March 2024 arXiv papers — page 86

Xiaoben Li, Mancheng Meng, Ziyan Wu, Terrence Chen

Human mesh recovery from arbitrary multi-view images involves two characteristics: the arbitrary camera poses and arbitrary number of camera views. Because of the variability, designing a unified framework to tackle this task is challenging. The challenges can be summarized as the dilemma of being able to simultaneously estimate arbitrary camera poses and re

Rui Yang, Evgenios M. Kornaropoulos, Yue Cheng

Learned Index Structures (LIS) view a sorted index as a model that learns the data distribution, takes a data element key as input, and outputs the predicted position of the key. The original LIS can only handle lookup operations with no support for updates, rendering it impractical to use for typical workloads. To address this limitation, recent studies hav

Benjamín Ojeda Magaña, José Guadalupe Robledo Hernández, Leopoldo Gómez Barba, Victor Manuel Rangel Cobián

This document describes the development of a video game prototype designed to encourage physical activity among children and older adults. The prototype consists of a laptop, a camera with 3D sensors, and optionally requires an LCD screen or a projector. The programming component of this prototype was developed in Scratch, a programming language geared towar

Vibhas K Vats, David J Crandall

Stereophotogrammetry is an established technique for scene understanding. Its origins go back to at least the 1800s when people first started to investigate using photographs to measure the physical properties of the world. Since then, thousands of approaches have been explored. The classic geometric technique of Shape from Stereo is built on using geometry

Wenhao Liu Yangzi Zheng, Aswin Lakshmi Narayanan Kondusamy, David L. Scherm, Anton. V. Malko

Transition metal dichalcogenides have received much attention in the past decade not only due to the new fundamental physics, but also due to the emergent applications in these materials. Currently chalcogenide deficiencies in TMDs are commonly believed either during the high temperature growth procedure or in the nanofabrication process resulting significan

Tsz-Him Cheung, Dit-Yan Yeung

Data augmentation improves the generalization power of deep learning models by synthesizing more training samples. Sample-mixing is a popular data augmentation approach that creates additional data by combining existing samples. Recent sample-mixing methods, like Mixup and Cutmix, adopt simple mixing operations to blend multiple inputs. Although such a heuri

Rahul N R, Vaibhav Katewa

We consider a sequential stochastic multi-armed bandit problem where the agent interacts with bandit over multiple episodes. The reward distribution of the arms remain constant throughout an episode but can change over different episodes. We propose an algorithm based on UCB to transfer the reward samples from the previous episodes and improve the cumulative

Max Regalado Kloos, Naoki Sasakura

Quantum field theories can be applied to compute various statistical properties of random tensors. In particular signed distributions of tensor eigenvalues/vectors are the easiest, which can be computed as partition functions of four-fermi theories. Though signed distributions are different from genuine ones because of extra signs of weights, they are expect

Chi Ho Wong

The realization of next-generation quantum computing devices is hindered by the formidable challenge of detecting and manipulating Majorana Fermion in nanomaterials. In this study, we explore a new approach of detecting Majorana Fermion in a metallated carbyne nanowire array. Through comprehensive optimizations, we successfully achieved a local magnetic mome

Jun Yu, Gongpeng Zhao, Yongqi Wang, Zhihong Wei

This paper presents our approach for the VA (Valence-Arousal) estimation task in the ABAW6 competition. We devised a comprehensive model by preprocessing video frames and audio segments to extract visual and audio features. Through the utilization of Temporal Convolutional Network (TCN) modules, we effectively captured the temporal and spatial correlations b

Stavros Garoufalidis, Tao Yu

We define a quantum trace map from the skein module of a 3-manifold with torus boundary components to a module (left and right quotient of a quantum torus) constructed from an ideal triangulation. Our map is a 3-dimensional version of the well-known quantum trace map on surfaces introduced by Bonahon and Wong and further developed by L\^e.

