Miracle-dz/UniFAR-FTUs huggingface dataset:
Measuring Societal Biases in Text Corpora via First-Order Co-occurrence

Text corpora are used to study societal biases, typically through statistical models such as word embeddings. The bias of a word towards a concept is typically estimated using vectors similarity, measuring whether the word and concept words share other words in their contexts. We argue that this second-order relationship introduces unrelated concepts into the measure, which causes an imprecise measurement of the bias. We propose instead to measure bias using the direct normalized co-occurrence associations between the word and the representative concept words, a first-order measure, by reconstructing the co-occurrence estimates inherent in the word embedding models. To study our novel corpus bias measurement method, we calculate the correlation of the gender bias values estimated from the text to the actual gender bias statistics of the U.S. job market, provided by two recent collections. The results show a consistently higher correlation when using the proposed first-order measure with a variety of word embedding models, as well as a more severe degree of bias, especially to female in a few specific occupations.
Twin-width I: tractable FO model checking

Inspired by a width invariant defined on permutations by Guillemot and Marx [SODA '14], we introduce the notion of twin-width on graphs and on matrices. Proper minor-closed classes, bounded rank-width graphs, map graphs, $K_{t^{-}}$ free unit $d$-dimensional ball graphs, posets with antichains of bounded size, and proper subclasses of dimension-2 posets all have bounded twin-width. On all these classes (except map graphs without geometric embedding) we show how to compute in polynomial time a sequence of $d$-contractions, witness that the twin-width is at most $d$. We show that FO model checking, that is deciding if a given first-order formula $\phi$ evaluates to true for a given binary structure $G$ on a domain $D$, is FPT in $\vert \phi\vert$ on classes of bounded twin-width, provided the witness is given. More precisely, being given a $d$-contraction sequence for $G$, our algorithm runs in time $f(d,\vert \phi\vert)\cdot\vert D\vert$ where $f$ is a computable but non-elementary function. We also prove that bounded twin-width is preserved by FO interpretations and transductions (allowing operations such as squaring or complementing a graph). This unifies and significantly extends the knowledge on fixed-parameter tractability of FO model checking on non-monotone classes, such as the FPT algorithm on bounded-width posets by Gajarský et al. [FOCS '15].
Learning nonlinear dynamical systems from a single trajectory

We introduce algorithms for learning nonlinear dynamical systems of the form $x_{t+1}=\sigma(\Theta^{\star}x_t)+\varepsilon_t$, where $\Theta^{\star}$ is a weight matrix, $\sigma$ is a nonlinear link function, and $\varepsilon_t$ is a mean-zero noise process. We give an algorithm that recovers the weight matrix $\Theta^{\star}$ from a single trajectory with optimal sample complexity and linear running time. The algorithm succeeds under weaker statistical assumptions than in previous work, and in particular i) does not require a bound on the spectral norm of the weight matrix $\Theta^{\star}$ (rather, it depends on a generalization of the spectral radius) and ii) enjoys guarantees for non-strictly-increasing link functions such as the ReLU. Our analysis has two key components: i) we give a general recipe whereby global stability for nonlinear dynamical systems can be used to certify that the state-vector covariance is well-conditioned, and ii) using these tools, we extend well-known algorithms for efficiently learning generalized linear models to the dependent setting.
Modeling Pharmacological Effects with Multi-Relation Unsupervised Graph Embedding

A pharmacological effect of a drug on cells, organs and systems refers to the specific biochemical interaction produced by a drug substance, which is called its mechanism of action. Drug repositioning (or drug repurposing) is a fundamental problem for the identification of new opportunities for the use of already approved or failed drugs. In this paper, we present a method based on a multi-relation unsupervised graph embedding model that learns latent representations for drugs and diseases so that the distance between these representations reveals repositioning opportunities. Once representations for drugs and diseases are obtained we learn the likelihood of new links (that is, new indications) between drugs and diseases. Known drug indications are used for learning a model that predicts potential indications. Compared with existing unsupervised graph embedding methods our method shows superior prediction performance in terms of area under the ROC curve, and we present examples of repositioning opportunities found on recent biomedical literature that were also predicted by our method.
On cosmological signatures of baryons-dark energy elastic couplings

