April 2024 arXiv papers — page 168

Li Xiaopeng, Zeng Zhaoyuan, Fan Cien, Zhao Chen

High dynamic range imaging (HDRI) for real-world dynamic scenes is challenging because moving objects may lead to hybrid degradation of low dynamic range and motion blur. Existing event-based approaches only focus on a separate task, while cascading HDRI and motion deblurring would lead to sub-optimal solutions, and unavailable ground-truth sharp HDR images

Ruoyou Wu, Jian Cheng, Cheng Li, Juan Zou

Deep learning-based dMRI super-resolution methods can effectively enhance image resolution by leveraging the learning capabilities of neural networks on large datasets. However, these methods tend to learn a fixed scale mapping between low-resolution (LR) and high-resolution (HR) images, overlooking the need for radiologists to scale the images at arbitrary

Sayantan Kumar, Sean Yu, Andrew Michelson, Thomas Kannampallil

Objective: We aimed to develop and validate a novel multimodal framework HiMAL (Hierarchical, Multi-task Auxiliary Learning) framework, for predicting cognitive composite functions as auxiliary tasks that estimate the longitudinal risk of transition from Mild Cognitive Impairment (MCI) to Alzheimer Disease (AD). Methods: HiMAL utilized multimodal longitudina

Tsuyoshi Hatano, Akihiro Fukawa, Hiroki Yamamoto, Keiichirou Akiba

We investigated the magnetic and electric properties of nanometer-sized vanadium dioxide (VO$_2$) particles. VO$_2$ nanoparticles were formed by milling VO$_2$ powder. We measured the magnetic field dependence of the magnetization of the VO$_2$ powder and nanoparticles. The VO$_2$ powder did not exhibit ferromagnetism, whereas the VO$_2$ nanoparticles exhibi

Mahasweta Chakraborti, Sailendra Akash Bonagiri, Santiago Virgüez-Ruiz, Seth Frey

Formal rules and policies are fundamental in formally specifying a social system: its operation, boundaries, processes, and even ontology. Recent scholarship has highlighted the role of formal policy in collective knowledge creation, game communities, the production of digital public goods, and national social media governance. Researchers have shown interes

Jason Stock, Jaideep Pathak, Yair Cohen, Mike Pritchard

This work presents an autoregressive generative diffusion model (DiffObs) to predict the global evolution of daily precipitation, trained on a satellite observational product, and assessed with domain-specific diagnostics. The model is trained to probabilistically forecast day-ahead precipitation. Nonetheless, it is stable for multi-month rollouts, which rev

Yuqi Qing, Wen-Long You, Yueheng Lan, Maoxin Liu

In this paper, we investigate the gauge dependence of various physical observables in the quantum Rabi model (QRM) under different potential fields, arising from the Hilbert-space truncation of the atomic degree of freedom. We discover that in both the square-well potential and oscillator potential,the optimal gauges for the ground-state energy of the QRM va

Detai Xin, Xu Tan, Kai Shen, Zeqian Ju

We present RALL-E, a robust language modeling method for text-to-speech (TTS) synthesis. While previous work based on large language models (LLMs) shows impressive performance on zero-shot TTS, such methods often suffer from poor robustness, such as unstable prosody (weird pitch and rhythm/duration) and a high word error rate (WER), due to the autoregressive

Dezhao Wu, Meng Ye, Haowei Chen, Yong Xu

The interplay of polarization and magnetism in materials with light can create rich nonlinear magneto-optical (NLMO) effects, and the recent discovery of two-dimensional (2D) van der Waals magnets provides remarkable control over NLMO effects due to their superb tunability. Here, based on first-principles calculations, we reported giant NLMO effects in CrI3-

Longwei Li, Huajian Huang, Sai-Kit Yeung, Hui Cheng

Photorealistic reconstruction relying on 3D Gaussian Splatting has shown promising potential in various domains. However, the current 3D Gaussian Splatting system only supports radiance field reconstruction using undistorted perspective images. In this paper, we present OmniGS, a novel omnidirectional Gaussian splatting system, to take advantage of omnidirec

Geoffrey Ramseyer, David Mazières

Groundhog is a novel design for a smart contract execution engine based around concurrent execution of blocks of transactions. Unlike prior work, transactions within a block in Groundhog are not ordered relative to one another. Instead, our key design insights are first, to design a set of commutative semantics that lets the Groundhog runtime deterministical

Quentin Jodelet, Xin Liu, Yin Jun Phua, Tsuyoshi Murata

Exemplar-Free Class Incremental Learning is a highly challenging setting where replay memory is unavailable. Methods relying on frozen feature extractors have drawn attention recently in this setting due to their impressive performances and lower computational costs. However, those methods are highly dependent on the data used to train the feature extractor

