October 2023 arXiv papers — page 154

Raphael Schoof, Johannes Niermann, Alexander Dyck, Thomas Böhlke

As an anode material for lithium-ion batteries, amorphous silicon offers a significantly higher energy density than the graphite anodes currently used. Alloying reactions of lithium and silicon, however, induce large deformation and lead to volume changes up to 300%. We formulate a thermodynamically consistent continuum model for the chemo-elasto-plastic dif

Phusit Nualpijit, Bumned Soodchomshom

We investigate the optical conductivity, along with longitudinal and transverse conductivities, in buckled hexagonal lattice such as silicene subjected to both an in-plane magnetic field and a perpendicular electric field. In this model, we neglect the effect of the spin-orbit interaction, which is of a smaller order compared to the strong staggered potentia

Z. V. Gareeva, N. V. Shulga, A. K. Zvezdin

The development of new computing technologies has given a new stimulus in the study of multiferroics. The use of multiferroics allows the realization of competitive energy efficient scalable logic and storage devices. The low-power consumption in Magneto Electric-Spin Orbital logics and Magnetic Random Access Memory components is provided by magnetoelectric

Kyuhwan Lee, Sol Kim, Taehoon Kim, Yong-il Shin

Half a century ago, T. Kibble proposed a scenario for topological defect formation from symmetry breaking during the expansion of the early Universe. W. Zurek later crystallized the concept to superfluid helium, predicting a power-law relation between the number of quantum vortices and the rate at which the system passes through the lambda transition. Here,

Nazanin Mosavian, Forrest Hubert, Janis Smits, Pauli Kehayias

Scanning-probe and wide-field magnetic microscopes based on Nitrogen-Vacancy (NV) centers in diamond have enabled remarkable advances in the study of biology and materials, but each method has drawbacks. Here, we implement an alternative method for nanoscale magnetic microscopy based on optical control of the charge state of NV centers in a dense layer near

Z. Huang, Y. Estaremi, S. Shimi

In this paper, we investigate the relation between the Deddens and spectral radius algebras of two bounded linear operators, noting a similarity between them. Additionally, we characterize the Deddens and spectral radius algebras related to rank one operators, operators that are similar to rank one operators, operators that are majorized by rank one operator

Evgenii E. Narimanov

We introduce the concept of the ``insurgent metamaterial'' -- which is a hyperbolic medium that contains quantum emitters with a forbidden optical transition. We show that the resulting electromagnetic response of the composite is dramatically different from that expected from the ``conventional'' hyperbolic medium, and discuss the experimental manifestation

Zhangchi Chen, Dinh Tuan Huynh, Ruiran Sun, Song-Yan Xie

Let $\{D_i\}_{i=1}^{n+1}$ be $n+1$ hypersurfaces in $\mathbb{P}^n(\mathbb{C})$ with total degrees $\sum_{i=1}^{n+1} \deg D_i\geqslant n+2$, in general position and satisfying a generic geometric condition: every $n$ hypersurfaces intersect only at smooth points and the intersection is transversal. Then, for every algebraically nondegenerate entire holomorphi

Geoffrey Goodell

We propose a novel payment mechanism for use by victims of large-scale conflict or natural disasters to conduct critical economic transactions and rebuild damaged infrastructure in the absence of both cash and traditional electronic payment mechanisms linked to bank accounts, such as debit cards or wire transfers. Claimants shall receive electronic tokens th

Haonan Yan, Wenjing Zhang, Qian Chen, Xiaoguang Li

Model poisoning attacks greatly jeopardize the application of federated learning (FL). The effectiveness of existing defenses is susceptible to the latest model poisoning attacks, leading to a decrease in prediction accuracy. Besides, these defenses are intractable to distinguish benign outliers from malicious gradients, which further compromises the model g

Haider Kamal, Muaz A. Niazi, Hammad Afzal

Reinforcement learning generates policies based on reward functions and hyperparameters. Slight changes in these can significantly affect results. The lack of documentation and reproducibility in Reinforcement learning research makes it difficult to replicate once-deduced strategies. While previous research has identified strategies using grounded maneuvers,

Mohamed F. Aboushelib, Abdelfady B. Morcos, Samir Nawar, Osama M. Shalabiea

Photoelectric observations of night sky brightness (NSB) at different zenith distances and azimuths, covering all the sky, at the Egyptian Kottamia Astronomical observatory (KAO) site of coordinates {\phi} = 29{\deg}55.9'N and {\lambda} = 31{\deg}49.5' E, were done using a fully automated photoelectric photometer (FAPP). The Bessel wide range system (UBVRI)

