April 2024 arXiv papers — page 183

Tatsuya Hosono, Philippe Laurençot

Global existence and boundedness of solutions to the Cauchy problem for the four dimensional fully parabolic chemotaxis system with indirect signal production are studied. We prove that solutions with initial mass below $(8\pi)^2$ exist globally in time. This value $(8\pi)^2$ is known as the four dimensional threshold value of the initial mass determining wh

Zhuoyuan Wang, Dong Sun, Xiangyun Zeng, Ruodai Wu

The segmentation of organs in volumetric medical images plays an important role in computer-aided diagnosis and treatment/surgery planning. Conventional 2D convolutional neural networks (CNNs) can hardly exploit the spatial correlation of volumetric data. Current 3D CNNs have the advantage to extract more powerful volumetric representations but they usually

Yuanyuan Lei, Ruihong Huang

Media outlets are becoming more partisan and polarized nowadays. In this paper, we identify media bias at the sentence level, and pinpoint bias sentences that intend to sway readers' opinions. As bias sentences are often expressed in a neutral and factual way, considering broader context outside a sentence can help reveal the bias. In particular, we observe

Serge Cantat, Christophe Dupont, Florestan Martin-Baillon

Consider the four punctured sphere ${\mathbb{S}}_4^2$. Each choice of four traces, one for each puncture, determines a relative character variety for the representations of the fundamental group of ${\mathbb{S}}_4^2$ in ${\sf{SL}}_2(\mathbb{C})$. We classify the stationary probability measures for the action of the mapping class group ${\sf{Mod}}({\mathbb{S}

Zhuo Chen, Zhao Zhang, Zixuan Li, Fei Wang

Temporal Knowledge Graph Question Answering (TKGQA) aims to answer questions with temporal intent over Temporal Knowledge Graphs (TKGs). The core challenge of this task lies in understanding the complex semantic information regarding multiple types of time constraints (e.g., before, first) in questions. Existing end-to-end methods implicitly model the time c

Alberto Elduque, Pavel Etingof, Arun S. Kannan

Kac's ten-dimensional simple Jordan superalgebra over a field of characteristic 5 is obtained from a process of semisimplification, via tensor categories, from the exceptional simple Jordan algebra (or Albert algebra), together with a suitable order 5 automorphism. This explains McCrimmon's 'bizarre result' asserting that, in characteristic 5, Kac's superalg

Alexey D. Kondorskiy

The optical properties of the hybrid core--shell nanostructures composed of a metallic core and an organic shell of molecular J-aggregates are determined by the electromagnetic coupling between plasmons localized at the surface of the metallic core and Frenkel excitons in the shell. In cases of strong and ultra-strong plasmon--exciton coupling, the use of th

Rui Xie, Chen Zhao, Kai Zhang, Zhenyu Zhang

Blind super-resolution methods based on stable diffusion showcase formidable generative capabilities in reconstructing clear high-resolution images with intricate details from low-resolution inputs. However, their practical applicability is often hampered by poor efficiency, stemming from the requirement of thousands or hundreds of sampling steps. Inspired b

Jinxi Guo, Niko Moritz, Yingyi Ma, Frank Seide

The internal language model (ILM) of the neural transducer has been widely studied. In most prior work, it is mainly used for estimating the ILM score and is subsequently subtracted during inference to facilitate improved integration with external language models. Recently, various of factorized transducer models have been proposed, which explicitly embrace

Yuanyuan Lei, Md Messal Monem Miah, Ayesha Qamar, Sai Ramana Reddy

Most previous research on moral frames has focused on social media short texts, little work has explored moral sentiment within news articles. In news articles, authors often express their opinions or political stance through moral judgment towards events, specifically whether the event is right or wrong according to social moral rules. This paper initiates

Jiawu Tian, Liwei Xu, Xiaowei Zhang, Yongqi Li

Training deep neural networks is a challenging task. In order to speed up training and enhance the performance of deep neural networks, we rectify the vanilla conjugate gradient as conjugate-gradient-like and incorporate it into the generic Adam, and thus propose a new optimization algorithm named CG-like-Adam for deep learning. Specifically, both the first-

Nassim Sehad, Lina Bariah, Wassim Hamidouche, Hamed Hellaoui

Over the past two decades, the Internet-of-Things (IoT) has become a transformative concept, and as we approach 2030, a new paradigm known as the Internet of Senses (IoS) is emerging. Unlike conventional Virtual Reality (VR), IoS seeks to provide multi-sensory experiences, acknowledging that in our physical reality, our perception extends far beyond just sig

Xinbao Qiao, Meng Zhang, Ming Tang, Ermin Wei

Machine unlearning strives to uphold the data owners' right to be forgotten by enabling models to selectively forget specific data. Recent advances suggest pre-computing and storing statistics extracted from second-order information and implementing unlearning through Newton-style updates. However, the Hessian matrix operations are extremely costly and previ

