July 2023 arXiv papers — page 67

Jinsong Li, Shaochen Wang, Ziyang Chen, Zhen Kan

Learning-based path planning is becoming a promising robot navigation methodology due to its adaptability to various environments. However, the expensive computing and storage associated with networks impose significant challenges for their deployment on low-cost robots. Motivated by this practical challenge, we develop a lightweight neural path planning arc

Peijie Dong, Lujun Li, Zimian Wei, Xin Niu

Mixed-Precision Quantization~(MQ) can achieve a competitive accuracy-complexity trade-off for models. Conventional training-based search methods require time-consuming candidate training to search optimized per-layer bit-width configurations in MQ. Recently, some training-free approaches have presented various MQ proxies and significantly improve search effi

Takashi J. Moriya

Some interaction-powered supernovae have long rise times of more than 100 days. We show that such long rise times are naturally expected if circumstellar matters (CSM) have a flat density structure (s <~ 1.5, where rho_CSM ~ r^{-s}). In such cases, bolometric luminosities from the CSM interaction keep increasing as long as the CSM interacts with the outer la

Miyuji Hirota, Yoshifumi Sakai

We propose efficient algorithms for enumerating maximal common subsequences (MCSs) of two strings. Efficiency of the algorithms are estimated by the preprocessing-time, space, and delay-time complexities. One algorithm prepares a cubic-space data structure in cubic time to output each MCS in linear time. This data structure can be used to search for particul

Kaiwen Wei, Jie Yao, Jingyuan Zhang, Yangyang Kang

Key Information Extraction (KIE) is a challenging multimodal task that aims to extract structured value semantic entities from visually rich documents. Although significant progress has been made, there are still two major challenges that need to be addressed. Firstly, the layout of existing datasets is relatively fixed and limited in the number of semantic

Daegyeom Kim, Seongho Hong, Yong-Hoon Choi

Expressive speech synthesis models are trained by adding corpora with diverse speakers, various emotions, and different speaking styles to the dataset, in order to control various characteristics of speech and generate the desired voice. In this paper, we propose a style control (SC) VALL-E model based on the neural codec language model (called VALL-E), whic

Yuanhao Gong

Training and deploying the large language models requires a large mount of computational resource because the language models contain billions of parameters and the text has thousands of tokens. Another problem is that the large language models are static. They are fixed after the training process. To tackle these issues, in this paper, we propose to train a

Leslie Hogben, Mark Hunnell, Kevin Liu, Houston Schuerger

We introduce a generalization of the concept of a chronological list of forces, called a relaxed chronology. This concept is used to introduce a new way of formulating the standard zero forcing process, which we refer to as parallel increasing path covers, or PIPs. The combinatorial properties of PIPs are utilized to identify bounds comparing standard zero f

Na Wang, Ting-Feng Yi, Liang Wang, Li-Sheng Mao

Fermi-LAT LCR provide continuous and regularly-sampled gamma-ray light curves, spanning about 14 years, for a large sample of blazars. The log-normal flux distribution and linear RMS-Flux relation of the light curves for a few of Fermi blazar have been examined in previous studies. However, the probability that blazars exhibit log-normal flux distribution an

Zijian Wu, Hamid Moradi, Shuojue Yang, Hyunwoo Song

Real-time transrectal ultrasound (TRUS) image guidance during robot-assisted laparoscopic radical prostatectomy has the potential to enhance surgery outcomes. Whether conventional or photoacoustic TRUS is used, the robotic system and the TRUS must be registered to each other. Accurate registration can be performed using photoacoustic (PA markers). However, t

Keyou Zeng

The well-known Loday-Quillen-Tsygan theorem calculates the Lie algebra homology of the infinite general linear Lie algebra $\mathfrak{gl}(A)$ over an unital associative algebra $A$. We generalize the Loday-Quillen-Tsygan theorem to an infinite Lie algebra associated with a (framed) quiver, where we assign to each vertex $v$ an infinite general linear Lie alg

Feng-Jiao Luo, Zhi-Min Wang, An-Bo Yang, Yue-Kun Heng

The Jiangmen Underground Observatory (JUNO) is a 20-kton liquid scintillator detector that employs 20,000 20-inch photomultiplier tubes (PMTs) as photon sensors, with 5,000 dynode-PMTs from HAMAMATSU Photonics K.K. (HPK), and 15,000 MCP-PMTs from North Night Vision Technology (NNVT) installed in pure water. JUNO aims to provide long-lasting and the best perf

