Miracle-dz/UniFAR-FTUs huggingface dataset:
The COVID-19 Social Media Infodemic Reflects Uncertainty and State-Sponsored Propaganda

Significant attention has been devoted to determining the credibility of online misinformation about the COVID-19 pandemic on social media. Here, we compare the credibility of tweets about COVID-19 to datasets pertaining to other health issues. We find that the quantity of information about COVID-19 is indeed overwhelming, but that the majority of links shared cannot be rated for its credibility. Reasons for this failure to rate include widespread use of social media and news aggregators. The majority of links that could be rated came from credible sources; however, we found a large increase in the proportion of state-sponsored propaganda among non-credible and less credible URLs, suggesting that COVID-19 may be used as a vector to spread misinformation and disinformation for political purposes. Overall, results indicate that COVID-19 is unfolding in a highly uncertain information environment that not may amenable to fact-checking as scientific understanding of the disease, and appropriate public health measures, evolve. As a consequence, public service announcements must adequately communicate the uncertainly underlying these recommendations, while still encouraging healthy behaviors.
Gate-tunable imbalanced Kane-Mele model in encapsulated bilayer jacutingaite

We study free, capped and encapsulated bilayer jacutingaite Pt\2HgSe\3 from first principles. While the free standing bilayer is a large gap trivial insulator, we find that the encapsulated structure has a small trivial gap due to the competition between sublattice symmetry breaking and sublattice-dependent next-nearest-neighbor hopping. Upon the application of a small perpendicular electric field, the encapsulated bilayer undergoes a topological transition towards a quantum spin Hall insulator. We find that this topological transition can be qualitatively understood by modeling the two layers as uncoupled and described by an imbalanced Kane-Mele model that takes into account the sublattice imbalance and the corresponding inversion-symmetry breaking in each layer. Within this picture, bilayer jacutingaite undergoes a transition from a 0+0 state, where each layer is trivial, to a 0+1 state, where an unusual topological state relying on Rashba-like spin orbit coupling emerges in only one of the layers.
Spatial entanglement and state engineering via four-photon Hong–Ou–Mandel interference

The phenomenon of entanglement is the basis of quantum information and quantum communication processes. Entangled systems with a large number of photons are of great interest at present because they provide a platform for streaming technologies based on photonics. In this paper we present a device which operates with four-photons and based on the Hong–Ou–Mandel interference. The presented device allows to maximize the degree of spatial entanglement and generate the highly entangled four-dimensional Bell states. Furthermore, the use of the interferometer in different regimes leads to fast interference fringes in the coincidence probability with period of oscillations twice smaller than the pump wavelength. We have a good agreement between theoretical simulations and experimental results.
Pampas Deer in Uruguay

Pampas deer, which are on the verge of extinction in Argentina, have declined in Uruguay to an estimated 1000 animals. Mostly they occur in small scattered herds, which have survived because of their remoteness and the absence of sheep, and because a small number of landowners have protected them. Sheep, however, are likely to spread because rising oil prices have made synthetic fibres more expensive and wool more competitive. The authors recommend the measures that should be taken quickly, including action against poaching, a study to find out the deer's needs, and steps to set up both reserves and captive breeding units.
Automating Involutive MCMC using Probabilistic and Differentiable Programming.

Involutive MCMC is a unifying mathematical construction for MCMC kernels that generalizes many classic and state-of-the-art MCMC algorithms, from reversible jump MCMC to kernels based on deep neural networks. But as with MCMC samplers more generally, implementing involutive MCMC kernels is often tedious and error-prone, especially when sampling on complex state spaces. This paper describes a technique for automating the implementation of involutive MCMC kernels given (i) a pair of probabilistic programs defining the target distribution and an auxiliary distribution respectively and (ii) a differentiable program that transforms the execution traces of these probabilistic programs. The technique, which is implemented as part of the Gen probabilistic programming system, also automatically detects user errors in the specification of involutive MCMC kernels and exploits sparsity in the kernels for improved efficiency. The paper shows example Gen code for a split-merge reversible jump move in an infinite Gaussian mixture model and a state-dependent mixture of proposals on a combinatorial space of covariance functions for a Gaussian process.
DIFFERENCES IN ANTIMICROBIAL RESISTANCE OF CAMPYLOBACTER JEJUNI ISOLATED FROM BROILER INTESTINES AND DRUMSTICKS IN LITHUANIA