Martial Morisse, Stuti Joshi, Jaromir Mika, Juan Capella

Light is characterized by its electric field, yet quantum optics has revealed the importance of monitoring photon-photon correlations at all orders. We here present a comparative study of two experimental setups, composed of cold and warm Rubidium atoms, respectively, which allow us to probe and compare photon correlations up to the fourth order. The former

Juno Hwang, Yong-Hyun Park, Junghyo Jo

Diffusion models have demonstrated superior performance across various generative tasks including images, videos, and audio. However, they encounter difficulties in directly generating high-resolution samples. Previously proposed solutions to this issue involve modifying the architecture, further training, or partitioning the sampling process into multiple s

Mehdi Ouadfel, Samy Merabia, Yasutaka Yamaguchi, Laurent Joly

Thermo-osmotic flows, generated by applying a thermal gradient along a liquid-solid interface, could be harnessed to convert waste heat into electricity. While this phenomenon has been known for almost a century, there is a crucial need to gain a better understanding of the molecular origins of thermo-osmosis. In this paper, we start by detailing the multipl

Egor Dobronravov, Dmitriy Stolyarov, Pavel Zatitskii

We enlarge the area of applicability of the Bellman function method to estimates in the spirit of the John--Nirenberg inequality abandoning certain convexity assumptions. As an application, we consider a characteristic of a function that is much smaller than the $\mathrm{BMO}$ norm, but whose finiteness leads to the exponential integrability of the function.

Yuanyuan Lei, Kaiqiang Song, Sangwoo Cho, Xiaoyang Wang

Opinion summarization is automatically generating summaries from a variety of subjective information, such as product reviews or political opinions. The challenge of opinions summarization lies in presenting divergent or even conflicting opinions. We conduct an analysis of previous summarization models, which reveals their inclination to amplify the polarity

Qinfeng Zhu, Yuanzhi Cai, Yuan Fang, Yihan Yang

High-resolution remotely sensed images pose a challenge for commonly used semantic segmentation methods such as Convolutional Neural Network (CNN) and Vision Transformer (ViT). CNN-based methods struggle with handling such high-resolution images due to their limited receptive field, while ViT faces challenges in handling long sequences. Inspired by Mamba, wh

Varsha Lohani, Anjali Sharma, Yatindra Nath Singh

The core network is experiencing bandwidth capacity constraints as internet traffic grows. As a result, the notion of a Multi-band flexible-grid optical network was established to increase the lifespan of an optical core network. In this paper, we use the C+L band for working traffic transmission and the S-band for protection against failure. Furthermore, we

Zhanwen Liu, Yuhang Li, Yang Wang, Bolin Gao

The environmental perception of autonomous vehicles in normal conditions have achieved considerable success in the past decade. However, various unfavourable conditions such as fog, low-light, and motion blur will degrade image quality and pose tremendous threats to the safety of autonomous driving. That is, when applied to degraded images, state-of-the-art

Petr Vanc, Radoslav Skoviera, Karla Stepanova

As human-robot collaboration is becoming more widespread, there is a need for a more natural way of communicating with the robot. This includes combining data from several modalities together with the context of the situation and background knowledge. Current approaches to communication typically rely only on a single modality or are often very rigid and not

Marcel Nawrath, Agnieszka Nowak, Tristan Ratz, Danilo C. Walenta

At the heart of the Pyramid evaluation method for text summarization lie human written summary content units (SCUs). These SCUs are concise sentences that decompose a summary into small facts. Such SCUs can be used to judge the quality of a candidate summary, possibly partially automated via natural language inference (NLI) systems. Interestingly, with the a

Biao Jiang, Xin Chen, Chi Zhang, Fukun Yin

Recent advancements in language models have demonstrated their adeptness in conducting multi-turn dialogues and retaining conversational context. However, this proficiency remains largely unexplored in other multimodal generative models, particularly in human motion models. By integrating multi-turn conversations in controlling continuous virtual human movem

Hai Su, ZhenWen Jian, Songsen Yu

Knowledge distillation is a widely adopted technique for model lightening. However, the performance of most knowledge distillation methods in the domain of object detection is not satisfactory. Typically, knowledge distillation approaches consider only the classification task among the two sub-tasks of an object detector, largely overlooking the regression t

Thomas J Mcgrath, Julien Saint-Vanne, Sébastien Hutinet, Walter Vetter

Bromochloro alkanes (BCAs) have been manufactured for use as flame retardants for decades and preliminary environmental risk screening suggests they are likely to behave similarly to polychlorinated alkanes (PCAs), subclasses of which are restricted as Stockholm Convention Persistent Organic Pollutants (POPs). BCAs have rarely been studied in the environment

