March 2024 arXiv papers — page 127

Jiafu Chen, Wei Xing, Jiakai Sun, Tianyi Chu

3D scene stylization refers to transform the appearance of a 3D scene to match a given style image, ensuring that images rendered from different viewpoints exhibit the same style as the given style image, while maintaining the 3D consistency of the stylized scene. Several existing methods have obtained impressive results in stylizing 3D scenes. However, the

Siyue Ren, Zhiyao Cui, Ruiqi Song, Zhen Wang

Social norms play a crucial role in guiding agents towards understanding and adhering to standards of behavior, thus reducing social conflicts within multi-agent systems (MASs). However, current LLM-based (or generative) MASs lack the capability to be normative. In this paper, we propose a novel architecture, named CRSEC, to empower the emergence of social n

Philip Beltracchi, Camilo Posada

The present paper is devoted to a study of the equilibrium configurations of slowly rotating anisotropic stars in the framework of general relativity. For that purpose, we provide the equations of structure where the rotation is treated to second order in the angular velocity. These equations extend those first derived by Hartle for slowly rotating isotropic

Zhijie Fan, Michael Lacey, Ji Li, Xiao Xiong

Let $\Delta_\lambda$ be the Bessel operator on the upper half space $\mathbb{R}_+^{n+1}$ with $n\geq 0$ and $\lambda>0$, and $R_{\lambda,j}$ be the $j-$th Bessel Riesz transform, $j=1,\ldots,n+1$. We demonstrate that the Schatten--Lorentz norm ($S^{p,q}$, $1<p<\infty$, $1\leq q\leq \infty$) of the commutator $[b,R_{\lambda,j}]$ can be characterized in terms

Haoxu Huang, Fanqi Lin, Yingdong Hu, Shengjie Wang

Foundation models pre-trained on web-scale data are shown to encapsulate extensive world knowledge beneficial for robotic manipulation in the form of task planning. However, the actual physical implementation of these plans often relies on task-specific learning methods, which require significant data collection and struggle with generalizability. In this wo

Hongyang Zhu, Xin Lu, Yanwei Qin, Xinran Yu

Ring artifacts in computed tomography images, arising from the undesirable responses of detector units, significantly degrade image quality and diagnostic reliability. To address this challenge, we propose a dual-domain regularization model to effectively remove ring artifacts, while maintaining the integrity of the original CT image. The proposed model corr

Yuting Liu, Yizhou Dang, Yuliang Liang, Qiang Liu

Recently, sign-aware graph recommendation has drawn much attention as it will learn users' negative preferences besides positive ones from both positive and negative interactions (i.e., links in a graph) with items. To accommodate the different semantics of negative and positive links, existing works utilize two independent encoders to model users' positive

Shawn Tan, Yikang Shen, Rameswar Panda, Aaron Courville

We present ScatterMoE, an implementation of Sparse Mixture-of-Experts (SMoE) on GPUs. ScatterMoE builds upon existing implementations, and overcoming some of the limitations to improve inference and training speed, and memory footprint. This implementation achieves this by avoiding padding and making excessive copies of the input. We introduce ParallelLinear

Howoun Jung, Nohjin Park, Jay H. Lee

Despite ongoing global initiatives to reduce CO2 emissions, implementing large-scale CO2 capture using amine solvents is fraught with economic uncertainties and technical hurdles. The Rotating Packed Bed (RPB) presents a promising alternative to traditional packed towers, offering compact design and adaptability. Nonetheless, scaling RPB processes to an indu

Matthew Fayers, Eoghan McDowell

For a finite group, it is interesting to determine when two ordinary irreducible representations have the same $p$-modular reduction; that is, when two rows of the decomposition matrix in characteristic $p$ are equal, or equivalently when the corresponding $p$-modular Brauer characters are the same. We complete this task for the double covers of the symmetri

Xu-Jia Ouyang, Yong Zhang, Juan Li, Jun-ichi Nakashima

Water fountain objects are generally defined as "evolved stars with low to intermediate initial mass accompanied by high-velocity molecular jets detectable in the 22.235 GHz H$_2$O maser line". They are the key objects of understanding the morphological transitions of circumstellar envelopes during the post asymptotic giant branch phase. Masers are useful to

Roger de Belsunce, Oliver H. E. Philcox, Vid Irsic, Patrick McDonald

We measure the three-dimensional power spectrum (P3D) of the transmitted flux in the Lyman-a (Ly-a) forest using the complete extended Baryon Oscillation Spectroscopic Survey data release 16 (eBOSS DR16). This sample consists of 205,012 quasar spectra in the redshift range 2 <= z <= 4 at an effective redshift z=2.334. We propose a pair-count spectral estimat

