a `classical navigation pipeline'.
This results in navigation policies that can work with raw sensory
inputs such as RGB images, are robust to state estimation errors,
and leverage the regularities of real-world layouts. This results in
extremely competitive performance over both geometry-based methods and
recent learning-based methods; at the same time requiring a fraction of
the number of samples.

More specifically, our proposed exploration architecture comprises
of a learned Neural SLAM module, a global policy, and a local policy,
that are interfaced via the map and an analytical path planner. The learned Neural SLAM module produces free space maps and estimates agent pose from input RGB images and motion sensors.
The global policy consumes this free-space map with the agent pose and employs learning to exploit structural regularities in layouts of real-world environments to produce long-term goals.
These long-term goals are used to generate short-term
goals for the local policy (using a geometric path-planner).
This local policy uses learning to directly map
raw RGB images to actions that the agent should execute.
Use of learning in the SLAM module provides flexibility with respect to
input modality, learned global policy can exploit regularities
in layouts of real-world environments, while learned local
policies can use visual feedback to exhibit more robust behavior.
At the same time, hierarchical and modular design and use of analytical planning, significantly cuts down the search space during training,
leading to better performance as well as sample efficiency

We demonstrate our proposed approach in \textit{visually} and
\textit{physically} realistic simulators for the task of geometric
exploration (visit as much area as possible). We work with the
Habitat simulator from \cite{savva2019habitat}. While Habitat is already visually
realistic (it uses real-world scans from
\cite{chang2017matterport3d} and \cite{xiazamirhe2018gibsonenv} as environments),
we improve its physical realism by
using actuation and odometry sensor noise models, that we collected
by conducting physical experiments on a real mobile robot.
Our experiments and ablations in this realistic simulation reveal
the effectiveness of our proposed approach for the task of exploration.
A straightforward modification of our method also tackles
point-goal navigation tasks, and won the AI Habitat challenge
at CVPR2019 across all tracks.

\renewcommand*{\thefootnote}{\arabic{footnote}}

\vspace{-5pt}
\section{Related Work}
\vspace{-10pt}
Navigation has been well studied in classical robotics. There has been a renewed
interest in the use of learning to arrive at navigation policies, for a variety of
tasks. Our work builds upon concepts in classical robotics and learning for navigation. We survey related works below.

\textbf{Navigation Approaches.} Classical approaches to navigation break the problem into two parts: mapping and path planning. Mapping is done via simultaneous localization and mapping~\citep{thrun2005probabilistic, hartley2003multiple, slam-survey:2015}, by fusing information from multiple views of the environment.
While sparse reconstruction can be done well with monocular RGB images~\citep{mur2017orb}, dense mapping is inefficient~\citep{newcombe2011dtam} or requires specialized scanners such as Kinect~\citep{izadiUIST11}.
Maps are used to compute paths to goal locations via path planning~\citep{kavraki1996probabilistic, lavalle2000rapidly, canny1988complexity}. These classical methods have inspired recent learning-based techniques.
Researchers have designed neural network policies that
reason via spatial representations~\citep{gupta2017cognitive, parisotto2017neural, zhang2017neural, henriques2018mapnet, gordon2018iqa},
topological representations~\citep{savinov2018semi, savinov2018episodic}, or use differentiable and trainable planners~\citep{tamar2016value, lee2018gated, gupta2017cognitive, khan2017end}.
Our work furthers this research, and we study a hierarchical and modular decomposition of the problem and employ learning inside these components instead of
end-to-end learning. Research also focuses on incorporating semantics in SLAM~\citep{pronobis2012large, walter2013learning}.

\textbf{Exploration in Navigation.}
While a number of works focus on passive map-building, path planning and
goal-driven policy learning, a much smaller body of work tackles the
the problem of active SLAM, i.e., how to actively control the camera for
map building. We point readers to \cite{slam-survey:2015} for a detailed survey,
and summarize the major themes below.
Most such works frame this problem as a Partially
Observable Markov Decision Process (POMDP) that are approximately
solved~\citep{martinez2009bayesian, kollar2008trajectory}, and or seek to find a sequence of
actions that minimizes uncertainty of maps~\citep{stachniss2005information, carlone2014active}.
Another line of work explores by picking vantage points (such
as on the frontier between explored and unexplored
regions~\citep{dornhege2013frontier, holz2010evaluating, yamauchi1997frontier, xu2017autonomous}).
Recent works from \cite{chen2019learning, savinov2018episodic, fang2019scene}
attack this problem via learning. Our proposed modular policies
unify the last two lines of research, and we show improvements over
representative methods from both these lines of work. Exploration
has also been studied more generally in RL in the context of exploration-exploitation trade-off~\citep{sutton2018reinforcement, kearns2002near, auer2002using, jaksch2010near}.

\textbf{Hierarchical and Modular Policies.} Hierarchical RL~\citep{dayan1993feudal, sutton1999between, barto2003recent} is an active area of research, aimed at automatically discovering
hierarchies to speed up learning. However, this has proven to be
challenging, and thus most work has resorted to using hand-defining
hierarchies. For example in the context of navigation, \cite{bansal2019combining}
and \cite{kaufmann2019beauty} design modular policies for navigation,
that interface learned policies with low-level feedback controllers.
Hierarchical and modular policies have also been used for Embodied Question Answering \citep{das2017embodied, gordon2018iqa, das2018neural}.

\section{Task Setup}
\vspace{-5pt}
We follow the exploration task setup proposed by~\Mycite{chen2019learning} where the objective is to maximize the coverage in a fixed time budget. The coverage is defined as the total area in the map known to be traversable. Our objective is to train a policy which takes in an observation $s_t$ at each time step $t$ and outputs a navigational action $a_t$ to maximize the coverage.

We try to make our experimental setup in simulation as realistic as possible with the goal of transferring trained policies to the real world. We use the Habitat simulator~\citep{savva2019habitat} with the Gibson~\citep{xiazamirhe2018gibsonenv} and Matterport (MP3D)~\citep{chang2017matterport3d} datasets for our experiments. Both Gibson and Matterport datasets are based on real-world scene reconstructions are thus significantly more realistic than synthetic SUNCG dataset~\citep{song2016ssc} used for past research on exploration~\citep{chen2019learning, fang2019scene}.

