October 2022 arXiv papers — page 118

Hanseul Cho, Chulhee Yun

Stochastic gradient descent-ascent (SGDA) is one of the main workhorses for solving finite-sum minimax optimization problems. Most practical implementations of SGDA randomly reshuffle components and sequentially use them (i.e., without-replacement sampling); however, there are few theoretical results on this approach for minimax algorithms, especially outsid

Aleksandr Beznosikov, Alexander Gasnikov

Variational inequalities are a broad formalism that encompasses a vast number of applications. Motivated by applications in machine learning and beyond, stochastic methods are of great importance. In this paper we consider the problem of stochastic finite-sum cocoercive variational inequalities. For this class of problems, we investigate the convergence of t

Homayun Afrabandpey, Michael Spranger

We present a human-in-the-loop approach to generate counterfactual (CF) explanations that preserve global and local feasibility constraints. Global feasibility constraints refer to the causal constraints that are necessary for generating actionable CF explanation. Assuming a domain expert with knowledge on unary and binary causal constraints, our approach ef

Ran Tamir

Majority dynamics on the binomial Erd\H{o}s-R\'enyi graph $\mathsf{G}(n,p)$ with $p=\lambda/\sqrt{n}$ is studied. In this process, each vertex has a state in $\{0,1\}$ and at each round, every vertex adopts the state of the majority of its neighbors, retaining its state in the case of a tie. It was conjectured by Benjamini et al. and proved by Fountoulakis e

Sayontan Ghosh, Tanvi Aggarwal, Minh Hoai, Niranjan Balasubramanian

Anticipating future actions in a video is useful for many autonomous and assistive technologies. Most prior action anticipation work treat this as a vision modality problem, where the models learn the task information primarily from the video features in the action anticipation datasets. However, knowledge about action sequences can also be obtained from ext

Haotian Wu, Peipei Wang, Xin Wang, Ji Xiang

Detecting manipulated facial images and videos on social networks has been an urgent problem to be solved. The compression of videos on social media has destroyed some pixel details that could be used to detect forgeries. Hence, it is crucial to detect manipulated faces in videos of different quality. We propose a new multi-stream network architecture named

Thom Badings, Licio Romao, Alessandro Abate, Nils Jansen

Capturing uncertainty in models of complex dynamical systems is crucial to designing safe controllers. Stochastic noise causes aleatoric uncertainty, whereas imprecise knowledge of model parameters leads to epistemic uncertainty. Several approaches use formal abstractions to synthesize policies that satisfy temporal specifications related to safety and reach

Pin-Hua Lai, Bo-Shan Wang, Wei-Chun Yang, Hsiang-Chieh Tsou

Human electroencephalography (EEG) is a brain monitoring modality that senses cortical neuroelectrophysiological activity in high-temporal resolution. One of the greatest challenges posed in applications of EEG is the unstable signal quality susceptible to inevitable artifacts during recordings. To date, most existing techniques for EEG artifact removal and

Yihang Gao, Michael K. Ng

The cubic regularization method (CR) and its adaptive version (ARC) are popular Newton-type methods in solving unconstrained non-convex optimization problems, due to its global convergence to local minima under mild conditions. The main aim of this paper is to develop a momentum-accelerated adaptive cubic regularization method (ARCm) to improve the convergen

Ayan Maiti

We generalize the work of Lindenstrauss and Venkatesh establishing Weyl's Law for cusp forms from the spherical spectrum to arbitrary Archimedean type. Weyl's law for the spherical spectrum gives an asymptotic formula for the number of cusp forms that are bi-spherical in terms of eigenvalue T of the Laplacian. We prove an analogous asymptotic holds for cusp

Tinggui Zhang, Naihuan Jing, Shao-Ming Fei

The Bell nonlocality is closely related to the foundations of quantum physics and has significant applications to security questions in quantum key distributions. In recent years, the sharing ability of the Bell nonlocality has been extensively studied. The nonlocality of quantum network states is more complex. We first discuss the sharing ability of the sim

Xuecheng Xu, Sha Lu, Jun Wu, Haojian Lu

Global localization plays a critical role in many robot applications. LiDAR-based global localization draws the community's focus with its robustness against illumination and seasonal changes. To further improve the localization under large viewpoint differences, we propose RING++ which has roto-translation invariant representation for place recognition, and

Luca Brusa, Catherine Matias

We propose a model to address the overlooked problem of node clustering in simple hypergraphs. Simple hypergraphs are suitable when a node may not appear multiple times in the same hyperedge, such as in co-authorship datasets. Our model generalizes the stochastic blockmodel for graphs and assumes the existence of latent node groups and hyperedges are conditi

