December 2023 arXiv papers — page 57

Anzhe Cheng, Zhenkun Wang, Chenzhong Yin, Mingxi Cheng

Backpropagation (BP) has been a successful optimization technique for deep learning models. However, its limitations, such as backward- and update-locking, and its biological implausibility, hinder the concurrent updating of layers and do not mimic the local learning processes observed in the human brain. To address these issues, recent research has suggeste

Daniel Lenz, Timo Spindeler, Nicolae Strungaru

This article deals with pure point diffraction and its connection to various notions of almost periodicity. We explain why the Fibonacci chain does not fit into the classical class of Bohr almost periodicity and how it fits into the classes of mean, Besicovitch and Weyl almost periodic point sets. We report on recent results which characterize pure point dif

Koichi Fujii, Sunyoung Kim, Masakazu Kojima, Hans D. Mittelmann

Tai256c is the largest unsolved quadratic assignment problem (QAP) instance in QAPLIB. It is known that QAP tai256c can be converted into a 256 dimensional binary quadratic optimization problem (BQOP) with a single cardinality constraint which requires the sum of the binary variables to be 92. As the BQOP is much simpler than the original QAP, the conversion

Yuan Zhang, Yaolei Qi, Xiaoming Qi, Lotfi Senhadji

Federated learning (FL) for histopathology image segmentation involving multiple medical sites plays a crucial role in advancing the field of accurate disease diagnosis and treatment. However, it is still a task of great challenges due to the sample imbalance across clients and large data heterogeneity from disparate organs, variable segmentation tasks, and

Xinyi Lei, Zhicheng Chen

Detecting damage in critical structures using monitored data is a fundamental task of structural health monitoring, which is extremely important for maintaining structures' safety and life-cycle management. Based on statistical pattern recognition paradigm, damage detection can be conducted by assessing changes in the distribution of properly extracted damag

Yuka Kotorii, Atsuhiko Mizusawa

Two links are called link-homotopic if they are transformed to each other by a sequence of self-crossing changes and ambient isotopies. The notion of link-homotopy is generalized to spatial graphs and it is called component-homotopy. The link-homotopy classes were classified by Habegger and Lin through the classification of the link-homotopy classes of strin

Yusun Shul, Jung-Woo Choi

Sound event localization and detection (SELD) is a task for the classification of sound events and the localization of direction of arrival (DoA) utilizing multichannel acoustic signals. Prior studies employ spectral and channel information as the embedding for temporal attention. However, this usage limits the deep neural network from extracting meaningful

Masaru Ikehata

In this paper, an integrated theory of the probe and singular sources methods for an inverse obstacle problem governed by the Stokes system in a bounded domain is developed. The main results consist of: the probe method for the Stokes system; the singular sources method by using the notion of the probe method; the completely integrated version of the probe a

Teng Zhong, Yinglei Teng, Shijun Ma, Jiaxuan Chen

The advent of Industrial Internet of Things (IIoT) has imposed more stringent requirements on industrial software in terms of communication delay, scalability, and maintainability. Microservice architecture (MSA), a novel software architecture that has emerged from cloud computing and DevOps, presents itself as the most promising solution due to its independ

Quentin Bouton, G Dutier, Nathalie Fabre, Eric Charron

We present a theoretical model of matter-wave diffraction through a material nanostructure. This model is based on the numerical solution of the time-dependent Schr{\"o}dinger equation, which goes beyond the standard semi-classical approach. In particular, we consider the dispersion force interaction between the atoms and the material, which is responsible f

Haozhi Sha, Jizhe Cui, Wenfeng Yang, Rong Yu

It is generally assumed that a high spatial resolution of a microscope requires a large numerical aperture of the imaging lens or detector. In this study, the information limit of 15 pm is achieved in transmission electron microscopy using only the bright-field disk (small numerical aperture) via multislice ptychography. The results indicate that high-freque

Yang Yue, Joseph Z. Shyu

As a pivotal innovation in digital infrastructure, blockchain ledger technology catalyzes the development of nascent business paradigms and applications globally. Utilizing Rothwell and Zegveld's taxonomy of twelve innovation policy tools, this study offers a nuanced comparison of domestic blockchain policies, dissecting supply, environment, and demand-drive

Zhangbin Li, Dan Guo, Jinxing Zhou, Jing Zhang

This paper focuses on the Audio-Visual Question Answering (AVQA) task that aims to answer questions derived from untrimmed audible videos. To generate accurate answers, an AVQA model is expected to find the most informative audio-visual clues relevant to the given questions. In this paper, we propose to explicitly consider fine-grained visual objects in vide