In addition to synthetic scenes, prior works on learning-based navigation have also assumed simplistic agent motion. Some works limit agent motion on a grid with 90 degree rotations~\citep{zhu2017target, gupta2017cognitive, chaplot2018active}. Other works which implement fine-grained control, typically assume unrealistic agent motion with no noise~\citep{savva2019habitat} or perfect knowledge of agent pose~\citep{chaplot2016transfer}. Since the motion is simplistic, it becomes trivial to estimate the agent pose in most cases even if it is not assumed to be known. The reason behind these assumptions on agent motion and pose is that motion and sensor noise models are not known. In order to relax both these assumptions, we collect motion and sensor data in the real-world and implement more realistic agent motion and sensor noise models in the simulator as described in the following subsection.

\subsection{Actuation and Sensor Noise Model}
\vspace{-7pt}
\label{sec:noise}
We represent the agent pose by $(x, y, o)$ where $x$ and $y$ represent the $xy$ co-ordinate of the agent measured in metres and $o$ represents the orientation of the agent in radians (measured counter-clockwise from $x$-axis). Without loss of generality, assume agents starts at $p_0 = (0, 0, 0)$. Now, suppose the agent takes an action $a_t$. Each action is implemented as a control command on a robot. Let the corresponding control command be $\Delta u_a = (x_a, y_a, o_a)$. Let the agent pose after the action be $p_{1} = (x^\star, y^\star, o^\star)$. The actuation noise ($\epsilon_{act}$) is the difference between the actual agent pose ($p_{1}$) after the action and the intended agent pose ($p_0 + \Delta u$):
\begin{equation*}
    \vspace{-5pt}
    \epsilon_{act} = p_1 - (p_0 + \Delta u) = (x^\star - x_a, y^\star - y_a, o^\star - o_a)
\end{equation*}

Mobile robots typically have sensors which estimate the robot pose as it moves. Let the sensor estimate of the agent pose after the action be $p_{1}' = (x', y', o')$. The sensor noise ($\epsilon_{sen}$) is given by the difference between the sensor pose estimate ($p_{1}'$) and the actual agent pose($p_{1}$):
\begin{equation*}
    \vspace{-5pt}
    \epsilon_{sen} = p_1' - p_1 = (x' - x^\star, y'- y^\star, o' - o^\star)
\end{equation*}

In order to implement the actuation and sensor noise models, we would like to collect data for navigational actions in the Habitat simulator. We use three default navigational actions: Forward: move forward by 25cm, Turn Right: on the spot rotation clockwise by 10 degrees, and Turn Left: on the spot rotation counter-clockwise by 10 degrees. The control commands are implemented as $u_{Forward} = (0.25, 0, 0)$, $u_{Right}: (0, 0, -10*\pi/180)$ and $u_{Left}: (0, 0, 10*\pi/180)$. In practice, a robot can also rotate slightly while moving forward and translate a bit while rotating on-the-spot, creating rotational actuation noise in forward action and similarly, a translation actuation noise in on-the-spot rotation actions.

We use a LoCoBot\footnote{http://locobot.org} to collect data for building the actuation and sensor noise models. We use the pyrobot API~\citep{pyrobot2019} along with ROS~\citep{Quigley09} to implement the control commands and get sensor readings. For each action $a$, we fit a separate Gaussian Mixture Model for the actuation noise and sensor noise, making a total of 6 models.  Each component in these Gaussian mixture models is a multi-variate Gaussian in 3 variables, $x$, $y$ and $o$. For each model, we collect 600 datapoints. The number of components in each Gaussian mixture model is chosen using cross-validation.
We implement these actuation and sensor noise models in the Habitat simulator for our experiments. We have released the noise models, along with their implementation in the Habitat simulator in the open-source \href{https://github.com/devendrachaplot/Neural-SLAM}{code}.

\section{Methods}
\vspace{-5pt}

\begin{figure*}
\centering
\includegraphics[width=0.99\linewidth,keepaspectratio]{images/anm}
\vspace{-5pt}
	\caption{\small \textbf{Overview of our approach}. The Neural SLAM module predicts a map and agent pose estimate from incoming RGB observations and sensor readings. This map and pose are used by a Global policy to output a long-term goal, which is converted to a short-term goal using an analytic path planner. A Local Policy is trained to navigate to this short-term goal.}
\label{fig:arch}
\vspace{-10pt}
\end{figure*}

We propose a modular navigation model, `Active Neural SLAM'. It consists of three components: a \textit{Neural SLAM module}, a \textit{Global policy} and a \textit{Local policy} as shown in Figure~\ref{fig:arch}. The Neural SLAM module predicts the map of the environment and the agent pose based on the current observations and previous predictions. The Global policy uses the predicted map and agent pose to produce a long-term goal. The long-term goal is converted into a short-term goal using path planning. The Local policy takes navigational actions based on the current observation to reach the short-term goal.

\textbf{Map Representation.} The Active Neural SLAM model internally maintains a spatial map, $m_t$ and pose of the agent $x_t$. The spatial map, $m_t$, is a ${2 \times M \times M}$ matrix where $M \times M$ denotes the map size and each element in this spatial map corresponds to a cell of size $25cm^2$ ($5cm \times 5cm$) in the physical world. Each element in the first channel denotes the probability of an obstacle at the corresponding location and each element in the second channel denotes the probability of that location being explored. A cell is considered to be explored when it is known to be free space or an obstacle. The spatial map is initialized with all zeros at the beginning of an episode, $m_0 = [0]^{2 \times M \times M}$. The pose $x_t \in \mathbb{R}^3$ denotes the $x$ and $y$ coordinates of the agent and the orientation of the agent at time $t$. The agent always starts at the center of the map facing east at the beginning of the episode, $x_0 = (M/2, M/2, 0.0)$.

