Miracle-dz/UniFAR-FTUs huggingface dataset:
A Probabilistic Interpretation of Self-Paced Learning with Applications to Reinforcement Learning

Across machine learning, the use of curricula has shown strong empirical potential to improve learning from data by avoiding local optima of training objectives. For reinforcement learning (RL), curricula are especially interesting, as the underlying optimization has a strong tendency to get stuck in local optima due to the exploration-exploitation trade-off. Recently, a number of approaches for an automatic generation of curricula for RL have been shown to increase performance while requiring less expert knowledge compared to manually designed curricula. However, these approaches are seldomly investigated from a theoretical perspective, preventing a deeper understanding of their mechanics. In this paper, we present an approach for automated curriculum generation in RL with a clear theoretical underpinning. More precisely, we formalize the well-known self-paced learning paradigm as inducing a distribution over training tasks, which trades off between task complexity and the objective to match a desired task distribution. Experiments show that training on this induced distribution helps to avoid poor local optima across RL algorithms in different tasks with uninformative rewards and challenging exploration requirements.
Inductive Mutual Information Estimation: A Convex Maximum-Entropy Copula Approach

We propose a novel estimator of the mutual information between two ordinal vectors $x$ and $y$. Our approach is inductive (as opposed to deductive) in that it depends on the data generating distribution solely through some nonparametric properties revealing associations in the data, and does not require having enough data to fully characterize the true joint distributions $P_{x, y}$. Specifically, our approach consists of (i) noting that $I\left(y; x\right) = I\left(u_y; u_x\right)$ where $u_y$ and $u_x$ are the \emph{copula-uniform dual representations} of $y$ and $x$ (i.e. their images under the probability integral transform), and (ii) estimating the copula entropies $h\left(u_y\right)$, $h\left(u_x\right)$ and $h\left(u_y, u_x\right)$ by solving a maximum-entropy problem over the space of copula densities under a constraint of the type $\alpha_m = E\left[\phi_m(u_y, u_x)\right]$. We prove that, so long as the constraint is feasible, this problem admits a unique solution, it is in the exponential family, and it can be learned by solving a convex optimization problem. The resulting estimator, which we denote MIND, is marginal-invariant, always non-negative, unbounded for any sample size $n$, consistent, has MSE rate $O(1/n)$, and is more data-efficient than competing approaches. Beyond mutual information estimation, we illustrate that our approach may be used to mitigate mode collapse in GANs by maximizing the entropy of the copula of fake samples, a model we refer to as Copula Entropy Regularized GAN (CER-GAN).
Domain Adapting Ability Of Self-Supervised Learning For Face Recognition

Although deep convolutional networks have achieved great performance in face recognition tasks, the challenge of domain discrepancy still exists in real world applications. Lack of domain coverage of training data (source domain) makes the learned models degenerate in a testing scenario (target domain). In face recognition tasks, classes in two domains are usually different, so classical domain adaptation approaches, assuming there are shared classes in domains, may not be reasonable solutions for this problem. In this paper, self-supervised learning is adopted to learn a better embedding space where the subjects in target domain are more distinguishable. The learning goal is maximizing the similarity between the embeddings of each image and its mirror in both domains. The experiments show its competitive results compared with prior works. To know the reason why it can achieve such performance, we further discuss how this approach affects the learning of embeddings.
An introduction to the determination of the probability of a successful trial: Frequentist and Bayesian approaches

Determination of posterior probability for go-no-go decision and predictive power are becoming increasingly common for resource optimization in clinical investigation. There are vast published literature on these topics; however, the terminologies are not consistently used across the literature. Further, there is a lack of consolidated presentation of various concepts of the probability of success. We attempted to fill this gap. This paper first provides a detailed derivation of these probability of success measures under the frequentist and Bayesian paradigms in a general setting. Subsequently, we have presented the analytical formula for these probability of success measures for continuous, binary, and time-to-event endpoints separately. This paper can be used as a single point reference to determine the following measures: (a) the conditional power (CP) based on interim results, (b) the predictive power of success (PPoS) based on interim results with or without prior distribution, and (d) the probability of success (PoS) for a prospective trial at the design stage. We have discussed both clinical success and trial success. This paper’s discussion is mostly based on the normal approximation for prior distribution and the estimate of the parameter of interest. Besides, predictive power using the beta prior for the binomial case is also presented. Some examples are given for illustration. R functions to calculate CP and PPoS are available through the LongCART package. An R shiny app is also available at https://ppos.herokuapp. com/.
Evaluate On-the-job Learning Dialogue Systems and a Case Study for Natural Language Understanding

