December 2023 arXiv papers — page 105

Haowen Bai, Zixiang Zhao, Jiangshe Zhang, Yichen Wu

Image fusion aims to combine information from multiple source images into a single one with more comprehensive informational content. Deep learning-based image fusion algorithms face significant challenges, including the lack of a definitive ground truth and the corresponding distance measurement. Additionally, current manually defined loss functions limit t

Hao Huang, Qian Yan, Keqi Han, Ting Gan

To infer a diffusion network based on observations from historical diffusion processes, existing approaches assume that observation data contain exact occurrence time of each node infection, or at least the eventual infection statuses of nodes in each diffusion process. They determine potential influence relationships between nodes by identifying frequent se

Qian Zhang, Mingjie Shao, Qiang Li, Ju Liu

Active reconfigurable intelligent surface (RIS) is a new RIS architecture that can reflect and amplify communication signals. It can provide enhanced performance gain compared to the conventional passive RIS systems that can only reflect the signals. On the other hand, the design problem of active RIS-aided systems is more challenging than the passive RIS-ai

Haiyong Wang, Lun Zhang

In this paper, we present a rigorous analysis of root-exponential convergence of Hermite approximations, including projection and interpolation methods, for functions that are analytic in an infinite strip containing the real axis and satisfy certain restrictions on the asymptotic behavior at infinity within this strip. Asymptotically sharp error bounds in t

Vadim Leshkov

In the present work we describe the category $\mathsf{WC}_2$ of weighted 2-complexes and its subcategory $\mathsf{WC}_1$ of weighted graphs. Since a Coxeter group is defined by its Coxeter graph, the construction of Coxeter groups defines a functor from $\mathsf{WC}_1$ to the category of groups. We generalize the notion of a Coxeter group by extending the do

Kazuki Yanagihara, Fumio Uchida, Tomohiro Fujita, Shinji Tsujikawa

Primordial magnetogenesis is an intriguing possibility to explain the origin of intergalactic magnetic fields (IGMFs). However, the baryon isocurvature problem has recently been pointed out, ruling out all magnetogenesis models operating above the electroweak scale. In this letter, we show that lower-scale inflationary scenarios with a Chern-Simons coupling

Wenqian Zhang, Molin Huang, Yuxuan Zhou, Juze Zhang

The recently emerging text-to-motion advances have spired numerous attempts for convenient and interactive human motion generation. Yet, existing methods are largely limited to generating body motions only without considering the rich two-hand motions, let alone handling various conditions like body dynamics or texts. To break the data bottleneck, we propose

Ziyue Zhang, Min Sheng, Junyu Liu, Jiandong Li

Leveraging the advantage of satellite and terrestrial networks, the integrated satellite terrestrial networks (ISTNs) can help to achieve seamless global access and eliminate the digital divide. However, the dense deployment and frequent handover of satellites aggravate intra- and inter-system interference, resulting in a decrease in downlink sum rate. To ad

Ranjan Sapkota, Dawood Ahmed, Manoj Karkee

Instance segmentation is an important image processing operation for agricultural automation, providing precise delineation of individual objects within images and enabling tasks such as selective harvesting and precision pruning. This study compares the one stage YOLOv8 model with the two stage Mask R CNN model for instance segmentation under varying orchar

Yuanbo Zhou, Yuyang Xue, Jiang Bi, Wenlin He

Real-world stereo image super-resolution has a significant influence on enhancing the performance of computer vision systems. Although existing methods for single-image super-resolution can be applied to improve stereo images, these methods often introduce notable modifications to the inherent disparity, resulting in a loss in the consistency of disparity be

Taiki Jikei, Takanobu Amano, Yosuke Matsumoto

We examine the evolution of ion-beam Weibel instability at strong collisionless shocks in weakly magnetized media. We find that a finite background magnetic field substantially affects both linear and nonlinear phases of the instability, depending on whether the background electrons behave magnetized or not. Particle-in-cell simulations for magnetized electr

Yixiong Chen

Image classification is a crucial task in machine learning with widespread practical applications. The existing classical framework for image classification typically utilizes a global pooling operation at the end of the network to reduce computational complexity and mitigate overfitting. However, this operation often results in a significant loss of informa

Anton Cedilnik

We considerably shorten axiom systems for Metric space Real normed space Euclidean space Algebra with scalar involution