Jixiang Luo, Yan Wang, Hongwei Qin

Learned Image Compression (LIC) has achieved dramatic progress regarding objective and subjective metrics. MSE-based models aim to improve objective metrics while generative models are leveraged to improve visual quality measured by subjective metrics. However, they all suffer from blurring or deformation at low bit rates, especially at below $0.2bpp$. Besid

Joshua Sparks, Markus Kuba, Srinivasan Balaji, Hosam Mahmoud

Early investigation of P\'{o}lya urns considered drawing balls one at a time. In the last two decades, several authors considered multiple drawing in each step, but mostly for schemes on two colors. In this manuscript, we consider multiple drawing from urns of balls of multiple colors, formulating asymptotic theory for specific urn classes and addressing mor

Haocheng Xi, Yuxiang Chen, Kang Zhao, Kai Jun Teh

Pretraining transformers are generally time-consuming. Fully quantized training (FQT) is a promising approach to speed up pretraining. However, most FQT methods adopt a quantize-compute-dequantize procedure, which often leads to suboptimal speedup and significant performance degradation when used in transformers due to the high memory access overheads and lo

Tianhao Wu, Yunchong Gan, Mingdong Wu, Jingbo Cheng

In real-world scenarios, objects often require repositioning and reorientation before they can be grasped, a process known as pre-grasp manipulation. Learning universal dexterous functional pre-grasp manipulation requires precise control over the relative position, orientation, and contact between the hand and object while generalizing to diverse dynamic sce

Baoying Wang, Huixu Dong

The Bin Packing Problem (BPP) has attracted enthusiastic research interest recently, owing to widespread applications in logistics and warehousing environments. It is truly essential to optimize the bin packing to enable more objects to be packed into boxes. Object packing order and placement strategy are the two crucial optimization objectives of the BPP. H

Sarthak Jain, Martina Cardone, Soheil Mohajer

In this work, we consider the sparsity-constrained community-based group testing problem, where the population follows a community structure. In particular, the community consists of $F$ families, each with $M$ members. A number $k_f$ out of the $F$ families are infected, and a family is said to be infected if $k_m$ out of its $M$ members are infected. Furth

Lincan Li, Hanchen Wang, Wenjie Zhang, Adelle Coster

Spatial-Temporal Graph (STG) data is characterized as dynamic, heterogenous, and non-stationary, leading to the continuous challenge of spatial-temporal graph learning. In the past few years, various GNN-based methods have been proposed to solely focus on mimicking the relationships among node individuals of the STG network, ignoring the significance of mode

Aswin Paul, Takuya Isomura, Adeel Razi

Given the rapid advancement of artificial intelligence, understanding the foundations of intelligent behaviour is increasingly important. Active inference, regarded as a general theory of behaviour, offers a principled approach to probing the basis of sophistication in planning and decision-making. In this paper, we examine two decision-making schemes in act

Chong Ma, Hanqi Jiang, Wenting Chen, Yiwei Li

In the medical multi-modal frameworks, the alignment of cross-modality features presents a significant challenge. However, existing works have learned features that are implicitly aligned from the data, without considering the explicit relationships in the medical context. This data-reliance may lead to low generalization of the learned alignment relationshi

Hao Wang, Jiayou Qin, Ashish Bastola, Xiwen Chen

This paper explores the potential of Large Language Models(LLMs) in zero-shot anomaly detection for safe visual navigation. With the assistance of the state-of-the-art real-time open-world object detection model Yolo-World and specialized prompts, the proposed framework can identify anomalies within camera-captured frames that include any possible obstacles,

T. Y. Guan, Y. P. Zhang, B. Wang, C. Guo

The Jiangmen Underground Neutrino Observatory(JUNO) is a state-of-the-art liquid scintillator-based neutrino physics experiment under construction in South China. To reduce the background from external radioactivities, a water Cherenkov detector composed of 35~kton ultra-pure water and 2,400 20-inch photomultiplier tubes is developed. Even after specialized

Rahul Nadkarni, Yizhong Wang, Noah A. Smith

Language model-based instruction-following systems have lately shown increasing performance on many benchmark tasks, demonstrating the capability of adapting to a broad variety of instructions. However, such systems are often not designed to be transparent about their limitations; a user may easily prompt a model with an instruction without any idea of wheth

Yongyun Qin

Let $B \subseteq A$ be an extension of finite dimensional algebras. We provide a sufficient condition for the existence of triangle equivalences of singularity categories (resp. Gorenstein defect categories) between $A$ and $B$. This result is applied to trivial extensions, Morita rings and triangular matrix algebras to give several reduction methods on sing

Tao E. Li

Vibrational polaritons form in a planar Fabry-Perot microcavity when a vibrational mode of a layer of molecules is near resonant with an infrared cavity mode. Herein, dispersion relations of vibrational polaritons are studied when the molecular density distribution breaks the macroscopic translational symmetry along the cavity mirror plane. Both perturbative

Byoung S. Ham

The classically defined minimum uncertainty of the optical phase is known as the standard quantum limit or shot-noise limit (SNL) originating in the uncertainty principle of quantum mechanics. Based on SNL, the phase sensitivity is inversely proportional to the square root K, where K is the number of interfering photons or statistically measured events. Thus