Luke Yoffe, Aditya Sharma, Tobias Höllerer

One key challenge in augmented reality is the placement of virtual content in natural locations. Existing automated techniques are only able to work with a closed-vocabulary, fixed set of objects. In this paper, we introduce a new open-vocabulary method for object placement. Our eight-stage pipeline leverages recent advances in segmentation models, vision-la

Meng-Yao Zhang, Hao Chen, Hassan Hassanabadi, Zheng-Wen Long

In the present study, we investigate the topological properties of black holes in terms of R\'{e}nyi statistics as an extension of the Gibbs-Boltzmann (GB) statistics, aiming to characterize the non-Boltzmannian thermodynamic topology of Kerr-Sen and dyonic Kerr-Sen black holes. Through this research, we discover that the topological number derived via R\'{e

Ryoto Shima, Masateru Tsunoda, Yukasa Murakami, Akito Monden

Background: Recently, code generation tools such as ChatGPT have drawn attention to their performance. Generally, a prior analysis of their performance is needed to select new code-generation tools from a list of candidates. Without such analysis, there is a higher risk of selecting an ineffective tool, negatively affecting software development productivity.

Ryosuke Itoh, Syota Maeno

Bisei town, located in the west part of Japan, is known as a place where the local community protects its beautiful night sky from light pollution through its unique ordinances and the efforts of the local residents. It is also important to monitor in the quantity and quality of light pollution for precise measurement of astronomical observations. The fluore

Sining Li, Ahad N. Zehmakan

We study a graph-based generalization of the Galam opinion formation model. Consider a simple connected graph which represents a social network. Each node in the graph is colored either blue or white, which indicates a positive or negative opinion on a new product or a topic. In each discrete-time round, all nodes are assigned randomly to groups of different

Jiachen Lian, Carly Feng, Naasir Farooqi, Steve Li

Dysfluent speech modeling requires time-accurate and silence-aware transcription at both the word-level and phonetic-level. However, current research in dysfluency modeling primarily focuses on either transcription or detection, and the performance of each aspect remains limited. In this work, we present an unconstrained dysfluency modeling (UDM) approach th

Kapil Agrawal, Sangeetha Abdu Jyothi

Cloud resilience is crucial for cloud operators and the myriad of applications that rely on the cloud. Today, we lack a mechanism that enables cloud operators to perform graceful degradation of applications while satisfying the application's availability requirements. In this paper, we put forward a vision for automated cloud resilience management with coope

Hiroki Onozeki, Zhiyang Qi, Kazuma Akiyama, Ryutaro Asahara

This paper describes our dialogue system submitted to Dialogue Robot Competition 2023. The system's task is to help a user at a travel agency decide on a plan for visiting two sightseeing spots in Kyoto City that satisfy the user. Our dialogue system is flexible and stable and responds to user requirements by controlling dialogue flow according to dialogue s

Seunghoo Hong, Juhun Lee, Simon S. Woo

Text-to-Image models such as Stable Diffusion have shown impressive image generation synthesis, thanks to the utilization of large-scale datasets. However, these datasets may contain sexually explicit, copyrighted, or undesirable content, which allows the model to directly generate them. Given that retraining these large models on individual concept deletion

Yan Cai, Linlin Wang, Ye Wang, Gerard de Melo

The emergence of various medical large language models (LLMs) in the medical domain has highlighted the need for unified evaluation standards, as manual evaluation of LLMs proves to be time-consuming and labor-intensive. To address this issue, we introduce MedBench, a comprehensive benchmark for the Chinese medical domain, comprising 40,041 questions sourced

Xinyuan Liu, Lizhi Wang, Lingen Li, Chang Chen

Computational spectral imaging is drawing increasing attention owing to the snapshot advantage, and amplitude, phase, and wavelength encoding systems are three types of representative implementations. Fairly comparing and understanding the performance of these systems is essential, but challenging due to the heterogeneity in encoding design. To overcome this

Sudipta Khamrui, Kamini Bharti, Daniella Goldfarb, Tilak Das

A microscopic control over the origin and dynamics of localised spin centres in lower dimensional solids turns out to be a key factor for next generation spintronics and quantum technologies. With the help of low temperature electron paramagnetic resonance (EPR) measurements, supported by the first-principles calculations within density functional theory (DF