We consider a scenario where dark energy and baryons are dynamically coupled without any energy transfer. In this scenario, the background cosmology is unaffected and, at the perturbations level, the coupling only appears through the corresponding Euler equations of dark energy and baryons. We then explore some phenomenological consequences of this scenario and their signatures in several cosmological observables. In particular, we show its ability to suppress the growth of cosmic structures. We also constrain the parameters of the model with cosmological data and show that an interaction of dark energy with baryons on cosmological scales is mildly favoured.
Discovery and characterization of the exoplanets WASP-148b and c

We present the discovery and characterization of WASP-148, a new extrasolar system that includes at least two giant planets. The host star is a slowly rotating inactive late-G dwarf with a V = 12 magnitude. The planet WASP-148b is a hot Jupiter of 0.72 RJup and 0.29 MJup that transits its host with an orbital period of 8.80 days. We found the planetary candidate with the SuperWASP photometric survey, then characterized it with the SOPHIE spectrograph. Our radial velocity measurements subsequently revealed a second planet in the system, WASP-148c, with an orbital period of 34.5 days and a minimum mass of 0.40 MJup. No transits of this outer planet were detected. The orbits of both planets are eccentric and fall near the 4:1 mean-motion resonances. This configuration is stable on long timescales, but induces dynamical interactions so that the orbits differ slightly from purely Keplerian orbits. In particular, WASP-148b shows transit-timing variations of typically 15 min, making it the first interacting system with transit-timing variations that is detected on ground-based light curves. We establish that the mutual inclination of the orbital plane of the two planets cannot be higher than 35°, and the true mass of WASP-148c is below 0.60 MJup. We present photometric and spectroscopic observations of this system that cover a time span of ten years. We also provide their Keplerian and Newtonian analyses; these analyses should be significantly improved through future TESS observations.
Knowledge Graph Empowered Entity Description Generation

Existing works on KG-to-text generation take as input a few RDF triples or key-value pairs conveying the knowledge of some entities to generate a natural language description. Existing datasets, such as WikiBIO, WebNLG, and E2E, basically have a good alignment between an input triple/pair set and its output text. However in practice, the input knowledge could be more than enough, because the output description may only want to cover the most significant knowledge. In this paper, we introduce a large-scale and challenging dataset to facilitate the study of such practical scenario in KG-to-text. Our dataset involves exploring large knowledge graphs (KG) to retrieve abundant knowledge of various types of main entities, which makes the current graph-to-sequence models severely suffered from the problems of information loss and parameter explosion while generating the description text. We address these challenges by proposing a multi-graph structure that is able to represent the original graph information more comprehensively. Furthermore, we also incorporate aggregation methods that learn to ensemble the rich graph information. Extensive experiments demonstrate the effectiveness of our model architecture.
Vocabulary Adaptation for Distant Domain Adaptation in Neural Machine Translation

Neural machine translation (NMT) models do not work well in domains different from the training data. The standard approach to this problem is to build a small parallel data in the target domain and perform domain adaptation from a source domain where massive parallel data is available. However, domain adaptation between distant domains (e.g., subtitles and research papers) does not perform effectively because of mismatches in vocabulary; it will encounter many domain-specific unknown words (e.g., `angstrom') and words whose meanings shift across domains (e.g., `conductor'). In this study, aiming to solve these vocabulary mismatches in distant domain adaptation, we propose vocabulary adaptation, a simple method for effective fine-tuning that adapts embedding layers in a given pre-trained NMT model to the target domain. Prior to fine-tuning, our method replaces word embeddings in embedding layers of the NMT model, by projecting general word embeddings induced from monolingual data in the target domain onto the source-domain embedding space. Experimental results on distant domain adaptation for English-to-Japanese translation and German-to-English translation indicate that our vocabulary adaptation improves the performance of fine-tuning by 3.6 BLEU points.
A Robust Hierarchical Graph Convolutional Network Model for Collaborative Filtering