Karan Vombatkere, Sepehr Mousavi, Savvas Zannettou, Franziska Roesner

Recommendation algorithms for social media feeds often function as black boxes from the perspective of users. We aim to detect whether social media feed recommendations are personalized to users, and to characterize the factors contributing to personalization in these feeds. We introduce a general framework to examine a set of social media feed recommendatio

Yongwei Chen, Tengfei Wang, Tong Wu, Xingang Pan

Generating high-quality 3D assets from a given image is highly desirable in various applications such as AR/VR. Recent advances in single-image 3D generation explore feed-forward models that learn to infer the 3D model of an object without optimization. Though promising results have been achieved in single object generation, these methods often struggle to m

Yifan Peng, Ilia Kulikov, Yilin Yang, Sravya Popuri

There have been emerging research interest and advances in speech-to-speech translation (S2ST), translating utterances from one language to another. This work proposes Multitask Speech Language Model (MSLM), which is a decoder-only speech language model trained in a multitask setting. Without reliance on text training data, our model is able to support multi

Xiaoyu Qiu, Yuechen Wang, Jiaxin Shi, Wengang Zhou

Based on multilingual pre-trained models, cross-lingual transfer with prompt learning has shown promising effectiveness, where soft prompt learned in a source language is transferred to target languages for downstream tasks, particularly in the low-resource scenario. To efficiently transfer soft prompt, we propose a novel framework, Multilingual Prompt Trans

Kuang-Da Wang, Wei-Yao Wang, Ping-Chun Hsieh, Wen-Chih Peng

In the dynamic and rapid tactic involvements of turn-based sports, badminton stands out as an intrinsic paradigm that requires alter-dependent decision-making of players. While the advancement of learning from offline expert data in sequential decision-making has been witnessed in various domains, how to rally-wise imitate the behaviors of human players from

Shiyu Xue, Mingyong Jing, Hao Zhang, Linjie Zhang

The laser's frequency noise is crucial to the sensitivity of quantum sensors. Two commonly used methods to suppress the laser's frequency noise are locking the laser to an atomic transition by the lock-in technique or to an ultra-low thermal expansion (ULE) glass cavity by the PDH technique. The former cannot suppress rapidly changing frequency noise and har

Goutam Mandal, Sujay Kr. Biswas

The present work aims to investigate an interacting Umami Chaplygin gas in the background dynamics of a spatially flat Friedmann-Lemaitre-Robertson-Walker (FLRW) universe when adiabatic particle creation is allowed. Here, the universe is taken to be an open thermodynamical model where the particle is created irreversibly and consequently, the creation pressu

Yifei Shen, Xinyang Jiang, Yezhen Wang, Yifan Yang

Adding additional control to pretrained diffusion models has become an increasingly popular research area, with extensive applications in computer vision, reinforcement learning, and AI for science. Recently, several studies have proposed training-free loss-based guidance by using off-the-shelf networks pretrained on clean images. This approach enables zero-

Ayushi Nirmal, Amrita Bhattacharjee, Paras Sheth, Huan Liu

Although social media platforms are a prominent arena for users to engage in interpersonal discussions and express opinions, the facade and anonymity offered by social media may allow users to spew hate speech and offensive content. Given the massive scale of such platforms, there arises a need to automatically identify and flag instances of hate speech. Alt

Yifan Peng, Ilia Kulikov, Yilin Yang, Sravya Popuri

Speech language models (LMs) are promising for high-quality speech synthesis through in-context learning. A typical speech LM takes discrete semantic units as content and a short utterance as prompt, and synthesizes speech which preserves the content's semantics but mimics the prompt's style. However, there is no systematic understanding on how the synthesiz

Zijian Zhao, Tingwei Chen, Fanyi Meng, Hang Li

Despite the development of various deep learning methods for Wi-Fi sensing, package loss often results in noncontinuous estimation of the Channel State Information (CSI), which negatively impacts the performance of the learning models. To overcome this challenge, we propose a deep learning model based on Bidirectional Encoder Representations from Transformer

Saurabh Sharma, Ambuj Singh

The problem of maximizing the adoption of a product through viral marketing in social networks has been studied heavily through postulated network models. We present a novel data-driven formulation of the problem. We use Graph Neural Networks (GNNs) to model the adoption of products by utilizing both topological and attribute information. The resulting Dynam

Pengyi Jia, Xianbin Wang, Xuemin Shen

Achieving a holistic and long-term understanding through accurate network modeling is essential for orchestrating future networks with increasing service diversity and infrastructure complexities. However, due to unselective data collection and uniform processing, traditional modeling approaches undermine the efficacy and timeliness of network orchestration.