Xiang Wei, Alan J. X. Guo, Sihan Sun, Mengyi Wei

Efficient computation or approximation of Levenshtein distance, a widely-used metric for evaluating sequence similarity, has attracted significant attention with the emergence of DNA storage and other biological applications. Sequence embedding, which maps Levenshtein distance to a conventional distance between embedding vectors, has emerged as a promising s

Hao Wu, Yuxuan Liang, Wei Xiong, Zhengyang Zhou

Efficiently modeling spatio-temporal (ST) physical processes and observations presents a challenging problem for the deep learning community. Many recent studies have concentrated on meticulously reconciling various advantages, leading to designed models that are neither simple nor practical. To address this issue, this paper presents a systematic study on e

Baihe Huang, Hanlin Zhu, Banghua Zhu, Kannan Ramchandran

We study statistical watermarking by formulating it as a hypothesis testing problem, a general framework which subsumes all previous statistical watermarking methods. Key to our formulation is a coupling of the output tokens and the rejection region, realized by pseudo-random generators in practice, that allows non-trivial trade-offs between the Type I error

Seyed A. Esmaeili, Suho Shin, Aleksandrs Slivkins

Motivated by applications such as online labor markets we consider a variant of the stochastic multi-armed bandit problem where we have a collection of arms representing strategic agents with different performance characteristics. The platform (principal) chooses an agent in each round to complete a task. Unlike the standard setting, when an arm is pulled it

Jonas M. Lindert, Lorenzo Mai

We present a fully automated implementation of next-to-leading order electroweak (NLO EW) corrections in the logarithmic approximation in OpenLoops. For energies above the electroweak scale NLO EW corrections are logarithmically enhanced and in tails of kinematic distributions of crucial LHC processes yield correction factors of several tens of percent. The

Gwo Dong Lin, Chin-Yuan Hu

Integral representation is one of the powerful tools for studying analytic continuation of the zeta functions. It is known that Hurwitz zeta function generalizes the famous Riemann zeta function which plays an important role in analytic number theory. They both have several multiple versions in the literature. In this paper, we introduce three novel multiple

Weiguang Zhang, Qiufeng Wang, Kaizhu Huang

Document dewarping, aiming to eliminate geometric deformation in photographed documents to benefit text recognition, has made great progress in recent years but is still far from being solved. While Cartesian coordinates are typically leveraged by state-of-the-art approaches to learn a group of deformation control points, such representation is not efficient

Xingjin Wang, Linjing Li, Daniel Zeng

With the rapid development of large language models (LLMs), it is highly demanded that LLMs can be adopted to make decisions to enable the artificial general intelligence. Most approaches leverage manually crafted examples to prompt the LLMs to imitate the decision process of human. However, designing optimal prompts is difficult and the patterned prompts ca

Othmane Dani, Abdelhak Abouqateb

Let $(G,H,\sigma)$ be a symmetric pair and $\mathfrak{g}=\mathfrak{m}\oplus\mathfrak{h}$ the canonical decomposition of the Lie algebra $\mathfrak{g}$ of $G$. We denote by ${\nabla}^0$ the canonical affine connection on the symmetric space $G/H$. A torsion-free $G$-invariant affine connection on $G/H$ is called special if it has the same curvature as ${\nabl

Xu-Lin Dong, Yu-Hua Yao, Yi-Qing Guo, Shu-Wang Cui

The AMS-02 experiment has observed new properties of primary cosmic rays (CRs) categorized into two groups: He-C-O-Fe and Ne-Mg-Si-S, which are independent of CR propagation. In this study, we investigate the unexpected properties of these nuclei using a spatial propagation model. All nuclei spectra are accurately reproduced and separated into primary and se

Hong Zhang, Yu Zhang

Spiking neural networks (SNNs) are potential competitors to artificial neural networks (ANNs) due to their high energy-efficiency on neuromorphic hardware. However, SNNs are unfolded over simulation time steps during the training process. Thus, SNNs require much more memory than ANNs, which impedes the training of deeper SNN models. In this paper, we propose

Xu He, Shu Wang, Pengbin Feng, Xinda Wang

A timely software update is vital to combat the increasing security vulnerabilities. However, some software vendors may secretly patch their vulnerabilities without creating CVE entries or even describing the security issue in their change log. Thus, it is critical to identify these hidden security patches and defeat potential N-day attacks. Researchers have

Xiaoyu Zhou, Zhiwei Lin, Xiaojun Shan, Yongtao Wang

We present DrivingGaussian, an efficient and effective framework for surrounding dynamic autonomous driving scenes. For complex scenes with moving objects, we first sequentially and progressively model the static background of the entire scene with incremental static 3D Gaussians. We then leverage a composite dynamic Gaussian graph to handle multiple moving

