October 2022 arXiv papers — page 135

Shivam Chhirolya, Sameer Malik, Rajiv Soundararajan

The design of deep learning methods for low light video enhancement remains a challenging problem owing to the difficulty in capturing low light and ground truth video pairs. This is particularly hard in the context of dynamic scenes or moving cameras where a long exposure ground truth cannot be captured. We approach this problem by training a model on stati

Sylvain D. Brechet

The Onsager reciprocal relations are established within the phenomenological framework of the thermodynamics of irreversible processes. In order to do so, the dissipated power densities associated to scalar and vectorial processes are written as positive semi-definite quadratic forms of the corresponding generalised forces, as required by the local expressio

Khoa D. Doan, Jianwen Xie, Yaxuan Zhu, Yang Zhao

Leveraging supervised information can lead to superior retrieval performance in the image hashing domain but the performance degrades significantly without enough labeled data. One effective solution to boost performance is to employ generative models, such as Generative Adversarial Networks (GANs), to generate synthetic data in an image hashing model. Howev

Feng Wang, Manling Li, Xudong Lin, Hairong Lv

Recent advances in pre-training vision-language models like CLIP have shown great potential in learning transferable visual representations. Nonetheless, for downstream inference, CLIP-like models suffer from either 1) degraded accuracy and robustness in the case of inaccurate text descriptions during retrieval-based inference (the challenge for zero-shot pr

Nathan Geer, Matthew B. Young

We introduce an unrolled quantization $U_q^E(\mathfrak{gl}(1 \vert 1))$ of the complex Lie superalgebra $\mathfrak{gl}(1 \vert 1)$ and use its categories of weight modules to construct and study new three dimensional non-semisimple topological quantum field theories. These theories are defined on categories of cobordisms which are decorated by ribbon graphs

Samra Irshad, Douglas P. S. Gomes, Seong Tae Kim

Quantitative assessment of the abdominal region from clinically acquired CT scans requires the simultaneous segmentation of abdominal organs. Thanks to the availability of high-performance computational resources, deep learning-based methods have resulted in state-of-the-art performance for the segmentation of 3D abdominal CT scans. However, the complex char

Shwai He, Liang Ding, Daize Dong, Miao Zhang

Adapter Tuning, which freezes the pretrained language models (PLMs) and only fine-tunes a few extra modules, becomes an appealing efficient alternative to the full model fine-tuning. Although computationally efficient, the recent Adapters often increase parameters (e.g. bottleneck dimension) for matching the performance of full model fine-tuning, which we ar

Zhiyu Liu

The winning rule of billiards is to drive the billiard ball on the table into the designated holes. We try to study the trajectory of the billiard ball, so that we can predict the direction of the ball. For rational slopes, we got cutting sequence by setting up the square torus. We simplified cutting sequence using shearing and flipping and we obtain the tra

Javeria Shabbir, M. Zeshan Alam, M. Umair Mukati

Hand-held light field cameras suffer from low spatial resolution due to the inherent spatio-angular tradeoff. In this paper, we propose a method to improve the spatial resolution of light field images with the aid of the Texture Transformer Network (TTSR). The proposed method consists of three modules: the first module produces an all-in focus high-resolutio

Sylvain D. Brechet, Marin C. A. Girard

The relativistic continuity equations for the extensive thermodynamic quantities are derived based on the divergence theorem in Minkowski space outlined by St\"uckelberg. This covariant approach leads to a relativistic formulation of the first and second laws of thermodynamics. The internal energy density and the pressure of a relativistic perfect fluid carr

Nilesh Khandekar, Peter J. Cameron, Vinayak Joshi

In this paper, nonzero component graphs and nonzero component union graphs of finite dimensional vector space are studied using the zero-divisor graph of specially constructed 0-1-distributive lattice and the zero-divisor graph of rings. Further, we define an equivalence relation on nonzero component graphs and nonzero component union graphs to deduce that t

Wenlin Bai, Peixuan Li, Xihua Zou, Ningyuan Zhong

A cost-effective millimeter-wave (MMW) joint radar-communication (JRC) system with super resolution is proposed and experimentally demonstrated, using optical heterodyne up-conversion and self-coherent detection down-conversion techniques. The point lies in the designed coherent dual-band constant envelope linear frequency modulation-orthogonal frequency div

H. Bartolomei, R. Bisognin, H. Kamata, J. -M. Berroir

Squeezing of the quadratures of the electromagnetic field has been extensively studied in optics and microwaves. However, previous works focused on the generation of squeezed states in a low impedance ($Z_0 \approx 50 \Omega$) environment. We report here on the demonstration of the squeezing of bosonic edge magnetoplasmon modes in a quantum Hall conductor wh

