Miracle-dz/UniFAR-FTUs huggingface dataset:
Unveiling interatomic distances influencing the reaction coordinates in alanine dipeptide isomerization: An explainable deep learning approach

The present work shows that the free energy landscape associated with alanine dipeptide isomerization can be effectively represented by specific interatomic distances without explicit reference to dihedral angles. Conventionally, two stable states of alanine dipeptide in vacuum, i.e., C$7_{\mathrm{eq}}$ ($\beta$-sheet structure) and C$7_{\mathrm{ax}}$ (left handed $\alpha$-helix structure), have been primarily characterized using the main chain dihedral angles, $\varphi$ (C-N-C$_\alpha$-C) and $\psi$ (N-C$_\alpha$-C-N). However, our recent deep learning combined with"Explainable AI"(XAI) framework has shown that the transition state can be adequately captured by a free energy landscape using $\varphi$ and $\theta$ (O-C-N-C$_\alpha$) [T. Kikutsuji, et al. J. Chem. Phys. 156, 154108 (2022)]. In perspective of extending these insights to other collective variables, a more detailed characterization of transition state is required. In this work, we employ the interatomic distances and bond angles as input variables for deep learning, rather than the conventional and more elaborate dihedral angles. Our approach utilizes deep learning to investigate whether changes in the main chain dihedral angle can be expressed in terms of interatomic distances and bond angles. Furthermore, by incorporating XAI into our predictive analysis, we quantified the importance of each input variable and succeeded in clarifying the specific interatomic distance that affects the transition state. The results indicate that constructing a free energy landscape based on using the identified interatomic distance can clearly distinguish between the two stable states and provide a comprehensive explanation for the energy barrier crossing.
Crossed Andreev Reflection in altermagnets

Crossed Andreev reflection (CAR) is a scattering phenomenon occurring in superconductors (SCs) connected to two metallic leads, where an incident electron on one side of the SC emerges on the opposite side as a hole. Despite its significance, CAR detection is often impeded by the prevalent electron tunneling (ET), where electrons exit on the opposing side. One approach to augment CAR over ET involves employing two antiparallel ferromagnets across the SC. However, this method is constrained by the low polarization in ferromagnets and necessitates the application of a magnetic field. Altermagnets (AMs) present a promising avenue for detecting and enhancing CAR due to their distinct Fermi surfaces for each spin. Here, we propose a novel configuration utilizing two AMs rotated by $90^{\circ}$ with respect to each other on either side of an SC to enhance CAR. We calculate local and nonlocal conductivities across the AM-SC-AM junction using the Landauer-B\"uttiker scattering approach. Our findings reveal that in the strong phase of AMs, CAR overwhelmingly dominates nonlocal transport. In the weak phase, CAR can exhibit significant enhancement for larger values of the altermagnetic parameter compared to the scenario where AMs are in the normal metallic phase. As a function of the length of SC, the conductivities exhibit oscillations reminiscent of Fabry-P\'erot interference.
Intrinsic Orbital Angular Momentum Originated from Optical Catastrophe Superposition

Conventionally, intrinsic orbital angular momentum (OAM) is associated with phase vortices. However, our investigation into the propagation dynamics of 2D superimposed catastrophe beams, termed cyclone catastrophe beams (CCBs), reveals that these beams inherently exhibit rotation and possess OAM, distinct from the typical connection to phase vortices. Our observations clearly show these beams rotating during autofocusing propagation and particle manipulation, confirming the presence of OAM. Theoretical calculations affirm that the OAM of these beams is intrinsic and can be adjusted by varying the number of superimposed beams. Furthermore, our interference and phase studies indicate that, although CCBs exhibit phase vortices, they do not rotate around the singularities of phase vortices and their total topological charges are zero. This implies that the manifestation of OAM within CCBs does not rely on nonzero topological charge of the presented phase vortices within CCBs. Especially, eigenstates decomposition analysis illustrates that CCBs can be decomposed as a composite of Laguerre-Gaussian (LG) modes with uneven fidelity, where the topological charges of LG modes align with multiples of the superimposed catastrophe beams but do not equal to the value of the OAM per photon within CCBs, emphasizing the intrinsic OAM within CCBs and the absence of a connection to phase vortices. Our findings not only advance the understanding of the relationship between OAM and phase vortices but also pave the way for different applications of OAM waves, catalyzing their development in optics and other domains.
Asymmetry of Westerly and Easterly Wind Events: Observational Evidence

