March 2024 arXiv papers — page 78

Md Towhidul Absar Chowdhury, Naveen Sharma, Ashiqur R. KhudaBukhsh

A community needs assessment is a tool used by non-profits and government agencies to quantify the strengths and issues of a community, allowing them to allocate their resources better. Such approaches are transitioning towards leveraging social media conversations to analyze the needs of communities and the assets already present within them. However, manua

Zhihong Sun, Chen Lyu, Bolun Li, Yao Wan

Large Language Models (LLMs) have recently made significant advances in code generation through the 'Chain-of-Thought' prompting technique. This technique empowers the model to autonomously devise "solution plans" to tackle intricate programming challenges, thereby improving its performance in code generation. Nevertheless, smaller models have been strugglin

Ge Qi, Huai-Liang Zheng, Chen-xi Liu, Li MA

For decades, aspects of the topological architecture, and of the mechanical as well as other physical behaviors of periodic lattice truss materials (PLTMs) have been massively studied. Their approximate infinite design space presents a double-edged sword, implying on one hand dramatic designability in fulfilling the requirement of various performance, but on

Zeyu Liu, Souvik Kundu, Anni Li, Junrui Wan

We present a novel Parameter-Efficient Fine-Tuning (PEFT) method, dubbed as Adaptive Freezing of Low Rank Adaptation (AFLoRA). Specifically, for each pre-trained frozen weight tensor, we add a parallel path of trainable low-rank matrices, namely a down-projection and an up-projection matrix, each of which is followed by a feature transformation vector. Based

Ningyi Liao, Zihao Yu, Ruixiao Zeng, Siqiang Luo

Graph Neural Networks (GNNs) have shown promising performance, but at the cost of resource-intensive operations on graph-scale matrices. To reduce computational overhead, previous studies attempt to sparsify the graph or network parameters, but with limited flexibility and precision boundaries. In this work, we propose Unifews, a joint sparsification techniq

Xinyu Li, Jinyang Huang, Xiang Zhang, Peng Zhao

Describing the dynamics of information dissemination within social networks poses a formidable challenge. Despite multiple endeavors aimed at addressing this issue, only a limited number of studies have effectively replicated and forecasted the evolving course of information dissemination. In this paper, we propose a novel model, DM-NAI, which not only consi

Ziqi Yang, Roberto Tron

This paper addresses security challenges in multi-robot systems (MRS) where adversaries may compromise robot control, risking unauthorized access to forbidden areas. We propose a novel multi-robot optimal planning algorithm that integrates mutual observations and introduces reachability constraints for enhanced security. This ensures that, even with adversar

Mario Damiano, Aaron Bello-Arufe, Jeehyun Yang, Renyu Hu

LHS 1140 b is a small planet orbiting in the habitable zone of its M4.5V dwarf host. Recent mass and radius constraints have indicated that it has either a thick H$_2$-rich atmosphere or substantial water by mass. Here we present a transmission spectrum of LHS 1140 b between 1.7 and 5.2 $\mu$m, obtained using the NIRSpec instrument on JWST. By combining spec

Benjamin S. Savino, Wen Wu

Direct numerical simulations of spanwise-rotating turbulent channel flow with a parabolic bump on the bottom wall are employed to investigate the effects of rotation on flow separation. Four rotation rates of $Ro_b := 2\Omega H/U_b = \pm 0.42, \; \pm 1.0$ are compared with the non-rotating scenario. The mild adverse pressure gradient induced by the lee side

Tongtian Yue, Jie Cheng, Longteng Guo, Xingyuan Dai

Recent trends in Large Vision Language Models (LVLMs) research have been increasingly focusing on advancing beyond general image understanding towards more nuanced, object-level referential comprehension. In this paper, we present and delve into the self-consistency capability of LVLMs, a crucial aspect that reflects the models' ability to both generate info

Bart Boom, Tadd Truscott, Frank Fish, Ed Habtour

High-speed water entry of projectiles and diving systems induces high forces and jerk to the entering bodies due to the development of large hydrodynamic pressure. Previous research has shown separately that the peak forces can be reduced by improving the aerodynamic shape of the head (nose) or, recently, by introducing a spring element between the head and

Kewei Wang, Yizheng Wu, Jun Cen, Zhiyu Pan

The perception of motion behavior in a dynamic environment holds significant importance for autonomous driving systems, wherein class-agnostic motion prediction methods directly predict the motion of the entire point cloud. While most existing methods rely on fully-supervised learning, the manual labeling of point cloud data is laborious and time-consuming.