Jungin Lee

We study the joint distribution of random abelian and non-abelian groups. In the abelian case, we prove several universality results for the joint distribution of the multiple cokernels for random $p$-adic matrices. In the non-abelian case, we compute the joint distribution of random groups given by the quotients of the free profinite group by random relatio

Peng Kang, Srutarshi Banerjee, Henry Chopp, Aggelos Katsaggelos

Tactile sensing is essential for a variety of daily tasks. And recent advances in event-driven tactile sensors and Spiking Neural Networks (SNNs) spur the research in related fields. However, SNN-enabled event-driven tactile learning is still in its infancy due to the limited representation abilities of existing spiking neurons and high spatio-temporal compl

M. Longinetti, P. Manselli, A. Venturi

Let an $R$-body be the complement of the union of open balls of radius $R$ in $\mathbb{E}^d$. The $R$-hulloid of a closed not empty set $A$, the minimal $R$-body containing $A$, is investigated; if $A$ is the set of the vertices of a simplex, the $R$-hulloid of $A$ is completely described (if $d=2$) and if $d> 2$ special examples are studied. The class of $R

Ian A. Cosden, Kenton McHenry, Daniel S. Katz

As software has become more essential to research across disciplines, and as the recognition of this fact has grown, the importance of professionalizing the development and maintenance of this software has also increased. The community of software professionals who work on this software have come together under the title Research Software Engineer (RSE) over

Georgii Kadantsev, Aleksandra Shutova

We prove that a subrack of a free rack is free and suggest a method to prove a similar statement about involutory racks.

Anthony Nguyen, Krishnakumar Balasubramanian

Functionally constrained stochastic optimization problems, where neither the objective function nor the constraint functions are analytically available, arise frequently in machine learning applications. In this work, assuming we only have access to the noisy evaluations of the objective and constraint functions, we propose and analyze stochastic zeroth-orde

Yuan Da Liao, Xiao Yan Xu, Zi Yang Meng, Yang Qi

Lately there are many SLAC fermion investigations on the (2+1)D Gross-Neveu criticality of a single Dirac cone [1,2]. While the SLAC fermion construction indeed gives rise to the linear energy-momentum relation for all lattice momenta at the non-interacting limit, the long-range hopping and its consequent violation of locality on the Gross-Neveu quantum crit

Shizhen Chang, Michael Kopp, Pedram Ghamisi

In recent years, multi-view subspace learning has been garnering increasing attention. It aims to capture the inner relationships of the data that are collected from multiple sources by learning a unified representation. In this way, comprehensive information from multiple views is shared and preserved for the generalization processes. As a special branch of

Yi Liao, Xiao-Dong Ma

The scotogenic neutrino seesaw model is a minimal extension of the standard model with three $\mathbb{Z}_2$-odd right-handed singlet fermions $N$ and one $\mathbb{Z}_2$-odd Higgs doublet $\eta$ that can accommodate the tiny neutrino mass and provide a dark matter candidate in a unified picture. Due to lack of experimental signatures for electroweak scale new

Clément Scherding, Georgios Rigas, Denis Sipp, Peter J. Schmid

In this paper, we present a novel model-agnostic machine learning technique to extract a reduced thermochemical model for reacting hypersonic flows simulation. A first simulation gathers all relevant thermodynamic states and the corresponding gas properties via a given model. The states are embedded in a low-dimensional space and clustered to identify region

Bowen Gang, Yuantao Shi, Wenguang Sun

The uncertainty quantification and error control of classifiers are crucial in many high-consequence decision-making scenarios. We propose a selective classification framework that provides an indecision option for any observations that cannot be classified with confidence. The false selection rate (FSR), defined as the expected fraction of erroneous classif

Omkar Gokhale, Aditya Kane, Shantanu Patankar, Tanmay Chavan

Pre-training large neural language models, such as BERT, has led to impressive gains on many natural language processing (NLP) tasks. Although this method has proven to be effective for many domains, it might not always provide desirable benefits. In this paper, we study the effects of hateful pre-training on low-resource hate speech classification tasks. Wh

Runmin Cong, Kepu Zhang, Chen Zhang, Feng Zheng

In recent years, RGB-T salient object detection (SOD) has attracted continuous attention, which makes it possible to identify salient objects in environments such as low light by introducing thermal image. However, most of the existing RGB-T SOD models focus on how to perform cross-modality feature fusion, ignoring whether thermal image is really always matt

