July 2023 arXiv papers — page 88

Roberto Rubboli, Ryuji Takagi, Marco Tomamichel

We prove that the stabilizer fidelity is multiplicative for the tensor product of an arbitrary number of single-qubit states. We also show that the relative entropy of magic becomes additive if all the single-qubit states but one belong to a symmetry axis of the stabilizer octahedron. We extend the latter results to include all the $\alpha$-$z$ R\'enyi relat

Gabriel German, Juan Carlos Hidalgo, Luis E. Padilla

This paper focuses on the Starobinsky model of inflation. We derive solutions for various cosmological observables, such as the scalar spectral index $n_s$, the tensor-to-scalar ratio $r$ and their runnings, as well as the number of $e$-folds of inflation, reheating, and radiation with minimal assumptions. We establish an equation that connects inflation and

Xin Zhang, Shenghui Song

This paper investigates the ultra reliable and low latency communication (URLLC) performance of the IRS-aided MIMO system. The upper and lower bounds of the optimal average error probability (OAEP) for the coding rate 1/sqrt(Mn) of the capacity are derived, where n and M represent the blocklength and the number of transmit antennas, respectively. To achieve

Karina Bzheumikhova, John Vinson, Rainer Unterumsberger, Malte Wansleben

Using well-calibrated experimental data we validate theoretical X-ray absorption spectroscopy (XAS) as well as X-ray emission spectroscopy (XES) calculations for titanium (Ti), titanium oxide (TiO), and titanium dioxide (TiO$_2$) at the Ti K- and L-edges as well as O K-edge. XAS and XES in combination with a multi-edge approach offer a detailed insight into

A. Longman, S. Ravichandran, L. Manzo, C. Z. He

Spatial distributions of electrons ionized and scattered from ultra-low pressure gases are proposed and experimentally demonstrated as a method to directly measure the intensity of an ultra-high intensity laser pulse. Analytic models relating the peak scattered electron energy to the peak laser intensity are derived and compared to paraxial Runge-Kutta simul

Takayuki Suzuki, Kaito Iwamura

We consider the time-dependent transverse field Ising chain with time-periodic perturbations. Without perturbations, this model is one of the famous models that obeys the scaling in the adiabatic limit predicted by the quantum Kibble-Zurek mechanism (QKZM). However, it is known that when oscillations are added to the system, the non-perturbative contribution

Kai Peng, Ying Zhang, Shuai Ling, Zhaoru Ke

Celebrities' whereabouts are of pervasive importance. For instance, where politicians go, how often they visit, and who they meet, come with profound geopolitical and economic implications. Although news articles contain travel information of celebrities, it is not possible to perform large-scale and network-wise analysis due to the lack of automatic itinera

Lu Yang, Liulei Li, Xueshi Xin, Yifan Sun

Location determination finds wide applications in daily life. Instead of existing efforts devoted to localizing tourist photos captured by perspective cameras, in this article, we focus on devising person positioning solutions using overhead fisheye cameras. Such solutions are advantageous in large field of view (FOV), low cost, anti-occlusion, and unaggress

Dongning Ma, Xun Jiao, Fred Lin, Mengshi Zhang

Deep recommendation systems (DRS) heavily depend on specialized HPC hardware and accelerators to optimize energy, efficiency, and recommendation quality. Despite the growing number of hardware errors observed in large-scale fleet systems where DRS are deployed, the robustness of DRS has been largely overlooked. This paper presents the first systematic study

David Martin, Daniel Seara, Yael Avni, Michel Fruchart

Two hallmarks of non-equilibrium systems, from active colloids to animal herds, are agents motility and nonreciprocal interactions. Their interplay creates feedback loops leading to complex spatiotemporal dynamics crucial to understand and control the nonlinear response of active systems. Here, we introduce a minimal model that captures these two features at

Mirza Karamehmedović, Faouzi Triki

We study the validity of the Neumann or Born series approach in solving the Helmholtz equation and coefficient identification in related inverse scattering problems. Precisely, we derive a sufficient and necessary condition under which the series is strongly convergent. We also investigate the rate of convergence of the series. The obtained condition is opti

Yanghao Wang, Zhongqi Yue, Xian-Sheng Hua, Hanwang Zhang

We show that classifiers trained with random region proposals achieve state-of-the-art Open-world Object Detection (OWOD): they can not only maintain the accuracy of the known objects (w/ training labels), but also considerably improve the recall of unknown ones (w/o training labels). Specifically, we propose RandBox, a Fast R-CNN based architecture trained

