March 2023 arXiv papers — page 133

Jiayi Zhu, Bart Bolsterlee, Brian V. Y. Chow, Yang Song

Semi-supervised learning has made significant progress in medical image segmentation. However, existing methods primarily utilize information acquired from a single dimensionality (2D/3D), resulting in sub-optimal performance on challenging data, such as magnetic resonance imaging (MRI) scans with multiple objects and highly anisotropic resolution. To addres

Zhiheng Liu, Ruili Feng, Kai Zhu, Yifei Zhang

Human brains respond to semantic features of presented stimuli with different neurons. It is then curious whether modern deep neural networks admit a similar behavior pattern. Specifically, this paper finds a small cluster of neurons in a diffusion model corresponding to a particular subject. We call those neurons the concept neurons. They can be identified

Abdolkarim Saeedi, Arash Maghsoudi, Fereidoun Nowshiravan Rahatabad

The Early diagnosis and treatment of depression is essential for effective treatment. Depression, while being one of the most common mental illnesses, is still poorly understood in both research and clinical practice. Among different treatments, drug prescription is widely used, however the drug treatment is not effective for many patients. In this work, we

F. Vazza, D. Wittor, M. Brueggen, G. Brunetti

{We simulate the evolution of relativistic electrons injected into the intracluster medium by five radio galaxies. We study the spatial transport and the emission properties of the injected radio plasma over a $\sim 5$ Gyr period, and the sequence of cooling and re-acceleration events experienced by electrons, using a Lagrangian approach joint with a numeric

Anastasiia Kornilova, Ivan Moskalenko, Timofei Pushkin, Fakhriddin Tojiboev

This paper presents an approach for creating a visual place recognition (VPR) database for localization in indoor environments from RGBD scanning sequences. The proposed approach is formulated as a minimization problem in terms of dominating set algorithm for graph, constructed from spatial information, and referred as DominatingSet. Our algorithm shows bett

Ning Liao, Xiaopeng Zhang, Min Cao, Junchi Yan

In realistic open-set scenarios where labels of a part of testing data are totally unknown, when vision-language (VL) prompt learning methods encounter inputs related to unknown classes (i.e., not seen during training), they always predict them as one of the training classes. The exhibited label bias causes difficulty in open set recognition (OSR), in which

Anna Meyer, André Kaup

In contrast to traditional compression techniques performing linear transforms, the latent space of popular compressive autoencoders is obtained from a learned nonlinear mapping and hard to interpret. In this paper, we explore a promising alternative approach for neural compression, with an autoencoder whose latent space represents a nonlinear wavelet decomp

Solmaz Seifollahi, Hossein Bevrani, Kaniav Kamary

In this paper, we consider the multicollinearity problem in the gamma regression model when model parameters are linearly restricted. The linear restrictions are available from prior information to ensure the validity of scientific theories or structural consistency based on physical phenomena. In order to make relevant statistical inference for a model any

Antoine Collas, Titouan Vayer, Rémi Flamary, Arnaud Breloy

Dimension reduction (DR) methods provide systematic approaches for analyzing high-dimensional data. A key requirement for DR is to incorporate global dependencies among original and embedded samples while preserving clusters in the embedding space. To achieve this, we combine the principles of optimal transport (OT) and principal component analysis (PCA). Ou

Gengwei Zhang, Liyuan Wang, Guoliang Kang, Ling Chen

The goal of continual learning is to improve the performance of recognition models in learning sequentially arrived data. Although most existing works are established on the premise of learning from scratch, growing efforts have been devoted to incorporating the benefits of pre-training. However, how to adaptively exploit the pre-trained knowledge for each i

Zhiwen Li, Xin He, Changling Zhang, Ke Lu

The binary polyhydrides of heavy rare earth lutetium that shares a similar valence electron configuration to lanthanum have been experimentally discovered to be superconductive. The lutetium polyhydrides were successfully synthesized at high pressure and high temperature conditions using a diamond anvil cell in combinations with the in-situ high pressure las

Xiangyu Huang, Caidan Zhao, Jinghui Yu, Chenxing Gao

Frame prediction based on AutoEncoder plays a significant role in unsupervised video anomaly detection. Ideally, the models trained on the normal data could generate larger prediction errors of anomalies. However, the correlation between appearance and motion information is underutilized, which makes the models lack an understanding of normal patterns. Moreo