On-the-job learning consists in continuously learning while being used in production, in an open environment, meaning that the system has to deal on its own with situations and elements never seen before. The kind of systems that seem to be especially adapted to on-the-job learning are dialogue systems, since they can take advantage of their interactions with users to collect feedback to adapt and improve their components over time. Some dialogue systems performing on-the-job learning have been built and evaluated but no general methodology has yet been defined. Thus in this paper, we propose a first general methodology for evaluating on-the-job learning dialogue systems. We also describe a task-oriented dialogue system which improves on-the-job its natural language component through its user interactions. We finally evaluate our system with the described methodology.
Heat Transfer and Entanglement–Non‐Equilibrium Correlation Spectra of Two Quantum Oscillators

The non‐equilibrium state of two oscillators with a mutual interaction and coupled to separate heat baths is discussed. Bosonic baths are considered, and an exact spectral representation for the elements of the covariance matrix is provided analytically. A wide class of spectral densities for the relevant bath modes is allowed for. The validity of the fluctuation‐dissipation relation is established for global equilibrium (both baths at the same temperature) in the stationary state. Spectral measures of entanglement are suggested by comparing to the equilibrium spectrum of zero‐point fluctuations. No rotating‐wave approximation is applied, and anomalous heat transport from cold to hot bath, as reported in earlier work, is demonstrated not to occur.
DOCENT: Learning Self-Supervised Entity Representations from Large Document Collections

This paper explores learning rich self-supervised entity representations from large amounts of associated text. Once pre-trained, these models become applicable to multiple entity-centric tasks such as ranked retrieval, knowledge base completion, question answering, and more. Unlike other methods that harvest self-supervision signals based merely on a local context within a sentence, we radically expand the notion of context to include any available text related to an entity. This enables a new class of powerful, high-capacity representations that can ultimately distill much of the useful information about an entity from multiple text sources, without any human supervision. We present several training strategies that, unlike prior approaches, learn to jointly predict words and entities – strategies we compare experimentally on downstream tasks in the TV-Movies domain, such as MovieLens tag prediction from user reviews and natural language movie search. As evidenced by results, our models match or outperform competitive baselines, sometimes with little or no fine-tuning, and are also able to scale to very large corpora. Finally, we make our datasets and pre-trained models publicly available. This includes Reviews2Movielens, mapping the ~1B word corpus of Amazon movie reviews (He and McAuley, 2016) to MovieLens tags (Harper and Konstan, 2016), as well as Reddit Movie Suggestions with natural language queries and corresponding community recommendations.
Braneworld‐Klein‐Gordon System in the Framework of Gravitational Decoupling

We analyze the effective field equations of the Randall‐Sundrum braneworld coupled with a Klein‐Gordon scalar field through the minimal geometric deformation decoupling method (MGD‐decoupling). We introduce two different ways to apply the MGD‐decoupling method to obtain new solutions for this enlarged system. We also compare the behavior of the new solutions with those obtained directly from the Randall‐Sumdrum braneworld without coupling to the scalar field.
Variation Control and Evaluation for Generative Slate Recommendations

