Miracle-dz/UniFAR-FTUs huggingface dataset:
Stable Training of Dnn for Speech Enhancement Based on Perceptually-Motivated Black-Box Cost Function

Improving subjective sound quality of enhanced signals is one of the most important missions in speech enhancement. For evaluating the subjective quality, several methods related to perceptually-motivated objective sound quality assessment (OSQA) have been proposed such as PESQ (perceptual evaluation of speech quality). However, direct use of such measures for training deep neural network (DNN) is not allowed in most cases because popular OSQAs are non-differentiable with respect to DNN parameters. Therefore, the previous study has proposed to approximate the score of OS-QAs by an auxiliary DNN so that its gradient can be used for training the primary DNN. One problem with this approach is instability of the training caused by the approximation error of the score. To overcome this problem, we propose to use stabilization techniques borrowed from reinforcement learning. The experiments, aimed to increase the score of PESQ as an example, show that the proposed method (i) can stably train a DNN to increase PESQ, (ii) achieved the state-of-the-art PESQ score on a public dataset, and (iii) resulted in better sound quality than conventional methods based on subjective evaluation.
Transport of atoms across an optical lattice using an external harmonic potential

Precise control of quantum particles is required for many interesting or novel experiments. Here we consider the task of transporting an atom using an external harmonic potential from one well of an optical lattice to another without motional excitations. To achieve this we apply techniques from Shortcuts to Adiabaticity (STA) enabling fast and robust state manipulation. The process is split up into three independent building blocks; first the atom is loaded into an additional external harmonic trap; this trap is then transported from one lattice site to another and finally the atom is unloaded back onto the lattice by opening the external harmonic trap. We design protocols for each of these building blocks separately using invariant-based inverse engineering. Additionally we extend this method to the transport of a Bose-Einstein condensate described by the Gross-Pitaevskii equation.
Uniform labelled calculi for preferential conditional logics based on neighbourhood semantics

The preferential conditional logic $ \mathbb{PCL} $, introduced by Burgess, and its extensions are studied. First, a natural semantics based on neighbourhood models, which generalizes Lewis’ sphere models for counterfactual logics, is proposed. Soundness and completeness of $ \mathbb{PCL} $ and its extensions with respect to this class of models are proved directly. Labelled sequent calculi for all logics of the family are then introduced. The calculi are modular and have standard proof-theoretical properties, the most important of which is admissibility of cut that entails a syntactic proof of completeness of the calculi. By adopting a general strategy, root-first proof search terminates, thereby providing a decision procedure for $ \mathbb{PCL} $ and its extensions. Finally, semantic completeness of the calculi is established: from a finite branch in a failed proof attempt it is possible to extract a finite countermodel of the root sequent. The latter result gives a constructive proof of the finite model property of all the logics considered.
An Inductive Bias for Distances: Neural Nets that Respect the Triangle Inequality

Distances are pervasive in machine learning. They serve as similarity measures, loss functions, and learning targets; it is said that a good distance measure solves a task. When defining distances, the triangle inequality has proven to be a useful constraint, both theoretically--to prove convergence and optimality guarantees--and empirically--as an inductive bias. Deep metric learning architectures that respect the triangle inequality rely, almost exclusively, on Euclidean distance in the latent space. Though effective, this fails to model two broad classes of subadditive distances, common in graphs and reinforcement learning: asymmetric metrics, and metrics that cannot be embedded into Euclidean space. To address these problems, we introduce novel architectures that are guaranteed to satisfy the triangle inequality. We prove our architectures universally approximate norm-induced metrics on $\mathbb{R}^n$, and present a similar result for modified Input Convex Neural Networks. We show that our architectures outperform existing metric approaches when modeling graph distances and have a better inductive bias than non-metric approaches when training data is limited in the multi-goal reinforcement learning setting.
Clustering based on Point-Set Kernel

Measuring similarity between two objects is the core operation in existing cluster analyses in grouping similar objects into clusters. Cluster analyses have been applied to a number of applications, including image segmentation, social network analysis, and computational biology. This paper introduces a new similarity measure called point-set kernel which computes the similarity between an object and a sample of objects generated from an unknown distribution. The proposed clustering procedure utilizes this new measure to characterize both the typical point of every cluster and the cluster grown from the typical point. We show that the new clustering procedure is both effective and efficient such that it can deal with large scale datasets. In contrast, existing clustering algorithms are either efficient or effective; and even efficient ones have difficulty dealing with large scale datasets without special hardware. We show that the proposed algorithm is more effective and runs orders of magnitude faster than the state-of-the-art density-peak clustering and scalable kernel k-means clustering when applying to datasets of millions of data points, on commonly used computing machines.
Unconventional Bio-Inspired Model for Design of Logic Gates

