December 2023 arXiv papers — page 89

Michiko Okudo, Keisuke Yano

Bayesian statistics has two common measures of central tendency of a posterior distribution: posterior means and Maximum A Posteriori (MAP) estimates. In this paper, we discuss a connection between MAP estimates and posterior means. We derive an asymptotic condition for a pair of prior densities under which the posterior mean based on one prior coincides wit

Hua Lan, Shijie Zhao, Jinjie Hu, Zengfu Wang

In this article, the state estimation problems with unknown process noise and measurement noise covariances for both linear and nonlinear systems are considered. By formulating the joint estimation of system state and noise parameters into an optimization problem, a novel adaptive Kalman filter method based on conjugate-computation variational inference, ref

David Kim, Sinhae Cha, Byeongkeun Kang

This work addresses the task of weakly-supervised object localization. The goal is to learn object localization using only image-level class labels, which are much easier to obtain compared to bounding box annotations. This task is important because it reduces the need for labor-intensive ground-truth annotations. However, methods for object localization tra

Tzu-Ting Yang, Hsin-Wei Wang, Berlin Chen

In recent years, end-to-end speech recognition has emerged as a technology that integrates the acoustic, pronunciation dictionary, and language model components of the traditional Automatic Speech Recognition model. It is possible to achieve human-like recognition without the need to build a pronunciation dictionary in advance. However, due to the relative s

Haiping Ma, Changqian Wang, Hengshu Zhu, Shangshang Yang

Cognitive diagnosis is a crucial task in computational education, aimed at evaluating students' proficiency levels across various knowledge concepts through exercises. Current models, however, primarily rely on students' answered exercises, neglecting the complex and rich information contained in un-interacted exercises. While recent research has attempted t

Atanu R Sinha, Tanay Anand, Paridhi Maheshwari, A V Lakshmy

A site's recommendation system relies on knowledge of its users' preferences to offer relevant recommendations to them. These preferences are for attributes that comprise items and content shown on the site, and are estimated from the data of users' interactions with the site. Another form of users' preferences is material too, namely, users' preferences for

Hayato Futami, Emiru Tsunoo, Yosuke Kashiwagi, Hiroaki Ogawa

In speech recognition applications, it is important to recognize context-specific rare words, such as proper nouns. Tree-constrained Pointer Generator (TCPGen) has shown promise for this purpose, which efficiently biases such words with a prefix tree. While the original TCPGen relies on grapheme-based encoding, we propose extending it with phoneme-aware enco

Xingyu Lu, Zhining Liu, Yanchu Guan, Hongxuan Zhang

Given the enormous number of users and items, industrial cascade recommendation systems (RS) are continuously expanded in size and complexity to deliver relevant items, such as news, services, and commodities, to the appropriate users. In a real-world scenario with hundreds of thousands requests per second, significant computation is required to infer person

Suvam Pal, Gourab Kumar Sar, Dibakar Ghosh, Arnab Pal

Synchronization is one of the emerging collective phenomena in interacting particle systems. Its ubiquitous presence in nature, science, and technology has fascinated the scientific community over the decades. Moreover, a great deal of research has been, and is still being, devoted to understand various physical aspects of the subject. In particular, the stu

Chih-Chyau Yang, Tian-Sheuan Chang

Low power deep learning accelerators on the speech processing enable real-time applications on edge devices. However, most of the existing accelerators suffer from high power consumption and focus on image applications only. This paper presents a low power accelerator for speech separation through algorithm and hardware optimizations. At the algorithm level,

Chaoning Zhang, Dongshen Han, Sheng Zheng, Jinwoo Choi

Segment anything model (SAM) addresses two practical yet challenging segmentation tasks: \textbf{segment anything (SegAny)}, which utilizes a certain point to predict the mask for a single object of interest, and \textbf{segment everything (SegEvery)}, which predicts the masks for all objects on the image. What makes SegAny slow for SAM is its heavyweight im

Kevin Wilkinghoff

State-of-the-art anomalous sound detection (ASD) systems are often trained by using an auxiliary classification task to learn an embedding space. Doing so enables the system to learn embeddings that are robust to noise and are ignoring non-target sound events but requires manually annotated meta information to be used as class labels. However, the less diffi

Xue Li, Weibin Zeng, Zhibin Wang, Diwen Zhu

Data lakes, increasingly adopted for their ability to store and analyze diverse types of data, commonly use columnar storage formats like Parquet and ORC for handling relational tables. However, these traditional setups fall short when it comes to efficiently managing graph data, particularly those conforming to the Labeled Property Graph (LPG) model. To add