Jincheng Wang, Dong Li, Chuan Li, Yijun Hou

Solar prominences, intricate structures on the Sun's limb, have been a subject of fascination due to their thread-like features and dynamic behaviors. Utilizing data from the New Vacuum Solar Telescope (NVST), Chinese H_alpha Solar Explorer (CHASE), and Solar Dynamics Observatory (SDO), this study investigates the transverse swaying motions observed in the v

Jiaqi Gu, Ruoxu Tan, Guosheng Yin

Two-sample hypothesis testing is a fundamental problem with various applications, which faces new challenges in the high-dimensional context. To mitigate the issue of the curse of dimensionality, high-dimensional data are typically assumed to lie on a low-dimensional manifold. To incorporate geometric information in the data, we propose to apply the Delaunay

Ran Wei, Arindam K. Das, Payman Arabshahi, Daniel S. Kirschen

Severe weather events can cause extensive damage to electrical distribution networks, requiring a multi-day restoration effort. Optimizing the dispatch of repair crews minimizes the severe socio-economic consequences of such events. Considering both repair times and travel times, we use graphical manipulations to transform this multiple crew scheduling probl

Abhijnan Nath, Shadi Manafi, Avyakta Chelle, Nikhil Krishnaswamy

In NLP, Event Coreference Resolution (ECR) is the task of connecting event clusters that refer to the same underlying real-life event, usually via neural systems. In this work, we investigate using abductive free-text rationales (FTRs) generated by modern autoregressive LLMs as distant supervision of smaller student models for cross-document coreference (CDC

Shengwen Gan

The Kakeya problem in $\mathbb{R}^n$ is about estimating the size of union of $k$-planes; the projection problem in $\mathbb{R}^n$ is about estimating the size of projection of a set onto every $k$-plane ($1\le k\le n-1$). The $k=1$ case has been studied on general manifolds in which $1$-planes become geodesics, while $k\ge 2$ cases were still only considere

Cynthia Keeler, Nikhil Monga

In our previous paper (arXiv:2005.04242) we discussed type-D and type-N fluid-dual spacetimes and provided their associated single copies in the context of the Weyl double copy. In this work we extend our analysis to more general fluids thereby requiring the application of the double copy picture to type-II space-times. By combining our previous type-D and t

Binyang Dai, Xiao Hu, Ke Yi

Sampling over joins is a fundamental task in large-scale data analytics. Instead of computing the full join results, which could be massive, a uniform sample of the join results would suffice for many purposes, such as answering analytical queries or training machine learning models. In this paper, we study the problem of how to maintain a random sample over

Mohammed Abouzaid, Andrew J. Blumberg

We construct a stable infinity category with objects flow categories and morphisms flow bimodules; our construction has many flavors, related to a choice of bordism theory, and we discuss in particular framed bordism and the bordism theory of complex oriented derived orbifolds. In this setup, the construction of homotopy types associated to Floer-theoretic d

Yuan Wang, Xuyang Wu, Hsin-Tai Wu, Zhiqiang Tao

The integration of Large Language Models (LLMs) in information retrieval has raised a critical reevaluation of fairness in the text-ranking models. LLMs, such as GPT models and Llama2, have shown effectiveness in natural language understanding tasks, and prior works (e.g., RankGPT) have also demonstrated that the LLMs exhibit better performance than the trad

Beibei Wang, Zijian Yu, Lu Zhang, Jingjing Huang

Numerous roadside perception datasets have been introduced to propel advancements in autonomous driving and intelligent transportation systems research and development. However, it has been observed that the majority of their concentrates is on urban arterial roads, inadvertently overlooking residential areas such as parks and campuses that exhibit entirely

Jianwei Ren

In self-supervised monocular depth estimation tasks, discrete disparity prediction has been proven to attain higher quality depth maps than common continuous methods. However, current discretization strategies often divide depth ranges of scenes into bins in a handcrafted and rigid manner, limiting model performance. In this paper, we propose a learnable mod

Noah Y. Siegel, Oana-Maria Camburu, Nicolas Heess, Maria Perez-Ortiz

In order to oversee advanced AI systems, it is important to understand their underlying decision-making process. When prompted, large language models (LLMs) can provide natural language explanations or reasoning traces that sound plausible and receive high ratings from human annotators. However, it is unclear to what extent these explanations are faithful, i

W. S. H. M. W. Ahmad, M. F. A. Fauzi, M. K. Abdullahi, Jenny T. H. Lee

Nasopharyngeal carcinoma (NPC) is one of the understudied yet deadliest cancers in South East Asia. In Malaysia, the prevalence is identified mainly in Sarawak, among the ethnic of Bidayuh. NPC is often late-diagnosed because it is asymptomatic at the early stage. There are several tissue representations from the nasopharynx biopsy, such as nasopharyngeal in