Ying Li, Zhidi Lin, Feng Yin, Michael Minyi Zhang

Gaussian process latent variable models (GPLVMs) are a versatile family of unsupervised learning models commonly used for dimensionality reduction. However, common challenges in modeling data with GPLVMs include inadequate kernel flexibility and improper selection of the projection noise, leading to a type of model collapse characterized by vague latent repr

Saadi Ishaq, Sajawal Zafar, Abdur Rehman, Ishtiaq Ahmed

Motivated by the study of heavy-light meson production within the framework of heavy quark effective theory (HQET) factorization, we extend the factorization formalism for a rather complicated process $W^+\to B^+\ell^+\ell^-$ in the limit of a non-zero invariant squared-mass of dilepton, $q^2$, at the lowest order in $1/m_b$ up to $\mathcal{O}(\alpha_s)$. Th

Zhongni Hou, Xiaolong Jin, Zixuan Li, Long Bai

Temporal Knowledge Graph (TKG), which characterizes temporally evolving facts in the form of (subject, relation, object, timestamp), has attracted much attention recently. TKG reasoning aims to predict future facts based on given historical ones. However, existing TKG reasoning models are unable to abstain from predictions they are uncertain, which will inev

Sota Yoshida, Takeshi Sato, Takumi Ogata, Tomoya Naito

Developing methods to solve nuclear many-body problems with quantum computers is an imperative pursuit within the nuclear physics community. Here, we introduce a quantum algorithm to accurately and precisely compute the ground state of valence two-neutron systems leveraging presently available Noisy Intermediate-Scale Quantum devices. Our focus lies on the n

Rong Han, Wenbing Huang, Lingxiao Luo, Xinyan Han

Understanding and leveraging the 3D structures of proteins is central to a variety of biological and drug discovery tasks. While deep learning has been applied successfully for structure-based protein function prediction tasks, current methods usually employ distinct training for each task. However, each of the tasks is of small size, and such a single-task

Jaeha Kim, Junghun Oh, Kyoung Mu Lee

In real-world scenarios, image recognition tasks, such as semantic segmentation and object detection, often pose greater challenges due to the lack of information available within low-resolution (LR) content. Image super-resolution (SR) is one of the promising solutions for addressing the challenges. However, due to the ill-posed property of SR, it is challe

Hui Li, Rong-Wang Li, Peng Shu, Yu-Qiang Li

Attitude is one of the crucial parameters for space objects and plays a vital role in collision prediction and debris removal. Analyzing light curves to determine attitude is the most commonly used method. In photometric observations, outliers may exist in the obtained light curves due to various reasons. Therefore, preprocessing is required to remove these

Hongjae Lee, Jun-Sang Yoo, Seung-Won Jung

Single image super-resolution (SISR) aims to reconstruct a high-resolution image from its low-resolution observation. Recent deep learning-based SISR models show high performance at the expense of increased computational costs, limiting their use in resource-constrained environments. As a promising solution for computationally efficient network design, netwo

Girish Sharma, Jyoti Grover, Abhishek Verma

In recent times, the Internet of Things (IoT) has a significant rise in industries, and we live in the era of Industry 4.0, where each device is connected to the Internet from small to big. These devices are Artificial Intelligence (AI) enabled and are capable of perspective analytics. By 2023, it's anticipated that over 14 billion smart devices will be avai

Anna Elisabeth Riha, Nikolas Siccha, Antti Oulasvirta, Aki Vehtari

When building statistical models for Bayesian data analysis tasks, required and optional iterative adjustments and different modelling choices can give rise to numerous candidate models. In particular, checks and evaluations throughout the modelling process can motivate changes to an existing model or the consideration of alternative models to ultimately obt

F. P. An, W. D. Bai, A. B. Balantekin, M. Bishai

This Letter presents results of a search for the mixing of a sub-eV sterile neutrino with three active neutrinos based on the full data sample of the Daya Bay Reactor Neutrino Experiment, collected during 3158 days of detector operation, which contains $5.55 \times 10^{6}$ reactor \anue candidates identified as inverse beta-decay interactions followed by neu

Duy-Tho Le, Chenhui Gou, Stavya Datta, Hengcan Shi

Autonomous robot systems have attracted increasing research attention in recent years, where environment understanding is a crucial step for robot navigation, human-robot interaction, and decision. Real-world robot systems usually collect visual data from multiple sensors and are required to recognize numerous objects and their movements in complex human-cro

Rachmad Vidya Wicaksana Putra, Muhammad Shafique

Spiking Neural Networks (SNNs) can offer ultra-low power/energy consumption for machine learning-based application tasks due to their sparse spike-based operations. Currently, most of the SNN architectures need a significantly larger model size to achieve higher accuracy, which is not suitable for resource-constrained embedded applications. Therefore, develo