Bo Yang, Hong Peng, Xiaohui Luo, Jun Wang

Breast cancer(BC) is a prevalent type of malignant tumor in women. Early diagnosis and treatment are vital for enhancing the patients' survival rate. Downsampling in deep networks may lead to loss of information, so for compensating the detail and edge information and allowing convolutional neural networks to pay more attention to seek the lesion region, we

Han Cai, Ying Miao, Moshe Schwartz, Xiaohu Tang

In this paper, the repair problem for erasures beyond locality in locally repairable codes is explored under a practical system setting, where a rack-aware storage system consists of racks, each containing a few parity checks. This is referred to as a rack-aware system with locality. Two repair schemes are devised to reduce the repair bandwidth for Tamo-Barg

Bo-Hung Chen, Dah-Wei Chiou

As a realistic model of a quantum system of matter, this paper investigates the gravitational-wave effects on a hydrogen-like atom. By formulating the tetrad formalism of linearized gravity, we naturally incorporate the gravitational-wave effects through minimal coupling in the covariant Dirac equation. The atomic electron transition rates induced by the gra

Misaki Mitsuno, Koji Hasegawa

We experimentally investigated the evaporation dynamics of acoustically levitated Ouzo droplets (a mixture of ethanol, water, and anise oil). Acoustic levitation has gained significant attention in various fields due to its potential to create a lab-in-a-drop. Although evaporation is a key process in nature and industry, many studies have focused on single a

Qing-Hua Zhang, Jing-Feng Wu, Shao-Ming Fei

The variance of quantum channels involving a mixed state gives a hybrid of classical and quantum uncertainties. We seek certain decomposition of variance into classical and quantum parts in terms of the Wigner-Yanase skew information. Generalizing the uncertainty relations for quantum observables to quantum channels, we introduce a new quantity with better q

N. Furey

An optimal algebraic model of particle physics has a number of checkpoints to pass. As a minimum, models should $\langle 1 \rangle$ conform to the Coleman-Mandula theorem (or establish a loophole), $\langle 2 \rangle$ evade familiar fermion doubling problems, $\langle 3 \rangle$ naturally explain the Standard Model's chirality, $\langle 4 \rangle$ exclude B-

Kyungmin Kwon, Seunghwan Ryu, Soyun Joo, Youngjoon Han

This study investigates the impact of resonant acoustic powder mixing on the delay time of the W-KClO4-BaCrO4 (WKB) mixture and its potential implications for powder and material synthesis. Through thermal analysis, an inverse linear relationship was found between thermal conductivity and delay time, allowing us to use thermal conductivity as a reliable prox

Ru Wang, Yufei Tao

Given a join with an acyclic set of degree constraints, we show how to draw a uniformly random sample from the join result in $O(\mathit{polymat}/ \max \{1, \mathrm{OUT} \})$ expected time after a preprocessing of $O(\mathrm{IN})$ expected time, where $\mathrm{IN}$, $\mathrm{OUT}$, and $\mathit{polymat}$ are the join's input size, output size, and polymatroi

A. Rod Gover, Valentina-Mira Wheeler

On a manifold we term a hypersurface foliation a slicing if it is the level set foliation of a slice function -- meaning some real valued function $f$ satisfying that $df$ is nowhere zero. On Riemannian manifolds we give a non-linear PDE on functions whose solutions are generic constant-mean-curvature (CMC) slice functions. Conversely, to any generic transve

Dafeng Zhu, Bo Yang, Yu Wu, Haoran Deng

This paper presents a carbon-energy coupling management framework for an industrial park, where the carbon flow model accompanying multi-energy flows is adopted to track and suppress carbon emissions on the user side. To deal with the quadratic constraint of gas flows, a bound tightening algorithm for constraints relaxation is adopted. The synergies among th

Sahil Singla, Yifan Wang

Sequential posted pricing auctions are popular because of their simplicity in practice and their tractability in theory. A usual assumption in their study is that the Bayesian prior distributions of the buyers are known to the seller, while in reality these priors can only be accessed from historical data. To overcome this assumption, we study sequential pos

W. S. Oh, T. Nordlander, G. S. Da Costa, M. S. Bessell

We present detailed abundance results based on UVES high dispersion spectra for 7 very and extremely metal-poor stars in the Large Magellanic Cloud. We confirm that all 7 stars, two of which have [Fe/H] $\leq$ --3.0, are the most metal-poor stars discovered so far in the Magellanic Clouds. The element abundance ratios are generally consistent with Milky Way

