April 2024 arXiv papers — page 112

Zhe Wang, Jiayi Zhang, Hongyang Du, Ruichen Zhang

Next-generation Multiple-Input Multiple-Output (MIMO) is expected to be intelligent and scalable. In this paper, we study Large Language Model (LLM)-enabled next-generation MIMO networks. Firstly, we provide an overview of the development, fundamentals, and challenges of the next-generation MIMO. Then, we propose the concept of the generative AI agent, which

Junjielong Xu, Ying Fu, Shin Hwei Tan, Pinjia He

Large language models (LLMs) have achieved decent results on automated program repair (APR). However, the next token prediction training objective of decoder-only LLMs (e.g., GPT-4) is misaligned with the masked span prediction objective of current infilling-style methods, which impedes LLMs from fully leveraging pre-trained knowledge for program repair. In

Jingjing Zhao, Yizhu Zhang, Yanjun Gao, Meng Li

Mixing the fundamental ($\omega$) and the second harmonic (2$\omega$) waves in gas phase is a widely employed technique for emitting terahertz (THz) pulses. The THz generation driven by bi-chromatic fields can be described by the photocurrent model, where the THz generation is attributed to free electrons ionized by the $\omega$ field, and the 2$\omega$ fiel

Hui Shi, Mingyuan Wang, Hongying Chen, Adrien Rousseau

Boron nitride (BN) is today considered as one of the most promising materials for many novel applications including bright single photon emission, deep UV opto-electronics, small sized solid-state neutron detector, and high-performance two-dimensional materials, etc. Despite the recent successful fabrication of large-area BN single-crystals (typically <= 5 a

ChungHa Lee

In this paper, I have implemented a timbre visualization system called Tailors. Through the experiment with 27 MIR users, Tailors was found to be effective in conveying timbral warmth, brightness, depth, shallowness, hardness, roughness, and sharpness features of music compared to the only music condition and basic visualization. All scores of Tailors in the

Jonathan Treviño-Marroquín

In algebraic topology, the fundamental groupoid is a classical homotopy invariant which is defined using continuous maps from the closed interval to a topological space. In this paper, we construct a semi-coarse version of this invariant, using as paths a finite sequences of maps from $\mathbb{Z}_1$ to a semi-coarse space, connecting their tails through semi

Nikita Gladkov, Aleksandr Zimin

We show that a site percolation is a stronger model than a bond percolation. We use the van den Berg -- Kesten (vdBK) inequality to prove that site percolation on a neighborhood of a vertex of degree $4$ cannot be simulated even approximately by bond percolation, and develop a decision tree technique to prove the same for a neighborhood of a vertex of degree

Abhishek Sebastian, Pragna R

Hydrogen evolution reaction (HER) has emerged as a promising technique for the production of clean and sustainable energy. In recent years, researchers have been exploring various materials for efficient HER activity. In this study, we report the synthesis of two different materials, namely MOS$_2$ and MoS$_2$-rGO, through a hydrothermal technique. X-ray dif

Binbin Lin, Lei Zou, Mingzheng Yang, Bing Zhou

The COVID-19 pandemic has changed human life. To mitigate the pandemic's impacts, different regions implemented various policies to contain COVID-19 and residents showed diverse responses. These human responses in turn shaped the uneven spatial-temporal spread of COVID-19. Consequently, the human-pandemic interaction is complex, dynamic, and interconnected.

Si Ung Noh, Junguk Hong, Chaemin Lim, Seongyeon Park

Recent dual in-line memory modules (DIMMs) are starting to support processing-in-memory (PIM) by associating their memory banks with processing elements (PEs), allowing applications to overcome the data movement bottleneck by offloading memory-intensive operations to the PEs. Many highly parallel applications have been shown to benefit from these PIM-enabled

Venkatesan Guruswami, Bingkai Lin, Xuandi Ren, Yican Sun

The Parameterized Inapproximability Hypothesis (PIH), which is an analog of the PCP theorem in parameterized complexity, asserts that, there is a constant $\varepsilon> 0$ such that for any computable function $f:\mathbb{N}\to\mathbb{N}$, no $f(k)\cdot n^{O(1)}$-time algorithm can, on input a $k$-variable CSP instance with domain size $n$, find an assignment

Peter Carragher, Evan M. Williams, Kathleen M. Carley

The proliferation of unreliable news domains on the internet has had wide-reaching negative impacts on society. We introduce and evaluate interventions aimed at reducing traffic to unreliable news domains from search engines while maintaining traffic to reliable domains. We build these interventions on the principles of fairness (penalize sites for what is i