Westerly (WWEs) and easterly (EWEs) wind events, short- lived anomalous westerly and easterly surface winds, are known to occur with unusual large magnitude over the equatorial Pacific. As their relative frequency of occurrence and dependence on background conditions are yet to be fully clarified, we analyzed daily surface winds for 1982−2013 from which WWEs and EWEs are detected. Both types of events appear over the Pacific warm pool, where sea surface temperature (SST) is sufficiently high for active deep convection, and favorably occur with increasing Niño4 SST. However, the frequency of occurrence of EWEs is less than that of WWEs, resulting in asymmetry in wind amplitude. Local and remote anomalous convections are equally important in exciting these events, but different local development processes cause the asymmetry in the frequency of occurrence. These results can also be seen in wind stress anomalies, albeit obscured due to nonlinearity therein. Asymmetry of westerly and easterly wind events: Observational evidence.
LOSS-GAT: Label Propagation and One-Class Semi-Supervised Graph Attention Network for Fake News Detection

In the era of widespread social networks, the rapid dissemination of fake news has emerged as a significant threat, inflicting detrimental consequences across various dimensions of people's lives. Machine learning and deep learning approaches have been extensively employed for identifying fake news. However, a significant challenge in identifying fake news is the limited availability of labeled news datasets. Therefore, the One-Class Learning (OCL) approach, utilizing only a small set of labeled data from the interest class, can be a suitable approach to address this challenge. On the other hand, representing data as a graph enables access to diverse content and structural information, and label propagation methods on graphs can be effective in predicting node labels. In this paper, we adopt a graph-based model for data representation and introduce a semi-supervised and one-class approach for fake news detection, called LOSS-GAT. Initially, we employ a two-step label propagation algorithm, utilizing Graph Neural Networks (GNNs) as an initial classifier to categorize news into two groups: interest (fake) and non-interest (real). Subsequently, we enhance the graph structure using structural augmentation techniques. Ultimately, we predict the final labels for all unlabeled data using a GNN that induces randomness within the local neighborhood of nodes through the aggregation function. We evaluate our proposed method on five common datasets and compare the results against a set of baseline models, including both OCL and binary labeled models. The results demonstrate that LOSS-GAT achieves a notable improvement, surpassing 10%, with the advantage of utilizing only a limited set of labeled fake news. Noteworthy, LOSS-GAT even outperforms binary labeled models.
Operation with Concentration Inequalities and Conjugate of Parallel Sum

Following the concentration of the measure approach, we consider the transformation $\Phi(Z)$ of a random variable $Z$ having a general concentration function $\alpha$. If the transformation $\Phi$ is $\lambda$-Lipschitz with $\lambda$ deterministic, the concentration function of $\Phi$ is immediately deduced to be equal to $\alpha(\cdot/\lambda)$. If the variations of $\Phi$ are bounded by a random variable $\Lambda$ having a concentration function (around $0$) $\beta: \mathbb R_+\to \mathbb R$, this paper sets that $\Phi(Z)$ has a concentration function analogous to $\gamma = (\alpha^{-1} \cdot \beta^{-1})^{-1}$. We apply this result to generalize Hanson Wright inequality, we express $\gamma$ with the conjugate functions of the parallel sum of $\alpha$ and $\beta$ and further consider the case where $\Lambda$ is the product of $n$ random variables to provide useful insights to the so-called"multilevel concentration".
Physics informed data-driven near-wall modelling for lattice Boltzmann simulation of high Reynolds number turbulent flows