Sander Borst, Daniel Dadush, Sophie Huiberts, Danish Kashaev

Explorable heap selection is the problem of selecting the $n$th smallest value in a binary heap. The key values can only be accessed by traversing through the underlying infinite binary tree, and the complexity of the algorithm is measured by the total distance traveled in the tree (each edge has unit cost). This problem was originally proposed as a model to

Wu Wang, Bin Tan, Shun Zhang

In this paper,the concepts of generalized ideal inf-limit and generalized ideal final lower bound limit are introduced in the directed complete poset,and their relations with Scott topology and Lawson topology are studied. The main results are as follows: (1) On directed complete posets,generalized ideal inf-limit topology is consistent with Scott topology;

Zichen Liu, Siyi Li, Wee Sun Lee, Shuicheng Yan

MuZero Unplugged presents a promising approach for offline policy learning from logged data. It conducts Monte-Carlo Tree Search (MCTS) with a learned model and leverages Reanalyze algorithm to learn purely from offline data. For good performance, MCTS requires accurate learned models and a large number of simulations, thus costing huge computing time. This

Byoung Jin Choi, Myeonghun Jeong, Minchan Kim, Sung Hwan Mun

Several recently proposed text-to-speech (TTS) models achieved to generate the speech samples with the human-level quality in the single-speaker and multi-speaker TTS scenarios with a set of pre-defined speakers. However, synthesizing a new speaker's voice with a single reference audio, commonly known as zero-shot multi-speaker text-to-speech (ZSM-TTS), is s

Minki Hhan, Tomoyuki Morimae, Takashi Yamakawa

Recently, Aaronson et al. (arXiv:2009.07450) showed that detecting interference between two orthogonal states is as hard as swapping these states. While their original motivation was from quantum gravity, we show its applications in quantum cryptography. 1. We construct the first public key encryption scheme from cryptographic \emph{non-abelian} group action

Antonio Candelieri, Andrea Ponti, Francesco Archetti

Optimal resource allocation is gaining a renewed interest due its relevance as a core problem in managing, over time, cloud and high-performance computing facilities. Semi-Bandit Feedback (SBF) is the reference method for efficiently solving this problem. In this paper we propose (i) an extension of the optimal resource allocation to a more general class of

Dong Wei, Huaijiang Sun, Bin Li, Jianfeng Lu

Stochastic human motion prediction aims to forecast multiple plausible future motions given a single pose sequence from the past. Most previous works focus on designing elaborate losses to improve the accuracy, while the diversity is typically characterized by randomly sampling a set of latent variables from the latent prior, which is then decoded into possi

Jean-François Berret

For applications of pulmonary surfactant delivery to the lungs, the question of rheology of the existing clinical formulations is of upmost importance. Recently, Ciutara and Zasadsinky (Soft Matter, 2021, 17, 5170-5182) measured the rheological properties of Infasurf, Survanta and Curosurf, three of the most used pulmonary surfactant substitutes. This study

Henry Ling-Hei Tsang, Thomas Dybdahl Ahle

Embedding tables are used by machine learning systems to work with categorical features. In modern Recommendation Systems, these tables can be very large, necessitating the development of new methods for fitting them in memory, even during training. We suggest Clustered Compositional Embeddings (CCE) which combines clustering-based compression like quantizat

Samuel Yanes Luis, Daniel Gutiérrez Reina, Sergio Toral Marín

Monitoring and patrolling large water resources is a major challenge for conservation. The problem of acquiring data of an underlying environment that usually changes within time involves a proper formulation of the information. The use of Autonomous Surface Vehicles equipped with water quality sensor modules can serve as an early-warning system agents for c

Antonio Agresti, Matthias Hieber, Amru Hussein, Martin Saal

In this paper, we introduce and study the primitive equations with $\textit{non}$-isothermal turbulent pressure and transport noise. They are derived from the Navier-Stokes equations by employing stochastic versions of the Boussinesq and the hydrostatic approximations. The temperature dependence of the turbulent pressure can be seen as a consequence of an ad

Takeru Miyato, Masanori Koyama, Kenji Fukumizu

In this study, we present meta-sequential prediction (MSP), an unsupervised framework to learn the symmetry from the time sequence of length at least three. Our method leverages the stationary property (e.g. constant velocity, constant acceleration) of the time sequence to learn the underlying equivariant structure of the dataset by simply training the encod

