Miracle-dz/UniFAR-FTUs huggingface dataset:
How to help university students to manage their interruptions and improve their attention and time management

Technology has changed both our way of life and the way in which we learn. Students now attend lectures with laptops and mobile phones, and this situation is accentuated in the case of students on Computer Science degrees, since they require their computers in order to participate in both theoretical and practical lessons. Problems, however, arise when the students' social networks are opened on their computers and they receive notifications that interrupt their work. We set up a workshop regarding time, thoughts and attention management with the objective of teaching our students techniques that would allow them to manage interruptions, concentrate better and definitively make better use of their time. Those who took part in the workshop were then evaluated to discover its effects. The results obtained are quite optimistic and are described in this paper with the objective of encouraging other universities to perform similar initiatives.
The dust-gas AGN torus as constrained from X-ray and mid-infrared observations

Context. In recent decades, several multiwavelength studies have been dedicated to exploring the properties of the obscuring material in active galactic nuclei (AGN). Various models have been developed to describe the structure and distribution of this material and constrain its physical and geometrical parameters through spectral fitting techniques. However, questions around the way in which torus mid-infrared (mid-IR) and X-ray emission are related remain unanswered. Aims. In this work, we aim to study whether the dust continuum at mid-IR and gas reflection at X-rays have the same distribution in a sample of AGN. Methods. We carefully selected a sample of 36 nearby AGN with NuSTAR and Spitzer spectra available that satisfy the following criteria: (1) the AGN component dominates the mid-IR spectra (i.e., the stellar and interstellar medium components contribute less than 50% to the spectrum), and (2) the reflection component contributes significantly to the X-ray spectrum. Furthermore, we discarded the sources whose reflection component could be produced by ionized material in the disk. We derived the properties of the nuclear dust and gas through a spectral fitting, using models developed for mid-IR and X-ray wavelengths assuming smooth and clumpy distributions for this structure. Results. We find that a combination of smooth and clumpy distributions of gas and dust, respectively, is preferred for ∼80% of sources with good spectral fits according to the Akaike criterion. However, considering extra information about each individual source, such as the absorption variability, we find that ∼50% of our sources are best described by a clumpy distribution of both dust and gas. The remaining ∼50% of our sources can still be explained with a smooth distribution of gas and a clumpy distribution of dust. Furthermore, we explored the torus dust-to-gas ratio, finding that it is [0.01–1] times that of the interstellar medium. Conclusions. The results presented in this paper suggest that the distribution of the gas and dust in AGN is complex. We find at least six scenarios to explain the observed properties of our sample. In these scenarios, three gas–dust distribution combinations are possible: clumpy–clumpy, smooth–smooth, and smooth–clumpy. Most of them are in agreement with the notion that gas could also be located in the dust-free region, which is consistent with the dust-to-gas ratio found.
LeBenchmark: A Reproducible Framework for Assessing Self-Supervised Representation Learning from Speech

Self-Supervised Learning (SSL) using huge unlabeled data has been successfully explored for image and natural language processing. Recent works also investigated SSL from speech. They were notably successful to improve performance on downstream tasks such as automatic speech recognition (ASR). While these works suggest it is possible to reduce dependence on labeled data for building efficient speech systems, their evaluation was mostly made on ASR and using multiple and heterogeneous experimental settings (most of them for English). This questions the objective comparison of SSL approaches and the evaluation of their impact on building speech systems. In this paper, we propose LeBenchmark: a reproducible framework for assessing SSL from speech. It not only includes ASR (high and low resource) tasks but also spoken language understanding, speech translation and emotion recognition. We also focus on speech technologies in a language different than English: French. SSL models of different sizes are trained from carefully sourced and documented datasets. Experiments show that SSL is beneficial for most but not all tasks which confirms the need for exhaustive and reliable benchmarks to evaluate its real impact. LeBenchmark is shared with the scientific community for reproducible research in SSL from speech.
Operator Augmentation for General Noisy Matrix Systems

In the computational sciences, one must often estimate model parameters from data subject to noise and uncertainty, leading to inaccurate results. In order to improve the accuracy of models with noisy parameters, we consider the problem of reducing error in a linear system with the operator corrupted by noise. To address this problem, we extend the elliptic operator augmentation framework [4] to the general nonsymmetric matrix case. We show that under the conditions of right-hand-side isotropy and noise symmetry that the optimal operator augmentation factor for the residual error is always positive, thereby making the framework amenable to a necessary bootstrapping step. While the above conditions are unnecessary for positive optimal augmentation factor in the elliptic case, we provide counter-examples that illustrate their necessity when applied to general matrices. When the noise in the operator is small, however, we show that the condition of noise symmetry is unnecessary. Finally, we demonstrate through numerical experiments on Markov chain problems that operator augmentation can signiﬁcantly reduce error in noisy matrix systems — even when the aforementioned conditions are not met.
Evaluating Deception Detection Model Robustness To Linguistic Variation

