Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Inductive Bias of a CNN for Orthogonal Patterns Distributions

Training overparameterized convolutional neural networks with gradient based methods is the most successful learning method for image classification. However, its theoretical properties are far from understood even for very simple learning tasks. In this work, we consider a simplified image classification task where images contain orthogonal patches and are learned with a 3-layer overparameterized convolutional network and stochastic gradient descent. We empirically identify a novel phenomenon where the dot-product between the learned pattern detectors and their detected patterns are governed by the pattern statistics in the training set. We call this phenomenon Pattern Statistics Inductive Bias (PSI) and prove that PSI holds for a simple setup with two points in the training set. Furthermore, we prove that if PSI holds, stochastic gradient descent has sample complexity $O(d^2\log(d))$ where $d$ is the filter dimension. In contrast, we show a VC dimension lower bound in our setting which is exponential in $d$. Taken together, our results provide strong evidence that PSI is a unique inductive bias of stochastic gradient descent, that guarantees good generalization properties.
Ground state energy of hydrogen-like ions in quantum plasmas

Using the asymptotic iteration method (AIM) we investigate the variation in the 1s energy levels of hydrogen and helium-like static ions in fully degenerate electron gas. The semiclassical Thomas-Fermi (TF), Shukla-Eliasson (SE) and corrected Shukla-Eliasson (cSE) models are compared. It is remarked that these models merge into the vacuum level for hydrogen and helium-like ions in the dilute classical electron gas regime. While in the TF model hydrogen ground state level lifts monotonically towards the continuum limit with increase in the electron concentration, in the SE and cSE models universal bound stabilization valley through the energy minimization occurs at a particular electron concentration range for the hydrogen-like ion which for cSE model closely matches the electron concentrations in typical metals. The later stabilizing mechanism appears to be due to the interaction between plasmon excitations and the Fermi lengthscales in metallic density regime. In the case of helium-like ions, however, no such stability mechanism is found. The application of cSE model with electron exchange and correlation effects reveals that cSE model qualitatively accounts for the number-density and lattice parameters of elemental metals within the framework of free electron assumption. According to the cSE model of static charge screening a simple metal-insulator transition criterion is defined. Current investigation may further elucidate the underlying physical mechanisms in the formation and dielectric properties of metallic compounds.
Estimating Economic Models with Testable Assumptions: Theory and Application to LATE

This paper studies the identification and estimation problem in incomplete economic models with testable assumptions. Testable assumptions give strong and interpretable empirical content to the models but they also carry the possibility that our distribution of observed outcome may reject these assumptions. A natural way is to find a set of relaxed assumptions $\tilde{A}$ that cannot be rejected by any reasonable distribution of observed outcome and preserves identified set of parameter of interest. The main contribution of this paper is to characterize the property of such relaxed assumption $\tilde{A}$ using a generalized definition of refutability and confirmability. A general estimation and inference procedure is proposed, and can be applied to most incomplete economic models. As the key application, I study the Imbens and Angrist Monotonicity assumption in potential outcome framework. I give a set of relaxed assumptions $\tilde{A}$ that can never be rejected and always preserve the identified set of local average treatment effect (LATE). The LATE is point identified and easy to estimate under $\tilde{A}$
Kullback–Leibler Divergence-Based Fuzzy C-Means Clustering Incorporating Morphological Reconstruction and Wavelet Frames for Image Segmentation

In this article, we elaborate on a Kullback–Leibler (KL) divergence-based Fuzzy ${C}$ -Means (FCM) algorithm by incorporating a tight wavelet frame transform and morphological reconstruction (MR). To make membership degrees of each image pixel closer to those of its neighbors, a KL divergence term on the partition matrix is introduced as a part of FCM, thus resulting in KL divergence-based FCM. To make the proposed FCM robust, a filtered term is augmented in its objective function, where MR is used for image filtering. Since tight wavelet frames provide redundant representations of images, the proposed FCM is performed in a feature space constructed by tight wavelet frame decomposition. To further improve its segmentation accuracy (SA), a segmented feature set is reconstructed by minimizing the inverse process of its objective function. Each reconstructed feature is reassigned to the closest prototype, thus modifying abnormal features produced in the reconstruction process. Moreover, a segmented image is reconstructed by using tight wavelet frame reconstruction. Finally, supporting experiments coping with synthetic, medical, and real-world images are reported. The experimental results exhibit that the proposed algorithm works well and comes with better segmentation performance than other peers. In a quantitative fashion, its average SA improvements over its peers are 4.06%, 3.94%, and 4.41%, respectively, when segmenting synthetic, medical, and real-world images. Moreover, the proposed algorithm requires less time than most of the FCM-related algorithms.
Microreversibility and quantum transport in Aharonov-Bohm rings