Tianyu Pan, Yiyao Shi, Xiang Zhang, Weining Shen

Gene therapies aim to address the root causes of diseases, particularly those stemming from rare genetic defects that can be life-threatening or severely debilitating. Although an increasing number of gene therapies have received regulatory approvals in recent years, understanding their long-term efficacy in trials with limited follow-up time remains challen

Scott Blyth, Markus Wagner, Christoph Treude

AI foundation models have the capability to produce a wide array of responses to a single prompt, a feature that is highly beneficial in software engineering to generate diverse code solutions. However, this advantage introduces a significant trade-off between diversity and correctness. In software engineering tasks, diversity is key to exploring design spac

Xian Lin, Yangyang Xiang, Zhehao Wang, Kwang-Ting Cheng

Segment anything model (SAM), a foundation model with superior versatility and generalization across diverse segmentation tasks, has attracted widespread attention in medical imaging. However, it has been proved that SAM would encounter severe performance degradation due to the lack of medical knowledge in training and local feature encoding. Though several

Charles Goddard, Shamane Siriwardhana, Malikeh Ehghaghi, Luke Meyers

The rapid expansion of the open-source language model landscape presents an opportunity to merge the competencies of these model checkpoints by combining their parameters. Advances in transfer learning, the process of fine-tuning pretrained models for specific tasks, has resulted in the development of vast amounts of task-specific models, typically specializ

Chanmin Kim, Corwin Zigler

Principal stratification analysis evaluates how causal effects of a treatment on a primary outcome vary across strata of units defined by their treatment effect on some intermediate quantity. This endeavor is substantially challenged when the intermediate variable is continuously scaled and there are infinitely many basic principal strata. We employ a Bayesi

Canchen Jiang, Hao Wang

Community battery systems have been widely deployed to provide services to the grid. Unlike a single battery storage system in the community, coordinating multiple community batteries can further unlock their value, enhancing the viability of community battery solutions. However, the centralized control of community batteries relies on the full information o

Tao Song

In a typical sound event detection (SED) system, the existence of a sound event is detected at a frame level, and consecutive frames with the same event detected are combined as one sound event. The median filter is applied as a post-processing step to remove detection errors as much as possible. However, detection errors occurring around the onset and offse

Todd K Moon, Jacob H. Gunther

Over the years there has been ongoing interest in detecting authorship of a text based on statistical properties of the text, such as by using occurrence rates of noncontextual words. In previous work, these techniques have been used, for example, to determine authorship of all of \emph{The Federalist Papers}. Such methods may be useful in more modern times

Tao Song, WenWen Zhang

In sound event detection (SED), convolutional neural networks (CNNs) are widely employed to extract time-frequency (TF) patterns from spectrograms. However, the ability of CNNs to recognize different sound events is limited by their insensitivity to shifts of TF patterns along the frequency dimension, caused by translation equivariance. To address this issue

Pengfei Lin, Ehsan Javanmardi, Yuze Jiang, Manabu Tsukada

Lane merging is one of the critical tasks for self-driving cars, and how to perform lane-merge maneuvers effectively and safely has become one of the important standards in measuring the capability of autonomous driving systems. However, due to the ambiguity in driving intentions and right-of-way issues, the lane merging process in autonomous driving remains

Huachuan Qiu, Shuai Zhang, Hongliang He, Anqi Li

Pornographic content occurring in human-machine interaction dialogues can cause severe side effects for users in open-domain dialogue systems. However, research on detecting pornographic language within human-machine interaction dialogues is an important subject that is rarely studied. To advance in this direction, we introduce CensorChat, a dialogue monitor

Zhenyi Wang, Yan Li, Li Shen, Heng Huang

Continual Learning (CL) focuses on learning from dynamic and changing data distributions while retaining previously acquired knowledge. Various methods have been developed to address the challenge of catastrophic forgetting, including regularization-based, Bayesian-based, and memory-replay-based techniques. However, these methods lack a unified framework and

Zhengqing Yuan, Yixin Liu, Yihan Cao, Weixiang Sun

Text-to-video generation has made significant strides, but replicating the capabilities of advanced systems like OpenAI Sora remains challenging due to their closed-source nature. Existing open-source methods struggle to achieve comparable performance, often hindered by ineffective agent collaboration and inadequate training data quality. In this paper, we i