Wendi Li, Wei Wei, Xiaoye Qu, Xian-Ling Mao

Conversational recommender systems (CRS) aim to timely trace the dynamic interests of users through dialogues and generate relevant responses for item recommendations. Recently, various external knowledge bases (especially knowledge graphs) are incorporated into CRS to enhance the understanding of conversation contexts. However, recent reasoning-based models

Larissa Markwardt, Hsing Wen Lin, David Gerdes, Fred C. Adams

In 2018, Jewitt identified the "The Trojan Color Conundrum", namely that Neptune's Trojan asteroids (NTs) had no ultra-red members, unlike the the nearby Kuiper Belt. Since then, numerous ultra-red NTs have been discovered, seemingly resolving this conundrum (Lin et al. 2019; Bolin et al.12 2023). However, it is still unclear whether or not the Kuiper Belt h

Adam W. Hall, Melissa Greeff, Angela P. Schoellig

Learning-based optimal control algorithms control unknown systems using past trajectory data and a learned model of the system dynamics. These controllers use either a linear approximation of the learned dynamics, trading performance for faster computation, or nonlinear optimization methods, which typically perform better but can limit real-time applicabilit

Ananya Parashar

In this paper, we study the portfolio optimization problem formulated by Lacker and Soret. They formulate a finite time horizon model that allows agents to be competitive, measuring their utility not only by their absolute wealth but also relative performance compared to the average of other agents. While the finite population or $n$-player game is tractable

Alice L. L. Gao, Ethan Y. H. Li, Matthew H. Y. Xie, Arthur L. B. Yang

Inspired by the notion of equivariant log-concavity, we introduce the concept of induced log-concavity for a sequence of representations of a finite group. For an equivariant matroid equipped with a symmetric group action or a finite general linear group action, we transform the problem of proving the induced log-concavity of matroid invariants to that of pr

Dohoon Kim, Shenghui Song

Power control for the device-to-device interference channel with single-antenna transceivers has been widely analyzed with both model-based methods and learning-based approaches. Although the learning-based approaches, i.e., datadriven and model-driven, offer performance improvement, the widely adopted graph neural network suffers from learning the heterophi

Ke Zeng, Xiaohui Yu, Martin B. Plenio, Zhen-Yu Wang

We present a quantum sensing technique that utilizes a sequence of $\pi$ pulses to cyclically drive the qubit dynamics along a geodesic path of adiabatic evolution. This approach effectively suppresses the effects of both decoherence noise and control errors while simultaneously removing unwanted resonance terms, such as higher harmonics and spurious respons

Mehdi Rostami, Olli Saarela

Estimation of the Average Treatment Effect (ATE) is often carried out in 2 steps, wherein the first step, the treatment and outcome are modeled, and in the second step the predictions are inserted into the ATE estimator. In the first steps, numerous models can be fit to the treatment and outcome, including using machine learning algorithms. However, it is a

Marco Astorino

A new, exact and analytical class of accelerating and charged black holes is built, in the Einstein-Maxwell theory, thanks to the Harrison transformation. The diagonal metric does not belong to the Petrov type D classification, therefore it is not part of the Plebanski-Demianski spacetimes. The simplest subcase of this family recovers the Reissner-Nordstrom

Guillermo Colin, Joseph Byrnes, Youngwoo Sim, Patrick Wensing

Teleoperated humanoid robots hold significant potential as physical avatars for humans in hazardous and inaccessible environments, with the goal of channeling human intelligence and sensorimotor skills through these robotic counterparts. Precise coordination between humans and robots is crucial for accomplishing whole-body behaviors involving locomotion and

Deniz O. Devecioglu, Mu-In Park

Recently, a ``no inner (Cauchy) horizon theorem" for static black holes with non-trivial scalar hairs has been proved in Einstein-Maxwell-scalar theories and also in Einstein-Maxwell-Horndeski theories with the non-minimal coupling of a charged (complex) scalar field to Einstein tensor. In this paper, we study an extension of the theorem to the static black

Robert Reynolds

This is a compendium of generating functions involving single, double sums and definite integrals. These generating functions also involve special functions in both the summand function and closed form solution.