Campylobacter jejuni isolates from broiler intestines and from retail poultry drumsticks were tested for antimicrobial susceptibility in Lithuania during 2009. The results were interpreted according to EUCAST epidemiological cut-off values using a broth microdilution method. The current study demonstrated that C. jejuni isolated from poultry drumsticks were more frequently resistant to different classes of antimicrobials – (fluoro)quinolones, macrolides, aminoglycosides and tetracyclines, than those isolated from intestines of live chickens; however, statistically significant results were obtained only for ciprofloxacin. All isolates from chicken intestines and drumsticks were susceptible to chloramphenicol. According to the data of the current study, it may be presumed that poultry carcasses could be contaminated by campylobacters, not only during evisceration but also from contaminated equipment or during handling. Antimicrobial susceptibility testing of campylobacters should not be limited to the monitoring of live (or slaughtered), broilers but also include testing of retail products. PRACTICAL APPLICATIONS Campylobacter jejuni is widely spread in raw poultry products and is the most common cause of bacterial human gastroenteritis worldwide. Although campylobacteriosis is often a self-limiting disease, some cases, particularly those caused by resistant strains, are severe. It was determined that the frequency of resistance to antimicrobials of C. jejuni isolated from poultry products differ from the resistance of strains isolated from poultry intestines. This could be associated with certain strains that are able to survive in a slaughterhouse environment. Hygienic measures in raw food processing should be strongly enforced. Personal hygiene during marketing is also very important since resistant bacteria could easily spread among salespersons and consumers.
Voice@SRIB at SemEval-2020 Task [9, 12]: Sentiment and Offensiveness detection in Social Media

In social-media platforms such as Twitter, Facebook, and Reddit, people prefer to use code-mixed language such as Spanish-English, Hindi-English to express their opinions. In this paper, we describe different models we used, using the external dataset to train embeddings, ensembling methods for Sentimix, and OffensEval tasks. The use of pre-trained embeddings usually helps in multiple tasks such as sentence classification, and machine translation. In this experiment, we haveused our trained code-mixed embeddings and twitter pre-trained embeddings to SemEval tasks. We evaluate our models on macro F1-score, precision, accuracy, and recall on the datasets. We intend to show that hyper-parameter tuning and data pre-processing steps help a lot in improving the scores. In our experiments, we are able to achieve 0.886 F1-Macro on OffenEval Greek language subtask post-evaluation, whereas the highest is 0.852 during the Evaluation Period. We stood third in Spanglish competition with our best F1-score of 0.756. Codalab username is asking28.
Counting Defiers: Examples from Health Care

I propose a finite sample inference procedure that uses a likelihood function derived from the randomization process within an experiment to conduct inference on various quantities that capture heterogeneous intervention effects. One such quantity is the number of defiers---individuals whose treatment runs counter to the intervention. Results from the literature make informative inference on this quantity seem impossible, but they rely on different assumptions and data. I only require data on the cross-tabulations of a binary intervention and a binary treatment. Replacing the treatment variable with a more general outcome variable, I can perform inference on important quantities analogous to the number of defiers. I apply the procedure to test safety and efficacy in hypothetical drug trials for which the point estimate of the average intervention effect implies that at least 40 out of 100 individuals would be saved. In one trial, I infer with 95% confidence that at least 3 individuals would be killed, which could stop the drug from being approved.
Multi-Principal Assistance Games

Assistance games (also known as cooperative inverse reinforcement learning games) have been proposed as a model for beneficial AI, wherein a robotic agent must act on behalf of a human principal but is initially uncertain about the humans payoff function. This paper studies multi-principal assistance games, which cover the more general case in which the robot acts on behalf of N humans who may have widely differing payoffs. Impossibility theorems in social choice theory and voting theory can be applied to such games, suggesting that strategic behavior by the human principals may complicate the robots task in learning their payoffs. We analyze in particular a bandit apprentice game in which the humans act first to demonstrate their individual preferences for the arms and then the robot acts to maximize the sum of human payoffs. We explore the extent to which the cost of choosing suboptimal arms reduces the incentive to mislead, a form of natural mechanism design. In this context we propose a social choice method that uses shared control of a system to combine preference inference with social welfare optimization.
High Performance Data Engineering Everywhere

The amazing advances being made in the fields of machine and deep learning are a highlight of the Big Data era for both enterprise and research communities. Modern applications require resources beyond a single node's ability to provide. However this is just a small part of the issues facing the overall data processing environment, which must also support a raft of data engineering for pre- and post-data processing, communication, and system integration. An important requirement of data analytics tools is to be able to easily integrate with existing frameworks in a multitude of languages, thereby increasing user productivity and efficiency. All this demands an efficient and highly distributed integrated approach for data processing, yet many of today's popular data analytics tools are unable to satisfy all these requirements at the same time. In this paper we present Cylon, an open-source high performance distributed data processing library that can be seamlessly integrated with existing Big Data and AI/ML frameworks. It is developed with a flexible C++ core on top of a compact data structure and exposes language bindings to C++, Java, and Python. We discuss Cylon's architecture in detail, and reveal how it can be imported as a library to existing applications or operate as a standalone framework. Initial experiments show that Cylon enhances popular tools such as Apache Spark and Dask with major performance improvements for key operations and better component linkages. Finally, we show how its design enables Cylon to be used cross-platform with minimum overhead, which includes popular AI tools such as PyTorch, Tensorflow, and Jupyter notebooks.
The Splashback Radius of Halos from Particle Dynamics. III. Halo Catalogs, Merger Trees, and Host–Subhalo Relations