Guanqi Chen, Guanbin Li

Cardiac function assessment aims at predicting left ventricular ejection fraction (LVEF) given an echocardiogram video, which requests models to focus on the changes in the left ventricle during the cardiac cycle. How to assess cardiac function accurately and automatically from an echocardiogram video is a valuable topic in intelligent assisted healthcare. E

Cicero A. de Lima

A hamiltonian path is a path walk P that can be a hamiltonian path or hamiltonian circuit. Determining whether such hamiltonian path exists in a given graph G = (V, E) is a NP-Complete problem. In this paper, a novel algorithm with chaotic behaviour for hamiltonian path problem is proposed. We show that our algorithm runs in $O(V^5(V + E))$ for hard sparse i

Hu Zhang, Xin Shen, Heming Du, Huiqiang Chen

In the wheat nutrient deficiencies classification challenge, we present the DividE and EnseMble (DEEM) method for progressive test data predictions. We find that (1) test images are provided in the challenge; (2) samples are equipped with their collection dates; (3) the samples of different dates show notable discrepancies. Based on the findings, we partitio

Sangmin Bae, Jongwoo Ko, Hwanjun Song, Se-Young Yun

To tackle the high inference latency exhibited by autoregressive language models, previous studies have proposed an early-exiting framework that allocates adaptive computation paths for each token based on the complexity of generating the subsequent token. However, we observed several shortcomings, including performance degradation caused by a state copying

Bing Liu, Pengyu Xu, Sijin Lu, Shijing Wang

With the development of Internet technology and the expansion of social networks, online platforms have become an important way for people to obtain information. The introduction of tags facilitates information categorization and retrieval. Meanwhile, the development of tag recommendation systems not only enables users to input tags more efficiently, but als

Fan-Ming Luo, Tian Xu, Xingchen Cao, Yang Yu

Learning a precise dynamics model can be crucial for offline reinforcement learning, which, unfortunately, has been found to be quite challenging. Dynamics models that are learned by fitting historical transitions often struggle to generalize to unseen transitions. In this study, we identify a hidden but pivotal factor termed dynamics reward that remains con

Keivalya Pandya, Mehfuza Holia

In the digital age, the dynamics of customer service are evolving, driven by technological advancements and the integration of Large Language Models (LLMs). This research paper introduces a groundbreaking approach to automating customer service using LangChain, a custom LLM tailored for organizations. The paper explores the obsolescence of traditional custom

Jeremy A. McCulloch, Skyler R. St. Pierre, Kevin Linka, Ellen Kuhl

Sparse regression and feature extraction are the cornerstones of knowledge discovery from massive data. Their goal is to discover interpretable and predictive models that provide simple relationships among scientific variables. While the statistical tools for model discovery are well established in the context of linear regression, their generalization to no

Yu Cheng, Weikang Lin, Jie Sheng, Tsutomu T. Yanagida

The f\'eeton is the gauge boson of the $U(1)_{B-L}$ gauge theory. If the gauge coupling constant is extremely small, it becomes a candidate for dark matter. We show that its decay to a pair of electron and positron explains the observed Galactic 511-keV gamma-ray excess in a consistent manner. This f\'eeton dark matter decays mainly into pairs of neutrino an

Surayya H. E, Jesmi Sunny, M. M Musthafa, C. V Midhun

A comprehensive test of level density models for explaining the decay of excited compound nuclei, 54 Mn, 56 Fe, 58 Co, 60 Ni, 61 Ni and 63 Cu, in the energy range of 28 - 36 MeV has been performed. The compound nuclei of interest in the desired ranges are populated using 6 Li based transfer reactions. The proton decay spectrum for each excitation energy bins

Zhilin Wang, Yu Ying Chiu, Yu Cheung Chiu

Just as computational simulations of atoms, molecules and cells have shaped the way we study the sciences, true-to-life simulations of human-like agents can be valuable tools for studying human behavior. We propose Humanoid Agents, a system that guides Generative Agents to behave more like humans by introducing three elements of System 1 processing: Basic ne

Gaomin Tang, Jian-Sheng Wang

The traditional approach to studying near-field thermal transfer is based on fluctuational electrodynamics. However, this approach may not be suitable for nonequilibrium states due to dynamic drivings. In our work, we introduce a theoretical framework to describe the phenomenon of near-field heat transfer between two objects when subjected to periodic time m