Vishnu Pandi Chellapandi, Antesh Upadhyay, Abolfazl Hashemi, Stanislaw H. Żak

A novel Decentralized Noisy Model Update Tracking Federated Learning algorithm (FedNMUT) is proposed that is tailored to function efficiently in the presence of noisy communication channels that reflect imperfect information exchange. This algorithm uses gradient tracking to minimize the impact of data heterogeneity while minimizing communication overhead. T

Fucai Ke, Hao Wang

Predicting electric vehicle (EV) charging events is crucial for load scheduling and energy management, promoting seamless transportation electrification and decarbonization. While prior studies have focused on EV charging demand prediction, primarily for public charging stations using historical charging data, home charging prediction is equally essential. H

Zhenyuan Yuan, Siyuan Xu, Minghui Zhu

This paper considers the problem of learning a control policy for robot motion planning with zero-shot generalization, i.e., no data collection and policy adaptation is needed when the learned policy is deployed in new environments. We develop a federated reinforcement learning framework that enables collaborative learning of multiple learners and a central

Peng Zhou, Jianmin Wang, Chunyan Li, Zixu Wang

While various models and computational tools have been proposed for structure and property analysis of molecules, generating molecules that conform to all desired structures and properties remains a challenge. Here, we introduce a multi-constraint molecular generation large language model, TSMMG, which, akin to a student, incorporates knowledge from various

Hao Yu

Interacting defect systems are ubiquitous in materials under realistic scenarios, yet gaining an atomic-level understanding of these systems from a computational perspective is challenging - it often demands substantial resources due to the necessity of employing supercell calculations. While machine learning techniques have shown potential in accelerating m

Ruizhe Zhang, Qingyao Ai, Ziyi Ye, Yueyue Wu

The tasks of legal case retrieval have received growing attention from the IR community in the last decade. Relevance feedback techniques with implicit user feedback (e.g., clicks) have been demonstrated to be effective in traditional search tasks (e.g., Web search). In legal case retrieval, however, collecting relevance feedback faces a couple of challenges

Jingyi Wang, Xiaobo Xia, Long Lan, Xinghao Wu

Given data with noisy labels, over-parameterized deep networks suffer overfitting mislabeled data, resulting in poor generalization. The memorization effect of deep networks shows that although the networks have the ability to memorize all noisy data, they would first memorize clean training data, and then gradually memorize mislabeled training data. A simpl

Jacob Parnell, Inigo Jauregi Unanue, Massimo Piccardi

Cross-lingual summarization (XLS) generates summaries in a language different from that of the input documents (e.g., English to Spanish), allowing speakers of the target language to gain a concise view of their content. In the present day, the predominant approach to this task is to take a performing, pretrained multilingual language model (LM) and fine-tun

Jie Chen, Hongze Li, Javier Gainza, Angel Munoz

A new perovskite KOsO3 has been stabilized under high-pressure and high temperature conditions. It is cubic at 500 K (Pm-3m) and undergoes subsequent phase transitions to tetragonal at 320 K (P4/mmm) and rhombohedral (R-3m) at 230 K as shown from refining synchrotron X-ray powder diffraction (SXRD) data. The larger orbital overlap integral and the extended w

Qitong Yang, Mingtao Feng, Zijie Wu, Shijie Sun

Directly learning to model 4D content, including shape, color, and motion, is challenging. Existing methods rely on pose priors for motion control, resulting in limited motion diversity and continuity in details. To address this, we propose a framework that generates volumetric 4D sequences, where 3D shapes are animated under given conditions (text-image gui

Jiana Liao, Jinbo Wen, Jiawen Kang, Changyan Yi

Web 3.0 is recognized as a pioneering paradigm that empowers users to securely oversee data without reliance on a centralized authority. Blockchains, as a core technology to realize Web 3.0, can facilitate decentralized and transparent data management. Nevertheless, the evolution of blockchain-enabled Web 3.0 is still in its nascent phase, grappling with cha

Jiarong Fan, Ariel Liebman, Hao Wang

The increasing integration of electric vehicles (EVs) into the grid can pose a significant risk to the distribution system operation in the absence of coordination. In response to the need for effective coordination of EVs within the distribution network, this paper presents a safety-aware reinforcement learning (RL) algorithm designed to manage EV charging

Mohamed Elnoor, Kasun Weerakoon, Adarsh Jagan Sathyamoorthy, Tianrui Guan

We present AMCO, a novel navigation method for quadruped robots that adaptively combines vision-based and proprioception-based perception capabilities. Our approach uses three cost maps: general knowledge map; traversability history map; and current proprioception map; which are derived from a robot's vision and proprioception data, and couples them to obtai

