February 2024 arXiv papers — page 17

Takashi Koide, Naoki Fukushi, Hiroki Nakano, Daiki Chiba

The proliferation of phishing sites and emails poses significant challenges to existing cybersecurity efforts. Despite advances in malicious email filters and email security protocols, problems with oversight and false positives persist. Users often struggle to understand why emails are flagged as potentially fraudulent, risking the possibility of missing im

Meidai Xuanyuan, Yuwang Wang, Honglei Guo, Qionghai Dai

In this paper, we consider a novel and practical case for talking face video generation. Specifically, we focus on the scenarios involving multi-people interactions, where the talking context, such as audience or surroundings, is present. In these situations, the video generation should take the context into consideration in order to generate video content n

Yuiga Wada, Kanta Kaneda, Daichi Saito, Komei Sugiura

Establishing an automatic evaluation metric that closely aligns with human judgments is essential for effectively developing image captioning models. Recent data-driven metrics have demonstrated a stronger correlation with human judgments than classic metrics such as CIDEr; however they lack sufficient capabilities to handle hallucinations and generalize acr

Shunsuke Inenaga, Takuya Mieno, Hiroki Arimura, Mitsuru Funakoshi

A string $w$ is said to be a minimal absent word (MAW) for a string $S$ if $w$ does not occur in $S$ and any proper substring of $w$ occurs in $S$. We focus on non-trivial MAWs which are of length at least 2. Finding such non-trivial MAWs for a given string is motivated for applications in bioinformatics and data compression. Fujishige et al. [TCS 2023] prop

Xinyu Wang, Xiaotian Sun, Yinhe Han, Xiaoming Chen

Processing-in-memory (PIM) has shown extraordinary potential in accelerating neural networks. To evaluate the performance of PIM accelerators, we present an ISA-based simulation framework including a dedicated ISA targeting neural networks running on PIM architectures, a compiler, and a cycleaccurate configurable simulator. Compared with prior works, this wo

Samuel O. Folorunsho, William R. Norris

The emergence of tethered drones represents a major advancement in unmanned aerial vehicles (UAVs) offering solutions to key limitations faced by traditional drones. This article explores the potential of tethered drones with a particular focus on their ability to tackle issues related to battery life constraints and data latency commonly experienced by batt

Mojtaba Esfandiari, Peter Gehlbach, Russell H. Taylor, Iulian Iordachita

Performing retinal vein cannulation (RVC) as a potential treatment for retinal vein occlusion (RVO) without the assistance of a surgical robotic system is very challenging to do safely. The main limitation is the physiological hand tremor of surgeons. Robot-assisted eye surgery technology may resolve the problems of hand tremors and fatigue and improve the s

Tor Ole B. Odden, Halvor Tyseng, Jonas Timmann Mjaaland, Markus Fleten Kreutzer

We propose a technique for performing deductive qualitative data analysis at scale on text-based data. Using a natural language processing technique known as text embeddings, we create vector-based representations of texts in a high-dimensional meaning space within which it is possible to quantify differences as vector distances. To apply the technique, we b

Chao Wu, Xiaobin Chang, Ruixuan Wang

Deep neural networks often severely forget previously learned knowledge when learning new knowledge. Various continual learning (CL) methods have been proposed to handle such a catastrophic forgetting issue from different perspectives and achieved substantial improvements. In this paper, a novel two-branch continual learning framework is proposed to further

Govind Mittal, Arthur Jakobsson, Kelly O. Marshall, Chinmay Hegde

The rise of AI voice-cloning technology, particularly audio Real-time Deepfakes (RTDFs), has intensified social engineering attacks by enabling real-time voice impersonation that bypasses conventional enrollment-based authentication. This technology represents an existential threat to phone-based authentication systems, while total identity fraud losses reac

Samuel O. Folorunsho, William R. Norris

Spannotation is an open source user-friendly tool developed for image annotation for semantic segmentation specifically in autonomous navigation tasks. This study provides an evaluation of Spannotation, demonstrating its effectiveness in generating accurate segmentation masks for various environments like agricultural crop rows, off-road terrains and urban r

Gaku Matsumoto, Ryo Ogawa, Koji Higasa, Tomoki Kobayashi

Complex conductivity measurements spanning the entire temperature range, including the vicinity of $T_c$, were conducted on systematically varied FeSe$_{1-x}$Te$_x$ ($x$ = 0 - 0.5) very thin films. By applying a novel cavity measurement technique employing microwave electric fields parallel to FeSe$_{1-x}$Te$_x$ films, we observed distinct temperature-depend