\textbf{Neural SLAM Module.}
The Neural SLAM Module ($f_{SLAM}$) takes in the current RGB observation, $s_t$, the current and last sensor reading of the agent pose $x_{t-1:t}'$, last agent pose and map estimates, $\hat{x}_{t-1}, m_{t-1}$ and outputs an updated map, $m_{t}$, and the current agent pose estimate, $\hat{x}_t$, (see Figure \ref{fig:map}): $m_t, \hat{x}_t = f_{SLAM}(s_t, x_{t-1:t}', \hat{x}_{t-1}, m_{t-1}| \theta_S)$, where $\theta_S$ denote the trainable parameters of the Neural SLAM module. It consists of two learned components, a Mapper and a Pose Estimator. The Mapper ($f_{Map}$) outputs a egocentric top-down 2D spatial map, $p_t^{ego} \in [0, 1]^{2 \times V \times V}$ (where $V$ is the vision range), predicting the obstacles and the explored area in the current observation. The Pose Estimator ($f_{PE}$) predicts the agent pose ($\hat{x_t}$) based on past pose estimate ($\hat{x}_{t-1}$) and last two egocentric map predictions ($p_{t-1:t}^{ego}$). It essentially compares the current egocentric map prediction to the last egocentric map prediction transformed to the current frame to predict the pose change between the two maps. The egocentric map from the Mapper is transformed to a geocentric map based on the pose estimate given by the Pose Estimator and then aggregated with the previous spatial map ($m_{t-1}$) to get the current map ($m_t$). More implementation details of the Neural SLAM module are provided in the Appendix.

\begin{figure*}
\centering
\vspace{-15pt}
\begin{minipage}{\textwidth}
\includegraphics[width=\linewidth,height=\textheight,keepaspectratio]{images/neural_slam}
\vspace{-15pt}
\caption{\textbf{Architecture of the Neural SLAM module:} The Neural SLAM module ($f_{Map}$) takes in the current RGB observation, $s_t$, the current and last sensor reading of the agent pose $x_{t-1:t}'$, last agent pose estimate, $\hat{x}_{t-1}$ and the map at the previous time step $m_{t-1}$ and outputs an updated map, $m_{t}$ and the current agent pose estimate, $\hat{x}_t$. `ST' denotes spatial transformation. }
\label{fig:map}
\vspace{-10pt}
\end{minipage}\hfill
\end{figure*}

\textbf{Global Policy.}
The Global Policy takes $h_t \in [0,1]^{4 \times M \times M}$ as input, where the first two channels of $h_t$ are the spatial map $m_t$ given by the SLAM module, the third channel represents the current agent position estimated by the SLAM module, the fourth channel represents the visited locations, i.e. $\forall i, j \in \{1, 2, \ldots, m\}$:
\vspace{-5pt}
\begin{equation*}
\begin{split}
    h_t[c, i, j] & = m_t[c, i, j] \quad \forall c \in \{0,1\}\\
    h_t[2, i, j] & = 1 \qquad \qquad \text{ if } i = \hat{x}_t[0] \text{ and } j = \hat{x}_t[1]\\
    h_t[3, i, j] & = 1 \qquad \qquad \text{ if } (i,j) \in [(\hat{x}_k[0], \hat{x}_k[1])]_{k \in \{0, 1, \ldots, t\}}
\end{split}
\end{equation*}
\vspace{-5pt}

We perform two transformations before passing $h_t$ to the Global Policy model. The first transformation subsamples a window of size $4 \times G \times G$ around the agent from $h_t$. The second transformation performs max pooling operations to get an output of size $4 \times G \times G$ from $h_t$. Both the transformations are stacked to form a tensor of size $8 \times G \times G$ and passed as input to the Global Policy model. The Global Policy uses a convolutional neural network to predict a long-term goal, $g_t^l$ in $G \times G$ space: $g_t^l = \pi_{G}(h_t|\theta_{G})$, where $\theta_{G}$ are the parameters of the Global Policy.

\textbf{Planner.}
The Planner takes the long-term goal ($g_t^l$), the spatial obstacle map ($m_t$) and the agnet pose estimate ($\hat{x}_t$) as input and computes the short-term goal $g_t^s$, i.e. $g_t^s = f_{Plan}(g_t^l, m_t, \hat{x}_t)$. It computes the shortest path from the current agent location to the long-term goal ($g_t^l$) using the Fast Marching Method~\citep{sethian1996fast} based on the current spatial map $m_t$. The unexplored area is considered as free space for planning. We compute a short-term goal coordinate (farthest point within $d_s(=0.25m)$ from the agent) on the planned path.

\textbf{Local Policy.}
The Local Policy takes as input the current RGB observation ($s_t$) and the short-term goal ($g_t^s$) and outputs a navigational action, $a_t = \pi_{L}(s_t, g_t^s|\theta_L)$, where $\theta_L$ are the parameters of the Local Policy. The short-term goal coordinate is transformed into relative distance and angle from the agent's location before being passed to the Local Policy. The Local Policy is a recurrent neural network consisting of a pretrained ResNet18~\citep{he2016deep} as the visual encoder.

\section{Experimental setup}
\vspace{-5pt}
We use the Habitat simulator~\citep{savva2019habitat} with the Gibson~\citep{xiazamirhe2018gibsonenv} and Matterport (MP3D)~\citep{chang2017matterport3d} datasets for our experiments. Both Gibson and MP3D consist of scenes which are 3D reconstructions of real-world environments, however, Gibson is collected using a different set of cameras, consists mostly of office spaces while MP3D consists of mostly homes with a larger average scene area. We will use Gibson as our training domain, and use MP3D for domain generalization experiments.
The observation space consists of RGB images of size $3 \times 128 \times 128$ and base odometry sensor readings of size $3 \times 1$ denoting the change in agent's x-y coordinates and orientation. The actions space consists of three actions: \texttt{move\_forward, turn\_left, turn\_right}. Both the base odometry sensor readings and the agent motion based on the actions are noisy. They are implemented using the sensor and actuation noise models based on real-world data as discussed in Section~\ref{sec:noise}.

We follow the Exploration task setup proposed by~\cite{chen2019learning} where the objective to maximize the coverage in a fixed time budget. Coverage is the total area in the map known to be traversable. We define a traversable point to be known if it is in the field-of-view of the agent and is less than $3.2m$ away. We use two evaluation metrics, the absolute coverage area in $m^2$ (\textbf{Cov}) and the percentage of area explored in the scene (\textbf{\% Cov}), i.e. ratio of coverage to maximum possible coverage in the corresponding scene. During training, each episode lasts for a fixed length of 1000 steps.

We use train/val/test splits provided by~\Mycite{savva2019habitat} for both the datasets. Note that the set of scenes used in each split is disjoint, which means the agent is tested on new scenes never seen during training. Gibson test set is not public but rather held out on an online evaluation server for the Pointgoal task. We use the validation as the test set for comparison and analysis for the Gibson domain. We do not use the validation set for hyper-parameter tuning. To analyze the performance of all the models with respect to the size of the scene, we split the Gibson validation set into two parts, a small set of 10 scenes with explorable area ranging from $16m^2$ to $36m^2$, and a large set of 4 scenes with explorable area ranging from $55m^2$ to $100m^2$. Note that the size of the map is usually much larger than the traversable area, with the largest map being about $23m$ long and $11m$ wide.