Xiangde Luo, Jia Fu, Yunxin Zhong, Shuolin Liu

Radiation therapy is a primary and effective NasoPharyngeal Carcinoma (NPC) treatment strategy. The precise delineation of Gross Tumor Volumes (GTVs) and Organs-At-Risk (OARs) is crucial in radiation treatment, directly impacting patient prognosis. Previously, the delineation of GTVs and OARs was performed by experienced radiation oncologists. Recently, deep

Jaesuk Hwang

Radiative cooling exploits the imbalance between the thermal emission from the radiative cooling surface and the downward atmospheric emission. Since the atmospheric emission power is polar angle-dependent, a mirror structure can be used to increase this imbalance and to amplify the net cooling power. The degree of amplification is determined by various para

Yiwen Liu, Haifeng Tang, Mengdong Ma, Hulei Yu

Revealing the hardening and strengthening mechanisms is crucial for facilitating the design of superhard and high-strength high-entropy ceramics (HECs). Here, we take high-entropy diborides (HEB$_2$) as the prototype to thoroughly investigate the hardening and strengthening mechanisms of HECs. Specifically, the equiatomic 4- to 9-cation single-phase HEB$_2$

Meng Li, Tao Cheng, Gongze Liu, He Huang

Visible transparent but infrared reflective materials are ideal candidates for both transparent conductive films and low-emissivity glass, which are highly desired in a broad variety of areas such as touchscreens and displays, photovoltaics, smart windows, and antistatic coatings. Ultrathin Ti3C2Tx MXene films are emerging as promising low-emissivity transpa

Sunghwa Lee, Younghoon Shin, Myungjong Kim, Jiwon Seo

Several sensing techniques have been proposed for silent speech recognition (SSR); however, many of these methods require invasive processes or sensor attachment to the skin using adhesive tape or glue, rendering them unsuitable for frequent use in daily life. By contrast, impulse radio ultra-wideband (IR-UWB) radar can operate without physical contact with

Weizhi Fei, Xueyan Niu, Pingyi Zhou, Lu Hou

Transformer-based Large Language Models (LLMs) often impose limitations on the length of the text input to ensure the generation of fluent and relevant responses. This constraint restricts their applicability in scenarios involving long texts. We propose a novel semantic compression method that enables generalization to texts that are 6-8 times longer, witho

Jiayi Liu, Hou In Ivan Tam, Ali Mahdavi-Amiri, Manolis Savva

We address the challenge of generating 3D articulated objects in a controllable fashion. Currently, modeling articulated 3D objects is either achieved through laborious manual authoring, or using methods from prior work that are hard to scale and control directly. We leverage the interplay between part shape, connectivity, and motion using a denoising diffus

Rei Kawashima

Channel wall sheath thickness in an anode-layer Hall thruster RAIJIN66 was investigated by a two-dimensional hybrid simulation and a one-dimensional sheath model. A hybrid particle-fluid model with the quasineutrality assumption was used to obtain the wall ion flux and plasma property at plasma-sheath edge. The plasma property inside the sheath was estimated

Xiaofeng Zhang, Zishan Xu, Hao Tang, Chaochen Gu

Low-light image enhancement is a crucial visual task, and many unsupervised methods tend to overlook the degradation of visible information in low-light scenes, which adversely affects the fusion of complementary information and hinders the generation of satisfactory results. To address this, our study introduces "Enlighten-Your-Voice", a multimodal enhancem

Xiaoting Li, Haochuan Li, Zhenzheng Wang, Zhaoxi Chen

Thin-film periodically poled lithium niobate (TF-PPLN) devices have recently gained prominence for efficient wavelength conversion processes in both classical and quantum applications. However, the patterning and poling of TF-PPLN devices today are mostly performed at chip scales, presenting a significant bottleneck for future large-scale nonlinear photonic

Mina Pak, Junhyun Baek, Joon Hyeop Lee, Aeree Chung

We present the discovery of a new H I structure in the NGC 7194 group from the observations using the Karl G. Jansky Very Large Array. NGC 7194 group is a nearby (z ~ 0.027) small galaxy group with five quiescent members. The observations reveal a 200 kpc-long H I plume that spans the entire group with a total mass of M$_{HI}$ = 3.4 x 10$^{10}$ M$_{\odot}$.