Aleksander Grochowicz, Fred Espen Benth, Marianne Zeyringer

In this article, we investigate mismatch of renewable electricity production to demand and how this is affected by flexibility options on the supply side. We assess the impact of spatial and temporal smoothing on reliability of production and whether they can reduce risks of variation. As a case study we pick a simplified (partial) representation of the Norw

Mario Cuoco, Angelo Di Bernardo

The need for faster and more miniaturised electronics is challenging scientists to develop novel forms of electronics based on quantum degrees of freedom different from electron charge. In this fast-developing field, often referred to as quantum electronics, the metal-oxide perovskite SrRuO3 can play an important role thanks to its diverse physical propertie

Georgia Kenyon, Stephan Lau, Michael A. Chappell, Mark Jenkinson

Segmentation of cerebral blood vessels from Magnetic Resonance Imaging (MRI) is an open problem that could be solved with deep learning (DL). However, annotated data for training is often scarce. Due to the absence of open-source tools, we aim to develop a classical segmentation method that generates vessel ground truth from Magnetic Resonance Angiography fo

Xiangyu Huang, Caidan Zhao, Chenxing Gao, Lvdong Chen

Due to the limited availability of anomalous samples for training, video anomaly detection is commonly viewed as a one-class classification problem. Many prevalent methods investigate the reconstruction difference produced by AutoEncoders (AEs) under the assumption that the AEs would reconstruct the normal data well while reconstructing anomalies poorly. How

R. P. Woodard

I share some reminiscences of the late Steven Weinberg. Then I discuss a topic in quantum field theory which he taught me: the role of state wave functionals in deriving the $i\epsilon$ term of the Feynman propagator when using the functional formalism. This is perhaps a curiosity for in-out scattering amplitudes on flat space backgrounds, but it is has much

Nchongmaje Ndipenoch, Alina Miron, Zidong Wang, Yongmin Li

Many eye diseases like Diabetic Macular Edema (DME), Age-related Macular Degeneration (AMD), and Glaucoma manifest in the retina, can cause irreversible blindness or severely impair the central version. The Optical Coherence Tomography (OCT), a 3D scan of the retina with high qualitative information about the retinal morphology, can be used to diagnose and m

Xiangyu Huang, Caidan Zhao, Zhiqiang Wu

Video anomaly detection is an essential but challenging task. The prevalent methods mainly investigate the reconstruction difference between normal and abnormal patterns but ignore the semantics consistency between appearance and motion information of behavior patterns, making the results highly dependent on the local context of frame sequences and lacking t

Qiangqiang Li, Qingjie Cao

Nonlinear systems exist widely in nature, however, how to construct systems with accurate expected non-linearity artificially is still a problem, which greatly limits their experimental study and engineering application. In this paper, we present a general nonlinear mechanical model (GNMM) based on special-shaped cam-spring mechanism (SCSM). GNMM consists of

Dicong Liang, Lijing Shao

Neutron stars (NSs) can be used to constrain dark matter (DM) since a NS can transform into a black hole (BH) if it captures sufficient DM particles and exceeds the Chandrasekhar limit. We extend earlier work and for the first time take into account the Galactic motion of individual NSs, which changes the amount of the captured DM by as large as one to two o

Tao Liu, Camelia M. Selcu, Binbin Wang, Núria Bagués

Creating materials that do not exist in nature can lead to breakthroughs in science and technology. Magnetic skyrmions are topological excitations that have attracted great attention recently for their potential applications in low power, ultrahigh density memory. A major challenge has been to find materials that meet the dual requirement of small skyrmions

Jannes Elstner, Raoul G. C. Schönhof, Steffen Tauber, Marco F Huber

When it comes to the optimization of CAD models in the automation domain, neural networks currently play only a minor role. Optimizing abstract features such as automation capability is challenging, since they can be very difficult to simulate, are too complex for rule-based systems, and also have little to no data available for machine-learning methods. On

Minh-Quan Le, Tam V. Nguyen, Trung-Nghia Le, Thanh-Toan Do

Few-shot instance segmentation extends the few-shot learning paradigm to the instance segmentation task, which tries to segment instance objects from a query image with a few annotated examples of novel categories. Conventional approaches have attempted to address the task via prototype learning, known as point estimation. However, this mechanism depends on

Mousumi Das, Roger Ianjamasimanana, Stacy S. McGaugh, James Schombert

We derive the oblateness parameter q of the dark matter halo of a sample of gas rich, face-on disk galaxies. We have assumed that the halos are triaxial in shape but their axes in the disk plane (a and b) are equal, so that q=c/a measures the halo flattening. We have used the HI velocity dispersion, derived from the stacked HI emission lines and the disk sur

