Miracle-dz/UniFAR-FTUs huggingface dataset:
Exact Machine Learning Topological States

Artificial neural networks play a prominent role in the rapidly growing field of machine learning and are recently introduced to quantum many-body systems to tackle complex problems. Here, we find that even topological states with long-range quantum entanglement can be represented with classical artificial neural networks. This is demonstrated by using two concrete spin systems, the one-dimensional (1D) symmetry-protected topological cluster state and the 2D toric code state with an intrinsic topological order. For both cases we show rigorously that the topological ground states can be represented by short-range neural networks in an {\it exact} fashion. This neural network representation, in addition to being exact, is surprisingly {\it efficient} as the required number of hidden neurons is as small as the number of physical spins. Our exact construction of topological-order neuron-representation demonstrates explicitly the exceptional power of neural networks in describing exotic quantum states, and at the same time provides valuable topological data to supervise machine learning topological quantum orders in generic lattice models.
SARS-CoV-2 hijacks neutralizing dimeric IgA for nasal infection and injury in Syrian hamsters1

ABSTRACT Prevention of robust severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection in nasal turbinate (NT) requires in vivo evaluation of IgA neutralizing antibodies. Here, we report the efficacy of receptor binding domain (RBD)-specific monomeric B8-mIgA1 and B8-mIgA2, and dimeric B8-dIgA1, B8-dIgA2 and TH335-dIgA1 against intranasal SARS-CoV-2 challenge in Syrian hamsters. These antibodies exhibited comparable neutralization potency against authentic virus by competing with human angiotensin converting enzyme-2 (ACE2) receptor for RBD binding. While reducing viral loads in lungs significantly, prophylactic intranasal B8-dIgA unexpectedly led to high amount of infectious viruses and extended damage in NT compared to controls. Mechanistically, B8-dIgA failed to inhibit SARS-CoV-2 cell-to-cell transmission, but was hijacked by the virus through dendritic cell-mediated trans-infection of NT epithelia leading to robust nasal infection. Cryo-EM further revealed B8 as a class II antibody binding trimeric RBDs in 3-up or 2-up/1-down conformation. Neutralizing dIgA, therefore, may engage an unexpected mode of SARS-CoV-2 nasal infection and injury.
Holographic Entanglement Entropy from Numerical Relativity

In this proceedings we show the evolution and equilibration of nonlocal quantities such as equal time two-point functions for operators of large conformal weight and holographic entanglement entropy in N = 4 super Yang Mills theory using the gauge gravity duality. We use two different scenarios. One where the system is initially anisotropic and thermalizes and one which also includes inhomogeneities corresponding to colliding shocks on the gravity side.
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In this paper, we present an embedding-based framework (TrQuery) for recommending solutions of a SPARQL query, including approximate solutions when exact querying solutions are not available due to incompleteness or inconsistencies of real-world RDF data. Within this framework, embedding is applied to score solutions together with edit distance so that we could obtain more fine-grained recommendations than those recommendations via edit distance. For instance, graphs of two querying solutions with a similar structure can be distinguished in our proposed framework while the edit distance depending on structural difference becomes unable. To this end, we propose a novel score model built on vector space generated in embedding system to compute the similarity between an approximate subgraph matching and a whole graph matching. Finally, we evaluate our approach on large RDF datasets DBpedia and YAGO, and experimental results show that TrQuery exhibits an excellent behavior in terms of both effectiveness and efficiency.
A DIRT-T Approach to Unsupervised Domain Adaptation

Domain adaptation refers to the problem of how to leverage labels in one source domain to boost up learning performance in a new target domain where labels are scarcely available or completely unavailable. In this paper, we address these issues through the lens of the cluster assumption, i.e., decision boundaries should not cross high-density data regions. We propose two novel and related models: (1) the Virtual Adversarial Domain Adaptation (VADA) model, which combines domain adversarial training with a penalty term that punishes the violation of the cluster assumption; (2) the Decision-boundary Iterative Reﬁnement Training with a Teacher (DIRT-T) model, which takes the VADA model as initialization and employs natural gradient steps to further minimize the cluster assumption violation. Extensive empirical results demonstrate that the combination of these two models signiﬁcantly improve the state-of-the-art performance on several visual domain adaptation benchmarks.
Hysteresis modeling in smart material systems