Tianrui Guan, Ruiqi Xian, Xijun Wang, Xiyang Wu

We present AGL-NET, a novel learning-based method for global localization using LiDAR point clouds and satellite maps. AGL-NET tackles two critical challenges: bridging the representation gap between image and points modalities for robust feature matching, and handling inherent scale discrepancies between global view and local view. To address these challeng

Henry Berger, Ian Abraham

This paper investigates performance guarantees on coverage-based ergodic exploration methods in environments containing disturbances. Ergodic exploration methods generate trajectories for autonomous robots such that time spent in each area of the exploration space is proportional to the utility of exploring in the area. We find that it is possible to use tec

Wenchen Luo, Muaath Abdulwahab, Xiang Liu, Hao Wang

The Landau level mixing is the key in understanding the mysterious $5/2$ fractional quantum Hall effect in GaAs quantum well. Theoretical calculations with and without Landau level mixing show striking differences. However, the way to deal with the considerable strong Landau level mixing in GaAs is still unsatisfactory. We develop a method combining the scre

Jiawei Li, Yue Zhang

Machine reading comprehension is an essential natural language processing task, which takes into a pair of context and query and predicts the corresponding answer to query. In this project, we developed an end-to-end question answering model incorporating BERT and additional linguistic features. We conclude that the BERT base model will be improved by incorp

Abhishek Tandon, Anujraaj Goyal, Henry M. Clever, Zackory Erickson

Accurately predicting the 3D human posture and the pressure exerted on the body for people resting in bed, visualized as a body mesh (3D pose & shape) with a 3D pressure map, holds significant promise for healthcare applications, particularly, in the prevention of pressure ulcers. Current methods focus on singular facets of the problem -- predicting only 2D/

Jielun Chen, Michael Lindsey

The quantum Fourier transform (QFT), which can be viewed as a reindexing of the discrete Fourier transform (DFT), has been shown to be compressible as a low-rank matrix product operator (MPO) or quantized tensor train (QTT) operator. However, the original proof of this fact does not furnish a construction of the MPO with a guaranteed error bound. Meanwhile,

Longfei Yan, Pei Yan, Shengzhou Xiong, Xuanyu Xiang

Monocular 3D object detection has attracted widespread attention due to its potential to accurately obtain object 3D localization from a single image at a low cost. Depth estimation is an essential but challenging subtask of monocular 3D object detection due to the ill-posedness of 2D to 3D mapping. Many methods explore multiple local depth clues such as obj

Houzhe Wang, Xiaojie Zhu, Chi Chen, Paulo Esteves-Veríssimo

With recent legislation on the right to be forgotten, machine unlearning has emerged as a crucial research area. It facilitates the removal of a user's data from federated trained machine learning models without the necessity for retraining from scratch. However, current machine unlearning algorithms are confronted with challenges of efficiency and validity.

Tiantian Geng, Teng Wang, Jinming Duan, Yanfu Zhang

Video event localization tasks include temporal action localization (TAL), sound event detection (SED) and audio-visual event localization (AVEL). Existing methods tend to over-specialize on individual tasks, neglecting the equal importance of these different events for a complete understanding of video content. In this work, we aim to develop a unified fram

Andrew P. C. Underwood, Andrew J. Groszek, Xiaoquan Yu, P. B. Blakie

An easy-plane ferromagnetic spin-1 Bose gas undergoes two Berezinskii-Kosterlitz-Thouless (BKT) transitions, associated with mass and spin superfluidity respectively. We study the effect of axial magnetization on the superfluid properties of this system. We find that nonzero axial magnetization couples mass and spin superflow, via a mechanism analogous to th

Joseph Vimal Vas, Rohit Medwal, Sourabh Manna, Mayank Mishra

Exploring new strategies for controlling the magnetic domain propagation is the key to realize ultrafast, high-density domain wall-based memory and logic devices for next generation computing. These strategies include strain modulation in multiferroic devices, geometric confinement and area-selective pinning of domain wall. 2D Van der Waals materials introdu

Haiyun He, Ziv Goldfeld

Deep neural networks (DNNs) exhibit an exceptional capacity for generalization in practical applications. This work aims to capture the effect and benefits of depth for supervised learning via information-theoretic generalization bounds. We first derive two hierarchical bounds on the generalization error in terms of the Kullback-Leibler (KL) divergence or th