Sameer Chavan, Shubham Jain, Paramita Pramanick

We consider the family $\mathcal P$ of $n$-tuples $P$ consisting of polynomials $P_1, \ldots, P_n$ with nonnegative coefficients which satisfy $\partial_i P_j(0) = \delta_{i, j},$ $i, j=1, \ldots, n.$ With any such $P,$ we associate a Reinhardt domain $\triangle^{\!n}_{_P}$ that we will call the generalized Hartogs triangle. We are particularly interested in

Yves Dumont, Michel Duprez

The sterile insect technique (SIT) is a biological control technique that can be used either to eliminate or decay a wild mosquito population under a given threshold to reduce the nuisance or the epidemiological risk. In this work, we propose a model using a differential system that takes into account the variations of rainfall and temperature over time and

Barbara Carretti, David Giofre, Enrico Toffalini, Cesare Cornoldi

Several models of working memory (WM) have been proposed in the literature. Most of the research on the architecture of WM is based on adults or older children, but less is known about younger children. In this study, we tested various models of WM on a sample of 739 Italian children from 3 to 8 years old. Participants were assessed with 12 WM tasks, systema

Philippe Grangier

With the Nobel Prize attributed to Aspect, Clauser, and Zeilinger, the international scientific community acknowledged the fundamental importance of the experimental violation of Bell's inequalities. It is however still debated what fails in Bell's hypotheses, leading to these inequalities, and usually summarized as "local realism", or maybe more appropriate

Zeyu Qin, Yanbo Fan, Yi Liu, Li Shen

Deep neural networks (DNNs) have been shown to be vulnerable to adversarial examples, which can produce erroneous predictions by injecting imperceptible perturbations. In this work, we study the transferability of adversarial examples, which is significant due to its threat to real-world applications where model architecture or parameters are usually unknown

Ravi Kalluri

This paper aims to investigate the mechanisms that contribute to propagation of competence in an Agile Scrum team. This study seeks to challenge the traditional view of bounded rationality (BR). An Agile Scrum team (Team) is expected to build problem solving competence quickly as the expected ramp up time continues to shrink. But the team has a mixture of ex

Peng Fan, Ning-Hua Tong, Zhen-Gang Zhu

We investigate the interplay between the Kondo effect and the ferromagnetism by an one dimension Anderson impurity model with a spin partially polarized bath, using the projective truncation approximation under Lacroix basis.The equal-time spatial spin-spin correlation function (SSCF) is calculated. For the case of spin-unpolarized conduction electrons, it a

Loay Mualem, Moran Feldman

In recent years, maximization of DR-submodular continuous functions became an important research field, with many real-worlds applications in the domains of machine learning, communication systems, operation research and economics. Most of the works in this field study maximization subject to down-closed convex set constraints due to an inapproximability res

Hsiao-Yi Chen, Marco Bernardi

Calculations combining first-principles electron-phonon ($e$-ph) interactions with the Boltzmann equation enable studies of ultrafast carrier and phonon dynamics. However, in materials with weak Coulomb screening, electrons and holes form bound excitons and their scattering processes become correlated, posing additional challenges for modeling nonequilibrium

Tetsuya Hara, Anna Suzuki, Masayoshi Kiguchi, Akika Nakamichi

It has been investigated the possibility of the various atmospheres over water oceans. We have considered the H$_2$ atmosphere and He atmosphere concerning to N$_2$ atmosphere over oceans. One of the main subjects in astrobiology is to estimate the habitable zone. If there is an ocean on the planet with an atmosphere, there is an upper limit to the outgoing

Mukesh Singh Raghav, Sharath Jose, Amit Apte, Rama Govindarajan

Linear modal stability analysis of a mean zonal shear flow is carried out in the framework of rotating shallow water equations (RSWE), both under the $\beta$-plane approximation and in the full spherical coordinate system. Two base flows -- equatorial easterly (EE) and westerly (EW) -- with Gaussian profiles highly confined to small latitudes are analyzed. A

Adam Haber, Elad Schneidman

Dale's principle implies that biological neural networks are composed of neurons that are either excitatory or inhibitory. While the number of possible architectures of such Daleian networks is exponentially smaller than non-Daleian ones, the computational and functional implications of using Daleian networks by the brain are mostly unknown. Here, we use mod

Lin Zhou, Haoming Cai, Jinjin Gu, Zheyuan Li

The attention mechanism plays a pivotal role in designing advanced super-resolution (SR) networks. In this work, we design an efficient SR network by improving the attention mechanism. We start from a simple pixel attention module and gradually modify it to achieve better super-resolution performance with reduced parameters. The specific approaches include:

Jian Kang, Qinghai Zhou, Hanghang Tong

Graph Convolutional Network (GCN) has exhibited strong empirical performance in many real-world applications. The vast majority of existing works on GCN primarily focus on the accuracy while ignoring how confident or uncertain a GCN is with respect to its predictions. Despite being a cornerstone of trustworthy graph mining, uncertainty quantification on GCN

Zhiying Lu, Hongtao Xie, Chuanbin Liu, Yongdong Zhang

There still remains an extreme performance gap between Vision Transformers (ViTs) and Convolutional Neural Networks (CNNs) when training from scratch on small datasets, which is concluded to the lack of inductive bias. In this paper, we further consider this problem and point out two weaknesses of ViTs in inductive biases, that is, the spatial relevance and

Xiao-Hua Fan, Zu-Xing Yang, Peng Yin, Peng-Hui Chen

We adapt the local density approximation to add the high-momentum tails (HMTs) to finite nuclei's Slater-determinant momentum distributions. The HMTs are extracted by the extended Brueckner-Hartree-Fock (EBHF) method or by the lowest order cluster approximation. With a correction factor being added to EBHF, it is sufficiently in agreement with the experiment

Yibo Yang, Hong Wang, Haobo Yuan, Zhouchen Lin

Automated machine learning has been widely explored to reduce human efforts in designing neural architectures and looking for proper hyperparameters. In the domain of neural initialization, however, similar automated techniques have rarely been studied. Most existing initialization methods are handcrafted and highly dependent on specific architectures. In th

Yuanyuan Wang, Wei Huang, Mingming Gong, Xi Geng

Ordinary Differential Equations (ODEs) have recently gained a lot of attention in machine learning. However, the theoretical aspects, e.g., identifiability and asymptotic properties of statistical estimation are still obscure. This paper derives a sufficient condition for the identifiability of homogeneous linear ODE systems from a sequence of equally-spaced

Chak Fong Chong, Yapeng Wang, Benjamin Ng, Wuman Luo

Classification on smartphone-captured chest X-ray (CXR) photos to detect pathologies is challenging due to the projective transformation caused by the non-ideal camera position. Recently, various rectification methods have been proposed for different photo rectification tasks such as document photos, license plate photos, etc. Unfortunately, we found that no

Meng-Xian Zhu, Yuan-Hai Shao

In this paper, we study the classification problem by estimating the conditional probability function of the given data. Different from the traditional expected risk estimation theory on empirical data, we calculate the probability via Fredholm equation, this leads to estimate the distribution of the data. Based on the Fredholm equation, a new expected risk

Yanbin Liu, Girish Dwivedi, Farid Boussaid, Mohammed Bennamoun

Generative models such as generative adversarial networks and autoencoders have gained a great deal of attention in the medical field due to their excellent data generation capability. This paper provides a comprehensive survey of generative models for three-dimensional (3D) volumes, focusing on the brain and heart. A new and elaborate taxonomy of unconditio

Ganesh Bagler

Cooking is a uniquely human endeavor for transforming raw ingredients into delicious dishes. Over centuries, cultures worldwide have evolved diverse cooking practices ingrained in their culinary traditions. Recipes, thus, are cultural capsules that capture culinary knowledge in elaborate cooking protocols. While simple quantitative models have probed the pat

A. Chilingarian, G. Hosepyan, T. Karapetyan, B. Sargsyan

Proceeding from a stormy day of 22 September 2022, when 7 thunderstorm ground enhancements occurred (TGEs, 3 of them very large), we perform an analysis of the most important conditions, on which depend the origination of the large particle fluxes in the thunderous atmosphere. Among these conditions are the near-surface electric field (NSEF), graupel fall, a

Chenjie Cao, Qiaole Dong, Yanwei Fu

Image inpainting involves filling missing areas of a corrupted image. Despite impressive results have been achieved recently, restoring images with both vivid textures and reasonable structures remains a significant challenge. Previous methods have primarily addressed regular textures while disregarding holistic structures due to the limited receptive fields

Taro Kimura, Norton Lee

We study the surface defect in $\mathcal{N}=2^*$ $U(N)$ gauge theory in four dimensions and its relation to quantum Hall states in two dimensions. We first prove that the defect partition function becomes the Jack polynomial of the variables describing the brane positions by imposing the Higgsing condition and taking the bulk decoupling limit. Further tuning