Xumin Gu, Dehua Wang, Feng Xie

In this paper we consider the vanishing viscosity limit of solutions to the initial boundary value problem for compressible viscoelastic equations in the half space. When the initial deformation gradient does not degenerate and there is no vacuum initially, we establish the uniform regularity estimates of solutions to the initial-boundary value problem for t

Nghia Hieu Nguyen, Kiet Van Nguyen

We present in this paper a novel scheme for multimodal learning named the Parallel Attention mechanism. In addition, to take into account the advantages of grammar and context in Vietnamese, we propose the Hierarchical Linguistic Features Extractor instead of using an LSTM network to extract linguistic features. Based on these two novel modules, we introduce

Jianbo Cui

To capture and simulate geometric surface evolutions, one effective approach is based on the phase field methods. Among them, it is important to design and analyze numerical approximations whose error bound depends on the inverse of the diffuse interface thickness (denoted by $\frac 1\epsilon$) polynomially. However, it has been a long-standing problem wheth

Roey Merchav, Shoham Sabach

In this paper, we propose the Bi-Sub-Gradient (Bi-SG) method, which is a generalization of the classical sub-gradient method to the setting of convex bi-level optimization problems. This is a first-order method that is very easy to implement in the sense that it requires only a computation of the associated proximal mapping or a sub-gradient of the outer non

Sangeet S. Kumar, Brendan C. Mulkerin, Meera M. Parish, Jesper Levinsen

Strong interactions between charges and light-matter coupled quasiparticles offer an intriguing prospect with applications from optoelectronics to light-induced superconductivity. Here, we investigate how the interactions between electrons and exciton-polaritons in a two-dimensional semiconductor microcavity can be resonantly enhanced due to a strong couplin

Wentao Bao, Lele Chen, Libing Zeng, Zhong Li

Hand trajectory forecasting from egocentric views is crucial for enabling a prompt understanding of human intentions when interacting with AR/VR systems. However, existing methods handle this problem in a 2D image space which is inadequate for 3D real-world applications. In this paper, we set up an egocentric 3D hand trajectory forecasting task that aims to

Anubhav Singh, Miquel Ramirez, Nir Lipovetzky, Peter J. Stuckey

This paper studies the possibilities made open by the use of Lazy Clause Generation (LCG) based approaches to Constraint Programming (CP) for tackling sequential classical planning. We propose a novel CP model based on seminal ideas on so-called lifted causal encodings for planning as satisfiability, that does not require grounding, as choosing groundings fo

Samuel Goldman, Jiayi Xin, Joules Provenzano, Connor W. Coley

Chemical formula annotation for tandem mass spectrometry (MS/MS) data is the first step toward structurally elucidating unknown metabolites. While great strides have been made toward solving this problem, the current state-of-the-art method depends on time-intensive, proprietary, and expert-parameterized fragmentation tree construction and scoring. In this w

Siwei Huang, Jianfeng Chen, Jisheng Bai, Yafei Jia

DNN-based methods have shown high performance in sound event localization and detection(SELD). While in real spatial sound scenes, reverberation and the imbalanced presence of various sound events increase the complexity of the SELD task. In this paper, we propose an effective SELD system in real spatial scenes.In our approach, a dynamic kernel convolution m

Hengcan Shi, Munawar Hayat, Jianfei Cai

In recent years, open-vocabulary (OV) dense visual prediction (such as OV object detection, semantic, instance and panoptic segmentations) has attracted increasing research attention. However, most of existing approaches are task-specific and individually tackle each task. In this paper, we propose a Unified Open-Vocabulary Network (UOVN) to jointly address

Jing Ma, Chen Chen, Anil Vullikanti, Ritwick Mishra

Methicillin-resistant Staphylococcus aureus (MRSA) is a type of bacteria resistant to certain antibiotics, making it difficult to prevent MRSA infections. Among decades of efforts to conquer infectious diseases caused by MRSA, many studies have been proposed to estimate the causal effects of close contact (treatment) on MRSA infection (outcome) from observat

K. A. Shaykhutdinov, S. A. Skorobogatov, Yu. V. Knyazev, T. N. Kamkova

HoFe1-xMnxO3 (0 < x < 1) single crystals have been grown by the optical floating zone technique. A structural transition from the orthorhombic to hexagonal modification has been established in the crystals in the concentration range of 0.7-0.8, which has been confirmed by the X-ray diffraction data. For a series of the rhombic crystals, the room-temperature