Shao-Ze Li, Yun-Wei Yu, He Gao, Lin Lan

The coalescence of binary neutron stars can yield the expulsion of a fast-moving, quasi-isotropic material, which may induce thermal radiation and give rise to kilonova emission. Moreover, the interaction between the ejected material and the surrounding environment generates an external shock, which can result in a long-lasting radio signal that persists for

William Borrelli, Ruijun Wu

Motivated by various geometric problems, we study the nodal set of solutions to Dirac equations on manifolds, of general form. We prove that such set has Hausdorff dimension less than or equal to $n-2$, $n$ being the ambient dimension. We extend this result, previously known only in the smooth case or in specific cases, working with locally Lipschitz coeffic

Ze Yu Zhao, Yue Ling Che, Sheng Luo, Gege Luo

This paper proposes a novel design on the wireless powered communication network (WPCN) in dynamic environments under the assistance of multiple unmanned aerial vehicles (UAVs). Unlike the existing studies, where the low-power wireless nodes (WNs) often conform to the coherent harvest-then-transmit protocol, under our newly proposed double-threshold based WN

Hokuto Nagatakiya, Naoyuki Sakumichi, Shunsuke Kobayashi, Ryuichi Tarumi

We derive an analytical expression for the strain field during steady-state crack propagation in viscoelastic solids described by the standard linear solid (Zener) model. This expression reveals three regions in the fracture profile and in the strain field ahead of the crack tip, each distinguished by power-law exponents that evolve with distance from the cr

Takahiro Minami, Miho Katsuragawa, Shunsaku Nagasawa, Shin'ichiro Takeda

We developed a 2-mm-thick CdTe double-sided strip detector (CdTe-DSD) with a 250 um strip pitch, which has high spatial resolution with a uniform large imaging area of 10 cm$^2$ and high energy resolution with high detection efficiency in tens to hundreds keV. The detector can be employed in a wide variety of fields for quantitative observations of hard X-ra

Hossein Sabzian, Nima Shahriari, Mohammad G. Nejad

The complex interplay between human societies and their environments has long been a subject of fascination for social scientists. Utilizing agent-based modeling, this study delves into the profound implications of seemingly inconsequential variations in social dynamics. Focusing on the nexus between food distribution, residential patterns, and population dy

Aiwei Liu, Leyi Pan, Yijian Lu, Jingjing Li

Text watermarking algorithms are crucial for protecting the copyright of textual content. Historically, their capabilities and application scenarios were limited. However, recent advancements in large language models (LLMs) have revolutionized these techniques. LLMs not only enhance text watermarking algorithms with their advanced abilities but also create a

Jinhao Tian, Zuchao Li, Jiajia Li, Ping Wang

The first step to apply deep learning techniques for symbolic music understanding is to transform musical pieces (mainly in MIDI format) into sequences of predefined tokens like note pitch, note velocity, and chords. Subsequently, the sequences are fed into a neural sequence model to accomplish specific tasks. Music sequences exhibit strong correlations betw

Yuzhe Zhang, Jiawei Zhang, Hao Li, Zhouxia Wang

Recovering degraded low-resolution text images is challenging, especially for Chinese text images with complex strokes and severe degradation in real-world scenarios. Ensuring both text fidelity and style realness is crucial for high-quality text image super-resolution. Recently, diffusion models have achieved great success in natural image synthesis and res

Kazufumi Kimoto, Masato Wakayama

The heat kernel (or propagator) of the quantum harmonic oscillator (qHO) is given by the Mehler formula, and the partition function is obtained by taking its trace. In general, the spectral zeta function of the given system is obtained by the Mellin transform of its partition function. In the case of non-commutative harmonic oscillators (NCHO), however, the

Oscar Chang, Hod Lipson

Weight-sharing plays a significant role in the success of many deep neural networks, by increasing memory efficiency and incorporating useful inductive priors about the problem into the network. But understanding how weight-sharing can be used effectively in general is a topic that has not been studied extensively. Chen et al. [2015] proposed HashedNets, whi

Yuxi Li, Hongzhi Jiang, Huijie Zhao, Xudong Li

We present projective parallel single-pixel imaging (pPSI), a 3D photography method that provides a robust and efficient way to analyze the light transport behavior and enables separation of light effect due to global illumination, thereby achieving 3D structured light scanning under global illumination. The light transport behavior is described by the light