Fei Cao, Sebastien Motsch

We study a variant of the dispersion process on the complete graph introduced in the recent work [17] under the mean-field framework. We adopt a kinetic perspective (as opposed to the probabilistic approach taken in [17] and many other related works) thanks to the reinterpretation of the model in terms of a novel econophysics model. Various analytical and qu

Huicong Zhong, Xiaobing Feng

In this paper, we develop and test a fast numerical algorithm, called MDI-LR, for efficient implementation of quasi-Monte Carlo lattice rules for computing $d$-dimensional integrals of a given function. It is based on the idea of converting and improving the underlying lattice rule into a tensor product rule by an affine transformation and adopting the multi

Furkan Mumcu, Yasin Yilmaz

Adversarial machine learning attacks on video action recognition models is a growing research area and many effective attacks were introduced in recent years. These attacks show that action recognition models can be breached in many ways. Hence using these models in practice raises significant security concerns. However, there are very few works which focus

Ioannis E. Livieris, Nikos Alimpertis, George Domalis, Dimitris Tsakalidis

Nowadays, the use of synthetic data has gained popularity as a cost-efficient strategy for enhancing data augmentation for improving machine learning models performance as well as addressing concerns related to sensitive data privacy. Therefore, the necessity of ensuring quality of generated synthetic data, in terms of accurate representation of real data, c

Daniel Machlab, Rick Battle

The proliferation of Large Language Models (LLMs) highlights the critical importance of conducting thorough evaluations to discern their comparative advantages, limitations, and optimal use cases. Particularly important is assessing their capacity to accurately retrieve information included in a given prompt. A model's ability to do this significantly influe

Naiara V. de Paulo, Pedro A. S. Salomão

Pseudo-holomorphic curves in symplectizations, as introduced by Hofer in 1993, and then developed by Hofer, Wysocki, and Zehnder, have brought new insights to Hamiltonian dynamics, providing new approaches to some classical questions in Celestial Mechanics. This short survey presents some recent developments in Reeb dynamics based on the theory of pseudo-hol

Kasia Jankiewicz, Kevin Schreve

Given a graph $\Gamma$ and a number $n$, the associated $n^{th}$ graph braid group $B_n(\Gamma)$ is the fundamental group of the unordered configuration space of $n$ points on $\Gamma$. \'{S}wi\k{a}tkowski showed that for a given $\Gamma$ and $n$ large enough, there is a free abelian subgroup of $B_n(\Gamma)$ of rank equal to the cohomological dimension of $

Katsuei Kenmotsu

We provide an elementary proof of a lemma that plays an important role in the classification of parallel mean curvature surfaces in two-dimensional complex space forms.

Jin-Long Jiao

Non-neutral plasmas can excite many nonlinear plasma phenomena, e.g., collisionless shocks, sheath layers, solitons, and plasma plumes. It is a fundamental issue in fields such as astrophysics, space physics, nuclear fusion, and plasma propulsion. Hybrid PIC methods are currently the most commonly used techniques for simulating non-neutral plasmas. However,

Lei Ding, Jeshwanth Bheemanpally, Yi Zhang

Many people use search engines to find online guidance to solve computer or mobile device problems. Users frequently encounter challenges in identifying effective solutions from search results, often wasting time trying ineffective solutions that seem relevant yet fail to solve real problems. This paper introduces a novel approach to improving the accuracy a

M. L. Gutiérrez Albarrán, D. Montes, H. M. Tabernero, J. I. González Hernández

The Li abundance observed in pre-main sequence and main sequence late-type stars is strongly age-dependent, but also shows a complex pattern depending on several parameters, such as rotation, chromospheric activity and metallicity. The best way to calibrate these effects, with the aim of studying Li as an age indicator for FGK stars, is to calibrate coeval g

Yan Ru Pei, Sasskia Brüers, Sébastien Crouzet, Douglas McLelland

Event-based data are commonly encountered in edge computing environments where efficiency and low latency are critical. To interface with such data and leverage their rich temporal features, we propose a causal spatiotemporal convolutional network. This solution targets efficient implementation on edge-appropriate hardware with limited resources in three way

Zhengyan Sheng, Yang Ai, Li-Juan Liu, Jia Pan

Despite recent advancements in speech generation with text prompt providing control over speech style, voice attributes in synthesized speech remain elusive and challenging to control. This paper introduces a novel task: voice attribute editing with text prompt, with the goal of making relative modifications to voice attributes according to the actions descr