During the last years, a well studied biological substrate, namely Physarum polycephalum, has been proven efficient on finding appropriate and efficient solutions in hard to solve complex mathematical problems. The plasmodium of P. polycephalum is a single-cell that serves as a prosperous bio-computational example. Consequently, it has been successfully utilized in the past to solve a variety of path problems in graphs and combinatorial problems. In this work, this interesting behaviour is mimicked by a robust unconventional computational model, drawing inspiration from the notion of Cellular and Learning Automata. Namely, we employ principles of Cellular Automata (CAs) enriched with learning capabilities to develop a robust computational model, able of modelling appropriately the aforementioned biological substrate and, thus, capturing its computational capabilities. CAs are very efficient in modelling biological systems and solving scientific problems, owing to their ability of incarnating essential properties of a system where global behaviour arises as an effect of simple components, interacting locally. The resulting computational tool, after combining CAs with learning capabilities, should be appropriate for modelling the behaviour of living organisms. Thus, the inherent abilities and computational characteristics of the proposed bio-inspired model are stressed towards the experimental verification of Physarum's ability to model Logic Gates, while trying to find minimal paths in properly configured mazes with food sources. The presented simulation results for various Logic Gates are found in good agreement, both qualitatively and quantitatively, with the corresponding experimental results, proving the efficacy of this unconventional bio-inspired model and providing useful insights for its enhanced usage in various computing applications.
Multivariate Monotone Inclusions in Saddle Form

We propose a novel approach to monotone operator splitting based on the notion of a saddle operator. Under investigation is a highly structured multivariate monotone inclusion problem involving a mix of set-valued, cocoercive, and Lipschitzian monotone operators, as well as various monotonicity-preserving operations among them. This model encompasses most formulations found in the literature. A limitation of existing primal-dual algorithms is that they operate in a product space that is too small to achieve full splitting of our problem in the sense that each operator is used individually. To circumvent this difficulty, we recast the problem as that of finding a zero of a saddle operator that acts on a bigger space. This leads to an algorithm of unprecedented flexibility, which achieves full splitting, exploits the specific attributes of each operator, is asynchronous, and requires to activate only blocks of operators at each iteration, as opposed to activating all of them. The latter feature is of critical importance in large-scale problems. The weak convergence of the main algorithm is established, as well as the strong convergence of a variant. Various applications are discussed, and instantiations of the proposed framework in the context of variational inequalities and minimization problems are presented.
DNA Torsion-based Model of Cell Fate Phase Transitions

All stem cell fate transitions, including the metabolic reprogramming of stem cells and the somatic reprogramming of fibroblasts into pluripotent stem cells, can be understood from a unified theoretical model of cell fates. Each cell fate transition can be regarded as a phase transition in DNA supercoiling. However, there has been a dearth of quantitative biophysical models to explain and predict the behaviors of these phase transitions. The generalized Ising model is proposed to define such phase transitions. The model predicts that, apart from temperature-induced phase transitions, there exists DNA torsion frequency-induced phase transitions. Major transitions in epigenetic states, from stem cell activation to differentiation and reprogramming, can be explained by such torsion frequency-induced phase transitions, with important implications for regenerative medicine and medical diagnostics in the future.
Online Algorithms for Multi-shop Ski Rental with Machine Learned Predictions

We study the problem of augmenting online algorithms with machine learned (ML) predictions. In particular, we consider the \emph{multi-shop ski rental} (MSSR) problem, which is a generalization of the classical ski rental problem. In MSSR, each shop has different prices for buying and renting a pair of skis, and a skier has to make decisions on when and where to buy. We obtain both deterministic and randomized online algorithms with provably improved performance when either a single or multiple ML predictions are used to make decisions. These online algorithms have no knowledge about the quality or the prediction error type of the ML predictions. The performance of these online algorithms are robust to the poor performance of the predictors, but improve with better predictions. We numerically evaluate the performance of our proposed online algorithms in practice.
SDSS IV MaNGA: Metallicity and ionisation parameter in local star-forming galaxies from Bayesian fitting to photoionisation models