Vasileios Fotopoulos, Jack Strand, Manuel Petersmann, Alexander L. Shluger

Metallic dopants have the potential to increase the mechanical strength of polycrystalline metals. These elements are expected to aggregate in regions of lower coordination, such as grain boundaries. At the grain boundaries, they can have a beneficial (toughening) or detrimental effect (e.g. grain boundary embrittlement). In this study, we employ Density Fun

Vasileios Fotopoulos, Corey S. O'Hern, Mark D. Shattuck, Alexander L. Shluger

The mechanical properties of Cu-Ti alloys have been characterized extensively through experimental studies. However, a detailed understanding of why the strength of Cu increases after a small fraction of Ti atoms is added to the alloy is still missing. In this work, we address this question using density functional theory (DFT) and molecular dynamics (MD) si

Ci-Jyun Liang, Thai-Hoa Le, Youngjib Ham, Bharadwaj R. K. Mantha

Artificial intelligence (AI) and robotics research and implementation emerged in the architecture, engineering, and construction (AEC) industry to positively impact project efficiency and effectiveness concerns such as safety, productivity, and quality. This shift, however, warrants the need for ethical considerations of AI and robotics adoption due to its p

Weilin Ma

In this paper, we have designed a low-cost scanning tunneling microscope (STM) priced at 300 USD or 2000 CNY. This microscope is suitable for educational purposes and low-demand research imaging at the nanometer level. This microscope's motion components and scanner are controlled using piezoelectric materials, avoiding the thermal drift associated with trad

Jia-Xin Peng, Baiqiang Zhu, Weiping Zhang, Keye Zhang

Cavity-magnon systems are emerging as a fruitful architecture for the integration of quantum technologies and spintronic technologies, where magnons are coupled to microwave photons via the magnetic-dipole interaction. Controllable the photon-magnon (P-M) couplings provide a powerful means of accessing and manipulating quantum states in such hybrid systems.

Zhonghua Qiao, Zhenli Xu, Qian Yin, Shenggao Zhou

This work considers charged systems described by the modified Poisson--Nernst--Planck (PNP) equations, which incorporate ionic steric effects and the Born solvation energy for dielectric inhomogeneity. Solving the steady-state modified PNP equations poses numerical challenges due to the emergence of sharp boundary layers caused by small Debye lengths, partic

Trang Nguyen, Naoaki Okazaki

Generalization in Visual Question Answering (VQA) requires models to answer questions about images with contexts beyond the training distribution. Existing attempts primarily refine unimodal aspects, overlooking enhancements in multimodal aspects. Besides, diverse interpretations of the input lead to various modes of answer generation, highlighting the role

Jian Teng, Sungwon La, Jesse T. Ault

A parallel-plate rotational rheometer measures the viscosity of a fluid by rotating the top plate relative to the bottom plate in order to induce a shear on the fluid and measuring the torques and forces that result as a function of the induced rotation rate. Manufacturing imperfections can often lead to unintentional misalignment of the plates of the rheome

Jiming Ma, Baohua Xie

It is well-known that complex hyperbolic triangle groups $\Delta(3,3,4)$ generated by three complex reflections $I_1,I_2,I_3$ in $\mbox{PU(2,1)}$ has 1-dimensional moduli space. Deforming the representations from the classical $\mathbb{R}$-Fuchsian one to $\Delta(3,3,4; \infty)$, that is, when $I_3I_2I_1I_2$ is accidental parabolic, the 3-manifolds at infini

Qingming Chen, Peng Liu, Guoqiang Li, Zhenpo Wang

The accuracy and robustness of vehicle localization are critical for achieving safe and reliable high-level autonomy. Recent results show that GPS is vulnerable to spoofing attacks, which is one major threat to autonomous driving. In this paper, a novel anomaly detection and mitigation method against GPS attacks that utilizes onboard camera and high-precisio

Crane He Chen, Joerg Liebelt

This paper proposes GradientSurf, a novel algorithm for real time surface reconstruction from monocular RGB video. Inspired by Poisson Surface Reconstruction, the proposed method builds on the tight coupling between surface, volume, and oriented point cloud and solves the reconstruction problem in gradient-domain. Unlike Poisson Surface Reconstruction which

T. Liu, Z. W. Zhao, M. Cai, D. Byer

The electro- and photo-production of $J/\psi$ meson near the threshold from the proton is relevant to the search of hidden charm pentaquark candidates reported by the LHCb collaboration, and the study of the QCD trace anomaly's contribution to the proton mass. It is also expected to be sensitive to the QCD van der Waals interaction, that is mediated by multi

Sina Bagheri Nezhad, Ameeta Agrawal

Multilingual language models have gained significant attention in recent years, enabling the development of applications that meet diverse linguistic contexts. In this paper, we present a comprehensive evaluation of three popular multilingual language models: mBERT, XLM-R, and GPT-3. We assess their performance across a diverse set of languages, with a focus