Luke T. Peterson, Gavin Brown, Àngel Jorba, Daniel Scheeres

This paper investigates the motion of a small particle moving near the triangular points of the Earth-Moon system. The dynamics are modeled in the Hill restricted 4-body problem (HR4BP), which includes the effect of the Earth and Moon as in the circular restricted 3-body problem (CR3BP), as well as the direct and indirect effect of the Sun as a periodic time

Peter Hu, Yangqiuting Li, Chandralekha Singh

Research-validated multiple-choice questions comprise an easy-to-implement instructional tool that serves to scaffold student learning and formatively assess students knowledge. We present findings from the implementation, in consecutive years, of research-validated multiple-choice question sequence on measurement uncertainty as it applies to two-state quant

Polina Baron

At the focus of the paper are applications of the well-known Moser transformation of the C. Neumann dynamical system. It yields us a new quadratic integrable dynamical system on $\mathbb{C}^{3n+1}$, which we call the Neumann-Moser dynamical system. We present an explicit formula of the inverse of the Moser transformation. Consequently, we obtain explicitly a

Yanzuo Lu, Manlin Zhang, Andy J Ma, Xiaohua Xie

Diffusion model is a promising approach to image generation and has been employed for Pose-Guided Person Image Synthesis (PGPIS) with competitive performance. While existing methods simply align the person appearance to the target pose, they are prone to overfitting due to the lack of a high-level semantic understanding on the source person image. In this pa

Parida Hashim, Fangyu Xu, Ya Wang, Weijie Men

In this paper, with a set of high-resolution He I 10830 \AA\ filtergrams, we select an area in a plage, very likely an EUV moss area, as an interface layer to follow the clues of coronal heating channels down to the photosphere. The filtergrams are obtained from the 1-meter aperture New Vacuum Solar Telescope (NVST). We make a distinction between the darker

Xi Luo, Shiying Dong, Jinlong Hong, Bingzhao Gao

This paper presents a neural network optimizer with soft-argmax operator to achieve an ecological gearshift strategy in real-time. The strategy is reformulated as the mixed-integer model predictive control (MIMPC) problem to minimize energy consumption. Then the outer convexification is introduced to transform integer variables into relaxed binary controls.

Peter Hu, Yangqiuting Li, Chandralekha Singh

Research-validated clicker questions as instructional tools for formative assessment are relatively easy to implement and can provide effective scaffolding when developed and implemented in a sequence. We present findings from the implementation of a research-validated clicker question sequence (CQS) on student understanding of the time-development of two-st

Chengyang Liu, Michael K. Ng

Image retargeting, which resizes images to one with a prescribed aspect ratio by determining an optimal warping map, has gained substantial interest in imaging science. Despite significant advances, existing methods often fail to ensure bijective warping maps essential for preserving visual information. This paper introduces a novel bijective image retargeti

Koichiro Yawata, Kai Fukami, Kunihiko Taira, Hiroya Nakao

We present a phase autoencoder that encodes the asymptotic phase of a limit-cycle oscillator, a fundamental quantity characterizing its synchronization dynamics. This autoencoder is trained in such a way that its latent variables directly represent the asymptotic phase of the oscillator. The trained autoencoder can perform two functions without relying on th

Dibyendu Adak, Duc P. Truong, Gianmarco Manzini, Kim Ø. Rasmussen

Emerging tensor network techniques for solutions of Partial Differential Equations (PDEs), known for their ability to break the curse of dimensionality, deliver new mathematical methods for ultrafast numerical solutions of high-dimensional problems. Here, we introduce a Tensor Train (TT) Chebyshev spectral collocation method, in both space and time, for solu

Peter Hu, Yangqiuting Li, Chandralekha Singh

Research-validated multiple-choice questions comprise an easy-to-implement instructional tool that serves to scaffold student learning and formatively assess students' knowledge. We present findings from the implementation, in consecutive years, of a research-validated multiple-choice question sequence [referred to in this study as a Clicker Question Sequenc

Junqing Jia, Xiaoqing Chi, Xiaoyun Jiang

In this paper, we derive the improved uniform error bounds for the long-time dynamics of the $d$-dimensional $(d=2,3)$ nonlinear space fractional sine-Gordon equation (NSFSGE). The nonlinearity strength of the NSFSGE is characterized by $\varepsilon^2$ where $0<\varepsilon \le 1$ is a dimensionless parameter. The second-order time-splitting method is applied