Hiromasa Hori, Koji Nakazawa, Makoto Tatsuta

Infinitary and cyclic proof systems are proof systems for logical formulas with fixed-point operators or inductive definitions. A cyclic proof system is a restriction of the corresponding infinitary proof system. Hence, these proof systems are generally not the same, as in the cyclic system may be weaker than the infinitary system. For several logics, the in

Ziang Liu, Genggeng Zhou, Jeff He, Tobia Marcucci

Learning predictive models from observations using deep neural networks (DNNs) is a promising new approach to many real-world planning and control problems. However, common DNNs are too unstructured for effective planning, and current control methods typically rely on extensive sampling or local gradient descent. In this paper, we propose a new framework for

Matthew B. Weiss

QBism is an interpretation of quantum theory which views quantum mechanics as standard probability theory supplemented with a few extra normative constraints. The fundamental gambit is to represent states and measurements, as well as time evolution, with respect to an informationally complete reference device. From this point of view, the Born rule appears a

Bo Yang, Hong Peng, Chenggang Guo, Xiaohui Luo

Prompt treatment for melanoma is crucial. To assist physicians in identifying lesion areas precisely in a quick manner, we propose a novel skin lesion segmentation technique namely SLP-Net, an ultra-lightweight segmentation network based on the spiking neural P(SNP) systems type mechanism. Most existing convolutional neural networks achieve high segmentation

Radhika Prosad Datta

This paper explores the application of Sample Entropy (SampEn) as a sophisticated tool for quantifying and predicting volatility in international oil price returns. SampEn, known for its ability to capture underlying patterns and predict periods of heightened volatility, is compared with traditional measures like standard deviation. The study utilizes a comp

Yasuhiro Matsumoto, Akihiro Yoshiki, Hiroshi Isakari

This paper proposes a collocation boundary element method based on the Burton--Miller method for solving transmission problems, which is rapidly convergent within the Krylov subspace solver framework. Our study enhances Burton--Miller-type boundary integral equations tailored for transmission problems by exploiting the Calderon formula. In cases where a sing

Ruzhang Zhao, Prosenjit Kundu, Arkajyoti Saha, Nilanjan Chatterjee

Development of comprehensive prediction models are often of great interest in many disciplines of science, but datasets with information on all desired features often have small sample sizes. We describe a transfer learning approach for building high-dimensional generalized linear models using data from a main study with detailed information on all predictor

Ashok K. Singal

We investigate dipole asymmetries in four large radio surveys, each spanning more than 80\% of the sky. Two of them, the Very Large Array Sky Survey (VLASS) and the Rapid ASKAP Continuum Survey (RACS), have recently yielded dipoles that appear incongruent with each other as well as seem inconsistent with previous radio survey dipoles and the Cosmic Microwave

Tianliang Ma, Guangxi Fan, Zhihui Deng, Xuguang Sun

Design technology co-optimization (DTCO) plays a critical role in achieving optimal power, performance, and area (PPA) for advanced semiconductor process development. Cell library characterization is essential in DTCO flow, but traditional methods are time-consuming and costly. To overcome these challenges, we propose a graph neural network (GNN)-based machi

Ashish Seth, Sreyan Ghosh, S. Umesh, Dinesh Manocha

Continued self-supervised (SSL) pre-training for adapting existing SSL models to the target domain has shown to be extremely effective for low-resource Automatic Speech Recognition (ASR). This paper proposes Stable Distillation, a simple and novel approach for SSL-based continued pre-training that boosts ASR performance in the target domain where both labele

Qian Qin, Nianqiao Ju, Guanyang Wang

Hybrid Gibbs samplers represent a prominent class of approximated Gibbs algorithms that utilize Markov chains to approximate conditional distributions, with the Metropolis-within-Gibbs algorithm standing out as a well-known example. Despite their widespread use in both statistical and non-statistical applications, little is known about their convergence prop

Mrinal Mathur, Sergey Plis

Deep learning models have become increasingly computationally intensive, requiring extensive computational resources and time for both training and inference. A significant contributing factor to this challenge is the uniform computational effort expended on each input example, regardless of its complexity. We introduce \textbf{DynaLay}, an alternative archi

S. M. Rafi-Ul-Islam, Zhuo Bin Siu, Haydar Sahin, Md. Saddam Hossain Razo

We investigate the twisted topology of the complex eigenspectrum of a one-dimensional non-Hermitian system under the influence of long-range unidirectional coupling. Unlike the complex energy spectrum of the conventional Hatano-Nelson chain, which takes the form of a single loop with a topological winding index of a definite sign, the introduction of long-ra