Mousumi Mandal, Shruti Priya

Let $(R, \mathfrak m)$ be a Cohen-Macaulay local ring of dimension $d \geq 2,$ and $I$ an $\mathfrak m$-primary ideal of $R.$ Denote $r_{J}(I)$ as the reduction number of $I$ with respect to a minimal reduction $J$ of $I,$ and $\rho(I)$ as the Ratliff-Rush index of $I$. We establish upper bounds on $\rho(I)$ in terms of Hilbert coefficients $e_{i}(I)$ for $0

Gyupil Kam, Kiseop Chung

The Internet of Things (IoT) is a communication scheme which allows various objects to exchange several types of information, enabling functions such as home automation, production management, healthcare, etc. In addition, energy-harvesting (EH) technology is considered for IoT environment in order to reduce the need for management and enhance maintainabilit

Charles S. Wright, Kunaal Joshi, Rudro R. Biswas, Srividya Iyer-Biswas

Organisms maintain the status quo, holding key physiological variables constant to within an acceptable tolerance, and yet adapt with precision and plasticity to dynamic changes in externalities. What organizational principles ensure such exquisite yet robust control of systems-level "state variables" in complex systems with an extraordinary number of moving

W. H. T. Vlemmings, B. Lankhaar, L. Velilla-Prieto

Polarisation observations of masers in the circumstellar envelopes (CSEs) around Asymptotic Giant Branch (AGB) stars have revealed strong magnetic fields. However, masers probe only specific lines-of-sight through the CSE. Non-masing molecular line polarisation observation can more directly reveal the large scale magnetic field morphology and hence probe the

Jiawei Liu, Hiroshi Suito

The presented multi-scale, closed-loop blood circulation model includes arterial, venous, and portal venous systems, heart-pulmonary circulation, and micro-circulation in capillaries. One-dimensional models simulate large blood vessel flow, whereas zerodimensional models are used for simulating blood flow in vascular subsystems corresponding to peripheral ar

Tanmay Parekh, Anh Mac, Jiarui Yu, Yuxuan Dong

Social media is an easy-to-access platform providing timely updates about societal trends and events. Discussions regarding epidemic-related events such as infections, symptoms, and social interactions can be crucial for informing policymaking during epidemic outbreaks. In our work, we pioneer exploiting Event Detection (ED) for better preparedness and early

Eisuke Otsuka

Multiple zeta values (MZVs for short) can be represented as iterated integrals of $\mathbb{Q}$-rational algebraic differential forms on $\mathbb{P}^1(\mathbb{C})\setminus\{0, 1, \infty\}$. This interpretation allows us to consider MZVs geometrically, and this is one of the motivations for Deligne--Goncharov, Terasoma et al. to give motivic interpretations of

Zhouhao Sun, Xiao Ding, Li Du, Bibo Cai

Large language models (LLMs) have achieved significant performance in various natural language reasoning tasks. However, they still struggle with performing first-order logic reasoning over formal logical theories expressed in natural language. This is because the previous LLMs-based reasoning systems have the theoretical incompleteness issue. As a result, i

Rachael Hwee Ling Sim, Yehong Zhang, Trong Nghia Hoang, Xinyi Xu

Collaborative machine learning involves training models on data from multiple parties but must incentivize their participation. Existing data valuation methods fairly value and reward each party based on shared data or model parameters but neglect the privacy risks involved. To address this, we introduce differential privacy (DP) as an incentive. Each party

Eirik Enger, Rune Graversen, Audun Theodorsen

We investigate the climatic effects of volcanic eruptions spanning from Mt.\ Pinatubo-sized events to super-volcanoes. The study is based on ensemble simulations in the Community Earth System Model Version 2 (CESM2) climate model using the Whole Atmosphere Community Climate Model Version 6 (WACCM6) atmosphere model. Our analysis focuses on the impact of diff

Kirill Muravyev, Alexander Melekhin, Dmitry Yudin, Konstantin Yakovlev

Mapping is one of the crucial tasks enabling autonomous navigation of a mobile robot. Conventional mapping methods output a dense geometric map representation, e.g. an occupancy grid, which is not trivial to keep consistent for prolonged runs covering large environments. Meanwhile, capturing the topological structure of the workspace enables fast path planni

Quanwei Liu, Yanni Dong, Tao Huang, Lefei Zhang

Hyperspectral image (HSI) classification techniques have been intensively studied and a variety of models have been developed. However, these HSI classification models are confined to pocket models and unrealistic ways of dataset partitioning. The former limits the generalization performance of the model and the latter is partitioned leading to inflated mode