Mukul Gagrani, Raghavv Goel, Wonseok Jeon, Junyoung Park

Inference with Multimodal Large Language Models (MLLMs) is slow due to their large-language-model backbone which suffers from memory bandwidth bottleneck and generates tokens auto-regressively. In this paper, we explore the application of speculative decoding to enhance the inference efficiency of MLLMs, specifically the LLaVA 7B model. We show that a langua

Dvij Kalaria, Shreya Sharma, Sarthak Bhagat, Haoru Xue

Off-road navigation is a challenging problem both at the planning level to get a smooth trajectory and at the control level to avoid flipping over, hitting obstacles, or getting stuck at a rough patch. There have been several recent works using classical approaches involving depth map prediction followed by smooth trajectory planning and using a controller t

Derek Knowles, Ashwin Vivek Kanhere, Daniel Neamati, Grace Gao

This paper presents gnss_lib_py, a Python library used to parse, analyze, and visualize data from a variety of GNSS (Global Navigation Satellite Systems) data sources. The gnss_lib_py library's ease of use, modular capabilities, testing coverage, and extensive documentation make it an attractive tool not only for scientific and industry users wanting a quick

Bala McRae-Posani, Andrei Holodny, Hrithwik Shalu, Joseph N Stember

Uncertainty quantification plays a vital role in facilitating the practical implementation of AI in radiology by addressing growing concerns around trustworthiness. Given the challenges associated with acquiring large, annotated datasets in this field, there is a need for methods that enable uncertainty quantification in small data AI approaches tailored to

Luo-bin Lin, Fu-quan Chen, Jin-ping Zhuang

Over-/under-break excavation is a common phenomenon in shallow tunnelling, which is nonetheless not generally considered in existing complex variable solutions. In this paper, a new equilibrium mechanical model on over-/under-break shallow tunnelling in gravitational geomaterial is established by fixing far-field ground surface to form a corresponding mixed

Jason Midkiff, Po-Yu Hsiao, Patrick T. Camp, Ray T. Chen

The extension of photonic technologies such as lidar and free-space optical communications from the traditional visible and near-infrared wavelengths to longer wavelengths can improve performance in adverse environments such as haze, fog, smoke, or strong solar background. Non-mechanical beam steerers will be a critical component of the low size, weight, and

Amit Sharma, Teodor-Dumitru Ene, Kishor Kunal, Mingjie Liu

This paper presents a comparative analysis of total cost of ownership (TCO) and performance between domain-adapted large language models (LLM) and state-of-the-art (SoTA) LLMs , with a particular emphasis on tasks related to coding assistance for chip design. We examine the TCO and performance metrics of a domain-adaptive LLM, ChipNeMo, against two leading L

Siyuan Chen, Yi Zhang, Yiming Wang, Zhuang Liu

Atmosphere modelling applications become increasingly memory-bound due to the inconsistent development rates between processor speeds and memory bandwidth. In this study, we mitigate memory bottlenecks and reduce the computational load of the GRIST dynamical core by adopting the mixed-precision computing strategy. Guided by a limited-degree of iterative deve

Willca Villafana, Andrew T. Powis, Sarveshwar Sharma, Igor D. Kaganovich

In this study, we conduct 2D3V Particle-In-Cell simulations of hollow cathodes, encompassing both the channel and plume region, with an emphasis on plasma switch applications. The plasma in the hollow cathode channel can exhibit kinetic effects depending on how fast electrons emitted from the insert are thermalized via Coulomb collisions. The criterion that

Juntaek Lim, Youngeun Kwon, Ranggi Hwang, Kiwan Maeng

Differential privacy (DP) is widely being employed in the industry as a practical standard for privacy protection. While private training of computer vision or natural language processing applications has been studied extensively, the computational challenges of training of recommender systems (RecSys) with DP have not been explored. In this work, we first p

Subhojyoti Mukherjee, Anusha Lalitha, Aniket Deshmukh, Ge Liu

One emergent ability of large language models (LLMs) is that query-specific examples can be included in the prompt at inference time. In this work, we use active learning for adaptive prompt design and call it Active In-context Prompt Design (AIPD). We design the LLM prompt by adaptively choosing few-shot examples from a training set to optimize performance

Yuta Hamada, Gregory J. Loges

The space of 6D supergravities with minimal supersymmetry is greatly constrained by anomaly cancellation. Nevertheless, a large number of models satisfy all low-energy consistency conditions and in this work we make progress towards exhaustively enumerating all anomaly-free models with at most one tensor multiplet. Generalizing previous techniques, we descri

