Miracle-dz/UniFAR-FTUs huggingface dataset:
A curated collection of COVID-19 online datasets

One of the defining moments of the year 2020 is the outbreak of Coronavirus Disease (Covid-19), a deadly virus affecting the body's respiratory system to the point of needing a breathing aid via ventilators. As of June 21, 2020 there are 12,929,306 confirmed cases and 569,738 confirmed deaths across 216 countries, areas or territories. The scale of spread and impact of the pandemic left many nations grappling with preventive and curative approaches. The infamous lockdown measure introduced to mitigate the virus spread has altered many aspects of our social routines in which demand for online-based services skyrocketed. As the virus propagate, so does misinformation and fake news around it via online social media, which seems to favour virality over veracity. With a majority of the populace confined to their homes for a long period, vulnerability to the toxic impact of online misinformation is high. A case in point is the various myths and disinformation associated with the Covid-19, which, if left unchecked, could lead to a catastrophic outcome and hamper the fight against the virus. While the scientific community is actively engaged in identifying the virus treatment, there is a growing interest in combating the associated harmful infodemic. To this end, researchers have been curating and documenting various datasets about Covid-19. In line with existing studies, we provide an expansive collection of curated datasets to support the fight against the pandemic, especially concerning misinformation. The collection consists of 3 categories of Twitter data, information about standard practices from credible sources and a chronicle of global situation reports. We describe how to retrieve the hydrated version of the data and proffer some research problems that could be addressed using the data.
DWMD: Dimensional Weighted Orderwise Moment Discrepancy for Domain-specific Hidden Representation Matching

Knowledge transfer from a source domain to a different but semantically related target domain has long been an important topic in the context of unsupervised domain adaptation (UDA). A key challenge in this field is establishing a metric that can exactly measure the data distribution discrepancy between two homogeneous domains and adopt it in distribution alignment, especially in the matching of feature representations in the hidden activation space. Existing distribution matching approaches can be interpreted as failing to either explicitly orderwise align higher-order moments or satisfy the prerequisite of certain assumptions in practical uses. We propose a novel moment-based probability distribution metric termed dimensional weighted orderwise moment discrepancy (DWMD) for feature representation matching in the UDA scenario. Our metric function takes advantage of a series for high-order moment alignment, and we theoretically prove that our DWMD metric function is error-free, which means that it can strictly reflect the distribution differences between domains and is valid without any feature distribution assumption. In addition, since the discrepancies between probability distributions in each feature dimension are different, dimensional weighting is considered in our function. We further calculate the error bound of the empirical estimate of the DWMD metric in practical applications. Comprehensive experiments on benchmark datasets illustrate that our method yields state-of-the-art distribution metrics.
On regularization of gradient descent, layer imbalance and flat minima

We analyze the training dynamics for deep linear networks using a new metric - layer imbalance - which defines the flatness of a solution. We demonstrate that different regularization methods, such as weight decay or noise data augmentation, behave in a similar way. Training has two distinct phases: 1) optimization and 2) regularization. First, during the optimization phase, the loss function monotonically decreases, and the trajectory goes toward a minima manifold. Then, during the regularization phase, the layer imbalance decreases, and the trajectory goes along the minima manifold toward a flat area. Finally, we extend the analysis for stochastic gradient descent and show that SGD works similarly to noise regularization.
A Machine Learning Approach for Task and Resource Allocation in Mobile-Edge Computing-Based Networks

In this article, a joint task, spectrum, and transmit power allocation problem is investigated for a wireless network in which the base stations (BSs) are equipped with mobile-edge computing (MEC) servers to jointly provide computational and communication services to users. Each user can request one computational task from three types of computational tasks. Since the data size of each computational task is different, as the requested computational task varies, the BSs must adjust their resource (subcarrier and transmit power) and task allocation schemes to effectively serve the users. This problem is formulated as an optimization problem whose goal is to minimize the maximal computational and transmission delay among all users. A multistack reinforcement learning (RL) algorithm is developed to solve this problem. Using the proposed algorithm, each BS can record the historical resource allocation schemes and users’ information in its multiple stacks to avoid learning the same resource allocation scheme and users’ states, thus improving the convergence speed and learning efficiency. The simulation results illustrate that the proposed algorithm can reduce the number of iterations needed for convergence and the maximal delay among all users by up to 18% and 11.1% compared to the standard $Q$ -learning algorithm.
Studying the nature of the unidentified gamma-ray source HESS J1841−055 with the MAGIC telescopes