Di Ma, Yu-Ge Chen, Yue Yu, Xi Luo

We propose an effective lattice model for the moir\'e structure of the twisted bilayer dice lattice. In the chiral limit, we find that there are flat bands at the zero-energy level at any twist angle besides the magic ones and these flat bands are broadened by small perturbation away from the chiral limit. The flat bands contain both bands with zero Chern nu

Yubo Zhang, Da Ke, Junxiong Wu, Chutong Zhang

VO2 is renowned for its electric transition from an insulating monoclinic (M1) phase characterized by V-V dimerized structures, to a metallic rutile (R) phase above 340 Kelvin. This transition is accompanied by a magnetic change: the M1 phase exhibits a non-magnetic spin-singlet state, while the R phase exhibits a state with local magnetic moments. Simultane

Bolian Li, Ruqi Zhang

Bayesian deep learning counts on the quality of posterior distribution estimation. However, the posterior of deep neural networks is highly multi-modal in nature, with local modes exhibiting varying generalization performance. Given a practical budget, targeting at the original posterior can lead to suboptimal performance, as some samples may become trapped

Shiyue Cao, Yueqin Yin, Lianghua Huang, Yu Liu

Vector-quantized image modeling has shown great potential in synthesizing high-quality images. However, generating high-resolution images remains a challenging task due to the quadratic computational overhead of the self-attention process. In this study, we seek to explore a more efficient two-stage framework for high-resolution image generation with improve

Chandrika Parimoo, Ashish Gupta

This paper describes what it means for a kernel to be debuggable and proposes a kernel design with debuggability in mind. We evaluate the proposed kernel design by comparing the iterations required in cyclic debugging for different classes of bugs in a vanilla monolithic kernel to a variant enhanced with our design rules for debuggability. We discuss the tra

Vincent Wang, Nikhil Sampath, Eric Yule, Ethan Wang

Wythoff's game is a modification of the well-known game of ``nim." Wythoff's game, which does not resemble the Fibonacci sequence, has direct relation to the Golden ratio. We will explore the sequence behind this surprising relationship, and consider the implications of our elementary methods.

Marco Antonio Pinto-Orellana, Hernando Ombao, Beth Lopour

Phase-amplitude coupling is a phenomenon observed in several neurological processes, where the phase of one signal modulates the amplitude of another signal with a distinct frequency. The modulation index (MI) is a common technique used to quantify this interaction by assessing the Kullback-Leibler divergence between a uniform distribution and the empirical

Yongxin Guo, Xiaoying Tang, Tao Lin

Federated Learning (FL) is an evolving distributed machine learning approach that safeguards client privacy by keeping data on edge devices. However, the variation in data among clients poses challenges in training models that excel across all local distributions. Recent studies suggest clustering as a solution to address client heterogeneity in FL by groupi

Ru Wang, Nihan Zhou, Tam Nguyen, Sanbrita Mondal

Cooking is a vital yet challenging activity for people with visual impairments (PVI). It involves tasks that can be dangerous or difficult without vision, such as handling a knife or adding a suitable amount of salt. A better understanding of these challenges can inform the design of technologies that mitigate safety hazards and improve the quality of the li

Agnibh Dasgupta, Xin Zhong

Image watermarking involves embedding and extracting watermarks within a cover image, with deep learning approaches emerging to bolster generalization and robustness. Predominantly, current methods employ convolution and concatenation for watermark embedding, while also integrating conceivable augmentation in the training process. This paper explores a robus

Gang Xu, Shuhao Wang, Lingyu Zhao, Xiao Chen

Histopathology image analysis plays a crucial role in cancer diagnosis. However, training a clinically applicable segmentation algorithm requires pathologists to engage in labour-intensive labelling. In contrast, weakly supervised learning methods, which only require coarse-grained labels at the image level, can significantly reduce the labeling efforts. Unf

Weifeng Lin, Ziheng Wu, Wentao Yang, Mingxin Huang

Fine-tuning pre-trained Vision Transformers (ViTs) has showcased significant promise in enhancing visual recognition tasks. Yet, the demand for individualized and comprehensive fine-tuning processes for each task entails substantial computational and memory costs, posing a considerable challenge. Recent advancements in Parameter-Efficient Transfer Learning (

Yunfeng Li, Bo Wang, Xueyi Wu, Zhuoyan Liu

Although single object trackers have achieved advanced performance, their large-scale models hinder their application on limited resources platforms. Moreover, existing lightweight trackers only achieve a balance between 2-3 points in terms of parameters, performance, Flops and FPS. To achieve the optimal balance among these points, this paper proposes a lig