Ziyi Chang, George Alex Koulieris, Hubert P. H. Shum

Acquiring the virtual equivalent of exhibits, such as sculptures, in virtual reality (VR) museums, can be labour-intensive and sometimes infeasible. Deep learning based 3D reconstruction approaches allow us to recover 3D shapes from 2D observations, among which single-view-based approaches can reduce the need for human intervention and specialised equipment

Haiyang Wang, Lihe Ding, Shaocong Dong, Shaoshuai Shi

We present a novel two-stage fully sparse convolutional 3D object detection framework, named CAGroup3D. Our proposed method first generates some high-quality 3D proposals by leveraging the class-aware local group strategy on the object surface voxels with the same semantic predictions, which considers semantic consistency and diverse locality abandoned in pr

E. Floratos, I. Tsohantjis

Following the method of induced group representations of Wigner-Mackay, the explicit construction of all the unitary irreducible representations of the discrete finite Heisenberg-Weyl group $HW_{2^s}$ over the discrete phase space lattice $Z_{2^s}$ $\otimes$ $Z_{2^s}$ is presented. We explicitly determine their characters and their fusion rules. We discuss p

V. I. Yukalov, E. P. Yukalova

A method is advanced allowing for fast regulation of magnetization direction in magnetic nanosystems. The examples of such systems are polarized nanostructures, magnetic nanomolecules, magnetic nanoclusters, magnetic graphene, dipolar and spinor trapped atoms, and quantum dots. The emphasis in the paper is on magnetic nanomolecules and nanoclusters. The meth

Emily Silcock, Luca D'Amico-Wong, Jinglin Yang, Melissa Dell

Identifying near duplicates within large, noisy text corpora has a myriad of applications that range from de-duplicating training datasets, reducing privacy risk, and evaluating test set leakage, to identifying reproduced news articles and literature within large corpora. Across these diverse applications, the overwhelming majority of work relies on N-grams.

Ruomin Huang, Jiawei Huang, Wenjie Liu, Hu Ding

Wasserstein distributionally robust optimization (\textsf{WDRO}) is a popular model to enhance the robustness of machine learning with ambiguous data. However, the complexity of \textsf{WDRO} can be prohibitive in practice since solving its ``minimax'' formulation requires a great amount of computation. Recently, several fast \textsf{WDRO} training algorithm

Wenhui Peng, Zelong Yuan, Zhijie Li, Jianchun Wang

Modeling three-dimensional (3D) turbulence by neural networks is difficult because 3D turbulence is highly-nonlinear with high degrees of freedom and the corresponding simulation is memory-intensive. Recently, the attention mechanism has been shown as a promising approach to boost the performance of neural networks on turbulence simulation. However, the stan

Muhammad Muneeb Saad, Mubashir Husain Rehmani, Ruairi O'Reilly

Imbalanced image datasets are commonly available in the domain of biomedical image analysis. Biomedical images contain diversified features that are significant in predicting targeted diseases. Generative Adversarial Networks (GANs) are utilized to address the data limitation problem via the generation of synthetic images. Training challenges such as mode co

Sara Baradaran, Mahdi Heidari, Ali Kamali, Maryam Mouzarani

Memory corruption is a serious class of software vulnerabilities, which requires careful attention to be detected and removed from applications before getting exploited and harming the system users. Symbolic execution is a well-known method for analyzing programs and detecting various vulnerabilities, e.g., memory corruption. Although this method is sound an

V. J. Dowling, Y. V. Pershin

The Ising model is of prime importance in the field of statistical mechanics. Here we show that Ising-type interactions can be realized in periodically-driven circuits of stochastic binary resistors with memory. A key feature of our realization is the simultaneous co-existence of ferromagnetic and antiferromagnetic interactions between two neighboring spins

Iris Abt, Chris Gooch, Felix Hagemann, Lukas Hauertmann

The electron drift in germanium detectors is modeled making many assumptions. Confronted with data, these assumptions have to be revisited. The temperature dependence of the drift of electrons was studied in detail for an n-type segmented point-contact germanium detector. The detector was mounted in a temperature controlled, electrically cooled cryostat. Sur

Luyi Han, Yunzhi Huang, Tao Tan, Ritse Mann

Domain adaptation has been widely adopted to transfer styles across multi-vendors and multi-centers, as well as to complement the missing modalities. In this challenge, we proposed an unsupervised domain adaptation framework for cross-modality vestibular schwannoma (VS) and cochlea segmentation and Koos grade prediction. We learn the shared representation fr