In this paper, we discuss several techniques for modeling hysteresis in smart material systems. The physical mechanisms leading to hysteresis vary greatly among materials and applications. This leads to a variety of models ranging from purely phenomenological models for systems in which the physics is not well understood to characterizations which incorporate significant physical properties of the system. We provide an overview of such approaches with examples drawn from current smart material applications.
Forbidden Words Taboo And The Censoring Of Language

Thank you for reading forbidden words taboo and the censoring of language. Maybe you have knowledge that, people have look hundreds times for their favorite readings like this forbidden words taboo and the censoring of language, but end up in harmful downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they are facing with some malicious bugs inside their computer.
Supplementary material for Variational Automatic Curriculum Learning for Sparse-Reward Cooperative Multi-Agent Problems

From Assumption 1, V (φ′, π) is constant within a small vicinity of φ; thus∇ V (T (φ), π)| =0 = 0; hence ∇ L2(π, q[T ])| =0 = Eφ∼q(φ) [ V (T (φ), π) · ∇ log p[T−1](φ)| =0 ] (10) Define sp(φ) = ∇φ log p(φ); we have ∇ log p[T−1](φ) = sp(T (φ))>∇ T (φ) + trace((∇φT (φ))−1 · ∇ ∇φT (φ)). (11) When T (φ) = φ+ f(φ) and = 0, we have T (φ) = φ, ∇ T (φ) = f(φ), ∇φT (φ) = I, ∇ ∇φT (φ) = ∇φf(φ), (12) where I is the identity matrix. Therefore: ∇ L2(π, q[T ])| =0 = Eφ∼q(φ) [ V (T (φ), π)(∇φ log p(φ)>f(φ) + trace(∇φf(φ))) ] (13)
Host ( CPU ) Device ( GPU ) Gaussian Blur XY Derivative Second Moment 3 x Gaussian Blur

Graphics processing units (GPUs) in embedded mobile platforms are reaching performance levels where they may be useful for computer vision applications. We compare two generations of embedded GPUs for mobile devices when running a state-of-the-art feature detection algorithm, i.e., HarrisHessian/FREAK. We compare architectural differences, execution time, temperature, and frequency on Sony Xperia Z3 and Sony Xperia XZ mobile devices. Our results indicate that the performance soon is sufficient for real-time feature detection, the GPUs have no temperature problems, and support for large work-groups is important.
Gesture as a conceptual mapping tool

Metaphoric understandings emerge when conceptual mappings link elements of one domain with elements of another. Cross-domain mappings seem powerful and creative, but even mundane meanings involve mappings that link elements with one another. Instructional interactions bring the process of conceptual mapping into the open as one person strives to shape the understanding of another. Using data from a study of time-telling instruction, this chapter describes two important functions of gestures during instruction: (1) guiding mappings that link conceptual models with structures in the environment, and (2) adding image-schematic structure to the conceptualization. Both help form the understanding needed to perform successfully. These critical functions of gesture are apparent only when discourse is treated as multimodal and contextual – as involving the interrelation of talk, gesture, and action with the setting and purpose of activity.
Classical and surround receptive field structure in cortical area MT as revealed by reverse correlation - eScholarship