We measured gas-phase metallicity, ionisation parameter, and dust extinction for a representative sample of 1795 local star-forming galaxies using integral field spectroscopy from the SDSS-IV MaNGA survey. We self-consistently derive these quantities by comparing observed line fluxes with photoionisation models using a Bayesian framework. We also present the first comprehensive study of the [S III]λλ9069,9532 nebular lines, which have long been predicted to be ideal tracers of the ionisation parameter. However, we find that current photoionisation model predictions substantially over-predict the intensity of the [S III] lines, while broadly reproducing other observed optical line ratios. We discuss how to nonetheless make use of the information provided by the [S III] lines by setting a prior on the ionisation parameter. Following this approach, we derive spatially resolved maps and radial profiles of metallicity and ionisation parameter. The metallicity radial profiles derived are comparable with previous works, with metallicity declining toward the outer parts and showing a flattening in the central regions. This is in agreement with infall models of galaxy formation, which predict that spiral discs build up through accretion of material, leading to an inside-out growth. On the other hand, ionisation parameter radial profiles are flat for low-mass galaxies, while their slope becomes positive as galaxy mass increases. However, the ionisation parameter maps we obtain are clumpy, especially for low-mass galaxies. The ionisation parameter is tightly correlated with the equivalent width of Hα[EW(Hα)], following a nearly universal relation, which we attribute to the change of the spectral shape of ionising sources due to ageing of H IIregions. We derive a positive correlation between ionisation parameter and metallicity at fixed EW(Hα), in disagreement with previous theoretical work that predict an anti-correlation.
Constraining gravity with eccentric gravitational waves: projected upper bounds and model selection

Gravitational waves allow us to test general relativity in the highly dynamical regime. While current observations have been consistent with waves emitted by quasi-circular binaries, eccentric binaries may also produce detectable signals in the near future with ground- and space-based detectors. We here explore how tests of general relativity scale with the orbital eccentricity of the source during the inspiral of compact objects up to e ∼ 0.8. We use a new, third post-Newtonian-accurate, eccentric waveform model for the inspiral of compact objects, which is fast enough for Bayesian parameter estimation and model selection, and highly accurate for modeling moderately eccentric inspirals. We derive and incorporate the eccentric corrections to this model induced in Brans–Dicke theory and in Einstein–dilaton–Gauss–Bonnet gravity at leading post-Newtonian order, which suggest a straightforward eccentric extension of the parameterized post-Einsteinian formalism. We explore the upper limits that could be set on the coupling parameters of these modified theories through both a confidence-interval- and Bayes-factor-based approach, using a Markov-Chain Monte Carlo and a trans-dimensional, reversible-jump, Markov-Chain Monte Carlo method. We find projected constraints with signals from sources with e ∼ 0.4 that are one order of magnitude stronger than that those obtained with quasi-circular binaries in advanced LIGO. In particular, eccentric gravitational waves detected at design sensitivity should be able to constrain the Brans–Dicke coupling parameter ω ≳ 3300 and the Gauss–Bonnet coupling parameter α1/2 ≲ 0.5 km at 90% confidence. Although the projected constraint on ω is weaker than other current constraints, the projected constraint on α1/2 is 10 times stronger than the current gravitational wave bound.
Punctual Hilbert scheme and certified approximate singularities

In this paper we provide a new method to certify that a nearby polynomial system has a singular isolated root and we compute its multiplicity structure. More precisely, given a polynomial system f = (f1, ., fN) ∈ C[x1, . , xn]N, we present a Newton iteration on an extended deflated system that locally converges, under regularity conditions, to a small deformation of f such that this deformed system has an exact singular root. The iteration simultaneously converges to the coordinates of the singular root and the coefficients of the so-called inverse system that describes the multiplicity structure at the root. We use α-theory test to certify the quadratic convergence, and to give bounds on the size of the deformation and on the approximation error. The approach relies on an analysis of the punctual Hilbert scheme, for which we provide a new description. We show in particular that some of its strata can be rationally parametrized and exploit these parametrizations in the certification. We show in numerical experimentation how the approximate inverse system can be computed as a starting point of the Newton iterations and the fast numerical convergence to the singular root with its multiplicity structure, certified by our criteria.
Chiral kinetic theory from the on-shell effective field theory: Derivation of collision terms

We show that the on-shell effective theory (OSEFT) is the quantum field theory counterpart of a Foldy-Wouthuysen diagonalization of relativistic quantum mechanics for massless fermions. Thus, it is free of the Zitterbewegung oscillations that would yield an ill-defined meaning to the semiclassical transport approach at short distances if derived from the pure Dirac picture. We present a detailed derivation of the collision terms in the chiral kinetic theory using the OSEFT. Collision integrals are derived up to order 1/E, where E is the energy of an on-shell fermion. At this order, the collision terms depends on the spin tensor of the fermion, and in the presence of chiral imbalance, it describes how a massless fermion of a given helicity interacts differently with the transverse photons of different circular polarization. In order to back up our results, we check that they allow us to reproduce the fermion decay rate in an ultradegenerate plasma with a chiral imbalance computed directly from QED.
The "Higher" Status Language does not Always Win: the Fall of English in India and the Rise of Hindi