Ruiyang Liu, Jinxu Xiang, Bowen Zhao, Ran Zhang

Neural Radiance Fields (NeRF) have significantly advanced the generation of highly realistic and expressive 3D scenes. However, the task of editing NeRF, particularly in terms of geometry modification, poses a significant challenge. This issue has obstructed NeRF's wider adoption across various applications. To tackle the problem of efficiently editing neura

Peng Chen, Xinghu Jin, Yimin Xiao, Lihu Xu

Let $(X_t)_{t \ge 0}$ be the solution of the stochastic differential equation $$dX_t = b(X_t) dt+A dZ_t, \quad X_{0}=x,$$ where $b: \mathbb{R}^d \rightarrow \mathbb R^d$ is a Lipschitz function, $A \in \mathbb R^{d \times d}$ is a positive definite matrix, $(Z_t)_{t\geq 0}$ is a $d$-dimensional rotationally invariant $\alpha$-stable L\'evy process with $\alp

Suyeon Son, Minjin Kim, Luis C. Ho

Using the multi-epoch mid-infrared (MIR) photometry from the Wide-field Infrared Survey Explorer spanning a baseline of $\sim10$ yr, we extensively investigate the MIR variability of nearby active galactic nuclei (AGNs) at $0.15 < z < 0.4$. We find that the ensemble structure function in the W1 band ($3.4\ \mu$m) can be modeled with a broken power law. Type

Zhihua Wen, Zhiliang Tian, Wei Wu, Yuxin Yang

Conditional story generation is significant in human-machine interaction, particularly in producing stories with complex plots. While Large language models (LLMs) perform well on multiple NLP tasks, including story generation, it is challenging to generate stories with both complex and creative plots. Existing methods often rely on detailed prompts to guide

Da Long, Wei W. Xing, Aditi S. Krishnapriyan, Robert M. Kirby

Discovering governing equations from data is important to many scientific and engineering applications. Despite promising successes, existing methods are still challenged by data sparsity and noise issues, both of which are ubiquitous in practice. Moreover, state-of-the-art methods lack uncertainty quantification and/or are costly in training. To overcome th

Vahe Gharakhanyan, Max Aalto, Aminah Alsoulah, Nongnuch Artrith

Local atomic environment descriptors (LAEDs) are used in the materials science and chemistry communities, for example, for the development of machine learning interatomic potentials. Despite the fact that LAEDs have been extensively studied and benchmarked for various applications, global structure descriptors (GSDs), i.e., descriptors for entire molecules o

Hangdong Zhao, Austen Z. Fan, Xiating Ouyang, Paraschos Koutris

In this paper, we study the complexity of evaluating Conjunctive Queries with negation (\cqneg). First, we present an algorithm with linear preprocessing time and constant delay enumeration for a class of CQs with negation called free-connex signed-acyclic queries. We show that no other queries admit such an algorithm subject to lower bound conjectures. Seco

Michael Lambert, Evan Patterson

The categorified theories known as "doctrines" specify a category equipped with extra structure, analogous to how ordinary theories specify a set with extra structure. We introduce a new framework for doctrines based on double category theory. A cartesian double theory is defined to be a small double category with finite products and a model of a cartesian d

Pengcheng Lei, Zaoming Yan, Tingting Wang, Faming Fang

Blurry video frame interpolation (BVFI) aims to generate high-frame-rate clear videos from low-frame-rate blurry videos, is a challenging but important topic in the computer vision community. Blurry videos not only provide spatial and temporal information like clear videos, but also contain additional motion information hidden in each blurry frame. However,

Zhixiang Hu, An Liu, Minjian Zhao

Future wireless networks are envisioned to provide ubiquitous sensing services, which also gives rise to a substantial demand for high-dimensional non-convex parameter estimation, i.e., the associated likelihood function is non-convex and contains numerous local optima. Variational Bayesian inference (VBI) provides a powerful tool for modeling complex estima

Yang Liu, Melissa Xiaohui Qin, Long Wang, Chao Huang

Language models have been foundations in various scenarios of NLP applications, but it has not been well applied in language variety studies, even for the most popular language like English. This paper represents one of the few initial efforts to utilize the NLP technology in the paradigm of World Englishes, specifically in creating a multi-variety corpus fo