Limin Jiang, Yi Shi, Yintao Liu, Qingyu Deng

Wireless baseband processing (WBP) is a key element of wireless communications, with a series of signal processing modules to improve data throughput and counter channel fading. Conventional hardware solutions, such as digital signal processors (DSPs) and more recently, graphic processing units (GPUs), provide various degrees of parallelism, yet they both fa

Peter Hu, Yangqiuting Li, Chandralekha Singh

Research-validated multiple-choice questions comprise an easy-to-implement instructional tool for scaffolding student learning and providing formative assessment of students' knowledge. We present findings from the implementation of a research-validated multiple-choice question sequence on the basics of two-state quantum systems, including inner products, ou

Bin Cao, Jianhao Yuan, Yexin Liu, Jian Li

In the rapidly evolving area of image synthesis, a serious challenge is the presence of complex artifacts that compromise perceptual realism of synthetic images. To alleviate artifacts and improve quality of synthetic images, we fine-tune Vision-Language Model (VLM) as artifact classifier to automatically identify and classify a wide range of artifacts and p

Chun-Chi Lin, The Dung Tran

This article presents a novel resolution to the problem of spline interpolation versus least-squares fitting on smooth Riemannian manifolds utilizing the method of gradient flows of networks. This approach represents a contribution to both geometric control theory and statistical shape data analysis. Our work encompasses a rigorous proof for the existence of

Banglei Guan, Ji Zhao, Laurent Kneip

Relative pose estimation using point correspondences (PC) is a widely used technique. A minimal configuration of six PCs is required for two views of generalized cameras. In this paper, we present several minimal solvers that use six PCs to compute the 6DOF relative pose of multi-camera systems, including a minimal solver for the generalized camera and two m

Ananya Trivedi, Mark Zolotas, Adeeb Abbas, Sarvesh Prajapati

Skid-Steer Wheeled Mobile Robots (SSWMRs) are increasingly being used for off-road autonomy applications. When turning at high speeds, these robots tend to undergo significant skidding and slipping. In this work, using Gaussian Process Regression (GPR) and Sigma-Point Transforms, we estimate the non-linear effects of tire-terrain interaction on robot velocit

Nicholas Harrison, Nathan Wallace, Salah Sukkarieh

The efficient collection of samples is an important factor in outdoor information gathering applications on account of high sampling costs such as time, energy, and potential destruction to the environment. Utilization of available a-priori data can be a powerful tool for increasing efficiency. However, the relationships of this data with the quantity of int

Charlotte Aten

We give a categorification of the notion of a mathematical structure originally given by Bourbaki in their set theory textbook. We show that any isomorphism-invariant property of a finite structure can be computed by counting the number of isomorphic copies of small substructures it contains. Our main theorem in this direction is a generalization of the clas

Guangyuan Liu, Nguyen Van Huynh, Hongyang Du, Dinh Thai Hoang

With recent advances in artificial intelligence (AI) and robotics, unmanned vehicle swarms have received great attention from both academia and industry due to their potential to provide services that are difficult and dangerous to perform by humans. However, learning and coordinating movements and actions for a large number of unmanned vehicles in complex a

Jianwei Wang, Tianyin Wang, Ziqian Zeng

The superior performance of supervised classification methods in the information extraction (IE) area heavily relies on a large amount of gold standard data. Recent zero-shot classification methods converted the task to other NLP tasks (e.g., textual entailment) and used off-the-shelf models of these NLP tasks to directly perform inference on the test data w

Hanjie Chen, Zhouxiang Fang, Yash Singla, Mark Dredze

LLMs have demonstrated impressive performance in answering medical questions, such as achieving passing scores on medical licensing examinations. However, medical board exams or general clinical questions do not capture the complexity of realistic clinical cases. Moreover, the lack of reference explanations means we cannot easily evaluate the reasoning of mo

Mingjia Huo, Sai Ashish Somayajula, Youwei Liang, Ruisi Zhang

Large language models generate high-quality responses with potential misinformation, underscoring the need for regulation by distinguishing AI-generated and human-written texts. Watermarking is pivotal in this context, which involves embedding hidden markers in texts during the LLM inference phase, which is imperceptible to humans. Achieving both the detecta

N. I. Nessonov

The full description of the stable factor-representations of the infinite hyperoctahedral group up to quasi-equivalence obtained.

