February 2024 arXiv papers — page 80

Arman Adibi, Nicolo Dal Fabbro, Luca Schenato, Sanjeev Kulkarni

Motivated by applications in large-scale and multi-agent reinforcement learning, we study the non-asymptotic performance of stochastic approximation (SA) schemes with delayed updates under Markovian sampling. While the effect of delays has been extensively studied for optimization, the manner in which they interact with the underlying Markov process to shape

Xi Lin, Yewei Huang, Fanfei Chen, Brendan Englot

Collision avoidance algorithms for Autonomous Surface Vehicles (ASV) that follow the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs) have been proposed in recent years. However, it may be difficult and unsafe to follow COLREGs in congested waters, where multiple ASVs are navigating in the presence of static obstacles an

Shivam Dangwal, Kaveh Edalati, Ruslan Z. Valiev, Terence G. Langdon

Strengthening by grain refinement via the Hall-Petch mechanism and softening by nanograin formation via the inverse Hall-Petch mechanism have been the subject of argument for decades, particularly for ultrafine-grained materials. In this study, the Hall-Petch relationship is examined for ultrafine-grained magnesium, aluminum, copper, and iron produced by sev

Robel Efrem, Alex Coutu, Sajad Saeedi

Multirotor Unmanned Aerial Vehicles (UAV)s have recently become an important instrument for collecting mineral data, enabling more effective and accurate geological investigations. This paper explores the difficulties in mounting high-sensitivity sensors on a UAV platform, including electromagnetic interference, payload dynamics, and maintaining stable senso

Hao Hu, Boshi Yang

Semidefinite programming (SDP) relaxations can provide strong bounds for binary and mixed-binary optimization problems, but their practical use is often limited by matrix variables of large order and by failures of Slater's condition, which can contribute to numerical difficulties for interior-point solvers. We propose \emph{affine facial reduction (affi

Hao Hu

Facial reduction (FR) is an important tool in linear and semidefinite programming, providing both algorithmic and theoretical insights into these problems. The maximum length of an FR sequence for a convex set is referred to as the maximum singularity degree (MSD). We observe that the behavior of certain FR algorithms can be explained through the MSD. Combin

Zizhong Li, Haopeng Zhang, Jiawei Zhang

Retrieval-augmented generation framework can address the limitations of large language models by enabling real-time knowledge updates for more accurate answers. An efficient way in the training phase of retrieval-augmented models is attention distillation, which uses attention scores as a supervision signal instead of manually annotated query-document pairs.

Harsh Shrivastava

We discovered that the neural networks, especially the deep ReLU networks, demonstrate an `over-generalization' phenomenon. That is, the output values for the inputs that were not seen during training are mapped close to the output range that were observed during the learning process. In other words, the neural networks learn a many-to-one mapping and this e

Jie Xu, Hanbo Zhang, Xinghang Li, Huaping Liu

Linguistic ambiguity is ubiquitous in our daily lives. Previous works adopted interaction between robots and humans for language disambiguation. Nevertheless, when interactive robots are deployed in daily environments, there are significant challenges for natural human-robot interaction, stemming from complex and unpredictable visual inputs, open-ended inter

Oluwafemi F Olaiyapo

The objective of this research is to examine how sentiment analysis can be employed to generate trading signals for the Foreign Exchange (Forex) market. The author assessed sentiment in social media posts and news articles pertaining to the United States Dollar (USD) using a combination of methods: lexicon-based analysis and the Naive Bayes machine learning

Jialei Xu, Wei Yin, Dong Gong, Junjun Jiang

Depth estimation is a critical technology in autonomous driving, and multi-camera systems are often used to achieve a 360$^\circ$ perception. These 360$^\circ$ camera sets often have limited or low-quality overlap regions, making multi-view stereo methods infeasible for the entire image. Alternatively, monocular methods may not produce consistent cross-view

Yankai Chen, Yixiang Fang, Qiongyan Wang, Xin Cao

Node importance estimation problem has been studied conventionally with homogeneous network topology analysis. To deal with network heterogeneity, a few recent methods employ graph neural models to automatically learn diverse sources of information. However, the major concern revolves around that their full adaptive learning process may lead to insufficient

Shipeng Zhong, Hongbo Chen, Yuhua Qi, Dapeng Feng

Collaborative state estimation using different heterogeneous sensors is a fundamental prerequisite for robotic swarms operating in GPS-denied environments, posing a significant research challenge. In this paper, we introduce a centralized system to facilitate collaborative LiDAR-ranging-inertial state estimation, enabling robotic swarms to operate without th