Lei Zhang, Kaixin Bai, Guowen Huang, Zhenshan Bing

The deep learning models has significantly advanced dexterous manipulation techniques for multi-fingered hand grasping. However, the contact information-guided grasping in cluttered environments remains largely underexplored. To address this gap, we have developed a method for generating multi-fingered hand grasp samples in cluttered settings through contact

Tomasz Penza, Anna B. Romanowska

The Mal'tsev product of two varieties of the same similarity type is not in general a variety, because it can fail to be closed under homomorphic images. In the previous paper we provided a new sufficient condition for such a product to be a variety. In this paper we extend that result by weakening the assumptions regarding the two varieties. We also explore

Alan Williams, Miroslav Krstic, Alexander Scheinker

We present Assignably Safe Extremum Seeking (ASfES), an algorithm designed to minimize a measured objective function while maintaining a measured metric of safety (a control barrier function or CBF) be positive in a practical sense. We ensure that for trajectories with safe initial conditions, the violation of safety can be made arbitrarily small with approp

Tomasz Penza, Anna B. Romanowska

We investigate the Mal'tsev product $\mathcal{V} \circ \mathcal{W}$ of two varieties $\mathcal{V}$ and $\mathcal{W}$ of the same similarity type. Such a product is usually a quasivariety but not necessarily a variety. We give an equational base for the variety generated by $\mathcal{V} \circ \mathcal{W}$ in terms of identities satisfied in $\mathcal{V}$ and

Ruben Louis

Any Lie algebroid $A$ admits a Nash-type blow-up $\mathrm{Nash}(A)$ that sits in a nice short exact sequence of Lie algebroids $0\rightarrow K\rightarrow \mathrm{Nash}(A)\rightarrow \mathcal{D}\rightarrow 0$ with $K$ a Lie algebra bundle and $\mathcal{D}$ a Lie algebroid whose anchor map is injective on an open dense subset. The base variety is a blowup dete

Victor Quintas-Martinez, Mohammad Taha Bahadori, Eduardo Santiago, Jeff Mu

Comparing two samples of data, we observe a change in the distribution of an outcome variable. In the presence of multiple explanatory variables, how much of the change can be explained by each possible cause? We develop a new estimation strategy that, given a causal model, combines regression and re-weighting methods to quantify the contribution of each cau

Tara Kelly, Jessica Gupta

Traffic congestion at intersections is a significant issue in urban areas, leading to increased commute times, safety hazards, and operational inefficiencies. This study aims to develop a predictive model for congestion at intersections in major U.S. cities, utilizing a dataset of trip-logging metrics from commercial vehicles across 4,800 intersections. The

Cláudio Gomes, João Paulo Fernandes, Gabriel Falcao, Soummya Kar

The rapid adoption of Electric Vehicles (EVs) poses challenges for electricity grids to accommodate or mitigate peak demand. Vehicle-to-Vehicle Charging (V2VC) has been recently adopted by popular EVs, posing new opportunities and challenges to the management and operation of EVs. We present a novel V2VC model that allows decision-makers to take V2VC into ac

Zezheng Li, Kingston Yip

This study introduces a novel BERT-LSH model that incorporates Locality Sensitive Hashing (LSH) to approximate the attention mechanism in the BERT architecture. We examine the computational efficiency and performance of this model compared to a standard baseline BERT model. Our findings reveal that BERT-LSH significantly reduces computational demand for the

Reza Ahmadvand, Sarah Safura Sharif, Yaser Mike Banad

This paper presents a novel cloud-edge framework for addressing computational and energy constraints in complex control systems. Our approach centers around a learning-based controller using Spiking Neural Networks (SNN) on physical plants. By integrating a biologically plausible learning method with local plasticity rules, we harness the efficiency, scalabi

Evan Patterson

The cartesian structure possessed by relations, spans, profunctors, and other such morphisms is elegantly expressed by universal properties in double categories. Though cartesian double categories were inspired in part by the older program of cartesian bicategories, the precise relationship between the double-categorical and bicategorical approaches has so f

Mohsen Asghari Ilani, Yaser Mike Banad

This analytical solution, based on Goldak's Semi-Ellipsoidal Heat Source model, captures the dynamic temperature evolution from a semi-ellipsoidal power density moving heat source within a semi-infinite body. It tackles the convection-diffusion heat transfer equation by integrating an instantaneous point heat source across the volume of the ellipsoidal shape