Kun Ma, Chenyuan Feng, Giovanni Geraci, Howard H. Yang

In this paper, we introduce a novel mathematical framework for assessing the performance of joint communication and sensing (JCAS) in wireless networks, employing stochastic geometry as an analytical tool. We focus on deriving the meta distribution of the signal-to-interference ratio (SIR) for JCAS networks. This approach enables a fine-grained quantificatio

Nazik Elsayed, Yousuf Babiker M. Osman, Cheng Li, Jiong Zhang

Cardio-cerebrovascular diseases are the leading causes of mortality worldwide, whose accurate blood vessel segmentation is significant for both scientific research and clinical usage. However, segmenting cardio-cerebrovascular structures from medical images is very challenging due to the presence of thin or blurred vascular shapes, imbalanced distribution of

Ilya B. Shapirovsky, Vladislav Sliusarev

In the product $L_1\times L_2$ of two Kripke complete consistent logics, local tabularity of $L_1$ and $L_2$ is necessary for local tabularity of $L_1\times L_2$. However, it is not sufficient: the product of two locally tabular logics may not be locally tabular. We provide extra semantic and axiomatic conditions that give criteria of local tabularity of the

Soham Poddar, Rajdeep Mukherjee, Subhendu Khatuya, Niloy Ganguly

The debate around vaccines has been going on for decades, but the COVID-19 pandemic showed how crucial it is to understand and mitigate anti-vaccine sentiments. While the pandemic may be over, it is still important to understand how the pandemic affected the anti-vaccine discourse, and whether the arguments against non-COVID vaccines (e.g., Flu, MMR, IPV, HP

Esther Cabezas-Rivas, Salvador Moll, Marcos Solera

We construct weak solutions of the anisotropic inverse mean curvature flow (A-IMCF) under very mild assumptions both on the anisotropy (which is simply a norm in $\mathbb R^N$ with no ellip\-ticity nor smoothness requirements, in order to include the crystalline case) and on the initial data. By means of an approximation procedure introduced by Moser, our so

Rishav Hada, Varun Gumma, Mohamed Ahmed, Kalika Bali

With the rising human-like precision of Large Language Models (LLMs) in numerous tasks, their utilization in a variety of real-world applications is becoming more prevalent. Several studies have shown that LLMs excel on many standard NLP benchmarks. However, it is challenging to evaluate LLMs due to test dataset contamination and the limitations of tradition

Xiao Fang, Song-Hao Liu, Qi-Man Shao, Yi-Kun Zhao

The question of whether the central limit theorem (CLT) holds for the total number of edges in exponential random graph models (ERGMs) in the subcritical region of parameters has remained an open problem. In this paper, we establish the CLT. As a result of our proof, we also derive a convergence rate for the CLT, an explicit formula for the asymptotic varian

Xi-Liang Yuan, Gang Lü

The direct CP asymmetry in the weak decay process of hadrons is commonly attributed to the weak phase of the CKM matrix and the indeterminate strong phase. We propose a way of creating a strong phase difference between two decay paths involving vector mesons $V= \omega,\rho$ decaying to $\pi^{+}\pi^{-}\pi^{0}$ considering the G-parity suppressed decay proces

Shaocong Xie, Rui Ye, Xiaolian Li, Zhongyi Huang

Nonreciprocal interaction crowd systems, such as human-human, human-vehicle, and human-robot systems, often have serious impacts on pedestrian safety and social order. A more comprehensive understanding of these systems is needed to optimize system stability and efficiency. Despite the importance of these interactions, empirical research in this area remains

Xuechen Liang, Yangfan He, Meiling Tao, Yinghui Xia

Open large language models (LLMs) have significantly advanced the field of natural language processing, showcasing impressive performance across various tasks.Despite the significant advancements in LLMs, their effective operation still relies heavily on human input to accurately guide the dialogue flow, with agent tuning being a crucial optimization techniq

Olexandr Polishchuk

A comparative analysis of structural and flow approaches to analysis of vulnerability of complex network systems (NS) from targeted attacks and non-target lesions of various types was carried out. Typical structural and functional scenarios of successive targeted attacks on the most important by certain characteristics system elements were considered, and sc

Dongryul Kim, Hyeonjeong Kim, Kyoungseok Han

This paper proposes collision-free optimal trajectory planning for autonomous vehicles in highway traffic, where vehicles need to deal with the interaction among each other. To address this issue, a novel optimal control framework is suggested, which couples the trajectory of surrounding vehicles with collision avoidance constraints. Additionally, we describ

Yuguang Shi, Jian Wang, Runzhang Wu, Jintian Zhu

In this paper, we investigate the topological obstruction problem for positive scalar curvature and uniformly positive scalar curvature on open manifolds. We present a definition for open Schoen-Yau-Schick manifolds and prove that there is no complete metric with positive scalar curvature on these manifolds. Similarly, we define weak Schoen-Yau-Shick manifol