Slate recommendation generates a list of items as a whole instead of ranking each item individually, so as to better model the intra-list positional biases and item relations. In order to deal with the enormous combinatorial space of slates, recent work considers a generative solution so that a slate distribution can be directly modeled. However, we observe that such approaches—despite their proved effectiveness in computer vision—suffer from a trade-off dilemma in recommender systems: when focusing on reconstruction, they easily over-fit the data and hardly generate satisfactory recommendations; on the other hand, when focusing on satisfying the user interests, they get trapped in a few items and fail to cover the item variation in slates. In this paper, we propose to enhance the accuracy-based evaluation with slate variation metrics to estimate the stochastic behavior of generative models. We illustrate that instead of reaching to one of the two undesirable extreme cases in the dilemma, a valid generative solution resides in a narrow “elbow” region in between. And we show that item perturbation can enforce slate variation and mitigate the over-concentration of generated slates, which expand the “elbow” performance to an easy-to-find region. We further propose to separate a pivot selection phase from the generation process so that the model can apply perturbation before generation. Empirical results show that this simple modification can provide even better variance with the same level of accuracy compared to post-generation perturbation methods.
Phase operator on $L^2(\mathbb{Q}_p)$ and the zeroes of Fisher and Riemann

The distribution of the non-trivial zeroes of the Riemann zeta function, according to the Riemann hypothesis, is tantalisingly similar to the zeroes of the partition functions (Fisher and Yang-Lee zeroes) of statistical mechanical models studied by physicists. The resolvent function of an operator akin to the phase operator, conjugate to the number operator in quantum mechanics, turns out to be important in this approach. The generalised Vladimirov derivative acting on the space $L^2(\mathbb{Q}_p)$ of complex valued locally constant functions on the $p$-adic field is rather similar to the number operator. We show that a `phase operator' conjugate to it can be constructed on a subspace $L^2(p^{-1}\mathbb{Z}_p)$ of $L^2(\mathbb{Q}_p)$. We discuss (at physicists' level of rigour) how to combine this for all primes to possibly relate to the zeroes of the Riemann zeta function. Finally, we extend these results to the family of Dirichlet $L$-functions, using our recent construction of Vladimirov derivative like pseudodifferential operators associated with the Dirichlet characters.
Lithium–Sulfur Cells: The Gap between the State‐of‐the‐Art and the Requirements for High Energy Battery Cells

Li–S cells are considered a highly attractive electrochemical storage system, especially due to their high potential gravimetric energy density. The long‐term target of all Li–S research activities must be to outperform state‐of‐the‐art Li‐ion cells. A current benchmark is the Panasonic NCR18650B, which has a gravimetric energy density of ≈240 Wh kg−1 and several hundred relatively stable cycles. The possible 18650 Li–S cell energies and cell costs are calculated for various sulfur loads, sulfur utilizations, and electrolyte/sulfur ratios with the aim of determining the cell and electrode property values required to outperform the NCR18650B. These values are compared with statistical information obtained from an extensive literature review of 274 Li–S publications over the last 12 years to show the gap between state‐of‐the‐art Li–S research and requirements for high energy density cells. Finally, a carbon nanotubes‐based electrode is introduced, which meets important criteria for obtaining high gravimetric cell energy densities.
Whole powder pattern decomposition methods and applications: A retrospection

Methods extracting fast all the peak intensities from a complete powder diffraction pattern are reviewed. The genesis of the modern whole powder pattern decomposition methods (the so-called Pawley and Le Bail methods) is detailed and their importance and domains of application are decoded from the most cited papers citing them. It is concluded that these methods represented a decisive step toward the possibility to solve more easily, if not routinely, a structure solely from a powder sample. The review enlightens the contributions from the Louër’s group during the rising years 1987–1993.
Caregivers’ feeding behaviour, children's eating behaviour and weight status among children of preschool age in China