This dissertation is organized into two main parts: physiology and new techniques. The physiology is organized into classical receptive fields, surround properties and global object selectivity of area MT. The first two physiology chapters used a novel sparse motion stimulus to drive MT cells and used reverse correlation to analyze the data. For the classical receptive field the spike triggered average was used as an estimate of the field. For the surround, maximally informative dimension analysis as originally developed by Sharpee at el. (2004) and further optimized for the stimulus distribution was used in these studies (see Chapter 5). Chapter 6 covers a circular statistic derived for use in Chapter 2. Using a new motion reverse correlation technique, we obtained high -resolution measurements of receptive field properties - spatial and directional selectivity - within cortical visual area MT. The standard, often implicit, view of these receptive fields is that they cover a contiguous area of the visual field and that the preferred direction is homogenous across this field. Contrary to this, we found that the receptive fields of many (19%) neurons were significantly patchy and that only half (48%) preferred only a single direction of motion within their receptive field. Both the spatial patchiness and directional preference variation were validated in single cells using more traditional stimuli. In the second chapter we used a variant of Maximally Informative Dimension (MID; Sharpee et al. 2004; Chapter 5) in order to find mechanisms that are not directly implied by the Spike Triggered Average (STA). Using MID we found a second kernel (receptive field, RF2) that interacted with the STA. We found 3 categories for RF2 structure : Surround Suppression, Off Preferred Direction Suppression and Omni-modulation. A divisive nonlinearity is found to fit the surround and off preferred direction suppression data well. In the third chapter we studied the response of area MT to a motion capture illusion. We first demonstrated a large psychophysical effect for humans, and then tested area MT's response to this stimulus. We found no modulation in area MT consistent with the global object motion, i.e. consistent with the illusion
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We address the problem of text-based activity retrieval in video. Given a sentence describing an activity, our task is to retrieve matching clips from an untrimmed video. To capture the inherent structures present in both text and video, we introduce a multilevel model that integrates vision and language features earlier and more tightly than prior work. First, we inject text features early on when generating clip proposals, to help eliminate unlikely clips and thus speed up processing and boost performance. Second, to learn a fine-grained similarity metric for retrieval, we use visual features to modulate the processing of query sentences at the word level in a recurrent neural network. A multi-task loss is also employed by adding query re-generation as an auxiliary task. Our approach significantly outperforms prior work on two challenging benchmarks: Charades-STA and ActivityNet Captions.
Transcriptome and DNA methylome analysis of peripheral blood samples reveals incomplete restoration and transposable element activation after 3-month recovery of COVID-19

Comprehensive analyses showed that SARS-CoV-2 infection caused COVID-19 and induced strong immune responses and sometimes severe illnesses. However, cellular features of recovered patients and long-term health consequences remain largely unexplored. In this study, we collected peripheral blood samples from recovered COVID-19 patients (average age of 35.7 years old) from Hubei province, China, 3 months after discharge; and carried out RNA-seq and whole-genome bisulfite sequencing (WGBS) to identify hallmarks of recovered COVID-19 patients. Our analyses showed significant changes both in expression and DNA methylation of genes and transposable elements (TEs) in recovered COVID-19 patients. We identified 639 misregulated genes and 18516 differentially methylated regions (DMRs) in total. Genes with aberrant expression and DMRs were found to be associated with immune responses and other related biological processes, implicating prolonged overreaction of the immune system in response to SARS-CoV-2 infection. Notably, a significant amount of TEs were aberrantly activated and TE activation was positively correlated with COVID-19 severity. Moreover, differentially methylated TEs may regulate adjacent gene expression as regulatory elements. Those identified transcriptomic and epigenomic signatures define and drive the features of recovered COVID-19 patients, helping determine the risks of long COVID-19, and providing guidance for clinical intervention.
ON COHOMOLOGICAL DIMENSION OF HOMOMORPHISMS

The (co)homological dimension of homomorphosm φ : G → H is the maximal number k such that the induced homomorphism is nonzero for some H -module. The following theorems are proven. 0.1. Theorem. For every homomorphism φ : G → H of a geometrically ﬁnite group G the homological dimension of φ equals the cohomological dimension, hd( φ ) = cd( φ ) . 0.2. Theorem. For every homomorphism φ : G → H of geometrically ﬁnite groups cd( φ × φ ) = 2cd( φ ) . 0.3. Theorem. Every homomorphism φ : G → H with cd( φ ) = 1 has geo- metric dimension 1.
Improving the Accuracy of the Mass-Energy Equivalence Law as an Effective Answer for a Seemingly Finely Tuned Structure of the Universe