Kaijie Zhu, Qinlin Zhao, Hao Chen, Jindong Wang

The evaluation of large language models (LLMs) is crucial to assess their performance and mitigate potential security risks. In this paper, we introduce PromptBench, a unified library to evaluate LLMs. It consists of several key components that are easily used and extended by researchers: prompt construction, prompt engineering, dataset and model loading, ad

R. Oishi-Tomiyasu

In ab-initio indexing, for a given diffraction/scattering pattern, the unit-cell parameters and the Miller indices assigned to reflections in the pattern are determined simultaneously. "Ab-initio" means a process performed without any good prior information on the crystal lattice. Newly developed ab-initio indexing software is frequently reported in crystall

Tianyun Tang, Kim-Chuan Toh

In this work, we consider the low rank decomposition (SDPR) of general convex semidefinite programming problems (SDP) that contain both a positive semidefinite matrix and a nonnegative vector as variables. We develop a rank-support-adaptive feasible method to solve (SDPR) based on Riemannian optimization. The method is able to escape from a saddle point to e

Sang Yun Kwon, Gagan Bhatia, El Moatez Billah Nagoudi, Muhammad Abdul-Mageed

Large language models (LLMs) finetuned to follow human instruction have recently exhibited significant capabilities in various English NLP tasks. However, their performance in grammatical error correction (GEC), especially on languages other than English, remains significantly unexplored. In this work, we evaluate the abilities of instruction finetuned LLMs

Oscar Chang, Otavio Braga, Hank Liao, Dmitriy Serdyuk

It has been shown that learning audiovisual features can lead to improved speech recognition performance over audio-only features, especially for noisy speech. However, in many common applications, the visual features are partially or entirely missing, e.g.~the speaker might move off screen. Multi-modal models need to be robust: missing video frames should n

Tao Li, A. R. Moghaddamfar, Andrey V. Vasil'ev, Zhigang Wang

The spectrum of a group is the set of orders of its elements. Finite groups with the same spectra as the direct squares of the finite simple groups with abelian Sylow 2-subgroups are considered. It is proved that the direct square $J_1\times J_1$ of the sporadic Janko group $J_1$ and the direct squares ${^2}G_2(q)\times{^2}G_2(q)$ of the simple small Ree gro

Hoang Truong

This paper investigates mapping spaces between enriched operads and relates these spaces to those between operadic bimodules via convenient fiber sequences. The main statements hold for simplicial operads, operads enriched in simplicial modules over a commutative ring, and for connective dg operads, with the last case relying on an operadic version of the Do

Mingle Xu, Ji Eun Park, Jaehwan Lee, Jucheng Yang

Plant disease recognition has witnessed a significant improvement with deep learning in recent years. Although plant disease datasets are essential and many relevant datasets are public available, two fundamental questions exist. First, how to differentiate datasets and further choose suitable public datasets for specific applications? Second, what kinds of

K. Nygård, S. A. McDonald, J. B. González, V. Haghighat

The ForMAX beamline at the MAX IV Laboratory provides multiscale and multimodal structural characterization of hierarchical materials in the nm to mm range by combining small- and wide-angle x-ray scattering with full-field microtomography. The modular design of the beamline is optimized for easy switching between different experimental modalities. The beaml

Oscar Chang, Dongseong Hwang, Olivier Siohan

In streaming settings, speech recognition models have to map sub-sequences of speech to text before the full audio stream becomes available. However, since alignment information between speech and text is rarely available during training, models need to learn it in a completely self-supervised way. In practice, the exponential number of possible alignments m

Anna Zuckerman, James Davenport, Steve Croft, Andrew Siemion

Never before has the detection and characterization of exoplanets via transit photometry been as promising and feasible as it is now, due to the increasing breadth and sensitivity of time domain optical surveys. Past works have made use of phase-folded stellar lightcurves in order to study the properties of exoplanet transits, because this provides the highe

Youngkyu Kim, Youngsoo Choi, Byounghyun Yoo

We introduce a novel data reconstruction algorithm known as Gappy auto-encoder (Gappy AE) to address the limitations associated with Gappy proper orthogonal decomposition (Gappy POD), a widely used method for data reconstruction when dealing with sparse measurements or missing data. Gappy POD has inherent constraints in accurately representing solutions char

Yuhao Kang, Song Gao, Robert E. Roth

The past decade has witnessed the rapid development of geospatial artificial intelligence (GeoAI) primarily due to the ground-breaking achievements in deep learning and machine learning. A growing number of scholars from cartography have demonstrated successfully that GeoAI can accelerate previously complex cartographic design tasks and even enable cartograp