Classical language dynamics explain language shift as a process in which speakers adopt a higher status language in lieu of a lower status language. This is well documented with English having out-competed languages such as Scottish Gaelic, Welsh, and Mandarin. The 1961–1991 Indian censuses report a sharper increase in Hindi/English Bilinguals compared to Monolingual Hindi speakers, suggesting that English is on the rise in India — and is out-competing Hindi. However, the 1991–2011 data show that Bilingual numbers have saturated, while Monolingual Hindi speakers continue to rise exponentially. To capture this counter-intuitive dynamic, we propose a novel language dynamics model of interaction between Monolingual Hindi speakers and Hindi/English Bilinguals, which captures the Indian census data of the last 50 years with greater accuracy, than other well-known language dynamics models. We thus provide a first example of a lower status language having out competed English, a higher status language.
FQuAD: French Question Answering Dataset

Recent advances in the field of language modeling have improved state-of-the-art results on many Natural Language Processing tasks. Among them, Reading Comprehension has made significant progress over the past few years. However, most results are reported in English since labeled resources available in other languages, such as French, remain scarce. In the present work, we introduce the French Question Answering Dataset (FQuAD). FQuAD is a French Native Reading Comprehension dataset of questions and answers on a set of Wikipedia articles that consists of 25,000+ samples for the 1.0 version and 60,000+ samples for the 1.1 version. We train a baseline model which achieves an F1 score of 92.2 and an exact match ratio of 82.1 on the test set. In an effort to track the progress of French Question Answering models we propose a leaderboard and we have made the 1.0 version of our dataset freely available at https://illuin-tech.github. io/FQuAD-explorer/.
Understanding patient complaint characteristics using contextual clinical BERT embeddings

In clinical conversational applications, extracted entities tend to capture the main subject of a patient’s com-plaint, namely symptoms or diseases. However, they mostly fail to recognize the characterizations of a complaint such as the time, the onset, and the severity. For example, if the input is "I have a headache and it is extreme", state-of-the-art models only recognize the main symptom entity - headache, but ignore the severity factor of extreme, that characterises headache. In this paper, we design a two-fold approach to detect the characterizations of entities like symptoms presented by general users in contexts where they would describe their symptoms to a clinician. We use Word2Vec and BERT models to encode clinical text given by the patients. We transform the output and re-frame the task as a multi-label classification problem. Finally, we combine the processed encodings with the Linear Discriminant Analysis (LDA) algorithm to classify the characterizations of the main entity. Experimental results demonstrate that our method achieves 40-50% improvement in the accuracy over the state-of-the-art models.
The Association Between Self Efficacy, Perceived Social Support and Adherence to Treatment in Patients with Heart Failure

BackgroundPoor adherence to treatment in heart failure patients is associated with frequent hospital readmissions, worsening of symptoms and premature death. Since the progression of heart failure is also affected by the psychological and social aspects of life, identifying psychosocial variables associated with health-promoting behaviors in these patients is essential.ObjectivesThe aim of this study was to investigate the relationship between cardiac self-efficacy, perceived social support (PSS) and adherence to treatment in patients with heart failure reduced ejection fraction (HFrEF). MethodsAmong patients referred to the outpatient heart failure clinic at Rajaie Cardiovascular, Medical and Research Center, a total of 120 patients with a diagnosis of HFrEF (left ventricular ejection fraction ≤ 35%), were consecutively enrolled. They were asked to complete demographic, cardiac self-efficacy, perceived social support (PSS) and adherence to treatment questionnaires.ResultsA total of 120 heart failure patients (70% male, 67% with ischemic etiology) were enrolled. Most patients were between 40 - 59 years old. The mean (SD) score of cardiac self-efficacy was 30.6 (2.4), which shows a fair self-efficacy in our study population. The mean (SD) score for PSS was 94.6 (10) which showed more than 75% of heart failure patients have enjoyed high levels of PSS. The mean (SD) scores for the three domains of adherence to treatment were 7.4 (1.5) for diet, 13.2 (2) for physical activity and 10 (1.4) for medications which shows a poor adherence to the diet. Both PSS and adherence to treatment were significantly correlated with NYHA function class. Multivariable logistic regression indicated that cardiac self-efficacy might be a better independent predictor of treatment adherence than PSS in patients with HFrEF.ConclusionsAccording to the analyzed data of this study, social support, treatment adherence and self-efficacy among all patients with HFrEF were in acceptable levels. However, both patients and their care givers should be more educated in this regard.