The question of the “finely tuned Universe” will be answered by defining a new dimensionless constant that is potentially fundamental for the future of physics. The constant will be named the “de Broglie constant” in honor of Louis de Broglie, and it will be defined with a symbol (Д). This is the constant that gives rise to the constant (α) known as the “fine structure constant” that defines the electromagnetic interaction but was mistakenly considered to be the constant that defines a “fine-tuned” Universe, it will be elaborated that it is the de Broglie constant that does so and that it arises from the fundamental law of mass-energy equivalence.
Part-based Graph Convolutional Network for Action Recognition : Supplementary Material

In this document, we present findings from further quantitative analysis on the action recognition results. Specifically, we compute the confusion matrices of the performance of different models and explain the useful model properties based on our observations. We find that graph-based models can understand actions which involve more motion better than those where skeleton motion is very less and contains object interactions. We also show the importance of using geometric and kinematic features instead of 3D joint locations by performing an experiment on graph-based model of Yan et al. [2].
#DebateNight: The Role and Influence of Socialbots on Twitter During the 1st U.S. Presidential Debate

Serious concerns have been raised about the role of 'socialbots' in manipulating public opinion and influencing the outcome of elections by retweeting partisan content to increase its reach. Here we analyze the role and influence of socialbots on Twitter by determining how they contribute to retweet diffusions. We collect a large dataset of tweets during the 1st U.S. Presidential Debate in 2016 (#DebateNight) and we analyze its 1.5 million users from three perspectives: user influence, political behavior (partisanship and engagement) and botness. First, we define a measure of user influence based on the user's active contributions to information diffusions, i.e. their tweets and retweets. Given that Twitter does not expose the retweet structure - it associates all retweets with the original tweet - we model the latent diffusion structure using only tweet time and user features, and we implement a scalable novel approach to estimate influence over all possible unfoldings. Next, we use partisan hashtag analysis to quantify user political polarization and engagement. Finally, we use the BotOrNot API to measure user botness (the likelihood of being a bot). We build a two-dimensional "polarization map" that allows for a nuanced analysis of the interplay between botness, partisanship and influence. We find that not only social bots are more active on Twitter - starting more retweet cascades and retweeting more -- but they are 2.5 times more influential than humans, and more politically engaged. Moreover, pro-Republican bots are both more influential and more politically engaged than their pro-Democrat counterparts. However we caution against blanket statements that software designed to appear human dominates political debates. We find that many highly influential Twitter users are in fact pro-Democrat and that most pro-Republican users are mid-influential and likely to be human (low botness).
Better Generalization by Efficient Trust Region Method

In this paper, we develop a trust region method for training deep neural networks. At each iteration, trust region method computes the search direction by solving a non-convex subproblem. Solving this subproblem is non-trivial---existing methods have only sub-linear convergence rate. In the first part, we show that a simple modification of gradient descent algorithm can converge to a global minimizer of the subproblem with an asymptotic linear convergence rate. Moreover, our method only requires Hessian-vector products, which can be computed efficiently by back-propagation in neural networks. In the second part, we apply our algorithm to train large-scale convolutional neural networks, such as VGG and MobileNets. Although trust region method is about 3 times slower than SGD in terms of running time, we observe it finds a model that has lower generalization (test) error than SGD, and this difference is even more significant in large batch training. We conduct several interesting experiments to support our conjecture that the trust region method can avoid sharp local minimas.
ItEM: A Vector Space Model to Bootstrap an Italian Emotive Lexicon

English. In recent years computational linguistics has seen a rising interest in subjectivity, opinions, feelings and emotions. Even though great attention has been given to polarity recognition, the research in emotion detection has had to rely on small emotion resources. In this paper, we present a methodology to build emotive lexicons by jointly exploiting vector space models and human annotation, and we provide the first results of the evaluation with a crowdsourcing experiment. Italiano. Negli ultimi anni si è affermato un crescente interesse per soggettività, opinioni e sentimenti. Nonostante sia stato dato molto spazio al riconoscimento della polarità, esistono ancora poche risorse disponibili per il riconoscimento di emozioni. In questo lavoro presentiamo una metodologia per la creazione di un lessico emotivo, sfruttando annotazione manuale e spazi distribuzionali, e forniamo i primi risultati della valutazione effettuata tramite crowdsourcing.
Prevalence of Autism Spectrum Disorder (ASD) in all Individuals Diagnosed with Down Syndrome (DS): A Systematic Review and Meta-analysis Protocol