Kevin C. Chen, Ian Christen, Hamza Raniwala, Marco Colangelo

A central challenge in quantum networking is transferring quantum states between different physical modalities, such as between flying photonic qubits and stationary quantum memories. One implementation entails using spin-photon interfaces that combine solid-state spin qubits, such as color centers in diamond, with photonic nanostructures. However, while hig

Koffi Kossi, Stephane Coulombe, Christian Desrosiers, Ghyslain Gagnon

With the increasing demand for audiovisual services, telecom service providers and application developers are compelled to ensure that their services provide the best possible user experience. Particularly, services such as videoconferencing are very sensitive to network conditions. Therefore, their performance should be monitored in real time in order to ad

Gilberto Bini

Let X be a smooth, projective variety over the field of complex numbers. Here we focus on a conjecture attributed to Shigefumi Mori, which claims that X is uniruled if and only if the Kodaira dimension of X is negative.

Biswadip Mandal, Xiangci Li, Jessica Ouyang

Abstractive citation text generation is usually framed as an infilling task, where a sequence-to-sequence model is trained to generate a citation given a reference paper and the context window around the target; the generated citation should be a brief discussion of the reference paper as it relates to the citing context. However, examining a recent LED-base

Jürgen Kritschgau, tahda queer, Cyrus Young, Wohua Zhou

Given an edge-colored graph $G$, we denote the number of colors as $c(G)$, and the number of edges as $e(G)$. An edge-colored graph is rainbow if no two edges share the same color. A proper $mK_3$ is a vertex disjoint union of $m$ rainbow triangles. Rainbow problems have been studied extensively in the context of anti-Ramsey theory, and more recently, in the

Nicolò Dalmasso, Nicha Leethochawalit, Michele Trenti, Kristan Boyett

We report measurements of the galaxy two-point correlation function at cosmic dawn, using photometrically-selected sources from the JWST Advanced Deep Extragalactic Survey (JADES). The JWST/NIRCam dataset comprises approximately $N_g \simeq 7000$ photometrically-selected Lyman Break Galaxies (LBGs), spanning in the redshift range $5\leq z<11$. The primary ob

Gideon Vos, Maryam Ebrahimpour, Liza van Eijk, Zoltan Sarnyai

Introduction. Low-cost health monitoring devices are increasingly being used for mental health related studies including stress. While cortisol response magnitude remains the gold standard indicator for stress assessment, a growing number of studies have started to use low-cost EEG devices as primary recorders of biomarker data. Methods. This study reviews p

H. Wang, T. Banerjee, T. G. Farinha, A. T. Hanbicki

A single crystal niobium thin film was grown using molecular beam epitaxy on a c-plane sapphire wafer. Several samples were fabricated into dc resistivity test devices and coplanar waveguide resonator chips using the same microfabrication procedures and solvent cleans. The samples were then subject to different acid cleaning treatments using different combin

Hannah Kim, Kushan Mitra, Rafael Li Chen, Sajjadur Rahman

Large language models (LLMs) can label data faster and cheaper than humans for various NLP tasks. Despite their prowess, LLMs may fall short in understanding of complex, sociocultural, or domain-specific context, potentially leading to incorrect annotations. Therefore, we advocate a collaborative approach where humans and LLMs work together to produce reliab

Molud Esmaili, Ken Christensen

Public permissionless blockchains facilitate peer-to-peer digital transactions, yet face performance challenges specifically minimizing transaction confirmation time to decrease energy and time consumption per transaction. Performance evaluation and prediction are crucial in achieving this objective, with performance modeling as a key solution despite the co

Fan Yin, Jayanth Srinivasa, Kai-Wei Chang

We study how to characterize and predict the truthfulness of texts generated from large language models (LLMs), which serves as a crucial step in building trust between humans and LLMs. Although several approaches based on entropy or verbalized uncertainty have been proposed to calibrate model predictions, these methods are often intractable, sensitive to hy

Shusaku Sasaki, Takahiro Kubo, Shodai Kitano

Ascertaining the number, type, and location of plant, insect, and animal species is essential for biodiversity conservation. However, comprehensively monitoring the situation only through public fixed-point surveys is challenging, and therefore information voluntarily provided by citizens assists in ascertaining the species distribution. To effectively encou

Sunwoong Choi, Samuel Kim

We present a novel data augmentation method to address the challenge of data scarcity in modeling longitudinal patterns in Electronic Health Records (EHR) of patients using natural language processing (NLP) algorithms. The proposed method generates augmented data by rearranging the orders of medical records within a visit where the order of elements are not