Teruyuki Katsuoka, Tomohiro Shiraishi, Daiki Miwa, Vo Nguyen Le Duy

Anomaly localization in images -- identifying regions that deviate from normal patterns -- is vital in applications such as medical diagnosis and industrial inspection. A recent trend is the use of image generation models in anomaly localization, where these models generate normal-looking counterparts of anomalous images, thereby allowing flexible and adapti

Raktim Kumar Mondol, Ewan K. A. Millar, Arcot Sowmya, Erik Meijering

Survival risk stratification is an important step in clinical decision making for breast cancer management. We propose a novel deep learning approach for this purpose by integrating histopathological imaging, genetic and clinical data. It employs vision transformers, specifically the MaxViT model, for image feature extraction, and self-attention to capture i

Jiang Zhou

A set of unit vectors in $\mathbb{R}^d$ is a called a spherical two-distance set if the inner products of distinct vectors only take two values. In this paper, we give explicit correspondence between spherical two-distance sets and graphs with specific spectral properties, and derive some bounds on the maximum size of spherical two-distance sets.

Jake Horder, Dominic Scognamiglio, Ádám Ganyecz, Viktor Ivády

Hexagonal boron nitride (hBN) has recently gained attention as a solid state host of quantum emitters. However, hBN emitters reported to date lack the properties needed for their deployment in scalable quantum technologies. Here we employ spectral hole burning spectroscopy and resonant polarization measurements to observe nearly-coherent hBN quantum emitters

Wenhao Gao, Priyanka Raghavan, Ron Shprints, Connor W. Coley

A synthetic method's substrate tolerance and generality are often showcased in a "substrate scope" table. However, substrate selection exhibits a frequently discussed publication bias: unsuccessful experiments or low-yielding results are rarely reported. In this work, we explore more deeply the relationship between such publication bias and chemical reactivi

Caohan Cheng, Lin Chen

The unextendible orthogonal matrices (UPBs) can be used for various problems in quantum information. We provide an algorithm to check if two UPBs are non-equivalent to each other. We give a method to construct UPBs and we apply this method to find all $5$-qubit UPBs of size eight. We apply the algorithm to check if the $5$-qubit UPBs of size eight are non-eq

Shivam Dangwal, Kaveh Edalati

The ability of high-entropy alloys (HEAs) for hydrogen storage is a rather new topic in the hydrogen community. HEAs with the C14 Laves phase have shown a high potential to reversibly store hydrogen at room temperature, but most of these alloys require a high-temperature activation treatment. This study explores the role of interphase boundaries on the easy

Daniel Cruz-Delgado, J. Enrique Antonio-Lopez, Armando Perez-Leija, Nicolas K. Fontaine

Multimode optical fibers represent the ideal platform for transferring multidimensional light states. However, dispersion degrades the correlations between the light's degrees of freedom, thus limiting the effective transport of ultrashort pulses between distant nodes of optical networks. Here, we demonstrate that tailoring the spatiotemporal structure of ul

Alexander Wan, Eric Wallace, Dan Klein

Retrieval-augmented language models are being increasingly tasked with subjective, contentious, and conflicting queries such as "is aspartame linked to cancer". To resolve these ambiguous queries, one must search through a large range of websites and consider "which, if any, of this evidence do I find convincing?". In this work, we study how LLMs answer this

Shivam Dangwal, Kaveh Edalati

The development of alloys that are hydrogenated and dehydrogenated quickly and actively at room temperature is a challenge for the safe and compact storage of hydrogen. In this study, a new high-entropy alloy (HEA) with AB-type configuration (A: hydride-forming elements, B: inert-to-hydrogen elements) was designed by considering valence electron concentratio

Souvik Kundu, Anthony Sarah, Vinay Joshi, Om J Omer

With the recent growth in demand for large-scale deep neural networks, compute in-memory (CiM) has come up as a prominent solution to alleviate bandwidth and on-chip interconnect bottlenecks that constrain Von-Neuman architectures. However, the construction of CiM hardware poses a challenge as any specific memory hierarchy in terms of cache sizes and memory

W. K. Schief, U. Hertrich-Jeromin, B. G. Konopelchenko

It is shown that a canonical geometric setting of the integrable TED equation is a Kahlerian tangent bundle of an affine manifold. The remarkable multi-dimensional consistency of this 4+4-dimensional dispersionless partial differential equation arises naturally in this context. In a particular 4-dimensional reduction, the affine manifolds turn out to be self