Graph Convolutional Network (GCN) has achieved great success and has been applied in various fields including recommender systems. However, GCN still suffers from many issues such as training difficulties, over-smoothing, vulnerable to adversarial attacks, etc. Distinct from current GCN-based methods which simply employ GCN for recommendation, in this paper we are committed to build a robust GCN model for collaborative filtering. Firstly, we argue that recursively incorporating messages from different order neighborhood mixes distinct node messages indistinguishably, which increases the training difficulty; instead we choose to separately aggregate different order neighbor messages with a simple GCN model which has been shown effective; then we accumulate them together in a hierarchical way without introducing additional model parameters. Secondly, we propose a solution to alleviate over-smoothing by randomly dropping out neighbor messages at each layer, which also well prevents over-fitting and enhances the robustness. Extensive experiments on three real-world datasets demonstrate the effectiveness and robustness of our model.
Indirect Identification of Psychosocial Risks from Natural Language

During the perinatal period, psychosocial health risks, including depression and intimate partner violence, are associated with serious adverse health outcomes for parents and children. To appropriately intervene, healthcare professionals must first identify those at risk, yet stigma often prevents people from directly disclosing the information needed to prompt an assessment. We examine indirect methods of eliciting and analyzing information that could indicate psychosocial risks. Short diary entries by peripartum women exhibit thematic patterns, extracted by topic modeling, and emotional perspective, drawn from dictionary-informed sentiment features. Using these features, we use regularized regression to predict screening measures of depression and psychological aggression by an intimate partner. Journal text entries quantified through topic models and sentiment features show promise for depression prediction, with performance almost as good as closed-form questions. Text-based features were less useful for prediction of intimate partner violence, but moderately indirect multiple-choice questioning allowed for detection without explicit disclosure. Both methods may serve as an initial or complementary screening approach to detecting stigmatized risks.
Zero-shot Neural Retrieval via Domain-targeted Synthetic Query Generation

Deep neural scoring models have recently been shown to improve ranking quality on a number of benchmarks (Guo et al., 2016; Daiet al., 2018; MacAvaney et al., 2019; Yanget al., 2019a). However, these methods rely on underlying ad-hoc retrieval systems to generate candidates for scoring, which are rarely neural themselves (Zamani et al., 2018). Re-cent work has shown that the performance of ad-hoc neural retrieval systems can be competitive with a number of baselines (Zamani et al.,2018), potentially leading the way to full end-to-end neural retrieval. A major road-block to the adoption of ad-hoc retrieval models is that they require large supervised training sets to surpass classic term-based techniques, which can be developed from raw corpora. Previous work shows weakly supervised data can yield competitive results, e.g., click data (Dehghaniet al., 2017; Borisov et al., 2016). Unfortunately for many domains, even weakly supervised data can be scarce. In this paper, we pro-pose an approach to zero-shot learning (Xianet al., 2018) for ad-hoc retrieval models that relies on synthetic query generation. Crucially, the query generation system is trained on general domain data, but is applied to documents in the targeted domain. This allows us to create arbitrarily large, yet noisy, query-document relevance pairs that are domain targeted. On a number of benchmarks, we show that this is an effective strategy for building neural retrieval models for specialised domains.
Multi-task Learning with Crowdsourced Features Improves Skin Lesion Diagnosis