Sheikh Shafayat, Eunsu Kim, Juhyun Oh, Alice Oh

Evaluating the factuality of long-form large language model (LLM)-generated text is an important challenge. Recently there has been a surge of interest in factuality evaluation for English, but little is known about the factuality evaluation of multilingual LLMs, specially when it comes to long-form generation. %This paper systematically evaluates multilingu

Liangyu Xu, Wanxuan Lu, Hongfeng Yu, Fanglong Yao

Extrapolating future weather radar echoes from past observations is a complex task vital for precipitation nowcasting. The spatial morphology and temporal evolution of radar echoes exhibit a certain degree of correlation, yet they also possess independent characteristics. {Existing methods learn unified spatial and temporal representations in a highly couple

Rrubaa Panchendrarajan, Geri Popova, Tony Russell-Rose

A prominent media topic in the UK in the early 2020s is the energy crisis affecting the UK and most of Europe. It brings into a single public debate issues of energy dependency and sustainability, fair distribution of economic burdens and cost of living, as well as climate change, risk, and sustainability. In this paper, we investigate the public discourse a

Makoto Sakagaito

Let $n\geq 0$ and $r>0$ be integers. Let $\mathcal{O}_{X, x}^{h}$ be the henselization of the local ring $\mathcal{O}_{X, x}$ of a scheme $X$ at a point $x\in X$. For a normal crossing variety $Y$ over the spectrum of a field $k$ of positive characteristic $p>0$, K$.$Sato defined an \'{e}tale logarithmic Hodge-Witt sheaf $\lambda^{n}_{Y, r}$ on the \'{e}tale

Yang Liu, Jiahuan Cao, Chongyu Liu, Kai Ding

This paper embarks on an exploration into the Large Language Model (LLM) datasets, which play a crucial role in the remarkable advancements of LLMs. The datasets serve as the foundational infrastructure analogous to a root system that sustains and nurtures the development of LLMs. Consequently, examination of these datasets emerges as a critical topic in res

Xiaoxin Yin

Recent advancements in the realm of deep learning, particularly in the development of large language models (LLMs), have demonstrated AI's ability to tackle complex mathematical problems or solving programming challenges. However, the capability to solve well-defined problems based on extensive training data differs significantly from the nuanced process of

Shuhua Shi, Shaohan Huang, Minghui Song, Zhoujun Li

As one of the most popular parameter-efficient fine-tuning (PEFT) methods, low-rank adaptation (LoRA) is commonly applied to fine-tune large language models (LLMs). However, updating the weights of LoRA blocks effectively and expeditiously is challenging due to the long calculation path in the original model. To address this, we propose ResLoRA, an improved

Curtis Taylor Peterson, Anna Hasenfratz

We present strong numerical evidence for the existence of an infrared fixed point in the renormalization group flow of the SU(3) gauge-fermion system with twelve massless fermions in the fundamental representation. Our numerical simulations using nHYP-smeared staggered fermions with Pauli-Villars improvement do not exhibit any first-order bulk phase transiti

Xiaoxin Yin, David S. Yin

Parameter estimation is one of the most important tasks in statistics, and is key to helping people understand the distribution behind a sample of observations. Traditionally parameter estimation is done either by closed-form solutions (e.g., maximum likelihood estimation for Gaussian distribution), or by iterative numerical methods such as Newton-Raphson me

Si-Yuan Qi, Geni Gupur, Yu-Chun Wu, Guo-Ping Guo

Equivalence between Positive Partial Transpose (PPT) entanglement and bound entanglement is a long-standing open problem in quantum information theory. So far limited progress has been made, even on the seemingly simple case of Werner states bound entanglement. The primary challenge is to give a concise mathematical representation of undistillability. To thi

Scott C. Egbert, Keeyoon Sung, Sean C. Coburn, Brian J. Drouin

We present broadband dual frequency comb laser absorption measurements of 2% H$_2$O (natural isotopic abundance of 99.7% H$_2^{16}$O) in air from 6600-7650 cm$^{-1}$ (1307-1515 nm) with a spectral point spacing of 0.0068 cm$^{-1}$. Twenty-nine datasets were collected at temperatures between 300 and 1300 K ($\pm$0.82% average uncertainty) and pressures rangin

Xinhui Ren, Jingbo Wang, Wenming Yan, Jintao Xie

Rotating Radio Transients (RRATs) are a relatively new subclass of pulsars that emit detectable radio bursts sporadically. We conducted an analysis of 10 RRATs observed using the Parkes telescope, with 8 of these observed via the Ultra-Wideband Receiver. We measured the burst rate and produced integrated profiles spanning multiple frequency bands for 3 RRATs