Haohui Wang, Weijie Guan, Jianpeng Chen, Zi Wang

Long-tailed data distributions pose challenges for a variety of domains like e-commerce, finance, biomedical science, and cyber security, where the performance of machine learning models is often dominated by head categories while tail categories are inadequately learned. This work aims to provide a systematic view of long-tailed learning with regard to thre

Shaoshi Ling, Yuxuan Hu, Shuangbei Qian, Guoli Ye

Most end-to-end (E2E) speech recognition models are composed of encoder and decoder blocks that perform acoustic and language modeling functions. Pretrained large language models (LLMs) have the potential to improve the performance of E2E ASR. However, integrating a pretrained language model into an E2E speech recognition model has shown limited benefits due

Liu Liu, Shuaifeng Zhi, Zhenhua Du, Li Liu

Radars, due to their robustness to adverse weather conditions and ability to measure object motions, have served in autonomous driving and intelligent agents for years. However, Radar-based perception suffers from its unintuitive sensing data, which lack of semantic and structural information of scenes. To tackle this problem, camera and Radar sensor fusion

Abubakar Isah, Hyeju Shin, Ibrahim Aliyu, Sangwon Oh

A new network named the "Digital Twin Network" (DTN) uses the "Digital Twin" (DT) technology to produce virtual twins of real things. The network load and size continue to grow as a result of the development of 5G, the Internet of Things, and cloud computing technology as well as the advent of new network services. As a result, network operation and maintena

Yanir Marmor, Kinneret Misgav, Yair Lifshitz

We introduce "ivrit.ai", a comprehensive Hebrew speech dataset, addressing the distinct lack of extensive, high-quality resources for advancing Automated Speech Recognition (ASR) technology in Hebrew. With over 3,300 speech hours and a over a thousand diverse speakers, ivrit.ai offers a substantial compilation of Hebrew speech across various contexts. It is

Jing Ma, Ruocheng Guo, Aidong Zhang, Jundong Li

Fairness-aware machine learning has attracted a surge of attention in many domains, such as online advertising, personalized recommendation, and social media analysis in web applications. Fairness-aware machine learning aims to eliminate biases of learning models against certain subgroups described by certain protected (sensitive) attributes such as race, ge

Chang-Qing Ye, Jin-Hong Chen, Jian-dong Zhang, Hui-Min Fan

Recently discovered regular X-ray bursts known as quasi-periodic eruptions have a proposed model that suggests a tidal stripping white dwarf inspiralling into the galaxy's central black hole on an eccentric orbit. According to this model, the interaction of the stripping white dwarf with the central black hole would emit gravitational wave signals as well, t

Hoang-Giang Cao, I Lee, Bo-Jiun Hsu, Zheng-Yi Lee

Deep reinforcement learning has achieved significant results in low-level controlling tasks. However, for some applications like autonomous driving and drone flying, it is difficult to control behavior stably since the agent may suddenly change its actions which often lowers the controlling system's efficiency, induces excessive mechanical wear, and causes u

Jiayue Yang, Andrew R. Frey

We investigate the holographic complexity of CFTs compactified on a circle with a Wilson line, dual to magnetized solitons in AdS$_4$ and AdS$_5$. These theories have a confinement-deconfinement phase transition as a function of the Wilson line, and the complexity of formation acts as an order parameter for this transition. Through explicit calculation, we s

Chao Ding, Mingyuan Lin, Haijian Zhang, Jianzhuang Liu

The stereo event-intensity camera setup is widely applied to leverage the advantages of both event cameras with low latency and intensity cameras that capture accurate brightness and texture information. However, such a setup commonly encounters cross-modality parallax that is difficult to be eliminated solely with stereo rectification especially for real-wo

Jihao Huang, Zhitao Liu, Jun Zeng, Xuemin Chi

In this paper, we propose a safety-critical controller based on time-varying control barrier functions (CBFs) for a robot with an unicycle model in the continuous-time domain to achieve navigation and dynamic collision avoidance. Unlike previous works, our proposed approach can control both linear and angular velocity to avoid collision with obstacles, overc

Linfeng Zhao, Owen Howell, Jung Yeon Park, Xupeng Zhu

In robotic tasks, changes in reference frames typically do not influence the underlying physical properties of the system, which has been known as invariance of physical laws.These changes, which preserve distance, encompass isometric transformations such as translations, rotations, and reflections, collectively known as the Euclidean group. In this work, we

Shuhao Zhang, Xianzhi Zeng, Yuhao Wu, Zhonghao Yang

Large Language Models (LLMs) have demonstrated extraordinary performance across a broad array of applications, from traditional language processing tasks to interpreting structured sequences like time-series data. Yet, their effectiveness in fast-paced, online decision-making environments requiring swift, accurate, and concurrent responses poses a significan

Aleksandra Sobieska, Jay Yang

Cellular resolutions are a technique for constructing resolutions of monomial ideals by giving a cell complex labeled by monomials, or more generally, by monomial modules. This \verb|Macaulay2| package allows us to work with cellular resolutions in a natural way.