Antonios M. Alvertis, David B. Williams-Young, Fabien Bruneval, Jeffrey B. Neaton

Electron-phonon interactions are of great importance to a variety of physical phenomena, and their accurate description is an important goal for first-principles calculations. Isolated examples of materials and molecular systems have emerged where electron-phonon coupling is enhanced over density functional theory (DFT) when using the Green's-function-based

Kento Kawaharazuka, Naoaki Kanazawa, Yoshiki Obinata, Kei Okada

The state recognition of the environment and objects by robots is generally based on the judgement of the current state as a classification problem. On the other hand, state changes of food in cooking happen continuously and need to be captured not only at a certain time point but also continuously over time. In addition, the state changes of food are comple

Junfei Li, Simon X. Yang

Natural disasters and urban accidents drive the demand for rescue robots to provide safer, faster, and more efficient rescue trajectories. In this paper, a feature learning-based bio-inspired neural network (FLBBINN) is proposed to quickly generate a heuristic rescue path in complex and dynamic environments, as traditional approaches usually cannot provide a

Parv Kapoor, Eunsuk Kang, Romulo Meira-Goes

Trajectory planning is a critical process that enables autonomous systems to safely navigate complex environments. Signal temporal logic (STL) specifications are an effective way to encode complex temporally extended objectives for trajectory planning in cyber-physical systems (CPS). However, planning from these specifications using existing techniques scale

Peichen Zhong, Sunny Gupta, Bowen Deng, KyuJung Jun

Li$_2$ZrCl$_6$ (LZC) is a promising solid-state electrolyte due to its affordability, moisture stability, and high ionic conductivity. We computationally investigate the role of cation disorder in LZC and its effect on Li-ion transport by integrating thermodynamic and kinetic modeling. The results demonstrate that fast Li-ion conductivity requires Li/vacancy

Zezeng Li, Weimin Wang, Ziliang Wang, Na Lei

This paper presents a novel point cloud compression method COT-PCC by formulating the task as a constrained optimal transport (COT) problem. COT-PCC takes the bitrate of compressed features as an extra constraint of optimal transport (OT) which learns the distribution transformation between original and reconstructed points. Specifically, the formulated COT

A. Karmakar, P. Datta, N. Rather, S. Pal

An experimental investigation of $^{105}$Pd has revealed, for the first time, the existence of two wobbling bands, both having one phonon configuration and originating from excitation which is the wobbling from the yrast band with the $h_{11/2}$ quasineutron fully aligned with the short axis, and from an excited band with the same quasineutron but with less

Heng Yu, Kan-Hao Xue, Nan Feng, Yunzhe Zheng

While ferroelectric hafnia ($\mathrm{HfO_2}$) has become a technically important material for microelectronics, the physical origin of its ferroelectricity remains poorly understood. The tetragonal $P4_2/nmc$ phase is commonly assigned as its paraelectric mother phase but has no soft mode at the Brillouin zone center. In this work, we propose that the parael

Qi Liu, Xuan Liu, Yu Tian, Zhaohua Tian

Gradient metasurface, formed by a set of subwavelength unit cells with different phase modulation, is widely used in polarized beam splitting (BS) in the classical and quantum optics. Specifically, its phase gradient allows the path and polarization of multiple output lights to be locked by corresponding inputs.Using this unique path-polarization locked prop

Annan Fan, Shi-Dong Liang

We propose the velocity field approach to characterize topological invariants of quantum states. We introduce the indexes of the velocity field flow based on the zero modes of the velocity field and find that these zero modes play the role of effective topological charges or defects linking to Euler characteristic by the Poincar\'{e}-Hopf theorem. The global

Shaoting Peng, Margaret X. Wang, Julie A. Shah, Nadia Figueroa

Object permanence, which refers to the concept that objects continue to exist even when they are no longer perceivable through the senses, is a crucial aspect of human cognitive development. In this work, we seek to incorporate this understanding into interactive robots by proposing a set of assumptions and rules to represent object permanence in multi-objec

Minyu Shen, Weihua Gu, Michael J. Cassidy, Yongjie Lin

We unveil that a previously-unreported vicious cycle can be created when bus queues form at curbside stops along a corridor. Buses caught in this cycle exhibit growing variation in headways as they travel from stop to stop. Bus (and patron) delays accumulate in like fashion and can grow large on long, busy corridors. We show that this damaging cycle can be a