Amit Ophir

Consider a commutative unital ring $A$ and a unital $A$-algebra $R$. Let $d$ be a positive integer. Chenevier proved that when $(2d)!$ is invertible in $A$, the map associating to a determinant its trace is a bijection between $A$-valued $d$-dimensional determinants of $R$ and $A$-valued $d$-dimensional pseudocharacters of $R$. In this paper, we show that as

Saeed Aghapour, Kasra Ahmadi, Mehran Mozaffari Kermani, Reza Azarderakhsh

Whether stemming from malicious intent or natural occurrences, faults and errors can significantly undermine the reliability of any architecture. In response to this challenge, fault detection assumes a pivotal role in ensuring the secure deployment of cryptosystems. Even when a cryptosystem boasts mathematical security, its practical implementation may rema

Aayush Gupta, Aditya Gulati, Himanshu, Lakshya LNU

Human shape and clothing estimation has gained significant prominence in various domains, including online shopping, fashion retail, augmented reality (AR), virtual reality (VR), and gaming. The visual representation of human shape and clothing has become a focal point for computer vision researchers in recent years. This paper presents a comprehensive surve

Yibin Lei, Yu Cao, Tianyi Zhou, Tao Shen

Recent studies demonstrate that query expansions generated by large language models (LLMs) can considerably enhance information retrieval systems by generating hypothetical documents that answer the queries as expansions. However, challenges arise from misalignments between the expansions and the retrieval corpus, resulting in issues like hallucinations and

L. Minah Yang, Edwin P. Gerber

Machine learning for the parameterization of subgrid-scale processes in climate models has been widely researched and adopted in a few models. A key challenge in developing data-driven parameterization schemes is how to properly represent rare, but important events that occur in geoscience datasets. We investigate and develop strategies to reduce errors caus

Jinlei Dong, Fang Li

One of the key points in Galois theory via field extensions is to build up a correspondence between subfields of a field and subgroups of its automorphism group, so as to study fields via methods of groups. As an analogue of the Galois theory, we want to discuss the relations between cluster subalgebras of a cluster algebra and subgroups of its automorphism

Xiaosong Wang, Xiaofan Zhang, Guotai Wang, Junjun He

The emerging trend of advancing generalist artificial intelligence, such as GPTv4 and Gemini, has reshaped the landscape of research (academia and industry) in machine learning and many other research areas. However, domain-specific applications of such foundation models (e.g., in medicine) remain untouched or often at their very early stages. It will requir

Xinhao Liu, Yingzhao Jiang, Zetao Lin

Model inversion attacks (MIAs) seek to infer the private training data of a target classifier by generating synthetic images that reflect the characteristics of the target class through querying the model. However, prior studies have relied on full access to the target model, which is not practical in real-world scenarios. Additionally, existing black-box MI

Pengcheng Hou, Tao Wang, Daniel Cerkoney, Xiansheng Cai

Quantum field theory (QFT) for interacting many-electron systems is fundamental to condensed matter physics, yet achieving accurate solutions confronts computational challenges in managing the combinatorial complexity of Feynman diagrams, implementing systematic renormalization, and evaluating high-dimensional integrals. We present a unifying framework that

R. Abbasi, M. Ackermann, J. Adams, S. K. Agarwalla

A measurement of the diffuse astrophysical neutrino spectrum is presented using IceCube data collected from 2011-2022 (10.3 years). We developed novel detection techniques to search for events with a contained vertex and exiting track induced by muon neutrinos undergoing a charged-current interaction. Searching for these starting track events allows us to no

Kexun Zhang, Yee Man Choi, Zhenqiao Song, Taiqi He

How can large language models (LLMs) process and translate endangered languages? Many languages lack a large corpus to train a decent LLM; therefore existing LLMs rarely perform well in unseen, endangered languages. On the contrary, we observe that 2000 endangered languages, though without a large corpus, have a grammar book or a dictionary. We propose LINGO

Zhuoer Xu, Jianping Zhang, Shiwen Cui, Changhua Meng

Large Language Models (LLMs) become the start-of-the-art solutions for a variety of natural language tasks and are integrated into real-world applications. However, LLMs can be potentially harmful in manifesting undesirable safety issues like social biases and toxic content. It is imperative to assess its safety issues before deployment. However, the quality

Kexue Zhang

This article studies the synchronization problem of complex dynamical networks. The impulsive control method is considered with a novel event-triggered pinning algorithm. Sufficient conditions on the network topology are obtained to ensure network synchronization. It is shown that synchronization can be realized with a careful selection of the pinning nodes.