Tetsuo Yabuki

In this study, the theoretical maximum efficiency $\eta_{max}$ and the Boltzmann-type factor giving the concentration ratio of excited-to-ground state pigment-molecules for photosynthetic systems under irradiation with arbitrary photon flux density $n_\gamma(\lambda)$, solid angle $\Omega$, and degree of polarization P, are formulated in the most fundamental

Guang Jiang, Jiahui Zhu, Yunsong Li, Pengcheng An

Teacher-student interaction (TSI) is essential for learning efficiency and harmonious teacher-student interpersonal relationships. However, studies on TSI support tools often focus on teacher needs while neglecting student needs and autonomy. To enhance both lecturer competence in delivering interpersonal interaction and student autonomy in TSI, we developed

Lizhou Liao, Wenlei Yan, Li Sun, Xinhui Bai

Loop-closure detection, also known as place recognition, aiming to identify previously visited locations, is an essential component of a SLAM system. Existing research on lidar-based loop closure heavily relies on dense point cloud and 360 FOV lidars. This paper proposes an out-of-the-box NDT (Normal Distribution Transform) based global descriptor, NDT-Map-C

Mohammed Latif Siddiq, Beatrice Casey, Joanna C. S. Santos

In recent years, the use of automated source code generation utilizing transformer-based generative models has expanded, and these models can generate functional code according to the requirements of the developers. However, recent research revealed that these automatically generated source codes can contain vulnerabilities and other quality issues. Despite

Yang Guo, Ning Zhang, Wenjian Liu

It has recently been shown that the SOiCI approach [J. Phys.: Condens. Matter 34 (2022) 224007], in conjunction with the spin-separated exact two-component relativistic Hamiltonian, can provide very accurate fine structures of systems containing heavy elements by treating electron correlation and spin-orbit coupling (SOC) on an equal footing. Nonetheless, or

Kohei Kawabata, Zhenyu Xiao, Tomi Ohtsuki, Ryuichi Shindou

The spectral statistics of non-Hermitian random matrices are of importance as a diagnostic tool for chaotic behavior in open quantum systems. Here, we investigate the statistical properties of singular values in non-Hermitian random matrices as an effective measure of quantifying dissipative quantum chaos. By means of Hermitization, we reveal the unique char

Tangyu Qian, Zhangli Zhou, Shaocheng Wang, Zhijun Li

Quadruped robots have received increasing attention for the past few years. However, existing works primarily focus on static environments or assume the robot has full observations of the environment. This limits their practical applications since real-world environments are often dynamic and partially observable. To tackle these issues, vision-based reactiv

Roshan Sharma, Kenneth Zheng, Siddhant Arora, Shinji Watanabe

End-to-end speech summarization has been shown to improve performance over cascade baselines. However, such models are difficult to train on very large inputs (dozens of minutes or hours) owing to compute restrictions and are hence trained with truncated model inputs. Truncation leads to poorer models, and a solution to this problem rests in block-wise model

Rui Chang, Jun He, Junmin Wang

External-cavity diode lasers (ECDL) are widely used as light sources in laser spectroscopy, atomic physics, and quantum optics. This study demonstrated a home-made 852-nm ECDL with variable feedback, using the combined dual narrow-band interference filters (IFs) as the laser longitudinal mode selection element. The combination of narrow-band IFs, mainly for

Fujie Tang, Kefeng Shi, Xifan Wu

X-ray absorption spectroscopy (XAS) is a powerful experimental tool to probe the local structure in materials with the core hole excitations. Here, the oxygen K-edge XAS spectra of the NaCl solution and pure water are computed by using a recently developed GW-BSE approach, based on configurations modeled by path-integral molecular dynamics with the deep-lear

Ruichen Li, Haotian Ye, Du Jiang, Xuelan Wen

Neural network-based variational Monte Carlo (NN-VMC) has emerged as a promising cutting-edge technique of ab initio quantum chemistry. However, the high computational cost of existing approaches hinders their applications in realistic chemistry problems. Here, we report the development of a new NN-VMC method that achieves a remarkable speed-up by more than