Aliaa Alnaggar, Faiza Farrukh

This paper investigates the optimal locations and capacities of hospital expansion facilities under uncertain future patient demands, considering both spatial and temporal correlations. We propose a novel two-stage distributionally robust optimization (DRO) model that integrates a Spatio-Temporal Neural Network (STNN). Specifically, we develop an STNN model

Shuaijiang Zhao, Xiaoquan Fang

In the era of flourishing large-scale models, the challenge of selecting and optimizing datasets from the vast and complex sea of data, to enhance the performance of large language models within the constraints of limited computational resources, has become paramount. This paper details our solution for the BetterMixture challenge, which focuses on the fine-

Jarah Evslin, Kehinde Ogundipe

Recently we have introduced a lightweight, perturbative approach to quantum solitons. Thus far, our approach has been largely limited to configurations consisting of a single soliton plus a finite number of mesons, whose classical limit is an isolated stationary or rigidly moving soliton. In this paper, with an eye to soliton collisions and oscillons, we gen

X. Xiang, G. Yang, S. Andrade, M. Askins

Water-based liquid scintillators (WbLS) are attractive neutrino detector materials because they allow us to tune the ratio of the Cherenkov and scintillation signals. Using WbLS large-scale neutrino experiments can benefit from both directional reconstruction and enhanced low-energy efficiency. Furthermore, broadening the science capability of such materials

Peiyao Sheng, Vishal Sevani, Ranvir Rana, Himanshu Tyagi

Internet platforms depend on accurately determining the geographical locations of online users to deliver targeted services (e.g., advertising). The advent of decentralized platforms (blockchains) emphasizes the importance of geographically distributed nodes, making the validation of locations more crucial. In these decentralized settings, mutually non-trust

Hao Zeng, Feng Wang, Chao Sun

Understanding the solidification dynamics of impacted water droplets is fundamental and crucial for applications, especially with the presence of frost and salt. Here, we experimentally investigate the spreading and freezing dynamics of saline droplets upon impact on a cold, frosty surface. Our findings demonstrate that the frost and salt can lead to an exte

Ruyong Feng, Zewang Guo, Wei Lu

We prove a differential analogue of Hilbert's irreducibility theorem. Let $\mathcal{L}$ be a linear differential operator with coefficients in $C(\mathbb{X})(x)$ that is irreducible over $\overline{C(\mathbb{X})}(x)$, where $\mathbb{X}$ is an irreducible affine algebraic variety over an algebraically closed field $C$ of characteristic zero. We show that the

Reid Johnson

We characterize boundedness and compactness of pullback operators under holomorphic maps between Bargmann spaces of entire holomorphic functions with quadratic strictly plurisubharmonic exponential weights, extending a result of Carswell-MacCluer-Schuster obtained in the case of the radial quadratic weight. We also show that the pullback operator between Bar

Nazmul Hasan, Apurba Kumar Saha, Andrew Wessman, Mohammed Shafae

Overheating anomaly detection is essential for the quality and reliability of parts produced by laser powder bed fusion (LPBF) additive manufacturing (AM). In this research, we focus on the detection of overheating anomalies using photodiode sensor data. Photodiode sensors can collect high-frequency data from the melt pool, reflecting the process dynamics an

Xi Sisi Shen, Pranay Talla

In this short note, we prove that $\alpha$-concavity of the pressure is not preserved for the porous medium equation in dimensions $n=3$ and higher for any $\alpha\in [0,1]\backslash \{\frac{1}{2}\}$. Together with the result of Chau-Weinkove for $n=2$, this fully resolves an open problem posed by V\'asquez on whether pressure concavity is preserved in gener

Linshan Wu, Zhun Zhong, Jiayi Ma, Yunchao Wei

Weakly-Supervised Semantic Segmentation (WSSS) aims to train segmentation models by weak labels, which is receiving significant attention due to its low annotation cost. Existing approaches focus on generating pseudo labels for supervision while largely ignoring to leverage the inherent semantic correlation among different pseudo labels. We observe that pseu

Colin Tang

We show that for the regular n-simplex, the 1-codimensional central slice that's parallel to a facet will achieve the minimum area (up to a 1-o(1) factor) among all 1-codimensional central slices, thus improving the previous best known lower bound (Brzezinski 2013) by a factor of $\frac{2\sqrt{3}}{e} \approx 1.27$. In addition to the standard technique of in