Anirudh Khatry, Yasharth Bajpai, Priyanshu Gupta, Sumit Gulwani

Information retrieval involves selecting artifacts from a corpus that are most relevant to a given search query. The flavor of retrieval typically used in classical applications can be termed as homogeneous and relaxed, where queries and corpus elements are both natural language (NL) utterances (homogeneous) and the goal is to pick most relevant elements fro

Han Gao, Matthew J. Zahr

We propose a new method, the continuous Galerkin method with globally and locally supported basis functions (CG-GL), to address the parametric robustness issues of reduced-order models (ROMs) by incorporating solution-based adaptivity with locally supported finite element basis functions. The CG-GL method combines the accuracy of locally supported basis func

Nick DiSanto, Anthony Corso, Benjamin Sanders, Gavin Harding

While transformers have pioneered attention-driven architectures as a cornerstone of language modeling, their dependence on explicitly contextual information underscores limitations in their abilities to tacitly learn overarching textual themes. This study challenges the heuristic paradigm of performance benchmarking by investigating social media data as a s

Qiqi Duan, Chang Shao, Guochen Zhou, Minghan Zhang

In the post-Moore era, main performance gains of black-box optimizers are increasingly depending on parallelism, especially for large-scale optimization (LSO). Here we propose to parallelize the well-established covariance matrix adaptation evolution strategy (CMA-ES) and in particular its one latest LSO variant called limited-memory CMA-ES (LM-CMA). To achi

Xu-Cheng Wang, Xiao Yan Xu, Yang Qi

We propose that the pseudogap and Fermi arcs can universally emerge due to thermal (static) phase fluctuations in the normal state of 2D nodal superconductors. By considering a minimal phenomenological model with spatially fluctuating superconducting pairings, we theoretically investigate the role of superconducting phase fluctuations in generic 2D supercond

Bohan Zeng, Shanglin Li, Yutang Feng, Ling Yang

Recent advances in 3D generation have been remarkable, with methods such as DreamFusion leveraging large-scale text-to-image diffusion-based models to guide 3D object generation. These methods enable the synthesis of detailed and photorealistic textured objects. However, the appearance of 3D objects produced by such text-to-3D models is often unpredictable,

Jianqiao Lu, Wenyong Huang, Nianzu Zheng, Xingshan Zeng

Training a high performance end-to-end speech (E2E) processing model requires an enormous amount of labeled speech data, especially in the era of data-centric artificial intelligence. However, labeled speech data are usually scarcer and more expensive for collection, compared to textual data. We propose Latent Synthesis (LaSyn), an efficient textual data uti

Zhongxiang Dai, Gregory Kang Ruey Lau, Arun Verma, Yao Shu

Kernelized bandits, also known as Bayesian optimization (BO), has been a prevalent method for optimizing complicated black-box reward functions. Various BO algorithms have been theoretically shown to enjoy upper bounds on their cumulative regret which are sub-linear in the number T of iterations, and a regret lower bound of Omega(sqrt(T)) has been derived wh

Zhiguo He, Yingzhong Lou, Haoyang Zhang, Xiqiu Han

Active hydrothermal vents provide the surrounding submarine environment with substantial amounts of matter and energy, thus serving as important habitats for diverse megabenthic communities in the deep ocean and constituting a unique, highly productive chemosynthetic ecosystem on Earth. Vent-endemic biological communities gather near the venting site and are

Anil B. Gavade, Neel Kanwal, Priyanka A. Gavade, Rajendra Nerli

Prostate cancer (PCa) is a severe disease among men globally. It is important to identify PCa early and make a precise diagnosis for effective treatment. For PCa diagnosis, Multi-parametric magnetic resonance imaging (mpMRI) emerged as an invaluable imaging modality that offers a precise anatomical view of the prostate gland and its tissue structure. Deep le

Conghao Wong, Beihao Xia, Ziqian Zou, Yulong Wang

Analyzing and forecasting trajectories of agents like pedestrians and cars in complex scenes has become more and more significant in many intelligent systems and applications. The diversity and uncertainty in socially interactive behaviors among a rich variety of agents make this task more challenging than other deterministic computer vision tasks. Researche

Li Wang, Jiaqi Li, Yuhao Luo, Jiahao Zheng

It is known that deep neural networks are vulnerable to adversarial attacks. Although Automatic Speaker Verification (ASV) built on top of deep neural networks exhibits robust performance in controlled scenarios, many studies confirm that ASV is vulnerable to adversarial attacks. The lack of a standard dataset is a bottleneck for further research, especially

Yinfeng Yu, Changan Chen, Lele Cao, Fangkai Yang

As humans, we hear sound every second of our life. The sound we hear is often affected by the acoustics of the environment surrounding us. For example, a spacious hall leads to more reverberation. Room Impulse Responses (RIR) are commonly used to characterize environment acoustics as a function of the scene geometry, materials, and source/receiver locations.