\textbf{Training Details.}
We train our model in the Gibson domain and transfer it to the Matterport domain. The Mapper is trained to predict egocentric projections, and the Pose Estimator is trained to predict agent pose using supervised learning. The ground truth egocentric projection is computed using geometric projections from ground truth depth.
The Global Policy is trained using Reinforcement Learning with reward proportional to the increase in coverage as the reward. The Local Policy is trained using Imitation Learning (behavioral cloning).
All the modules are trained simultaneously. Their parameters are independent, but the data distribution is inter-dependent. Based on the actions taken by the Local policy, the future input to Neural SLAM module changes, which in turn changes the map and agent pose input to the Global policy and consequently affects the short-term goal given to the Local Policy.
For more architecture and hyperparameter details, please refer to the supplementary material and the open-source \href{https://github.com/devendrachaplot/Neural-SLAM}{code}.

\textbf{Baselines.} We use a range of end-to-end Reinforcement Learning (RL) methods as baselines:\\
\textbf{RL + 3LConv:} An RL Policy with 3 layer convolutional network followed by a GRU~\citep{cho2014properties} as described by~\Mycite{savva2019habitat}.\\
\textbf{RL + Res18:} A RL Policy initialized with ResNet18~\citep{he2016deep} pre-trained on ImageNet followed by a GRU.\\
\textbf{RL + Res18 + AuxDepth:} This baseline is adapted from~\Mycite{mirowski2016learning} who use depth prediction as an auxiliary task. We use the same architecture as our Neural SLAM module (conv layers from ResNet18) with one additional deconvolutional layer for Depth prediction followed by 3 layer convolution and GRU for the policy.\\
\textbf{RL + Res18 + ProjDepth:} This baseline is adapted form~\Mycite{chen2019learning} who project the depth image in an egocentric top-down in addition to the RGB image as input to the RL policy. Since we do not have depth as input, we use the architecture from RL + Res18 + AuxDepth for depth prediction and project the predicted depth before passing to 3Layer Conv and GRU policy.

For all the baselines, we also feed a 32-dimensional embedding of the sensor pose reading to the GRU along with the image-based representation. This embedding is also learnt end-to-end using RL. All baselines are trained using PPO~\citep{schulman2017proximal} with increase in coverage as the reward (identical to the reward used for Global policy). All the baselines require access to the ground-truth map during training for computing the reward. The supervision for the Global Policy, the Local Policy and the Mapper can also be obtained from the ground-truth map. The Pose Estimator requires additional supervision in the form of the ground-truth agent pose. We study the effect of this additional supervision in ablation experiments.

\setlength{\tabcolsep}{10pt}
\begin{table}[]
\centering
{\small'
\caption{\small Exploration performance of the proposed model, Active Neural SLAM (ANS) and baselines. The baselines are adated from [1]~\Mycite{savva2019habitat}, [2]~\Mycite{mirowski2016learning} and [3]~\Mycite{chen2019learning}.}
\label{tab:exp_results}
\begin{tabular}{@{}llccl
>{\columncolor[HTML]{DEDEDE}}c
>{\columncolor[HTML]{DEDEDE}}c l@{}}
\toprule
                       &  &               &                &  & \multicolumn{2}{c}{\cellcolor[HTML]{DEDEDE}Domain Generalization} &  \\
                       &  & \multicolumn{2}{c}{Gibson Val} &  & \multicolumn{2}{c}{\cellcolor[HTML]{DEDEDE}MP3D Test}             &  \\ \midrule
Method                 &  & \% Cov.       & Cov. (m2)      &  & \% Cov.                        & Cov. (m2)                        &  \\ \midrule
RL + 3LConv [1]            &  & 0.737         & 22.838         &  & 0.332                          & 47.758                           &  \\
RL + Res18                    &  & 0.747         & 23.188         &  & 0.341                          & 49.175                           &  \\
RL + Res18 + AuxDepth [2]  &  & 0.779         & 24.467         &  & 0.356                          & 51.959                           &  \\
RL + Res18 + ProjDepth [3] &  & 0.789         & 24.863         &  & 0.378                          & 54.775                           &  \\
\textbf{Active Neural SLAM (ANS)}&  & \textbf{0.948} & \textbf{32.701} &  & \textbf{0.521} & \textbf{73.281} &  \\ \bottomrule
\end{tabular}    }
\end{table}

\begin{figure*}[t!]
\centering
\includegraphics[width=0.99\linewidth,height=\textheight,keepaspectratio]{images/plot_ratio.pdf}
\vspace{-5pt}
\caption{\small Plot showing the $\%$ Coverage as the episode progresses for ANS and the baselines on the large and small scenes in the Gibson Val set as well as the overall Gibson Val set.}
\label{fig:plot_ratio}
\vspace{-5pt}
\end{figure*}

\vspace{-5pt}
\section{Results}
\vspace{-10pt}
We train the proposed ANS model and all the baselines for the Exploration task with 10 million frames on the Gibson training set. The results are shown in Table~\ref{tab:exp_results}. The results on the Gibson Val set are averaged over a total of 994 episodes in 14 different unseen scenes. The proposed model achieves an average absolute and relative coverage of $32.701m^2 / 0.948$ as compared to $24.863m^2 / 0.789$ for the best baseline. This indicates that the proposed model is more efficient and effective at exhaustive exploration as compared to the baselines. This is because our hierarchical policy architecture reduces the horizon of the long-term exploration problem as instead of taking tens of low-level navigational actions, the Global policy only takes few long-term goal actions. We also report the domain generalization performance on the Exploration task in Table~\ref{tab:exp_results}~(see shaded region), where all models trained on Gibson are evaluated on the Matterport domain. ANS leads to higher domain generalization performance ($73.281m^2 / 0.521$ vs $54.775m^2 / 0.378$). The absolute coverage is higher and \% Cov is lower for the Matterport domain as it consists of larger scenes on average. On a set of small MP3D test scenes (comparable to Gibson scene sizes), ANS achieved a performance of $31.407m^2 / 0.836$ as compared to $23.091m^2 / 0.620$ for the best baseline.  Some visualizations of policy execution are provided in Figure~\ref{fig:exp_traj}\footnote{See {\scriptsize \url{https://devendrachaplot.github.io/projects/Neural-SLAM}} for visualization videos.}.