Lorenzo Giannelli, Giulio Chiribella

We provide a derivation of quantum theory in which the existence of an energy observable that generates the reversible dynamics follows directly from information-theoretic principles. Our first principle is that every reversible dynamics is implementable through a collision model, i.e. a sequence of fast collisions with an array of identically prepared syste

Tomohiro Hirano, Alexis Akira Toda

This paper provides a detailed analysis of the local determinacy of monetary and non-monetary steady states in Tirole (1985)'s classical two-period overlapping generations model with capital and production. We show that the sufficient condition for local determinacy in endowment economies provided by Scheinkman (1980) does not generalize to models with produ

Masashi Okada, Mayumi Komatsu, Tadahiro Taniguchi

In this paper, a novel approach is proposed for learning robot control in contact-rich tasks such as wiping, by developing Diffusion Contact Model (DCM). Previous methods of learning such tasks relied on impedance control with time-varying stiffness tuning by performing Bayesian optimization by trial-and-error with robots. The proposed approach aims to reduc

Lucas Neves, Oscar Campos, Robson Santos, Italo Santos

Testing is an essential quality activity in the software development process. Usually, a software system is tested on several levels, starting with unit testing that checks the smallest parts of the code until acceptance testing, which is focused on the validations with the end-user. Historically, unit testing has been the domain of developers, who are respo

Zihao Li, Hui Yuan, Kaixuan Huang, Chengzhuo Ni

Generative AI has redefined artificial intelligence, enabling the creation of innovative content and customized solutions that drive business practices into a new era of efficiency and creativity. In this paper, we focus on diffusion models, a powerful generative AI technology, and investigate their potential for black-box optimization over complex structure

Calvin Yeung, Prathyush Poduval, Mohsen Imani

Vector Symbolic Architectures (VSAs) have emerged as a novel framework for enabling interpretable machine learning algorithms equipped with the ability to reason and explain their decision processes. The basic idea is to represent discrete information through high dimensional random vectors. Complex data structures can be built up with operations over vector

Cristian Mejía-Cortés, Mario I. Molina

We study the spectrum and transmission coefficient of plane waves propagating along square ribbons of varying widths, containing a square-shaped, PT-symmetric impurity region. We start with a zero-width ribbon (1D chain) and place a PT symmetric dimer. The spectrum is computed numerically and the instability gain is computed as a function of the gain/loss di

Poul H. Damgaard, Kanghoon Lee

Applying the quantum field theoretic perturbiner approach to Einstein gravity, we compute the metric of a Schwarzschild black hole order by order in perturbation theory. Using recursion, this calculation can be carried out in de Donder gauge to all orders in Newton's constant. The result is a geometric series which is convergent outside a disk of finite radi

Raima Carol Appaw, Nicholas Fountain-Jones, Michael A. Charleston

The ability to simulate realistic networks based on empirical data is an important task across scientific disciplines, from epidemiology to computer science. Often simulation approaches involve selecting a suitable network generative model such as Erd\"os-R\'enyi or small-world. However, few tools are available to quantify if a particular generative model is

Austin E. Y. T. Lefebvre, Gabriel Sturm, Ting-Yu Lin, Emily Stoops

The analysis of dynamic organelles remains a formidable challenge, though key to understanding biological processes. We introduce Nellie, an automated and unbiased user-friendly pipeline for segmentation, tracking, and feature extraction of diverse intracellular structures. Nellie adapts to image metadata, eliminating user input. Nellie's preprocessing pipel

Khaoula Chehbouni, Megha Roshan, Emmanuel Ma, Futian Andrew Wei

Recent progress in large language models (LLMs) has led to their widespread adoption in various domains. However, these advancements have also introduced additional safety risks and raised concerns regarding their detrimental impact on already marginalized populations. Despite growing mitigation efforts to develop safety safeguards, such as supervised safety

Tianjiao Wang, Xiang Xu, Yue Zhao

We present stability estimates for the inverse source problem of the stochastic Helmholtz equation in two and three dimensions by either near-field or far-field data. The random source is assumed to be a microlocally isotropic generalized Gaussian random function. For the direct problem, by exploring the regularity of the Green function, we demonstrate that

Hubert Bray, Yiyue Zhang

Riemannian Penrose Inequalities are precise geometric statements that imply that the total mass of a zero second fundamental form slice of a spacetime is at least the mass contributed by the black holes, assuming that the spacetime has nonnegative matter density everywhere. In this paper, we remove this last assumption, and prove stronger statements that the