Machine learning has a recognised need for large amounts of annotated data. Due to the high cost of expert annotations, crowdsourcing, where non-experts are asked to label or outline images, has been proposed as an alternative. Although many promising results are reported, the quality of diagnostic crowdsourced labels is still lacking. We propose to address this by instead asking the crowd about visual features of the images, which can be provided more intuitively, and by using these features in a multi-task learning framework. We compare our proposed approach to a baseline model with a set of 2000 skin lesions from the ISIC 2017 challenge dataset. The baseline model only predicts a binary label from the skin lesion image, while our multi-task model also predicts one of the following features: asymmetry of the lesion, border irregularity and color. We show that crowd features in combination with multi-task learning leads to improved generalisation. The area under the receiver operating characteristic curve is 0.754 for the baseline model and 0.782, 0.785 and 0.789 for multi-task models with border, color and asymmetry respectively. Finally, we discuss the findings, identify some limitations and recommend directions for further research.
A more secure IPv6 neighborhood process

The process of neighborhood establishment in an IPv6 network is made out through the NDP (Neighbor Discovery Protocol). Using ICMPv6 messages (NS - Neighbor Solicitation and NA - Neighbor Advertisement) which contains the IP address to be resolved, is exposed during the exchange of messages, making communication vulnerable to various types of attacks. This paper presents a proposal for a safer neighborhood establishment using the DH and HMAC algorithms. Experiments were performed in virtualized environments with the objective of analyzing the efficiency of the modification proposed in the NDP.
Closed-loop experiments on the BrainScaleS-2 architecture

The evolution of biological brains has always been contingent on their embodiment within their respective environments, in which survival required appropriate navigation and manipulation skills. Studying such interactions thus represents an important aspect of computational neuroscience and, by extension, a topic of interest for neuromorphic engineering. Here, we present three examples of embodiment on the BrainScaleS-2 architecture, in which dynamical timescales of both agents and environment are accelerated by several orders of magnitude with respect to their biological archetypes.
Rethinking Class-Discrimination Based CNN Channel Pruning

Channel pruning has received ever-increasing focus on network compression. In particular, class-discrimination based channel pruning has made major headway, as it fits seamlessly with the classification objective of CNNs and provides good explainability. Prior works singly propose and evaluate their discriminant functions, while further study on the effectiveness of the adopted metrics is absent. To this end, we initiate the first study on the effectiveness of a broad range of discriminant functions on channel pruning. Conventional single-variate binary-class statistics like Student's T-Test are also included in our study via an intuitive generalization. The winning metric of our study has a greater ability to select informative channels over other state-of-the-art methods, which is substantiated by our qualitative and quantitative analysis. Moreover, we develop a FLOP-normalized sensitivity analysis scheme to automate the structural pruning procedure. On CIFAR-10, CIFAR-100, and ILSVRC-2012 datasets, our pruned models achieve higher accuracy with less inference cost compared to state-of-the-art results. For example, on ILSVRC-2012, our 44.3% FLOPs-pruned ResNet-50 has only a 0.3% top-1 accuracy drop, which significantly outperforms the state of the art.
Maladaptive daydreaming : une histoire pour survivre

Cette étude s'intéresse aux « rêveries diurnes inadaptées », dont la traduction provient de l’anglais « Maladaptive daydreaming ». Celles-ci sont définies comme des activités de rêveries excessives, pouvant remplacer l’interaction humaine et/ou interférer avec le fonctionnement académique ou vocationnel. Le but de cette étude est de comprendre ce phénomène, en investiguant ses antécédents traumatiques. Des analyses ont été effectuées sur un échantillon de 583 participants, récupérés dans les données internationales de l’étude de Bigelsen, Lehrfeld, Jopp et Somer (2016). Au sein de cet échantillon, 435 participants s’identifient en tant que rêveurs compulsifs ou « MDers » et 142 ne s’identifient pas en tant que MDers et représentent le groupe contrôle. Successivement, les analyses ont porté sur 162 participants se considérant comme MDers et ayant identifié un traumatisme catalyseur, et ensuite sur 164 participants correspondant aux MDers n’ayant pas identifié un traumatisme catalyseur et représentant le groupe contrôle. Les résultats indiquent des associations entre le développement du MD et l’existence de certains traumatismes spécifiques. Cette relation est renforcée entre autres par la prédisposition de la dissociation, des éléments de la répétition compulsive, ainsi que par la présence des liens entre les événements traumatiques et les rêves diurnes.
memeBot: Towards Automatic Image Meme Generation