Fanghua Guo, Yanbo Zhang, Yunqing Zhang

Recently, Caro, Patk\'os, and Tuza (2022) introduced the concept of connected Tur\'an number. We study a similar parameter in Ramsey theory. Given two graphs $G_1$ and $G_2$, the size Ramsey number $\hat{r}(G_1,G_2)$ refers to the smallest number of edges in a graph $G$ such that for any red-blue edge-coloring of $G$, either a red subgraph $G_1$ or a blue su

Sergio Mendoza, Milton Jair Santibañez-Armenta

We present comparisons of the deflection angles of massless and massive particles in the Schwarzschild space-time. For the case of photons in a general static space-time, we construct a spatial 3D equation of motion for their path that leads to an implicit formula for the deflection angle. We then compare our results with well known results of the literature

Qiangbo Zhang, Zeqing Yu, Mengguang Wang, Yiyang Liu

Single metalenses are limited by their physical constraints, precluding themselves from achieving high numerical aperture across a wide visible spectral band in large-aperture applications. A hybrid system that integrates a metalens with a refractive lens can address this issue, yet previous designs lacked sufficient flexibility. Here, by reanalyzing the gen

Yufeng Li, Qiusong Yang, Yiwei Ci, Enyuan Tian

Symbolic quick error detection (SQED) has greatly improved efficiency in formal chip verification. However, it has a limitation in detecting single-instruction bugs due to its reliance on the self-consistency property. To address this, we propose a new variant called symbolic quick error detection by semantically equivalent program execution (SEPE-SQED), whi

Hanxian Huang, Jishen Zhao

The increasing adoption of WebAssembly (Wasm) for performance-critical and security-sensitive tasks drives the demand for WebAssembly program comprehension and reverse engineering. Recent studies have introduced machine learning (ML)-based WebAssembly reverse engineering tools. Yet, the generalization of task-specific ML solutions remains challenging, becaus

Nitesh Goyal, Minsuk Chang, Michael Terry

Our ability to build autonomous agents that leverage Generative AI continues to increase by the day. As builders and users of such agents it is unclear what parameters we need to align on before the agents start performing tasks on our behalf. To discover these parameters, we ran a qualitative empirical research study about designing agents that can negotiat

Glenn Bruda

Suppose a complex function $f$ has a Lebesgue measurable inverse Laplace transform. We show that the $n$th order forward and backward differences of $f$ at $z_0\in\mathbb{C}$ tend to zero as $n\to\infty$ whenever $z_0$ lies in the region of absolute convergence of $f$. Under the same hypothesis, we show that the Newton series of $f$ centered at $z_0$ exists

Pouria Rouzrokh, Shahriar Faghani, Cooper U. Gamble, Moein Shariatnia

Retrieval-augmented generation (RAG) frameworks enable large language models (LLMs) to retrieve relevant information from a knowledge base and incorporate it into the context for generating responses. This mitigates hallucinations and allows for the updating of knowledge without retraining the LLM. However, RAG does not guarantee valid responses if retrieval

Wenhao Wang, Miao Yang, Zongmin Hu, Peng Zhang

This study proposes a dynamic model to reflect the physical image of the droplet breakup process in two-phase detonation flows. This breakup model is implemented in a two-phase detonation solver developed based on an open-source computational fluid dynamic platform, OpenFOAM, and compared with three prevalent models (TAB, PilchErdman, and ReitzKH-RT model) u

Ali Tahzibi, Richard Cubas

In this paper we establish a dichotomy for the ergodic measures of maximal entropy for partially hyperbolic diffeomorphisms with one-dimensional compact center leaves which are virtually skew products over (transitive) Anosov homeomorphism. We prove that if the whole manifold is the unique minimal invariant set saturated by unstable foliation, then either th

Owen Ekblad, Eloy Moreno-Nadales, Lubashan Pathirana, Jeffrey Schenker

We develop a general framework to study quantum trajectories resulting from repeated random measurements subject to stationary noise, and generalize results of K\"ummerer and Maassen to this setting. The resulting trajectory of quantum states is a time-inhomogeneous Markov chain in a random environment. K\"ummerer and Maassen introduced the concept of dark s

Qingxiang Liu, Sheng Sun, Yuxuan Liang, Jingjing Xue

The existing federated learning (FL) methods for spatio-temporal forecasting fail to capture the inherent spatio-temporal heterogeneity, which calls for personalized FL (PFL) methods to model the spatio-temporally variant patterns. While contrastive learning approach is promising in addressing spatio-temporal heterogeneity, the existing methods are noneffect

Tavis Bennett, Lyle Noakes, Jingbo Wang

This paper introduces a non-variational quantum algorithm designed to solve a wide range of combinatorial optimisation problems, including constrained and non-binary problems. The algorithm leverages an engineered interference process achieved through repeated application of two unitaries; one inducing phase-shifts dependent on objective function values, and