Le Hao, Sravan K. R. Vuyyuru, Sergei A. Tretyakov, Artan Salihu

In this paper, we systematically study the electromagnetic (EM) and communication aspects of an RIS through EM simulations, system-level and ray-tracing simulations, and finally measurements. We simulate a nearly perfect, lossless RIS, and a realistic lossy anomalous reflector (AR) in different ray tracers and analyze the large-scale fading of simple RIS-ass

Yiliu Li, Eric A. Arsenault, Birui Yang, Xi Wang

Van der Waals (vdW) structures of two-dimensional materials host a broad range of physical phenomena. New opportunities arise if different functional layers may be remotely modulated or coupled in a device structure. Here we demonstrate the in-situ coherent modulation of moiré excitons and correlated Mott insulators in transition metal dichalcogenide (TMD) h

Keisuke Himeno

The Upsilon invariant of a knot is a concordance invariant derived from knot Floer homology theory. It is a piecewise linear continuous function defined on the interval $[0,2]$. Borodzik and Hedden gave a question asking for which knots the Upsilon invariant is a convex function. It is known that the Upsilon invariant of any $L$-space knot, and a Floer thin

Matěj Trödler, Jan Volec, Jan Vybíral

We give a new lower bound for the minimal dispersion of a point set in the unit cube and its inverse function in the high dimension regime. This is done by considering only a very small class of test boxes, which allows us to reduce bounding the dispersion to a problem in extremal set theory. Specifically, we translate a lower bound on the size of $r$-cover-

Fan Liu, Yuan-Sen Ting, David Yong, Bertram Bitsch

Stellar chemical compositions can be altered by ingestion of planetary material and/or planet formation which removes refractory material from the proto-stellar disc. These "planet signatures" appear as correlations between elemental abundance differences and the dust condensation temperature. Detecting these planet signatures, however, is challenging due to

Peide Huang, Wenhao Ding, Benjamin Stoler, Jonathan Francis

Simulation is an indispensable tool in the development and testing of autonomous vehicles (AVs), offering an efficient and safe alternative to road testing. An outstanding challenge with simulation-based testing is the generation of safety-critical scenarios, which are essential to ensure that AVs can handle rare but potentially fatal situations. This paper

Paul J. Robinson, Adam Rettig, Hieu Q. Dinh, Meng-Fu Chen

Molecular quantum chemistry has seen enormous progress in the last few decades thanks to the more advanced and sophisticated numerical techniques and computing power. Following the recent interest in extending these capabilities to condensed-phase problems, we summarize basic knowledge of condensed-phase quantum chemistry for ones with experience in molecula

Anita Rau, Josiah Aklilu, F. Christopher Holsinger, Serena Yeung-Levy

Neural Radiance Fields (NeRFs) are trained to minimize the rendering loss of predicted viewpoints. However, the photometric loss often does not provide enough information to disambiguate between different possible geometries yielding the same image. Previous work has thus incorporated depth supervision during NeRF training, leveraging dense predictions from

Myriam Nonaka, Monica Agüero, Alejandro Hnilo, Marcelo Kovalsky

In this paper we present a nonlinear autoregressive neural network with a hidden layer of 50 neurons, three delays and one output layer that accurately is capable of predict the appearence of extreme events in a Kerr lens mode locking Ti:Sapphire laser with ultrashort pulses. Extreme events are produced in the context of a chaotic atractor and with chirped p

Anh Bui, Vy Vo, Tung Pham, Dinh Phung

There has long been plenty of theoretical and empirical evidence supporting the success of ensemble learning. Deep ensembles in particular take advantage of training randomness and expressivity of individual neural networks to gain prediction diversity, ultimately leading to better generalization, robustness and uncertainty estimation. In respect of generali

Minhyeok Ko, Konstantinos G. Papakonstantinou

This paper presents in detail the originally developed Quadratic Point Estimate Method (QPEM), aimed at efficiently and accurately computing the first four output moments of probabilistic distributions, using 2n^2+1 sample (or sigma) points, with n, the number of input random variables. The proposed QPEM particularly offers an effective, superior, and practi

D. Jones, A. Östlin, A. Weh, F. Beiuseanu

Ordinary superconductors are widely assumed insensitive to small concentrations of random nonmagnetic impurities, whereas strong disorder suppresses superconductivity, ultimately leading to a superconductor-insulator transition. In between these limiting cases, a most fascinating regime may emerge where disorder enhances superconductivity. This effect is dis