Latsamy Xayavong, Yeunhwan Lim

The isospin-symmetry breaking correction, denoted as $\delta_C$, is introduced for the first time within the shell-model framework to the nuclear matrix element of Gamow-Teller transitions. $\delta_C$ is separated into two components: the isospin mixing term, $\delta_{C1}$, induced by the Coulomb and nuclear charge-dependent forces in the effective Hamiltoni

Qiaosi Tang, Ranjala Ratnayake, Gustavo Seabra, Zhe Jiang

Morphological profiling is a valuable tool in phenotypic drug discovery. The advent of high-throughput automated imaging has enabled the capturing of a wide range of morphological features of cells or organisms in response to perturbations at the single-cell resolution. Concurrently, significant advances in machine learning and deep learning, especially in c

Donghyun Yu, Sungho Lee, Ruei-Hau Hsu, Jemin Lee

As advanced V2X applications emerge in the connected and autonomous vehicle (CAV), the data communications between in-vehicle end-devices and outside nodes increase, which make the end-to-end (E2E) security to in-vehicle end-devices as the urgent issue to be handled. However, the E2E security with fine-grained access control still remains as a challenging is

Declan Finn Keogh, Mark Baldry, Victoria Timchenko, John Reizes

Liquid metal batteries (LMBs) are a promising grid-scale storage device however, the scalability of this technology and its electrochemical performance is limited by mass transport overpotentials. In this work, a numerical model of a three-layer LMB was developed using a multi-region approach. An alternative design concept for the battery aimed at reducing m

Joshua Groen, Zixian Yang, Divyadharshini Muruganandham, Mauro Belgiovine

5G and beyond networks promise advancements in bandwidth, latency, and connectivity. The Open Radio Access Network (O-RAN) framework enhances flexibility through network slicing and closed-loop RAN control. Central to this evolution is integrating machine learning (ML) for dynamic network control. This paper presents a framework to optimize O-RAN operation.

Oscar Chang, Lampros Flokas, Hod Lipson

Hypernetworks are meta neural networks that generate weights for a main neural network in an end-to-end differentiable manner. Despite extensive applications ranging from multi-task learning to Bayesian deep learning, the problem of optimizing hypernetworks has not been studied to date. We observe that classical weight initialization methods like Glorot & Be

Yuqi Ye, Li You, Jue Wang, Hao Xu

In conventional multiple-input multiple-output (MIMO) communication systems, the positions of antennas are fixed. To take full advantage of spatial degrees of freedom, a new technology called fluid antenna (FA) is proposed to obtain higher achievable rate and diversity gain. Most existing works on FA exploit instantaneous channel state information (CSI). How

Mohammadali Kargar, Xingyong Song

Technology advancement for on-road vehicles has gained significant momentum in the past decades, particularly in the field of vehicle automation and powertrain electrification. The optimization of powertrain controls for autonomous vehicles typically involves a separated consideration of the vehicle's external dynamics and powertrain dynamics, with one key a

Oscar Chang, Hod Lipson

The success of gradient-based meta-learning is primarily attributed to its ability to leverage related tasks to learn task-invariant information. However, the absence of interactions between different tasks in the inner loop leads to task-specific over-fitting in the initial phase of meta-training. While this is eventually corrected by the presence of these

Zhen Lu, Yue Yang

We report the quantum computing of reacting flows by simulating the Hamiltonian dynamics. The scalar transport equation for reacting flows is transformed into a Hamiltonian system, mapping the dissipative and non-Hermitian problem in physical space to a Hermitian one in a higher-dimensional space. Using this approach, we develop the quantum spectral and fini

Yan-Yi Wang, Chun-Wang Wu, Wei Wu, Ping-Xing Chen

Quantum sensing utilizing unique quantum properties of non-Hermitian systems to realize ultra-precision measurements has been attracting increasing attention. However, the debate on whether non-Hermitian systems are superior to Hermitian counterparts in sensing remains an open question. Here, we investigate the quantum information in PT-symmetric quantum sen

Oscar Chang, Lampros Flokas, Hod Lipson, Michael Spranger

SATNet is an award-winning MAXSAT solver that can be used to infer logical rules and integrated as a differentiable layer in a deep neural network. It had been shown to solve Sudoku puzzles visually from examples of puzzle digit images, and was heralded as an impressive achievement towards the longstanding AI goal of combining pattern recognition with logica