Anarya Ray, Ignacio Magaña Hernandez, Katelyn Breivik, Jolien Creighton

Astrophysically motivated population models for binary black hole observables are often insufficient to capture the imprints of multiple formation channels. This is mainly due to the strongly parametrized nature of such investigations. Using a non-parametric model for the joint population-level distributions of binary black hole component masses and effectiv

Mamtaj Akter, Zainab Agha, Ashwaq Alsoubai, Naima Ali

Traditional online safety technologies often overly restrict teens and invade their privacy, while parents often lack knowledge regarding their digital privacy. As such, prior researchers have called for more collaborative approaches on adolescent online safety and networked privacy. In this paper, we propose family-centered approaches to foster parent-teen

Haonan Zhang, Dongxia Wang, Zhu Sun, Yanhui Li

Recommender systems (RSs) are designed to provide personalized recommendations to users. Recently, knowledge graphs (KGs) have been widely introduced in RSs to improve recommendation accuracy. In this study, however, we demonstrate that RSs do not necessarily perform worse even if the KG is downgraded to the user-item interaction graph only (or removed). We

Xinmeng Huang, Shuo Li, Mengxin Yu, Matteo Sesia

Language Models (LMs) have shown promising performance in natural language generation. However, as LMs often generate incorrect or hallucinated responses, it is crucial to correctly quantify their uncertainty in responding to given inputs. In addition to verbalized confidence elicited via prompting, many uncertainty measures ($e.g.$, semantic entropy and aff

Xiaoxiao Liu, Fan Xu, Nan Wang, Qinxin Zhao

Advanced Persistent Threat (APT) is challenging to detect due to prolonged duration, infrequent occurrence, and adept concealment techniques. Existing approaches primarily concentrate on the observable traits of attack behaviors, neglecting the intricate relationships formed throughout the persistent attack lifecycle. Thus, we present an innovative APT detec

Tomohiro Nishimoto, Taichi Nishimura, Koki Yamamoto, Keisuke Shirai

This paper introduces BioVL-QR, a biochemical vision-and-language dataset comprising 23 egocentric experiment videos, corresponding protocols, and vision-and-language alignments. A major challenge in understanding biochemical videos is detecting equipment, reagents, and containers because of the cluttered environment and indistinguishable objects. Previous s

Yang Ba, Michelle V. Mancenido, Erin K. Chiou, Rong Pan

As crowdsourcing emerges as an efficient and cost-effective method for obtaining labels for machine learning datasets, it is important to assess the quality of crowd-provided data, so as to improve analysis performance and reduce biases in subsequent machine learning tasks. Given the lack of ground truth in most cases of crowdsourcing, we refer to data quali

Zitong Zhang, Shizhe Chen

Technological advancements have enabled the recording of spiking activities from large neuron ensembles, presenting an exciting yet challenging opportunity for statistical analysis. This project considers the challenges from a common type of neuroscience experiments, where randomized interventions are applied over the course of each trial. The objective is t

Wencan Cheng, Hao Tang, Luc Van Gool, Jong Hwan Ko

Extracting keypoint locations from input hand frames, known as 3D hand pose estimation, is a critical task in various human-computer interaction applications. Essentially, the 3D hand pose estimation can be regarded as a 3D point subset generative problem conditioned on input frames. Thanks to the recent significant progress on diffusion-based generative mod

Halil Ibrahim Kurt, Wenxian Shen

The current paper is concerned with the stabilization in the following parabolic-parabolic-elliptic chemotaxis system with singular sensitivity and Lotka-Volterra competitive kinetics, \begin{equation} \begin{cases} u_t=\Delta u-\chi_1 \nabla\cdot (\frac{u}{w} \nabla w)+u(a_1-b_1u-c_1v) ,\quad &x\in \Omega\cr v_t=\Delta v-\chi_2 \nabla\cdot (\frac{v}{w} \nab

Emma Brakkee, Chiara Camere, Annalisa Grossi, Laura Pertusi

Generalizing work of Markushevich--Tikhomirov and Arbarello--Sacc\`a--Ferretti, we use relative Prym varieties to construct Lagrangian fibered symplectic varieties in infinitely many dimensions. We then give criteria for when the construction yields primitive symplectic varieties, respectively, irreducible symplectic varieties. The starting point of the cons

Yossef Zenati, Julian Krolik, Leonardo Werneck, Zachariah Etienne

The detection of GW170817/AT2017gfo inaugurated an era of multimessenger astrophysics, in which gravitational wave and multiwavelength photon observations complement one another to provide unique insight on astrophysical systems. A broad theoretical consensus exists in which the photon phenomenology of neutron star mergers largely rests upon the evolution of