The consequences of microreversibility for the linear and nonlinear transport properties of systems subjected to external magnetic fields are systematically investigated in Aharonov-Bohm rings connected to two, three, and four terminals. Within the independent electron approximation, the cumulant generating function, which fully specifies the statistics of the nonequilibrium currents, is expressed in terms of the scattering matrix of these circuits. The time-reversal symmetry relations up to the third responses of the currents and the fourth cumulants are analytically investigated and numerically tested as a function of the magnetic flux. The validity of such relations is thus firmly confirmed in this class of open quantum systems.
A-polynomials, Ptolemy varieties and Dehn filling

The A-polynomial encodes hyperbolic geometric information on knots and related manifolds. Historically, it has been difficult to compute, and particularly difficult to determine A-polynomials of infinite families of knots. Here, we show how to compute A-polynomials by starting with a triangulation of a manifold, similar to Champanerkar, then using symplectic properties of the Neumann-Zagier matrix encoding the gluings to change the basis of the computation. The result is a simplicifation of the defining equations. Our methods are a refined version of Dimofte's symplectic reduction, and we conjecture that the result is equivalent to equations arising from the enhanced Ptolemy variety of Zickert, which would connect these different approaches to the A-polynomial. We apply this method to families of manifolds obtained by Dehn filling, and show that the defining equations of their A-polynomials are Ptolemy equations which, up to signs, are equations between cluster variables in the cluster algebra of the cusp torus. Thus the change in A-polynomial under Dehn filling is given by an explicit twisted cluster algebra. We compute the equations for Dehn fillings of the Whitehead link.
Modelling the 3D climate of Venus with oasis

Flexible 3D models to explore the vast diversity of terrestrial planets and interpret observational data are still in their early stages. In this work, we present OASIS: a novel and flexible 3D virtual planet laboratory. With OASIS we envision a platform that couples self-consistently seven individual modules representing the main physical and chemical processes that shape planetary environments. Additionally, OASIS is capable of producing simulated spectra from different instruments and observational techniques. In this work we focus on the benchmark test of coupling four of the physical modules: fluid dynamics, radiation, turbulence and surface/soil. To test the OASIS platform, we produced 3D simulations of the Venus climate and its atmospheric circulation and study how the modeled atmosphere changes with various cloud covers, atmospheric heat capacity, and surface friction. 3D simulations of Venus are challenging because they require long integration times with a computationally expensive radiative transfer code. By comparing OASIS results with observational data, we verify that the new model is able to successfully simulate Venus. With simulated spectra produced directly from the 3D simulations, we explore the capabilities of future missions, like LUVOIR, to observe Venus analogs located at a distance of 10 pc. With OASIS, we have taken the first steps to build a sophisticated and very flexible platform capable of studying the environment of terrestrial planets, which will be an essential tool to characterize observed terrestrial planets and plan future observations.
Application of ERA5 and MENA simulations to predict offshore wind energy potential

This study explores wind energy resources in different locations through the Gulf of Oman and also their future variability due climate change impacts. In this regard, EC-EARTH near-surface wind outputs obtained from CORDEX-MENA simulations are used for historical and future projection of the energy. The ERA5 wind data are employed to assess the suitability of the climate model. Moreover, the ERA5 wave data over the study area are applied to compute sea surface roughness as an important variable for converting near-surface wind speeds to those of wind speed at turbine hub height. Considering the power distribution, bathymetry and distance from the coats, some spots as tentative energy hotspots to provide a detailed assessment of directional and temporal variability and also to investigate climate change impact studies. RCP8.5 is a common climatic scenario is used to project and extract future variation of the energy in the selected sites. The results of this study demonstrate that the selected locations have a suitable potential for wind power turbine plans and constructions.
Analytic Model of Chiral-Induced Spin Selectivity

Organic materials are known to feature long spin-diffusion times, originating in a generally small spin–orbit coupling observed in these systems. From that perspective, chiral molecules acting as efficient spin selectors pose a puzzle that attracted a lot of attention in recent years. Here, we revisit the physical origins of chiral-induced spin selectivity (CISS) and propose a simple analytic minimal model to describe it. The model treats a chiral molecule as an anisotropic wire with molecular dipole moments aligned arbitrarily with respect to the wire’s axes and is therefore quite general. Importantly, it shows that the helical structure of the molecule is not necessary to observe CISS and other chiral nonhelical molecules can also be considered as potential candidates for the CISS effect. We also show that the suggested simple model captures the main characteristics of CISS observed in the experiment, without the need for additional constraints employed in the previous studies. The results pave the way for understanding other related physical phenomena where the CISS effect plays an essential role.
Stochastic Normalizing Flows