With the increasing use of machine-learning driven algorithmic judgements, it is critical to develop models that are robust to evolving or manipulated inputs. We propose an extensive analysis of model robustness against linguistic variation in the setting of deceptive news detection, an important task in the context of misinformation spread online. We consider two prediction tasks and compare three state-of-the-art embeddings to highlight consistent trends in model performance, high confidence misclassifications, and high impact failures. By measuring the effectiveness of adversarial defense strategies and evaluating model susceptibility to adversarial attacks using character- and word-perturbed text, we find that character or mixed ensemble models are the most effective defenses and that character perturbation-based attack tactics are more successful.
The nicolet lettuce model : a theme with variations

The NICOLET model has been developed to predict the growth and nitrate content of greenhouse lettuce. Four single-organ versions have been developed: [1] abundant supply of nitrogen (1998), [2] mild N-stress (1999), [3] severe N-stress (2003), and [4] ontogenetic changes of organic-N and water content (in preparation). The `abundant-N? model [1] and the `mild-stress? model [2] have two compartments: `structure? and `vacuole? , while the `severe-stress? model [3] requires a third compartment: `excess-carbon? , and the `ontogenetic? model [4] has separate `metabolic? and `support? structural sub-compartments. The main special features of the NICOLET model are [1] the osmotica balance of the `vacuole? , where nitrate and hydrocarbons play a complementary role in maintaining a constant osmotic potential, [2] the excess-carbon compartment, where `dry? carbon compounds are stored, and [3] the sub-division of the `structure? into sub-compartments of different compositions. Loosely speaking, the first feature controls the nitrate concentration, the second controls the organic-N and water contents, and the third controls the ontogenetic changes. The NICOLET model has been able to mimic `normal? seasonal variations of nitrate content, as well as the effects of drastic N-stress treatments. These results are illustrated by comparing measured data with model-simulations. Accurate prediction of nitrate concentration is difficult, due to its sensitivity to changes in the environment. Exact control of nitrate under commercial conditions may require transient corrective measures, such as N-interruption, in conjunction with a good plant-nitrate monitoring system.
Nontrivial fixed points and truncated SU(4) Kondo models in a quasi-quartet multipolar quantum impurity problem

The multipolar Kondo problem, wherein the quantum impurity carries higher-rank multipolar moments, has seen recent theoretical and experimental interest due to proposals of novel non-Fermi liquid states and the availability of a variety of material platforms. The multipolar nature of local moments, in conjunction with constraining crystal field symmetries, leads to a vast array of possible interactions and resulting non-Fermi liquid ground states. Previous works on Kondo physics have typically focussed on impurities that have two degenerate internal states. In this work, inspired by recent experiments on the tetragonal material YbRu2Ge2, which has been shown to exhibit a local moment with a quasi-fourfold degenerate ground state, we consider the Kondo effect for such a quasi-quartet multipolar impurity. In the tetragonal crystal field environment, the local moment supports dipolar, quadrupolar, and octupolar moments, which interact with conduction electrons in entangled spin and orbital states. Using renormalization group analysis, we uncover a number of emergent quantum ground states characterized by non-trivial fixed points. It is shown that these previously unidentified fixed points are described by truncated SU(4) Kondo models, where only some of the SU(4) generators (representing the impurity degrees of freedom) are coupled to conduction electrons. Such novel non-trivial fixed points are unique to the quasi-quartet multipolar impurity, reinforcing the idea that an unexplored rich diversity of phenomena may be produced by multipolar quantum impurity systems.
A Dimension-Insensitive Algorithm for Stochastic Zeroth-Order Optimization

This paper concerns a convex, stochastic zeroth-order optimization (S-ZOO) problem. The objective is to minimize the expectation of a cost function whose gradient is not directly accessible. For this problem, traditional optimization algorithms mostly yield query complexities that grow polynomially with dimensionality (the number of decision variables). Consequently, these methods may not perform well in solving massive-dimensional problems arising in many modern applications. Although more recent methods can be provably dimension-insensitive, almost all of them require arguably more stringent conditions such as everywhere sparse or compressible gradient. In this paper, we propose a sparsity-inducing stochastic gradient-free (SI-SGF) algorithm, which provably yields a dimension-free (up to a logarithmic term) query complexity in both convex and strongly convex cases. Such insensitivity to the dimensionality growth is proven, for the first time, to be achievable when neither gradient sparsity nor gradient compressibility is satisfied. Our numerical results demonstrate a consistency between our theoretical prediction and the empirical performance.
SBNet: Segmentation-based Network for Natural Language-based Vehicle Search