We investigate the physical nature and origin of the gamma-ray emission from the extended source HESS J1841−055 observed at TeV and GeV energies. We observed HESS J1841−055 at TeV energies for a total effective time of 43 h with the MAGIC telescopes, in 2012 and 2013. Additionally, we analysed the GeV counterpart making use of about 10 yr of Fermi-LAT data. Using both Fermi-LAT and MAGIC, we study both the spectral and energy-dependent morphology of the source for almost four decades of energy. The origin of the gamma-ray emission from this region is investigated using multiwaveband information on sources present in this region, suggested to be associated with this unidentified gamma-ray source. We find that the extended emission at GeV–TeV energies is best described by more than one source model. We also perform the first energy-dependent analysis of the HESS J1841−055 region at GeV–TeV. We find that the emission at lower energies comes from a diffuse or extended component, while the major contribution of gamma rays above 1 TeV arises from the southern part of the source. Moreover, we find that a significant curvature is present in the combined observed spectrum of MAGIC and Fermi-LAT. The first multiwavelength spectral energy distribution of this unidentified source shows that the emission at GeV–TeV energies can be well explained with both leptonic and hadronic models. For the leptonic scenario, bremsstrahlung is the dominant emission compared to inverse Compton. On the other hand, for the hadronic model, gamma-ray resulting from the decay of neutral pions (π0) can explain the observed spectrum. The presence of dense molecular clouds overlapping with HESS J1841−055 makes both bremsstrahlung and π0-decay processes the dominant emission mechanisms for the source.
Estimation for High-Dimensional Multi-Layer Generalized Linear Model - Part II: The ML-GAMP Estimator

This is Part II of a two-part work on the estimation for a multi-layer generalized linear model (ML-GLM) in large system limits. In Part I, we had analyzed the asymptotic performance of an exact MMSE estimator, and obtained a set of coupled equations that could characterize its MSE performance. To work around the implementation difficulty of the exact estimator, this paper continues to propose an approximate solution, ML-GAMP, which could be derived by blending a moment-matching projection into the Gaussian approximated loopy belief propagation. The ML-GAMP estimator is then shown to enjoy a great simplicity in its implementation, where its per-iteration complexity is as low as GAMP. Further analysis on its asymptotic performance also reveals that, in large system limits, its dynamical MSE behavior is fully characterized by a set of simple one-dimensional iterating equations, termed state evolution (SE). Interestingly, this SE of ML-GAMP share exactly the same fixed points with an exact MMSE estimator whose fixed points were obtained in Part I via a replica analysis. Given the Bayes-optimality of the exact implementation, this proposed estimator (if converged) is optimal in the MSE sense.
Avoiding Spurious Local Minima in Deep Quadratic Networks.

Despite their practical success, a theoretical understanding of the loss landscape of neural networks has proven challenging due to the high-dimensional, non-convex, and highly nonlinear structure of such models. In this paper, we characterize the training landscape of the mean squared error loss for neural networks with quadratic activation functions. We prove existence of spurious local minima and saddle points which can be escaped easily with probability one when the number of neurons is greater than or equal to the input dimension and the norm of the training samples is used as a regressor. We prove that deep overparameterized neural networks with quadratic activations benefit from similar nice landscape properties. Our theoretical results are independent of data distribution and fill the existing gap in theory for two-layer quadratic neural networks. Finally, we empirically demonstrate convergence to a global minimum for these problems.
Attention2AngioGAN: Synthesizing Fluorescein Angiography from Retinal Fundus Images using Generative Adversarial Networks

Fluorescein Angiography (FA) is a technique that employs the designated camera for Fundus photography incorporating excitation and barrier filters. FA also requires fluorescein dye that is injected intravenously, which might cause adverse effects ranging from nausea, vomiting to even fatal anaphylaxis. Currently, no other fast and non-invasive technique exists that can generate FA without coupling with Fundus photography. To eradicate the need for an invasive FA extraction procedure, we introduce an Attention-based Generative network that can synthesize Fluorescein Angiography from Fundus images. The proposed gan incorporates multiple attention based skip connections in generators and comprises novel residual blocks for both generators and discriminators. It utilizes reconstruction, feature-matching, and perceptual loss along with adversarial training to produces realistic Angiograms that is hard for experts to distinguish from real ones. Our experiments confirm that the proposed architecture surpasses recent state-of-the-art generative networks for fundus-to-angio translation task.
Event Prediction in the Big Data Era