Masafumi Hattori

We show that the CM line bundle on a proper family parametrizing specially K-stable varieties with maximal variation is ample. As an application, we show projectivity of any proper subspace of the coarse moduli space of uniformly adiabatically K-stable klt--trivial fibrations over curves constructed in [HH23].

SungHo Moon, JinWoo Bae, SungHoon Im

Research on monocular 3D object detection is being actively studied, and as a result, performance has been steadily improving. However, 3D object detection performance is significantly reduced when applied to a camera system different from the system used to capture the training datasets. For example, a 3D detector trained on datasets from a passenger car mo

Pengcheng Xu, Tao Feng, Tianfan Fu, Siddhartha Laghuvarapu

In this work, we introduce a method to fine-tune a Transformer-based generative model for molecular de novo design. Leveraging the superior sequence learning capacity of Transformers over Recurrent Neural Networks (RNNs), our model can generate molecular structures with desired properties effectively. In contrast to the traditional RNN-based models, our prop

Bolin Zhu, Xiaoze Liu, Xin Mao, Zhuo Chen

The objective of Entity Alignment (EA) is to identify equivalent entity pairs from multiple Knowledge Graphs (KGs) and create a more comprehensive and unified KG. The majority of EA methods have primarily focused on the structural modality of KGs, lacking exploration of multi-modal information. A few multi-modal EA methods have made good attempts in this fie

Jake D. Turner, Philippe Zarka, Jean-Mathias Griessmeier, Emilie Mauduit

Studying the magnetic fields of exoplanets will provide valuable information about their interior structures, atmospheric properties (escape and dynamics), and potential habitability. One of the most promising methods to detect exoplanetary magnetic fields is to study their auroral radio emission. However, there are no confirmed detections of an exoplanet in

Scott M. Cohen

We prove a necessary condition that a quantum channel on a multipartite system may be approximated arbitrarily closely using local operations and classical communication (LOCC). We then extend those arguments to obtain a condition that applies to all quantum instruments, which range from the most refined case, a generalized measurement, to the most coarse-gr

George Glauberman, Justin Lynd

We study here the higher derived limits of mod $p$ cohomology on the centric orbit category of a saturated fusion system on a finite $p$-group. It is an open problem whether all such higher limits vanish. This is known in many cases, including for fusion systems realized by a finite group and for many classes of fusion systems which are not so realized. We p

Jia Zhao, Yu Qiao

In this paper, we first construct a differential graded Lie algebra that controls deformations of a Lie-Yamaguti algebra. Furthermore, a relative Rota-Baxter operator on a Lie-Yamaguti algebra is characterized as a Maurer-Cartan element in an appropriate $L_\infty$-algebra that we build through the graded Lie bracket of Lie-Yamaguti algebra's controlling alg

Theresa Fisher, Estelle Janin, Sara Imari Walker

The near-term capability to characterize terrestrial exoplanet atmospheres may bring us closer to discovering alien life through atmospheric data. However, remotely detectable candidate biosignature gases are subject to false positive signals because they can also be produced abiotically, raising a critical need to develop methods to determine whether a gas

Arda Aydin, Max A. Alekseyev, Alexander Barg

We construct a new family of permutationally invariant codes that correct $t$ Pauli errors for any $t\ge 1$. We also show that codes in the new family correct quantum deletion errors as well as spontaneous decay errors. Our construction contains some of the previously known permutationally invariant quantum codes as particular cases, which also admit transve

Nour Khoudari, Rabie Ramadan, Megan Ross, Benjamin Seibold

Traffic waves can rise even from single lane car-following behaviour. To better understand and mitigate traffic waves, it is necessary to use analytical tools like mathematical models, data analysis, and micro-simulations that can capture the dynamics of real traffic flow. In this study, we isolate car-following dynamics and present a systematic hierarchy of

Zhen Zhang, Zhichao Zhou, Xiaoyu Wang, Huiqian Wang

The quantum anomalous Hall state with a large band gap and a high Chern number is significant for practical applications in spintronics. By performing first-principles calculations, we investigate electronic properties of the fully fluorinated 1T-MoSe$_{2}$ monolayer. Without considering the spin-orbit coupling, the band structure demonstrates single-spin se

Ruizhi Wang, Xiangtao Wang, Jie Zhou, Thomas Lukasiewicz

In clinical scenarios, multiple medical images with different views are usually generated simultaneously, and these images have high semantic consistency. However, most existing medical report generation methods only consider single-view data. The rich multi-view mutual information of medical images can help generate more accurate reports, however, the depen