Keisuke Kawano, Takuro Kutsuna, Ryoko Tokuhisa, Akihiro Nakamura

One major challenge in machine learning applications is coping with mismatches between the datasets used in the development and those obtained in real-world applications. These mismatches may lead to inaccurate predictions and errors, resulting in poor product quality and unreliable systems. In this study, we propose StyleDiff to inform developers of the dif

Hanlin Yu, Marcelo Hartmann, Bernardo Williams, Arto Klami

Stochastic-gradient sampling methods are often used to perform Bayesian inference on neural networks. It has been observed that the methods in which notions of differential geometry are included tend to have better performances, with the Riemannian metric improving posterior exploration by accounting for the local curvature. However, the existing methods oft

Luxuan Yang, Ting Gao, Wei Wei, Min Dai

Time series classification faces two unavoidable problems. One is partial feature information and the other is poor label quality, which may affect model performance. To address the above issues, we create a label correction method to time series data with meta-learning under a multi-task framework. There are three main contributions. First, we train the lab

J. Matoušek, J. Duník, M. Brandner

This paper deals with state estimation of stochastic models with linear state dynamics, continuous or discrete in time. The emphasis is laid on a numerical solution to the state prediction by the time-update step of the grid-point-based point-mass filter (PMF), which is the most computationally demanding part of the PMF algorithm. A novel efficient PMF (ePMF

J. Licandro, M. Popescu, E. Tatsumi, M. R. Alarcon

In the framework of the Visible NEAs Observations Survey (ViNOS) that uses several telescopes at the Canary Islands observatories since 2018, we observed two super fast rotator NEAs, 2021 NY$_1$ and 2022 AB. We obtained photometry and spectrophotometry of both targets and visible spectroscopy of 2022 AB. Light curves of 2021 NY$_1$ obtained in 4 different ni

Christodoulos Stylianou, Michele Weiland

Sparse matrices are an integral part of scientific simulations. As hardware evolves new sparse matrix storage formats are proposed aiming to exploit optimizations specific to the new hardware. In the era of heterogeneous computing, users often are required to use multiple formats for their applications to remain optimal across the different available hardwar

Ya-Dong Wu, Yan Zhu, Giulio Chiribella, Nana Liu

The characterization of continuous-variable quantum states is crucial for applications in quantum communication, sensing, simulation and computing. However, a full characterization of multimode quantum states requires a number of experiments that grows exponentially with the number of modes. Here we propose an alternative approach where the goal is not to re

Zuyi Zhang

In this article, we study the singularities of Lagrangian immersions into Cartesian product of surfaces. After applying a Hamiltonian isotopy in the Weinstein tubular neighbourhood of the Lagrangian immersion, the singular points of the Lagrangian immersion can be expressed locally as fold points with finitely many cusp points. This result has applications i

Xiaogang Peng, Siyuan Mao, Zizhao Wu

Multi-person pose forecasting remains a challenging problem, especially in modeling fine-grained human body interaction in complex crowd scenarios. Existing methods typically represent the whole pose sequence as a temporal series, yet overlook interactive influences among people based on skeletal body parts. In this paper, we propose a novel Trajectory-Aware

Rui-Feng Wang, Yan-Ling Xiong, Hang Yan, Xiaopeng Hu

Motivated by the discovery of superconductivity in the infinite-layer nickelate family, we report an experimental endeavor to clean the surface of nickelate superconductor Pr0.8Sr0.2NiO2 films by Ar+ ion sputtering and subsequent annealing, and we study their electronic structures by cryogenic scanning tunneling microscopy and spectroscopy. The annealed surf

Xiaobao Guo, Nithish Muthuchamy Selvaraj, Zitong Yu, Adams Wai-Kin Kong

Deception detection in conversations is a challenging yet important task, having pivotal applications in many fields such as credibility assessment in business, multimedia anti-frauds, and custom security. Despite this, deception detection research is hindered by the lack of high-quality deception datasets, as well as the difficulties of learning multimodal

Feng He, Qi Wang, Zhifan Feng, Wenbin Jiang

Video retrieval is becoming increasingly important owing to the rapid emergence of videos on the Internet. The dominant paradigm for video retrieval learns video-text representations by pushing the distance between the similarity of positive pairs and that of negative pairs apart from a fixed margin. However, negative pairs used for training are sampled rand

Chengyang Ying, Xinning Zhou, Zhongkai Hao, Hang Su

A long-standing goal of reinforcement learning is to acquire agents that can learn on training tasks and generalize well on unseen tasks that may share a similar dynamic but with different reward functions. The ability to generalize across tasks is important as it determines an agent's adaptability to real-world scenarios where reward mechanisms might vary.