Brannon Basilio, Chaeryn Lee, Joseph Malionek

Finding a totally geodesic surface, an embedded surface where the geodesics in the surface are also geodesics in the surrounding manifold, has been a problem of interest in the study of 3-manifolds. This has especially been of interest in hyperbolic 3-manifolds and knot complements, complements of piecewise-linearly embedded circles in the 3-sphere. This is

Junhao Cai, Yisheng He, Weihao Yuan, Siyu Zhu

This paper studies a new open-set problem, the open-vocabulary category-level object pose and size estimation. Given human text descriptions of arbitrary novel object categories, the robot agent seeks to predict the position, orientation, and size of the target object in the observed scene image. To enable such generalizability, we first introduce OO3D-9D, a

Yuxi Chen, Gabor Toth, Erick Powell, Talha Arshad

The charge exchange between the interstellar medium (ISM) and the solar wind plasma is crucial for determining the structures of the heliosphere. Since both the neutral-ion and neutral-neutral collision mean free paths are either comparable to or larger than the size of the heliosphere, the neutral phase space distribution can deviate far away from the Maxwe

Mengqiu Shao, Yulu Tian, Liang Zhao

We have established a coherent framework for applying variational methods to partial differential equations on hypergraphs, which includes the propositions of calculus and function spaces on hypergraphs. Several results related to the maximum principle on hypergraphs have also been proven. As applications, we demonstrated how these can be used to study parti

Yuan Gao, Yiheng Zhu, Yuanbin Cao, Yinzhi Zhou

Open Domain Multi-Hop Question Answering (ODMHQA) plays a crucial role in Natural Language Processing (NLP) by aiming to answer complex questions through multi-step reasoning over retrieved information from external knowledge sources. Recently, Large Language Models (LLMs) have demonstrated remarkable performance in solving ODMHQA owing to their capabilities

Faisal Qarah

Arabic poetry, with its rich linguistic features and profound cultural significance, presents a unique challenge to the Natural Language Processing (NLP) field. The complexity of its structure and context necessitates advanced computational models for accurate analysis. In this paper, we introduce AraPoemBERT, an Arabic language model pretrained exclusively

Gengyu Lin, Zhengyang Zhou, Qihe Huang, Kuo Yang

Spatiotemporal learning plays a crucial role in mobile computing techniques to empower smart cites. While existing research has made great efforts to achieve accurate predictions on the overall dataset, they still neglect the significant performance heterogeneity across samples. In this work, we designate the performance heterogeneity as the reason for unfai

Bogumił Pilecki, Ian B. Thompson, Felipe Espinoza-Arancibia, Gergely Hajdu

Binary Cepheids with giant companions are crucial for studying the physical properties of Cepheid variables, providing the best means to measure their masses. Systems composed of two Cepheids are even more important but to date, only one such system in the Large Magellanic Cloud (LMC) was known. Our current aim is to increase the number of these systems tenf

Pengfei He, Jin-Kao Hao, Jinhui Xia

The minmax multiple traveling salesman problem involves minimizing the longest tour among a set of tours. The problem is of great practical interest because it can be used to formulate several real-life applications. To solve this computationally challenging problem, we propose a leaning-driven iterated local search approach that combines an aggressive local

Ying-Chun Lin, Jennifer Neville, Jack W. Stokes, Longqi Yang

Accurate and interpretable user satisfaction estimation (USE) is critical for understanding, evaluating, and continuously improving conversational systems. Users express their satisfaction or dissatisfaction with diverse conversational patterns in both general-purpose (ChatGPT and Bing Copilot) and task-oriented (customer service chatbot) conversational syst

Kojiro Tanaka, Akito Mizuno, Toranosuke Kato, Masahiko Mikawa

There are various proposals for employing grass materials as a green landscape-friendly display. However, it is difficult for current techniques to display smooth animations using 8-bit images and to adjust display resolution, similar to conventional displays. We present ProgrammableGrass, an artificial grass display with scalable resolution, capable of swif

Pengchao Wu, Xuefeng Li, Jinghang Gu, Longhua Qian

Biomedical event extraction is an information extraction task to obtain events from biomedical text, whose targets include the type, the trigger, and the respective arguments involved in an event. Traditional biomedical event extraction usually adopts a pipelined approach, which contains trigger identification, argument role recognition, and finally event co