Xisheng Li, Wei Li, Pinhao Song, Mingjun Zhang

The inherent characteristics and light fluctuations of water bodies give rise to the huge difference between different layers and regions in underwater environments. When the test set is collected in a different marine area from the training set, the issue of domain shift emerges, significantly compromising the model's ability to generalize. The Domain Adver

Alex Iosevich, Ruixiang Zhang

For several weights based on lattice point constructions in $\mathbb{R}^d (d \geq 2)$, we prove that the sharp $L^2$ weighted restriction inequality for the sphere is very different than the corresponding result for the paraboloid. The proof uses Poisson summation, linear algebra, and lattice counting. We conjecture that the $L^2$ weighted restriction is gen

Joshin John Bejoy, G. Ambika

We present a study on the spatio-temporal pattern underlying the climate dynamics in various locations spread over India, including the Himalayan region, coastal region, central and northeastern parts of India. We try to capture the variations in the complexity of their dynamics derived from temperature and relative humidity data from 1948-2022. By estimatin

Olga Cherednichenko, Fahad Muhammad

The present-day business landscape necessitates novel methodologies that integrate intelligent technologies and tools capable of swiftly providing precise and dependable information for decision-making purposes. Contemporary society is characterized by vast amounts of accumulated data across various domains, which hold considerable potential for informing an

Guangbin Ren, Xin Zhao

This article explores octonionic analysis on the lattice $\mathbb Z^8$, emphasizing the octonionic discrete Cauchy integral within a bounded domain, the Sokhotski-Plemelj jump formulas, and the convergence of discrete regular functions. We address the challenge of articulating the discrete Cauchy-Pompeiu integral formula, particularly the complexities tied t

Felix Schmidt, Stefan Schuß, Christian Hesch

In this contribution we investigate the application of phase-field fracture models on non-linear multiscale computational homogenization schemes. In particular, we introduce different phase-fields on a two-scale problem and develop a thermodynamically consistent model. This allows on the one hand for the prediction of local micro-fracture patterns, which eff

Kotaro Osato, Takanori Taniguchi, Hirotaka Okabe, Takafumi Kitazawa

We report on the quantum criticality of YbCu$_4$Ni as revealed by our combined micro- and macro-measurements. We determine the crystal structure of YbCu$_4$Ni with site mixing by neutron diffraction measurements, which suggests the possible presence of Kondo disorder. However, decreasing the local spin susceptibility distribution and the development of spin

Nassi Ebadifard, Ajitesh Parihar, Youry Khmelevsky, Gaetan Hains

A data warehouse efficiently prepares data for effective and fast data analysis and modelling using machine learning algorithms. This paper discusses existing solutions for the Data Extraction, Transformation, and Loading (ETL) process and automation for algorithmic trading algorithms. Integrating the Data Warehouses and, in the future, the Data Lakes with t

Carol Anderson, Phil Crone

Text segmentation, the task of dividing a document into sections, is often a prerequisite for performing additional natural language processing tasks. Existing text segmentation methods have typically been developed and tested using clean, narrative-style text with segments containing distinct topics. Here we consider a challenging text segmentation task: di

Donglin Yang, Zhenfeng Liu, Wentao Jiang, Guohang Yan

Employing data augmentation methods to enhance perception performance in adverse weather has attracted considerable attention recently. Most of the LiDAR augmentation methods post-process the existing dataset by physics-based models or machine-learning methods. However, due to the limited environmental annotations and the fixed vehicle trajectories in the ex

Christian Hesch, Felix Schmidt, Stefan Schuß

In this contribution, we derive a consistent variational formulation for computational homogenization methods and show that traditional FE2 and IGA2 approaches are special discretization and solution techniques of this most general framework. This allows us to enhance dramatically the numerical analysis as well as the solution of the arising algebraic system

Wen-Xuan Zhang, Chang-Le Liu, Duojie Jia

In light of the recently observed resonance states $P_{\psi s}^{\Lambda}(4338)^0$ and $P_{cs}(4459)^0$ by LHCb Collaboration in $J/\psi\Lambda$ decay channel, we perform a systematical study of all possible hidden-heavy pentaquarks with strangeness $S=0,-1,-2,-3$, in unified framework of MIT bag model. The color-spin wavefunctions presented in terms of Young

Marcel Jackiewicz, Adam Kasperski, Pawel Zielinski

This paper discusses a class of combinatorial optimization problems with uncertain costs in the objective function. It is assumed that a sample of the cost realizations is available, which defines an empirical probability distribution for the random cost vector. A Wasserstein ball, centered at the empirical distribution, is used to define an ambiguity set of