Simeon C. Calvert, Stig Johnsen, Ashwin George

Ensuring operational control over automated vehicles is not trivial and failing to do so severely endangers the lives of road users. An integrated approach is necessary to ensure that all agents play their part including drivers, occupants, vehicle designers and governments. While progress is being made, a comprehensive approach to the problem is being ignor

Cristel Chandre, J. Pablo Salas

We consider a diatomic molecule driven by a linearly polarized laser pulse with a polarization axis rotating with a constant acceleration. This setup is referred to as optical centrifuge, and it is known to lead to high-angular momenta for the molecule (superrotor states) and, possibly, to dissociation. Here we elucidate the dynamical mechanisms behind the c

François-Marie Le Régent, Pierre Rouchon

A numerical method is proposed for simulation of composite open quantum systems. It is based on Lindblad master equations and adiabatic elimination. Each subsystem is assumed to converge exponentially towards a stationary subspace, slightly impacted by some decoherence channels and weakly coupled to the other subsystems. This numerical method is based on a p

Nasir Shaheed, Xun Chen, Meng Wang

The PandaX dark matter detection project searches for dark matter particles using the technology of dual phase xenon time projection chamber. The low expected rate of the signal events makes the control of backgrounds crucial for the experiment success. In addition to reducing external and internal backgrounds during the construction and operation of the det

Jie Jiang, Philippe Laurençot

Global existence and boundedness of classical solutions are shown for a parabolic-elliptic chemotaxis system with local sensing when the motility function is assumed to be unbounded at infinity. The cornerstone of the proof is the derivation of $L^\infty$-estimates on the second component of the system and is achieved by various comparison arguments.

Kunfeng Wang, Kaichun Zhao, Zheng You

Event-based cameras are new type vision sensors whose pixels work independently and respond asynchronously to brightness change with microsecond resolution, instead of providing standard intensity frames. Compared with traditional cameras, event-based cameras have low latency, no motion blur, and high dynamic range (HDR), which provide possibilities for robo

Hironori Yoshioka, Jun-Ichi Iwata, Yu-ichiro Matsushita

To clarify the cause of the low channel conductivity at the SiO2/4H-SiC interface, the wavefunction at the SiC conduction band minimum was calculated using density functional theory under an applied electric field. We found that the wavefunction for a 4H-SiC (0001) slab tends to be localized at the cubic site closest to the interface. Importantly, because th

Zhuoran Geng, Ilari J. Maasilta

In contradictin to the common concept that acoustic phonons can only travel inside a material medium, they can in fact "tunnel" across a vacuum gap with the help of piezoelectricity, transmitting a significantly stronger heat flux than that of blackbody radiation. Here, we present a theoretical formulation for the heat flux of such piezoelectrically mediated

Chengyuan Wu, Heran Xiong, Jie Lin, Yunlang Guo

Although multidimensional simulations have investigated the processes of double WD mergers, post-merger evolution only focused on the carbon-oxygen (CO) WD or helium (He) WD merger remnants. In this work, we investigate for the first time the evolution of the remnants stemmed from the merger of oxygen-neon (ONe) WDs with CO WDs. Our simulation results indica

Jing Yang, Bin Ji, Shasha Li, Jun Ma

Recently, many studies incorporate external knowledge into character-level feature based models to improve the performance of Chinese relation extraction. However, these methods tend to ignore the internal information of the Chinese character and cannot filter out the noisy information of external knowledge. To address these issues, we propose a mixture-of-v

Ngoc Hoang Anh Mai, Victor Magron

The problem of characterizing a real polynomial $f$ as a sum of squares of polynomials on a real algebraic variety $V$ dates back to the pioneering work of Hilbert in [Mathematische Annalen 32.3 (1888): 342-350]. In this paper, we investigate this problem with a focus on cases where the real zeros of $f$ on $V$ are singular points of $V$. By using optimality

Taom Sakal, Stephen Proulx

We investigate how well traditional fiction genres like Fantasy, Thriller, and Literature represent readers' preferences. Using user data from Goodreads we construct a book network where two books are strongly linked if the same people tend to read or enjoy them both. We then partition this network into communities of similar books and assign each a list of