Longbing Cao

In the approximately century-long journey of robotics, humanoid robots made their debut around six decades ago. While current humanoids bear human-like appearances, none have embodied true humaneness, remaining distant from achieving human-like to human-level intelligence. The rapid recent advancements in generative AI and (multimodal) large language models

José Navarro-Salas

Cosmic censorship protects the outside world from black hole singularities and paves the way for assigning entropy to gravity at the event horizons. We point out a tension between cosmic censorship and the quantum backreacted geometry of Schwarzschild black holes, induced by vacuum polarization and driven by the conformal anomaly. A similar tension appears f

Joachim Baumann, Celestine Mendler-Dünner

We investigate algorithmic collective action in transformer-based recommender systems. Our use case is a music streaming platform where a collective of fans aims to promote the visibility of an underrepresented artist by strategically placing one of their songs in the existing playlists they control. We introduce two easily implementable strategies to select

Barna Schefler

The exact value of the separating Noether number of an arbitrary finite abelian group of rank two is determined. This is done by a detailed study of the monoid of zero-sum sequences over the group.

Zaid Tasneem, Akshat Dave, Abhishek Singh, Kushagra Tiwary

Neural radiance fields (NeRFs) show potential for transforming images captured worldwide into immersive 3D visual experiences. However, most of this captured visual data remains siloed in our camera rolls as these images contain personal details. Even if made public, the problem of learning 3D representations of billions of scenes captured daily in a central

James F. Mullen, Dinesh Manocha

Large language models (LLMs) showcase many desirable traits for intelligent and helpful robots. However, they are also known to hallucinate predictions. This issue is exacerbated in robotics where LLM hallucinations may result in robots confidently executing plans that are contrary to user goals or relying more frequently on human assistance. In this work, w

Robin Requadt, Manuel Fink, Patrick Kubica, Claudia Steinem

Recent experimental studies have shed light on the intriguing possibility that ion channels exhibit cooperative behaviour. However, a comprehensive understanding of such cooperativity remains elusive, primarily due to limitations in measuring separately the response of each channel. Rather, only the superimposed channel response can be observed, challenging

Masih Eskandar, Tooba Imtiaz, Zifeng Wang, Jennifer Dy

The performance of deep models, including Vision Transformers, is known to be vulnerable to adversarial attacks. Many existing defenses against these attacks, such as adversarial training, rely on full-model fine-tuning to induce robustness in the models. These defenses require storing a copy of the entire model, that can have billions of parameters, for eac

Konstantinos K. Delibasis, Iro Oikonomou, Aristides I. Kechriniotis, Georgios N. Tsigaridas

A number of basic image processing tasks, such as any geometric transformation require interpolation at subpixel image values. In this work we utilize the multidimensional coordinate Hermite spline interpolation defined on non-equal spaced, rectilinear grids and apply it to a very common image processing task, image zooming. Since Hermite interpolation utili

Miriam Gudiño, Elena Jiménez-Bailón, Anna Lia Longinotti, Matteo Guainazzi

In accordance with the AGN Unified Model, observed polarization can be related to the orientation of the line of sight with respect to the torus. AGN X-ray emission arises from the central region and carries the imprints of the obscuring material. We aim to test a unified scheme based on optical polarization using X-ray absorption. Using the XMM-Newton data

Tan Khang Le, Saba Alimadadi, Steven Y. Ko

In recent years, JavaScript has become the most widely used programming language, especially in web development. However, writing secure JavaScript code is not trivial, and programmers often make mistakes that lead to security vulnerabilities in web applications. Large Language Models (LLMs) have demonstrated substantial advancements across multiple domains,

Dennis Lartey Quayesam, Anani Lotsi, Felix Okoe Mettle

Modeling financial data often relies on assumptions that may prove insufficient or unrealistic in practice. The Geometric Brownian Motion (GBM) model is frequently employed to represent stock price processes. This study investigates whether the behavior of weekly and monthly returns of selected equities listed on the Ghana Stock Exchange conforms to the GBM

Somayeh Gholipour, Maryam Bahreini, Mohamad Reza Jafarfard

Raman spectroscopy is one of the widely used methods in the analysis of various samples including carbon-based materials. This study aimed to identify the number of layers and defects in graphene using micro-Raman spectroscopy. More specifically, the study examined tracing the oxidation process of graphene under UV exposure. Investigation of the effect of th