Jingwen Ye, Ruonan Yu, Songhua Liu, Xinchao Wang

Adversarial attacks constitute a notable threat to machine learning systems, given their potential to induce erroneous predictions and classifications. However, within real-world contexts, the essential specifics of the deployed model are frequently treated as a black box, consequently mitigating the vulnerability to such attacks. Thus, enhancing the transfe

Miranda D. Louwerse, David A. Sivak

A central concern across the natural sciences is a quantitative understanding of the mechanism governing rare transitions between two metastable states. Recent research has uncovered a fundamental equality between the time-reversal asymmetry of the ensemble of such transition paths and the informativeness of system dynamics about the reactivity of a given tr

Wenhao Sun, Wendi Sun, Song Chen, Yi Kang

Sparse matrix multiplication (SpMM) is widely applied to numerous domains, such as graph processing, machine learning, and data analytics. However, inner product based SpMM induces redundant zero-element computing for mismatched nonzero operands, while outer product based approach lacks input reuse across Process Elements (PEs) and poor output locality for a

Yifei Sun, Sunny Hy, Nelson Hua, James Wingert

Discontinuous solid-solid phase transformations play a pivotal role in determining properties of rechargeable battery electrodes. By leveraging operando Bragg Coherent Diffractive Imaging (BCDI), we investigate the discontinuous phase transformation in LixNi0.5Mn1.5O4 within a fully operational battery. Throughout Li-intercalation, we directly observe the nu

Atsunori Ogawa, Naohiro Tawara, Marc Delcroix, Shoko Araki

We investigate the effectiveness of using a large ensemble of advanced neural language models (NLMs) for lattice rescoring on automatic speech recognition (ASR) hypotheses. Previous studies have reported the effectiveness of combining a small number of NLMs. In contrast, in this study, we combine up to eight NLMs, i.e., forward/backward long short-term memor

Beibei Jing, Youjia Zhang, Zikai Song, Junqing Yu

Generating realistic human motion sequences from text descriptions is a challenging task that requires capturing the rich expressiveness of both natural language and human motion. Recent advances in diffusion models have enabled significant progress in human motion synthesis. However, existing methods struggle to handle text inputs that describe complex or l

Cristian Camilo Cárdenas

We establish the deformation theory of Lie groupoid morphisms, describe the corresponding deformation cohomology of morphisms, and show the properties of the cohomology. We prove its invariance under isomorphisms of morphisms. Additionally, we establish stability properties of the morphisms using Moser-type arguments. Furthermore, we demonstrate the Morita i

Zhiyuan Liu

We study the topology of the regular loci of two complexified Hamiltonian integrable systems using the Zariski-van Kampen method. In particular, we show that the fundamental group of the regular locus for the complexified planar Kepler problem is the free Abelian group $\mathbb{Z}\oplus \mathbb{Z}$, whereas that for the complexified spherical pendulum is $\m

Wei Sun

We present a purely analytical inequality which is equivalent to the Riemann hypothesis (RH). The proof of the equivalence is based on a representation of the modulus of the Riemann $\xi$ function. As the first step to analyze the inequality, we consider polynomial approximations. We also show that the RH is equivalent to the statement that some wave functio

Chuanzheng Wang, Yiming Meng, Jun Liu, Stephen Smith

Control barrier functions are widely used to synthesize safety-critical controls. However, the presence of Gaussian-type noise in dynamical systems can generate unbounded signals and potentially result in severe consequences. Although research has been conducted in the field of safety-critical control for stochastic systems, in many real-world scenarios, we

Jiun-Shiuan Shiu, Zi-Yu Liu, Chin-Yao Cheng, Yu-Chiao Huang

The pairing ratio, a crucial metric assessing a biphoton source's ability to generate correlated photon pairs, remains underexplored despite theoretical predictions. This study presents experimental findings on the pairing ratio, utilizing a double-$\Lambda$ spontaneous four-wave mixing biphoton source in cold atoms. At an optical depth (OD) of 20, we achiev

Haohan Wang, Wei Feng, Yaoyu Li, Zheng Zhang

The state-of-the-art methods for e-commerce product background generation suffer from the inefficiency of designing product-wise prompts when scaling up the production, as well as the ineffectiveness of describing fine-grained styles when customizing personalized backgrounds for some specific brands. To address these obstacles, we integrate the category comm