Zhenrong Cheng, Jiayan Guo, Hao Sun, Yan Zhang

Current disfluency detection methods heavily rely on costly and scarce human-annotated data. To tackle this issue, some approaches employ heuristic or statistical features to generate disfluent sentences, partially improving detection performance. However, these sentences often deviate from real-life scenarios, constraining overall model enhancement. In this

Fujing Xie, Sören Schwertfeger

Large Language Models (LLMs) have demonstrated great potential in robotic applications by providing essential general knowledge. Mobile robots rely on map comprehension for tasks like localization and navigation. In this paper, we explore enabling LLMs to comprehend the topology and hierarchy of Area Graph, a text-based hierarchical, topometric semantic map

Yusuke Marumo, Kazuhiko Kawamoto, Satomi Tanaka, Shigenobu Hirano

Not identical but similar objects are ubiquitous in our world, ranging from four-legged animals such as dogs and cats to cars of different models and flowers of various colors. This study addresses a novel task of matching such non-identical objects at the pixel level. We propose a weighting scheme of descriptors, Semantic Enhancement Weighting (SEW), that i

Fabo Feng

As the most ancient branch of astronomy, astrometry has been developed for thousands of years. However, it has only recently become possible to utilize astrometry for the detection of exoplanets. Gaia, an astrometric surveyor of 1 billion stars, is capable of measuring the position of stars with a precision as high as 20 $\mu$as. Gaia is expected to discover

E. Cruz-Albaro, A. Gutiérrez-Rodríguez, D. Espinosa-Gómez, T. Cisneros-Pérez

In the Bestest Little Higgs Model (BLHM) scenario, we analyze the branching ratios and production cross-section of the heavy Higgs boson $H_0$. The analysis is performed at the tree level and the one-loop level. In addition, we present results of the possible production of the heavy Higgs boson $H_0$ via gluon fusion for the center-of-mass energies and the i

Ruochen Zheng, Jiahao Hong, Changxin Gao, Nong Sang

The presence of noise in acquired data invariably leads to performance degradation in cross-modal matching. Unfortunately, obtaining precise annotations in the multimodal field is expensive, which has prompted some methods to tackle the mismatched data pair issue in cross-modal matching contexts, termed as noisy correspondence. However, most of these existin

Menquan Liu, Jie Zhang, Cong Wang

GW170817 represents the first observed binary neutron star merger event by humanity. The observation of GW170817 has identified the correlation between Kilonova, gravitational wave and short GRB. The shocks from GW170817 have the capacity to inject significant thermal and kinetic energies into the interstellar medium and evolve for over a million years. In t

Yongkang Guo, Yuqing Kong

We consider a robust aggregation problem in the presence of both truthful and adversarial experts. The truthful experts will report their private signals truthfully, while the adversarial experts can report arbitrarily. We assume experts are marginally symmetric in the sense that they share the same common prior and marginal posteriors. The rule maker needs

Yuanyang Teng, Connor Courtien, David Angel Rios, Yves M. Tseng

Blind and low-vision (BLV) people face many challenges when venturing into public environments, often wishing it were easier to get help from people nearby. Ironically, while many sighted individuals are willing to help, such interactions are infrequent. Asking for help is socially awkward for BLV people, and sighted people lack experience in helping BLV peo

Lianghao Cao, Thomas O'Leary-Roseberry, Omar Ghattas

We propose an operator learning approach to accelerate geometric Markov chain Monte Carlo (MCMC) for solving infinite-dimensional Bayesian inverse problems (BIPs). While geometric MCMC employs high-quality proposals that adapt to posterior local geometry, it requires repeated computations of gradients and Hessians of the log-likelihood, which becomes prohibi

Viorel Silaghi, Zobaida Alssadi, Ben Mathew, Majed Alotaibi

Public availability of Artificial Intelligence generated information can change the markets forever, and its factoring into economical dynamics may take economists by surprise, out-dating models and schools of thought. Real estate hyper-inflation is not a new phenomenon but its consistent and almost monotonous persistence over 12 years, coinciding with promi

Xiaojun Xu, Yuanshun Yao, Yang Liu

We study how to watermark LLM outputs, i.e. embedding algorithmically detectable signals into LLM-generated text to track misuse. Unlike the current mainstream methods that work with a fixed LLM, we expand the watermark design space by including the LLM tuning stage in the watermark pipeline. While prior works focus on token-level watermark that embeds signa