Hao Fu, Peiqing Tong

We study the quantum phase transitions (QPTs) in extended Kitaev chains with long-range ($1/r^{\alpha}$) hopping. Formally, there are two QPT points at $\mu=\mu_0(\alpha)$ and $\mu_\pi(\alpha)$ ($\mu$ is the chemical potential) which correspond to the summations of $\sum_{m=1}^{\infty}m^{-\alpha}$ and $\sum_{m=1}^{\infty}(-1)^{m-1}m^{-\alpha}$, respectively.

Lukas Brunke, Siqi Zhou, Mingxuan Che, Angela P. Schoellig

Providing safety guarantees for learning-based controllers is important for real-world applications. One approach to realizing safety for arbitrary control policies is safety filtering. If necessary, the filter modifies control inputs to ensure that the trajectories of a closed-loop system stay within a given state constraint set for all future time, referre

Zirui Chen, Xing Hu, Xin Xia, Yi Gao

In software development, developers extensively utilize third-party libraries to avoid implementing existing functionalities. When a new third-party library vulnerability is disclosed, project maintainers need to determine whether their projects are affected by the vulnerability, which requires developers to invest substantial effort in assessment. However,

Chunhui Wu, Junhao Pei, Yihua Wu, Shengmei Zhao

Quantum machine learning is an important application of quantum computing in the era of noisy intermediate-scale quantum devices. Domain adaptation is an effective method for addressing the distribution discrepancy problem between the training data and the real data when the neural network model is deployed. In this paper, a variational quantum domain adapta

Animesh Chakravarthy, Debasish Ghose

In this paper, we address the problem of predicting collision for objects moving on the surface of a spherical manifold. Toward this end, we develop the notion of a collision triangle on such manifolds. We use this to determine analytical conditions governing the speed ratios and direcions of motion of objects that lead to collisions on the sphere. We first

Muneera Bano, Didar Zowghi, Pip Shea, Georgina Ibarra

Scientific research organizations that are developing and deploying Artificial Intelligence (AI) systems are at the intersection of technological progress and ethical considerations. The push for Responsible AI (RAI) in such institutions underscores the increasing emphasis on integrating ethical considerations within AI design and development, championing co

Zilong He

Based on BONGs theory, we prove the norm principle for integral and relative integral spinor norms of quadratic forms over general dyadic local fields, respectively. By virtue of these results, we further establish the arithmetic version of Springer's theorem for indefinite quadratic forms. Moreover, we solve the lifting problems on $n$-universality over arb

Krzysztof Czarnecki, Hiroshi Kuwajima

Driving Automation Systems (DAS) are subject to complex road environments and vehicle behaviors and increasingly rely on sophisticated sensors and Artificial Intelligence (AI). These properties give rise to unique safety faults stemming from specification insufficiencies and technological performance limitations, where sensors and AI introduce errors that va

Yao Huang, Yinpeng Dong, Shouwei Ruan, Xiao Yang

Compared with transferable untargeted attacks, transferable targeted adversarial attacks could specify the misclassification categories of adversarial samples, posing a greater threat to security-critical tasks. In the meanwhile, 3D adversarial samples, due to their potential of multi-view robustness, can more comprehensively identify weaknesses in existing

Tanisha Chauhan, Kaushik Kalyanaraman, Sarthok Sircar

We present a theory to quantify the formation of spatiotemporal macrostructures (or the non-homogeneous regions of high viscosity at moderate to high fluid inertia) for viscoelastic sub-diffusive flows, by introducing a mathematically consistent decomposition of the polymer conformation tensor, into the so-called structure tensor. Our approach bypasses an in

Yuechen Yang, Yang Shen, Kailu Zhou, Chenhua Hu

Optical ranging is a key technology in metrology. Optical frequency combs are shown to provide several advantages in light ranging, offering high precision with high acquisition rate. However, performance of traditional ranging systems based on microcombs is limited by the short ambiguity distance and non-real-time processing. Here, we show that dual-comb ra

Rubèn Tito, Khanh Nguyen, Marlon Tobaben, Raouf Kerkouche

Document Visual Question Answering (DocVQA) has quickly grown into a central task of document understanding. But despite the fact that documents contain sensitive or copyrighted information, none of the current DocVQA methods offers strong privacy guarantees. In this work, we explore privacy in the domain of DocVQA for the first time, highlighting privacy is

Florian Neukart

The resolution of the P vs. NP problem, a cornerstone in computational theory, remains elusive despite extensive exploration through mathematical logic and algorithmic theory. This paper takes a novel approach by integrating information theory, thermodynamics, and computational complexity, offering a comprehensive landscape of interdisciplinary study. We foc