F. Pegoraro, P. J. Morrison

A starting point for deriving the Vlasov equation is the BBGKY hierarchy that describes the dynamics of coupled marginal distribution functions. With a large value of the plasma parameter one can justify eliminating 2-point correlations in terms of the 1-point function in order to derive the Vlasov Landau Lenard Balescu (VLLB) theory. Because of the high dim

Harsh Dolhare, Vivek Borkar

We revisit the classical model of Tsitsiklis, Bertsekas and Athans for distributed stochastic approximation with consensus. The main result is an analysis of this scheme using the ODE approach to stochastic approximation, leading to a high probability bound for the tracking error between suitably interpolated iterates and the limiting differential equation.

Aisha Chandio, Gong Gui, Teerath Kumar, Irfan Ullah

There are still two problems in SDD causing some inaccurate results: (1) In the process of feature extraction, with the layer-by-layer acquisition of semantic information, local information is gradually lost, resulting into less representative feature maps; (2) During the Non-Maximum Suppression (NMS) algorithm due to inconsistency in classification and regr

Jiafei Lyu, Aicheng Gong, Le Wan, Zongqing Lu

We present state advantage weighting for offline reinforcement learning (RL). In contrast to action advantage $A(s,a)$ that we commonly adopt in QSA learning, we leverage state advantage $A(s,s^\prime)$ and QSS learning for offline RL, hence decoupling the action from values. We expect the agent can get to the high-reward state and the action is determined b

Amarjyoti Choudhury, N. Mohanta, T. Maitra

Recent discovery of the time reversal symmetry breaking magnetic Weyl semimetals has created a huge surge of activities in the field of quantum topological materials. In this work, we have studied systematically the ground state magnetic order, electronic structure and the interplay between the magnetic order and band topology in one such materials, EuMg$_2$

Jiaxiang Chen, Qingyuan Yang, Ruomin Huang, Hu Ding

A coreset is a small set that can approximately preserve the structure of the original input data set. Therefore we can run our algorithm on a coreset so as to reduce the total computational complexity. Conventional coreset techniques assume that the input data set is available to process explicitly. However, this assumption may not hold in real-world scenar

Philip M. Winter, Christoph Burger, Sebastian Lehner, Johannes Kofler

Fast and accurate treatment of collisions in the context of modern N-body planet formation simulations remains a challenging task due to inherently complex collision processes. We aim to tackle this problem with machine learning (ML), in particular via residual neural networks. Our model is motivated by the underlying physical processes of the data-generatin

Nzar Rauf Abdullah, Yousif Hussein Azeez, Botan Jawdat Abdullah, Hunar Omar Rashid

We report the electronic, the thermal, and the optical properties of a Germagraphene (GeC) monolayer taking into account buckling effects. The relatively wide direct band gap of a flat GeC nanosheet can be changed by tuning the planar buckling. A GeC monolayer has an sp$^2$ hybridization in which the contribution of an $s$-orbital is half of the contribution

Haojie Zhang, Mingfei Liang, Ruobing Xie, Zhenlong Sun

In this work, we revisit the Transformer-based pre-trained language models and identify two different types of information confusion in position encoding and model representations, respectively. Firstly, we show that in the relative position encoding, the joint modeling about relative distances and directions brings confusion between two heterogeneous inform

Giulia Pagnini, Paola Di Matteo, Sergey Khoperskov, Alessandra Mastrobuono-Battisti

Reconstructing how all the stellar components of the Galaxy formed and assembled over time, by studying the properties of the stars which make it, is the aim of Galactic archeology. In these last years, thanks to the launch of the ESA Gaia astrometric mission, and the development of many spectroscopic surveys, we are for the first time in the position to del

Yinger Zhang, Zhouyi Wu, Peiying Lin, Yuting Wu

Lensless cameras are characterized by several advantages (e.g., miniaturization, ease of manufacture, and low cost) as compared with conventional cameras. However, they have not been extensively employed due to their poor image clarity and low image resolution, especially for tasks that have high requirements on image quality and details such as text detecti

Junjie Li, Meng Ge, Zexu Pan, Longbiao Wang

Speaker extraction seeks to extract the target speech in a multi-talker scenario given an auxiliary reference. Such reference can be auditory, i.e., a pre-recorded speech, visual, i.e., lip movements, or contextual, i.e., phonetic sequence. References in different modalities provide distinct and complementary information that could be fused to form top-down