Wenbo Zhao, Shengjie Wang, Yixuan Fan, Yang Gao

Space robots have played a critical role in autonomous maintenance and space junk removal. Multi-arm space robots can efficiently complete the target capture and base reorientation tasks due to their flexibility and the collaborative capabilities between the arms. However, the complex coupling properties arising from both the multiple arms and the free-float

Lei Xiao, Yaoming Chu, Quan Lin, Haiqing Lin

Open systems possess unique potentials in high-precision sensing, yet the majority of previous studies rely on the spectral singularities known as exceptional points. Here we theoretically propose and experimentally demonstrate universal non-Hermitian sensing in the absence of exceptional points. The scheme makes use of the intrinsic sensitivity of a non-Her

Yichao Wu, Zhengyu Jin, Chenxi Shi, Penghao Liang

This paper explores the application of deep learning techniques, particularly focusing on BERT models, in sentiment analysis. It begins by introducing the fundamental concept of sentiment analysis and how deep learning methods are utilized in this domain. Subsequently, it delves into the architecture and characteristics of BERT models. Through detailed expla

Qinglong Meng, Chongkun Xia, Xueqian Wang

Normalizing flow is a generative modeling approach with efficient sampling. However, Flow-based models suffer two issues: 1) If the target distribution is manifold, due to the unmatch between the dimensions of the latent target distribution and the data distribution, flow-based models might perform badly. 2) Discrete data might make flow-based models collaps

Sicen Guo, Ziwei Long, Zhiyuan Wu, Qijun Chen

Despite the impressive performance achieved by data-fusion networks with duplex encoders for visual semantic segmentation, they become ineffective when spatial geometric data are not available. Implicitly infusing the spatial geometric prior knowledge acquired by a data-fusion teacher network into a single-modal student network is a practical, albeit less ex

Shuangjian Li, Tao Zhu, Mingxing Nie, Huansheng Ning

Traditional deep learning methods struggle to simultaneously segment, recognize, and forecast human activities from sensor data. This limits their usefulness in many fields such as healthcare and assisted living, where real-time understanding of ongoing and upcoming activities is crucial. This paper introduces P2LHAP, a novel Patch-to-Label Seq2Seq framework

Baixiang Huang, Canyu Chen, Kai Shu

The ability to accurately identify authorship is crucial for verifying content authenticity and mitigating misinformation. Large Language Models (LLMs) have demonstrated an exceptional capacity for reasoning and problem-solving. However, their potential in authorship analysis remains under-explored. Traditional studies have depended on hand-crafted stylistic

Thomas Creutzig, Justine Fasquel, Andrew R. Linshaw, Shigenori Nakatsuka

We formulate and prove examples of a conjecture which describes the W-algebras in type A as successive quantum Hamiltonian reductions of affine vertex algebras associated with several hook-type nilpotent orbits. This implies that the affine coset subalgebras of hook-type W-algebras are building blocks of the W-algebras in type A. In the rational case, it tur

Kenta Tsukahara, Kanji Tanaka, Daiki Iwata

A typical assumption in state-of-the-art self-localization models is that an annotated training dataset is available in the target workspace. However, this does not always hold when a robot travels in a general open-world. This study introduces a novel training scheme for open-world distributed robot systems. In our scheme, a robot ("student") can ask the ot

Liang Yao

Prompting methods play a crucial role in enhancing the capabilities of pre-trained large language models (LLMs). We explore how contrastive prompting (CP) significantly improves the ability of large language models to perform complex reasoning. We demonstrate that LLMs are decent contrastive reasoners by simply adding "Let's give a correct and a wrong answer

George Kontogeorgiou, Maya Stein

We prove an upper bound of $n+9$ for the strong separation number of the complete graph $K_n$, and an upper bound of $n+1$ for its weak separation number. This improves on the previous best known bound of $(1+o(1))n$ for both cases.

Hideo Bannai, Mitsuru Funakoshi, Diptarama Hendrian, Myuji Matsuda

We introduce height-bounded LZ encodings (LZHB), a new family of compressed representations that are variants of Lempel-Ziv parsings with a focus on bounding the worst-case access time to arbitrary positions in the text directly via the compressed representation. An LZ-like encoding is a partitioning of the string into phrases of length $1$ which can be enco

Khondoker Murad Hossain, Tim Oates

In the rapidly evolving landscape of communication and network security, the increasing reliance on deep neural networks (DNNs) and cloud services for data processing presents a significant vulnerability: the potential for backdoors that can be exploited by malicious actors. Our approach leverages advanced tensor decomposition algorithms Independent Vector A