Soh Kumabe, Yuichi Yoshida

Combinatorial algorithms are widely used for decision-making and knowledge discovery, and it is important to ensure that their output remains stable even when subjected to small perturbations in the input. Failure to do so can lead to several problems, including costly decisions, reduced user trust, potential security concerns, and lack of replicability. Unf

Zongchen Chen

We present a simple combinatorial framework for establishing approximate tensorization of variance and entropy in the setting of spin systems (a.k.a. undirected graphical models) based on balanced separators of the underlying graph. Such approximate tensorization results immediately imply as corollaries many important structural properties of the associated

Konstantin Tikhomirov

Let $A$ be an $n\times n$ matrix with mutually independent centered Gaussian entries. Define \begin{align*} \sigma^*:=\max\limits_{i,j\leq n}\sqrt{{\mathbb E}\,|A_{i,j}|^2}, \quad \sigma:=\max\bigg(\max\limits_{j\leq n}\sqrt{{\mathbb E}\,\|{\rm col}_j(A)\|_2^2}, \max\limits_{i\leq n}\sqrt{{\mathbb E}\,\|{\rm row}_i(A)\|_2^2}\bigg). \end{align*} Assume that $

Jieni Zhang, Yong Zeng

For millimeter wave (mmWave) or Terahertz (THz) communications, by leveraging the high spatial resolution offered by large antenna arrays and the multi-path sparsity of mmWave/THz channels, a novel inter-symbol interference (ISI) mitigation technique called delay alignment modulation (DAM) has been recently proposed. The key ideas of DAM are delay pre-compen

Tianchen Zhao, Xuefei Ning, Ke Hong, Zhongyuan Qiu

Voxel-based methods have achieved state-of-the-art performance for 3D object detection in autonomous driving. However, their significant computational and memory costs pose a challenge for their application to resource-constrained vehicles. One reason for this high resource consumption is the presence of a large number of redundant background points in Lidar

Hanbo Cai, Pengcheng Zhang, Hai Dong, Yan Xiao

Deep neural networks (DNNs) have been widely and successfully adopted and deployed in various applications of speech recognition. Recently, a few works revealed that these models are vulnerable to backdoor attacks, where the adversaries can implant malicious prediction behaviors into victim models by poisoning their training process. In this paper, we revisi

Hui-hui Miao

In this paper, we try to study the quantum discord dynamics in a complex correlated photon-matter model, which is modified from the Tavis-Cummings-Hubbard model - a common cavity quantum electrodynamics model. The target model consists of two hydrogen atoms. A neutral hydrogen molecule can be obtained through an association reaction and disintegrated through

Tianyu Chen, Lin Li, Bingjie Shan, Guangtai Liang

To address security vulnerabilities arising from third-party libraries, security researchers maintain databases monitoring and curating vulnerability reports. Application developers can identify vulnerable libraries by directly querying the databases with their used libraries. However, the querying results of vulnerable libraries are not reliable due to the

Bing Han, Zhengyang Chen, Yanmin Qian

The mismatch between close-set training and open-set testing usually leads to significant performance degradation for speaker verification task. For existing loss functions, metric learning-based objectives depend strongly on searching effective pairs which might hinder further improvements. And popular multi-classification methods are usually observed with

Gowri Namratha Meedinti, Kandukuri Sai Srirekha, Radhakrishnan Delhibabu

This paper presents a comprehensive evaluation of the potential of Quantum Convolutional Neural Networks (QCNNs) in comparison to classical Convolutional Neural Networks (CNNs) and Artificial / Classical Neural Network (ANN) models. With the increasing amount of data, utilizing computing methods like CNN in real-time has become challenging. QCNNs overcome th

Sarthak Johari, Gowri Namratha Meedinti, Radhakrishnan Delhibabu, Deepak Joshi

Over many decades, research is being attempted for the removal of noise in the ambulatory EEG. In this respect, an enormous number of research papers is published for identification of noise removal, It is difficult to present a detailed review of all these literature. Therefore, in this paper, an attempt has been made to review the detection and removal of

Anqi Zhao, Peng Ding

Randomized trials balance all covariates on average and provide the gold standard for estimating treatment effects. Chance imbalances nevertheless exist more or less in realized treatment allocations and intrigue an important question: what should we do in case the treatment groups differ with respect to some important baseline characteristics? A common stra

Afsoon Alidadi Shamsabadi, Animesh Yadav, Halim Yanikomeroglu

Located in the stratospheric layer of Earth's atmosphere, high altitude platform station (HAPS) is a promising network infrastructure, which can bring significant advantages to sixth-generation (6G) and beyond wireless communications systems by forming vertical heterogeneous networks (vHetNets). However, if not dealt with properly, integrated networks suffer

Zachary Newman

Automated certificate authorities (CAs) have expanded the reach of public key infrastructure on the web and for software signing. The certificates that these CAs issue attest to proof of control of some digital identity. Some of these automated CAs issue certificates in response to client authentication using OpenID Connect (OIDC, an extension of OAuth 2.0).