Qi Li, Tzu-Chen Chiu, Hsiang-Wei Huang, Min-Te Sun

In the dynamic and evolving field of computer vision, action recognition has become a key focus, especially with the advent of sophisticated methodologies like Convolutional Neural Networks (CNNs), Convolutional 3D, Transformer, and spatial-temporal feature fusion. These technologies have shown promising results on well-established benchmarks but face unique

Yifan Liu, Kangning Zhang, Xiangyuan Ren, Yanhua Huang

With the development of multimedia systems, multimodal recommendations are playing an essential role, as they can leverage rich contexts beyond interactions. Existing methods mainly regard multimodal information as an auxiliary, using them to help learn ID features; However, there exist semantic gaps among multimodal content features and ID-based features, f

Yiran Xu, Ly Kim Ha, Haina Li, Zexi Wang

In this paper, we investigate some priori estimates to provide the critical regularity criteria for incompressible Navier-Stokes equations on $\mathbb{R}^3$ and super critical surface quasi-geostrophic equations on $\mathbb{R}^2$. Concerning the Navier-Stokes equation, we demonstrate that a Leray-Hopf solution $u$ is regular if $u\in L_T^{\frac{2}{1-\alpha}}

Jintong Hu, Bin Xia, Bingchen Li, Wenming Yang

Deep learning-based denoiser has been the focus of recent development on image denoising. In the past few years, there has been increasing interest in developing self-supervised denoising networks that only require noisy images, without the need for clean ground truth for training. However, a performance gap remains between current self-supervised methods an

Weihong Zhai, Xiupeng Shi, Yiik Diew Wong, Qing Han

Enhancing yield is recognized as a paramount driver to reducing production costs in semiconductor smart manufacturing. However, optimizing and ensuring high yield rates is a highly complex and technical challenge, especially while maintaining reliable yield diagnosis and prognosis, and this shall require understanding all the confounding factors in a complex

Daiki Watarai, Naritaka Oshita, Daichi Tsuna

Electromagnetic observations reveal that almost all galaxies have supermassive black holes (SMBHs) at their centers, but their properties, especially their spins, are not fully understood. Some of the authors have recently shown [Oshita and Tsuna (2023)] that rapid spins of $>0.9$, inferred for masses around $10^7\ M_\odot$ from observations of local SMBHs a

Xun Shen, Ye Wang, Kazumune Hashimoto, Yuhu Wu

Validating and controlling safety-critical systems in uncertain environments necessitates probabilistic reachable sets of future state evolutions. The existing methods of computing probabilistic reachable sets normally assume that stochastic uncertainties are independent of system states, inputs, and other environment variables. However, this assumption fall

Joshua Pilipovsky, Panagiotis Tsiotras

Precise control under uncertainty requires a good understanding and characterization of the noise affecting the system. This paper studies the problem of steering state distributions of dynamical systems subject to partially known uncertainties. We model the distributional uncertainty of the noise process in terms of Wasserstein ambiguity sets, which, based

Hiroya Makino, Seigo Ito

Managing delivery deadlines in automated warehouses and factories is crucial for maintaining customer satisfaction and ensuring seamless production. This study introduces the problem of online multi-agent pickup and delivery with task deadlines (MAPD-D), an advanced variant of the online MAPD problem incorporating delivery deadlines. In the MAPD problem, age

Hiroya Makino, Yoshihiro Ohama, Seigo Ito

In environments where many automated guided vehicles (AGVs) operate, planning efficient, collision-free paths is essential. Related research has mainly focused on environments with pre-defined passages, resulting in space inefficiency. We attempt to relax this assumption. In this study, we define multi-agent and multi-rack path finding (MARPF) as the problem

Takahiro Azuma, Takao Koikawa

The inverse scattering problem is applied to 2-dimensional partial differential equations called soliton equations such as the KdV equation and so on. It is also used to integrate the Einstein equations with axial symmetry. These inverse scattering problems look different. We show that they can be understood in a unified way. As an application to the Einstei

Cheng Peng, Zehao Yu, Kaleb E Smith, Wei-Hsuan Lo-Ciganic

The progress in natural language processing (NLP) using large language models (LLMs) has greatly improved patient information extraction from clinical narratives. However, most methods based on the fine-tuning strategy have limited transfer learning ability for cross-domain applications. This study proposed a novel approach that employs a soft prompt-based l

Chi Hu, Yuan Ge, Xiangnan Ma, Hang Cao

Large Language Models (LLMs) have achieved impressive performance across various reasoning tasks. However, even state-of-the-art LLMs such as ChatGPT are prone to logical errors during their reasoning processes. Existing solutions, such as deploying task-specific verifiers or voting over multiple reasoning paths, either require extensive human annotations or