Shi Fu, Sen Zhang, Yingjie Wang, Xinmei Tian

This paper tackles the emerging challenge of training generative models within a self-consuming loop, wherein successive generations of models are recursively trained on mixtures of real and synthetic data from previous generations. We construct a theoretical framework to rigorously evaluate how this training procedure impacts the data distributions learned

Pedro Freire, ChengCheng Tan, Adam Gleave, Dan Hendrycks

Do language models implicitly learn a concept of human wellbeing? We explore this through the ETHICS Utilitarianism task, assessing if scaling enhances pretrained models' representations. Our initial finding reveals that, without any prompt engineering or finetuning, the leading principal component from OpenAI's text-embedding-ada-002 achieves 73.9% accuracy

Jeffrey Lim, Po T. Wang, Wonjoon Sohn, Claudia Serrano-Amenos

Current treatments for paraplegia induced by spinal cord injury (SCI) are often limited by the severity of the injury. The accompanying loss of sensory and motor functions often results in reliance on wheelchairs, which in turn causes reduced quality of life and increased risk of co-morbidities. While brain-computer interfaces (BCIs) for ambulation have show

Mateus Oliveira da Silva, Caio Pinheiro Santana, Diedre Santos do Carmo, Letícia Rittner

Identifying and characterizing brain fiber bundles can help to understand many diseases and conditions. An important step in this process is the estimation of fiber orientations using Diffusion-Weighted Magnetic Resonance Imaging (DW-MRI). However, obtaining robust orientation estimates demands high-resolution data, leading to lengthy acquisitions that are n

Georgios Tsekenis, Giulio Cimini, Marinos Kalafatis, Achille Giacometti

We define bipartite and monopartite relational networks of chemical elements and compounds using two different datasets of inorganic chemical and material compounds, as well as study their topology. We discover that the connectivity between elements and compounds is distributed exponentially for materials, and with a fat tail for chemicals. Compounds network

Kohei Obata, Koki Kawabata, Yasuko Matsubara, Yasushi Sakurai

Subsequence clustering of time series is an essential task in data mining, and interpreting the resulting clusters is also crucial since we generally do not have prior knowledge of the data. Thus, given a large collection of tensor time series consisting of multiple modes, including timestamps, how can we achieve subsequence clustering for tensor time series

Shuman Sun, Mark W. Harrington, Fatemehsadat Tabatabaei, Samin Hanifi

Optical frequency division (OFD) has revolutionized microwave and mmWave generation and set spectral purity records owing to its unique capability to transfer high fractional stability from optical to electronic frequencies. Recently, rapid developments in integrated optical reference cavities and microresonator-based optical frequency combs (microcombs) hav

Niclas Boehmer, Yash Nair, Sanket Shah, Lucas Janson

When resources are scarce, an allocation policy is needed to decide who receives a resource. This problem occurs, for instance, when allocating scarce medical resources and is often solved using modern ML methods. This paper introduces methods to evaluate index-based allocation policies -- that allocate a fixed number of resources to those who need them the

Md Arafat Sultan, Jatin Ganhotra, Ramón Fernandez Astudillo

We introduce a structured chain-of-thought (SCoT) prompting approach to generating content-grounded multi-turn question-answer conversations using a pre-trained large language model (LLM). At the core of our proposal is a structured breakdown of the complex task into a number of states in a state machine, so that actions corresponding to various subtasks, e.

Cheng Feng, Kedi Zheng, Lanqing Shan, Hani Alers

Peer-to-peer (P2P) trading is seen as a viable solution to handle the growing number of distributed energy resources in distribution networks. However, when dealing with large-scale consumers, there are several challenges that must be addressed. One of these challenges is limited communication capabilities. Additionally, prosumers may have specific preferenc

Teng Guo, Jingjin Yu

Multi-Robot Path Planning (MRPP) on graphs, equivalently known as Multi-Agent Path Finding (MAPF), is a well-established NP-hard problem with critically important applications. As serial computation in (near)-optimally solving MRPP approaches the computation efficiency limit, parallelization offers a promising route to push the limit further, especially in h

Teng Guo, Jingjin Yu

Path planning for multiple non-holonomic robots in continuous domains constitutes a difficult robotics challenge with many applications. Despite significant recent progress on the topic, computationally efficient and high-quality solutions are lacking, especially in lifelong settings where robots must continuously take on new tasks. In this work, we make it

Teng Guo, Jingjin Yu

Parking lots and autonomous warehouses for accommodating many vehicles/robots adopt designs in which the underlying graphs are \emph{well-connected} to simplify planning and reduce congestion. In this study, we formulate and delve into the \emph{largest well-connected set} (LWCS) problem and explore its applications in layout design for multi-robot path plan