Jingyu Li, Zhe Liu, Jinghua Hou, Dingkang Liang

In this paper, we present a simple yet effective semi-supervised 3D object detector named DDS3D. Our main contributions have two-fold. On the one hand, different from previous works using Non-Maximal Suppression (NMS) or its variants for obtaining the sparse pseudo labels, we propose a dense pseudo-label generation strategy to get dense pseudo-labels, which

Mao Ye, Gregory P. Meyer, Yuning Chai, Qiang Liu

Balancing efficiency and accuracy is a long-standing problem for deploying deep learning models. The trade-off is even more important for real-time safety-critical systems like autonomous vehicles. In this paper, we propose an effective approach for accelerating transformer-based 3D object detectors by dynamically halting tokens at different layers depending

Dev Seth, Rickard Stureborg, Danish Pruthi, Bhuwan Dhingra

Many adversarial attacks in NLP perturb inputs to produce visually similar strings ('ergo' $\rightarrow$ '$\epsilon$rgo') which are legible to humans but degrade model performance. Although preserving legibility is a necessary condition for text perturbation, little work has been done to systematically characterize it; instead, legibility is typically loosel

Jingzhe Ma, Dingqiang Ye, Chao Fan, Shiqi Yu

As a kind of biometrics, the gait information of pedestrians has attracted widespread attention from both industry and academia since it can be acquired from long distances without the cooperation of targets. In recent literature, this line of research has brought exciting chances along with alarming challenges: On the positive side, gait recognition used fo

Muqing Cao, Xinhang Xu, Shenghai Yuan, Kun Cao

We present the dynamic model and control of DoubleBee, a novel hybrid aerial-ground vehicle consisting of two propellers mounted on tilting servo motors and two motor-driven wheels. DoubleBee exploits the high energy efficiency of a bicopter configuration in aerial mode, and enjoys the low power consumption of a two-wheel self-balancing robot on the ground.

Senbei Du, Hui Li, Zhaoming Gan, Xiangrong Fu

Solar wind turbulence is often perceived as weakly compressible and the density fluctuations remain poorly understood both theoretically and observationally. Compressible magnetohydrodynamic simulations provide useful insights into the nature of density fluctuations. We discuss a few important effects related to 3D simulations of turbulence and in-situ obser

Yaohui Zhu, Linhu Liu, Jiang Tian

Food recognition has a wide range of applications, such as health-aware recommendation and self-service restaurants. Most previous methods of food recognition firstly locate informative regions in some weakly-supervised manners and then aggregate their features. However, location errors of informative regions limit the effectiveness of these methods to some

Jan Hendrik Metzen, Robin Hutmacher, N. Grace Hua, Valentyn Boreiko

Despite excellent average-case performance of many image classifiers, their performance can substantially deteriorate on semantically coherent subgroups of the data that were under-represented in the training data. These systematic errors can impact both fairness for demographic minority groups as well as robustness and safety under domain shift. A major cha

Tian-Xing Xu, Yuan-Chen Guo, Yu-Kun Lai, Song-Hai Zhang

3D single object tracking has been a crucial problem for decades with numerous applications such as autonomous driving. Despite its wide-ranging use, this task remains challenging due to the significant appearance variation caused by occlusion and size differences among tracked targets. To address these issues, we present MBPTrack, which adopts a Memory mech

Zhentian Zhang, Jian Dang, Zaichen Zhang, Liang Wu

Massive machine-type communications (mMTC) or massive access is a critical scenario in the fifth generation (5G) and the future cellular network. With the surging density of devices from millions to billions, unique pilot allocation becomes inapplicable in the user ID-incorporated grant-free random access protocol. Unsourced random access (URA) manifests its

Shaohui Peng, Xing Hu, Rui Zhang, Jiaming Guo

Despite the broad application of deep reinforcement learning (RL), transferring and adapting the policy to unseen but similar environments is still a significant challenge. Recently, the language-conditioned policy is proposed to facilitate policy transfer through learning the joint representation of observation and text that catches the compact and invarian

Yuwei Zhang, Chih-Hui Ho, Nuno Vasconcelos

The problem of realistic VQA (RVQA), where a model has to reject unanswerable questions (UQs) and answer answerable ones (AQs), is studied. We first point out 2 drawbacks in current RVQA research, where (1) datasets contain too many unchallenging UQs and (2) a large number of annotated UQs are required for training. To resolve the first drawback, we propose

Marc Goerigk, Romain Guillaume, Adam Kasperski, Paweł Zieliński

In this paper, an optimization problem with uncertain objective function coefficients is considered. The uncertainty is specified by providing a discrete scenario set, containing possible realizations of the objective function coefficients. The concept of belief function in the traditional and possibilistic setting is applied to define a set of admissible pr