In Fig.~\ref{fig:plot_ratio}, we plot the relative coverage (\% Cov) of all the models as the episode progresses on the large and small scene sets, as well as the overall Gibson Val set. The plot on the small scene set shows that ANS is able to almost completely explore the small scenes in around 500 steps, however, the baselines are only able to explore 85-90\% of the small scenes in 1000 steps (see Fig.~\ref{fig:plot_ratio} center). This indicates that ANS explores more efficiently in small scenes. The plot on the large scenes set shows that the performance gap between ANS and baselines widens as the episode progresses (see Fig.~\ref{fig:plot_ratio} left). Looking at the behavior of the baselines, we saw that they often got stuck in local areas. This behavior indicates that they are unable to remember explored areas over long-time horizons and are ineffective at long-term planning. On the other hand, ANS uses a Global policy on the map which allows it to have the memory of explored areas over long-time horizons, and plan effectively to reach distant long-term goals by leveraging analytical planners. As a result, it is able to explore effectively in large scenes with long episode lengths.

\setlength{\tabcolsep}{3.2pt}
\begin{table}[t]
    \centering
{\small
\caption{\small Results of the ablation experiments on the Gibson environment.}
\label{tab:ablation}
\vspace{-10pt}

\begin{tabular}{@{}llcccccccc@{}}
\toprule
                        &           & \multicolumn{2}{c}{Gibson Val Overall} &           & \multicolumn{2}{c}{Gibson Val Large} &           & \multicolumn{2}{c}{Gibson Val Small} \\
Method                  &           & \% Cov.           & Cov. (m2)          &           & \% Cov.          & Cov. (m2)         &           & \% Cov.          & Cov. (m2)         \\ \midrule
ANS w/o Local Policy + Det. Planner  &           & 0.941             & 32.188             &           & 0.845            & 53.999            &           & 0.980            & 23.464            \\
ANS w/o Global Policy + FBE  &           & 0.925             & 30.981             &           & 0.782            & 49.731            &           & 0.982            & 23.481            \\
ANS w/o Pose Estimation &           & 0.916             & 30.746             &           & 0.771            & 49.518            &           & 0.973            & 23.237            \\
\textbf{ANS}            & \textbf{} & \textbf{0.948}    & \textbf{32.701}    & \textbf{} & \textbf{0.862}   & \textbf{55.608}   & \textbf{} & \textbf{0.983}   & \textbf{23.538}   \\ \bottomrule
\end{tabular}    }
\end{table}

\begin{figure*}[ht!]
\includegraphics[width=0.99\linewidth,height=\textheight,keepaspectratio]{images/exp_example_s.pdf}
\vspace{-5pt}
	\caption{\small \textbf{Exploration visualization}. Figure showing a sample trajectory of the Active Neural SLAM model in the Exploration task. \textit{Top:} RGB observations seen by the agent. \textit{Inset:} Global ground truth map and pose (not visible to the agent). \textit{Bottom:} Local map and pose predictions. Long-term goals selected by the Global policy are shown by blue circles. The ground-truth map and pose are under-laid in grey. Map prediction is overlaid in green, with dark green denoting correct predictions and light green denoting false positives. Agent pose predictions are shown in red. The light blue shaded region shows the explored area.}
\label{fig:exp_traj}
\vspace{-10pt}
\end{figure*}

\subsection{Ablations}
\vspace{-5pt}

\textbf{Local Policy.} An alternative to learning a Local Policy is to have a deterministic policy which follows the plan given by the Planner. As shown in Table~\ref{tab:ablation}, the ANS model performs slightly worse without the Local Policy. The Local Policy is designed to adapt to small errors in Mapping. We observed Local policy overcoming false positives encountered in mapping. For example, the Neural SLAM module could sometime wrongly predict a carpet as an obstacle. In this case, the planner would plan to go around the carpet. However, if the short-term goal is beyond the carpet, the Local policy can understand that the carpet is not an obstacle based on the RGB observation and learn to walk over it.

\textbf{Global Policy.} An alternative to learning a Global Policy for sampling long-term goals is to use a classical algorithm called Frontier-based exploration (FBE)~\citep{yamauchi1997frontier}. A frontier is defined as the boundary between the explored free space and the unexplored space. Frontier-based exploration essentially sample points on this frontier as goals to explore the space. There are different variants of Frontier-based exploration based on the sampling strategy. \cite{holz2010evaluating} compare different sampling strategies and find that sampling the point on the frontier closest to the agent gives the best results empirically. We implement this variant and replace it with our learned Global Policy. As shown in Table~\ref{tab:ablation}, the performance of the Frontier-based exploration policy is comparable on small scenes, but around 10\% lower on large scenes, relative to the Global policy. This indicates the importance of learning as compared to classical exploration methods in larger scenes. Qualitatively, we observed that Frontier-based exploration spent a lot of time exploring corners or small areas behind furniture. In contrast, the trained Global policy ignored small spaces and chose distant long-term goals which led to higher coverage.

\textbf{Pose Estimation.} A difference between ANS and the baselines is that ANS uses additional supervision to train the Pose Estimator. In order to understand whether the performance gain is coming from this additional supervision, we remove the Pose Estimator from ANS and just use the input sensor reading as our pose estimate. Results in Table~\ref{tab:ablation} show that the ANS still outperforms the baselines even without the Pose Estimator. We also observed that performance without the pose estimator drops only about 1\% on small scenes, but around 10\% on large scenes. This is expected because larger scenes take longer to explore, and pose errors accumulate over time to cause drift. Passing the ground truth pose as input the baselines instead of the sensor reading did not improve their performance.

\begin{figure*}[t!]
\vspace{5pt}
\centering
\includegraphics[width=0.99\linewidth,height=\textheight,keepaspectratio]{images/real_world_living_s.pdf}\\
\vspace{5pt}
\includegraphics[width=0.99\linewidth,height=\textheight,keepaspectratio]{images/real_world_bedroom_s.pdf}

\vspace{-5pt}
\caption{\small \textbf{Real-world Transfer.} \textbf{Left:} Image showing the living area in an apartment used for the real-world experiments. \textbf{Right:} Sample images seen by the robot and the predicted map. The long-term goal selected by the Global Policy is shown by a blue circle on the map.}
\label{fig:real_world_living}
\vspace{-10pt}
\end{figure*}

\subsection{Real-world transfer}
\vspace{-5pt}
We deploy the trained ANS policy on a Locobot in the real-world. In order to match the real-world observations to the simulator observations as closely as possible, we change the simulator input configuration to match the camera intrinsics on the Locobot. This includes the camera height and horizontal and vertical field-of-views. In Figure~\ref{fig:real_world_living}, we show an episode of ANS exploring the living area in an apartment. The figure shows that the policy transfers well to the real-world and is able to effectively explore the environment. The long-term goals sampled by the Global policy (shown by blue circles on the map) are often towards open spaces in the explored map, which indicates that it is learning to exploit the structure in the map. Please refer to the project \href{https://devendrachaplot.github.io/projects/Neural-SLAM}{webpage} for real-world transfer videos.