Niclas Boehmer, Piotr Faliszewski, Łukasz Janeczko, Andrzej Kaczmarczyk

We analyze how numerical experiments regarding elections were conducted within the computational social choice literature (focusing on papers published in the IJCAI, AAAI, and AAMAS conferences). We analyze the sizes of the studied elections and the methods used for generating preference data, thereby making previously hidden standards and practices explicit

Pengrui Han, Rafal Kocielnik, Adhithya Saravanan, Roy Jiang

Large Language models (LLMs), while powerful, exhibit harmful social biases. Debiasing is often challenging due to computational costs, data constraints, and potential degradation of multi-task language capabilities. This work introduces a novel approach utilizing ChatGPT to generate synthetic training data, aiming to enhance the debiasing of LLMs. We propos

Shayan Azizi, Ehsan Asadi, Shaun Howard, Benjamin W. Muir

This paper addresses the gap between the capabilities and utilisation of robotics and automation in laboratory settings and builds upon the concept of Self Driving Labs (SDL). %to significantly impact laboratory operations. We introduce an innovative approach to the temporal characterisation of materials. The article discusses the challenges posed by manual

Tomoharu Suda

Chemical reaction network theory is a powerful framework to describe and analyze chemical systems. While much about the concentration profile in an equilibrium state can be determined in terms of the graph structure, the overall reaction's time evolution depends on the network's kinetic rate function. In this article, we consider the problem of the effective

Dohoon Choi, Min Lee, Youngmin Lee, Subong Lim

We obtain an upper bound for the dimension of the cuspidal automorphic forms for $\mathrm{GL}_2$ over a number field, whose archimedean local representations are not tempered. More precisely, we prove the following result. Let $F$ be a number field and $\mathbb{A}_{F}$ be the ring of adeles of $F$. Let $\mathcal{O}_{F}$ be the ring of integers of $F$. Let $\

Bharat Srikishan, Anika Tabassum, Srikanth Allu, Ramakrishnan Kannan

Deep learning architectures have achieved state-of-the-art (SOTA) performance on computer vision tasks such as object detection and image segmentation. This may be attributed to the use of over-parameterized, monolithic deep learning architectures executed on large datasets. Although such architectures lead to increased accuracy, this is usually accompanied

Sinya Aoki, Takumi Doi

In this chapter, the current status on baryon-baryon interactions such as nuclear forces in lattice Quantum ChromoDynamics (QCD) is reviewed. In studies of baryon-baryon interactions in lattice QCD, the most reliable method so far is the potential method, proposed by the Hadrons to Atomic nuclei from Lattice QCD (HAL QCD) collaboration, whose formulation, pr

Stefan Steinerberger, Rekha R. Thomas

Given a finite, simple, connected graph $G=(V,E)$ with $|V|=n$, we consider the associated graph Laplacian matrix $L = D - A$ with eigenvalues $0 = \lambda_1 < \lambda_2 \leq \dots \leq \lambda_n$. One can also consider the same graph equipped with positive edge weights $w:E \rightarrow \mathbb{R}_{> 0}$ normalized to $\sum_{e \in E} w_e = |E|$ and the assoc

Shuai Wang, Shengyao Zhuang, Guido Zuccon

Text stemming is a natural language processing technique that is used to reduce words to their base form, also known as the root form. The use of stemming in IR has been shown to often improve the effectiveness of keyword-matching models such as BM25. However, traditional stemming methods, focusing solely on individual terms, overlook the richness of context

Nicholas J. Rattenbury, Joseph Ashby, Francis Bennet, Marcus Birch

Networks of ground stations designed to transmit and receive at optical wavelengths through the atmosphere offer an opportunity to provide on-demand, high-bandwidth, secure communications with spacecraft in Earth orbit and beyond. This work describes the operation and activities of current Free Space Optical Communication (FSOC) ground stations in Germany an

Yavuz Faruk Bakman, Duygu Nur Yaldiz, Baturalp Buyukates, Chenyang Tao

Generative Large Language Models (LLMs) are widely utilized for their excellence in various tasks. However, their tendency to produce inaccurate or misleading outputs poses a potential risk, particularly in high-stakes environments. Therefore, estimating the correctness of generative LLM outputs is an important task for enhanced reliability. Uncertainty Esti

Reshabh K Sharma, Vinayak Gupta, Dan Grossman

Large language models (LLMs) have profoundly transformed natural language applications, with a growing reliance on instruction-based definitions for designing chatbots. However, post-deployment the chatbot definitions are fixed and are vulnerable to attacks by malicious users, emphasizing the need to prevent unethical applications and financial losses. Exist