Tayyab Naseer, M. Sharif

In this paper, we consider isotropic solution and extend it to two different exact well-behaved spherical anisotropic solutions through minimal geometric deformation method in $f(R,T,R_{\rho\eta}T^{\rho\eta})$ gravity. We only deform the radial metric component that separates the field equations into two sets corresponding to their original sources. The firs

Debora Mroczek, Nanxi Yao, Katherine Zine, Jacquelyn Noronha-Hostler

In this work we provide a new, well-controlled expansion of the equation of state of dense matter from zero to finite temperatures ($T$) while covering a wide range of charge fractions ($Y_Q$), from pure neutron to isospin symmetric nuclear matter. Our expansion can be used to describe neutron star mergers using the equation of state inferred from neutron st

Kei Sawada, Tianyu Zhao, Makoto Shing, Kentaro Mitsui

AI democratization aims to create a world in which the average person can utilize AI techniques. To achieve this goal, numerous research institutes have attempted to make their results accessible to the public. In particular, large pre-trained models trained on large-scale data have shown unprecedented potential, and their release has had a significant impac

Hongyan Gu, Zihan Yan, Ayesha Alvi, Brandon Day

The expansion of artificial intelligence (AI) in pathology tasks has intensified the demand for doctors' annotations in AI development. However, collecting high-quality annotations from doctors is costly and time-consuming, creating a bottleneck in AI progress. This study investigates eye-tracking as a cost-effective technology to collect doctors' behavioral

Tao Hu, Fangzhou Hong, Zhaoxi Chen, Ziwei Liu

We present FashionEngine, an interactive 3D human generation and editing system that creates 3D digital humans via user-friendly multimodal controls such as natural languages, visual perceptions, and hand-drawing sketches. FashionEngine automates the 3D human production with three key components: 1) A pre-trained 3D human diffusion model that learns to model

Xiang Xiang, Zihan Zhang, Jing Ma, Yao Deng

Parkinson's Disease (PD) is the second most common neurodegenerative disorder. The existing assessment method for PD is usually the Movement Disorder Society - Unified Parkinson's Disease Rating Scale (MDS-UPDRS) to assess the severity of various types of motor symptoms and disease progression. However, manual assessment suffers from high subjectivity, lack

Satbir Kaur, V. V. Parkar, S. K. Pandit, A. Shrivastava

In order to investigate the contribution of $\alpha$ production in the reaction cross sections, measurements of elastic scattering and inclusive $\alpha$ particle angular distributions have been carried out with the $^9$Be projectile on $^{89}$Y, $^{124}$Sn, $^{159}$Tb, $^{198}$Pt, and $^{209}$Bi targets over a wide angular range at energies near the Coulomb

Zixuan Zhang, Revanth Gangi Reddy, Kevin Small, Tong Zhang

Open-domain Question Answering (OpenQA) aims at answering factual questions with an external large-scale knowledge corpus. However, real-world knowledge is not static; it updates and evolves continually. Such a dynamic characteristic of knowledge poses a vital challenge for these models, as the trained models need to constantly adapt to the latest informatio

Kristina Gligoric, Myra Cheng, Lucia Zheng, Esin Durmus

The use of words to convey speaker's intent is traditionally distinguished from the `mention' of words for quoting what someone said, or pointing out properties of a word. Here we show that computationally modeling this use-mention distinction is crucial for dealing with counterspeech online. Counterspeech that refutes problematic content often mentions harm

Shuaicheng Niu, Chunyan Miao, Guohao Chen, Pengcheng Wu

Test-time adaptation has proven effective in adapting a given trained model to unseen test samples with potential distribution shifts. However, in real-world scenarios, models are usually deployed on resource-limited devices, e.g., FPGAs, and are often quantized and hard-coded with non-modifiable parameters for acceleration. In light of this, existing method

Borwankar C., Sharma M., Hariharan J., Venugopal K.