We introduce stochastic normalizing flows, an extension of continuous normalizing flows for maximum likelihood estimation and variational inference (VI) using stochastic differential equations (SDEs). Using the theory of rough paths, the underlying Brownian motion is treated as a latent variable and approximated, enabling efficient training of neural SDEs as random neural ordinary differential equations. These SDEs can be used for constructing efficient Markov chains to sample from the underlying distribution of a given dataset. Furthermore, by considering families of targeted SDEs with prescribed stationary distribution, we can apply VI to the optimization of hyperparameters in stochastic MCMC.
Predicting Subjective Features from Questions on QA Websites using BERT

Community Question-Answering websites, such as StackOverflow and Quora, expect users to follow specific guidelines in order to maintain content quality. These systems mainly rely on community reports for assessing contents, which has serious problems, such as the slow handling of violations, the loss of normal and experienced users' time, the low quality of some reports, and discouraging feedback to new users. Therefore, with the overall goal of providing solutions for automating moderation actions in Q&A websites, we aim to provide a model to predict 20 quality or subjective aspects of questions in QA websites. To this end, we used data gathered by the CrowdSource team at Google Research in 2019 and fine-tuned pre-trained BERT model on our problem. Based on our evaluation, model achieved value of 0.046 for Mean-Squared-Error (MSE) after 2 epochs of training, which did not improve substantially in the next ones. Results confirm that by simple fine-tuning, we can achieve accurate models in little time and on less amount of data.11Code is available at: https://github.com/Moradnejad/Predicting-Subjective-Features-on-QA-Websites
Adaptation in General Temporally Changing Environments

We analyze a nonlocal PDE model describing the dynamics of adaptation of a phenotypically structured population, under the effects of mutation and selection, in a changing environment. Previous studies have analyzed the large-time behavior of such models, with particular forms of environmental changes, either linearly changing or periodically fluctuating. We use here a completely different mathematical approach, which allows us to consider very general forms of environmental variations and to give an analytic description of the full trajectories of adaptation, including the transient phase, before a stationary behavior is reached. The main idea behind our approach is to study a bivariate distribution of two `fitness components' which contains enough information to describe the distribution of fitness at any time. This distribution solves a degenerate parabolic equation that is dealt with by defining a multidimensional cumulant generating function associated with the distribution, and solving the associated transport equation. We apply our results to several examples, and check their accuracy, using stochastic individual-based simulations as a benchmark. These examples illustrate the importance of being able to describe the transient dynamics of adaptation to understand the development of drug resistance in pathogens.
Distributional Soft Actor-Critic: Off-Policy Reinforcement Learning for Addressing Value Estimation Errors

In reinforcement learning (RL), function approximation errors are known to easily lead to the <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula>-value overestimations, thus greatly reducing policy performance. This article presents a distributional soft actor–critic (DSAC) algorithm, which is an off-policy RL method for continuous control setting, to improve the policy performance by mitigating <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula>-value overestimations. We first discover in theory that learning a distribution function of state–action returns can effectively mitigate <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula>-value overestimations because it is capable of adaptively adjusting the update step size of the <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula>-value function. Then, a distributional soft policy iteration (DSPI) framework is developed by embedding the return distribution function into maximum entropy RL. Finally, we present a deep off-policy actor–critic variant of DSPI, called DSAC, which directly learns a continuous return distribution by keeping the variance of the state–action returns within a reasonable range to address exploding and vanishing gradient problems. We evaluate DSAC on the suite of MuJoCo continuous control tasks, achieving the state-of-the-art performance.
Active Lighting Recurrence by Parallel Lighting Analogy for Fine-Grained Change Detection

This paper studies a new problem, namely active lighting recurrence (ALR) that physically relocalizes a light source to reproduce the lighting condition from single reference image for a same scene, which may suffer from fine-grained changes during twice observations. ALR is of great importance for fine-grained visual inspection and change detection, because some phenomena or minute changes can only be clearly observed under particular lighting conditions. Therefore, effective ALR should be able to online navigate a light source toward the target pose, which is challenging due to the complexity and diversity of real-world lighting and imaging processes. To this end, we propose to use the simple parallel lighting as an analogy model and based on Lambertian law to compose an instant navigation ball for this purpose. We theoretically prove the feasibility, i.e., equivalence and convergence, of this ALR approach for realistic near point light source and small near surface light source. Besides, we also theoretically prove the invariance of our ALR approach to the ambiguity of normal and lighting decomposition. The effectiveness and superiority of the proposed approach have been verified by both extensive quantitative experiments and challenging real-world tasks on fine-grained change detection of cultural heritages. We also validate the generality of our approach to non-Lambertian scenes.
End-To-End Graph-based Deep Semi-Supervised Learning