Erik Bates, Wai-Tong Louis Fan, Timo Seppäläinen

We study the Busemann process and competition interfaces of the planar directed polymer model with i.i.d.\ weights on the vertices of the planar square lattice, in both the general case and the solvable inverse-gamma case. We prove new regularity properties of the Busemann process without reliance on unproved assumptions on the shape function. For example, e

Nedelina Teneva

Generative language models produce highly abstractive outputs by design, in contrast to extractive responses in search engines. Given this characteristic of LLMs and the resulting implications for content Licensing & Attribution, we propose the the so-called Extractive-Abstractive axis for benchmarking generative models and highlight the need for developing

Jiaxiang Wang, Bin Zhou

Let $u$ be a smooth, strictly $k$-plurisubharmonic function on a bounded domain $\Omega\in\mathbb C^n$ with $2\leq k\leq n$. The purpose of this paper is to study the regularity of solution to the linearized complex Monge-Amp\`ere and Hessian equations when the complex $k$-Hessian $H_k[u]$ of $u$ is bounded from above and below. We first establish some estim

Xueying Ding, Yue Zhao, Leman Akoglu

Outlier detection (OD) finds many applications with a rich literature of numerous techniques. Deep neural network based OD (DOD) has seen a recent surge of attention thanks to the many advances in deep learning. In this paper, we consider a critical-yet-understudied challenge with unsupervised DOD, that is, effective hyperparameter (HP) tuning/model selectio

Chenfeng Zhu, Dachun Yang, Wen Yuan

Let $\gamma\in\mathbb{R}\setminus\{0\}$ and $X(\mathbb{R}^n)$ be a ball Banach function space satisfying some extra mild assumptions. Assume that $\Omega=\mathbb{R}^n$ or $\Omega\subset\mathbb{R}^n$ is an $(\varepsilon,\infty)$-domain for some $\varepsilon\in(0,1]$. In this article, the authors prove that a function $f$ belongs to the homogeneous ball Banach

Alvin Chiu, David Eppstein, Michael T. Goodrich

We study methods to manipulate weights in stress-graph embeddings to improve convex straight-line planar drawings of 3-connected planar graphs. Stress-graph embeddings are weighted versions of Tutte embeddings, where solving a linear system places vertices at a minimum-energy configuration for a system of springs. A major drawback of the unweighted Tutte emb

Stephen M. Slivan, Francis P. Wilkin, Claire McLellan-Cassivi, Michael J. Person

A sidereal rotation counting approach is demonstrated by resolving an ambiguity in the synodic rotation period of Koronis family member (3032) Evans, whose rotation lightcurves' features did not easily distinguish between doubly- and quadruply-periodic. It confirms that Evans's spin rate does not exceed the rubble-pile spin barrier and thus presents no incon

Ziquan Zhuang

We survey some recent development in the stability theory of klt singularities. The main focus is on the solution of the stable degeneration conjecture.

Ian P. Roberts, Yu Zhang, Tawfik Osman, Ahmed Alkhateeb

Noteworthy strides continue to be made in the development of full-duplex millimeter wave (mmWave) communication systems, but most of this progress has been built on theoretical models and validated through simulation. In this work, we conduct a long overdue real-world evaluation of full-duplex mmWave systems using off-the-shelf 60 GHz phased arrays. Using an

Somayeh Ghanbarzadeh, Yan Huang, Hamid Palangi, Radames Cruz Moreno

Recent studies have revealed that the widely-used Pre-trained Language Models (PLMs) propagate societal biases from the large unmoderated pre-training corpora. Existing solutions require debiasing training processes and datasets for debiasing, which are resource-intensive and costly. Furthermore, these methods hurt the PLMs' performance on downstream tasks.

Wenzhen Qu, Yan Gu, Shengdong Zhao, Fajie wang

This paper presents a novel approach called the boundary integrated neural networks (BINNs) for analyzing acoustic radiation and scattering. The method introduces fundamental solutions of the time-harmonic wave equation to encode the boundary integral equations (BIEs) within the neural networks, replacing the conventional use of the governing equation in phy

M. Hiraishi, A. Koda, H. Okabe, R. Kadono

Using muon spin relaxation ($\mu$SR) measurements on formamidinium lead iodide [FAPbI$_3$, where FA denotes HC(NH$_2)_2$] we show that, among the five structurally distinct phases of FAPbI$_3$ exhibited through two different temperature hysteresis, the reorientation motion of FA molecules is quasi-static below $\approx50$ K over the time scale of 10$^{-6}$ s

Johan S. Obando-Ceron, Victor Romero-Cano, Sildomar Monteiro

Multi-modal depth estimation is one of the key challenges for endowing autonomous machines with robust robotic perception capabilities. There have been outstanding advances in the development of uni-modal depth estimation techniques based on either monocular cameras, because of their rich resolution, or LiDAR sensors, due to the precise geometric data they p

Josh Myers-Dean, Yifei Fan, Brian Price, Wilson Chan

Interactive segmentation entails a human marking an image to guide how a model either creates or edits a segmentation. Our work addresses limitations of existing methods: they either only support one gesture type for marking an image (e.g., either clicks or scribbles) or require knowledge of the gesture type being employed, and require specifying whether mar

Dennis Kriventsov, Zongyuan Li

We prove three theorems about the asymptotic behavior of solutions $u$ to the homogeneous Dirichlet problem for the Laplace equation at boundary points with tangent cones. First, under very mild hypotheses, we show that the doubling index of $u$ either has a unique finite limit, or goes to infinity; in other words, there is a well-defined order of vanishing.