The Major Atmospheric Cherenkov Experiment (MACE) is a large size (21m) Imaging Atmospheric Cherenkov Telescope (IACT) installed at an altitude of 4270m above sea level at Hanle, Ladakh in northern India. Here we report the detection of Very High Energy (VHE) gamma-ray emission from Crab Nebula above 80 GeV. We analysed ~15 hours of data collected at low zen

I Gusti Ayu Putu Arya Wulandari, I Putu Ade Andre Payadnya, Kadek Rahayu Puspadewi, Sompob Saelee

The field of ethnomathematics holds significance in the pursuit of comprehending how students can grasp, express, manipulate, and ultimately apply mathematical concepts. However, ethnomathematics is also considered a complex concept in Asian countries such as Indonesia and Thailand, which can pose challenges as it needs to be comprehensively understood. This

Shuai Tan, Bin Ji, Mengxiao Bi, Ye Pan

Achieving disentangled control over multiple facial motions and accommodating diverse input modalities greatly enhances the application and entertainment of the talking head generation. This necessitates a deep exploration of the decoupling space for facial features, ensuring that they a) operate independently without mutual interference and b) can be preser

Sujal Bhavsar, Vera Zaychik Moffitt, Justin Appleby

Stochastic battery bidding in real-time energy markets is a nuanced process, with its efficacy depending on the accuracy of forecasts and the representative scenarios chosen for optimization. In this paper, we introduce a pioneering methodology that amalgamates Transformer-based forecasting with weighted constrained Dynamic Time Warping (wcDTW) to refine sce

Minseop Jung, Minseong Kim, Jibum Kim

The success of Transformer-based models has encouraged many researchers to learn CAD models using sequence-based approaches. However, learning CAD models is still a challenge, because they can be represented as complex shapes with long construction sequences. Furthermore, the same CAD model can be expressed using different CAD construction sequences. We prop

sundaraparipurnan Narayanan, Sandeep Vishwakarma

Amidst escalating concerns about the detriments inflicted by AI systems, risk management assumes paramount importance, notably for high-risk applications as demanded by the European Union AI Act. Guidelines provided by ISO and NIST aim to govern AI risk management; however, practical implementations remain scarce in scholarly works. Addressing this void, our

Luoxuan Weng, Xingbo Wang, Junyu Lu, Yingchaojie Feng

The proliferation of large language models (LLMs) has revolutionized the capabilities of natural language interfaces (NLIs) for data analysis. LLMs can perform multi-step and complex reasoning to generate data insights based on users' analytic intents. However, these insights often entangle with an abundance of contexts in analytic conversations such as code

Chih-Chung Hsu, Chia-Ming Lee, Yang Fan Chiang, Yi-Shiuan Chou

Conventional Computed Tomography (CT) imaging recognition faces two significant challenges: (1) There is often considerable variability in the resolution and size of each CT scan, necessitating strict requirements for the input size and adaptability of models. (2) CT-scan contains large number of out-of-distribution (OOD) slices. The crucial features may onl

Zhiming Chi, Jianan Ma, Pengfei Yang, Cheng-Chao Huang

Deep neural networks (DNNs) are prone to various dependability issues, such as adversarial attacks, which hinder their adoption in safety-critical domains. Recently, NN repair techniques have been proposed to address these issues while preserving original performance by locating and modifying guilty neurons and their parameters. However, existing repair appr

Yun-Shi Dai, Peng-Fei Dai, Wei-Xing Zhou

The current international landscape is turbulent and unstable, with frequent outbreaks of geopolitical conflicts worldwide. Geopolitical risk has emerged as a significant threat to regional and global peace, stability, and economic prosperity, causing serious disruptions to the global food system and food security. Focusing on the international food market,

Honghong Lin, Yun Shang

This paper presents a deterministic search algorithm on complete bipartite graphs. Our algorithm adopts the simple form of alternating iterations of an oracle and a continuous-time quantum walk operator, which is a generalization of Grover's search algorithm. We address the most general case of multiple marked states, so there is a problem of estimating the

Chiara Lisotti, Ciaran A. J. O'Hare, Elisabetta Baracchini, Victoria U. Bashu

CYGNUS is a proposed global network of large-scale gas time projection chambers (TPCs) with the capability of directionally detecting nuclear and electron recoils at $\gtrsim$keV energies. The primary focus of CYGNUS so far has been the detection of dark matter, with directional sensitivity providing a means of circumventing the so-called neutrino fog. Howev

Xiaonan Liu, Zi-Xia Song, Zhiyu Wang

For integers $k>\ell\ge0$, a graph $G$ is $(k,\ell)$-stable if $\alpha(G-S)\geq \alpha(G)-\ell$ for every $S\subseteq V(G)$ with $|S|=k$. A recent result of Dong and Wu [SIAM J. Discrete Math., 36 (2022) 229--240] shows that every $(k,\ell)$-stable graph $G$ satisfies $\alpha(G) \le \lfloor ({|V(G)|-k+1})/{2}\rfloor+\ell$. A $(k,\ell)$-stable graph $G$ is ti

Junjie Wu, Xuming Fang

As artificial intelligence (AI)-enabled wireless communication systems continue their evolution, distributed learning has gained widespread attention for its ability to offer enhanced data privacy protection, improved resource utilization, and enhanced fault tolerance within wireless communication applications. Federated learning further enhances the ability