Mingyue Cheng, Xiaoyu Tao, Qi Liu, Hao Zhang

Advancements in self-supervised pre-training (SSL) have significantly advanced the field of learning transferable time series representations, which can be very useful in enhancing the downstream task. Despite being effective, most existing works struggle to achieve cross-domain SSL pre-training, missing valuable opportunities to integrate patterns and featu

Mingyue Cheng, Yiheng Chen, Qi Liu, Zhiding Liu

For the advancements of time series classification, scrutinizing previous studies, most existing methods adopt a common learning-to-classify paradigm - a time series classifier model tries to learn the relation between sequence inputs and target label encoded by one-hot distribution. Although effective, this paradigm conceals two inherent limitations: (1) en

Luyu Qiu, Jianing Li, Lei Wen, Chi Su

Current approaches in pose estimation primarily concentrate on enhancing model architectures, often overlooking the importance of comprehensively understanding the rationale behind model decisions. In this paper, we propose XPose, a novel framework that incorporates Explainable AI (XAI) principles into pose estimation. This integration aims to elucidate the

Liyang Lu, Ke Ma, Yue Wang, Zhaocheng Wang

In the context of extremely large-scale antenna arrays deployed in sixth-generation (6G) mobile networks, near-field (NF) communications have gained considerable attention. Unlike the planar waves formulated in the far-field, electromagnetic radiation propagates as spherical waves in the NF. This alteration affects the NF channel characteristics, particularl

Xi Wang, Hongliang Dai, Shen Gao, Piji Li

The advancement of Large Language Models (LLMs) has led to significant enhancements in the performance of chatbot systems. Many researchers have dedicated their efforts to the development of bringing characteristics to chatbots. While there have been commercial products for developing role-driven chatbots using LLMs, it is worth noting that academic research

Hongzhe Zhang, Jiasheng Shi, Jing Huang

Matching methods are widely used to reduce confounding effects in observational studies, but conventional approaches often treat all covariates as equally important, which can result in poor performance when covariates differ in their relevance to the study. We propose the Priority-Aware one-to-one Matching Algorithm (PAMA), a novel semi-supervised framework

Feiyu Lu

Machine learning techniques have seen a tremendous rise in popularity in weather and climate sciences. Data assimilation (DA), which combines observations and numerical models, has great potential to incorporate machine learning and artificial intelligence (ML/AI) techniques. In this paper, we use U-Net, a type of convolutional neutral network (CNN), to pred

Quankai Gao, Qiangeng Xu, Zhe Cao, Ben Mildenhall

Creating 4D fields of Gaussian Splatting from images or videos is a challenging task due to its under-constrained nature. While the optimization can draw photometric reference from the input videos or be regulated by generative models, directly supervising Gaussian motions remains underexplored. In this paper, we introduce a novel concept, Gaussian flow, whi

Balamurali Murugesan, Julio Silva-Rodriguez, Ismail Ben Ayed, Jose Dolz

In this work, we present a novel approach to calibrate segmentation networks that considers the inherent challenges posed by different categories and object regions. In particular, we present a formulation that integrates class and region-wise constraints into the learning objective, with multiple penalty weights to account for class and region differences.

Cong Dong, Jiahai Yang, Yun Li, Yue Wu

In recent years, DNS over Encrypted (DoE) methods have been regarded as a novel trend within the realm of the DNS ecosystem. In these DoE methods, DNS over HTTPS (DoH) provides encryption to protect data confidentiality while providing better obfuscation to avoid censorship by multiplexing port 443 with web services. This development introduced certain incon

Jianlong Hu, Xu Chen, Zhenye Gan, Jinlong Peng

Training a unified model is considered to be more suitable for practical industrial anomaly detection scenarios due to its generalization ability and storage efficiency. However, this multi-class setting, which exclusively uses normal data, overlooks the few but important accessible annotated anomalies in the real world. To address the challenge of real-worl

Kwan-Ho Kim, Zirun Han, Yinuo Zhang, Pariasadat Musavigharavi

The growth in data generation necessitates efficient data processing technologies to address the von Neumann bottleneck in conventional computer architecture. Memory-driven computing, which integrates non-volatile memory (NVM) devices in a 3D stack, is gaining attention, with CMOS back-end-of-line (BEOL) compatible ferroelectric (FE) diodes being ideal due t