The quality of a graph is determined jointly by three key factors of the graph: nodes, edges and similarity measure (or edge weights), and is very crucial to the success of graph-based semi-supervised learning (SSL) approaches. Recently, dynamic graph, which means part/all its factors are dynamically updated during the training process, has demonstrated to be promising for graph-based semi-supervised learning. However, existing approaches only update part of the three factors and keep the rest manually specified during the learning stage. In this paper, we propose a novel graph-based semi-supervised learning approach to optimize all three factors simultaneously in an end-to-end learning fashion. To this end, we concatenate two neural networks (feature network and similarity network) together to learn the categorical label and semantic similarity, respectively, and train the networks to minimize a unified SSL objective function. We also introduce an extended graph Laplacian regularization term to increase training efficiency. Extensive experiments on several benchmark datasets demonstrate the effectiveness of our approach.
DeepSign: Deep On-Line Signature Verification

Deep learning has become a breathtaking technology in the last years, overcoming traditional handcrafted approaches and even humans for many different tasks. However, in some tasks, such as the verification of handwritten signatures, the amount of publicly available data is scarce, what makes difficult to test the real limits of deep learning. In addition to the lack of public data, it is not easy to evaluate the improvements of novel proposed approaches as different databases and experimental protocols are usually considered. The main contributions of this study are: <italic>i)</italic> we provide an in-depth analysis of state-of-the-art deep learning approaches for on-line signature verification, <italic>ii)</italic> we present and describe the new DeepSignDB on-line handwritten signature biometric public database,<xref ref-type="fn" rid="fn1"><sup>1</sup></xref> <italic>iii)</italic> we propose a standard experimental protocol and benchmark to be used for the research community in order to perform a fair comparison of novel approaches with the state of the art, and <italic>iv)</italic> we adapt and evaluate our recent deep learning approach named Time-Aligned Recurrent Neural Networks (TA-RNNs)<xref ref-type="fn" rid="fn2"><sup>2</sup></xref>. for the task of on-line handwritten signature verification. This approach combines the potential of Dynamic Time Warping and Recurrent Neural Networks to train more robust systems against forgeries. Our proposed TA-RNN system outperforms the state of the art, achieving results even below 2.0% EER when considering skilled forgery impostors and just one training signature per user. <fn id="fn1"><label><sup>1</sup></label><p><uri>https://github.com/BiDAlab/DeepSignDB</uri> </p></fn><fn id="fn2"><label><sup>2</sup></label><p>Spanish Patent Application (P202030060). </p></fn>
Algebraic links in lens spaces

The lens space $L_{p,q}$ is the orbit space of a $\mathbb{Z}_{p}$-action on the three sphere. We investigate polynomials of two complex variables that are invariant under this action, and thus define links in $L_{p,q}$. We study properties of these links, and their relationship with the classical algebraic links. We prove that all algebraic links in lens spaces are fibered, and obtain results about their Seifert genus. We find some examples of algebraic knots in lens spaces, whose lift in the $3$-sphere is a torus link.
Delineation and Analysis of Seismocardiographic Systole and Diastole Profiles

Precise estimation of fiducial points of a seismocardiogram (SCG) signal is a challenging problem for its clinical usage. Delineation techniques proposed in the existing literature do not estimate all the clinically significant points of an SCG signal simultaneously. The aim of this research work is to propose a delineation framework to identify various systole and diastole fiducial points with the help of a photoplethysmogram (PPG) signal. The proposed delineation method processes a wavelet-based scalographic PPG, and an envelope construction scheme is proposed to estimate the prominent peaks of the PPG signal. A set of amplitude-histogram-based decision rules is developed for estimation of three fiducial points of SCG diastole. Subsequently, three systolic phases are detected by applying diastole masking on SCG and decision rules. Experimental results on real-time SCG signals acquired from our designed data acquisition circuitry and their analysis shows the effectiveness of the proposed scheme. In addition, these estimated parameters are analyzed to show the discrimination between normal breathing and breathlessness conditions.
Conceptual Game Expansion

Automated game design is the problem of automatically producing games through computational processes. Traditionally, these methods have relied on the authoring of search spaces by a designer, defining the space of all possible games for the system to the author. In this article, we instead learn representations of existing games from gameplay video and use these to approximate a search space of novel games. In a human subject study, we demonstrate that these novel games are indistinguishable from human games in terms of challenge and that one of the novel games was equivalent to one of the human games in terms of fun, frustration, and likeability.