Yuqi Ren, Renren Jin, Tongxuan Zhang, Deyi Xiong

Large Language Models (LLMs) have demonstrated remarkable abilities in text comprehension and logical reasoning, indicating that the text representations learned by LLMs can facilitate their language processing capabilities. In neuroscience, brain cognitive processing signals are typically utilized to study human language processing. Therefore, it is natural

Diogo P. L. Bragança

In general relativity, there is a velocity dependent term in the gravitational acceleration of a test particle for an observer at infinity. Depending on the direction of motion and the speed, that term can be repulsive. We show that this is also the case in the Parametrized Post-Newtonian (PPN) formalism. We compute the magnitude of that repulsive term for a

Fangguo Zhao, Jiahao Mei, Jin Zhou, Yuanyi Chen

The flying speed of autonomous quadrotors has increased significantly over the past 5 years, particularly in the field of autonomous drone racing. However, most research primarily focuses on the aggressive flight of a single quadrotor, simplifying the racing gate traversal problem to a waypoint passing problem that neglects the orientations of the racing gat

Monika Henzinger, A. R. Sricharan, Leqi Zhu

Computing the core decomposition of a graph is a fundamental problem that has recently been studied in the differentially private setting, motivated by practical applications in data mining. In particular, Dhulipala et al. [FOCS 2022] gave the first mechanism for approximate core decomposition in the challenging and practically relevant setting of local diff

Fuyuki Ando, Takamasa Hirai, Abdulkareem Alasli, Hossein Sepehri-Amin

Permanent magnets are used in various products and essential for human society. If omnipresent permanent magnets could directly convert heat into electricity, they would lead to innovative energy harvesting and thermal management technologies. However, achieving such "multifunctionality" has been difficult due to poor thermoelectric performance of convention

Bin Wang, Jun Fang, Hongbin Li, Yonina C. Eldar

Federated learning (FL) is a machine learning paradigm that targets model training without gathering the local data dispersed over various data sources. Standard FL, which employs a single server, can only support a limited number of users, leading to degraded learning capability. In this work, we consider a multi-server FL framework, referred to as \emph{Co

Dewei Wang, Bhaskar Mitra, Sameer Nekkalapu, Sohom Datta

As the power system continues to be flooded with intermittent resources, it becomes more important to accurately assess the role of hydro and its impact on the power grid. While hydropower generation has been studied for decades, dependency of power generation on water availability and constraints in hydro operation are not well represented in power system m

Yosuke Fukuchi, Seiji Yamada

This paper addresses the challenge of selecting explanations for XAI (Explainable AI)-based Intelligent Decision Support Systems (IDSSs). IDSSs have shown promise in improving user decisions through XAI-generated explanations along with AI predictions, and the development of XAI made it possible to generate a variety of such explanations. However, how IDSSs

Weitian Wu, Xinmin Yang

In multiobjective optimization, most branch and bound algorithms provide the decision maker with the whole Pareto front, and then decision maker could select a single solution finally. However, if the number of objectives is large, the number of candidate solutions may be also large, and it may be difficult for the decision maker to select the most interesti

Bingchu Nie, Dejian Tian, Long Jiang

In this paper, we introduce a new class of set-valued risk measures, named set-valued star-shaped risk measures. Motivated by the results of scalar monetary and star-shaped risk measures, this paper investigates the representation theorems in the set-valued framework. It is demonstrated that set-valued risk measures can be represented as the union of a famil

Youpeng Zhao, Ming Lin, Huadong Tang, Qiang Wu

Generative Large Language Models (LLMs) stand as a revolutionary advancement in the modern era of artificial intelligence (AI). However, scaling down LLMs for resource-constrained hardware, such as Internet-of-Things (IoT) devices requires non-trivial efforts and domain knowledge. In this paper, we propose a novel information-entropy framework for designing

Zihao Yi, Jiarui Ouyang, Zhe Xu, Yuwen Liu

This survey provides a comprehensive review of research on multi-turn dialogue systems, with a particular focus on multi-turn dialogue systems based on large language models (LLMs). This paper aims to (a) give a summary of existing LLMs and approaches for adapting LLMs to downstream tasks; (b) elaborate recent advances in multi-turn dialogue systems, coverin