H. Sugama

The Eulerian variational formulation is presented to obtain governing equations of the electromagnetic turbulent gyrokinetic system. A local momentum balance in the system is derived from the invariance of the Lagrangian of the system under an arbitrary spatial coordinate transformation by extending the previous work [H. Sugama et al., Phys. Plasmas 28, 0223

Shiliang Gao, Dinglong Wang

For a simple Lie algebra $\mathfrak{g}$ of type $A_n,B_n,C_n$ or $D_n$, we give a characterization of the set of dominant integral weights $\lambda$ such that for any rational point $\mu$ in the fundamental Weyl chamber, $2\lambda-\mu$ is a non-negative rational combination of the simple roots if and only if $V_{m\mu}\subseteq V_{m\lambda}\otimes V_{m\lambda

Juliang Li, Pete Barry, Tom Cecil, Marharyta Lisovenko

We present a design and implementation of frequency-tunable superconducting resonator. The resonance frequency tunability is achieved by flux-coupling a superconducting LC-loop to a current-biased feedline; the resulting screening current leads to a change of the kinetic inductance and shift in the resonance frequency. The thin film aluminum resonator consis

Wenhao Xu, Rongtao Xu, Changwei Wang, Shibiao Xu

Recently, CLIP has found practical utility in the domain of pixel-level zero-shot segmentation tasks. The present landscape features two-stage methodologies beset by issues such as intricate pipelines and elevated computational costs. While current one-stage approaches alleviate these concerns and incorporate Visual Prompt Training (VPT) to uphold CLIP's gen

Ioannis Tsouknidas

We use the theory of Harbater-Katz-Gabber curves to derive a generalization of the Hasse-Arf theorem for complete local field extensions in positive characteristic.

Arko P. Sen, Kalia Pfeffer, Paul Ries, Geoffrey Blewitt

We explore a novel, exotic physics, modality in multi-messenger astronomy. We are interested in exotic fields emitted by the mergers and their direct detection with a network of atomic clocks. We specifically focus on the rubidium clocks onboard satellites of the Global Positioning System. Bursts of exotic fields may be produced during the coalescence of bla

Riordan Alfredo, Vanessa Echeverria, Yueqiao Jin, Lixiang Yan

The rapid expansion of Learning Analytics (LA) and Artificial Intelligence in Education (AIED) offers new scalable, data-intensive systems but also raises concerns about data privacy and agency. Excluding stakeholders -- like students and teachers -- from the design process can potentially lead to mistrust and inadequately aligned tools. Despite a shift towa

Zhiguang Yang, Lu Wang, Chun Gan, Liufang Sang

"Creativity is the heart and soul of advertising services". Effective creatives can create a win-win scenario: advertisers can reach target users and achieve marketing objectives more effectively, users can more quickly find products of interest, and platforms can generate more advertising revenue. With the advent of AI-Generated Content, advertisers now can

Nitish Arya, Aditya G. Nair

In the realm of big data, discerning patterns in nonlinear systems affected by external control inputs is increasingly challenging. Our approach blends the coarse-graining strengths of centroid-based unsupervised clustering with the clarity of sparse regression in a unique way to enhance the closed-loop feedback control of nonlinear dynamical systems. A key

Richard Shaw, Michal Nazarczuk, Jifei Song, Arthur Moreau

Novel view synthesis has shown rapid progress recently, with methods capable of producing increasingly photorealistic results. 3D Gaussian Splatting has emerged as a promising method, producing high-quality renderings of scenes and enabling interactive viewing at real-time frame rates. However, it is limited to static scenes. In this work, we extend 3D Gauss

Ruixian Liu, Wenliang Zhang, Yuan Wei, Zhen Tao

The occurrence of charge-density-wave (CDW) order is a common thread in the phase diagram of cuprate high-transition-temperature ($T_c$) superconductors. In iron-based superconductors (FeSCs), nematic order and fluctuations play a decisive role in driving other emergent orders. CDW order has been observed by scanning tunneling microscopy for various FeSCs su

Yanling Zhang, Yin Li, Xiaojie Chen, Guangming Xie

Recently, reputation-based indirect reciprocity has been widely applied to the study on fairness behavior. Previous works mainly investigate indirect reciprocity by considering compulsory participation. While in reality, individuals may choose voluntary participation according to the opponent's reputation. It is still unclear how such reputation-based volunt