Shigeki Sugimoto, Yu-ki Suzuki

We consider dimensional reduction of cigar geometries which are obtained by a Wick rotation of black hole solutions. Originally the cigar geometry is smooth around the tip, but after the dimensional reduction along the Euclidean time direction, there appears an end-of-the-world brane (ETW brane). We derive the tension of the brane by two methods: bulk equati

Eunju Shin

For integers $a\neq0$, $k$, and $n\geq3$, we consider the Markoff-Hurwitz equation given by $x_1^1+\cdots+x_n^2-ax_1\cdots x_n=k$. By defining graphs associated with a height function and by using their properties, we find an exact fundamental domain for a symmetric group generated by involution maps sending $(x_1,\dots,x_n)$ to $(x_1,\dots,ax_1\cdots x_{i-1

Zepeng Wen, Qiong Pan, Xiaoya Zhai, Hongmei Kang

This paper proposes an Adaptive Isogeometric Topology Optimization framework for shell structures based on PHT-splines (PHT-AITO). In this framework, the design domain, displacement, and density are represented by PHT-splines. Leveraging the local refinement capability of PHT-splines, mesh elements defining the density function are adaptively refined to achi

Hanan Hamamera, Filipe Souza Mendes Guimarães, Manuel dos Santos Dias, Samir Lounis

The field of orbitronics has emerged with great potential to impact information technology by enabling environmentally friendly electronic devices. The main electronic degree of freedom at play is the orbital angular momentum, which can give rise to a myriad of phenomena such as the orbital Hall effect (OHE), torques and orbital magnetoelectric effects. Here

Junhao Zheng, Shengjie Qiu, Qianli Ma

Incremental Learning (IL) has been a long-standing problem in both vision and Natural Language Processing (NLP) communities. In recent years, as Pre-trained Language Models (PLMs) have achieved remarkable progress in various NLP downstream tasks, utilizing PLMs as backbones has become a common practice in recent research of IL in NLP. Most assume that catast

Kai Huang, Boyuan Yang, Wei Gao

Large Language Models (LLMs) are capable of reasoning over diverse input data modalities through pre-trained encoders. However, the growing diversity of input data modalities prevents incorporating all modalities into LLMs, especially when LLMs are deployed on resource-constrained edge devices for embodied AI applications. Instead, a better option is to adap

Mohammadhossein Amouei, Mohsen Rezvani, Mansoor Fateh

Due to the increasing sophistication of web attacks, Web Application Firewalls (WAFs) have to be tested and updated regularly to resist the relentless flow of web attacks. In practice, using a brute-force attack to discover vulnerabilities is infeasible due to the wide variety of attack patterns. Thus, various black-box testing techniques have been proposed

Yufeng Liu

Nighttime unmanned aerial vehicle (UAV) tracking has been facilitated with indispensable plug-and-play low-light enhancers. However, the introduction of low-light enhancers increases the extra computational burden for the UAV, significantly hindering the development of real-time UAV applications. Meanwhile, these state-of-the-art (SOTA) enhancers lack tight

Denis S. Krotov, Ivan Yu. Mogilnykh

Additive one-weight codes over a finite field of non-prime order are equivalent to special subspace coverings of the points of a projective space, which we call multispreads. The current paper is devoted to the characterization of the parameters of multispreads, which is equivalent to the characterization of the parameters of additive one-weight codes and, v

Sourav Mukherjee, Ziqian Yang, Rohit K Patra, Kshitij Khare

We focus on online second price auctions, where bids are made sequentially, and the winning bidder pays the maximum of the second-highest bid and a seller specified starting price. For many such auctions, the seller does not see all the bids or the total number of bidders accessing the auction, and only observes the current selling prices throughout the cour

Jushan Bai

This paper studies the problem of efficient estimation of panel data models in the presence of an increasing number of incidental parameters. We formulate the dynamic panel as a simultaneous equations system, and derive the efficiency bound under the normality assumption. We then show that the Gaussian quasi-maximum likelihood estimator (QMLE) applied to the

Shijie Dong, Kuijie Li, Jingya Zhao

We are interested in massless cubic Dirac equations in two and three space dimensions, known as the Soler model. The solution to this model is known as a wave function, which has the unit $L^2$ norm. We aim to show global existence and asymptotic behavior for the cubic Dirac model with a class of initial data that can be large in $L^2$.