Roshan Maharana, Kabir Ramola

We derive exact results for stress correlations in near-crystalline systems in two and three dimensions. We study energy minimized configurations of particles interacting through Harmonic as well as Lennard-Jones potentials, for varying degrees of microscopic disorder and quenched forces on grains. Our findings demonstrate that the macroscopic elastic proper

Yitong Sun, Yao Huang, Xingxing Wei

As physical adversarial attacks become extensively applied in unearthing the potential risk of security-critical scenarios, especially in dynamic scenarios, their vulnerability to environmental variations has also been brought to light. The non-robust nature of physical adversarial attack methods brings less-than-stable performance consequently. Although met

Shuanghao Bai, Min Zhang, Wanqi Zhou, Siteng Huang

Recently, despite the unprecedented success of large pre-trained visual-language models (VLMs) on a wide range of downstream tasks, the real-world unsupervised domain adaptation (UDA) problem is still not well explored. Therefore, in this paper, we first experimentally demonstrate that the unsupervised-trained VLMs can significantly reduce the distribution d

Yagub N. Aliyev

In the paper we prove that the number of graphs inscribed into graph of a convex polyhedron and circumscribed around another graph does not exceed 4. For this we first studied Poncelet type problem about the number of convex $n$-gons inscribed into one convex $n$-gon and circumscribed around another convex $n$-gon. It is proved that their number is also at m

Kwon Byung-Ki, Oh Hyun-Bin, Kim Jun-Seong, Hyunwoo Ha

Video motion magnification amplifies invisible small motions to be perceptible, which provides humans with a spatially dense and holistic understanding of small motions in the scene of interest. This is based on the premise that magnifying small motions enhances the legibility of motions. In the real world, however, vibrating objects often possess convoluted

Danho Ahn, Yeong-Bok Bae, Sang Hui Im, Chan Park

The detection method of gravitational waves (GW) using electromagnetic (EM) cavities has garnered significant attention in recent years. This paper thoroughly examines the analysis for the perturbation of the EM field and raises some issues in the existing literature. Our work demonstrates that the rigidity condition imposed on the material, as provided in t

Özcan Sert

Since the general relativistic approach requires exotic matter with negative energy density, constructing wormholes containing realistic matter is a crucial challenge. Therefore, extending General Relativity to non-minimal cases may be an alternative option. In this paper, we investigate wormholes which are supported by the non-minimally coupled electromagne

Ramteja Sajja, Yusuf Sermet, David Cwiertny, Ibrahim Demir

This research study explores the conceptualization, development, and deployment of an innovative learning analytics tool, leveraging OpenAI's GPT-4 model to quantify student engagement, map learning progression, and evaluate diverse instructional strategies within an educational context. By analyzing critical data points such as students' stress levels, curi

Cheng-Wei Ching, Liting Hu

The proliferation of mobile social networks (MSNs) has transformed information dissemination, leading to increased reliance on these platforms for news consumption. However, this shift has been accompanied by the widespread propagation of fake news, posing significant challenges in terms of public panic, political influence, and the obscuring of truth. Tradi

Warisa Sritriratanarak, Paulo Garcia

Without an agreed-upon definition of intelligence, asking "is this system intelligent?"" is an untestable question. This lack of consensus hinders research, and public perception, on Artificial Intelligence (AI), particularly since the rise of generative- and large-language models. Most work on precisely capturing what we mean by "intelligence" has come from

Ljubisa Bojic, Predrag Kovacevic, Milan Cabarkapa

As Large Language Models (LLMs) become increasingly integrated into everyday life as general purpose multimodal AI systems, their capabilities to simulate human understanding are under examination. This study investigates LLMs ability to interpret linguistic pragmatics, which involves context and implied meanings. Using Grice communication principles, we eva

Justin Loye, Sandrine Mouysset, Katia Jaffrès-Runser

Nestedness is a property of bipartite complex networks that has been shown to characterize the peculiar structure of biological and economical networks. In a nested network, a node of low degree has its neighborhood included in the neighborhood of nodes of higher degree. Emergence of nestedness is commonly due to two different schemes: i) mutualistic behavio

Mariano E. Brito, Gerhard Nägele, Alan R. Denton

Charge-stabilized colloidal suspensions display a rich variety of microstructural and thermodynamic properties, which are determined by electro-steric interactions between all ionic species. The large size asymmetry between molecular-scale microions and colloidal macroions allows the microion degrees of freedom to be integrated out, leading to an effective o