Pak Hung Au, Mark Whitmeyer

We study the ramifications of increased commitment power for information provision in an oligopolistic market with search frictions. Although prices are posted and, therefore, guide search, if firms cannot commit to information provision policies, there is no active search at equilibrium so consumers visit (and purchase from) at most one firm. If firms can g

Fengqing Jiang, Zhangchen Xu, Luyao Niu, Zhen Xiang

Safety is critical to the usage of large language models (LLMs). Multiple techniques such as data filtering and supervised fine-tuning have been developed to strengthen LLM safety. However, currently known techniques presume that corpora used for safety alignment of LLMs are solely interpreted by semantics. This assumption, however, does not hold in real-wor

Dominik Wagner, Basim Khajwal, C. -H. Luke Ong

It is well-known that the reparameterisation gradient estimator, which exhibits low variance in practice, is biased for non-differentiable models. This may compromise correctness of gradient-based optimisation methods such as stochastic gradient descent (SGD). We introduce a simple syntactic framework to define non-differentiable functions piecewisely and pr

Farzad Faramarzi, Ryan Stephenson, Sasha Sypkens, Byeong H. Eom

Kinetic inductance traveling-wave parametric amplifiers (KI-TWPA) have a wide instantaneous bandwidth with near quantum-limited performance and a relatively high dynamic range. Because of this, they are suitable readout devices for cryogenic detectors and superconducting qubits and have a variety of applications in quantum sensing. This work discusses the de

Mohammad Heydari Rad, Farhan Farsi, Shayan Bali, Romina Etezadi

Nowadays, the usage of Large Language Models (LLMs) has increased, and LLMs have been used to generate texts in different languages and for different tasks. Additionally, due to the participation of remarkable companies such as Google and OpenAI, LLMs are now more accessible, and people can easily use them. However, an important issue is how we can detect AI

Vinay M. S., Minh-Hao Van, Xintao Wu

Large Language Models (LLMs) have showcased their In-Context Learning (ICL) capabilities, enabling few-shot learning without the need for gradient updates. Despite its advantages, the effectiveness of ICL heavily depends on the choice of demonstrations. Selecting the most effective demonstrations for ICL remains a significant research challenge. To tackle th

Rafael Luque Merino, Dumitru Calugaru, Haoyu Hu, Jaime Diez-Merida

It has been recently postulated, that the strongly correlated flat bands of magicangle twisted bilayer graphene (MATBG) can host coexisting heavy and light carriers. While transport and spectroscopic measurements have shown hints of this behavior, a more direct experimental proof is still lacking. Here, we explore the thermoelectric response of MATBG through

Saeid Haghighatshoar, Dylan R Muir

Sound source localisation is used in many consumer devices, to isolate audio from individual speakers and reject noise. Localization is frequently accomplished by ``beamforming'', which combines phase-shifted audio streams to increase power from chosen source directions, under a known microphone array geometry. Dense band-pass filters are often needed to obt

Nineli Lashkarashvili, Wen Wu, Guangzhi Sun, Philip C. Woodland

Foundation models have shown superior performance for speech emotion recognition (SER). However, given the limited data in emotion corpora, finetuning all parameters of large pre-trained models for SER can be both resource-intensive and susceptible to overfitting. This paper investigates parameter-efficient finetuning (PEFT) for SER. Various PEFT adaptors ar

Rishabh Bhardwaj, Do Duc Anh, Soujanya Poria

Aligned language models face a significant limitation as their fine-tuning often results in compromised safety. To tackle this, we propose a simple method RESTA that performs LLM safety realignment. RESTA stands for REstoring Safety through Task Arithmetic. At its core, it involves a simple arithmetic addition of a safety vector to the weights of the comprom

Pietro Capuozzo, John Estes, Brandon Robinson, Benjamin Suzzoni

Two-dimensional (2d) $\mathcal{N}=(4,4)$ Lie superalgebras can be either "small" or "large", meaning their R-symmetry is either $\mathfrak{so}(4)$ or $\mathfrak{so}(4) \oplus \mathfrak{so}(4)$, respectively. Both cases admit a superconformal extension and fit into the one-parameter family $\mathfrak{d}\left(2,1;\gamma\right)\oplus \mathfrak{d}\left(2,1;\gamm

Zhongping Zhang, Wenda Qin, Bryan A. Plummer

Machine-Generated Text (MGT) detection aims to identify a piece of text as machine or human written. Prior work has primarily formulated MGT detection as a binary classification task over an entire document, with limited work exploring cases where only part of a document is machine generated. This paper provides the first in-depth study of MGT that localizes