Events are occurrences in specific locations, time, and semantics that nontrivially impact either our society or the nature, such as earthquakes, civil unrest, system failures, pandemics, and crimes. It is highly desirable to be able to anticipate the occurrence of such events in advance to reduce the potential social upheaval and damage caused. Event prediction, which has traditionally been prohibitively challenging, is now becoming a viable option in the big data era and is thus experiencing rapid growth, also thanks to advances in high performance computers and new Artificial Intelligence techniques. There is a large amount of existing work that focuses on addressing the challenges involved, including heterogeneous multi-faceted outputs, complex (e.g., spatial, temporal, and semantic) dependencies, and streaming data feeds. Due to the strong interdisciplinary nature of event prediction problems, most existing event prediction methods were initially designed to deal with specific application domains, though the techniques and evaluation procedures utilized are usually generalizable across different domains. However, it is imperative yet difficult to cross-reference the techniques across different domains, given the absence of a comprehensive literature survey for event prediction. This article aims to provide a systematic and comprehensive survey of the technologies, applications, and evaluations of event prediction in the big data era. First, systematic categorization and summary of existing techniques are presented, which facilitate domain experts’ searches for suitable techniques and help model developers consolidate their research at the frontiers. Then, comprehensive categorization and summary of major application domains are provided to introduce wider applications to model developers to help them expand the impacts of their research. Evaluation metrics and procedures are summarized and standardized to unify the understanding of model performance among stakeholders, model developers, and domain experts in various application domains. Finally, open problems and future directions are discussed. Additional resources related to event prediction are included in the paper website: http://cs.emory. edu/∼lzhao41/projects/event_prediction_site. html.
A framework to evaluate whether to pool or separate behaviors in a multilayer network

Abstract A multilayer network approach combines different network layers, which are connected by interlayer edges, to create a single mathematical object. These networks can contain a variety of information types and represent different aspects of a system. However, the process for selecting which information to include is not always straightforward. Using data on 2 agonistic behaviors in a captive population of monk parakeets (Myiopsitta monachus), we developed a framework for investigating how pooling or splitting behaviors at the scale of dyadic relationships (between 2 individuals) affects individual- and group-level social properties. We designed 2 reference models to test whether randomizing the number of interactions across behavior types results in similar structural patterns as the observed data. Although the behaviors were correlated, the first reference model suggests that the 2 behaviors convey different information about some social properties and should therefore not be pooled. However, once we controlled for data sparsity, we found that the observed measures corresponded with those from the second reference model. Hence, our initial result may have been due to the unequal frequencies of each behavior. Overall, our findings support pooling the 2 behaviors. Awareness of how selected measurements can be affected by data properties is warranted, but nonetheless our framework disentangles these efforts and as a result can be used for myriad types of behaviors and questions. This framework will help researchers make informed and data-driven decisions about which behaviors to pool or separate, prior to using the data in subsequent multilayer network analyses.
Is the Free-Energy Principle a Formal Theory of Semantics? From Variational Density Dynamics to Neural and Phenotypic Representations

The aim of this paper is twofold: (1) to assess whether the construct of neural representations plays an explanatory role under the variational free-energy principle and its corollary process theory, active inference; and (2) if so, to assess which philosophical stance—in relation to the ontological and epistemological status of representations—is most appropriate. We focus on non-realist (deflationary and fictionalist-instrumentalist) approaches. We consider a deflationary account of mental representation, according to which the explanatorily relevant contents of neural representations are mathematical, rather than cognitive, and a fictionalist or instrumentalist account, according to which representations are scientifically useful fictions that serve explanatory (and other) aims. After reviewing the free-energy principle and active inference, we argue that the model of adaptive phenotypes under the free-energy principle can be used to furnish a formal semantics, enabling us to assign semantic content to specific phenotypic states (the internal states of a Markovian system that exists far from equilibrium). We propose a modified fictionalist account—an organism-centered fictionalism or instrumentalism. We argue that, under the free-energy principle, pursuing even a deflationary account of the content of neural representations licenses the appeal to the kind of semantic content involved in the ‘aboutness’ or intentionality of cognitive systems; our position is thus coherent with, but rests on distinct assumptions from, the realist position. We argue that the free-energy principle thereby explains the aboutness or intentionality in living systems and hence their capacity to parse their sensory stream using an ontology or set of semantic factors.
SYSTEMATICS AND REDESCRIPTION OF THE EUROPEAN MEIOFAUNAL SLUG MICROHEDYLE GLANDULIFERA (KOWALEVSKY, 1901) (HETEROBRANCHIA: ACOCHLIDIA): EVIDENCE FROM MOLECULES AND MORPHOLOGY