Danilo Braghini, Vinicius D. de Lima, Danilo Beli, Matheus I. N. Rosa

In this work, we investigate non-Hermitian acoustic waveguides designed with periodically applied feedback efforts using electrodynamic actuators. One-dimensional spectral (infinite-dimensional) and finite element (finite-dimensional) models for plane acoustic waves in ducts are used. It is shown that dispersion diagrams of this family of metamaterials exhib

Yuxin Liu, Yawen Li, Yingxia Shao, Zeli Guan

In the age of big data, the demand for hidden information mining in technological intellectual property is increasing in discrete countries. Definitely, a considerable number of graph learning algorithms for technological intellectual property have been proposed. The goal is to model the technological intellectual property entities and their relationships th

Wenhui Zhu, Peijie Qiu, Natasha Lepore, Oana M. Dumitrascu

Lesion appearance is a crucial clue for medical providers to distinguish referable diabetic retinopathy (rDR) from non-referable DR. Most existing large-scale DR datasets contain only image-level labels rather than pixel-based annotations. This motivates us to develop algorithms to classify rDR and segment lesions via image-level labels. This paper leverages

Manoj Kummini, Mandira Mondal

Let $\Bbbk$ be a field of characteristic $p>0$, $V$ a finite-dimensional $\Bbbk$-vector-space, and $G$ a finite $p$-group acting $\Bbbk$-linearly on $V$. Let $S = \Sym V^*$. We show that $S^G$ is a polynomial ring if and only if the dimension of its singular locus is less than $\rank_\Bbbk V^G$. Confirming a conjecture of Shank-Wehlau-Broer, we show that if

Alexis Roger

Technology is now omnipresent around us. Especially with the recent health crisis, many people started working remotely, bringing home an additional computer. Combining this with our smartphones that we could never leave behind, we are always surrounded by these technological marvels. However, they come along with a rather dark side from which many people ch

Kehan Li, Zhennan Wang, Zesen Cheng, Runyi Yu

Recently, self-supervised large-scale visual pre-training models have shown great promise in representing pixel-level semantic relationships, significantly promoting the development of unsupervised dense prediction tasks, e.g., unsupervised semantic segmentation (USS). The extracted relationship among pixel-level representations typically contains rich class

Jongwon Lee

In this article, we prove that small localized data yield solutions to Higher order Korteweg-de Vries type equation with scattering-supercritical nonlinearity have linear dispersive decay in only a finite length of time. The proof is done by using space-time resonance method and analyzing the oscillatory integrals on the Fourier side.

G. Andreolas, M. Anoussis, C. Magiatis

We characterize the hypocompact radical of a semicrossed product in terms of properties of the dynamical system. We show that an element A of a semicrossed product is in the hypocompact radical if and only if the Fourier coefficients of A vanish on the closure of the recurrent points and the 0-Fourier coefficient vanishes also on the largest perfect subset o

Donghyeon Baek, Youngmin Oh, Sanghoon Lee, Junghyup Lee

Class-incremental semantic segmentation (CISS) labels each pixel of an image with a corresponding object/stuff class continually. To this end, it is crucial to learn novel classes incrementally without forgetting previously learned knowledge. Current CISS methods typically use a knowledge distillation (KD) technique for preserving classifier logits, or freez

Haritha T, Chithra A.

For a connected graph $G$, we present the concept of a new graph matrix related to its distance and Seidel matrix, called distance Seidel matrix $\mathcal{D}^S(G)$. Suppose that the eigenvalues of $\mathcal{D}^S(G)$ be $\partial_{1}^{S}(G) \geq \cdots \geq \partial_{n}^{S}(G).$ In this article, we establish a relationship between distance Seidel eigenvalues

Junjie Mao, G. A. Kriss, H. Landt, M. Mehdipour

Obscuring winds driven away from active supermassive black holes are rarely seen due to their transient nature. They have been observed with multi-wavelength observations in a few Seyfert 1 galaxies and one broad absorption line radio-quiet quasar so far. An X-ray obscuration event in MR 2251-178 was caught in late 2020, which triggered multi-wavelength (NIR

Julia Grabinski, Paul Gavrikov, Janis Keuper, Margret Keuper

Despite the success of convolutional neural networks (CNNs) in many academic benchmarks for computer vision tasks, their application in the real-world is still facing fundamental challenges. One of these open problems is the inherent lack of robustness, unveiled by the striking effectiveness of adversarial attacks. Current attack methods are able to manipula