Lei Zhang, Yunshui Li, Ziqiang Liu, Jiaxi yang

With the advancement of large language models (LLMs) and the expansion of their context windows, existing long-context benchmarks fall short in effectively evaluating the models' comprehension and reasoning abilities in extended texts. Moreover, conventional benchmarks relying on F1 metrics often inaccurately score responses: they may undervalue correct answ

Zhengyuan Liu, Nancy F. Chen

The Transformer-based models with the multi-head self-attention mechanism are widely used in natural language processing, and provide state-of-the-art results. While the pre-trained language backbones are shown to implicitly capture certain linguistic knowledge, explicitly incorporating structure-aware features can bring about further improvement on the down

Lujia Wang, Hairong Wang, Yi Su, Fleming Lure

Ordinal learning (OL) is a type of machine learning models with broad utility in health care applications such as diagnosis of different grades of a disease (e.g., mild, modest, severe) and prediction of the speed of disease progression (e.g., very fast, fast, moderate, slow). This paper aims to tackle a situation when precisely labeled samples are limited i

Jens Müller, Lars Kühmichel, Martin Rohbeck, Stefan T. Radev

In this work, we analyze the conditions under which information about the context of an input $X$ can improve the predictions of deep learning models in new domains. Following work in marginal transfer learning in Domain Generalization (DG), we formalize the notion of context as a permutation-invariant representation of a set of data points that originate fr

Xiao Du, Yutong Ye, Pengyu Zhang, Yaning Yang

Learning to collaborate has witnessed significant progress in multi-agent reinforcement learning (MARL). However, promoting coordination among agents and enhancing exploration capabilities remain challenges. In multi-agent environments, interactions between agents are limited in specific situations. Effective collaboration between agents thus requires a nuan

Yuhang Ming, Jian Ma, Xingrui Yang, Weichen Dai

We present AEGIS-Net, a novel indoor place recognition model that takes in RGB point clouds and generates global place descriptors by aggregating lower-level color, geometry features and higher-level implicit semantic features. However, rather than simple feature concatenation, self-attention modules are employed to select the most important local features t

Shiyi Ma

We study the evolution of a Jordan curve on the plane by curvature flow, also known as curve shortening flow, and by level-set flow, which is a weak formulation of curvature flow. We show that the evolution of the curve depends continuously on the initial curve in Fr\'echet distance.

Mikiya Doi, Yoshihiro Nakao, Takuro Tanaka, Masami Sako

In materials informatics, searching for chemical materials with desired properties is challenging due to the vastness of the chemical space. Moreover, the high cost of evaluating properties necessitates a search with a few clues. In practice, there is also a demand for proposing compositions that are easily synthesizable. In the real world, such as in the ex

Alex Devonport, Peter Seiler, Murat Arcak

Complex-valued Gaussian processes are commonly used in Bayesian frequency-domain system identification as prior models for regression. If each realization of such a process were an $H_\infty$ function with probability one, then the same model could be used for probabilistic robust control, allowing for robustly safe learning. We investigate sufficient condit

Maria Antoniak, Anjalie Field, Jimin Mun, Melanie Walsh

Riveter provides a complete easy-to-use pipeline for analyzing verb connotations associated with entities in text corpora. We prepopulate the package with connotation frames of sentiment, power, and agency, which have demonstrated usefulness for capturing social phenomena, such as gender bias, in a broad range of corpora. For decades, lexical frameworks have

Anindya Ganguly, Angshuman Karmakar, Nitin Saxena

Hard lattice problems are predominant in constructing post-quantum cryptosystems. However, we need to continue developing post-quantum cryptosystems based on other quantum hard problems to prevent a complete collapse of post-quantum cryptography due to a sudden breakthrough in solving hard lattice problems. Solving large multivariate quadratic systems is one

Blake Gella, Howard Zhang, Rishi Upadhyay, Tiffany Chang

The introduction of large, foundational models to computer vision has led to drastically improved performance on the task of semantic segmentation. However, these existing methods exhibit a large performance drop when testing on images degraded by weather conditions such as rain, fog, or snow. We introduce a general paired-training method that can be applied

Rollin Omari, Junae Kim, Paul Montague

In this paper we explore the challenges and strategies for enhancing the robustness of $k$-means clustering algorithms against adversarial manipulations. We evaluate the vulnerability of clustering algorithms to adversarial attacks, emphasising the associated security risks. Our study investigates the impact of incremental attack strength on training, introd