\subsection{Pointgoal Task Transfer.}
\vspace{-5pt}
PointGoal has been the most studied task in recent literature on navigation where the objective is to navigate to a goal location whose relative coordinates are given as input in a limited time budget. In this task, each episode ends when either the agent takes the \texttt{stop} action or at a maximum of 500 timesteps. An episode is considered a success when the final position of the agent is within $0.2\text{m}$ of the goal location. In addition to Success rate (Succ), Success weighted by (normalized inverse) Path Length or SPL is also used as a metric for evaluation as proposed by \cite{anderson2018evaluation}.

All the baseline models trained for the task of Exploration either need to be retrained or at least fine-tuned to be transferred to the Pointgoal task. The modularity of ANS provides it another advantage that it can be transferred to the Pointgoal task without any additional training. For transferring to the Pointgoal task, we just fix the Global policy to always output the PointGoal coordinates as the long-term goal and use the Local Policy and Neural SLAM module trained for the Exploration task. We found that an ANS policy trained on exploration, when transferred to the Pointgoal task performed better than several RL and Imitation Learning baselines trained on the Pointgoal task. The transferred ANS model achieves a success rate/SPL of 0.950/0.846 as compared to 0.827/0.730 for the best baseline model on Gibson val set. The ANS model also generalized significantly better than the baselines to harder goals and to the Matterport domain. In addition to better performance, ANS was also 10 to 75 times more sample efficient than the baselines. This transferred ANS policy was also the winner of the CVPR 2019 Habitat Pointgoal Navigation Challenge for both RGB and RGB-D tracks among over 150 submissions from 16 teams. These results highlight a key advantage of our model. It allows us to transfer the knowledge of obstacle avoidance and control in low-level navigation across tasks, as the Local Policy and Neural SLAM module are task-invariant. More details about the Pointgoal experiments, baselines, results including domain and goal generalization on the Pointgoal task are provided in the supplementary material.

\vspace{-5pt}
\section{Conclusion}
\vspace{-10pt}
In this paper, we proposed a modular navigational model which leverages the strengths of classical and learning-based navigational methods. We show that the proposed model outperforms prior methods on both Exploration and PointGoal tasks and shows strong generalization across domains, goals, and tasks. In the future, the proposed model can be extended to complex semantic tasks such as Semantic Goal Navigation and Embodied Question Answering by using a semantic Neural SLAM module which creates a multi-channel map capturing semantic properties of the objects in the environment. The model can also be combined with prior work on Localization to relocalize in a previously created map for efficient navigation in subsequent episodes.

\vspace{-5pt}
\section*{Acknowledgements}
\vspace{-10pt}
This work was supported by IARPA DIVA D17PC00340, ONR Grant N000141812861, ONR MURI, ONR Young Investigator, DARPA MCS, and Apple. We would also like to acknowledge NVIDIA's GPU support. We thank Guillaume Lample for discussions and coding during the initial stages of this project.

\textbf{Licenses for referenced datasets.}\\
Gibson: {\footnotesize \url{http://svl.stanford.edu/gibson2/assets/GDS_agreement.pdf}}\\
Matterport3D: {\footnotesize \url{http://kaldir.vc.in.tum.de/matterport/MP_TOS.pdf}}\\

redpajama_set_name: RedPajamaArXiv


Prof. Grigorij Kuzmin (1917-1988) was an early pioneer of the dynamics of stellar systems, invoking elegant mathematical models. Some results of his investigations form part of the theoretical basis of modern dynamical equilibria. Recent progress in the dynamics of galaxies has focused more on their formation and evolution, driven mainly by both space and ground-based observations and computer simulations. As a celebration of Kuzmin's contributions, the 90th anniversary of his birth will be honored by a major conference on "DYNAMICS OF GALAXIES" at which the tremendous progress of recent years will be reviewed.
The conference will be hosted by the Main Astronomical (Pulkovo) Observatory of Russian Academy of Sciences and Saint Petersburg State University under support of section "Structure and Dynamics of the Galaxy" of Astronomical Council of Russian Academy of Sciences. The conference will be held at the Pulkovo Observatory just outside St. Petersburg, during the week August 5 to 10, 2007. Leading experts and young researchers from Russia and the West will discuss recent advances in galaxy dynamics, including LCDM and Galaxy Formation, Dynamical Processes in Mergers, Limits on the Baryonic Mass Content of Galaxies, Dynamics of Disk Galaxies, and Galactic Modelling.


Only those within normal instrument time demands. But please enquire, we will always make efforts to pilot studies and explore new projects.
Gilson 215 liquid handler and SampleJet for high-throughout sample preparation and acquisition.


text:
Q: Fullscreen UIImage in Swift 4 I have a UICollectionView with cells that contain UIImages. Here is my didSelectIndex method to grab the image and fullscreen it.
override func collectionView(_ collectionView: UICollectionView, didSelectItemAt indexPath: IndexPath) {
    let media = self.media[indexPath.row]
    guard let photoURL = URL(string: media.photoURL) else { return }

    // Get the image from the cache
    if let image = mediaCache.object(forKey: photoURL.absoluteString as AnyObject) as? UIImage {
        let imageView = UIImageView(image: image)
        imageView.frame = UIScreen.main.bounds
        imageView.backgroundColor = .black
        imageView.contentMode = .scaleAspectFill
        imageView.clipsToBounds = true
        imageView.isUserInteractionEnabled = true

        self.navigationController?.isNavigationBarHidden = true
        self.tabBarController?.tabBar.isHidden = true

        let dismissTap = UITapGestureRecognizer(target: self, action: #selector(dismissFullscreenImage))
        imageView.addGestureRecognizer(dismissTap)

        self.view.addSubview(imageView)
    }
}

However, changing the content mode from scaleAspectFill to scaleAspectFit doesn't solve the issue. The image doesn't fit inside the UIImageView frame.