Natural language-based vehicle retrieval is a task to find a target vehicle within a given image based on a natural language description as a query. This technology can be applied to various areas including police searching for a suspect vehicle. However, it is challenging due to the ambiguity of language descriptions and the difficulty of processing multi-modal data. To tackle this problem, we propose a deep neural network called SBNet that performs natural language-based segmentation for vehicle retrieval. We also propose two task-specific modules to improve performance: a substitution module that helps features from different domains to be embedded in the same space and a future prediction module that learns temporal information. SBnet has been trained using the CityFlow-NL dataset that contains 2,498 tracks of vehicles with three unique natural language descriptions each and tested 530 unique vehicle tracks and their corresponding query sets. SBNet achieved a significant improvement over the baseline in the natural language-based vehicle tracking track in the AI City Challenge 2021. Source Code: https://github.com/lsrock1/nlp_search
Charged black-bounce spacetimes

Given the recent development of rotating black-bounce-Kerr spacetimes, for both theoretical and observational purposes it becomes interesting to see whether it might be possible to construct black-bounce variants of the entire Kerr-Newman family. Specifically, herein we shall consider black-bounce-Reissner-Nordström and black-bounce-Kerr-Newman spacetimes as particularly simple and clean everywhere-regular black hole “mimickers” that deviate from the Kerr-Newman family in a precisely controlled and minimal manner, and smoothly interpolate between regular black holes and traversable wormholes. While observationally the electric charges on astrophysical black holes are likely to be extremely low, |Q|/m ≪ 1, introducing any non-zero electric charge has a significant theoretical impact. In particular, we verify the existence of a Killing tensor (and associated Carter-like constant) but without the full Killing tower of principal tensor and Killing-Yano tensor, also we discuss how, assuming general relativity, the black-bounce-Kerr-Newman solution requires an interesting, non-trivial matter/energy content.
Learning in Deep Neural Networks Using a Biologically Inspired Optimizer

Plasticity circuits in the brain are known to be influenced by the distribution of the synaptic weights through the mechanisms of synaptic integration and local regulation of synaptic strength. However, the complex interplay of stimulation-dependent plasticity with local learning signals is disregarded by most of the artificial neural network training algorithms devised so far. Here, we propose a novel biologically inspired optimizer for artificial (ANNs) and spiking neural networks (SNNs) that incorporates key principles of synaptic integration observed in dendrites of cortical neurons: GRAPES (Group Responsibility for Adjusting the Propagation of Error Signals). GRAPES implements a weight-distribution dependent modulation of the error signal at each node of the neural network. We show that this biologically inspired mechanism leads to a systematic improvement of the convergence rate of the network, and substantially improves classification accuracy of ANNs and SNNs with both feedforward and recurrent architectures. Furthermore, we demonstrate that GRAPES supports performance scalability for models of increasing complexity and mitigates catastrophic forgetting by enabling networks to generalize to unseen tasks based on previously acquired knowledge. The local characteristics of GRAPES minimize the required memory resources, making it optimally suited for dedicated hardware implementations. Overall, our work indicates that reconciling neurophysiology insights with machine intelligence is key to boosting the performance of neural networks.
Identification of high-energy astrophysical point sources via hierarchical Bayesian nonparametric clustering

The light we receive from distant astrophysical objects carries information about their origins and the physical mechanisms that power them. The study of these signals, however, is complicated by the fact that observations are often a mixture of the light emitted by multiple localized sources situated in a spatially-varying background. A general algorithm to achieve robust and accurate source identification in this case remains an open question in astrophysics. This paper focuses on high-energy light (such as X-rays and γ-rays), for which observatories can detect individual photons (quanta of light), measuring their incoming direction, arrival time, and energy. Our proposed Bayesian methodology uses both the spatial and energy information to identify point sources, that is, separate them from the spatiallyvarying background, to estimate their number, and to compute the posterior probabilities that each photon originated from each identified source. This is accomplished via a Dirichlet process mixture while the background is simultaneously reconstructed via a flexible Bayesian nonparametric model based on B-splines. Our proposed method is validated with a suite of simulation studies and illustrated with an application to a complex region of the sky observed by the Fermi Gamma-ray Space Telescope. 2 Sottosanti et al. (2021)
Transfer training from smaller language model

Large language models have led to state-of-the-art accuracies across a range of tasks. However,training large language model needs massive computing resource, as more and more open source pre-training models are available, it is worthy to study how to take full advantage of available model. We find a method to save training time and resource cost by changing the small well-trained model to large model. We initialize a larger target model from a smaller source model by copy weight values from source model and padding with zeros or small initialization values on it to make the source and target model have approximate outputs, which is valid due to block matrix multiplication and residual connection in transformer structure. We test the target model on several data sets and find it is still comparable with the source model. When we continue training the target model, the training loss can start from a smaller value.
SPECTRE: Defending Against Backdoor Attacks Using Robust Statistics