Bing Liu

A core function of intelligence is grounding, which is the process of connecting the natural language and abstract knowledge to the internal representation of the real world in an intelligent being, e.g., a human. Human cognition is grounded in our sensorimotor experiences in the external world and subjective feelings in our internal world. We use languages

Biao-Liang Ye, Yao-Kun Wang, Shao-Ming Fei

In this paper, we investigate steered quantum coherence, i.e., the $l_1$ norm of steered coherence and the relative entropy of steered coherence, and the quantum Fisher information in the Gibbs state of two-qubit $XXZ$ systems. Their variations with respect to the temperature, external magnetic field, and interaction intensities are analyzed both analyticall

Chi-Hung Lee, Cheng-Yen Lin, Guan-Yu Chen

Unique zero thermal expansion (ZTE) materials are valuable for use in precision instruments, including electronics, aerospace parts, and engines. However, most ZTE materials have a temperature range less than 1000 K under which they do not expand. In this study, we present a uniaxial ZTE in the low-cost Mn2OBO3 with a thermal expansion coefficient of $\alpha

Kazuma Kobayashi, Yasuyuki Takamizawa, Mototaka Miyake, Sono Ito

Explainability is key to enhancing artificial intelligence's trustworthiness in medicine. However, several issues remain concerning the actual benefit of explainable models for clinical decision-making. Firstly, there is a lack of consensus on an evaluation framework for quantitatively assessing the practical benefits that effective explainability should pro

Volodymyr Vovchenko

We overview recent theoretical developments in the search for QCD critical point at finite temperature and density, including from lattice QCD, effective QCD theories, and proton number cumulants in heavy-ion collisions. We summarize the available constraints and predictions for the critical point location and discuss future challenges and opportunities.

Ruijie Zhu, Jiahao Chang, Ziyang Song, Jiahuan Yu

This report describes the solution that secured the first place in the "View Synthesis Challenge for Human Heads (VSCHH)" at the ICCV 2023 workshop. Given the sparse view images of human heads, the objective of this challenge is to synthesize images from novel viewpoints. Due to the complexity of textures on the face and the impact of lighting, the baseline

Yichen Liu

Given a symplectic toric manifold, the moment maps of sub-circle actions can be modified to be admissible functions in the sense of Hofer-Zehnder. By exploiting the relationship between the period of Hamiltonian sub-circle actions of a symplectic toric manifold and its Delzant polytope, we develop an invariant of Delzant polytopes which gives a lower bound o

Gensheng Pei, Fumin Shen, Yazhou Yao, Tao Chen

The optical flow guidance strategy is ideal for obtaining motion information of objects in the video. It is widely utilized in video segmentation tasks. However, existing optical flow-based methods have a significant dependency on optical flow, which results in poor performance when the optical flow estimation fails for a particular scene. The temporal consi

Minhyun Lee, Song Park, Byeongho Heo, Dongyoon Han

Recent advancements in Deep Neural Network (DNN) models have significantly improved performance across computer vision tasks. However, achieving highly generalizable and high-performing vision models requires expansive datasets, resulting in significant storage requirements. This storage challenge is a critical bottleneck for scaling up models. A recent brea

Ferdinand Ihringer

Matthew Kwan and Yuval Wigderson showed that for an infinite family of graphs, the Lov\'asz number gives an upper bound of $O(n^{3/4})$ for the size of an independent set (where $n$ is the number of vertices), while the weighted inertia bound cannot do better than $\Omega(n)$. Here we point out that there is an infinite family of graphs for which the Lov\'as

Arjun Balasingam, Joseph Chandler, Chenning Li, Zhoutong Zhang

This paper presents DriveTrack, a new benchmark and data generation framework for long-range keypoint tracking in real-world videos. DriveTrack is motivated by the observation that the accuracy of state-of-the-art trackers depends strongly on visual attributes around the selected keypoints, such as texture and lighting. The problem is that these artifacts ar

David A. Croydon, Daisuke Shiraishi, Satomi Watanabe

We derive sub-Gaussian bounds for the annealed transition density of the simple random walk on a high-dimensional loop-erased random walk. The walk dimension that appears in these is the exponent governing the space-time scaling of the process with respect to the extrinsic Euclidean distance, which contrasts with the exponent given by the intrinsic graph dis

Mahdi Ghorbani, Leo Gendelev, Paul Beroza, Michael J. Keiser

In this work, we introduce AutoFragDiff, a fragment-based autoregressive diffusion model for generating 3D molecular structures conditioned on target protein structures. We employ geometric vector perceptrons to predict atom types and spatial coordinates of new molecular fragments conditioned on molecular scaffolds and protein pockets. Our approach improves