Junwei Su, Lingjun Mao, Zheng Da, Chuan Wu

Heterogeneous graphs, comprising diverse node and edge types connected through varied relations, are ubiquitous in real-world applications. Message-passing heterogeneous graph neural networks (HGNNs) have emerged as a powerful model class for such data. However, existing HGNNs typically allocate a separate set of learnable weights for each relation type to m

Qijiong Liu, Hengchang Hu, Jiahao Wu, Jieming Zhu

Incorporating item content information into click-through rate (CTR) prediction models remains a challenge, especially with the time and space constraints of industrial scenarios. The content-encoding paradigm, which integrates user and item encoders directly into CTR models, prioritizes space over time. In contrast, the embedding-based paradigm transforms i

Chao Yang, Jiancheng Liu, Li Du

In this paper, we prove that PMCV (i.e. \Delta\vec{H} is proportional to \vec{H}) hypersurface M^n_r of a non-flat pseudo-Riemannian space form N^{n+1}_s(c) with at most two distinct principal curvatures is minimal or locally isoparametric, and compute the mean curvature for the isoparametric ones. As an application, we give full classification results of su

Siqi Li, Jun Chen, Jingyang Xiang, Chengrui Zhu

Structured pruning methods are developed to bridge the gap between the massive scale of neural networks and the limited hardware resources. Most current structured pruning methods rely on training datasets to fine-tune the compressed model, resulting in high computational burdens and being inapplicable for scenarios with stringent requirements on privacy and

Yanting Yang, Beidi Zhao, Zhuohao Ni, Yize Zhao

Neuroscientific research has revealed that the complex brain network can be organized into distinct functional communities, each characterized by a cohesive group of regions of interest (ROIs) with strong interconnections. These communities play a crucial role in comprehending the functional organization of the brain and its implications for neurological con

Ziyi Xu, Xue Cheng

We investigate a market with a normal-speed informed trader (IT) who may employ mixed strategy and multiple anticipatory high-frequency traders (HFTs) who are under different inventory pressures, in a three-period Kyle's model. The pure- and mixed-strategy equilibria are considered and the results provide recommendations for IT's randomization strategy with

Keito Ogawa, Kenta Ishimoto

Transport phenomena of microswimmers in fluid flows play a crucial role in various biological processes, including bioconvection and cell sorting. In this paper, we investigate the dispersion behavior of chiral microswimmers in a simple shear flow utilizing the generalized Taylor dispersion (GTD) theory, motivated by biased locomotion of bacterial swimmers k

Zhuoyin Dai, Di Wu, Zhenjun Dong, Kun Li

Channel knowledge map (CKM), which aims to directly reflect the intrinsic channel properties of the local wireless environment, is a novel technique for achieving environmentaware communication. In this paper, to alleviate the large training overhead in millimeter wave (mmWave) beam alignment, an environment-aware and training-free beam alignment prototype i

Gantavya Bhatt, Arnav Das, Jeff Bilmes

Submodular functions, crucial for various applications, often lack practical learning methods for their acquisition. Seemingly unrelated, learning a scaling from oracles offering graded pairwise preferences (GPC) is underexplored, despite a rich history in psychometrics. In this paper, we introduce deep submodular peripteral networks (DSPNs), a novel paramet

Shuo Jiang, Weifeng Li, Yuping Qian, Yangjun Zhang

Various ideation methods, such as morphological analysis and design-by-analogy, have been developed to aid creative problem-solving and innovation. Among them, the Theory of Inventive Problem Solving (TRIZ) stands out as one of the best-known methods. However, the complexity of TRIZ and its reliance on users' knowledge, experience, and reasoning capabilities

Jonathan Dunn

This paper investigates the impact of corpus creation decisions on large multi-lingual geographic web corpora. Beginning with a 427 billion word corpus derived from the Common Crawl, three methods are used to improve the quality of sub-corpora representing specific language-country pairs like New Zealand English: (i) the agreement of independent language ide

A. M. Silva

This article presents a comprehensive study of the impact of decoherence on the average correlation for pure quantum states. We explore two primary mechanisms of decoherence: phase damping and amplitude damping, each having distinct effects on quantum systems. Phase damping, which describes the loss of quantum coherence without energy loss, primarily affects

Chia-Hao Li, Niraj K. Jha

We propose PAGE, a domain-incremental adaptation strategy with past-agnostic generative replay for smart healthcare. PAGE enables generative replay without the aid of any preserved data or information from prior domains. When adapting to a new domain, it exploits real data from the new distribution and the current model to generate synthetic data that retain