William Shaddix, Mahyar Samani, Marjan Fariborz, S. J. Ben Yoo

Graphs are essential for representing relationships in various domains, driving modern AI applications such as graph analytics and neural networks across science, engineering, cybersecurity, transportation, and economics. However, the size of modern graphs are rapidly expanding, posing challenges for traditional CPUs and GPUs in meeting real-time processing

Minwoo Kim, Jonggyu Jang, Youngchol Choi, Hyun Jong Yang

The growth in artificial intelligence (AI) technology has attracted substantial interests in latency-aware task offloading of mobile edge computing (MEC)-namely, minimizing service latency. Additionally, the use of MEC systems poses an additional problem arising from limited battery resources of MDs. This paper tackles the pressing challenge of latency-aware

Yi Ren, Shangmin Guo, Linlu Qiu, Bailin Wang

With the widespread adoption of Large Language Models (LLMs), the prevalence of iterative interactions among these models is anticipated to increase. Notably, recent advancements in multi-round self-improving methods allow LLMs to generate new examples for training subsequent models. At the same time, multi-agent LLM systems, involving automated interactions

Arindam Roy

The partition function $p(n)$ and many of its related restricted partition functions have recently been shown independently to satisfy log-concavity: $p(n)^2 \geq p(n-1)p(n+1)$ for $n\geq 26$, and satisfy the inequality: $p(n)p(m) \geq p(n+m)$ for $n\geq m\geq 2$ with only finitely many instances of equality or failure. This paper proves that this is no coin

Antik Chakraborty, Jonelle B. Walsh, Louis Strigari, Bani K. Mallick

The orbital superposition method originally developed by Schwarzschild (1979) is used to study the dynamics of growth of a black hole and its host galaxy, and has uncovered new relationships between the galaxy's global characteristics. Scientists are specifically interested in finding optimal parameter choices for this model that best match physical measurem

Ngoc-Du Tran, Thi-Thao Tran, Quang-Huy Nguyen, Manh-Hung Vu

The emergence of deep learning techniques has advanced the image segmentation task, especially for medical images. Many neural network models have been introduced in the last decade bringing the automated segmentation accuracy close to manual segmentation. However, cutting-edge models like Transformer-based architectures rely on large scale annotated trainin

Lior Alon, John Urschel

The nodal edge count of an eigenvector of the Laplacian of a graph is the number of edges on which it changes sign. This quantity extends to any real symmetric $n\times n$ matrix supported on a graph $G$ with $n$ vertices. The average nodal count, averaged over all eigenvectors of a given matrix, is known to be bounded between $\frac{n-1}{2}$ and $\frac{n-1}

Anish Pahilajani, Samyak Rajesh Jain, Devasha Trivedi

This paper presents our submission to the SemEval 2024 Task 5: The Legal Argument Reasoning Task in Civil Procedure. We present two approaches to solving the task of legal answer validation, given an introduction to the case, a question and an answer candidate. Firstly, we fine-tuned pre-trained BERT-based models and found that models trained on domain knowl

Sibo Yang, Lincong Luo, Wei Chuan Law, Youlong Wang

We developed a 3D end-effector type of upper limb assistive robot, named as Assistive Robotic Arm Extender (ARAE), that provides transparency movement and adaptive arm support control to achieve home-based therapy and training in the real environment. The proposed system composes five degrees of freedom, including three active motors and two passive joints a

Mark Mandelkern

Constructive properties of uniform convexity, strict convexity, near convexity, and metric convexity in real normed linear spaces are considered. Examples show that certain classical theorems, such as the existence of points of osculation, are constructively invalid. The methods used are in accord with principles introduced by Errett Bishop

Tomás Vergara-Browne, Álvaro Soto, Akiko Aizawa

We introduce eigenpruning, a method that removes singular values from weight matrices in an LLM to improve its performance in a particular task. This method is inspired by interpretability methods designed to automatically find subnetworks of a model which solve a specific task. In our tests, the pruned model outperforms the original model by a large margin,

Swati Shaw, Subhradip Ghosh

The compositional and structural flexibility of functionalised two-dimensional metal carbonitrides or MXenes has been exploited through a combinatorial search for new materials that can act as catalysts for photo-assisted water splitting by absorbing sunlight with energy in the infra-red region. Detailed calculations on 49 Janus MXenes where two surfaces are

Michelle Shu, Charles Herrmann, Richard Strong Bowen, Forrester Cole

Text-conditioned diffusion models can generate impressive images, but fall short when it comes to fine-grained control. Unlike direct-editing tools like Photoshop, text conditioned models require the artist to perform "prompt engineering," constructing special text sentences to control the style or amount of a particular subject present in the output image.