Jiapeng Xu, Xiang Chen, Ying Tan, Kemin Zhou

This paper proposes a novel multi-objective control framework for linear time-invariant systems in which performance and robustness can be achieved in a complementary way instead of a trade-off. In particular, a state-space solution is first established for a new stabilizing control structure consisting of two independently designed controllers coordinated w

Chen Ma, Ningfei Wang, Qi Alfred Chen, Chao Shen

In Autonomous Driving (AD), real-time perception is a critical component responsible for detecting surrounding objects to ensure safe driving. While researchers have extensively explored the integrity of AD perception due to its safety and security implications, the aspect of availability (real-time performance) or latency has received limited attention. Exi

Mohammed O. Alziyadi, Alan R. Denton

In recent years, ionic microgels have garnered much attention due to their unique properties, especially their stimulus-sensitive swelling behavior. The tunable response of these soft, permeable, compressible, charged colloidal particles is increasingly attractive for applications in medicine and biotechnologies, such as controlled drug delivery, tissue engi

Pedro C. S. Costa, Philipp Schleich, Mauro E. S. Morales, Dominic W. Berry

The solution of large systems of nonlinear differential equations is needed for many applications in science and engineering. In this study, we present three main improvements to existing quantum algorithms based on the Carleman linearisation technique. First, by using a high-precision technique for the solution of the linearised differential equations, we a

Fouad Naderi

A positive non-commutative (NC) measure is a positive linear functional on the free disk operator system which is generated by a $d$-tuple of non-commuting isometries. By introducing the hybrid forms, their Cauchy transforms, and techniques from NC reproducing kernel Hilbert spaces (RKHS), we construct a natural Lebesgue decomposition for any positive NC mea

Yongsong Wang, Zhixin Li, Zhaohui Guo, Yin Ding

Lumbar disc herniation (LDH) is a prevalent orthopedic condition in clinical practice. Inertial measurement unit sensors (IMUs) are an effective tool for monitoring and assessing gait impairment in patients with lumbar disc herniation (LDH). However, the current gait assessment of LDH focuses solely on single-source acceleration signal data, without consider

Hongxuan Zhu, T. Stoltzfus-Dueck, R. Hager, S. Ku

Gyrokinetic tokamak plasmas can exhibit intrinsic toroidal rotation driven by the residual stress. While most studies have attributed the residual stress to the parallel-momentum flux from the turbulent $\boldsymbol{E}\times\boldsymbol{B}$ motion, the parallel-momentum flux from the drift-orbit motion (denoted $\Pi_\parallel^D$) and the $\boldsymbol{E}\times

Mohammed O. Alziyadi, Alan R. Denton

Ionic microcapsules are hollow shells of hydrogel, typically 10-1000 nm in radius, composed of cross-linked polymer networks that become charged and swollen in a good solvent. The ability of microcapsules to swell/deswell in response to changes in external stimuli (e.g., temperature, pH, ionic strength) suits them to applications, such as drug delivery, bios

Zhiqiang Li, Hengrong Lan, Lijie Huang, Qiong He

Reconstruction of ultrasound (US) images from radio-frequency data can be conceptualized as a linear inverse problem. Traditional deep learning approaches, which aim to improve the quality of US images by directly learning priors, often encounter challenges in generalization. Recently, diffusion-based generative models have received significant attention wit

Feng Lu, Wei Li, Yifei Sun, Cheng Song

Artificial intelligence (AI) has immense potential in time series prediction, but most explainable tools have limited capabilities in providing a systematic understanding of important features over time. These tools typically rely on evaluating a single time point, overlook the time ordering of inputs, and neglect the time-sensitive nature of time series app

Zhong-Xi Shen, Ke-Ke Wang, Shao-Ming Fei

Monogamy and polygamy of quantum correlations are the fundamental properties of quantum systems. We study the monogamy and polygamy relations satisfied by any quantum correlations in multipartite quantum systems. General monogamy relations are presented for the $\alpha$th $(0\leq\alpha \leq\gamma$, $\gamma\geq2)$ power of quantum correlation, and general pol

Yungi Kim, Taeri Kim, Won-Yong Shin, Sang-Wook Kim

In this paper, we focus on multimedia recommender systems using graph convolutional networks (GCNs) where the multimodal features as well as user-item interactions are employed together. Our study aims to exploit multimodal features more effectively in order to accurately capture users' preferences for items. To this end, we point out following two limitatio