Yerko Torres-Nova

Roughly speaking, the problem of geography asks for the existence of varieties of general type after we fix some invariants. In dimension $1$, where we fix the genus, the geography question is trivial, but already in dimension $2$, it becomes a hard problem in general. In higher dimensions, this problem is essentially wide open. In this paper, we focus on ge

Praveen Venkatesh, Corbett Bennett, Sam Gale, Tamina K. Ramirez

Recent advances in neuroscientific experimental techniques have enabled us to simultaneously record the activity of thousands of neurons across multiple brain regions. This has led to a growing need for computational tools capable of analyzing how task-relevant information is represented and communicated between several brain regions. Partial information dec

Sunipa Dev, Jaya Goyal, Dinesh Tewari, Shachi Dave

With rapid development and deployment of generative language models in global settings, there is an urgent need to also scale our measurements of harm, not just in the number and types of harms covered, but also how well they account for local cultural contexts, including marginalized identities and the social biases experienced by them. Current evaluation p

Hamza Mohammed

This research paper presents a meta-analysis of the multifaceted role of technology in mental health. The pervasive influence of technology on daily lives necessitates a deep understanding of its impact on mental health services. This study synthesizes literature covering Behavioral Intervention Technologies (BITs), digital mental health interventions during

R. D. Smirnov, S. I. Krasheninnikov

The 2D3V Aspherical Particle-in-Cell (APIC) code is developed for modeling of interactions of non-spherical dust grains with plasmas. It simulates the motion of plasma electrons and ions in a self-consistent electric field of plasma-screened charged dust particle. Due to absorption/recombination of plasma particles impinging on the grain surface, they transf

Rongsheng Wang, Yaofei Duan, ChanTong Lam, Jiexi Chen

General large language models (LLMs) such as ChatGPT have shown remarkable success. However, such LLMs have not been widely adopted for medical purposes, due to poor accuracy and inability to provide medical advice. We propose IvyGPT, an LLM based on LLaMA that is trained and fine-tuned with high-quality medical question-answer (QA) instances and Reinforceme

Xiaoxiao Li, Gaosheng Zhang, An Zhu, Weiyong Li

This paper presents a speech recognition system developed by the Transsion Speech Understanding Processing Team (TSUP) for the ASRU 2023 MADASR Challenge. The system focuses on adapting ASR models for low-resource Indian languages and covers all four tracks of the challenge. For tracks 1 and 2, the acoustic model utilized a squeezeformer encoder and bidirect

Florian Felice, Christophe Ley

We propose a Statistically Enhanced Learning (aka. SEL) model to predict handball games. Our Machine Learning model augmented with SEL features outperforms state-of-the-art models with an accuracy beyond 80%. In this work, we show how we construct the data set to train Machine Learning models on past female club matches. We then compare different models and

Teng Sun, Juntong Ni, Wenjie Wang, Liqiang Jing

Existing work on Multimodal Sentiment Analysis (MSA) utilizes multimodal information for prediction yet unavoidably suffers from fitting the spurious correlations between multimodal features and sentiment labels. For example, if most videos with a blue background have positive labels in a dataset, the model will rely on such correlations for prediction, whil

Zohair Raza Hassan, Edith Hemaspaandra, Stanisław Radziszowski

For fixed nonnegative integers $k$ and $\ell$, the $(P_k, P_\ell)$-Arrowing problem asks whether a given graph, $G$, has a red/blue coloring of $E(G)$ such that there are no red copies of $P_k$ and no blue copies of $P_\ell$. The problem is trivial when $\max(k,\ell) \leq 3$, but has been shown to be coNP-complete when $k = \ell = 4$. In this work, we show t

F. M. Bayer, A. J. Kozakevicius, R. J. Cintra

This paper introduces an adaptive filtering process based on shrinking wavelet coefficients from the corresponding signal wavelet representation. The filtering procedure considers a threshold method determined by an iterative algorithm inspired by the control charts application, which is a tool of the statistical process control (SPC). The proposed method, c