Kaixin Zhang, Zhixiang Yuan, Tao Huang

Recently, zero-shot multi-label classification has garnered considerable attention for its capacity to operate predictions on unseen labels without human annotations. Nevertheless, prevailing approaches often use seen classes as imperfect proxies for unseen ones, resulting in suboptimal performance. Drawing inspiration from the success of text-to-image gener

Giovanni Ferrami, Stuart Wyithe

Photometric wide-area observations in the next decade will be capable of detecting a large number of galaxy-scale strong gravitational lenses, increasing the gravitational lens sample size by orders of magnitude. To aid in forecasting and analysis of these surveys, we construct a flexible model based on observed distributions for the lens and source properti

Marc Besson, Sam Jeralds, Joshua Kiers

For an untwisted affine Kac-Moody Lie algebra $\mathfrak{g}$ with Cartan and Borel subalgebras $\mathfrak{h} \subset \mathfrak{b} \subset \mathfrak{g}$, affine Demazure modules are certain $U(\mathfrak{b})$-submodules of the irreducible highest-weight representations of $\mathfrak{g}$. We introduce here the associated affine Demazure weight polytopes, given

Elham Arabahmadi, Siamak Akhshabi

The torsion of spacetime, if exists, plays an important role at the very early universe when the spin density of particles was very high. It is generally believed that in extremely high energies of the early universe a new physics called trans-Planckian physics should be considered. Since the initial conditions for inflation are probably a result of this new

L. De Keukeleere, D. Rozpedzik, N. Severijns, K. Bodek

Spectrum shape measurements in nuclear $\beta$ decay can be used to test physics beyond the Standard Model with results being complementary to high-energy collider experiments. In particular, Beyond Standard Model sensitivity of the weak interaction is expressed through the so-called Fierz interference term. Additionally, the $\beta$ spectrum shape is a usef

Arjun Subramonian, Jian Kang, Yizhou Sun

Graph Neural Networks (GNNs) often perform better for high-degree nodes than low-degree nodes on node classification tasks. This degree bias can reinforce social marginalization by, e.g., privileging celebrities and other high-degree actors in social networks during social and content recommendation. While researchers have proposed numerous hypotheses for wh

Hyomin Kim, Yucheol Jung, Seungyong Lee

Many surface reconstruction methods incorporate normal integration, which is a process to obtain a depth map from surface gradients. In this process, the input may represent a surface with discontinuities, e.g., due to self-occlusion. To reconstruct an accurate depth map from the input normal map, hidden surface gradients occurring from the jumps must be han

Hannah Moring, Harsha Nagarajan, Kshitij Girigoudar, David M. Fobes

To mitigate the vulnerability of distribution grids to severe weather events, some electric utilities use preemptive de-energization as the primary line of defense, causing significant power outages. In such instances, networked microgrids could improve resiliency and maximize load delivery, though the modeling of three-phase unbalanced network physics and c

Narges Alavisamani, Suhas Vittal, Ramin Ayanzadeh, Poulami Das

Fault-tolerant quantum computing relies on Quantum Error Correction, which encodes logical qubits into data and parity qubits. Error decoding is the process of translating the measured parity bits into types and locations of errors. To prevent a backlog of errors, error decoding must be performed in real-time. Minimum Weight Perfect Matching (MWPM) is an acc

Alex Levchenko

The thermal resistance of a spin-polarized hydrodynamic Dirac plasma in graphene is considered. A mechanism for the coupling of heat and spin flows is discussed, demonstrating that spin diffusion and spin thermocurrent modify viscous dissipation, leading to a significant enhancement of thermal resistance. Practical calculations are then presented for graphen

Vagrant Gautam, Eileen Bingert, Dawei Zhu, Anne Lauscher

Robust, faithful and harm-free pronoun use for individuals is an important goal for language model development as their use increases, but prior work tends to study only one or two of these characteristics at a time. To measure progress towards the combined goal, we introduce the task of pronoun fidelity: given a context introducing a co-referring entity and

Jing Dong, Baoxiang Wang, Yaoliang Yu

In this work, we study potential games and Markov potential games under stochastic cost and bandit feedback. We propose a variant of the Frank-Wolfe algorithm with sufficient exploration and recursive gradient estimation, which provably converges to the Nash equilibrium while attaining sublinear regret for each individual player. Our algorithm simultaneously

Amnon Attali, Stav Ashur, Isaac Burton Love, Courtney McBeth

Randomized sampling based algorithms are widely used in robot motion planning due to the problem's intractability, and are experimentally effective on a wide range of problem instances. Most variants bias their sampling using various heuristics related to the known underlying structure of the search space. In this work, we formalize the intuitive notion of g