Muhammad Sohaib, Sang-Woon Jeon, Wei Yu

We consider a multi-user multi-server mobile edge computing (MEC) system, in which users arrive on a network randomly over time and generate computation tasks, which will be computed either locally on their own computing devices or be offloaded to one of the MEC servers. Under such a dynamic network environment, we propose a novel task offloading policy base

Rudy Geelen, Laura Balzano, Stephen Wright, Karen Willcox

We present a novel method for learning reduced-order models of dynamical systems using nonlinear manifolds. First, we learn the manifold by identifying nonlinear structure in the data through a general representation learning problem. The proposed approach is driven by embeddings of low-order polynomial form. A projection onto the nonlinear manifold reveals

Chiraag Kaushik, Ran Liu, Chi-Heng Lin, Amrit Khera

Classification models are expected to perform equally well for different classes, yet in practice, there are often large gaps in their performance. This issue of class bias is widely studied in cases of datasets with sample imbalance, but is relatively overlooked in balanced datasets. In this work, we introduce the concept of spectral imbalance in features a

Anxin Guo, Jingwei Li, Pattara Sukprasert, Samir Khuller

In this work, we study the cost efficient data versioning problem, where the goal is to optimize the storage and reconstruction (retrieval) costs of data versions, given a graph of datasets as nodes and edges capturing edit/delta information. One central variant we study is MinSum Retrieval (MSR) where the goal is to minimize the total retrieval costs, while

Naoki Sugishita, Kayo Kinjo, Jun Ohkubo

Nonlinearity plays a crucial role in deep neural networks. In this paper, we investigate the degree to which the nonlinearity of the neural network is essential. For this purpose, we employ the Koopman operator, extended dynamic mode decomposition, and the tensor-train format. The Koopman operator approach has been recently developed in physics and nonlinear

Yejiang Yang, Zihao Mo, Hoang-Dung Tran, Weiming Xiang

This paper proposes a transition system abstraction framework for neural network dynamical system models to enhance the model interpretability, with applications to complex dynamical systems such as human behavior learning and verification. To begin with, the localized working zone will be segmented into multiple localized partitions under the data-driven Ma

Hiroki Sukeno, Kazuki Ikeda, Tzu-Chieh Wei

In quantum many-body spin systems, the interplay between the entangling effect of multi-qubit Pauli measurements and the disentangling effect of single-qubit Pauli measurements may give rise to two competing effects. By introducing a randomized measurement pattern with such bases, a phase transition can be induced by altering the ratio between them. In this

Zihao Mo, Yejiang Yang, Shuaizheng Lu, Weiming Xiang

In this paper, we propose a method of repairing compressed Feedforward Neural Networks (FNNs) based on equivalence evaluation of two neural networks. In the repairing framework, a novel neural network equivalence evaluation method is developed to compute the output discrepancy between two neural networks. The output discrepancy can quantitatively characteriz

Jingyu Song, Lingjun Zhao, Katherine A. Skinner

We propose LiRaFusion to tackle LiDAR-radar fusion for 3D object detection to fill the performance gap of existing LiDAR-radar detectors. To improve the feature extraction capabilities from these two modalities, we design an early fusion module for joint voxel feature encoding, and a middle fusion module to adaptively fuse feature maps via a gated network. W

Jiajia Wang, Jimmy X. Huang, Xinhui Tu, Junmei Wang

Recent years have witnessed a substantial increase in the use of deep learning to solve various natural language processing (NLP) problems. Early deep learning models were constrained by their sequential or unidirectional nature, such that they struggled to capture the contextual relationships across text inputs. The introduction of bidirectional encoder rep

Shraddha Barke, Christian Poelitz, Carina Suzana Negreanu, Benjamin Zorn

Large language models (LLMs) are rapidly replacing help forums like StackOverflow, and are especially helpful for non-professional programmers and end users. These users are often interested in data-centric tasks, such as spreadsheet manipulation and data wrangling, which are hard to solve if the intent is only communicated using a natural-language descripti

Vijaya Raghavan T Ramkumar, Bahram Zonooz, Elahe Arani

Deep neural networks are susceptible to adversarial attacks, which can compromise their performance and accuracy. Adversarial Training (AT) has emerged as a popular approach for protecting neural networks against such attacks. However, a key challenge of AT is robust overfitting, where the network's robust performance on test data deteriorates with further t

Sergey Frolov, Davide Polvara, Alessandro Sfondrini

We propose the dressing factors for the scattering of massive particles on the worldsheet of mixed-flux $AdS_3\times S^3\times T^4$ superstrings, in the string and mirror kinematics. The proposal passes all self-consistency checks in the both kinematics, including for bound states. It matches with perturbative and semiclassical computations from the string s

Marjorie Drake, Charles Fefferman, Kevin Ren, Anna Skorobogatova

In this paper, we establish the existence of a bounded, linear extension operator $T: L^{2,p}(E) \to L^{2,p}(\mathbb{R}^2)$ when $1<p<2$ and $E$ is a finite subset of $\mathbb{R}^2$ contained in a line.