Abbey E. Corson, Meaghan MacDonald, Velislava Tzaneva, Chris M. Edwards

Historically, females were excluded from clinical research due to their reproductive roles, hindering medical understanding and healthcare quality. Despite guidelines promoting equal participation, females are underrepresented in exercise science, perpetuating misconceptions about female physiology. Even less attention has been given to exercise in the pregn

Siobhan Light, Mark Gurwell, Alexander Thelen, Nicholas Lombardo

Saturn's moon Titan possesses stratospheric zonal winds that places it among a sparse class of planetary bodies known to have superrotation in their atmospheres. Few measurements have been made of these speeds in the upper stratosphere, leaving their seasonal variations still not well understood. We examined observations made with the extended Submillimeter

Mohammed Adnan, Qinle Ba, Nazim Shaikh, Shivam Kalra

Recent years have seen significant efforts to adopt Artificial Intelligence (AI) in healthcare for various use cases, from computer-aided diagnosis to ICU triage. However, the size of AI models has been rapidly growing due to scaling laws and the success of foundational models, which poses an increasing challenge to leverage advanced models in practical appl

Yi Ma

This paper proposes sG-hyperbolicity as a new tool for studying hyperbolicity on complex manifolds. It demonstrates that this notion leads to a wider class of divisorially hyperbolic manifolds compared to balanced hyperbolicity. We also introduce weakly p-K\"ahler hyperbolic structures and pluriclosed star split hyperbolic metrics as possible new avenues for

Andrés Abeliuk, Alfonso Valderrama, Simón Campos, Marcelo Mendoza

Recommender Systems (RS) shape the filtering and curation of online content, yet we have limited understanding of how predictable their recommendation outputs are. We propose data-driven metrics that quantify the predictability of recommendation datasets by measuring the structural complexity of the user-item interaction matrix. High complexity indicates int

Mudit Verma, Katherine Metcalf

Preference based Reinforcement Learning (PbRL) removes the need to hand specify a reward function by learning a reward from preference feedback over policy behaviors. Current approaches to PbRL do not address the credit assignment problem inherent in determining which parts of a behavior most contributed to a preference, which result in data intensive approa

James F. Mullen, Prasoon Goyal, Robinson Piramuthu, Michael Johnston

Home robots intend to make their users lives easier. Our work assists in this goal by enabling robots to inform their users of dangerous or unsanitary anomalies in their home. Some examples of these anomalies include the user leaving their milk out, forgetting to turn off the stove, or leaving poison accessible to children. To move towards enabling home robo

George Yu, Ziv Scully

We study the problem of scheduling jobs in a queueing system, specifically an M/G/1 with light-tailed job sizes, to asymptotically optimize the response time tail. This means scheduling to make $\mathbf{P}[T > t]$, the chance a job's response time exceeds $t$, decay as quickly as possible in the $t \to \infty$ limit. For some time, the best known policy was

Jan-Gerrit Habekost, Connor Gäde, Philipp Allgeuer, Stefan Wermter

This paper introduces a novel zero-shot motion planning method that allows users to quickly design smooth robot motions in Cartesian space. A B\'ezier curve-based Cartesian plan is transformed into a joint space trajectory by our neuro-inspired inverse kinematics (IK) method CycleIK, for which we enable platform independence by scaling it to arbitrary robot

Nadezda Kirillova, M. Jehanzeb Mirza, Horst Bischof, Horst Possegger

State-of-the-art Multiple Object Tracking (MOT) approaches have shown remarkable performance when trained and evaluated on current benchmarks. However, these benchmarks primarily consist of clear weather scenarios, overlooking adverse atmospheric conditions such as fog, haze, smoke and dust. As a result, the robustness of trackers against these challenging c

Theodora Bourni, Nathan Burns, Spencer Catron

We classify convex ancient curve shortening flows with free boundary on general bounded convex domains.

Xun Tang, Leah Collis, Lexing Ying

Solving high-dimensional partial differential equations necessitates methods free of exponential scaling in the dimension of the problem. This work introduces a tensor network approach for the Kolmogorov backward equation via approximating directly the Markov operator. We show that the high-dimensional Markov operator can be obtained under a functional hiera

Shuo Li

Let $(s_2(n))_{n\in \mathbb{N}}$ be a $0,1$-sequence such that, for any natural number $n$, $s_2(n) = 1$ if and only if $n$ is a sum of two squares. In a recent article, Tahay proved that the sequence $(s_2(n))_{n\in \mathbb{N}}$ is not $k$-automatic for any integer $k$, and asked if this sequence can be morphic. In this note, we give a negative answer to th