Jihye Hwang, Jongsoo Kim, Kate Pattle, Chang Won Lee

We present and analyze observations of polarized dust emission at 850 $\mu$m towards the central 1 pc $\times$ 1 pc hub-filament structure of Monoceros R2 (Mon R2). The data are obtained with SCUBA-2/POL-2 on the James Clerk Maxwell Telescope (JCMT) as part of the BISTRO (B-fields in Star-forming Region Observations) survey. The orientations of the magnetic

Jaewoong Cho, Moonseok Choi, Changho Suh

We explore the fairness issue that arises in recommender systems. Biased data due to inherent stereotypes of particular groups (e.g., male students' average rating on mathematics is often higher than that on humanities, and vice versa for females) may yield a limited scope of suggested items to a certain group of users. Our main contribution lies in the intr

Ge-Yang Ke, Yan Pan, Jian Yin, Chang-Qin Huang

Multi-task learning (MTL) aims to improve the generalization performance of multiple tasks by exploiting the shared factors among them. Various metrics (e.g., F-score, Area Under the ROC Curve) are used to evaluate the performances of MTL methods. Most existing MTL methods try to minimize either the misclassified errors for classification or the mean squared

T. Akutsu, M. Ando, K. Arai, Y. Arai

KAGRA, the underground and cryogenic gravitational-wave detector, was operated for its solo observation from February 25th to March 10th, 2020, and its first joint observation with the GEO 600 detector from April 7th -- 21st, 2020 (O3GK). This study presents an overview of the input optics systems of the KAGRA detector, which consist of various optical syste

Aravind Asok, Tom Bachmann, Michael J. Hopkins

We improve some foundational connectivity results and the relative Hurewicz theorem in motivic homotopy theory, study functorial central series in motivic local group theory, establish the existence of functorial Moore--Postnikov factorizations for nilpotent morphisms of motivic spaces under a mild technical hypothesis and establish an analog of the Whitehea

Anish Pal, Riddhideep Biswas, Sourav Sarkar, Aranyak Chakravarty

Increasing power demands on multicore processors necessitate effective thermal management. The present study investigates natural convection heat transfer inside a square cavity with an alternately active bi-heater that mimics two cores of a dual-core processor. Pulsating heat flux condition is implemented on two discrete heaters with a certain switching fre

Felix Feit, Andreas Metzger, Klaus Pohl

Design time uncertainty poses an important challenge when developing a self-adaptive system. As an example, defining how the system should adapt when facing a new environment state, requires understanding the precise effect of an adaptation, which may not be known at design time. Online reinforcement learning, i.e., employing reinforcement learning (RL) at r

Mahdiyeh Gholizadeh, Mohammad Hossein Adabia, Abbas Sadeghi, Mohammadfarid Ghasemi

The Ilam Formation Cenomanian to Santonian in age is considered one of the main rock reservoirs of the Bangestan Group in the southwest of Iran. This formation mostly consists of carbonate rocks. To examine the sedimentary environment, diagenesis, sequence stratigraphy, and reservoir quality of Ilam Formation in Dezful embayment and Abadan Plain, four subsur

Jonathan Hayase, Sewoong Oh

In a backdoor attack, an attacker injects corrupted examples into the training set. The goal of the attacker is to cause the final trained model to predict the attacker's desired target label when a predefined trigger is added to test inputs. Central to these attacks is the trade-off between the success rate of the attack and the number of corrupted training

Amine Mezghani, Faouzi Bellili, Ekram Hossain

Conventional Reconfigurable intelligent surfaces (RIS) for wireless communications have a local position-dependent (phase-gradient) scattering response on the surface. We consider more general RIS structures, called nonlocal (or redirective) RIS, that are capable of selectively manipulate the impinging waves depending on the incident angle. Redirective RIS h

Yongyuan Liang, Yanchao Sun, Ruijie Zheng, Furong Huang

Recent studies reveal that a well-trained deep reinforcement learning (RL) policy can be particularly vulnerable to adversarial perturbations on input observations. Therefore, it is crucial to train RL agents that are robust against any attacks with a bounded budget. Existing robust training methods in deep RL either treat correlated steps separately, ignori

Carllos Eduardo Holanda

We show that additive and asymptotically additive families of continuous functions with respect to suspension flows are physically equivalent. In particular, the equivalence result holds for hyperbolic flows and some classes of expansive flows in general. Moreover, we show how this equivalence result can be used to extend the nonadditive thermodynamic formal

Jiayu Shen, Patrick Draper, Aida X. El-Khadra

The decay rate of a metastable vacuum is usually calculated using a semiclassical approximation to the Euclidean path integral. The extension to a complete Euclidean lattice Monte Carlo computation, however, is hampered by analytic continuations that are ill-suited to numerical treatment, and the nonequilibrium nature of a metastable state. In this paper we