Xiao Xiao, Jian-Xin Zhu

We show that nonzero drag current in a double quantum-dot system, consisting of a biased drive dot and an unbiased passive dot coupled capacitively, can be generated by a Majorana bound state located at one of the leads connected to the passive dot. Importantly, the drag current induced by Majorana bound states, either an isolated Majorana bound state or two

Andrew Lytle, Carleton DeTar, Aida El-Khadra, Elvira Gámiz

We present an update for results on $B$-meson semileptonic decays using the highly improved staggered quark (HISQ) action for both valence and 2+1+1 sea quarks. The use of the highly improved action, combined with the MILC collaboration's gauge ensembles with lattice spacings down to $\sim$0.03 fm, allows the $b$ quark to be treated with the same discretizat

Oluwole Fagbohun, Rachel M. Harrison, Anton Dereventsov

Due to rapid advancements in the development of Large Language Models (LLMs), programming these models with prompts has recently gained significant attention. However, the sheer number of available prompt engineering techniques creates an overwhelming landscape for practitioners looking to utilize these tools. For the most efficient and effective use of LLMs

Gautam Machiraju, Alexander Derry, Arjun Desai, Neel Guha

Feature attribution, the ability to localize regions of the input data that are relevant for classification, is an important capability for ML models in scientific and biomedical domains. Current methods for feature attribution, which rely on "explaining" the predictions of end-to-end classifiers, suffer from imprecise feature localization and are inadequate

Miho Itoi, Kazuyoshi Yoshimi, Hanming Ma, Takahiro Misawa

We present a combined experimental and theoretical study on the quasi-one-dimensional organic conductor (TMTTF)$_2$PF$_6$, and elucidate the variation of its physical properties under pressure. We fully resolve the crystal structure by single crystal x-ray diffraction measurements using a diamond anvil cell up to 8 GPa, and based on the structural data, we p

Agam Shah, Arnav Hiray, Pratvi Shah, Arkaprabha Banerjee

In this paper, we investigate the influence of claims in analyst reports and earnings calls on financial market returns, considering them as significant quarterly events for publicly traded companies. To facilitate a comprehensive analysis, we construct a new financial dataset for the claim detection task in the financial domain. We benchmark various languag

Pietro Di Gianantonio, Abbas Edalat

We present a novel, yet rather simple construction within the traditional framework of Scott domains to provide semantics to probabilistic programming, thus obtaining a solution to a long-standing open problem in this area. Unlike current main approaches that employ some probability measures or continuous valuations on non-standard or rather complex structur

Zelio Fusco, Dirk Koenig, Sean C. Smith, Fiona Jean Beck

Photoreduction of carbon dioxide (CO$_2$) on plasmonic structures is of great interest in photocatalysis to aid selectivity. While species commonly found in reaction environments and associated intermediates can steer the reaction down different pathways by altering the potential energy landscape of the system, they are often not addressed when designing eff

Kai Chen, Zihao He, Jun Yan, Taiwei Shi

Large Language Models (LLMs) possess the potential to exert substantial influence on public perceptions and interactions with information. This raises concerns about the societal impact that could arise if the ideologies within these models can be easily manipulated. In this work, we investigate how effectively LLMs can learn and generalize ideological biase

Vedh Kannan

In protein structure analysis, the accurate characterization of secondary structure elements is crucial for understanding protein function and dynamics. This paper presents a software system designed for the comprehensive analysis of the secondary structure of protein models. Leveraging phi ({\phi}) and psi ({\psi}) torsion angles, the system utilises K-mean

Jianling Wang, Haokai Lu, James Caverlee, Ed Chi

The reasoning and generalization capabilities of LLMs can help us better understand user preferences and item characteristics, offering exciting prospects to enhance recommendation systems. Though effective while user-item interactions are abundant, conventional recommendation systems struggle to recommend cold-start items without historical interactions. To