Virtually any investigation involving dark matter halos relies on a definition of their radius, of their mass, and of whether they are a subhalo. The halo boundary is most commonly defined to include a spherical overdensity contrast (such as , , and ), but different thresholds lead to significant differences in radius and mass. The splashback radius has recently been suggested as a more physically motivated (and generally larger) halo boundary, adding to the range of definitions. It is often difficult to assess the impact of a particular choice because most halo catalogs contain only one or a few definitions and generally only one set of host–subhalo relations. To alleviate this issue, we present halo catalogs and merger trees for 14 N-body simulations of ΛCDM and self-similar universes. Based on Rockstar catalogs, we compute additional halo properties using the Sparta code and recombine them with the original catalogs. The new catalogs contain numerous variants of spherical overdensity and splashback radii and masses and, most critically, host–subhalo relations for each definition. We also present a new merger tree format where the data are stored as a compressed, two-dimensional matrix. We perform basic tests of the relation between different definitions and present an updated model for the splashback–spherical overdensity connection. The Sparta code, as well as our catalogs and merger trees, are publicly available.
Monte Carlo Dropout Ensembles for Robust Illumination Estimation

Computational color constancy is a preprocessing step used in many camera systems. The main aim is to discount the effect of the illumination on the colors in the scene and restore the original colors of the objects. Recently, several deep learning-based approaches have been proposed to solve this problem and they often led to state-of-the-art performance in terms of average errors. However, for extreme samples, these methods fail and lead to high errors. In this paper, we address this limitation by proposing to aggregate different deep learning methods according to their output uncertainty. We estimate the relative uncertainty of each approach using Monte Carlo dropout and the final illumination estimate is obtained as the sum of the different model estimates weighted by the log-inverse of their corresponding uncertainties. The proposed framework leads to state-of-the-art performance on INTEL-TAU dataset.
How are you? Introducing stress-based text tailoring

Can stress affect not only your life but also how you read and interpret a text? Healthcare has shown evidence of such dynamics and in this short paper we discuss customising texts based on user stress level, as it could represent a critical factor when it comes to user engagement and behavioural change. We first show a real-world example in which user behaviour is influenced by stress, then, after discussing which tools can be employed to assess and measure it, we propose an initial method for tailoring the document by exploiting complexity reduction and affect enforcement. The result is a short and encouraging text which requires less commitment to be read and understood. We believe this work in progress can raise some interesting questions on a topic that is often overlooked in NLG.
Measurement of growth of bifidobacteria on inulofructosaccharides

A simple method for continuous measurement of growth of bifidobacteria is described. An inoculated broth in a small tube was evacuated at 100mmHg through a two‐way stopcock and incubated at 37°C. Growth was periodically measured by inserting the tube into a spectrophotometer. Doubling times of three bifidobacterium strains in broth containing various inulofructosaccharide preparations were compared. Inulofructosacchandes from the Jerusalem artichoke tubers supported good growth of these bacteria.
XMixup: Efficient Transfer Learning with Auxiliary Samples by Cross-domain Mixup

Transferring knowledge from large source datasets is an effective way to fine-tune the deep neural networks of the target task with a small sample size. A great number of algorithms have been proposed to facilitate deep transfer learning, and these techniques could be generally categorized into two groups - Regularized Learning of the target task using models that have been pre-trained from source datasets, and Multitask Learning with both source and target datasets to train a shared backbone neural network. In this work, we aim to improve the multitask paradigm for deep transfer learning via Cross-domain Mixup (XMixup). While the existing multitask learning algorithms need to run backpropagation over both the source and target datasets and usually consume a higher gradient complexity, XMixup transfers the knowledge from source to target tasks more efficiently: for every class of the target task, XMixup selects the auxiliary samples from the source dataset and augments training samples via the simple mixup strategy. We evaluate XMixup over six real world transfer learning datasets. Experiment results show that XMixup improves the accuracy by 1.9% on average. Compared with other state-of-the-art transfer learning approaches, XMixup costs much less training time while still obtains higher accuracy.
Ecological niche modeling on the effect of climatic change and conservation of Ternstroemia lineata DC. (Ternstroemiaceae) in Mesoamerica