Kaustubh Khedkar, Amirreza Charchi Mamaghani, Pieter Ghysels, Neelesh A. Patankar

For decades, the computational multiphase flow community has grappled with mass loss in the level set method. Numerous solutions have been proposed, from fixing the reinitialization step to combining the level set method with other conservative schemes. However, our work reveals a more fundamental culprit: the smooth Heaviside and delta functions inherent to

Haizi Yu, Lav R. Varshney

Data-driven artificial intelligence (AI) techniques are becoming prominent for learning in support of data compression, but are focused on standard problems such as text compression. To instead address the emerging problem of semantic compression, we argue that the lattice theory of information is particularly expressive and mathematically precise in capturi

Fabiana Ferracina, Bala Krishnamoorthy, Mahantesh Halappanavar, Shengwei Hu

We explore the application of machine learning algorithms specifically to enhance the selection process of Russet potato clones in breeding trials by predicting their suitability for advancement. This study addresses the challenge of efficiently identifying high-yield, disease-resistant, and climate-resilient potato varieties that meet processing industry st

Zhihan Zhang, Mallory Parker, Kuotian Liao, Jerry Cao

The sense of touch is fundamental to how we interact with the physical and digital world. Conventional interactive surfaces and tactile interfaces use electronic sensors embedded into objects, however this approach poses serious challenges both for environmental sustainability and a future of truly ubiquitous interaction systems where information is encoded

Xing Zhang, Chenghan Li, Hong-Zhou Ye, Timothy C. Berkelbach

In this work, we introduce a differentiable implementation of the local natural orbital coupled cluster (LNOCC) method within the automatic differentiation framework of the PySCFAD package. The implementation is comprehensively tuned for enhanced performance, which enables the calculation of first-order static response properties on medium-sized molecular sy

Hiroyuki Hirayama, Yasuyuki Oka

The aim of this paper is to give existence and uniqueness results for solutions of the Cauchy problem for semilinear heat equations on stratified Lie groups $\mathbb{G}$ with the homogeneous dimension $N$. We consider the nonlinear function behaves like $|u|^{\alpha}$ or $|u|^{\alpha-1}u$ $(\alpha>1)$ and the initial data $u_0$ belongs to the Sobolev spaces

Ismaïla Ba, Maxime Turgeon, Simona Veniamin, Juan Joel

Dimension reduction techniques are among the most essential analytical tools in the analysis of high-dimensional data. Generalized principal component analysis (PCA) is an extension to standard PCA that has been widely used to identify low-dimensional features in high-dimensional discrete data, such as binary, multi-category and count data. For microbiome co

Emmanouil Nikolakakis, Utkarsh Gupta, Jonathan Vengosh, Justin Bui

We present GaSpCT, a novel view synthesis and 3D scene representation method used to generate novel projection views for Computer Tomography (CT) scans. We adapt the Gaussian Splatting framework to enable novel view synthesis in CT based on limited sets of 2D image projections and without the need for Structure from Motion (SfM) methodologies. Therefore, we

Martin Alkämper, Stephan Hilb, Andreas Langer

Based on previous work we extend a primal-dual semi-smooth Newton method for minimizing a general $L^1$-$L^2$-$TV$ functional over the space of functions of bounded variations by adaptivity in a finite element setting. For automatically generating an adaptive grid we introduce indicators based on a-posteriori error estimates. Further we discuss data interpol

Tianyu Yang, Yang Yang

The paper studies an imaging problem in the diffusive ultrasound-modulated bioluminescence tomography with partial boundary measurement in an anisotropic medium. Assuming plane-wave modulation, we transform the imaging problem to an inverse problem with internal data, and derive a reconstruction procedure to recover the bioluminescent source. Subsequently, a

Linhua Jiang, Zhiwei Pan, Jessica Nicole Aguilar, Steven Ahlen

We present strong constraints on the spacetime variation of the fine-structure constant $\alpha$ using the Dark Energy Spectroscopic Instrument (DESI). In this pilot work, we utilize $\sim110,000$ galaxies with strong and narrow O III $\lambda\lambda$4959,5007 emission lines to measure the relative variation $\Delta\alpha/\alpha$ in space and time. The O III

Chetan Arora, John Grundy, Louise Puli, Natasha Layton

In the rapidly evolving field of assistive technology (AT), ensuring that products meet national and international standards is essential for user safety, efficacy, and accessibility. In this vision paper, we introduce CompliAT, a pioneering framework designed to streamline the compliance process of AT product specifications with these standards through the