Vincent Cartillier, Neha Jain, Irfan Essa

We study the task of 3D multi-object re-identification from embodied tours. Specifically, an agent is given two tours of an environment (e.g. an apartment) under two different layouts (e.g. arrangements of furniture). Its task is to detect and re-identify objects in 3D - e.g. a "sofa" moved from location A to B, a new "chair" in the second layout at location

Jay Gopalakrishnan, Johnny Guzman, Jeonghun J. Lee

Mixed methods for linear elasticity with strongly symmetric stresses of lowest order are studied in this paper. On each simplex, the stress space has piecewise linear components with respect to its Alfeld split (which connects the vertices to barycenter), generalizing the Johnson--Mercier two-dimensional element to higher dimensions. Further reductions in th

Kasi Viswanath, Peng Jiang, Srikanth Saripalli

LiDAR semantic segmentation frameworks predominantly use geometry-based features to differentiate objects within a scan. Although these methods excel in scenarios with clear boundaries and distinct shapes, their performance declines in environments where boundaries are indistinct, particularly in off-road contexts. To address this issue, recent advances in 3

Takuya Akiba, Makoto Shing, Yujin Tang, Qi Sun

Large language models (LLMs) have become increasingly capable, but their development often requires substantial computational resources. While model merging has emerged as a cost-effective promising approach for creating new models by combining existing ones, it currently relies on human intuition and domain knowledge, limiting its potential. Here, we propos

Sergio E. Aguilar-Gutierrez

How can we define complexity in dS space from microscopic principles? Based on recent developments pointing towards a correspondence between a pair of double-scaled Sachdev-Ye-Kitaev (DSSYK) models/ 2D Liouville-de Sitter (LdS$_2$) field theory/ 3D Schwarzschild de Sitter (SdS$_3$) space in arXiv:2310.16994, arXiv:2402.00635, arXiv:2402.02584, we study concr

Abraão J. S. Capistrano, Rafael C. Nunes, Luís A. Cabral

This paper simplifies the induced four-dimensional gravitational equations originating from a five-dimensional bulk within the framework of Nash's embeddings, incorporating them into a well-known $\mu-\Sigma$ modified gravity (MG) parametrization. By leveraging data from Planck Public Release 4 (PR4), BICEP/Keck Array 2018, Planck cosmic microwave background

Radost Waszkiewicz, Maciej Lisicki

We reconsider the classical problem of a freely joined chain of Brownian particles connected by elastic springs and study its conformational probability distribution function in the overdamped regime in the limit of infinite stiffness of constraints. We show that the well-known solution by Fixman [Proc. Natl. Acad. Sci. USA 71, 3050 (1974)] is missing a shap

Asif Abbas, Christopher W. Churchill, Glenn G. Kacprzak, Christopher Lidman

We present an all-southern sky survey for MgII doublet absorbers in 951 z < 4 AGN/quasar spectra from the Australian Dark Energy Survey (OzDES). The spectral resolution ranges from R = 1400-1700 over the wavelengths 3700 A-8800 A. The survey has a 5sigma detection completeness of 50% and above for rest-frame equivalent widths W_r(2796) >= 0.3 A. We studied 6

Jan Bok, Antoine Dailly, Tuomo Lehtilä

A \emph{resolving set} $R$ in a graph $G$ is a set of vertices such that every vertex of $G$ is uniquely identified by its distances to the vertices of $R$. Introduced in the 1970s, this concept has been since then extensively studied from both combinatorial and algorithmic points of view. We propose a generalization of the concept of resolving sets to tempo

Matthew Krauel, Jamal Noel Shafiq, Simon Wood

Conformal field theory and its axiomatisation in terms of vertex operator algebras or chiral algebras are most commonly considered on the Riemann sphere. However, an important constraint in physics and an interesting source of mathematics is the fact that conformal field theories are expected to be well defined on any Riemann surface. To this end, a thorough

Samuel H. Kramer, Ian H. Redmount

An open or hyperbolic Friedmann-Robertson-Walker spacetime dominated by tachyonic dark matter can exhibit an ``inflected'' expansion -- initially decelerating, later accelerating -- similar but not identical to that of now-standard $\Lambda$CDM models dominated by dark energy. The features of the tachyonic model can be extracted by fitting the redshift-dista

Congquan Mei, Lian Chen, Junfeng Zhou, Ming Du

Given a data graph G, a source vertex u and a target vertex v of a reachability query, the reachability query is used to answer whether there exists a path from u to v in G. Reachability query processing is one of the fundamental operations in graph data management, which is widely used in biological networks, communication networks, and social networks to a