Hongchi Lin, Qiyue yu

Currently, network topology becomes increasingly complex with the increased number of various network nodes, bringing in the challenge of network design and analysis. Most of the current studies are deduced based on the binary system stochastic geometry, overlooking the coupling and collaboration among nodes. This limitation makes it difficult to accurately

Xingzhe Su, Daixi Jia, Fengge Wu, Junsuo Zhao

Diffusion Models are a potent class of generative models capable of producing high-quality images. However, they often inadvertently favor certain data attributes, undermining the diversity of generated images. This issue is starkly apparent in skewed datasets like CelebA, where the initial dataset disproportionately favors females over males by 57.9%, this

Wenze Liu, Hao Lu, Yuliang Liu, Zhiguo Cao

We introduce the notion of point affiliation into feature upsampling. By abstracting a feature map into non-overlapped semantic clusters formed by points of identical semantic meaning, feature upsampling can be viewed as point affiliation -- designating a semantic cluster for each upsampled point. In the framework of kernel-based dynamic upsampling, we show

Elias Najarro, Shyam Sudhakaran, Sebastian Risi

Biological nervous systems are created in a fundamentally different way than current artificial neural networks. Despite its impressive results in a variety of different domains, deep learning often requires considerable engineering effort to design high-performing neural architectures. By contrast, biological nervous systems are grown through a dynamic self

Deng-Shan Wang, Xiaodong Zhu

Both Sawada-Kotera (SK) equation and Kaup-Kupershmidt (KK) equation are integrable systems with third-order Lax operator. Moreover, they are related with the same modified nonlinear equation (called modified SK-KK equation) by Miura transformations. This work first constructs the Riemann-Hilbert problem associated with the SK equation, KK equation and modifi

Ali Mohammadi Teshnizi, Majid Ghaderi, Dennis Goeckel

Hiding the wireless communication by transmitter Alice to intended receiver Bob from a capable and attentive adversary Willie has been widely studied under the moniker "covert communications". However, when such covert communication is done in the presence of allowable system communications, there has been little study of both hiding the signal and preservin

Kang Wang, Yiou Zhang, Vineetha Bheemarasetty, See-Chen Ying

To enable the practical use of skyrmion-based devices, it is essential to achieve a balance between energy efficiency and thermal stability, while also ensuring reliable electrical detection against noise. Understanding how a skyrmion interacts with material disorder and external perturbations is thus essential. Here we investigate the electronic noise of a

Hao-Yue Qi, Wei Zheng

Recently synthetic gauge fields have been implemented on quantum simulators. Unlike the gauge fields in the real world, in synthetic gauge fields, the gauge charge can fluctuate and gauge invariance can be violated, which leading rich physics unexplored before. In this work, we propose the gauge violation spectroscopy as a useful experimentally accessible me

Tingxiong Xiao, Weihang Zhang, Yuxiao Cheng, Jinli Suo

Despite their remarkable performance, deep neural networks remain mostly ``black boxes'', suggesting inexplicability and hindering their wide applications in fields requiring making rational decisions. Here we introduce HOPE (High-order Polynomial Expansion), a method for expanding a network into a high-order Taylor polynomial on a reference input. Specifica

Zhiding Liang, Jinglei Cheng, Rui Yang, Hang Ren

Large Language Models (LLMs) contribute significantly to the development of conversational AI and has great potentials to assist the scientific research in various areas. This paper attempts to address the following questions: What opportunities do the current generation of generative pre-trained transformers (GPTs) offer for the developments of noisy interm

Na Wang, Chuan Qin, Sian-Jheng Lin

Reversible data hiding (RDH) has been extensively studied in the field of information security. In our previous work [1], an explicit implementation approaching the rate-distortion bound of RDH has been proposed. However, there are two challenges left in our previous method. Firstly, this method suffers from computing precision problem due to the use of arit

Zhengping Zhou, Lezhi Li, Xinxi Chen, Andy Li

ChatGPT is phenomenal. However, it is prohibitively expensive to train and refine such giant models. Fortunately, small language models are flourishing and becoming more and more competent. We call them "mini-giants". We argue that open source community like Kaggle and mini-giants will win-win in many ways, technically, ethically and socially. In this articl

Morgan Bauer, Keith Copenhaver

The pop-stack-sorting process is a variation of the stack-sort process. We consider a deterministic version of this process, and provide a new lower bound of $\frac{3}{5}n$ for the number of sorts to fully sort a uniformly randomly chosen permutation via a useful lemma.