Jiaqi Li, Li Wang, Liumeng Xue, Lei Wang

Deep Learning has advanced Automatic Speaker Verification (ASV) in the past few years. Although it is known that deep learning-based ASV systems are vulnerable to adversarial examples in digital access, there are few studies on adversarial attacks in the context of physical access, where a replay process (i.e., over the air) is involved. An over-the-air atta

Guorong Gao, Jie Ma, Mingyuan Rong, Tuan Tran

Since its introduction by Vapnik and Chervonenkis in the 1960s, the VC dimension and its variants have played a central role in numerous fields. In this paper, we investigate several variants of the VC dimension and their applications to dynamical systems. First, we prove a new bound for a recently introduced generalization of VC dimension, which unifies and

Ying Shi, Dong Wang, Lantian Li, Jiqing Han

This paper investigates the possibility of extracting a target sentence from multi-talker speech using only a keyword as input. For example, in social security applications, the keyword might be "help", and the goal is to identify what the person who called for help is articulating while ignoring other speakers. To address this problem, we propose using the

Jiachen Jiang, Jinxin Zhou, Peng Wang, Qing Qu

Neural collapse provides an elegant mathematical characterization of learned last layer representations (a.k.a. features) and classifier weights in deep classification models. Such results not only provide insights but also motivate new techniques for improving practical deep models. However, most of the existing empirical and theoretical studies in neural c

Yixuan Even Xu, Steven Jecmen, Zimeng Song, Fei Fang

The assignment of papers to reviewers is a crucial part of the peer review processes of large publication venues, where organizers (e.g., conference program chairs) rely on algorithms to perform automated paper assignment. As such, a major challenge for the organizers of these processes is to specify paper assignment algorithms that find appropriate assignme

Michael Benington, Leo Phan, Chris Pierre Paul, Evan Shoemaker

AI accelerator processing capabilities and memory constraints largely dictate the scale in which machine learning workloads (e.g., training and inference) can be executed within a desirable time frame. Training a state of the art, transformer-based model today requires use of GPU-accelerated high performance computers with high-speed interconnects. As datase

Pengfei Li, Wenqing Wei, Rong Zhu, Bolin Ding

For efficient query processing, DBMS query optimizers have for decades relied on delicate cardinality estimation methods. In this work, we propose an Attention-based LEarned Cardinality Estimator (ALECE for short) for SPJ queries. The core idea is to discover the implicit relationships between queries and underlying dynamic data using attention mechanisms in

Yong Lin, Fan Zhou, Lu Tan, Lintao Ma

Invariance learning methods aim to learn invariant features in the hope that they generalize under distributional shifts. Although many tasks are naturally characterized by continuous domains, current invariance learning techniques generally assume categorically indexed domains. For example, auto-scaling in cloud computing often needs a CPU utilization predi

Guiyu Zhang, Qunbo Lv, Zui Tao, Baoyu Zhu

Infrared small target detection plays an important role in the remote sensing fields. Therefore, many detection algorithms have been proposed, in which the infrared patch-tensor (IPT) model has become a mainstream tool due to its excellent performance. However, most IPT-based methods face great challenges, such as inaccurate measure of the tensor low-ranknes

Zhenyu Wu, Xiuwei Xu, Ziwei Wang, Chong Xia

In this paper, we propose a novel network framework for indoor 3D object detection to handle variable input frame numbers in practical scenarios. Existing methods only consider fixed frames of input data for a single detector, such as monocular RGB-D images or point clouds reconstructed from dense multi-view RGB-D images. While in practical application scene

Frederik Benirschke

We construct a linear system on a general curve in a totally geodesic subvariety of the moduli space of curves. As a consequence, we obtain rank bounds for totally geodesic subvarieties of dimension at least two. Furthermore, we classify totally geodesic subvarieties of dimension at least two in strata with at most two zeros.

Yi Dong, Zhilin Wang, Makesh Narsimhan Sreedhar, Xianchao Wu

Model alignment with human preferences is an essential step in making Large Language Models (LLMs) helpful and consistent with human values. It typically consists of supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF) stages. However, RLHF faces inherent limitations stemming from a complex training setup and its tendency to ali

C. Tanner Fredieu

In this paper, the use of third-generation machine learning, also known as spiking neural network architecture, for continuous learning was investigated and compared to conventional models. The experimentation was divided into three separate phases. The first phase focused on training the conventional models via transfer learning. The second phase trains a N