Ji-Ung Lee, Marc E. Pfetsch, Iryna Gurevych

This work proposes a novel method to generate C-Tests; a deviated form of cloze tests (a gap filling exercise) where only the last part of a word is turned into a gap. In contrast to previous works that only consider varying the gap size or gap placement to achieve locally optimal solutions, we propose a mixed-integer programming (MIP) approach. This allows

Yueh-Cheng Huang, Hsin-Yi Chen, Cheng-Jui Hung, Jen-Hui Chuang

Confronting the critical challenge of insufficient training data in the field of complex image recognition, this paper introduces a novel 3D viewpoint augmentation technique specifically tailored for wine label recognition. This method enhances deep learning model performance by generating visually realistic training samples from a single real-world wine lab

William Merrill, Jackson Petty, Ashish Sabharwal

State-space models (SSMs) have emerged as a potential alternative architecture for building large language models (LLMs) compared to the previously ubiquitous transformer architecture. One theoretical weakness of transformers is that they cannot express certain kinds of sequential computation and state tracking (Merrill & Sabharwal, 2023), which SSMs are exp

Zehong Ke, Yanbo Jiang, Yuning Wang, Hao Cheng

With the advancement of deep learning technology, data-driven methods are increasingly used in the decision-making of autonomous driving, and the quality of datasets greatly influenced the model performance. Although current datasets have made significant progress in the collection of vehicle and environment data, emphasis on human-end data including the dri

Sreenitha Kasarapu, Sathwika Bavikadi, Sai Manoj Pudukotai Dinakarrao

The widespread integration of embedded systems across various industries has facilitated seamless connectivity among devices and bolstered computational capabilities. Despite their extensive applications, embedded systems encounter significant security threats, with one of the most critical vulnerabilities being malicious software, commonly known as malware.

Yewei Song, Cedric Lothritz, Daniel Tang, Tegawendé F. Bissyandé

This paper revisits recent code similarity evaluation metrics, particularly focusing on the application of Abstract Syntax Tree (AST) editing distance in diverse programming languages. In particular, we explore the usefulness of these metrics and compare them to traditional sequence similarity metrics. Our experiments showcase the effectiveness of AST editin

Dipkamal Bhusal, Md Tanvirul Alam, Monish K. Veerabhadran, Michael Clifford

Deep neural networks for classification are vulnerable to adversarial attacks, where small perturbations to input samples lead to incorrect predictions. This susceptibility, combined with the black-box nature of such networks, limits their adoption in critical applications like autonomous driving. Feature-attribution-based explanation methods provide relevan

R. Michael Alvarez, Jacob Morrier

This article proposes a new approach for assessing the quality of answers in political question-and-answer sessions. We measure the quality of an answer based on how easily and accurately it can be recognized in a random set of candidate answers given the question's text. This measure reflects the answer's relevance and depth of engagement with the question.

Jasel Berra-Montiel, Hugo Garcia-Compean, Alberto Molgado

In this paper we address the relation between the star exponentials emerging within the Deformation Quantization formalism and Feynman's path integrals associated with propagators in quantum dynamics. In order to obtain such a relation, we start by visualizing the quantum propagator as an integral transform of the star exponential by means of the symbol corr

Aref Azizpour, Tai D. Nguyen, Manil Shrestha, Kaidi Xu

As generative AI progresses rapidly, new synthetic image generators continue to emerge at a swift pace. Traditional detection methods face two main challenges in adapting to these generators: the forensic traces of synthetic images from new techniques can vastly differ from those learned during training, and access to data for these new generators is often l

Yichun Zhao, George Tzanetakis

Sonification can provide valuable insights about data but most existing approaches are not designed to be controlled by the user in an interactive fashion. Interactions enable the designer of the sonification to more rapidly experiment with sound design and allow the sonification to be modified in real-time by interacting with various control parameters. In

Sreenitha Kasarapu, Sanket Shukla, Sai Manoj Pudukotai Dinakarrao

In recent years, networked IoT systems have revolutionized connectivity, portability, and functionality, offering a myriad of advantages. However, these systems are increasingly targeted by adversaries due to inherent security vulnerabilities and limited computational and storage resources. Malicious applications, commonly known as malware, pose a significan

Camelia D. Brumar, Sam Molnar, Gabriel Appleby, Kristi Potter

Despite decision-making being a vital goal of data visualization, little work has been done to differentiate decision-making tasks within the field. While visualization task taxonomies and typologies exist, they often focus on more granular analytical tasks that are too low-level to describe large complex decisions, which can make it difficult to reason abou