Jinfeng Wang, Sifan Song, Jionglong Su, S. Kevin Zhou

Self-supervised learning is well known for its remarkable performance in representation learning and various downstream computer vision tasks. Recently, Positive-pair-Only Contrastive Learning (POCL) has achieved reliable performance without the need to construct positive-negative training sets. It reduces memory requirements by lessening the dependency on t

Soichi Shirai, Hokuto Iwakiri, Keita Kanno, Takahiro Horiba

The analysis of a chemical reaction along the ground state potential energy surface in conjunction with an unknown spin state is challenging because electronic states must be separately computed several times using different spin multiplicities to find the lowest energy state. However, in principle, the ground state could be obtained with just a single calcu

Yi Yang, Hauyu Baobab Liu, Takayuki Muto, Jun Hashimoto

Crescent-like asymmetric dust structures discovered in protoplanetary disks indicate dust aggregations. Thus, the research on them helps us understand the planet formation process. Here we analyze the ALMA data of the protoplanetary disk around the T-Tauri star SR 21, which has asymmetric structures detected in previous sub-millimeter observations. Imaged at

Mohammad Qatawneh

This paper provides a comprehensive and high level technical overview of BC technology by creating a common language through business and technology about BC. Then an overview of a BC technology, its architectures, classification, challenges, etc. is presented. Finally, some recommendations are provided to re-searchers that will have to be tackled before dep

Jiabang He, Lei Wang, Yi Hu, Ning Liu

Large language models (LLMs), such as GPT-3 and ChatGPT, have demonstrated remarkable results in various natural language processing (NLP) tasks with in-context learning, which involves inference based on a few demonstration examples. Despite their successes in NLP tasks, no investigation has been conducted to assess the ability of LLMs to perform document i

Chattu Bhargavi, Vikash Kumar Singh

In crowdsourcing, a group of common people is asked to execute the tasks and in return will receive some incentives. In this article, one of the crowdsourcing scenarios with multiple heterogeneous tasks and multiple IoT devices (as task executors) is studied as a two-tiered process. In the first tier of the proposed model, it is assumed that a substantial nu

Guang Yang, Yu Zhou, Xiang Chen, Xiangyu Zhang

Due to the development of pre-trained language models, automated code generation techniques have shown great promise in recent years. However, the generated code is difficult to meet the syntactic constraints of the target language, especially in the case of Turducken-style code, where declarative code snippets are embedded within imperative programs. In thi

Gladwin James V., Thomas Xavier, Nicy Sebastian

The Type-1 Pathway Generated Exponential distribution (PGE-1) is introduced. We have considered the estimate of the stress-strength parameter when the stress and strength components are statistically independent and follow PGE-1 distributions with distinct parameters. The point estimate of the stress-strength reliability is obtained using maximum likelihood

Charles Y Zhang, Ashish Shrivastava

Advancements in graphics technology has increased the use of simulated data for training machine learning models. However, the simulated data often differs from real-world data, creating a distribution gap that can decrease the efficacy of models trained on simulation data in real-world applications. To mitigate this gap, sim-to-real domain transfer modifies

Shenxing Zhang

Let $e_1,e_2,e_3$ be nonzero integers satisfying $e_1+e_2+e_3=0$. Let $(a,b,c)$ be a primitive triple of odd integers satisfying $e_1a^2+e_2b^2+e_3c^2=0$. Denote by $E: y^2=x(x-e_1)(x+e_2)$ and $\mathcal E: y^2=x(x-e_1a^2)(x+e_2b^2)$. Assume that the $2$-Selmer groups of $E$ and $\mathcal E$ are minimal. Let $n$ be a positive square-free odd integer, where t

Zhangjie Wang, Shenxing Zhang

Let $E: y^2=x(x-a^2)(x+b^2)$ be an elliptic curve with full $2$-torsion group, where $a$ and $b$ are coprime integers and $2(a^2+b^2)$ is a square. Assume that the $2$-Selmer group of $E$ has rank two. We characterize all quadratic twists of $E$ with Mordell-Weil rank zero and $2$-primary Shafarevich-Tate groups $(\mathbb Z/2\mathbb Z)^2$, under certain cond

Dongsu Bak, Chanju Kim, Sang-Heon Yi

We study canonical quantization of Jackiw-Teibelboim (JT) gravity coupled to a massless scalar field. We provide concrete expressions of matter SL(2,{\,\bf R}) charges and the boundary matter operators in terms of the creation and annihilation operators in the scalar field. The matter charges are represented in the form of an oscillator (Jordon-Schwinger) re

Masaaki Harada, Keita Ishizuka

In this note, we study the existence of Hadamard matrices of order $36$ formed by codewords of weight $36$ in some ternary near-extremal self-dual codes of length $36$.