Despite the long history of meiofaunal research in Europe our knowledge of its Acochlidia—the most diverse, abundant and widespread group of interstitial slugs—is still fragmentary. Distribution ranges and modes of dispersal are unknown and taxonomic hypotheses based on traditional light-microscopical examination have never been tested against a modern integrative approach combining microanatomical techniques with molecular analyses. This study redescribes Microhedyle glandulifera (Kowalevsky, 1901), a key species for microhedylid taxonomy and focus of taxonomic disorder. Three-dimensional reconstructions from histological semithin serial sections reveal several previously unknown characters, in particular concerning the nervous system (e.g. presence of gastro-oesophageal ganglia). There are no jaws, but a ‘cuticular element’ is attached anteriorly to the radula cushion. Scanning electron microscopic examination shows a radula with the formula 34 –38 � 1.1. 1. Microhedyle glandulifera can be distinguished from other Microhedylidae by a combination of external and radular features, and the unique presence of triaxonic spicules. Population genetic analyses based on mitochondrial markers support M. glandulifera as a widespread European species known to range from the North Sea to the Sea of Marmara (eastern Mediterranean). Accordingly, northern Atlantic ‘M. lactea’ and Mediterranean ‘M. glomerans’ are confirmed as junior synonyms of M. glandulifera. Molecular data indicate a recent radiation of M. glandulifera in European waters and potential means of dispersal in meiofaunal slugs with low reproductive output and no pelagic larval stages are discussed. Based on our molecular phylogeny and revision of distinguishing morphological characters, four valid Microhedylidae species occur in European waters: Pontohedyle milaschewitchii, Parhedyle tyrtowii, Parhedyle cryptophthalma and Microhedyle glandulifera. Morphological and molecular evidence indicate that Microhedyle odhneri is a member of the genus Parhedyle, and possibly a junior synonym of Parhedyle tyrtowii.
Analysis of Slotted ALOHA With an Age Threshold

We present a comprehensive steady-state analysis of <italic>threshold-ALOHA</italic>, a distributed age-aware modification of slotted ALOHA proposed in recent literature. In threshold-ALOHA, each terminal suspends its transmissions until the Age of Information (AoI) of the status update flow it is sending reaches a certain threshold <inline-formula> <tex-math notation="LaTeX">$\Gamma $ </tex-math></inline-formula>. Once the age exceeds <inline-formula> <tex-math notation="LaTeX">$\Gamma $ </tex-math></inline-formula>, the terminal attempts transmission with constant probability <inline-formula> <tex-math notation="LaTeX">$\tau $ </tex-math></inline-formula> in each slot, as in standard slotted ALOHA. We analyze the time-average expected AoI attained by this policy, and explore its scaling with network size, <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>. We derive the probability distribution of the number of active users at steady state, and show that as network size increases the policy converges to one that runs slotted ALOHA with fewer sources: on average about one fifth of the users is active at any time. We obtain an expression for steady-state expected AoI and use this to optimize the parameters <inline-formula> <tex-math notation="LaTeX">$\Gamma $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\tau $ </tex-math></inline-formula>, resolving the conjectures in previous literature by confirming that the optimal age threshold and transmission probability are <inline-formula> <tex-math notation="LaTeX">$2.2n$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$4.69/n$ </tex-math></inline-formula>, respectively. We find that the optimal AoI scales with the network size as <inline-formula> <tex-math notation="LaTeX">$1.4169n$ </tex-math></inline-formula>, which is almost half the minimum AoI achievable with slotted ALOHA, while the loss from the maximum throughput of <inline-formula> <tex-math notation="LaTeX">$e^{-1}$ </tex-math></inline-formula> remains below 1%. We compare the performance of this rudimentary algorithm to that of the SAT policy <xref ref-type="bibr" rid="ref2">[2]</xref> that dynamically adapts its transmission probabilities.
The Rhythms of the Night: increase in online night activity and emotional resilience during the Covid-19 lockdown

Context: The lockdown orders established in multiple countries in response to the Covid-19 pandemics are perhaps the widest and deepest shock experienced by human behaviors in recent years. Studying the impact of the lockdown, trough the lens of social media, offers an unprecedented opportunity for analyzing the susceptibility and the resilience of circadian rhythms to large-scale exogenous shocks. In this context, we address two interconnected research questions: Can variations of online activity cycles provide information on the impact of lockdown on human activities? How do online circadian rhythms react to such a disruption? Data: We base our research on the analysis and comparison of two independent databases about the French cyberspace: a fine-grained temporal record of YouTube videos and a large collection of Tweets on {Covid-19}. Findings: In both datasets we observe a reshaping of the circadian rhythms with a substantial increase of night activity during the lockdown. The analysis of the videos and tweets published during lockdown shows a general decrease in emotional contents and a shift from themes like work and money to themes like death and safety. However, the daily patterns of emotions remain mostly unchanged, thereby suggesting that emotional cycles are resilient to exogenous shocks.
Learning Interpretable Models for Black-Box Agents

This paper develops a new approach for learning a STRIPS-like model of a non-stationary black-box autonomous agent that can plan and act. In this approach, the user may ask an autonomous agent a series of questions, which the agent answers truthfully. Our main contribution is an al-gorithm that generates an interrogation policy in the form of a contingent sequence of questions to be posed to the agent. Answers to these questions are used to learn a minimal, functionally indistinguishable class of agent models. This approach requires a minimal query-answering capability from the agent. Empirical evaluation of our approach shows that despite the intractable space of possible models, our approach can learn inter-pretable agent models for a class of black-box autonomous agents in a scalable manner.