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In this paper we provide a generalization of the concept of cohesion as introduced recently by Berenhaut, Moore and Melvin [Proceedings of the National Academy of Sciences, 119 (4) (2022)]. The formulation presented builds on the technique of partitioned local depth by distilling two key probabilistic concepts: local relevance and support division. Earlier results are extended within the new context, and examples of applications to revealing communities in data with uncertainty are included., Comment: Improved exposition, examples in 5.1 & 5.3 expanded, 17 pages, 7 figures

Networks can be combined in various ways, such as overlaying one on top of another or setting two side by side. We introduce "network models" to encode these ways of combining networks. Different network models describe different kinds of networks. We show that each network model gives rise to an operad, whose operations are ways of assembling a network of the given kind from smaller parts. Such operads, and their algebras, can serve as tools for designing networks. Technically, a network model is a lax symmetric monoidal functor from the free symmetric monoidal category on some set to $\mathbf{Cat}$, and the construction of the corresponding operad proceeds via a symmetric monoidal version of the Grothendieck construction., 46 pages

Modern advances in natural language processing (NLP) and information retrieval (IR) provide for the ability to automatically analyze, categorize, process and search textual resources. However, generalizing these approaches remains an open problem: models that appear to understand certain types of data must be re-trained on other domains. Often, models make assumptions about the length, structure, discourse model and vocabulary used by a particular corpus. Trained models can often become biased toward an original dataset, learning that – for example – all capitalized words are names of people or that short documents are more relevant than longer documents. As a result, small amounts of noise or shifts in style can cause models to fail on unseen data. The key to more robust models is to look at text analytics tasks on more challenging and diverse data. Poetry is an ancient art form that is believed to pre-date writing and is still a key form of expression through text today. Some poetry forms (e.g., haiku and sonnets) have rigid structure but still break our traditional expectations of text. Other poetry forms drop punctuation and other rules in favor of expression. Our contributions include a set of novel, challenging datasets that extend traditional tasks: a text classification task for which content features perform poorly, a named entity recognition task that is inherently ambiguous, and a retrieval corpus over the largest public collection of poetry ever released. We begin by looking at poetry identification - the task of finding poetry within existing textual collections, and devise an effective method of extracting poetry based on how it is usually formatted within digitally scanned books, since content models do not generalize well. Then we work on the content of poetry: we construct a dataset of around 6,000 tagged spans that identify the people, places, organizations and personified concepts within poetry. We show that cross-training with existing datasets based on news-corpora helps modern models to learn to recognize entities within poetry. Finally, we return to IR, and construct a dataset of queries and documents inspired by real-world data that expose some of the key challenges of searching through poetry. Our work is the first significant effort to use poetry in these three tasks and our datasets and models will provide strong baselines for new avenues of research on this challenging domain.

Figure S1. Chronic endurance exercise model. Figure S2. Running protocol. Figure S3. Effect of endurance training on mtDNA copy number in muscle and liver. Figure S4. Wild-type animal weight and food intake. Figure S5. Effect of endurance training on circulating leukocytes. Figure S6. Antitumor immune response in exercised vs. sedentary wild type mice. (PDF 20084 kb)

Traditional information retrieval treats named entity recognition as a pre-indexing corpus annotation task, allowing entity tags to be indexed and used during search. Named entity taggers themselves are typically trained on thousands or tens of thousands of examples labeled by humans. However, there is a long tail of named entities classes, and for these cases, labeled data may be impossible to find or justify financially. We propose exploring named entity recognition as a search task, where the named entity class of interest is a query, and entities of that class are the relevant "documents". What should that query look like? Can we even perform NER-style labeling with tens of labels? This study presents an exploration of CRF-based NER models with handcrafted features and of how we might transform them into search queries., Comment: 6 pages

It is a widely accepted principle that software without tests has bugs. Testing reinforcement learning agents is especially difficult because of the stochastic nature of both agents and environments, the complexity of state-of-the-art models, and the sequential nature of their predictions. Recently, the Arcade Learning Environment (ALE) has become one of the most widely used benchmark suites for deep learning research, and state-of-the-art Reinforcement Learning (RL) agents have been shown to routinely equal or exceed human performance on many ALE tasks. Since ALE is based on emulation of original Atari games, the environment does not provide semantically meaningful representations of internal game state. This means that ALE has limited utility as an environment for supporting testing or model introspection. We propose ToyBox, a collection of reimplementations of these games that solves this critical problem and enables robust testing of RL agents., Comment: NeurIPS Systems for ML Workshop

This dissertation concerns the homotopical group theory of Kac-Moody groups. Applications stem from homotopical expressions of infinite and noncompact group classifying spaces in terms of finite and compact group classifying spaces through local to global constructions. New homotopy decompositions for the "unipotent" factors of parabolic subgroups of a discrete Kac-Moody group are given in terms of unipotent algebraic groups. As in the Lie case [23], a map is constructed from the classifying space of the discrete Kac-Moody group over the algebraic closure of the field with p elements to the complex topological Kac-Moody group of the same type. Rank 2, non-Lie, Kac-Moody groups are studied to show that in contrast to the Lie case [22] the classifying space of the discrete Kac-Moody group over the field with the kth power of p elements and the homotopy fixed points of the complex topological Kac-Moody group of the same type with respect to a newly constructed kth power of p unstable Adams operation frequently have different homotopy types after localization with respect to homology with coefficients in a field of characteristic relatively prime to p

Venezuela is experiencing a transitional political process in which the government and the majority of Venezuelans want to move from a capitalist representative democracy to a more socialist participatory democracy. This transition is enmeshed in complexities, contradictions, and political opposition. Reflection on the experience of accompanying neighborhood groups in local decision making in Caracas from 2002 to 2006 suggests that planning practitioners and scholars can be allies in the grassroots processes of empowerment and self-determination of local communities and advocates and active agents in the "trickling-up" of greater planning participation to upper levels of government.

CHDS State/Local This paper explores pandemic planning efforts across federal and state jurisdictions and how the absence of collaboration could have major consequences upon the population of the United States. How adequate are state and federal pandemic plans, and what must be done nationally to address common shortfalls? The methodology used a hybrid approach by combining a secondary analysis of available data with a modified case study approach. Analyzing the individual state plans and HHS' Pandemic Influenza Plan revealed common deficiencies, and disclosed distinct functional areas where stringent collaboration across multiple jurisdictions and functional areas would mitigate the deficiencies and provide a blueprint for potential development into an all-hazards national catastrophe plan. This resultant comprehensive plan would provide a solid template for all stakeholders to use in further development of their individual plans, and additionally provide a mechanism to propagate proactive planning efforts among international disaster preparedness partners. http://archive.org/details/thepandemicpendu109454864 Department of the Air Force author (civilian).

As discussed in the introduction, many scholars have argued that public space is a prerequisite for the expression, representation, preservation and enhancement of democracy (Boudreau, 2000; Caldeira, 2000; Holston and Appadurai, 1999; Low, 2000; Sassen, 1996; Low and Smith, 2006). This has not been more true than in the capital cities of Latin America in recent decades, where political demonstrations have played a critical role in the demise of totalitarian regimes and the reestablishment of democracy. Caracas, capital city of Venezuela, is a prime example in that key urban spaces have been sites for popular demonstrations since Hugo Chavez became President in 1998. Public buildings such as the seats of political and military power, and private ones such as headquarters of media corporations functioned as architectural icons and background to these events.