Christopher J. Lustri, Ines Aniceto, Daniel J. VandenHeuvel, Scott W. McCue

Burgers' equation is an important mathematical model used to study gas dynamics and traffic flow, among many other applications. Previous analysis of solutions to Burgers' equation shows an infinite stream of simple poles born at t = 0^+, emerging rapidly from the singularities of the initial condition, that drive the evolution of the solution for t > 0. We

Minghui Chen, Meirui Jiang, Qi Dou, Zehua Wang

Cross-silo federated learning (FL) enables the development of machine learning models on datasets distributed across data centers such as hospitals and clinical research laboratories. However, recent research has found that current FL algorithms face a trade-off between local and global performance when confronted with distribution shifts. Specifically, pers

Subhashis Suara, Aayush Jha, Pratik Sinha, Arif Ahmed Sekh

Explainable Deep Learning has gained significant attention in the field of artificial intelligence (AI), particularly in domains such as medical imaging, where accurate and interpretable machine learning models are crucial for effective diagnosis and treatment planning. Grad-CAM is a baseline that highlights the most critical regions of an image used in a de

Jeremy Borden

We construct an exact analytic solution of the revised small-$x$ helicity evolution equations derived recently. The equations we solve are obtained in the large-$N_c$ limit (with $N_c$ the number of quark colors) and are double-logarithmic (summing powers of $\alpha_s \ln^2(1/x)$ with $\alpha_s$ the strong coupling constant and $x$ the Bjorken $x$ variable).

Neha Kalibhat, Shweta Bhardwaj, Bayan Bruss, Hamed Firooz

We propose Automatic Feature Explanation using Contrasting Concepts (FALCON), an interpretability framework to explain features of image representations. For a target feature, FALCON captions its highly activating cropped images using a large captioning dataset (like LAION-400m) and a pre-trained vision-language model like CLIP. Each word among the captions

Felipe Arbulú, Fabien Durand, Bastián Espinoza

In the light of recent developments of the ${\mathcal S}$-adic study of subshifts, we revisit, within this framework, a well-known result on Toeplitz subshifts due to Jacobs--Keane giving a sufficient combinatorial condition to ensure discrete spectrum.We show that the notion of coincidences, originally introduced in the &#39;$70$s for the study of the discr

Hiroshi Yamaguchi, Daiki Hatanaka, Motoki Asano

Skyrmions are topological solitons in two-dimensional systems and have been observed in various physical systems. Generating and controlling skyrmions in artificial resonator arrays lead to novel acoustic, photonic, and electric devices, but it is a challenge to implement a vector variable with the chiral exchange interaction. Here, we propose to use quadrat

R. Noah Padgett, Grant B. Morgan, Tim Lomas

Using instruments comprising ordered responses to items are ubiquitous for studying many constructs of interest. However, using such an item response format may lead to items with response categories infrequently endorsed or unendorsed completely. In maximum likelihood estimation, this results in non-existing estimates for thresholds. This work focuses on a

Luc Jaulin

This paper proposes a new interval-based contractor for nonlinear equations which is minimal when dealing with narrow boxes. The method is based on the centered form classically used by interval algorithms combined with a Gauss Jordan band diagonalization preconditioning. As an illustration in stability analysis, we propose to compute the set of all paramete

Tareq Babaqi, Manar Jaradat, Ayse Erdem Yildirim, Saif H. Al-Nimer

Eye is the essential sense organ for vision function. Due to the fact that certain eye disorders might result in vision loss, it is essential to diagnose and treat eye diseases early on. By identifying common eye illnesses and performing an eye check, eye care providers can safeguard patients against vision loss or blindness. Convolutional neural networks (C

Binghao Chai, Christoforos Efstathiou, Haoran Yue, Viji M. Draviam

With the growth of artificial intelligence (AI), there has been an increase in the adoption of computer vision and deep learning (DL) techniques for the evaluation of microscopy images and movies. This adoption has not only addressed hurdles in quantitative analysis of dynamic cell biological processes, but it has also started supporting advances in drug dev

Guangzhi Wang, Yangyang Guo, Mohan Kankanhalli

Human-Object Interaction Detection is a crucial aspect of human-centric scene understanding, with important applications in various domains. Despite recent progress in this field, recognizing subtle and detailed interactions remains challenging. Existing methods try to use human-related clues to alleviate the difficulty, but rely heavily on external annotati