Huanru Henry Mao

Autoregressive Transformers are strong language models but incur O(T) complexity during per-token generation due to the self-attention mechanism. Recent work proposes kernel-based methods to approximate causal self-attention by replacing it with recurrent formulations with various update rules and feature maps to achieve O(1) time and memory complexity. We e

Yi Cheng, Wenge Liu, Wenjie Li, Jiashuo Wang

Providing Emotional Support (ES) to soothe people in emotional distress is an essential capability in social interactions. Most existing researches on building ES conversation systems only considered single-turn interactions with users, which was over-simplified. In comparison, multi-turn ES conversation systems can provide ES more effectively, but face seve

David Sweeney, Peter Tuthill, Sanjib Sharma, Ryosuke Hirai

We chart the expected Galactic distribution of neutron stars and black holes. These compact remnants of dead stars -- the Galactic underworld -- are found to exhibit a fundamentally different distribution and structure to the visible Galaxy. Compared to the visible Galaxy, concentration into a thin flattened disk structure is much less evident with the scale

G. G. L. Nashed

We show that the spherically symmetric { black hole (BH)} solution of a charged (linear case) field equation of Rastall gravitational theory is not affected by the Rastall parameter and this is consistent with the results presented in the literature. However, when we apply the field equation of Rastall's theory to a special form of nonlinear electrodynamics

Md. Tahrim Faroque, Yan Yang, Md Zakir Hossain, Sheikh Motahar Naim

Smile veracity classification is a task of interpreting social interactions. Broadly, it distinguishes between spontaneous and posed smiles. Previous approaches used hand-engineered features from facial landmarks or considered raw smile videos in an end-to-end manner to perform smile classification tasks. Feature-based methods require intervention from human

Qiyong Cao, Hongjun Gao, Bjorn Schmalfuss

This paper is devoted to the smooth and stationary Wong-Zakai approximations for a class of rough differential equations driven by a geometric fractional Brownian rough path $\boldsymbol{\omega}$ with Hurst index $H\in(\frac{1}{3},\frac{1}{2}]$. We first construct the approximation $\boldsymbol{\omega}_{\delta}$ of $\boldsymbol{\omega}$ by probabilistic argu

I. M. Chertok

In addition to several recent articles devoted to the rare event of ground-level enhancement of the solar relativistic proton flux observed on 2021 October 28 (GLE73), we study the 10-100 MeV solar energetic particle (SEP) component of this event. Based on the GOES satellite data for 26 GLEs recorded since 1986, we have formed a scatter plot displaying the r

Masazumi Honda, Etsuko Itou, Yuya Tanizaki

The charge-$q$ Schwinger model is the $(1+1)$-dimensional quantum electrodynamics (QED) with a charge-$q$ Dirac fermion. It has the $\mathbb{Z}_q$ $1$-form symmetry and also enjoys the $\mathbb{Z}_q$ chiral symmetry in the chiral limit, and there is a mixed 't Hooft anomaly between those symmetries. We numerically study the charge-$q$ Schwinger model in the

Ali Safa, Tim Verbelen, Ilja Ocket, André Bourdoux

This work proposes a first-of-its-kind SLAM architecture fusing an event-based camera and a Frequency Modulated Continuous Wave (FMCW) radar for drone navigation. Each sensor is processed by a bio-inspired Spiking Neural Network (SNN) with continual Spike-Timing-Dependent Plasticity (STDP) learning, as observed in the brain. In contrast to most learning-base

Khadka B. Chhetri, Supriyo Naskar, Prabal K. Maiti

The oxidative damage of DNA is a compelling issue in molecular biophysics as it plays a vital role in the epigenetic control of gene expression and is believed to be associated with mutagenesis, carcinogenesis, and ageing. To understand the microscopic structural changes in physical properties of DNA and the resulting influence on its function due to oxidati

Zhengbao Jiang, Jun Araki, Haibo Ding, Graham Neubig

Generative question answering (QA) models generate answers to questions either solely based on the parameters of the model (the closed-book setting) or additionally retrieving relevant evidence (the open-book setting). Generative QA models can answer some relatively complex questions, but the mechanism through which they do so is still poorly understood. We

Nishant Jain, Suryansh Kumar, Luc Van Gool

Neural Radiance Fields (NeRF) recently emerged as a new paradigm for object representation from multi-view (MV) images. Yet, it cannot handle multi-scale (MS) images and camera pose estimation errors, which generally is the case with multi-view images captured from a day-to-day commodity camera. Although recently proposed Mip-NeRF could handle multi-scale im