Peter Olamide Olanipekun

In this thesis, a four dimensional conformally invariant energy is studied. This energy generalises the well known two-dimensional Willmore energy. Although not positive definite, it includes minimal hypersurfaces as critical points. We compute its first variation and by applying the Noether theorem to the invariances, we derive some conservation laws which

Wei Huang, Jiaxiang Li, Shuming Jiao, Zibang Zhang

Single-pixel imaging (SPI) is a novel optical imaging technique by replacing the pixelated sensor array in a conventional camera with a single-pixel detector. In previous works, SPI is usually used for capturing object images or performing image processing tasks. In this work, we propose a SPI scheme for processing other types of data in addition to images.

Shentao Yang, Shujian Zhang, Yihao Feng, Mingyuan Zhou

In offline model-based reinforcement learning (offline MBRL), we learn a dynamic model from historically collected data, and subsequently utilize the learned model and fixed datasets for policy learning, without further interacting with the environment. Offline MBRL algorithms can improve the efficiency and stability of policy learning over the model-free al

Xin Lv, Yankai Lin, Zijun Yao, Kaisheng Zeng

Knowledge graphs, as the cornerstone of many AI applications, usually face serious incompleteness problems. In recent years, there have been many efforts to study automatic knowledge graph completion (KGC), most of which use existing knowledge to infer new knowledge. However, in our experiments, we find that not all relations can be obtained by inference, wh

Zhichun Guo, Chunhui Zhang, Yujie Fan, Yijun Tian

Graph neural networks (GNNs) have shown remarkable performance on diverse graph mining tasks. Although different GNNs can be unified as the same message passing framework, they learn complementary knowledge from the same graph. Knowledge distillation (KD) is developed to combine the diverse knowledge from multiple models. It transfers knowledge from high-cap

Yanjie Zhang, Qingmin Zhang, Jun Dai, Dong Li

In this paper, we report the multiwavelength observations of the partial filament eruption associated with a C1.2 class flare in NOAA active region 11236 on 13 June 2011. The event occurred at the eastern limb in the field of view (FOV) of Atmospheric Imaging Assembly (AIA) on board the Solar Dynamics Observatory (SDO) spacecraft and was close to the disk ce

Gandharv Patil, Prashanth L. A., Dheeraj Nagaraj, Doina Precup

We study the finite-time behaviour of the popular temporal difference (TD) learning algorithm when combined with tail-averaging. We derive finite time bounds on the parameter error of the tail-averaged TD iterate under a step-size choice that does not require information about the eigenvalues of the matrix underlying the projected TD fixed point. Our analysi

Junwoo Park, Youngwoo Cho, Gyuhyeon Sim, Hojoon Lee

Recently, deep learning-based methods have drawn huge attention due to their simple yet high performance without domain knowledge in sound classification and localization tasks. However, a lack of gun sounds in existing datasets has been a major obstacle to implementing a support system to spot criminals from their gunshots by leveraging deep learning models

Gokul Karthik Kumar, Karthik Nandakumar

Hateful memes are a growing menace on social media. While the image and its corresponding text in a meme are related, they do not necessarily convey the same meaning when viewed individually. Hence, detecting hateful memes requires careful consideration of both visual and textual information. Multimodal pre-training can be beneficial for this task because it

Naoki Kitazawa

As Reeb's theorem shows, Morse functions with exactly two singular points on closed manifolds are very simple and important. They characterize spheres whose dimensions are not $4$ topologically and the $4$-dimensional unit sphere. Special generic maps are generalized versions of these maps. Canonical projections of unit spheres are special generic. Studies o

Trent English, Nick Solomey

I discuss the feasibility of a conceptual space-based neutrino detector that utilizes the Ice Giants as Targets for Galactic Neutrinos. The purpose of this research stems from the concept of wanting to find a new method of observing the Galactic Core (GC) of the Milky Way and the Supermassive black hole, Sag A*. Observations of the GC have been made in every

Runmin Cong, Weiyu Song, Jianjun Lei, Guanghui Yue

For the video salient object detection (VSOD) task, how to excavate the information from the appearance modality and the motion modality has always been a topic of great concern. The two-stream structure, including an RGB appearance stream and an optical flow motion stream, has been widely used as a typical pipeline for VSOD tasks, but the existing methods u