Jiayu Du, Jinpeng Li, Guoguo Chen, Wei-Qiang Zhang

In the wake of the surging tide of deep learning over the past decade, Automatic Speech Recognition (ASR) has garnered substantial attention, leading to the emergence of numerous publicly accessible ASR systems that are actively being integrated into our daily lives. Nonetheless, the impartial and replicable evaluation of these ASR systems encounters challen

Zhenning Liu, Dhruv Devulapalli, Dominik Hangleiter, Yi-Kai Liu

Existing schemes for demonstrating quantum computational advantage are subject to various practical restrictions, including the hardness of verification and challenges in experimental implementation. Meanwhile, analog quantum simulators have been realized in many experiments to study novel physics. In this work, we propose a quantum advantage protocol based

Yubo Ye, Sumeet Vadhavkar, Xiajun Jiang, Ryan Missel

Modern applications increasingly require unsupervised learning of latent dynamics from high-dimensional time-series. This presents a significant challenge of identifiability: many abstract latent representations may reconstruct observations, yet do they guarantee an adequate identification of the governing dynamics? This paper investigates this challenge fro

Yuyang Ye, Peng Xu, Lizheng Ren, Tinghuan Chen

Gate sizing plays an important role in timing optimization after physical design. Existing machine learning-based gate sizing works cannot optimize timing on multiple timing paths simultaneously and neglect the physical constraint on layouts. They cause sub-optimal sizing solutions and low-efficiency issues when compared with commercial gate sizing tools. In

Zhishuai Li, Xiang Wang, Jingjing Zhao, Sun Yang

Recent advancements in Text-to-SQL (Text2SQL) emphasize stimulating the large language models (LLM) on in-context learning, achieving significant results. Nevertheless, they face challenges when dealing with verbose database information and complex user intentions. This paper presents a two-stage framework to enhance the performance of current LLM-based natu

Krzysztof A. Maliszewski, Magdalena A. Urbanska, Varvara Vetrova, Sylwia M. Kolenderska

Many instruments performing optical and non-optical imaging and sensing, such as Optical Coherence Tomography (OCT), Magnetic Resonance Imaging or Fourier-transform spectrometry, produce digital signals containing modulations, sine-like components, which only after Fourier transformation give information about the structure or characteristics of the investig

Xingyu Lu, He Cao, Zijing Liu, Shengyuan Bai

Large language models are playing an increasingly significant role in molecular research, yet existing models often generate erroneous information, posing challenges to accurate molecular comprehension. Traditional evaluation metrics for generated content fail to assess a model's accuracy in molecular understanding. To rectify the absence of factual evaluati

Wenhao Li, Shishun Zhang, Sisi Dai, Hui Huang

Synchronized dual-arm rearrangement is widely studied as a common scenario in industrial applications. It often faces scalability challenges due to the computational complexity of robotic arm rearrangement and the high-dimensional nature of dual-arm planning. To address these challenges, we formulated the problem as cooperative mTSP, a variant of mTSP where

Jonathan Weinberger

We provide a generalized treatment of (co)cartesian arrows, fibrations, and functors. Compared to the classical conditions, the endpoint inclusions get replaced by arbitrary shape inclusions. Our framework is Riehl--Shulman's simplicial homotopy type theory which supports the development of synthetic internal $(\infty,1)$-category theory.

Changbing Yang, Garrett Nicolai, Miikka Silfverberg

We investigate automatic interlinear glossing in low-resource settings. We augment a hard-attentional neural model with embedded translation information extracted from interlinear glossed text. After encoding these translations using large language models, specifically BERT and T5, we introduce a character-level decoder for generating glossed output. Aided b

Hiroyasu Koizumi

Recently, a new theory of superconductivity has been put forward that attributes the origin of superconductivity to the appearance of a non-trivial Berry connection from many-electron wave functions. This theory reproduces the major results of the BCS theory with conserving the particle number, and predicts the single-electron supercurrent tunneling across t

Taekyung Ahn, Yeonjung Hong, Younggon Im, Do Hyung Kim

This study presents a model of automatic speech recognition (ASR) designed to diagnose pronunciation issues in children with speech sound disorders (SSDs) to replace manual transcriptions in clinical procedures. Since ASR models trained for general purposes primarily predict input speech into real words, employing a well-known high-performance ASR model for

Shihao Zhuang, Xufeng Zhang, Yujie Zhu, Nian X. Sun

Terahertz (THz) spin waves or their quanta, magnons, can be efficiently excited by acoustic phonons because these excitations have similar wavevectors in the THz regime. THz acoustic phonons can be produced using photoacoustic phenomena but typically have a low population and thus a relatively low displacement amplitude. The magnetization amplitude and popul