Yuting Dong, Zhixue He, Chen Shen, Lei Shi

Existing studies have revealed a paradoxical phenomenon in public goods games, wherein destructive agents, harming both cooperators and defectors, can unexpectedly bolster cooperation. Building upon this intriguing premise, our paper introduces a novel concept: constructive agents, which confer additional benefits to both cooperators and defectors. We invest

Kyunghyun Lee, Ukcheol Shin, Byeong-Uk Lee

Adjusting camera exposure in arbitrary lighting conditions is the first step to ensure the functionality of computer vision applications. Poorly adjusted camera exposure often leads to critical failure and performance degradation. Traditional camera exposure control methods require multiple convergence steps and time-consuming processes, making them unsuitab

Seokho Lee, Hideko Nomura, Kenji Furuya

Carbon isotope fractionation of CO has been reported in the disk around TW Hya,where elemental carbon is more abundant than elemental oxygen ([C/O]$_{\rm elem}$> 1). We investigated the effects of the [C/O]$_{\rm elem}$ ratio on carbon fractionation using astrochemical models that incorporate isotope-selective photodissociation and isotope-exchange reactions

Daisuke Naimen

We establish a series of concentration and oscillation estimates for elliptic equations with exponential nonlinearity $e^{u^p}$ in a disc. Especially, we show various new results on the supercritical case $p>2$ which are left open in the previous works. We begin with the concentration analysis of blow-up solutions by extending the scaling and pointwise techn

Hao Yang, Jun Jiang, Bingwei Long

We study the double heavy baryon $\Xi_{QQ'}$ and tetraquark $T_{QQ}$ production through photon-photon and photon-gluon fusion via ultraperipheral collisions at the LHC and FCC within the framework of nonrelativistic QCD factorization formalism. Various ion-ion collisions are taken into account, two cc(bb)-diquark configurations ($[cc(bb),{^3S_1}\mbox{-}\bar{

Ruqi Liao, Chuqing Zhao, Jin Li, Weiqi Feng

In response to the rising interest in large multimodal models, we introduce Cross-Attention Token Pruning (CATP), a precision-focused token pruning method. Our approach leverages cross-attention layers in multimodal models, exemplified by BLIP-2, to extract valuable information for token importance determination. CATP employs a refined voting strategy across

Ayush Arunachalam, Ian Kintz, Suvadeep Banerjee, Arnab Raha

Given the widespread use of safety-critical applications in the automotive field, it is crucial to ensure the Functional Safety (FuSa) of circuits and components within automotive systems. The Analog and Mixed-Signal (AMS) circuits prevalent in these systems are more vulnerable to faults induced by parametric perturbations, noise, environmental stress, and o

Wei Zhang, Hiroaki Katsuragi, Ken Yamamoto

Drop impact events on wet granular bed show rich variety by changing the substrate composition. We observe the drop impact onto dry/wet granular substrates with different grain size (50-400 {\mu}m) and water content (0-22 vol %). Although the impactor condition is fixed (impact velocity: 4.0 m/s, water drop radius: 1.8 mm), the experiment reveals that the po

Tommaso Bonato, Abdul Kabbani, Ahmad Ghalayini, Anup Agarwal

With the rapid growth of artificial intelligence (AI) workloads in datacenters, the Ultra Ethernet Consortium (UEC) has defined a new high-performance transport layer to deliver the required performance at scale. A core component of this new standard is the Network Signal-based Congestion Control (NSCC) algorithm. This paper presents SMaRTT, the algorithm th

Muhamad Doris, Khaulah Sulaiman, Azzuliani Supangat

PCPDTBT nanostructures have been synthesized via template-assisted method and polymer-melt technique. The morphological, optical and structural properties of the PCPDTBT have been investigated. Melting polymer was used as a driving force to infiltrate the Anodic Aluminum Oxide (AAO) template in which the applied temperatures are 200, 250, 300, 350, 400 and 4

Minhyuk Seo, Hyunseo Koh, Wonje Jeung, Minjae Lee

Online continual learning suffers from an underfitted solution due to insufficient training for prompt model update (e.g., single-epoch training). To address the challenge, we propose an efficient online continual learning method using the neural collapse phenomenon. In particular, we induce neural collapse to form a simplex equiangular tight frame (ETF) str

Seong-Hoon Jang, Randy Jalem, Yoshitaka Tateyama

Given the vast compositional possibilities Na$_nM_mM_{m'}$Si$_{3-p-a}$P$_p$As$_a$O$_{12}$, Na-ion superionic conductors (NASICON) are attractive but complicate for designing materials with enhanced room-temperature Na-ion conductivity $\sigma_{\rm Na,300K}$. We propose an explicit regression model for $\sigma_{\rm Na,300K}$ with easily-accessible descriptors