Lucas Caudill

This thesis explores the missing baryon problem in a computational context. An overview of the problem is given, along with a discussion regarding the relevance of the Circumgalactic Medium (CMG) and cosmological Zoom-in simulations. The mechanisms underlying the N-body code ChaNGa (H. Menon, et al., Computational Astrophysics and Cosmology 2, 1 (2015), arXi

Han-Han Sheng, Bao-Feng Feng, Guo-Fu Yu

In this paper, we propose fully discrete analogues of a generalized sine-Gordon (gsG) equation $u_{t x}=\left(1+\nu \partial_x^2\right) \sin u$. The bilinear equations of the discrete KP hierarchy and the proper definition of discrete hodograph transformations are the keys to the construction. Then we derive semi-discrete analogues of the gsG equation from t

Okezzi F. Ukorigho, Opeoluwa Owoyele

Complex systems in science and engineering sometimes exhibit behavior that changes across different regimes. Traditional global models struggle to capture the full range of this complex behavior, limiting their ability to accurately represent the system. In response to this challenge, we propose a novel competitive learning approach for obtaining data-driven

James Chapman, Bohan Chen, Zheng Tan, Jeff Calder

Active learning improves the performance of machine learning methods by judiciously selecting a limited number of unlabeled data points to query for labels, with the aim of maximally improving the underlying classifier's performance. Recent gains have been made using sequential active learning for synthetic aperture radar (SAR) data arXiv:2204.00005. In each

Nicholas Hindley, Stephen J. DeVience, Ella Zhang, Leo L. Cheng

The discovery of novel experimental techniques often lags behind contemporary theoretical understanding. In particular, it can be difficult to establish appropriate measurement protocols without analytic descriptions of the underlying system-of-interest. Here we propose a statistical learning framework that avoids the need for such descriptions for ergodic s

Jahid Hasan

Over the years of challenges on detecting the crash consistency of non-volatile persistent memory (PM) bugs and developing new tools to identify those bugs are quite stretching due to its inconsistent behavior on the file or storage systems. In this paper, we evaluated an open-sourced automatic bug detector tool (i.e. AGAMOTTO) to test NVM level hashing PM a

Amir Jaberzadeh, Ajay Kumar Shrestha, Faijan Ahamad Khan, Mohammed Afaan Shaikh

With the increasing importance of data sharing for collaboration and innovation, it is becoming more important to ensure that data is managed and shared in a secure and trustworthy manner. Data governance is a common approach to managing data, but it faces many challenges such as data silos, data consistency, privacy, security, and access control. To address

Jiarui Gan, Annika Hennes, Rupak Majumdar, Debmalya Mandal

Canonical models of Markov decision processes (MDPs) usually consider geometric discounting based on a constant discount factor. While this standard modeling approach has led to many elegant results, some recent studies indicate the necessity of modeling time-varying discounting in certain applications. This paper studies a model of infinite-horizon MDPs wit

Eugene Bagdasaryan, Tsung-Yin Hsieh, Ben Nassi, Vitaly Shmatikov

We demonstrate how images and sounds can be used for indirect prompt and instruction injection in multi-modal LLMs. An attacker generates an adversarial perturbation corresponding to the prompt and blends it into an image or audio recording. When the user asks the (unmodified, benign) model about the perturbed image or audio, the perturbation steers the mode

Domenico Campolo, Franco Cardin

In this work, we propose a geometric framework for analyzing mechanical manipulation, for instance, by a robotic agent. Under the assumption of conservative forces and quasi-static manipulation, we use energy methods to derive a metric. In the first part of the paper, we review how quasi-static mechanical manipulation tasks can be naturally described via the

Nandan Thakur, Kexin Wang, Iryna Gurevych, Jimmy Lin

Traditionally, sparse retrieval systems relied on lexical representations to retrieve documents, such as BM25, dominated information retrieval tasks. With the onset of pre-trained transformer models such as BERT, neural sparse retrieval has led to a new paradigm within retrieval. Despite the success, there has been limited software supporting different spars

Yize Cheng, Wenbin Hu, Minhao Cheng

Deep neural networks (DNNs) have shown unprecedented success in object detection tasks. However, it was also discovered that DNNs are vulnerable to multiple kinds of attacks, including Backdoor Attacks. Through the attack, the attacker manages to embed a hidden backdoor into the DNN such that the model behaves normally on benign data samples, but makes attac

Pietro Giavedoni

For every $g\geq 2$ we distinguish real period matrices of real Riemann surfaces of topological type $(g,0,0)$ from the ones of topological type $(g,k,1)$, with $k$ equal to one or two for $g$ even or odd respectively (Theorem B). To that purpose, we exhibit new invariants of real principally polarized abelian varieties of orthosymmetric type (Theorem A.1).