BACKGROUNDTraditionally, autism spectrum disorder in people with Down syndrome was believed to be uncommon. This misconception is rooted in the challenges that a dual diagnosis poses. In fact, evidence indicates that children with Down syndrome are at risk for autism spectrum disorder with a potentially higher prevalence than the typically developing population. The purpose of this review is to determine the reported prevalence rate of autism spectrum disorder in all individuals with Down syndrome in comparison to the prevalence rate of autism spectrum disorder in the typical population when specific diagnostic tools are used. METHODSWe will conduct a systematic review of the prevalence and incidence data and perform a meta-analysis on these results. This study will consider all studies that reported on children and adults with an existing diagnosis of Down syndrome and diagnosed by the standardized assessments for autism spectrum disorder. We will also consider the diagnoses made by team assessment (psychologist, psychiatrist & developmental pediatrician), according to DSM-III, DSM-IV or DSM-V criteria for diagnosing autism spectrum disorder or if they use autism spectrum disorder screeners. Studies will be considered from all countries that have data reporting prevalence on this topic. We will not apply language restrictions, attempting to translate studies that are not in English. We will search five databases (MEDLINE, Embase, PsychINFO, Scopus, and CINAHL). Two reviewers will conduct all screening and data extraction independently. The articles will be categorized according to key findings and a critical appraisal performed.DISCUSSIONThe results of this review will bring increased awareness of the presence of autism spectrum disorder in individuals with Down syndrome. In doing so, this may facilitate a recommendation for screening and diagnosis for autism spectrum disorder in all individuals with Down syndrome. Based on the research demonstrating the benefits of early identification and intervention on the outcomes of children with autism, we anticipate similar benefits in this population. This will guide the allocation of resources and direct future research.SYSTEMATIC REVIEW REGISTRATIONWe registerd the review title prospectively on PROSPERO on November 8, 2021. Registration number: CRD42020213282
La loi de Pascal restreinte et ses cas particuliers; Constrained Pascal distribution and its special cases

Pascal distribution, Pa(r, π), also called Negative binomial distribution, pertains to the trial number n at which the first r successes have been obtained, each trial being the realization of a Bernoulli variable with success probability π. We review basic concepts of the Bernoulli process and delve into the “constrained Pascal” distribution, or Negative binomial distribution of order k, Pa(r, k, π), which concerns the trial number n at which r successes have been recorded within the last k trials for the first time. Practical functions and procedures are given for producing probability mass and distribution functions, along with indications for estimating the π parameter.
A Neurobiological Evaluation Metric for Neural Network Model Search: Supplemental Material

In this section we provide additional details about the datasets that we used for our experiments, as well as information about the pre-processing of each dataset. In the cases where datasets were composed of images, rather that video, we presented the stimuli to PredNet formatted as though it were a static video in order to match the network’s input layer. The activation patterns from PredNet for this style of stimulus presentation mimic biological neural responses and perception [4].
On Achromatic Coloring of Corona Graphs

Let \(G = (V(G),E(G))\) be a simple graph and an achromatic coloring of \(G\) is a proper vertex coloring of \(G\) in which every pair of colors appears on at least one pair of adjacent vertices. The achromatic number of \(G\) denoted by \(\psi(G)\), is the greatest number of colors in an achromatic coloring of \(G\). In this paper, we find out the achromatic number for Corona graph of Cycle with Path graphs on the same order \(n\), Path with Cycle graphs on the same order \(n\), Path with Complete graphs on the same order \(n\), Path of order \(n\) with Star graph on order \(n+1\), Path with Wheel graphs on the same order \(n\) and Ladder graph with Path graph on the same order \(n\).