Data-driven approaches offer novel opportunities for improving the performance of turbulent flow simulations, which are critical to wide-ranging applications from wind farms and aerodynamic designs to weather and climate forecasting. While conventional continuum Navier-Stokes solvers have been the subject of a significant amount of work in this domain, there has hitherto been very limited effort in the same direction for the more scalable and highly performant lattice Boltzmann method (LBM), even though it has been successfully applied to a variety of turbulent flow simulations using large-eddy simulation (LES) techniques. In this work, we establish a data-driven framework for the LES-based lattice Boltzmann simulation of near-wall turbulent flow fields. We do this by training neural networks using improved delayed detached eddy simulation data. Crucially, this is done in combination with physics-based information that substantially constrains the data-driven predictions. Using data from turbulent channel flow at a friction Reynolds number at $5200$, our simulations accurately predict the behaviour of the wall model at arbitrary friction Reynolds numbers up to $1.0 \times 10^6$. In contradistinction with other models that use direct numerical simulation datasets, our physics-informed model requires data from very limited regions within the wall-bounded plane, reducing by three orders of magnitude the quantity of data needed for training. We also demonstrate that our model can handle data configurations when the near-wall grid is sparse. Our physics-informed neural network approach opens up the possibility of employing LBM in combination with highly specific and therefore much more limited quantities of macroscopic data, substantially facilitating the investigation of a wide-range of turbulent flow applications at very large scale.
Exploration by Optimization with Hybrid Regularizers: Logarithmic Regret with Adversarial Robustness in Partial Monitoring

Partial monitoring is a generic framework of online decision-making problems with limited observations. To make decisions from such limited observations, it is necessary to find an appropriate distribution for exploration. Recently, a powerful approach for this purpose, exploration by optimization (ExO), was proposed, which achieves the optimal bounds in adversarial environments with follow-the-regularized-leader for a wide range of online decision-making problems. However, a naive application of ExO in stochastic environments significantly degrades regret bounds. To resolve this problem in locally observable games, we first establish a novel framework and analysis for ExO with a hybrid regularizer. This development allows us to significantly improve the existing regret bounds of best-of-both-worlds (BOBW) algorithms, which achieves nearly optimal bounds both in stochastic and adversarial environments. In particular, we derive a stochastic regret bound of $O(\sum_{a \neq a^*} k^2 m^2 \log T / \Delta_a)$, where $k$, $m$, and $T$ are the numbers of actions, observations and rounds, $a^*$ is an optimal action, and $\Delta_a$ is the suboptimality gap for action $a$. This bound is roughly $\Theta(k^2 \log T)$ times smaller than existing BOBW bounds. In addition, for globally observable games, we provide a new BOBW algorithm with the first $O(\log T)$ stochastic bound.
Tracking solar radio bursts using Bayesian multilateration

Solar radio bursts (SRBs), such as Type IIs and IIIs, are emitted by electrons propagating through the corona and interplanetary space. Tracking such bursts is key to understanding the properties of accelerated electrons and radio wave propagation as well as the local plasma environment that they propagate through. In this work, we present a novel multilateration algorithm called BayEsian LocaLisation Algorithm (BELLA) and validate the algorithm using simulated and observed SRBs. In addition, apparent SRB positions from BELLA are compared with comparable localisation methods and the predictions of solar wind models. BELLA uses Bayesian inference to create probabilistic distributions of source positions and their uncertainties. This facilitates the estimation of algorithmic, instrumental, and physical uncertainties in a quantitative manner. We validated BELLA using simulations and a Type III SRB observed by STEREO A and STEREO B at pm 116 degr from the Sun-Earth line and by Wind at L1. BELLA tracked the Type III source from sim 10--150 $R_ (2--0.15 MHz) along a spiral trajectory. This allowed for an estimate of an apparent solar wind speed of sw 400 km $ and a source longitude of $ 30degr . We compared these results with well-established methods of positioning: Goniopolarimetric (GP), analytical time-difference-of-arrival (TDOA), and Solar radio burst Electron Motion Tracker (SEMP). We found them to be in agreement with the results obtained by BELLA. Additionally, the results aligned with solar wind properties assimilated by the Heliospheric Upwind Extrapolation with time dependence (HUXt) model. We have validated BELLA and used it to identify apparent source positions as well as velocities and densities of the solar wind. Furthermore, we identified higher than expected electron densities, suggesting that the true emission sources were at lower altitudes than those identified by BELLA, an effect that may be due to appreciable scattering of electromagnetic waves by electrons in interplanetary space.
Learned Image Compression with Text Quality Enhancement