Hiroki Naganuma, Ryuichiro Hataya, Kotaro Yoshida, Ioannis Mitliagkas

In the field of computer vision, fine-tuning pre-trained models has become a prevalent strategy for out-of-distribution (OOD) generalization tasks. Different from most prior work that has focused on advancing learning algorithms, we systematically examined how pre-trained model size, pre-training dataset size, and training strategies impact generalization an

Joon-Suh Park, Soon Wei Daniel Lim, Arman Amirzhan, Hyukmo Kang

Metasurfaces, optics made from subwavelength-scale nanostructures, have been limited to millimeter-sizes by the scaling challenge of producing vast numbers of precisely engineered elements over a large area. In this study, we demonstrate an all-glass 100 mm diameter metasurface lens (metalens) comprising 18.7 billion nanostructures that operates in the visib

Shuailiang Ge

We study a new source of stochastic gravitational wave background (SGWB) from the final collapse of a network of topological defects. Typically, the final collapse is considered negligible for generating gravitational waves (GWs) due to its subdominance compared with the network's long-term evolution in the scaling regime. However, in some cases, a network c

Teruhisa Sugimoto

Non-periodic tilings with Tile(1, 1) using the substitution method, as presented by Smith et al. in [2] and [3], can be converted into non-periodic tilings with three types of pentagons. When arbitrary replacements are excluded, the resulting non-periodic tilings with three types of pentagons exhibit two patterns. Note that, during the conversion process in

Geoff Vooys

In this paper we show that if $\mathscr{C}$ is a tangent category then the Ind-category $\operatorname{Ind}(\mathscr{C})$ is a tangent category as well with a tangent structure which locally looks like the tangent structure on $\mathscr{C}$. Afterwards we give a pseudolimit description of $\operatorname{Ind}(\mathscr{C})_{/X}$ when $\mathscr{C}$ admits finit

Tamera Lanham, Anna Chen, Ansh Radhakrishnan, Benoit Steiner

Large language models (LLMs) perform better when they produce step-by-step, "Chain-of-Thought" (CoT) reasoning before answering a question, but it is unclear if the stated reasoning is a faithful explanation of the model's actual reasoning (i.e., its process for answering the question). We investigate hypotheses for how CoT reasoning may be unfaithful, by ex

Jianqi Chen, Yilan Zhang, Zhengxia Zou, Keyan Chen

We propose a zero-shot approach to image harmonization, aiming to overcome the reliance on large amounts of synthetic composite images in existing methods. These methods, while showing promising results, involve significant training expenses and often struggle with generalization to unseen images. To this end, we introduce a fully modularized framework inspi

Ruixian Liu, Wenliang Zhang, Yuan Wei, Zhen Tao

We use resonant inelastic X-ray scattering (RIXS) at the Fe-L$_3$ edge to study the spin excitations of uniaxial-strained and unstrained FeSe$_{1-x}$S$_{x}$ ($0\leq x\leq0.21$) samples. The measurements on unstrained samples reveal dispersive spin excitations in all doping levels, which show only minor doping dependence in energy dispersion, lifetime, and in

Maxim Jeffs, Yuan Yao, Ziwen Zhao

We show that for singular hypersurfaces, a version of their genus-zero Gromov-Witten theory may be described in terms of a direct limit of fixed point Floer cohomology groups, a construction which is more amenable to computation and easier to define than the technical foundations of the enumerative geometry of more general singular symplectic spaces. As an i

Ansh Radhakrishnan, Karina Nguyen, Anna Chen, Carol Chen

As large language models (LLMs) perform more difficult tasks, it becomes harder to verify the correctness and safety of their behavior. One approach to help with this issue is to prompt LLMs to externalize their reasoning, e.g., by having them generate step-by-step reasoning as they answer a question (Chain-of-Thought; CoT). The reasoning may enable us to ch