Peng Gao, Liangyi Zhao

We evaluate asymptotically the negative first moment at points larger than $1/2$ of the family of quadratic twists of automorphic $L$-functions using multiple Dirichlet series under the generalized Riemann hypothesis and the Ramanujan-Petersson conjecture.

Patrick S. Kamieneski, Brenda L. Frye, Massimo Pascale, Seth H. Cohen

Gradients in the mass-to-light ratio of distant galaxies impede our ability to characterize their size and compactness. The long-wavelength filters of $JWST$'s NIRCam offer a significant step forward. For galaxies at Cosmic Noon ($z\sim2$), this regime corresponds to the rest-frame near-infrared, which is less biased towards young stars and captures emission

ChunChi Liu, LiuHao Li, Jin An

Topology plays an important role in non-hermitian systems. How to characterize a non-hermitian topological system under open-boundary conditions(OBCs) is a challenging problem. A one-dimensional(1D) topological invariant defined on a generalized Brillion zone(GBZ) was recently found to successfully describe the topological property of the two-band Su-Schrief

Kento Kawaharazuka, Yoshiki Obinata, Naoaki Kanazawa, Kei Okada

State recognition of objects and environment in robots has been conducted in various ways. In most cases, this is executed by processing point clouds, learning images with annotations, and using specialized sensors. In contrast, in this study, we propose a state recognition method that applies Visual Question Answering (VQA) in a Pre-Trained Vision-Language

Gu Lim, Myungshin Im, Gregory S. H. Paek, Sung-Chul Yoon

The progenitor system of Type Ia supernovae (SNe Ia) is expected to be a close binary system of a carbon/oxygen white dwarf (WD) and a non-degenerate star or another WD. Here, we present results from a high-cadence monitoring observation of SN 2021hpr in a spiral galaxy, NGC 3147, and constraints on the progenitor system based on its early multi-color light

Junjie Hu, Chenyou Fan, Liguang Zhou, Qing Gao

With the rapid advancements in autonomous driving and robot navigation, there is a growing demand for lifelong learning models capable of estimating metric (absolute) depth. Lifelong learning approaches potentially offer significant cost savings in terms of model training, data storage, and collection. However, the quality of RGB images and depth maps is sen

Mude Hui, Zhizheng Zhang, Xiaoyi Zhang, Wenxuan Xie

Layout generation aims to synthesize realistic graphic scenes consisting of elements with different attributes including category, size, position, and between-element relation. It is a crucial task for reducing the burden on heavy-duty graphic design works for formatted scenes, e.g., publications, documents, and user interfaces (UIs). Diverse application sce

Wanli Ni, Jingheng Zheng, Hui Tian

Implementing existing federated learning in massive Internet of Things (IoT) networks faces critical challenges such as imbalanced and statistically heterogeneous data and device diversity. To this end, we propose a semi-federated learning (SemiFL) framework to provide a potential solution for the realization of intelligent IoT. By seamlessly integrating the

Wenrui Liu, Hong Chang, Bingpeng Ma, Shiguang Shan

Reconstruction-based anomaly detection models achieve their purpose by suppressing the generalization ability for anomaly. However, diverse normal patterns are consequently not well reconstructed as well. Although some efforts have been made to alleviate this problem by modeling sample diversity, they suffer from shortcut learning due to undesired transmissi

Satarupa Guha, Rahul Ambavat, Ankur Gupta, Manish Gupta

Word error rate (WER) is a standard metric for the evaluation of Automated Speech Recognition (ASR) systems. However, WER fails to provide a fair evaluation of human perceived quality in presence of spelling variations, abbreviations, or compound words arising out of agglutination. Multiple spelling variations might be acceptable based on locale/geography, a

Karthik Gajulapalli, Alexander Golovnev, Satyajeet Nagargoje, Sidhant Saraogi

Range Avoidance (AVOID) is a total search problem where, given a Boolean circuit $C\colon\{0,1\}^n\to\{0,1\}^m$, $m>n$, the task is to find a $y\in\{0,1\}^m$ outside the range of $C$. For an integer $k\geq 2$, $\mathrm{NC}^0_k$-AVOID is a special case of AVOID where each output bit of $C$ depends on at most $k$ input bits. While there is a very natural rando