Curtis Murray, Lewis Mitchell, Jonathan Tuke, Mark Mackay

Understanding patient experience in healthcare is increasingly important and desired by medical professionals in a patient-centered care approach. Healthcare discourse on social media presents an opportunity to gain a unique perspective on patient-reported experiences, complementing traditional survey data. These social media reports often appear as first-ha

Yuda Chen, Chenghan Wang, Meng Guo, Zhongkui Li

This article presents a multi-robot trajectory planning method which not only guarantees optimization feasibility and but also resolves deadlocks in obstacle-dense environments. The method is proposed via formulating a recursive optimization problem, where a novel safe corridor is generated online to ensure obstacle avoidance in trajectory planning. A dynami

Victor Croisfelt, Fabio Saggese, Israel Leyva-Mayorga, Radosław Kotaba

The widespread adoption of Reconfigurable Intelligent Surfaces (RISs) in future practical wireless systems is critically dependent on the integration of the RIS into higher-layer protocols beyond the physical (PHY) one, an issue that has received minimal attention in the research literature. In light of this, we consider a classical random access (RA) proble

Emmanuel Esposito, Federico Fusco, Dirk van der Hoeven, Nicolò Cesa-Bianchi

The framework of feedback graphs is a generalization of sequential decision-making with bandit or full information feedback. In this work, we study an extension where the directed feedback graph is stochastic, following a distribution similar to the classical Erd\H{o}s-R\'enyi model. Specifically, in each round every edge in the graph is either realized or n

Li Tang, Hai-Nan Lin, Liang Liu

The cosmic distance duality relation (DDR) is constrained from the combination of type-Ia supernovae (SNe Ia) and strong gravitational lensing (SGL) systems using deep learning method. To make use of the full SGL data, we reconstruct the luminosity distance from SNe Ia up to the highest redshift of SGL using deep learning, then it is compared with the angula

Yu Cai, Hao Chen, Xin Yang, Yu Zhou

Medical anomaly detection is a crucial yet challenging task aimed at recognizing abnormal images to assist in diagnosis. Due to the high-cost annotations of abnormal images, most methods utilize only known normal images during training and identify samples deviating from the normal profile as anomalies in the testing phase. Many readily available unlabeled i

Xiaole Tang, Xile Zhao, Jun Liu, Jianli Wang

Non-blind deblurring methods achieve decent performance under the accurate blur kernel assumption. Since the kernel uncertainty (i.e. kernel error) is inevitable in practice, semi-blind deblurring is suggested to handle it by introducing the prior of the kernel (or induced) error. However, how to design a suitable prior for the kernel (or induced) error rema

Naofumi Honda, Kohei Umeta

The paper studies several properties of Laplace hyperfunctions introduced by H.~Komatsu in the one dimensional case and by the authors in the higher dimensional cases from the viewpoint of \v{C}ech-Dolbeault cohomology theory, which enables us, for example, to construct the Laplace transformation and its inverse in a simple way. We also give some application

Pankaj Kumar Mishra, Nishchal K Verma

This paper deals with the tracking control problem for a very simple class of unknown nonlinear systems. In this paper, we presents a design strategy for tracking control of time-varying state constrained nonlinear systems in an adaptive framework. The controller is designed using the backstepping method. While designing it, Barrier Lyapunov Function (BLF) i

Cas van der Oord, Matthias Sachs, Dávid Péter Kovács, Christoph Ortner

Data-driven interatomic potentials have emerged as a powerful class of surrogate models for {\it ab initio} potential energy surfaces that are able to reliably predict macroscopic properties with experimental accuracy. In generating accurate and transferable potentials the most time-consuming and arguably most important task is generating the training set, w

Belle II Collaboration, K. Adamczyk, L. Aggarwal, P. Ahlburg

We present an analysis of the charmless semileptonic decay $B^0\to\pi^- \ell^+ \nu_{\ell}$, where $\ell = e, \mu$, from 198.0 million pairs of $B\bar{B}$ mesons recorded by the Belle II detector at the SuperKEKB electron-positron collider. The decay is reconstructed without identifying the partner $B$ meson. The partial branching fractions are measured indep

Vladislav Gennadievich Malyshkin, Mikhail Gennadievich Belov

An attempt to obtain market directional information from non-stationary solution of the dynamic equation: "future price tends to the value maximizing the number of shares traded per unit time" is presented. A remarkable feature of the approach is an automatic time scale selection. It is determined from the state of maximal execution flow calculated on past t

Bariscan Bozkurt, Ates Isfendiyaroglu, Cengiz Pehlevan, Alper T. Erdogan

The brain effortlessly extracts latent causes of stimuli, but how it does this at the network level remains unknown. Most prior attempts at this problem proposed neural networks that implement independent component analysis which works under the limitation that latent causes are mutually independent. Here, we relax this limitation and propose a biologically

Christopher K I Williams

This paper describes the Elliptical Quartic Exponential distribution in $\mathbb{R}^D$, obtained via a maximum entropy construction by imposing second and fourth moment constraints. I discuss relationships to related work, analytical expressions for the normalization constant and the entropy, and the conditional and marginal distributions.