Zhenduo Zhang, Bo-Wen Zhang, Guang Liu

Current text-to-image editing models often encounter challenges with smoothly manipulating multiple attributes using a single instruction. Taking inspiration from the Chain-of-Thought prompting technique utilized in language models, we present an innovative concept known as Chain-of-Instruct Editing (CoIE), which enhances the capabilities of these models thr

Rishit Dagli

Imaging under extremely low-light conditions presents a significant challenge and is an ill-posed problem due to the low signal-to-noise ratio (SNR) caused by minimal photon capture. Previously, diffusion models have been used for multiple kinds of generative tasks and image-to-image tasks, however, these models work as a post-processing step. These diffusio

Martin Semmann, Mahei Manhei Li

The rise of new modes of interaction with AI skyrocketed the popularity, applicability, and amount of use cases. Despite this evolution, conceptual integration is falling behind. Studies suggest that there is hardly a systematization in using AI in organizations. Thus, by taking a service-dominant logic perspective, specifically, the concept of resource inte

Zilin Shen, Imtiaz Karim, Elisa Bertino

The IEEE 802.11 family of standards, better known as WiFi, is a widely used protocol utilized by billions of users. Previous works on WiFi formal verification have mostly focused on the four-way handshake and other security aspects. However, recent works have uncovered severe vulnerabilities in functional aspects of WiFi, which can cause information leakage

Wei Zhao, Zhe Li, Jun Sun

Large Language Models (LLMs) such as GPT and Llama2 are increasingly adopted in many safety-critical applications. Their security is thus essential. Even with considerable efforts spent on reinforcement learning from human feedback (RLHF), recent studies have shown that LLMs are still subject to attacks such as adversarial perturbation and Trojan attacks. Fu

Guangming Zhu, Siyuan Wang, Tianci Wu, Liang Zhang

Free-hand sketches are appealing for humans as a universal tool to depict the visual world. Humans can recognize varied sketches of a category easily by identifying the concurrence and layout of the intrinsic semantic components of the category, since humans draw free-hand sketches based a common consensus that which types of semantic components constitute e

Jiafeng Mao, Xueting Wang, Kiyoharu Aizawa

Text-to-image diffusion models allow users control over the content of generated images. Still, text-to-image generation occasionally leads to generation failure requiring users to generate dozens of images under the same text prompt before they obtain a satisfying result. We formulate the lottery ticket hypothesis in denoising: randomly initialized Gaussian

Tristan Montoya, David W. Zingg

We present a new class of efficient and robust discontinuous spectral-element methods of arbitrary order for nonlinear hyperbolic systems of conservation laws on curved triangular and tetrahedral unstructured grids. Such discretizations employ a recently introduced family of sparse tensor-product summation-by-parts (SBP) operators in collapsed coordinates wi

Subharup Guha, Yi Li

Comparative meta-analyses of groups of subjects by integrating multiple observational studies rely on estimated propensity scores (PSs) to mitigate covariate imbalances. However, PS estimation grapples with the theoretical and practical challenges posed by high-dimensional covariates. Motivated by an integrative analysis of breast cancer patients across seve

Yong-Beom Choi, Chang-Hwan Lee, Myeong-Hwan Mun, Soonchul Choi

We investigate {\alpha}-decay half-lives for 74 {\le} Z {\le} 92 even-even nuclei within the semiclassical WKB approximation in deformed relativistic Hartree-Bogoliubov theory in continuum (DRHBc). The {\alpha}-particle preformation factors are estimated from cluster-formation model using both empirical AME2020 binding energies and numerical ones obtained by

Yanzuo Lu, Meng Shen, Andy J Ma, Xiaohua Xie

Universal domain adaptation (UniDA) is a practical but challenging problem, in which information about the relation between the source and the target domains is not given for knowledge transfer. Existing UniDA methods may suffer from the problems of overlooking intra-domain variations in the target domain and difficulty in separating between the similar know

Bang Wu, Xingliang Yuan, Shuo Wang, Qi Li

The deployment of Graph Neural Networks (GNNs) within Machine Learning as a Service (MLaaS) has opened up new attack surfaces and an escalation in security concerns regarding model-centric attacks. These attacks can directly manipulate the GNN model parameters during serving, causing incorrect predictions and posing substantial threats to essential GNN appli

Anil Kumar Rajapitamahuni, Sreejith Nair, Zhifei Yang, Anusha Kamath Manjeshwar

Epitaxially grown RuO2 films on TiO2 (110) exhibit significant in-plane strain anisotropy, with a compressive strain of - 4.7% along the [001] crystalline direction and a tensile strain of +2.3% along [1-10]. As the film thickness increases, anisotropic strain relaxation is expected. By fabricating Hall bar devices with current channels along two in-plane di