Hengrong Lan, Zhiqiang Li, Qiong He, Jianwen Luo

Image reconstruction from radio-frequency data is pivotal in ultrafast plane wave ultrasound imaging. Unlike the conventional delay-and-sum (DAS) technique, which relies on somewhat imprecise assumptions, deep learning-based methods perform image reconstruction by training on paired data, leading to a notable enhancement in image quality. Nevertheless, these

Jonathan Sanderson, Syed Rafay Hasan

Neural Networks (NNs) have become indispensable for applications of Computer Vision (CV) and their use has been ever-growing. NNs are commonly trained for long periods of time on datasets like ImageNet and COCO that have been carefully created to represent common "real-world" environments. When deployed in the field, such as applications of autonomous vehicl

Saiful Haq, Ashutosh Sharma, Pushpak Bhattacharyya

In this paper, we introduce Neural Information Retrieval resources for 11 widely spoken Indian Languages (Assamese, Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Oriya, Punjabi, Tamil, and Telugu) from two major Indian language families (Indo-Aryan and Dravidian). These resources include (a) INDIC-MARCO, a multilingual version of the MSMARCO dataset

Huy Le, Tung Kieu, Anh Nguyen, Ngan Le

Text-video retrieval, a prominent sub-field within the domain of multimodal information retrieval, has witnessed remarkable growth in recent years. However, existing methods assume video scenes are consistent with unbiased descriptions. These limitations fail to align with real-world scenarios since descriptions can be influenced by annotator biases, diverse

Xu Yang, Yingzhe Peng, Haoxuan Ma, Shuo Xu

As Archimedes famously said, ``Give me a lever long enough and a fulcrum on which to place it, and I shall move the world'', in this study, we propose to use a tiny Language Model (LM), \eg, a Transformer with 67M parameters, to lever much larger Vision-Language Models (LVLMs) with 9B parameters. Specifically, we use this tiny \textbf{Lever-LM} to configure

Jonathan Sanderson, Syed Rafay Hasan

Use of edge computing in application of Computer Vision (CV) is an active field of research. Today, most CV applications make use of Convolutional Neural Networks (CNNs) to inference on and interpret video data. These edge devices are responsible for several CV related tasks, such as gathering, processing and enhancing, inferencing on, and displaying video d

Mengmeng Sheng, Zeren Sun, Zhenhuang Cai, Tao Chen

There has been significant attention devoted to the effectiveness of various domains, such as semi-supervised learning, contrastive learning, and meta-learning, in enhancing the performance of methods for noisy label learning (NLL) tasks. However, most existing methods still depend on prior assumptions regarding clean samples amidst different sources of nois

Fan Zhang, Jining Chen, Kunlun Wang, Wen Chen

Federated learning (FL) leverages data distributed at the edge of the network to enable intelligent applications. The efficiency of FL can be improved by using over-the-air computation (AirComp) technology in the process of gradient aggregation. In this paper, we propose a relay-assisted large-scale FL framework, and investigate the device scheduling problem

Mustafa Hajij, Ghada Zamzmi, Theodore Papamarkou, Aldo Guzmán-Sáenz

Graph-based signal processing techniques have become essential for handling data in non-Euclidean spaces. However, there is a growing awareness that these graph models might need to be expanded into `higher-order' domains to effectively represent the complex relations found in high-dimensional data. Such higher-order domains are typically modeled either as h

Vivek Mohan, Wee Peng Tay, Arindam Basu

This work introduces a neuromorphic compression based neural sensing architecture with address-event representation inspired readout protocol for massively parallel, next-gen wireless iBMI. The architectural trade-offs and implications of the proposed method are quantitatively analyzed in terms of compression ratio and spike information preservation. For the

Jia-Yi Li, Zhong-Xi Shen, Shao-Ming Fei

Multipartite entanglement holds great importance in quantum information processing. The distribution of entanglement among subsystems can be characterized by monogamy relations. Based on the $\beta$th power of concurrence and negativity, we provide two new monogamy inequalities. Through detailed examples, we demonstrate that these inequalities are tighter th

Longzhong Lin, Xuewu Lin, Tianwei Lin, Lichao Huang

Motion prediction is a crucial task in autonomous driving, and one of its major challenges lands in the multimodality of future behaviors. Many successful works have utilized mixture models which require identification of positive mixture components, and correspondingly fall into two main lines: prediction-based and anchor-based matching. The prediction clus