Fei Zhao, Yuchen Shen, Zhen Wu, Xinyu Dai

Multi-Label Few-Shot Aspect Category Detection (FS-ACD) is a new sub-task of aspect-based sentiment analysis, which aims to detect aspect categories accurately with limited training instances. Recently, dominant works use the prototypical network to accomplish this task, and employ the attention mechanism to extract keywords of aspect category from the sente

Corentin Bodart

We provide new examples of groups without rational cross-sections (also called regular normal forms), using connections with bounded generation and rational orders on groups. Specifically, our examples are extensions of infinite torsion groups, groups of Grigorchuk type, wreath products similar to $C_2\wr(C_2\wr \mathbb Z)$ and $\mathbb Z\wr F_2$, a group of

Ahan M R, Roshan Roy, Shreyas Sunil Kulkarni, Vaibhav Soni

Floods are large-scale natural disasters that often induce a massive number of deaths, extensive material damage, and economic turmoil. The effects are more extensive and longer-lasting in high-population and low-resource developing countries. Early Warning Systems (EWS) constantly assess water levels and other factors to forecast floods, to help minimize da

Xiu Li, Xiao Li, Yan Lu

We present a new method for estimating the Neural Reflectance Field (NReF) of an object from a set of posed multi-view images under unknown lighting. NReF represents 3D geometry and appearance of objects in a disentangled manner, and are hard to be estimated from images only. Our method solves this problem by exploiting the Neural Radiance Field (NeRF) as a

Eduardo Vital Brasil

Nowadays, Computational Fluid Dynamics (CFD) is a fundamental tool for industrial design. However, the computational cost of doing such simulations is expensive and can be detrimental for real-world use cases where many simulations are necessary, such as the task of shape optimization. Recently, Deep Learning (DL) has achieved a significant leap in a wide sp

Hongxin Lin, Yunwei Chiu, Peiyuan Wu

The graph convolutional networks (GCNs) have been applied to model the physically connected and non-local relations among human joints for 3D human pose estimation (HPE). In addition, the purely Transformer-based models recently show promising results in video-based 3D HPE. However, the single-frame method still needs to model the physically connected relati

Svetlana Suleymanova, Anna Bilous

B0943+10 is known to switch between two distinct, hours-long modes of radio emission, Bright (B) and Quiet (Q). Up to now the switches in both directions were believed to occur instantly (on the scale of a spin period). We have found a transitive process around the Q-to-B-mode switch, which consists of two additional short-lived modes, each with distinct ave

Matteo Bortolon, Alessio Del Bue, Fabio Poiesi

NeRF aims to learn a continuous neural scene representation by using a finite set of input images taken from various viewpoints. A well-known limitation of NeRF methods is their reliance on data: the fewer the viewpoints, the higher the likelihood of overfitting. This paper addresses this issue by introducing a novel method to generate geometrically consiste

Yao Zhu, Yuefeng Chen, Xiaodan Li, Kejiang Chen

Transferable adversarial attacks against Deep neural networks (DNNs) have received broad attention in recent years. An adversarial example can be crafted by a surrogate model and then attack the unknown target model successfully, which brings a severe threat to DNNs. The exact underlying reasons for the transferability are still not completely understood. Pr

Mohak Chadha, Victor Pacyna, Anshul Jindal, Jianfeng Gu

Microservice architecture is the common choice for developing cloud applications these days since each individual microservice can be independently modified, replaced, and scaled. As a result, application development and operating cloud infrastructure were bundled together into what is now commonly called DevOps. However, with the increasing popularity of th

Pankaj Kumar Mishra, Nishchal K Verma

This paper deals with the tracking control problem for a class of unknown pure feedback system with pure state constraints on the state variables and unknown time-varying bounded disturbances. An adaptive controller is presented for such systems for the very first time. The controller is designed using the backstepping method. While designing it, Barrier Lya