A bioclimatic modeling of the tree Ternstroemia lineata DC. (Ternstroemiaceae) was undertaken. The genus itself is considered as diagnostic or characteristic of the Mexican cloud forest. Ternstroemia lineata has the broadest distribution of all species in this family in Mexico and northern Central America. This species consists of two subspecies, T. lineata subsp. lineata and T. lineata subsp. chalicophila (Loes.) B.M. Barthol. Ecological niche models for both subspecies were generated using the genetic algorithm for rule-set production method. The ecological models for the years 2020, 2050, and 2080 were obtained under the effects of global climatic change considering two emission scenarios (A2a and B2a). When climatic change values were introduced, the ecological niche representation for both subspecies contracted in such a way that they became almost lost throughout their entire geographical range. In both cases, predictions for all years in both scenarios contracted more than 90%. This species may not.
Algebraic foliations and derived geometry II: the Grothendieck-Riemann-Roch theorem

This is the second of series of papers on the study of foliations in the setting of derived algebraic geometry based on the central notion of derived foliation. We introduce sheaf-like coefficients for derived foliations, called quasi-coherent crystals, and construct a certain sheaf of dg-algebras of differential operators along a given derived foliation, with the property that quasi-coherent crystals can be interpreted as modules over this sheaf of differential operators. We use this interpretation in order to introduce the notion of good filtrations on quasi-coherent crystals, and define the notion of characteristic cycle. Finally, we prove a Grothendieck-Riemann-Roch (GRR) formula expressing that formation of characteristic cycles is compatible with push-forwards along proper and quasi-smooth morphisms. Several examples and applications are deduced from this, e.g. a GRR formula for D-modules on possibly singular schemes, and a foliated index formula for weakly Fredholm operators.
Multimessenger observations of counterparts to IceCube-190331A

High-energy neutrinos are a promising tool for identifying astrophysical sources of high and ultra-high energy cosmic rays (UHECR). Prospects of detecting neutrinos at high energies ($\gtrsim$TeV) from blazars have been boosted after the recent association of IceCube-170922A and TXS 0506+056. We investigate the high-energy neutrino, IceCube-190331A, a high-energy starting event (HESE) with a high likelihood of being astrophysical in origin. We initiated a Swift/XRT and UVOT tiling mosaic of the neutrino localisation, and followed up with ATCA radio observations, compiling a multiwavelength SED for the most likely source of origin. NuSTAR observations of the neutrino location and a nearby X-ray source were also performed. We find two promising counterpart in the 90% confidence localisation region and identify the brightest as the most likely counterpart. However, no Fermi/LAT $\gamma$-ray source and no prompt Swift/BAT source is consistent with the neutrino event. At this point it is unclear whether any of the counterparts produced IceCube-190331A. We note that the Helix Nebula is also consistent with the position of the neutrino event, and we calculate that associated particle acceleration processes cannot produce the required energies to generate a high-energy HESE neutrino.
BOUNDEDNESS AND COMPACTNESS OF CAUCHY-TYPE INTEGRAL COMMUTATOR ON WEIGHTED MORREY SPACES

Abstract In this paper we study boundedness and compactness characterizations of the commutators of Cauchy type integrals on bounded strongly pseudoconvex domains D in $\mathbb C^{n}$ with boundaries $bD$ satisfying the minimum regularity condition $C^{2}$ , based on the recent results of Lanzani–Stein and Duong et al. We point out that in this setting the Cauchy type integral is the sum of the essential part which is a Calderón–Zygmund operator and a remainder which is no longer a Calderón–Zygmund operator. We show that the commutator is bounded on the weighted Morrey space $L_{v}^{p,\kappa }(bD)$ ( $v\in A_{p}, 1<p<\infty $ ) if and only if b is in the BMO space on $bD$ . Moreover, the commutator is compact on the weighted Morrey space $L_{v}^{p,\kappa }(bD)$ ( $v\in A_{p}, 1<p<\infty $ ) if and only if b is in the VMO space on $bD$ .
Tracking the Untrackable

Although short-term fully occlusion happens rare in visual object tracking, most trackers will fail under these circumstances. However, humans can still catch up the target by anticipating the trajectory of the target even the target is invisible. Recent psychology also has shown that humans build the mental image of the future. Inspired by that, we present a HAllucinating Features to Track (HAFT) model that enables to forecast the visual feature embedding of future frames. The anticipated future frames focus on the movement of the target while hallucinating the occluded part of the target. Jointly tracking on the hallucinated features and the real features improves the robustness of the tracker even when the target is highly occluded. Through extensive experimental evaluations, we achieve promising results on multiple datasets: OTB100, VOT2018, LaSOT, TrackingNet, and UAV123.