Image memes have become a widespread tool used by people for interacting and exchanging ideas over social media, blogs, and open messengers. This work proposes to treat automatic image meme generation as a translation process, and further present an end to end neural and probabilistic approach to generate an image-based meme for any given sentence using an encoder-decoder architecture. For a given input sentence, an image meme is generated by combining a meme template image and a text caption where the meme template image is selected from a set of popular candidates using a selection module, and the meme caption is generated by an encoder-decoder model. An encoder is used to map the selected meme template and the input sentence into a meme embedding and a decoder is used to decode the meme caption from the meme embedding. The generated natural language meme caption is conditioned on the input sentence and the selected meme template. The model learns the dependencies between the meme captions and the meme template images and generates new memes using the learned dependencies. The quality of the generated captions and the generated memes is evaluated through both automated and human evaluation. An experiment is designed to score how well the generated memes can represent the tweets from Twitter conversations. Experiments on Twitter data show the efficacy of the model in generating memes for sentences in online social interaction.
Robust Time-Frequency Reconstruction by Learning Structured Sparsity

Time-frequency distributions (TFDs) play a vital role in providing descriptive analysis of non-stationary signals involved in realistic scenarios. It is well known that low time-frequency (TF) resolution and the emergency of cross-terms (CTs) are two main issues, which make it difficult to analyze and interpret practical signals using TFDs. In order to address these issues, we propose the U-Net aided iterative shrinkage-thresholding algorithm (U-ISTA) for reconstructing a near-ideal TFD by exploiting structured sparsity in signal TF domain. Specifically, the signal ambiguity function is firstly compressed, followed by unfolding the ISTA as a recurrent neural network. To consider continuously distributed characteristics of signals, a structured sparsity constraint is incorporated into the unfolded ISTA by regarding the U-Net as an adaptive threshold block, in which structure-aware thresholds are learned from enormous training data to exploit the underlying dependencies among neighboring TF coefficients. The proposed U-ISTA model is trained by both non-overlapped and overlapped synthetic signals including closely and far located non-stationary components. Experimental results demonstrate that the robust U-ISTA achieves superior performance compared with state-of-the-art algorithms, and gains a high TF resolution with CTs greatly eliminated even in low signal-to-noise ratio (SNR) environments.
Comparison of jejunal digestive enzyme activities, expression of nutrient transporter genes, and apparent fecal digestibility in weaned piglets fed diets with varied sources of fiber

The objective of this study was to evaluate the effect of different fiber sources on jejunal digestive and absorptive physiology in weaned piglets. One hundred weaned piglets were allotted according to body weight, gender and litter to four dietary treatments. Each treatment was replicated in 5 pens of 5 pigs each. The experimental diets contained 10% of a fiber source: wheat bran (WB), maize fiber (MF), soyabean fiber (SF), or pea fiber (PF). Piglets were fed ad libitum for 30 d. Reduced villus height and villus height-to-crypt depth ratio (P PF > MF > WB. Our study indicates that SF and PF could promote apparent faecal digestibility in piglets, but this is not accompanied by better jejunal morphology or digestive enzyme activities as compared with WB.
Born-Infeld Black Holes in novel 4D Einstein-Gauss-Bonnet gravity

Ke Yanga∗, Bao-Min Gub,c†, Shao-Wen Weid,e‡, Yu-Xiao Liud,e§ School of Physical Science and Technology, Southwest University, Chongqing 400715, China Department of Physics, Nanchang University, Nanchang 330031, China Center for Relativistic Astrophysics and High Energy Physics, Nanchang University, Nanchang 330031, China d Institute of Theoretical Physics, Lanzhou University, Lanzhou 730000, China Joint Research Center for Physics, Lanzhou University and Qinghai Normal University, Lanzhou 730000 and Xining 810000, China