Samir F Matar

From crystal chemistry rationale and density functional DFT calculations, novel tetragonal carbon C5 is proposed as simplest ultrahard allotrope with mixed hybrid carbon hybridizations (sp2 - sp3). Novel pentacarbon is identified as cohesive and stable both dynamically and mechanically. Whereas charge density is localized about tetrahedral C-sp3, it is found

Yao Mu, Yuzheng Zhuang, Fei Ni, Bin Wang

Adapting to the changes in transition dynamics is essential in robotic applications. By learning a conditional policy with a compact context, context-aware meta-reinforcement learning provides a flexible way to adjust behavior according to dynamics changes. However, in real-world applications, the agent may encounter complex dynamics changes. Multiple confou

Xu Yan, Heshen Zhan, Chaoda Zheng, Jiantao Gao

Although recent point cloud analysis achieves impressive progress, the paradigm of representation learning from a single modality gradually meets its bottleneck. In this work, we take a step towards more discriminative 3D point cloud representation by fully taking advantages of images which inherently contain richer appearance information, e.g., texture, col

Shu Xiao, Huaixin Cao, Zhihua Guo, Kanyuan Han

In the literature, bilocality and $n$-locality of correlation tensors (CTs) are described by integration local hidden variable models (called C-LHVMs) rather than by summation LHVMs (called D-LHVMs). Obviously, C-LHVMs are easier to be constructed than D-LHVMs, while the later are easier to be used than the former, e.g., in discussing on the topological and

Rang Meng, Xianfeng Li, Weijie Chen, Shicai Yang

Convolutional neural networks (CNNs) have demonstrated gratifying results at learning discriminative features. However, when applied to unseen domains, state-of-the-art models are usually prone to errors due to domain shift. After investigating this issue from the perspective of shortcut learning, we find the devils lie in the fact that models trained on dif

Michele Caraglio, Thomas Franosch

We provide an analytical solution for the time-dependent Fokker-Planck equation for a two-dimensional active Brownian particle trapped in an isotropic harmonic potential. Using the passive Brownian particle as basis states we show that the Fokker-Planck operator becomes lower diagonal, implying that the eigenvalues are unaffected by the activity. The propaga

Congpei An, Mou Cai

In this paper, we propose a fully discrete soft thresholding trigonometric polynomial approximation on $[-\pi,\pi],$ named Lasso trigonometric interpolation. This approximation is an $\ell_1$-regularized discrete least squares approximation under the same conditions of classical trigonometric interpolation on an equidistant grid. Lasso trigonometric interpol

Ming-Jie Sheng, Hao-Ran Yu, Sijia Li, Shihong Liao

Recent studies illustrate the correlation between the angular momenta of cosmic structures and their Lagrangian properties. However, only baryons are observable and it is unclear whether they reliably trace the cosmic angular momenta. We study the Lagrangian mass distribution, spin correlation, and predictability of dark matter, gas, and stellar components o

Jonathan Sterling

Jacobs has proposed definitions for (weak, strong, split) generic objects for a fibered category; building on his definition of (split) generic objects, Jacobs develops a menagerie of important fibrational structures with applications to categorical logic and computer science, including higher order fibrations, polymorphic fibrations, ${\lambda}2$-fibrations

Zirui Dong, Yubo Zhang, Jun Luo, Ying Jiang

Searching for high-performance thermoelectric (TE) materials in the paradigm of narrow-bandgap semiconductors has lasted for nearly 70 years and is obviously hampered by a bottleneck of research now. Here we report on the discovery of a few metallic compounds, TiFexCu2x-1Sb and TiFe1.33Sb, showing the thermopower exceeding many TE semiconductors and the dime

Yao Zhu, YueFeng Chen, Chuanlong Xie, Xiaodan Li

Out-of-distribution (OOD) detection is a critical task for ensuring the reliability and safety of deep neural networks in real-world scenarios. Different from most previous OOD detection methods that focus on designing OOD scores or introducing diverse outlier examples to retrain the model, we delve into the obstacle factors in OOD detection from the perspec

Qi Zhang, Meizhu Li

Nonextensivity is foreseeable in network ensembles, as heterogeneous interactions generally exist in complex networked systems that need to be described by network ensembles. But this nonextensivity has not been literatured proved yet. In this work, the existence of nonextensivity in the binary and weighted network ensembles is theoretically proved for the f

Jinjin Gu, Haoming Cai, Chenyu Dong, Ruofan Zhang

Rendering high-resolution (HR) graphics brings substantial computational costs. Efficient graphics super-resolution (SR) methods may achieve HR rendering with small computing resources and have attracted extensive research interests in industry and research communities. We present a new method for real-time SR for computer graphics, namely Super-Resolution b