Paolo Faraboschi, Ellis Giles, Justin Hotard, Konstanty Owczarek

The world has recently witnessed an unprecedented acceleration in demands for Machine Learning and Artificial Intelligence applications. This spike in demand has imposed tremendous strain on the underlying technology stack in supply chain, GPU-accelerated hardware, software, datacenter power density, and energy consumption. If left on the current technologic

Rodolfo Araya, Alfonso Caiazzo, Franz Chouly

We discuss how slip conditions for the Stokes equation can be handled using Nitsche method, for a stabilized finite element discretization. Emphasis is made on the interplay between stabilization and Nitsche terms. Well-posedness of the discrete problem and optimal convergence rates, in natural norm for the velocity and the pressure, are established, and ill

Zongren Zou, Tingwei Meng, Paula Chen, Jérôme Darbon

Uncertainty quantification (UQ) in scientific machine learning (SciML) combines the powerful predictive power of SciML with methods for quantifying the reliability of the learned models. However, two major challenges remain: limited interpretability and expensive training procedures. We provide a new interpretation for UQ problems by establishing a new theor

Sreenitha Kasarapu, Sanket Shukla, Sai Manoj Pudukotai Dinakarrao

The widespread integration of IoT devices has greatly improved connectivity and computational capabilities, facilitating seamless communication across networks. Despite their global deployment, IoT devices are frequently targeted for security breaches due to inherent vulnerabilities. Among these threats, malware poses a significant risk to IoT devices. The l

Qile Zhang, Fan Guo, William Daughton, Xiaocan Li

Understanding plasma dynamics and nonthermal particle acceleration in 3D magnetic reconnection has been a long-standing challenge. In this paper, we explore these problems by performing large-scale fully kinetic simulations of multi-xline plasmoid reconnection with various parameters in both the weak and strong guide field regimes. In each regime, we have id

Matthew DeLorenzo, Vasudev Gohil, Jeyavijayan Rajendran

Large Language Models (LLMs) have proved effective and efficient in generating code, leading to their utilization within the hardware design process. Prior works evaluating LLMs' abilities for register transfer level code generation solely focus on functional correctness. However, the creativity associated with these LLMs, or the ability to generate novel an

Manideep Reddy Aliminati, Bharatesh Chakravarthi, Aayush Atul Verma, Arpitsinh Vaghela

Recently, event-based vision sensors have gained attention for autonomous driving applications, as conventional RGB cameras face limitations in handling challenging dynamic conditions. However, the availability of real-world and synthetic event-based vision datasets remains limited. In response to this gap, we present SEVD, a first-of-its-kind multi-view ego

Baochang Zhang, Mai Bui, Cheng Wang, Felix Bourier

During endovascular interventions, physicians have to perform accurate and immediate operations based on the available real-time information, such as the shape and position of guidewires observed on the fluoroscopic images, haptic information and the patients' physiological signals. For this purpose, real-time and accurate guidewire segmentation and tracking

Mohammed Al Otmi, Ping Lin, William Schertzer, Coray M. Colina

Anion exchange membranes are used in alkaline fuel cells and offer a promising alternative to the more expensive proton exchange membrane fuel cells. However, hydroxide ion conductivity in anion exchange membranes is low, and the quest for membranes with superior ion conductivity, mechanical robustness, and chemical stability is ongoing. In this study, we us

Thomas Bonis, Laurent Decreusefond, Viet Chi Tran, Zhihan Iris Zhang

The notion of Laplacian of a graph can be generalized to simplicial complexes and hypergraphs, and contains information on the topology of these structures. Even for a graph, the consideration of associated simplicial complexes is interesting to understand its shape. Whereas the Laplacian of a graph has a simple probabilistic interpretation as the generator

Thomas Boeck, Mattias Brynjell-Rahkola, Yohann Duguet

We study the energy stability of pressure-driven laminar magnetohydrodynamic flow in a rectangular duct with transverse homogeneous magnetic field and electrically insulating walls. For sufficiently strong fields, the laminar velocity distribution has a uniform core and convex Hartmann and Shercliff boundary layers on the walls perpendicular and parallel to

Xuezhe Ma, Xiaomeng Yang, Wenhan Xiong, Beidi Chen

The quadratic complexity and weak length extrapolation of Transformers limits their ability to scale to long sequences, and while sub-quadratic solutions like linear attention and state space models exist, they empirically underperform Transformers in pretraining efficiency and downstream task accuracy. We introduce Megalodon, a neural architecture for effic