Abstract Background Childhood overweight and obesity have become significant public health challenges worldwide. The present study aimed to investigate whether caregivers’ feeding behaviour and children's eating behaviour were associated with the weight status of preschool children in China. Methods A cross‐sectional questionnaire was administered to 912 caregivers of preschool children from April to July 2016. Caregivers’ feeding behaviours were assessed by the Chinese Preschooler's Caregiver Feeding Behaviour Scale. Children's eating behaviours were evaluated using the Chinese Preschooler's Eating Behaviour Questionnaire. After controlling for demographic characteristics, multiple linear regression and logistic regression analyses were performed to evaluate the relationship between caregivers’ feeding behaviour, children's eating behaviour and children's body mass index (BMI). Results The results showed that weight concerns on the part of caregivers (β = 0.53) and food responsiveness on the part of children (β = 0.93) were positively correlated with children's BMI, whereas caregivers’ responsibility for feeding (β = −0.68) and children's external eating (β = −0.53) were negatively correlated with BMI. Among caregiver feeding behaviours, weight concerns [odds ratio (OR) =4.54, p < 0.001] and behaviour‐restricted feeding (OR =0.29, p < 0.001) were positively correlated with children's BMI. A child's food responsiveness (OR =4.04, p < 0.001) was also positively correlated with his/her BMI, whereas the child's satiety responsiveness (OR =0.42, p < 0.001) and emotional eating habits (OR =0.56, p < 0.001) were negatively correlated with overweight/obesity status. Conclusions Our study demonstrated that children's eating behaviour and caregivers’ feeding behaviour were associated with weight status among preschool children in China. Behaviour interventions on caregivers and their children may prevent or reduce weight problems in preschool children.
Structure-based analyses of Salmonella RcsB variants unravel new features of the Rcs regulon

Abstract RcsB is a transcriptional regulator that controls expression of numerous genes in enteric bacteria. RcsB accomplishes this role alone or in combination with auxiliary transcriptional factors independently or dependently of phosphorylation. To understand the mechanisms by which RcsB regulates such large number of genes, we performed structural studies as well as in vitro and in vivo functional studies with different RcsB variants. Our structural data reveal that RcsB binds promoters of target genes such as rprA and flhDC in a dimeric active conformation. In this state, the RcsB homodimer docks the DNA-binding domains into the major groove of the DNA, facilitating an initial weak read-out of the target sequence. Interestingly, comparative structural analyses also show that DNA binding may stabilize an active conformation in unphosphorylated RcsB. Furthermore, RNAseq performed in strains expressing wild-type or several RcsB variants provided new insights into the contribution of phosphorylation to gene regulation and assign a potential role of RcsB in controlling iron metabolism. Finally, we delimited the RcsB box for homodimeric active binding to DNA as the sequence TN(G/A)GAN4TC(T/C)NA. This RcsB box was found in promoter, intergenic and intragenic regions, facilitating both increased or decreased gene transcription.
Overlapping but distinct: a new model for G-quadruplex biochemical specificity

Abstract G-quadruplexes are noncanonical nucleic acid structures formed by stacked guanine tetrads. They are capable of a range of functions and thought to play widespread biological roles. This diversity raises an important question: what determines the biochemical specificity of G-quadruplex structures? The answer is particularly important from the perspective of biological regulation because genomes can contain hundreds of thousands of G-quadruplexes with a range of functions. Here we analyze the specificity of each sequence in a 496-member library of variants of a reference G-quadruplex with respect to five functions. Our analysis shows that the sequence requirements of G-quadruplexes with these functions are different from one another, with some mutations altering biochemical specificity by orders of magnitude. Mutations in tetrads have larger effects than mutations in loops, and changes in specificity are correlated with changes in multimeric state. To complement our biochemical data we determined the solution structure of a monomeric G-quadruplex from the library. The stacked and accessible tetrads rationalize why monomers tend to promote a model peroxidase reaction and generate fluorescence. Our experiments support a model in which the sequence requirements of G-quadruplexes with different functions are overlapping but distinct. This has implications for biological regulation, bioinformatics, and drug design.
Levodopa affects spike and local field synchronisation in the pedunculopontine nucleus of a rat model of Parkinson’s disease