Lingkai Kong, Yuanqi Du, Wenhao Mu, Kirill Neklyudov

Addressing real-world optimization problems becomes particularly challenging when analytic objective functions or constraints are unavailable. While numerous studies have addressed the issue of unknown objectives, limited research has focused on scenarios where feasibility constraints are not given explicitly. Overlooking these constraints can lead to spurio

Bach-Thuan Bui, Huy-Hoang Bui, Dinh-Tuan Tran, Joo-Ho Lee

Recent advancements in visual localization and mapping have demonstrated considerable success in integrating point and line features. However, expanding the localization framework to include additional mapping components frequently results in increased demand for memory and computational resources dedicated to matching tasks. In this study, we show how a lig

Lei Wang, Huanyuan Shan, Lin Nie, Dezi Liu

We have developed a novel method for co-adding multiple under-sampled images that combines the iteratively reweighted least squares and divide-and-conquer algorithms. Our approach not only allows for the anti-aliasing of the images but also enables PSF deconvolution, resulting in enhanced restoration of extended sources, the highest PSNR, and reduced ringing

Abby Bault, David Kirkby, Julien Guy, Allyson Brodzeller

The Dark Energy Spectroscopic Instrument (DESI) will measure millions of quasar spectra by the end of its 5 year survey. Quasar redshift errors impact the shape of the Lyman-$\alpha$ forest correlation functions, which can affect cosmological analyses and therefore cosmological interpretations. Using data from the DESI Early Data Release and the first two mo

Luyang Hou, Jun Yan, Yuankai Wu, Chun Wang

Energy Internet (EI) is emerging as new share economy platform for flexible local energy supplies in smart cities. Empowered by the Internet-of-Things (IoT) and Artificial Intelligence (AI), EI aims to unlock peer-to-peer energy trading and sharing among prosumers, who can adeptly switch roles between providers and consumers in localized energy markets with

Zhen Zhang

Smart meter is not only a device used to measure the amount of electricity, but also a core component of the smart grid, realizing the efficient monitoring, prediction and management of power use. With an insight into the evolution of smart meter technology, I realized that this change didn't happen overnight. It has undergone a long journey from the initial

Shen Cai, Zhanhao Wu, Lingxi Guo, Jiachun Wang

In this paper, we present two fast and interpretable decomposition methods for 2D homography, which are named Similarity-Kernel-Similarity (SKS) and Affine-Core-Affine (ACA) transformations respectively. Under the minimal $4$-point configuration, the first and the last similarity transformations in SKS are computed by two anchor points on target and source p

Qingfeng Ji, Yuxin Wang, Letong Sun

Recently, MLP structures have regained popularity, with MLP-Mixer standing out as a prominent example. In the field of computer vision, MLP-Mixer is noted for its ability to extract data information from both channel and token perspectives, effectively acting as a fusion of channel and token information. Indeed, Mixer represents a paradigm for information ex

Haoyu Wang, Yoichi Aso, Matteo Leonardi, Marc Eisenmann

Cooling down test masses to cryogenic temperatures is a way to reduce the thermal noise of gravitational wave detectors. Crystalline materials are considered the most promising materials for fabricating cryogenic test masses and their coatings because of their excellent thermal and optical properties at low temperatures. However, birefringence owing to local

Miao Li, Jey Han Lau, Eduard Hovy

Modern natural language generation systems with Large Language Models (LLMs) exhibit the capability to generate a plausible summary of multiple documents; however, it is uncertain if they truly possess the capability of information consolidation to generate summaries, especially on documents with opinionated information. We focus on meta-review generation, a

Qianqian Du, Mudong Du, Yun Guo

By utilizing a background field effective theory, we compute the collisional energy loss of a heavy quark moving through a semiquark-gluon plasma characterized by nontrivial holonomy for Polyakov loops. We consider the elastic scatterings between the incident heavy quark and the thermal partons with both hard and soft momentum transfers. As compared to the e

Jiqian Zhao, An-Bao Xu

In recent years, there has been a noteworthy focus on infrared small target detection, given its vital importance in processing signals from infrared remote sensing. The considerable computational cost incurred by prior methods, relying excessively on nuclear norm for noise separation, necessitates the exploration of efficient alternatives. The aim of this r

Arunkumar Velayudhan Pillai

The last decade has been marked by the evolution of cryptocurrencies, which have captured the interest of the public through the offered opportunities and the feeling of freedom, resulting from decentralization and lack of authority to oversee how cryptocurrency transactions are conducted. The innovation in crypto space is often compared to the impact intern