Shuvashree Mondal, Shanya Baghel

Recently, a growing amount interest is quite evident in modelling dependent competing risks in life time prognosis problem. In this work, we propose to model the dependent competing risks by Marshal-Olkin bivariate exponential distribution. The observable data consists of number of failures due to different causes across different time intervals. The failure

R. Watanabe, M. Shinjo, Y. Yamamoto, M. Iwasaki

The Toda equation is one of the most famous integrable systems, and its time-discretization is simply the recursion formula of the quotient-difference (qd) algorithm for computing eigenvalues of tridiagonal matrices. An extension of the Toda equation is the q-Toda equation, which is derived by replacing standard derivatives with the so-called q-derivatives i

H. Sakai, Y. Tokiwa, P. Opletal, M. Kimata

The superconducting (SC) phase diagram in uranium ditelluride is explored under magnetic fields ($H$) along the hard magnetic b-axis using a high-quality single crystal with $T_{\rm c} = 2.1$ K. Simultaneous electrical resistivity and AC magnetic susceptibility measurements discern low- and high-field SC (LFSC and HFSC, respectively) phases with contrasting

Romeo Brunetti, Michael Dütsch, Klaus Fredenhagen, Kasia Rejzner

The C*-algebraic construction of QFT by Buchholz and one of us relies on the causal structure of spacetime and a classical Lagrangian. In one of our previous papers we have introduced additional structure into this construction, namely an action of symmetries, which is related to fixing renormalisation conditions. This action characterizes anomalies and sati

Yun Liu, Julia Wiktor, Bartomeu Monserrat

Antimony sulphide (Sb$_2$S$_3$) is an Earth-abundant and non-toxic material that is under investigation for solar energy conversion applications. However, it still suffers from poor power conversion efficiency and a large open circuit voltage loss that have usually been attributed to point or interfacial defects and trap states. More recently, a self-trapped

Pingbang Hu

In this paper, we provide a novel strategy for solving Traveling Salesman Problem, which is a famous combinatorial optimization problem studied intensely in the TCS community. In particular, we consider the imitation learning framework, which helps a deterministic algorithm making good choices whenever it needs to, resulting in a speed up while maintaining t

Wei-Jen Ko, Yating Wu, Cutter Dalton, Dananjay Srinivas

Automatic discourse processing is bottlenecked by data: current discourse formalisms pose highly demanding annotation tasks involving large taxonomies of discourse relations, making them inaccessible to lay annotators. This work instead adopts the linguistic framework of Questions Under Discussion (QUD) for discourse analysis and seeks to derive QUD structur

Oliver Gorton, Calvin Johnson, Jutta Escher

Reaction measurements on fission products are being planned at both Argonne National Lab and at the Facility for Rare Isotope Beams. These indirect experiments produce specific short-lived nuclei via beta decay, and the subsequent neutron and gamma emission are studied. Some initial experiments found a surprising overabundance of gamma emission, which theory

Guangsheng Yu, Xu Wang, Qin Wang, Tingting Bi

Web3 is leading a wave of the next generation of web services that even many Web2 applications are keen to ride. However, the lack of Web3 background for Web2 developers hinders easy and effective access and transition. On the other hand, Web3 applications desire for encouragement and advertisement from conventional Web2 companies and projects due to their l

Eric Thoma

We prove several results for the Coulomb gas in any dimension $d \geq 2$ that follow from isotropic averaging, a transport method based on Newton's theorem. First, we prove a high-density Jancovici-Lebowitz-Manificat law, extending the microscopic density bounds of Armstrong and Serfaty and establishing strictly sub-Gaussian tails for charge excess in dimens

Hui-Chi Kuo, Yun-Nung Chen

Intelligent virtual assistants are currently designed to perform tasks or services explicitly mentioned by users, so multiple related domains or tasks need to be performed one by one through a long conversation with many explicit intents. Instead, human assistants are capable of reasoning (multiple) implicit intents based on user utterances via commonsense k

Peijun Li, Xu Wang

We consider the inverse random potential scattering problem for the two- and three-dimensional biharmonic wave equation in lossy media. The potential is assumed to be a microlocally isotropic Gaussian rough field. The main contributions of the work are twofold. First, the unique continuation principle is proved for the fourth order biharmonic wave equation w

Xiaosu Zhu, Jingkuan Song, Yu Lei, Lianli Gao

As a crucial approach for compact representation learning, hashing has achieved great success in effectiveness and efficiency. Numerous heuristic Hamming space metric learning objectives are designed to obtain high-quality hash codes. Nevertheless, a theoretical analysis of criteria for learning good hash codes remains largely unexploited. In this paper, we