Alexander G. Abanov, Andrea Cappelli

Euler hydrodynamics of perfect fluids can be viewed as an effective bosonic field theory. In cases when the underlying microscopic system involves Dirac fermions, the quantum anomalies should be properly described. In 1+1 dimensions the action formulation of hydrodynamics at zero temperature is reconsidered and shown to be equal to standard field-theory boso

Antonio M. García-García, Jacobus J. M. Verbaarschot, Jie-ping Zheng

A distinct feature of Hermitian quantum chaotic dynamics is the exponential increase of certain out-of-time-order-correlation (OTOC) functions around the Ehrenfest time with a rate given by a Lyapunov exponent. Physically, the OTOCs describe the growth of quantum uncertainty that crucially depends on the nature of the quantum motion. Here, we employ the OTOC

Jeffrey W. Reep, Roger B. Scott, Sherry Chhabra, John Unverferth

An expansion of cross-sectional area directly impacts the mass flow along a coronal loop, and significantly alters the radiative and hydrodynamic evolution of that loop as a result. Previous studies have found that an area expansion from chromosphere to corona significantly lengthens the cooling time of the corona, and appears to suppress draining from the c

Aramayis Dallakyan, Mohsen Pourahmadi

The Graphical Lasso (GLasso) algorithm is fast and widely used for estimating sparse precision matrices (Friedman et al., 2008). Its central role in the literature of high-dimensional covariance estimation rivals that of Lasso regression for sparse estimation of the mean vector. Some mysteries regarding its optimization target, convergence, positive-definite

Samia Menon, Sitong Wang, Lydia Chilton

Emotion is vital to information and message processing, playing a key role in attitude formation. Consequently, creating a mood that evokes an emotional response is essential to any compelling piece of outreach communication. Many nonprofits and charities, despite having established messages, face challenges in creating advocacy campaign videos for social me

Jonathan Hermon, Xiangying Huang

We consider the random Cayley graphs of a sequence of finite nilpotent groups of diverging sizes $G=G(n)$, whose ranks and nilpotency classes are uniformly bounded. For some $k=k(n)$ such that $1\ll\log k \ll \log |G|$, we pick a random set of generators $S=S(n)$ by sampling $k$ elements $Z_1,\ldots,Z_k$ from $G$ uniformly at random with replacement, and set

Jiyi Chen, Pengyu Li, Yutong Wang, Pei-Cheng Ku

This work proposes a deep learning (DL)-based framework, namely Sim2Real, for spectral signal reconstruction in reconstructive spectroscopy, focusing on efficient data sampling and fast inference time. The work focuses on the challenge of reconstructing real-world spectral signals under the extreme setting where only device-informed simulated data are availa

Zhongze Cai, Shang Liu, Hanzhao Wang, Huaiyang Zhong

In this paper, we study the problem of watermarking large language models (LLMs). We consider the trade-off between model distortion and detection ability and formulate it as a constrained optimization problem based on the red-green list watermarking algorithm. We show that the optimal solution to the optimization problem enjoys a nice analytical property wh

Jiajun Wu, Shan Zhong, Songbai Kang

The optical feedback intensity is an important parameter for realizing narrow linewidth lasers in Whispering-gallery-mode resonator (WGMR) self-injection locking technology. We proposed an approach that enhances the intensity of intracavity feedback in crystalline WGMR by using only a single coated auxiliary prism. Compared to the Rayleigh scattering, the fe

Zijun Chen

We prove that the Cauchy problem for the dispersion generalized Benjamin-Ono equation where $0<\alpha \leq 1$ \begin{eqnarray*} \left\{ \begin{array}{l} \partial_t u+|\partial_x|^{1+\alpha}\partial_x u+uu_x=0,\\ u(x,0)=u_0(x), \end{array} \right. \end{eqnarray*} is locally well-posed in the Fourier-Lebesgue space $\widehat{H}^{s}_{r}(\mathbb{R})$. This is pr

Xue Xiong, Beixiong Zheng, A. Lee Swindlehurst, Jie Tang

Electromagnetic wave absorbing material (EWAM) plays an essential role in manufacturing stealth aircraft, which can achieve the electromagnetic stealth (ES) by reducing the strength of the signal reflected back to the radar system. However, the stealth performance is limited by the coating thickness, incident wave angles, and working frequencies. To tackle t

Miki Kurihara, Wataru Buz Iwakiri, Masahiro Tsujimoto, Ken Ebisawa

We detected a giant X-ray flare from the RS-CVn type binary star UX Ari using MAXI on 2020 August 17 and started a series of NICER observations 89 minutes later. For a week, the entire duration of the flare was covered with 32 snapshot observations including the rising phase. The X-ray luminosity reached 2$\times$10$^{33}$ erg s$^{-1}$ and the entire energy