Learned image compression has gained widespread popularity for their efficiency in achieving ultra-low bit-rates. Yet, images containing substantial textual content, particularly screen-content images (SCI), often suffers from text distortion at such compressed levels. To address this, we propose to minimize a novel text logit loss designed to quantify the disparity in text between the original and reconstructed images, thereby improving the perceptual quality of the reconstructed text. Through rigorous experimentation across diverse datasets and employing state-of-the-art algorithms, our findings reveal significant enhancements in the quality of reconstructed text upon integration of the proposed loss function with appropriate weighting. Notably, we achieve a Bjontegaard delta (BD) rate of -32.64% for Character Error Rate (CER) and -28.03% for Word Error Rate (WER) on average by applying the text logit loss for two screenshot datasets. Additionally, we present quantitative metrics tailored for evaluating text quality in image compression tasks. Our findings underscore the efficacy and potential applicability of our proposed text logit loss function across various text-aware image compression contexts.
BdSLW60: A Word-Level Bangla Sign Language Dataset

Sign language discourse is an essential mode of daily communication for the deaf and hard-of-hearing people. However, research on Bangla Sign Language (BdSL) faces notable limitations, primarily due to the lack of datasets. Recognizing wordlevel signs in BdSL (WL-BdSL) presents a multitude of challenges, including the need for well-annotated datasets, capturing the dynamic nature of sign gestures from facial or hand landmarks, developing suitable machine learning or deep learning-based models with substantial video samples, and so on. In this paper, we address these challenges by creating a comprehensive BdSL word-level dataset named BdSLW60 in an unconstrained and natural setting, allowing positional and temporal variations and allowing sign users to change hand dominance freely. The dataset encompasses 60 Bangla sign words, with a significant scale of 9307 video trials provided by 18 signers under the supervision of a sign language professional. The dataset was rigorously annotated and cross-checked by 60 annotators. We also introduced a unique approach of a relative quantization-based key frame encoding technique for landmark based sign gesture recognition. We report the benchmarking of our BdSLW60 dataset using the Support Vector Machine (SVM) with testing accuracy up to 67.6% and an attention-based bi-LSTM with testing accuracy up to 75.1%. The dataset is available at https://www.kaggle. com/datasets/hasaniut/bdslw60 and the code base is accessible from https://github.com/hasanssl/BdSLW60_Code.
New limiter regions for multidimensional flows

Accurate transport algorithms are crucial for computational fluid dynamics and more accurate and efficient schemes are always in development. One dimensional limiting is a commonly employed technique used to suppress nonphysical oscillations. However, the application of such limiters can reduce accuracy. It is important to identify the weakest set of sufficient conditions required on the limiter as to allow the development of successful numerical algorithms. The main goal of this paper is to identify new less restrictive sufficient conditions for flux form in-compressible advection to remain monotonic. First, we identify conditions in which the Spekreijse limiter region can fail to be monotonic for incompressible flux form advection and demonstrate this numerically. Then a convex combination argument is used to derive new sufficient conditions that are less restrictive than the Sweby region for a discrete maximum principle. This allows the introduction of two new more general limiter regions suitable for flux form incompressible advection.
Applications, challenges and ethical issues of AI and ChatGPT in education