Zhiting Mei, Anushri Dixit, Meghan Booker, Emily Zhou

Rapid advances in perception have enabled large pre-trained models to be used out of the box for transforming high-dimensional, noisy, and partial observations of the world into rich occupancy representations. However, the reliability of these models and consequently their safe integration onto robots remains unknown when deployed in environments unseen duri

Ashish Joshi, Robert Peters, Thore Posske

We study the dynamics of quantum skyrmions under a magnetic field gradient using neural network quantum states. First, we obtain a quantum skyrmion lattice ground state using variational Monte Carlo with a restricted Boltzmann machine as the variational ansatz for a quantum Heisenberg model with Dzyaloshinskii-Moriya interaction. Then, using the time-depende

Feng Xiao, Hongbin Xu, Qiuxia Wu, Wenxiong Kang

3D visual grounding aims to automatically locate the 3D region of the specified object given the corresponding textual description. Existing works fail to distinguish similar objects especially when multiple referred objects are involved in the description. Experiments show that direct matching of language and visual modal has limited capacity to comprehend

Dhrubajit Chowdhury, Raman Goyal, Shantanu Rane

We introduce a novel approach to make the tracking error of a class of nonlinear systems differentially private in addition to guaranteeing the tracking error performance. We use funnel control to make the tracking error evolve within a performance funnel that is pre-specified by the user. We make the performance funnel differentially private by adding a bou

Zhangxuan Dang, Yu Zheng, Xinglin Lin, Chunlei Peng

With the rapid development of the Internet, various types of anomaly traffic are threatening network security. We consider the problem of anomaly network traffic detection and propose a three-stage anomaly detection framework using only normal traffic. Our framework can generate pseudo anomaly samples without prior knowledge of anomalies to achieve the detec

Chunqiang Xu, Heda Zhang, Caitlin Carnahan, Pengpeng Zhang

CrI3 is a prototypical van der Waals ferromagnet with a magnetic honeycomb lattice. Previous inelastic neutron scattering studies have suggested topological nature of its magnetic excitations with a magnon gap at the Dirac points, which are anticipated to give rise to magnon thermal Hall effect. Here we report thermal transport properties of CrI3 and show th

Yao Lei, Yin Haiyan, Zhu Mengmeng

The main concern of this paper is to study large-time behavior of the sheath to the full Euler-Poisson system. As is well known, the monotone stationary solution under the Bohm criterion can be referred to as the sheath which is formed by interactions of plasma with wall. So far, the existence and asymptotic stability of stationary solutions in one-dimension

Feiyu Li, Xiangrong Fu, Seth Dorfman

Shear Alfven wave parametric decay instability (PDI) provides a potential path toward significant wave dissipation and plasma heating. However, fundamental questions regarding how PDI is excited in a realistic three-dimensional (3D) open system and how critically the finite perpendicular wave scale--as found in both laboratory and space plasmas--affects the

Martin Schonger, Hugo T. M. Kussaba, Lingyun Chen, Luis Figueredo

Established techniques that enable robots to learn from demonstrations are based on learning a stable dynamical system (DS). To increase the robots' resilience to perturbations during tasks that involve static obstacle avoidance, we propose incorporating barrier certificates into an optimization problem to learn a stable and barrier-certified DS. Such optimi

Tianheng Wang, Stergios I. Roumeliotis

In this paper, we address the problem of estimating the rotational extrinsics, as well as the scale factors of two gyroscopes rigidly mounted on the same device. In particular, we formulate the problem as a least-squares minimization and introduce a direct algorithm that computes the estimated quantities without any iterations, hence avoiding local minima an

Shikha Gupta, Animesh Kumar

Heretofore, the only way to evaluate an author has been frequency-based citation metrics that assume citations to be of a neutral sentiment. However, considering the sentiment behind citations aids in a better understanding of the viewpoints of fellow researchers for the scholarly output of an author.