Tao Li, Pan Zhou, Zhengbao He, Xinwen Cheng

Sharpness-Aware Minimization (SAM) has been instrumental in improving deep neural network training by minimizing both training loss and loss sharpness. Despite the practical success, the mechanisms behind SAM's generalization enhancements remain elusive, limiting its progress in deep learning optimization. In this work, we investigate SAM's core components f

Yang Yu, Alexander F. Kemper, Chao Yang, Emanuel Gull

Imaginary-time response functions of finite-temperature quantum systems are often obtained with methods that exhibit stochastic or systematic errors. Reducing these errors comes at a large computational cost -- in quantum Monte Carlo simulations, the reduction of noise by a factor of two incurs a simulation cost of a factor of four. In this paper, we relate

Jing Xu

An operator tuple $\mathbf{T}=(T_{1},\ldots,T_{n})$ is called strongly irreducible (SI), if the joint commutant of $\mathbf{T}$ does not any nontrivial idempotent operator. In this paper, we study the uniqueness of finitely strong irreducible decomposition of operator tuples up to similarity by $K$-theory of operator algebra, and give the algebraically simil

Tiandao Chen, Jinyu Pan, Zhiyuan Huang, Yue Yu

Coherent dispersive wave emission, as an important phenomenon of soliton dynamics, manifests itself in multiple platforms of nonlinear optics from fibre waveguides to integrated photonics. Limited by its resonance nature, efficient generation of coherent dispersive wave with ultra-broad bandwidth has, however, proved difficult to realize. Here, we unveil a n

Shivam Bajaj, Bhargav Jha, Shaunak D. Bopardikar, Alexander Von Moll

We formulate a novel planar motion planning problem for a Dubins-Laser system that consists of a Dubins vehicle with an attached controllable laser. The vehicle moves with unit speed and the laser, having a finite range, can rotate in a clockwise or anti-clockwise direction with a bounded angular rate. From an arbitrary initial position and orientation, the

John Tramm, Paul Romano, Patrick Shriwise, Amanda Lund

OpenMC is an open source Monte Carlo neutral particle transport application that has recently been ported to GPU using the OpenMP target offloading model. We examine the performance of OpenMC at scale on the Frontier, Polaris, and Aurora supercomputers, demonstrating that performance portability has been achieved by OpenMC across all three major GPU vendors

Wiesław Kopeć, Grzegorz Pochwatko, Monika Kornacka, Wiktor Stawski

As humanity pushes the boundaries of space exploration, human factors research becomes more important. Human factors encompass a broad spectrum of psychological, physiological, and ergonomic factors that affect human performance, well-being, and safety in the unique and challenging space environment. This panel explores the multifaceted field of human factor

Zack While, Tanja Blascheck, Yujie Gong, Petra Isenberg

We present results of a replication study on smartwatch visualizations with adults aged 65 and older. The older adult population is rising globally, coinciding with their increasing interest in using small wearable devices, such as smartwatches, to track and view data. Smartwatches, however, pose challenges to this population: fonts and visualizations are of

Wenlei Shan, Sohei Ezaki

Investigations into the propagation characteristics, specifically loss and wave velocity, of superconducting coplanar waveguides and microstrip lines were conducted at a 2 mm wavelength. This was achieved through the measurement of on-chip half-wavelength resonators, employing superconductor-insulator-superconductor tunnel junctions as detectors. A continuou

Dong Han Kim, Seul Bee Lee, Lingmin Liao

For a given irrational number, we consider the properties of best rational approximations of given parities. There are three different kinds of rational numbers according to the parity of the numerator and denominator, say odd/odd, even/odd and odd/even rational numbers. We study algorithms to find best approximations by rational numbers of given parities an

Zhengbang Zhou, Huanhuan Ma, Wentiao Wu, Weiguo Gao

The dielectric response function and its inverse are crucial physical quantities in materials science. We propose an accurate and efficient strategy to invert the dielectric function matrix. The GW approximation, a powerful approach to accurately describe many-body excited states, is taken as an application to demonstrate accuracy and efficiency. We incorpor

Jielin Qiu, William Han, Winfred Wang, Zhengyuan Yang

Open-domain real-world entity recognition is essential yet challenging, involving identifying various entities in diverse environments. The lack of a suitable evaluation dataset has been a major obstacle in this field due to the vast number of entities and the extensive human effort required for data curation. We introduce Entity6K, a comprehensive dataset f