Daniel Gedon, Antôni H. Ribeiro, Niklas Wahlström, Thomas B. Schön

Kernel principal component analysis (kPCA) is a widely studied method to construct a low-dimensional data representation after a nonlinear transformation. The prevailing method to reconstruct the original input signal from kPCA -- an important task for denoising -- requires us to solve a supervised learning problem. In this paper, we present an alternative m

Arundhati Dasgupta, José Fajardo-Montenegro

With the discovery of gravitational waves, the search for the quantum of gravity, the graviton, is imminent. We discuss the current status of the bounds on graviton mass from experiments as well as the theoretical understanding of these particles. We provide an overview of current experiments in astrophysics such as the search for Hawking radiation in gamma-

Fenghao Zhu, Bohao Wang, Zhaohui Yang, Chongwen Huang

Beamforming with large-scale antenna arrays has been widely used in recent years, which is acknowledged as an important part in 5G and incoming 6G. Thus, various techniques are leveraged to improve its performance, e.g., deep learning, advanced optimization algorithms, etc. Although its performance in many previous research scenarios with deep learning is qu

Tian Le, Ruihan Zhang, Changcun Li, Ruiyang Jiang

Topological materials with boundary (surface/edge/hinge) states have attracted tremendous research interest. Besides, unconventional (obstructed atomic) materials have recently drawn lots of attention owing to their obstructed boundary states. Experimentally, Josephson junctions (JJs) constructed on materials with boundary states produce the peculiar boundar

Zaid Sawlan, Marco Scavino, Raul Tempone

This work employs the Birnbaum--Saunders distribution to model the fatigue life of metallic materials under cyclic loading and compares it with the normal distribution. Fatigue-limit models are fitted to three datasets of unnotched specimens of 75S-T6 aluminum alloys and carbon laminate with different loading types. A new equivalent stress definition that ac

Xinyuan Lu, Min-Yen Kan

Personality is a psychological factor that reflects people's preferences, which in turn influences their decision-making. We hypothesize that accurate modeling of users' personalities improves recommendation systems' performance. However, acquiring such personality profiles is both sensitive and expensive. We address this problem by introducing a novel metho

Hao Tang, Aref Miri Rekavandi, Dharjinder Rooprai, Girish Dwivedi

This study investigates the effectiveness of Explainable Artificial Intelligence (XAI) techniques in predicting suicide risks and identifying the dominant causes for such behaviours. Data augmentation techniques and ML models are utilized to predict the associated risk. Furthermore, SHapley Additive exPlanations (SHAP) and correlation analysis are used to ra

Benedict Quartey, Ankit Shah, George Konidaris

Reinforcement learning requires interaction with an environment, which is expensive for robots. This constraint necessitates approaches that work with limited environmental interaction by maximizing the reuse of previous experiences. We propose an approach that maximizes experience reuse while learning to solve a given task by generating and simultaneously l

Ning Liu, Benjamin Grimmer

We consider feasibility and constrained optimization problems defined over smooth and/or strongly convex sets. These notions mirror their popular function counterparts but are much less explored in the first-order optimization literature. We propose new scalable, projection-free, accelerated first-order methods in these settings. Our methods avoid linear opt

Sabrina Osmany

We perform a set of experiments to demonstrate that images generated using a Generative Adversarial Network can be modified using 'semiotics.' We show that just as physical attributes such as the hue and saturation of an image can be modified, so too can its non-physical, abstract properties using our method. For example, the design of a flight attendant's u

AprilPyone MaungMaung, Hitoshi Kiya

In this paper, we propose a novel generative model-based attack on learnable image encryption methods proposed for privacy-preserving deep learning. Various learnable encryption methods have been studied to protect the sensitive visual information of plain images, and some of them have been investigated to be robust enough against all existing attacks. Howev

Valeriy Imaykin

We consider the system of Maxwell equations and Lorentz torque equation which describes a motion of charge in electromagnetic field. Under certain symmetry conditions on charge distribution and on initial fields the mass center of the charge remains fixed and the charge rotates around it. The system admits stationary soliton-type solutions. We study the Lyap

Caiyuan Chu, Ya Li, Yifan Liu, Jia-Chen Gu

Intent recognition is critical for task-oriented dialogue systems. However, for emerging domains and new services, it is difficult to accurately identify the key intent of a conversation due to time-consuming data annotation and comparatively poor model transferability. Therefore, the automatic induction of dialogue intention is very important for intelligen