Paramveer S. Dhillon, Somayeh Molaei, Jiaqi Li, Maximilian Golub

Advances in language modeling have paved the way for novel human-AI co-writing experiences. This paper explores how varying levels of scaffolding from large language models (LLMs) shape the co-writing process. Employing a within-subjects field experiment with a Latin square design, we asked participants (N=131) to respond to argumentative writing prompts und

Da Long, Zhitong Xu, Qiwei Yuan, Yin Yang

Fourier Neural Operator (FNO) is a powerful and popular operator learning method. However, FNO is mainly used in forward prediction, yet a great many applications rely on solving inverse problems. In this paper, we propose an invertible Fourier Neural Operator (iFNO) for jointly tackling the forward and inverse problems. We developed a series of invertible F

Satoru Hayami

Multiple-$Q$ states are expressed as a superposition of spin density waves at multiple ordering wave vectors, which results in unconventional complicated spin textures, such as skyrmion, hedgehog, and vortex. We investigate the multiple-$Q$ instability by focusing on the low-symmetric ordering wave vectors in momentum space. By systematically performing the

A. Giachero, M. R. Vissers, J. D. Wheeler, M. Malnou

A quantum-limited amplification chain is a fundamental advantage for any application that may benefit from the detection of very faint signals. Reading out arrays of superconducting detectors (TESs or MKIDs), resonant cavities, or qubits, calls for large bandwidth amplifiers in addition to having the lowest possible noise. At millikelvin temperatures, Kineti

Bodhinanda Chandra, Ryota Hashimoto, Ken Kamrin, Kenichi Soga

This paper presents a novel stabilized mixed material point method (MPM) designed for the unified modeling of free-surface and seepage flow. The unified formulation integrates the Navier-Stokes equation with the Darcy-Brinkman-Forchheimer equation, effectively capturing flows in both non-porous and porous domains. In contrast to the conventional Eulerian com

Leonardo F. Calderón, Paul Brumer

The interplay between electronic and intramolecular high-frequency vibrational degrees of freedom is ubiquitous in natural light-harvesting systems. Recent studies have indicated that an intramolecular vibrational donor-acceptor frequency difference can enhance energy transport. Here, we analyze the extent to which different intramolecular donor-acceptor vib

Deven Panchal

With increasing data requirements of users, cellular operators are finding new ways to fulfil these requirements. These attempts involve the practice of deploying Wi-Fi access points nearer to the user and backhauling it to the nearest eNB (in case of LTE and LTE-A). The paper studies LTE-U, an extension of LTE which works in the unlicensed spectrum, as a po

Hans-Christian Herbig, Daniel Herden, Christopher Seaton

We give an explicit solution formula for the polynomial regression problem in terms of Schur polynomials and Vandermonde determinants. We thereby generalize the work of Chang, Deng, and Floater to the case of model functions of the form $\sum _{i=1}^{n} a_{i} x^{d_{i}}$ for some integer exponents $d_{1} >d_{2} >\dotsc >d_{n} \geq 0$ and phrase the results us

C. Deffayet, R. P. Woodard

We consider the classic question posed by Pardo and Spergel about the price of abandoning dark matter in the context of an invariant, metric-based theory of gravity. Our answer is that the price is nonlocality. This has been known for some time in the context of the quasi-static regime. We show that it also applies for cosmology and we exhibit a model which

José Miguel Flores-Contró

In this article, we consider a risk process to model the capital of a household. Our work focuses on the analysis of the trapping time of such a process, where trapping occurs when a household's capital level falls into the poverty area. A function analogous to the classical Gerber-Shiu function is introduced, which incorporates information on the trapping t

Fredy Yip, A. C. H. Cheung

Dynamics generated from Hamiltonians enjoy potential pathways to quantisation, but standard Hamiltonians are only capable of generating conservative forces. Classes of Hamiltonians have been proposed in Berry et al. capable of generating non-conservative velocity-independent forces. Such Hamiltonians have been classified in the past, under the strict assumpt

Roméo Leylekian

We prove the existence of an open set minimizing the first eigenvalue of the Dirichlet polylaplacian of order $m\geq1$ under volume constraint. Moreover, the corresponding eigenfunction is shown to enjoy $C^{m-1,\alpha}$ H\"older regularity. This is performed for dimension $2\leq d\leq 4m$. In particular, our analysis answers the question of the existence of

Farhad Moghimifar, Yuan-Fang Li, Robert Thomson, Gholamreza Haffari

Coalition negotiations are a cornerstone of parliamentary democracies, characterised by complex interactions and strategic communications among political parties. Despite its significance, the modelling of these negotiations has remained unexplored with the domain of Natural Language Processing (NLP), mostly due to lack of proper data. In this paper, we intr