Modern machine learning increasingly requires training on a large collection of data from multiple sources, not all of which can be trusted. A particularly concerning scenario is when a small fraction of poisoned data changes the behavior of the trained model when triggered by an attacker-specified watermark. Such a compromised model will be deployed unnoticed as the model is accurate otherwise. There have been promising attempts to use the intermediate representations of such a model to separate corrupted examples from clean ones. However, these defenses work only when a certain spectral signature of the poisoned examples is large enough for detection. There is a wide range of attacks that cannot be protected against by the existing defenses. We propose a novel defense algorithm using robust covariance estimation to amplify the spectral signature of corrupted data. This defense provides a clean model, completely removing the backdoor, even in regimes where previous methods have no hope of detecting the poisoned examples. Code and pre-trained models are available at https://github.com/SewoongLab/spectre-defense .
Generation and effects of electromotive force in turbulent stochastic reconnection

Reconnection is an important process that rules dissipation and diffusion of magnetic energy in plasmas. It is already clear that its rate is enhanced by turbulence, and that reconnection itself may increase its stochasticity, but the main mechanism that connects these two effects is still not completely understood. The aim of this work is to identify, from the terms of the electromotive force, the dominant physical process responsible for enhancing the reconnection rate in turbulent plasmas. We employ full three-dimensional numerical simulations of turbulence driven by stochastic reconnection and estimate the production and dissipation of turbulent energy and cross-helicity, the amount of produced residual helicity, and determine the relation between these quantities and the reconnection rate. We observe the development of the electromotive force in the studied models with plasma-β = 0.1−2 and the Lundquist number S = 10−5− 10−4. The turbulent energy and residual helicity develop in the large-scale current sheet, with the latter decreasing the effects of turbulent magnetic diffusion. We demonstrate that the stochastic reconnection, apart from the turbulence, can produce a significant amount of cross-helicity as well. The cross-helicity to turbulent energy ratio, however, has no correlation with the reconnection rate. We show that its sufficiently large value is not a necessary condition for fast reconnection to occur. The results suggest that cross-helicity is inherent to turbulent fields, but the reconnection rate enhancement is possibly caused by the effects of magnetic turbulent diffusion and controlled by the residual helicity.
QMUL-SDS at SCIVER: Step-by-Step Binary Classification for Scientific Claim Verification

Scientific claim verification is a unique challenge that is attracting increasing interest. The SCIVER shared task offers a benchmark scenario to test and compare claim verification approaches by participating teams and consists in three steps: relevant abstract selection, rationale selection and label prediction. In this paper, we present team QMUL-SDS’s participation in the shared task. We propose an approach that performs scientific claim verification by doing binary classifications step-by-step. We trained a BioBERT-large classifier to select abstracts based on pairwise relevance assessments for each and continued to train it to select rationales out of each retrieved abstract based on . We then propose a two-step setting for label prediction, i.e. first predicting “NOT_ENOUGH_INFO” or “ENOUGH_INFO”, then label those marked as “ENOUGH_INFO” as either “SUPPORT” or “CONTRADICT”. Compared to the baseline system, we achieve substantial improvements on the dev set. As a result, our team is the No. 4 team on the leaderboard.
Development of advanced linearized gyrokinetic collision operators using a moment approach

The derivation and numerical implementation of a linearized version of the gyrokinetic (GK) Coulomb collision operator (Jorge et al., J. Plasma Phys., vol. 85, 2019, 905850604) and of the widely used linearized GK Sugama collision operator (Sugama et al., Phys. Plasmas, vol. 16, 2009, 112503) is reported. An approach based on a Hermite–Laguerre moment expansion of the perturbed gyrocentre distribution function is used, referred to as gyromoment expansion. This approach allows the considering of arbitrary perpendicular wavenumber and expressing the two linearized GK operators as a linear combination of gyromoments where the expansion coefficients are given by closed analytical expressions that depend on the perpendicular wavenumber and on the temperature and mass ratios of the colliding species. The drift-kinetic (DK) limits of the GK linearized Coulomb and Sugama operators are also obtained. Comparisons between the gyromoment approach and the DK Coulomb and GK Sugama operators in the continuum GK code GENE are reported, focusing on the ion-temperature-gradient instability and zonal flow damping, finding an excellent agreement. It is confirmed that stronger collisional damping of the zonal flow residual by the Sugama GK model compared with the GK linearized Coulomb (Pan et al., Phys. Plasmas, vol. 27, 2020, 042307) persists at higher collisionality. Finally, we show that the numerical efficiency of the gyromoment approach increases with collisionality, a desired property for boundary plasma applications.