text:
A Beeston Historian's Journal
Aspects of the history of Beeston in Nottinghamshire and it's people - a discussion centred around current research by David Hallam about Beeston much of it response to requests for help and assistance from those with Beeston connections. David welcomes your comments and input on any of the topics...
Bendigo - a Boxing Legend
Tuesday, 11th October 2011 - we were to learn - was a memorable day for one of Beeston's worthies from the past. As near as could be seen, it was the 200th anniversary of the birth of the Victorian bare-knuckle boxing legend, William Thompson, better known as 'Bendigo'.
Once again there was a good crowd of local people to witness the unveiling of the latest local blue plaques to mark the time, during his later life, when Bendigo lived in Beeston.
We all gathered at the Pearson Centre where, fittingly, we were joined for the occasion by a modern-day local boxing champion, Jason Booth, currently the holder of the British and the Commonwealth super-bantamweight titles. We also welcome the Mayor of Broxtowe, Councillor Jacky Williams as well as a representative of Nottingham Castle Rock Brewery which had recently launched a 'Bendigo' beer.
Alan Dance outlined the story of Bendigo's life and career as a boxer. Like modern-day celebrities, the story of his life has become confused by much repeated flowery 'facts'. Although it is generally believed that he was one of triplets born to Mary and Benjamin Thompson, only William and his brother Richard were baptised at St Marys Nottingham on 16 October 1811, and Richard died about two weeks later. And the story that he was the last of 21 born to the couple is also difficult to square with the facts - they were married, apparently, in 1805 and only a relatively small number of their children - perhaps seven - are recorded in the baptismal register.
But doubts about the details cannot take anything away from his remarkable career as a prize fighter which started after his father died when Bendigo was 15 and he and his mother spent time in the workhouse. Determined to support himself and his mother, he turned to the boxing ring - where winners could take away large purses, sometimes £300 or more - and great acclaim. So it was to be with Bendigo whose career took in many grueling contests, each lasting as many as 96 rounds and all attracting a huge following. These included three marathon fights with his local rival Ben Caunt. The second of these contests was the only fight that Bendigo lost - and then only on a disputed technicality - and in his final fight, aged 39 and fittingly against his old rival Caunt, he was able to finish with a dramatic win.
His flamboyant style and agility and athleticism in the ring earned him a huge popular following, something which was to continue for the rest of his life. At first, his retirement years were spent following his love of fishing but he soon became a heavy drinker and became involved with the Nottingham Lambs, a violent, politically motivated mob. Now a drunken mess, he was committed 28 times to the House of Correction.
To escape this life, he became interested in religion and moved to Beeston - to a cottage on what is now Wollaton Road, immediately to the north of Anglo-Scotian Mills. This cottage, now demolished, had another claim for fame - its chimney, which can still be seen in the side of the mill building, was reputed to be the tallest house chimney in the land.
Bendigo now threw all his energies into preaching - in his own indomitable style - attracting great crowds, eager to see and hear their hero.
He died at Beeston on 23 August 1880, following a fall on the stairs of his cottage. His funeral procession, reputed to be a mile in length, took him to his final resting place in what is now Bath Street Rest Gardens, where his distinctive memorial survives.
Following Alan Dance's outline of Bendigo's life, we walked around to a spot opposite the site of his cottage where the plaque had been fixed to an adjacent pillar to watch its unveiling by Jason Booth (shown second from the right in our photo, accompanied by, left the right, Alan Dance, the Mayor and the representative from Castle Brewery)
The purpose of the blue plaque scheme is to link people with place and to make people aware of the importance of both. And, it seemed to have an immediate effect - a young woman stopped as she passed and told the writer that she thought that she was related to Bendigo and would be certainly looking into it. It seems, the plaques are working well !
Posted by David Hallam at 3:56 PM No comments:
More Blue Plaques
Unfortunately, I was away when the plaque was unveiled to mark the factory in Beeston where Thomas Humber made bicycles and where Humber motorcycles and eventually cars were made. By all accounts it was an excellent occasion so I was sorry to miss it. All the more reason to make sure not to miss the latest unveiling.
There will be many Beeston residents who will not have a clear idea - if any - of the life and work of this latest Beeston person to be commemorated with a plaque. They may have wondered about the origin of the name of 'Clifford Avenue' in Beeston and they may also be unsure why Nether Street School was renamed The John Clifford School. They may have spotted the date stone on the building when walking through the short piece of Nether Street that is on the west side of Station Road - ' Erected in 1806, Enlarged in 1836'. Its put to good use today as a nursery but they may have wondered what it was built for and what happened there all those years ago This latest plaque will put person and place together and help to answer those questions.
We were welcomed by the nursery's owners, Roy and Hilary Ruddock who take much pride in the history of their building and the way they had been able to rescue the building and convert it for an excellent modern use. Although drizzling rain threatened the ceremony and there was a possibility that the ceremony would have had to be indoors, everyone was determined to do it properly and gathered around the site of the plaque to hear Professor John Beckett give an outline of the life of John Clifford.
Though he was not born and did not die in Beeston, his parents moved their family here from Sawley when he was very young and it was undoubtedly the influences of his early education, his time as a lad in the local lace factories and his conversion and commitment which arose from his membership of the local Baptist chapel - which was then based in the very building where we had gathered - that shaped his life. It was this chapel community that sponsored his time at theological college from where he went on to a lifetime ministry in west London. It was from that base that he developed as a national and, indeed, an international figure for social issues, in particular as a campaigner for non-denominational education. Rev Kevin Dare, the present Baptist Minister in Beeston, then read an extract from Dr Clifford's sermon which he delivered on the occasion of the 50th anniversary of his ministry, which illustrated clearly the power of his oratory and his commitment to his life's beliefs
The plaque was then unveiled by the Deputy Mayor of Broxtowe, Councillor Margaret Handley (accompanied by her husband on the left of our photograph, which also shows, left to right, Rev Kevin Dare, Hilary and Roy Rudduck and Professor John Beckett)
Now, thanks to this simple memorial, the contribution of Rev Dr John Clifford and the part paid by Beeston, and particularly this building, will surely not be forgotten. Those interested to learn more about this, might wish to read our account of his life and work.
Posted by David Hallam at 10:32 AM 1 comment:
Blue Plaques for Beeston
It is very pleasing to see real progress with the local joint initiative by Beeston Civic Society, Beeston & District Local History Society and Stapleford & District Local History Society, to mark those who have historic links to the local area with a blue plaque. These plaques, already familiar in London and other major centres, link the person with the place with which they were associated very effectively and its good to see the idea being taken up in smaller towns - and in the wider Beeston area in particular. The first was placed in Chilwell earlier this year to commemorate the life of Thomas H Barton, the local bus pioneer and, in Stapleford, a plaque for Arthur Mee, the writer and journalist, has been unveiled - with more, we understand, in the pipeline.
On Saturday afternoon last, there was an excellent gathering to witness the latest placing, this one on Church Street Schools, Beeston - now, of course, converted to apartments - to commemorate the life of teacher and historian, Arthur Cossons and the exceptional contribution he made in Beeston over many years.
We all gathered at the Methodist Church hall, where Alan Clayton, the Chairman of Beeston & District Local History Society welcomed three generations of the Cossons family headed by Arthur Cosson's son, Sir Neil Cossons and his daughter, Hilda Stoddard. Peter Robinson, Chairman of the local blue plaque project, spoke of its objective of linking 'person with place' - and today it was the well deserved 'person', Arthur Cossons, and the 'place', therefore, undoubtedly Church Street Junior Boys' School. For Sir Neil it was a time to remember their father in the context of his own early life in Beeston and his time at his father's school and the family home on Union Street, now lost through redevelopment.
We had also gathered to witness the unveiling of an associated plaque to mark the remains of the Village Cross which had, until about 1860, it is said, stood in the area previously known as 'The Cross, where the War Memorial now stands, until it was taken down and used as part of a nearby wall. It was there, in 1929, that it was rediscovered by Arthur Cossons, who had it erected next to the school. During his lifetime, there is no doubt that he made sure that his pupils and the wider population of Beeston were aware of what it was but now, with memories fading, there was a need to add an explanation of what it was. Professor John Beckett set out the evidence to the audience. While there was no evidence that Beeston had held ancient rights to hold a market, the old name for Middle Street - Market Street - may point to its local use to mark a corn market and its proximity to the Church pointed to its use as a focus for processions at Harvest time.
We all then walked around to Church Street, with plenty of opportunity to compare our experiences of Arthur Cossons time at the school there. We readily agreed that there was no doubt that his enthusiasm and dedication to local history has had a lasting effect on local people and their continuing interest in their town's history.
The unveiling itself was performed by Sir Neil, assisted by Hilda, his sister (left) and the other members of the family. On his right are (left to right) Alan Clayton, Peter Robinson and Professor John Beckett. The plaque is fixed to the side of what was the caretaker's house at the school, as the building which housed the Boys' Junior School - of which Arthur Cossons was Headmaster - which stood to the rear of the site, adjacent to what was Church Lane - as distinct from the fully restored original Board School building on Church Street - was demolished as part of the redevelopment.
Everyone then moved around the corner where Margaret Cooper, a Beeston historian who taught in Beeston schools, was invited to unveil the plaque which now describes the 14th Century cross.
We all then returned to the Methodist hall for light refreshments, to view a small exhibition of the life of Arthur Cossons - and, of course, to continue to chat and swap memories between friends. An excellent occasion to mark one of Beeston's worthy 20th Century figures.
Posted by David Hallam at 6:04 AM No comments:
Visit David Hallam's History of Beeston site
Exploring the History of a Midlands Town