James D. Cunningham, Conrad S. Tucker

A significant amount of society's infrastructure can be modeled using graph structures, from electric and communication grids, to traffic networks, to social networks. Each of these domains are also susceptible to the cascading spread of negative impacts, whether this be overloaded devices in the power grid or the reach of a social media post containing misi

Leonid Petrov

Very long baseline interferometry (VLBI) technique allows us to determine positions of thousands of radio sources using the absolute astrometry approach. I have investigated the impact of a selection of observing frequencies in a range from 2 to 43 GHz in single-band, dual-band, and quad-band observing modes on astrometric results. I processed seven datasets

Brinnae Bent

In this study, we identify the need for an interpretable, quantitative score of the repeatability, or consistency, of image generation in diffusion models. We propose a semantic approach, using a pairwise mean CLIP (Contrastive Language-Image Pretraining) score as our semantic consistency score. We applied this metric to compare two state-of-the-art open-sou

B. M. O. Fraga, C. R. Bom, A. Santos, E. Russeil

The upcoming Legacy Survey of Space and Time (LSST) is expected to detect a few million transients per night, which will generate a live alert stream during the entire ten years of the survey. This stream will be distributed via community brokers whose task is to select subsets of the stream and direct them to scientific communities. Given the volume and com

Katie Christensen, Lyric Otto, Seth Bassetti, Claudia Tebaldi

Earth system models (ESMs) are the principal tools used in climate science to generate future climate projections under various atmospheric emissions scenarios on a global or regional scale. Generative deep learning approaches are suitable for emulating these tools due to their computational efficiency and ability, once trained, to generate realizations in a

Zekai Qu, Ruobing Xie, Chaojun Xiao, Xingwu Sun

Sequential recommendation (SR) has seen significant advancements with the help of Pre-trained Language Models (PLMs). Some PLM-based SR models directly use PLM to encode user historical behavior's text sequences to learn user representations, while there is seldom an in-depth exploration of the capability and suitability of PLM in behavior sequence modeling.

Larry R Nittler, Jens Barosch, Katherine Burgess, Rhonda M Stroud

We report the H, C, and N isotopic compositions of microscale (0.2 to 2$\mu$m) organic matter in samples of asteroid Ryugu and the Orgueil CI carbonaceous chondrite. Three regolith particles of asteroid Ryugu, returned by the Hayabusa2 spacecraft, and several fragments of Orgueil were analyzed by NanoSIMS isotopic imaging. The isotopic distributions of the R

The Tracker Group of the CMS Collaboration

A new tracking detector will be installed as part of the Phase-2 upgrade of the CMS detector for the high-luminosity LHC era. This tracking detector includes the Inner Tracker, equipped with silicon pixel sensor modules, and the Outer Tracker, consisting of modules with two parallel stacked silicon sensors. The Outer Tracker front-end ASICs will be able to c

Yingchaojie Feng, Zhizhang Chen, Zhining Kang, Sijia Wang

The proliferation of large language models (LLMs) has underscored concerns regarding their security vulnerabilities, notably against jailbreak attacks, where adversaries design jailbreak prompts to circumvent safety mechanisms for potential misuse. Addressing these concerns necessitates a comprehensive analysis of jailbreak prompts to evaluate LLMs' defensiv

Sean Oesch, Phillipe Austria, Amul Chaulagain, Brian Weber

Defenders are overwhelmed by the number and scale of attacks against their networks.This problem will only be exacerbated as attackers leverage artificial intelligence to automate their workflows. We propose a path to autonomous cyber agents able to augment defenders by automating critical steps in the cyber defense life cycle.

Manuel Arnese, Daniel Lacker

Mean field variational inference (VI) is the problem of finding the closest product (factorized) measure, in the sense of relative entropy, to a given high-dimensional probability measure $\rho$. The well known Coordinate Ascent Variational Inference (CAVI) algorithm aims to approximate this product measure by iteratively optimizing over one coordinate (fact

Malek Mechergui, Sarath Sreedharan

Detecting and handling misspecified objectives, such as reward functions, has been widely recognized as one of the central challenges within the domain of Artificial Intelligence (AI) safety research. However, even with the recognition of the importance of this problem, we are unaware of any works that attempt to provide a clear definition for what constitut

Tingjie Zhou, Zhihong Xia

As an application of the representation theory for the dihedral groups, we study the symmetric central configurations in the n-body problem where $n$ equal masses are placed at the vertices of a regular $n$-gon. Since the Hessian matrices at these configurations are typically very large, particularly when $n$ is large, computations of their eigenvalues prese