Kai Behrend, Hsuan-Yi Liao, Ping Xu

We prove that an \'etale fibration between $L_\infty$-bundles admits local sections composed of several elementary morphisms of particularly simple and accessible type. As applications, we establish an inverse function theorem for $L_\infty$-bundles and provide an elementary proof that every weak equivalence of $L_\infty$-bundles induces a quasi-isomorphism

Hankun Deng, Donghao Li, Colin Nitroy, Andrew Wertz

Fish locomotion emerges from a diversity of interactions among deformable structures, surrounding fluids and neuromuscular activations, i.e., fluid-structure interactions (FSI) controlled by fish's motor systems. Previous studies suggested that such motor-controlled FSI may possess embodied traits. However, their implications in motor learning, neuromuscular

Daye Nam, Andrew Macvean, Vincent Hellendoorn, Bogdan Vasilescu

Understanding code is challenging, especially when working in new and complex development environments. Code comments and documentation can help, but are typically scarce or hard to navigate. Large language models (LLMs) are revolutionizing the process of writing code. Can they do the same for helping understand it? In this study, we provide a first investig

Zihan Liu, Jiaqi Wang, Yun Luo, Shuang Zhao

In recent years, there has been an explosion of research on the application of deep learning to the prediction of various peptide properties, due to the significant development and market potential of peptides. Molecular dynamics has enabled the efficient collection of large peptide datasets, providing reliable training data for deep learning. However, the l

Charles W. Neville

We prove the Invariant Subspace Conjecture for separable Hilbert spaces.

Kai Behrend, Hsuan-Yi Liao, Ping Xu

We prove that dg manifolds of finite positive amplitude, i.e. bundles of positively graded curved $L_\infty[1]$-algebras, form a category of fibrant objects. As a main step in the proof, we obtain a factorization theorem using path spaces. First we construct an infinite-dimensional factorization of a diagonal morphism using actual path spaces motivated by th

Lennart Schneider, Bernd Bischl, Janek Thomas

We present a model-agnostic framework for jointly optimizing the predictive performance and interpretability of supervised machine learning models for tabular data. Interpretability is quantified via three measures: feature sparsity, interaction sparsity of features, and sparsity of non-monotone feature effects. By treating hyperparameter optimization of a m

Hiraku Atobe

In this paper, we define compact open subgroups of quasi-split even unitary groups for each even non-negative integers, and establish the theory of local newforms for irreducible tempered generic representations with a certain condition on the central characters. To do this, we use the local Gan-Gross-Prasad conjecture, the local Rankin-Selberg integrals, an

Luigi Quarantiello, Simone Marzeddu, Antonio Guzzi, Vincenzo Lomonaco

In the last few decades we have witnessed a significant development in Artificial Intelligence (AI) thanks to the availability of a variety of testbeds, mostly based on simulated environments and video games. Among those, roguelike games offer a very good trade-off in terms of complexity of the environment and computational costs, which makes them perfectly

Dandan Hu, József Z. Farkas, Gang Huang

In this paper we investigate a structured population model with distributed delay. Our model incorporates two different types of nonlinearities. Specifically we assume that individual growth and mortality are affected by scramble competition, while fertility is affected by contest competition. In particular, we assume that there is a hierarchical structure i

Valentin Dannenberg, Achill Schürmann

In this paper we give a first study of perfect copositive $n \times n$ matrices. They can be used to find rational certificates for completely positive matrices. We describe similarities and differences to classical perfect, positive definite matrices. Most of the differences occur only for $n \geq 3$, where we find for instance lower rank and indefinite per

Zeynep Senel, Ruby Phul, Ahmet Faruk Yazıcı, Akrema

The development of novel strategies for self-assembly in the field of nanotechnology has witnessed remarkable progress in recent years. Here, we present a DNA-driven programmable self-assembly to fabricate the targeted nanophotonic structures. The exploitation of the programmable properties of DNA and the unique optical properties of QDs unfolds the ability

Thuy Ngoc Nguyen, Chase McDonald, Cleotilde Gonzalez

Temporal credit assignment is crucial for learning and skill development in natural and artificial intelligence. While computational methods like the TD approach in reinforcement learning have been proposed, it's unclear if they accurately represent how humans handle feedback delays. Cognitive models intend to represent the mental steps by which humans solve

Yihan Fu, Daijing Shi, Anjunyi Fan, Wenshuo Yue

Markov chain Monte Carlo (MCMC) is a widely used sampling method in modern artificial intelligence and probabilistic computing systems. It involves repetitive random number generations and thus often dominates the latency of probabilistic model computing. Hence, we propose a compute-in-memory (CIM) based MCMC design as a hardware acceleration solution. This