Yizhe Yang, Heyan Huang, Yihang Liu, Yang Gao

Knowledge-grounded dialogue is a task of generating an informative response based on both the dialogue history and external knowledge source. In general, there are two forms of knowledge: manually annotated knowledge graphs and knowledge text from website. From various evaluation viewpoints, each type of knowledge has advantages and downsides. To further dis

Xiaojie Hong, Zixin Song, Liangzhi Li, Xiaoli Wang

Medical Visual Question Answering (Med-VQA) is a very important task in healthcare industry, which answers a natural language question with a medical image. Existing VQA techniques in information systems can be directly applied to solving the task. However, they often suffer from (i) the data insufficient problem, which makes it difficult to train the state

Wenliang Li

As a simple lattice model that exhibits a phase transition, the Ising model plays a fundamental role in statistical and condensed matter physics. The Ising transition is realized by physical systems, such as the liquid-vapor transition. Its continuum limit also furnishes a basic example of interacting quantum field theories and universality classes. Motivate

Wen-Xuan Long, Marco Moretti, Andrea Abrardo, Luca Sanguinetti

Consider a communication system in which a single antenna user equipment exchanges information with a multi-antenna base station via a reconfigurable intelligent surface (RIS) in the presence of spatially correlated channels and electromagnetic interference (EMI). To exploit the attractive advantages of RIS technology, accurate configuration of its reflectin

Imtiaz Khan, Waqas Ahmed, Tianjun Li, Shabbar Raza

We study the light right-handed slepton bulk regions for dark matter from the Generalized Minimal Supergravity (GmSUGRA) in the Minimal Supersymmetric Standard Model (MSSM). In our comprehensive numerical studies, we show that $\mathcal{R_{\tilde{\phi}}}\gtrsim10\%$ is a conservative criteria to formulate bulk region, where $\mathcal{R_{\tilde{\phi}}}\equiv(

Shutian Liu, Quanyan Zhu

We propose a dynamic information manipulation game (DIMG) to investigate the incentives of an information manipulator (IM) to influence the transition rules of a partially observable Markov decision process (POMDP). DIMG is a hierarchical game where the upper-level IM stealthily designs the POMDP's joint state distributions to influence the lower-level contr

Bang Wu, He Zhang, Xiangwen Yang, Shuo Wang

The emergence of Graph Neural Networks (GNNs) in graph data analysis and their deployment on Machine Learning as a Service platforms have raised critical concerns about data misuse during model training. This situation is further exacerbated due to the lack of transparency in local training processes, potentially leading to the unauthorized accumulation of l

Yoshiro Kitamura, Yuanzhong Li, Wataru Ito, Hiroshi Ishikawa

We propose a novel segmentation method based on energy minimization of higher-order potentials. We introduce higher-order terms into the energy to incorporate prior knowledge on the shape of the segments. The terms encourage certain sets of pixels to be entirely in one segment or the other. The sets can for instance be smooth curves in order to help delineat

Zhe Xu, Menghai Pan, Yuzhong Chen, Huiyuan Chen

Rationale discovery is defined as finding a subset of the input data that maximally supports the prediction of downstream tasks. In the context of graph machine learning, graph rationale is defined to locate the critical subgraph in the given graph topology. In contrast to the rationale subgraph, the remaining subgraph is named the environment subgraph. Grap

Yuhang Hao, Zengfu Wang, José Niño-Mora, Jing Fu

A smart target, also referred to as a reactive target, can take maneuvering motions to hinder radar tracking. We address beam scheduling for tracking multiple smart targets in phased array radar networks. We aim to mitigate the performance degradation in previous myopic tracking methods and enhance the system performance, which is measured by a discounted co

Graham V. Weinberg

Searching for concealed threats within a surveillance region is an important role for military sensors. One prominent case is the search for a submarine by a helicopter deploying a dipping sonar in anti-submarine warfare. Another is the utilisation of an uncrewed aerial vehicle for remote detection of land mines. These platforms will deploy an appropriate se

Minghao Fu, Ke Zhu, Jianxin Wu

When pre-trained models become rapidly larger, the cost of fine-tuning on downstream tasks steadily increases, too. To economically fine-tune these models, parameter-efficient transfer learning (PETL) is proposed, which only tunes a tiny subset of trainable parameters to efficiently learn quality representations. However, current PETL methods are facing the