Xiao-Yang Liu, Guoxuan Wang, Hongyang Yang, Daochen Zha

Large language models (LLMs) have demonstrated remarkable proficiency in understanding and generating human-like texts, which may potentially revolutionize the finance industry. However, existing LLMs often fall short in the financial field, which is mainly attributed to the disparities between general text data and financial text data. Unfortunately, there

Jonathan Castello, Patrick Redmond, Lindsey Kuper

The Lamport diagram is a pervasive and intuitive tool for informal reasoning about "happens-before" relationships in a concurrent system. However, traditional axiomatic formalizations of Lamport diagrams can be painful to work with in a mechanized setting like Agda. We propose an alternative, inductive formalization -- the causal separation diagram (CSD) --

Roberto Murcio, Nilufer Sari Aslam, Joana Barros

Understanding human mobility is crucial for urban and transport studies in cities. People's daily activities provide valuable insight, such as where people live, work, shop, leisure or eat during midday or after-work hours. However, such activities are changed due to travel behaviours after COVID-19 in cities. This study examines the mobility patterns captur

José Francisco de Oliveira, Jeferson Silva

Critical Sobolev-type inequality for a class of weighted Sobolev spaces on the entire space is established. We also investigate the existence of extremal function for the associated variational problem. As an application, we prove the existence of a weak solution for a general class of critical semilinear elliptic equations related to the polyharmonic operat

Heiko Kabutz, Kaushik Jayaram

Miniature robots provide unprecedented access to confined environments and show promising potential for novel applications such as search-and-rescue and high-value asset inspection. The capability of body deformation further enhances the reachability of these small robots in complex cluttered terrains similar to those of insects and soft arthropods. Motivate

Yuanzhe Jin, Tim J. A. de Jong, Martijn Tennekes, Min Chen

Icicles and sunbursts are two commonly-used visual representations of trees. While icicle trees can map data values faithfully to rectangles of different sizes, often some rectangles are too narrow to be noticed easily. When an icicle tree is transformed into a sunburst tree, the width of each rectangle becomes the length of an annular sector that is usually

Xiangtai Zuo

Text analysis is the process of constructing structured data from unstructured textual content, usually implemented in Python. In terms of the principles of text analysis, a computer program with the ability to read a file and match it with a regular expression is all that is needed for basic text analysis. However, few researchers have used Stata as their m

Nico Hezel, Kai Uwe Barthel, Konstantin Schall, Klaus Jung

For approximate nearest neighbor search, graph-based algorithms have shown to offer the best trade-off between accuracy and search time. We propose the Dynamic Exploration Graph (DEG) which significantly outperforms existing algorithms in terms of search and exploration efficiency by combining two new ideas: First, a single undirected even regular graph is i

Alexander Vinogradov, Olga Alexandrova, Pascal Démoulin, Anton Artemyev

We study intermittent coherent structures in solar wind magnetic turbulence from MHD to kinetic plasma scales using Parker Solar Probe data during its first perihelion (at 0.17 au), when the satellite was in the Alfv\'enic slow wind of 340 km/s. The coherent structures are energetic events localized in time and covering wide range of scales. We detect them u

Elizabeth Tilly, Magnus Handley, MIGDAL Collaboration

We demonstrate three-dimensional track reconstruction of electrons in a low pressure (50 Torr) optical TPC consisting of two glass GEMs with an ITO strip readout in CF4 and CF4/Ar mixtures. The reconstructed tracks show a variety of event topologies, including short tracks from photoelectrons induced by 55Fe 5.9 keV X-rays and long tracks from gamma ray inte

Jaydeep Borkar

Large Language Models (LLMs) have a privacy concern because they memorize training data (including personally identifiable information (PII) like emails and phone numbers) and leak it during inference. A company can train an LLM on its domain-customized data which can potentially also include their users' PII. In order to comply with privacy laws such as the

S Suryavardan, Shreyash Mishra, Megha Chakraborty, Parth Patwa

With social media usage growing exponentially in the past few years, fake news has also become extremely prevalent. The detrimental impact of fake news emphasizes the need for research focused on automating the detection of false information and verifying its accuracy. In this work, we present the outcome of the Factify 2 shared task, which provides a multi-

Tom Lütjen, Fabian Schönfeld, Alice Oberacker, Johannes Leuschner

Imaging problems such as the one in nanoCT require the solution of an inverse problem, where it is often taken for granted that the forward operator, i.e., the underlying physical model, is properly known. In the present work we address the problem where the forward model is inexact due to stochastic or deterministic deviations during the measurement process