The pedunculopontine nucleus (PPN) undergoes significant anatomic and electrophysiological alterations in Parkinson’s disease (PD), severely impacting locomotion. However, the effect of 6-hydroxydopamine (6-OHDA) lesion and levodopa (L-DOPA) therapy on the relationships between spike activities and local field potential (LFP) within the PPN is not well-understood. Synchronisation between the spike activity of individual neurones and LFP of neuronal ensembles is a crucial problem in the pathogenesis of PD. In this study, LFP signals and spikes in the PPN of rats in control, lesioned, and L-DOPA groups were recorded synchronously with a multi-unit electrical signal acquisition system and analysed for their coherence value, spike-field coherence, and phase-lock relationship. The spike-LFP relationship in the PPN was markedly increased in specific frequency bands because of the 6-OHDA lesion but differed depending on the animal locomotion state and neuronal type. L-DOPA had a limited therapeutic effect on the 6-OHDA-induced increase in the coherence value. Our study demonstrates that the PPN spike-LFP relationship is involved in the pathogenesis of PD and is critical for the effects of L-DOPA, providing a basis for the clinical treatment of refractory PD symptoms.
Frailty is an independent risk factor of one-year mortality after elective orthopedic surgery: a prospective cohort study

Frailty is associated with perioperative adverse outcomes, especially for the elderly. This study aimed to assess whether frailty was an independent risk factor of one-year mortality in frail patients after elective orthopedic surgery. In this prospective study, three hundred and thirteen patients aged ≥ 65 years, undergoing elective orthopedic surgery were finally included. Frailty assessed by the Clinical Frailty Score (CFS) before the surgery was present in 29.7% (93/313). Among them, 7.7% of patients (24/313) died at one year after surgery. In multivariate logistic analysis, higher CFS (OR = 2.271, 95% CI= 1.472–3.504) was found to be an independent risk factor of one-year mortality after surgery in elderly orthopedic patients. The area under the receiver operating characteristic curve of the model was 0.897 (95% CI 0.834–0.959). In addition, we found higher Charlson comorbidity index (OR= 1.498, 95% CI = 1.082–2.073) was also a significant risk factor. In conclusion, frailty is associated with increased one-year mortality in elderly patients after elective orthopedic surgery, which should be considered as a routine assessment tool in preoperative practice.
Forensic Characterization of Polycyclic Aromatic Hydrocarbons and Hopanes in Aerosols from Peninsular Malaysia

A forensic study on the characterization of polycyclic aromatic hydrocarbons (PAHs) and hopanes in atmospheric aerosols has been carried out on nine locations in peninsular Malaysia in July 2006. There were 18 individual PAHs and 16 individual hopanes detected in the samples. Total PAH concentrations ranged from 0.58 to 10.49 ng/m3, and hopanes ranged from 1.62 to 10.49 ng/m3. Source identification using diagnostic ratios suggests that the biomass burning and vehicular emissions are the main sources of PAHs in the samples. Additionally, the strong influence of crankcase oil incorporated in street dust was identified pointing out a significant source of hopanes in the study area.
Monte Carlo study of the exchange bias and the training effect in nanoparticles with core/shell morphology

We have employed the Monte Carlo (MC) simulation method to study at an atomic level the exchange bias effect, the vertical shift and the training effect observed in nanoparticles consisting of a ferromagnetic (FM) core and an antiferromagnetic (AFM) shell. Heisenberg exchange interaction is assumed between the spins in the ferromagnetic core, in the AFM shell and along the FM/AFM interface. We have demonstrated that the interface exchange interaction together with a strong interface anisotropy results in an exchange bias field (Hex) and a strong coercive field (Hc). Our simulations show that while the interface is responsible for the exchange bias, in the vertical shift we have a contribution from the whole shell of the nanoparticles. In the training effect we have a contribution from the interface and we can also have a contribution from the core and the shell depending on the particle size. The shape of the Hc(T) and Hex(T) curves depends on the type and the strength of the shell anisotropy. Using the MC simulations, we have also studied the effect of interparticle dipolar interactions in the exchange bias properties of an assembly of FM core/AFM shell nanoparticles for different nanoparticle densities. We show that these interactions result in a decrease of the coercive field and an increase of the exchange bias field with the concentration of the assembly. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)