Edmund Mills, Shiye Su, Stuart Russell, Scott Emmons

How do we measure the efficacy of language model explainability methods? While many explainability methods have been developed, they are typically evaluated on bespoke tasks, preventing an apples-to-apples comparison. To help fill this gap, we present ALMANACS, a language model explainability benchmark. ALMANACS scores explainability methods on simulatabilit

M Tran, C Sun

In healthcare, the emphasis on patient safety and the minimization of medical errors cannot be overstated. Despite concerted efforts, many healthcare systems, especially in low-resource regions, still grapple with preventing these errors effectively. This study explores a pioneering application aimed at addressing this challenge by assisting caregivers in ga

Qingwei Liu, Nicolas Privault

We consider the count of subgraphs with an arbitrary configuration of endpoints in the random-connection model based on a Poisson point process on ${\Bbb R}^d$. We present combinatorial expressions for the computation of the cumulants and moments of all orders of such subgraph counts, which allow us to estimate the growth of cumulants as the intensity of the

Shiwei Cheng, Yuejiang Hao

Due to the limitations in the accuracy and robustness of current electroencephalogram (EEG) classification algorithms, applying motor imagery (MI) for practical Brain-Computer Interface (BCI) applications remains challenging. This paper proposed a model that combined a three-dimensional convolutional neural network (CNN) with a long short-term memory (LSTM)

Lipeng Gu, Xuefeng Yan, Liangliang Nan, Dingkun Zhu

Current methodologies in point cloud analysis predominantly explore 3D geometries, often achieved through the introduction of intricate learnable geometric extractors in the encoder or by deepening networks with repeated blocks. However, these approaches inevitably lead to a significant number of learnable parameters, resulting in substantial computational c

Wenhao Li, Xiu Su, Yu Han, Shan You

Diffusion models have demonstrated remarkable efficacy in various generative tasks with the predictive prowess of denoising model. Currently, diffusion models employ a uniform denoising model across all timesteps. However, the inherent variations in data distributions at different timesteps lead to conflicts during training, constraining the potential of dif

Zhaoyang Zhang, Wenqi Shao, Yixiao Ge, Xiaogang Wang

This work introduces a new Transformer model called Cached Transformer, which uses Gated Recurrent Cached (GRC) attention to extend the self-attention mechanism with a differentiable memory cache of tokens. GRC attention enables attending to both past and current tokens, increasing the receptive field of attention and allowing for exploring long-range depend

Masahiro Kato

We address the problem of best arm identification (BAI) with a fixed budget for two-armed Gaussian bandits. In BAI, given multiple arms, we aim to find the best arm, an arm with the highest expected reward, through an adaptive experiment. Kaufmann et al. (2016) develops a lower bound for the probability of misidentifying the best arm. They also propose a str

Chenglu Pan, Jiarong Xu, Yue Yu, Ziqi Yang

Graph federated learning (FL) has emerged as a pivotal paradigm enabling multiple agents to collaboratively train a graph model while preserving local data privacy. Yet, current efforts overlook a key issue: agents are self-interested and would hesitant to share data without fair and satisfactory incentives. This paper is the first endeavor to address this i

Yasmin Moslem, Rejwanul Haque, Andy Way

This paper presents the outcomes of fine-tuning Mistral 7B, a general-purpose large language model (LLM), for adaptive machine translation (MT). The fine-tuning process involves utilising a combination of zero-shot and one-shot translation prompts within the medical domain. The primary objective is to enhance real-time adaptive MT capabilities of Mistral 7B,

Shuichi Yokoyama

Recently, the author and collaborators proposed a method to construct a new conserved charge different from the Noether one for general relativistic field theory on curved space-time with energy-momentum tensor covariantly conserved, and that the new conserved charge describes the entropy of the system. As concrete evidence of the proposals, it was shown tha

Matthew G. Walker, Nelson Caldwell, Mario Mateo, Edward W. Olszewski

We present spectroscopic data for 16369 stellar targets within and/or toward 38 dwarf spheroidal galaxies and faint star clusters within the Milky Way halo environment. All spectra come from observations with the multi-object, fiber-fed echelle spectrographs M2FS at the Magellan/Clay telescope or Hectochelle at the MMT, reaching a typical limiting magnitude

Rebecca M. Neeser, Bruno Correia, Philippe Schwaller

Determining whether a molecule can be synthesized is crucial in chemistry and drug discovery, as it guides experimental prioritization and molecule ranking in de novo design tasks. Existing scoring approaches to assess synthetic feasibility struggle to extrapolate to new chemical spaces or fail to discriminate based on subtle differences such as chirality. T