text:
ConnectOne Bank Awards The Community Chest a Grant for Professional Development Series
Wednesday, January 25 2023 @ 07:06 am
Posted in News and Views
(Eastern Bergen County, New Jersey; January 24, 2023) -- The Community Chest of Eastern Bergen County announces the organization received a grant of $2,500 from ConnectOne Bank. The grant provides partial support for The Community Chest of Eastern Bergen County's Professional Development Series.
The three-part series, which runs from February through April, offers nonprofit management and supervisory staff workshops on a range of topics, including adult mental health first aid, leadership and adapting to changing times quickly, and rethinking personnel management in a post-pandemic period.
"We thank ConnectOne Bank for their support of our Professional Development Series for nonprofit staff. The series will provide nonprofit managers and leaders with training to respond to clients' needs in a rapidly changing environment resulting from the COVID-19 pandemic," said Dr. Shelly Wimpfheimer, Executive Director, The Community Chest of Eastern Bergen County.
ConnectOne Bank has participated in The Chest's activities and events. The ConnectOne Bank Community Connections Team Building Event created benches, planter boxes, craft artwork and blankets and presented them to The Chest to give to non-profit organizations that could use them. In 2018, the company was recognized for its involvement in the community and received the Champion Award at The Chest's annual gala.
With the motto "Neighbors Helping Neighbors", The Community Chest of Eastern Bergen County leads initiatives and supports nonprofits that make communities stronger and benefit the people of eastern Bergen County in New Jersey. The founders' vision was to fill a chest with individuals and businesses' generous contributions and to empty the contents by awarding grant funds to support worthy projects led by local nonprofits in the area.
Funds raised in The Chest's annual campaign support nonprofits' programs providing social, cultural, recreational, and educational services to residents in the area. Coordinating and planning services are also provided in its communities, serving as a bridge between agencies and organizations and investing in the stability and quality of the service network. The organization's Board of Managers implements a thorough process of screening, research, and monitoring to assure the funds raised are directed to the most worthy programs and the areas of greatest need.
For more information about The Chest, visit the organization's web site, www.thecommunitychestebc.org, or call the office at 201-568-7474.
About The Community Chest
Founded in 1933, The Community Chest, a nonprofit, tax-exempt organization, has supported local not-for-profit agencies for 90 years. The Chest leads initiatives and supports nonprofits that make our communities stronger and benefit people in need in eastern Bergen County. Each year, The Chest raises funds and allocates them to agencies demonstrating the capacity to provide services to local citizens. The organization also provides coordinating and planning services in its communities, serving as a bridge between agencies and organizations and investing in the stability and quality of the service network.
The Community Chest's Board of Managers is comprised of community leaders dedicated to improving the lives of neighbors in need. People interested in being considered for board membership may contact Executive Director Dr. Shelly Wimpfheimer at (201) 568-7474.
Photo Caption: Shown left to right. Angelos Kouzinas, Assistant Vice President at ConnectOne Bank, presents a donation of $2,500 to Dr. Shelly Wimpfheimer, Executive Director of The Community Chest of Eastern Bergen County.
Photo Credit: The Community Chest of Eastern Bergen County
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