Artificial Intelligence (AI) in recent years has shown an unprecedentedly impressive development, tending to play a catalytic role in all aspects of life. The interest of the academic community, but also of governments, is huge in the dynamics of AI and is reflected by the truly explosive amount of investment and research that is underway. Enthusiastic opinions and statements about AI are made every day, but at the same time they also bring to the fore alarming predictions about its effects. This paper aims to describe the opportunities emerging from the use of artificial intelligence and ChatGPT to improve education, but also to identify the challenges and ethical issues that arise.
Tuning residual stress, directional memory and aging in soft glassy materials

When glassy materials are rapidly quenched from the liquid to the solid state upon flow cessation or cooling, they solidify in an out-of-equilibrium configuration, retaining the memory of the processing conditions for very long times. This is the origin of various phenomena, such as residual stresses and directional memory, which greatly affect their properties. At the same time, annealing the mechanical history encoded in disordered materials constitute a great challenge. Here, we address this problem for the case of colloidal glasses made of soft particles densely packed at a high volume fraction, using experiments and particle dynamic simulations. We demonstrate that periodically training soft particle glasses with a sequence of stress-controlled oscillations successfully anneals residual stress and directional memory when the stress amplitude corresponds to the yield point. At the microscopic level, annealing provides a fine tuning of the local distribution of the stress carried by the particles. Through the simulations, we show that the first moments of this distribution have precise physical meaning: the mean value of the distribution corresponds to the macroscopic stress; the skewness carries information about directional memory; and the standard deviation is related to mechanical aging. The same methodology is successfully applied to silica gels with thixotropic properties, suggesting that it is general and may be extended to other classes of disordered materials.
World Model on Million-Length Video And Language With Blockwise RingAttention

Current language models fall short in understanding aspects of the world not easily described in words, and struggle with complex, long-form tasks. Video sequences offer valuable temporal information absent in language and static images, making them attractive for joint modeling with language. Such models could develop a understanding of both human textual knowledge and the physical world, enabling broader AI capabilities for assisting humans. However, learning from millions of tokens of video and language sequences poses challenges due to memory constraints, computational complexity, and limited datasets. To address these challenges, we curate a large dataset of diverse videos and books, utilize the Blockwise RingAttention technique to scalably train on long sequences, and gradually increase context size from 4K to 1M tokens. This paper makes the following contributions: (a) Largest context size neural network: We train one of the largest context size transformers on long video and language sequences, setting new benchmarks in difficult retrieval tasks and long video understanding. (b) Solutions for overcoming vision-language training challenges, including using masked sequence packing for mixing different sequence lengths, loss weighting to balance language and vision, and model-generated QA dataset for long sequence chat. (c) A highly-optimized implementation with RingAttention, Blockwise Transformers, masked sequence packing, and other key features for training on millions-length multimodal sequences. (d) Fully open-sourced a family of 7B parameter models capable of processing long text documents (LWM-Text, LWM-Text-Chat) and videos (LWM, LWM-Chat) of over 1M tokens. This work paves the way for training on massive datasets of long video and language to develop understanding of both human knowledge and the multimodal world, and broader capabilities.
Reinforcement Learning for Docking Maneuvers with Prescribed Performance

We propose a two-component data-driven controller to safely perform docking maneuvers for satellites. Reinforcement Learning is used to deduce an optimal control policy based on measurement data. To safeguard the learning phase, an additional feedback law is implemented in the control unit, which guarantees the evolution of the system within predefined performance bounds. We define safe and safety-critical areas to train the feedback controller based on actual measurements. To avoid chattering, a dwell-time activation scheme is implemented. We provide numerical evidence for the performance of the proposed controller for a satellite docking maneuver with collision avoidance.