Benjamin Metha, Simon Birrer, Tommaso Treu, Michele Trenti

Historically, metallicity profiles of galaxies have been modelled using a radially symmetric, two-parameter linear model, which reveals that most galaxies are more metal-rich in their central regions than their outskirts. However, this model is known to yield inaccurate results when the point-spread function (PSF) of a telescope is large. Furthermore, a radi

Yuta Mukobara, Yutaro Shigeto, Masashi Shimbo

We explore loss functions for fact verification in the FEVER shared task. While the cross-entropy loss is a standard objective for training verdict predictors, it fails to capture the heterogeneity among the FEVER verdict classes. In this paper, we develop two task-specific objectives tailored to FEVER. Experimental results confirm that the proposed objectiv

Shuma Yamamoto

The Ramanujan Machine project predicts new continued fraction representations of numbers expressed by important mathematical constants. Generally, the value of a continued fraction is found by reducing it to a second order linear difference equation. In this paper, we prove 38 conjectures by solving the equation in two ways, use of a differential equation or

Cyril Cohen, Kazuhiko Sakaguchi

We present a novel characterization of stable mergesort functions using relational parametricity, and show that it implies the functional correctness of mergesort. As a result, one can prove the correctness of several variations of mergesort (e.g., top-down, bottom-up, tail-recursive, non-tail-recursive, smooth, and non-smooth mergesorts) by proving the char

Takemi Yamada, Yuki Yanagi, Keisuke Mitsumoto

We calculate the RKKY interactions derived from ab initio calculations for the intermetallic compound CeCoSi exhibiting the hidden nonmagnetic order at $T_0$ and examine the instability towards possible multipole orders within the random phase approximation. All 36 multipole interactions up to rank 5 are investigated, and the maximum susceptibility exhibits

Yang Cai, Constantinos Daskalakis, Haipeng Luo, Chen-Yu Wei

While Online Gradient Descent and other no-regret learning procedures are known to efficiently converge to a coarse correlated equilibrium in games where each agent's utility is concave in their own strategy, this is not the case when utilities are non-concave -- a common scenario in machine learning applications involving strategies parameterized by deep ne

Haibo Zhang, Zhihua Yao, Kouichi Sakurai

Adversarial attacks present a significant security risk to image recognition tasks. Defending against these attacks in a real-life setting can be compared to the way antivirus software works, with a key consideration being how well the defense can adapt to new and evolving attacks. Another important factor is the resources involved in terms of time and cost

Yueyao Li, Chenglong Bao, Wenxun Xing

It is essential to capture the true probability distribution of uncertain data in the distributionally robust optimization (DRO). The uncertain data presents multimodality in numerous application scenarios, in the sense that the probability density function of the uncertain data has two or more modes (local maximums). In this paper, we propose a globalized d

Arian Eamaz, Farhang Yeganegi, Yunqiao Hu, Mojtaba Soltanalian

This paper investigates the effects of coarse quantization with mixed precision on measurements obtained from sparse linear arrays, synthesized by a collaborative automotive radar sensing strategy. The mixed quantization precision significantly reduces the data amount that needs to be shared from radar nodes to the fusion center for coherent processing. We u

Teng Xiao, Chao Cui, Huaisheng Zhu, Vasant G. Honavar

Recent advancements in biology and chemistry have leveraged multi-modal learning, integrating molecules and their natural language descriptions to enhance drug discovery. However, current pre-training frameworks are limited to two modalities, and designing a unified network to process different modalities (e.g., natural language, 2D molecular graphs, 3D mole

David Wiedemann, Malte A. Peter

We consider the homogenisation of the instationary Stokes equations in a porous medium with an a-priori given evolving microstructure. In order to pass to the homogenisation limit, we transform the Stokes equations to a domain with a fixed periodic microstructure. The homogenisation result is a Darcy-type equation with memory term and has the form of an inte

Kaustav K. Das, Christoffer Fremling, Mansi M. Kasliwal, Steve Schulze

We present SN 2023zaw $-$ a sub-luminous ($\mathrm{M_r} = -16.7$ mag) and rapidly-evolving supernova ($\mathrm{t_{1/2,r}} = 4.9$ days), with the lowest nickel mass ($\approx0.002$ $\mathrm{M_\odot}$) measured among all stripped-envelope supernovae discovered to date. The photospheric spectra are dominated by broad He I and Ca NIR emission lines with velociti

Bruno Gavranović

Deep learning, despite its remarkable achievements, is still a young field. Like the early stages of many scientific disciplines, it is marked by the discovery of new phenomena, ad-hoc design decisions, and the lack of a uniform and compositional mathematical foundation. From the intricacies of the implementation of backpropagation, through a growing zoo of

Ziqi Liang, Haoxiang Shi, Jiawei Wang, Keda Lu

Recently, deep learning-based Text-to-Speech (TTS) systems have achieved high-quality speech synthesis results. Recurrent neural networks have become a standard modeling technique for sequential data in TTS systems and are widely used. However, training a TTS model which includes RNN components requires powerful GPU performance and takes a long time. In cont