Hojoon Ryu, Junzhe Kang, Minseong Park, Byungjoon Bae

In this paper, we demonstrate low-thermal-budget ferroelectric field-effect transistors (FeFETs) based on two-dimensional ferroelectric CuInP2S6 (CIPS) and oxide semiconductor InZnO (IZO). The CIPS/IZO FeFETs exhibit non-volatile memory windows of ~1 V, low off-state drain currents, and high carrier mobilities. The ferroelectric CIPS layer serves a dual purp

Omkar Dige, Jacob-Junqi Tian, David Emerson, Faiza Khan Khattak

As the breadth and depth of language model applications continue to expand rapidly, it is increasingly important to build efficient frameworks for measuring and mitigating the learned or inherited social biases of these models. In this paper, we present our work on evaluating instruction fine-tuned language models' ability to identify bias through zero-shot

Junaid Ahmed Ghauri, Eric Müller-Budack, Ralph Ewerth

Due to the swift growth of patent applications each year, information and multimedia retrieval approaches that facilitate patent exploration and retrieval are of utmost importance. Different types of visualizations (e.g., graphs, technical drawings) and perspectives (e.g., side view, perspective) are used to visualize details of innovations in patents. The c

A. Srinivasan, L. A. Calvo

For a positive integer $m>1$, if the generalized Markoff equation $a^2+b^2+c^2=3abc+m$ has a solution triple, then it has infinitely many solutions. We show that all positive solution triples are generated by a finite set of triples that we call minimal triples. We exhibit a correspondence between the set of minimal triples with first or second element equal

Nikita Iltyakov, Mark Obozov, Igor Dyslevski, Demyan Yarmoshik

The search for equilibrium in a two-stage traffic flow model reduces to the solution of a special nonsmooth convex optimization problem with two groups of different variables. For numerical solution of this problem, the paper proposes to use the accelerated block-coordinate Nesterov-Stich method with a special choice of block probabilities at each iteration.

Ming-Tang Chen, Keiichi Asada, Satoki Matsushita, Philippe Raffin

In 2018, the Greenland Telescope (GLT) started scientific observation in Greenland. Since then, we have completed several significant improvements and added new capabilities to the telescope system. This paper presents a full review of the GLT system, a summary of our observation activities since 2018, the lessons learned from the operations in the Arctic re

David Goedicke, Mark Colley, Sebastian S. Feger, Michael Goedicke

We discuss important aspects of HCI research regarding Research Data Management (RDM) to achieve better publication processes and higher reuse of HCI research results. Various context elements of RDM for HCI are discussed, including examples of existing and emerging infrastructures for RDM. We briefly discuss existing approaches and come up with additional a

Nima Anari, Vishesh Jain, Frederic Koehler, Huy Tuan Pham

We study Glauber dynamics for sampling from discrete distributions $\mu$ on the hypercube $\{\pm 1\}^n$. Recently, techniques based on spectral independence have successfully yielded optimal $O(n)$ relaxation times for a host of different distributions $\mu$. We show that spectral independence is universal: a relaxation time of $O(n)$ implies spectral indepe

Paola D'Aquino, Angus Macintyre

The classical work of Feferman Vaught gives a powerful, constructive analysis of definability in (generalized) product structures, and certain associated enriched Boolean structures. %structures in terms of definability in the component structures. Here, by closely related methods, but in the special setting of commutative unital rings, we obtain a kind of c

Michail Chabanov, Luciano Rezzolla

In the violent post-merger of binary neutron-star mergers strong oscillations are present that impact the emitted gravitational-wave (GW) signal. The frequencies, temperatures and densities involved in these oscillations allow for violations of the chemical equilibrium promoted by weak-interactions, thus leading to a nonzero bulk viscosity that can impact dy

Eloi Tanguy, Rémi Flamary, Julie Delon

The Sliced Wasserstein (SW) distance has become a popular alternative to the Wasserstein distance for comparing probability measures. Widespread applications include image processing, domain adaptation and generative modelling, where it is common to optimise some parameters in order to minimise SW, which serves as a loss function between discrete probability

Dimitri J. Papageorgiou, Jan Kronqvist, Krishnan Kumaran

This paper describes a simple, but effective sampling method for optimizing and learning a discrete approximation (or surrogate) of a multi-dimensional function along a one-dimensional line segment of interest. The method does not rely on derivative information and the function to be learned can be a computationally-expensive ``black box'' function that must