February 2024 arXiv papers — page 63

Hengjia Wei, Ron M. Roth

Error-correcting codes over the real field are studied which can locate outlying computational errors when performing approximate computing of real vector--matrix multiplication on resistive crossbars. Prior work has concentrated on locating a single outlying error and, in this work, several classes of codes are presented which can handle multiple errors. It

Satyabrat Sahoo, Narasimha Kumar

In this article, we study the asymptotic solutions of the generalized Fermat-type equation of signature $(p,p,3)$ over totally real number fields $K$, i.e., $Ax^p+By^p=Cz^3$ with prime exponent $p$ and $A,B,C \in \mathcal{O}_K \setminus \{0\}$. For certain class of fields $K$, we prove that $Ax^p+By^p=Cz^3$ has no asymptotic solutions over $K$ (resp., soluti

T. Zhang, Q. Hao, P. F. Chen

Solar active regions (ARs) are areas on the Sun with very strong magnetic fields where various activities take place. Prominences are one of the typical solar features in the solar atmosphere, whose eruptions often lead to solar flares and coronal mass ejections (CMEs). Therefore, studying their morphological features and their relationship with solar activi

Xiao Shi, Yun Shang

Variational quantum algorithms is one of the most representative algorithms in quantum computing, which has a wide range of applications in quantum machine learning, quantum simulation and other related fields. However, they face challenges associated with the barren plateau phenomenon, especially when dealing with large numbers of qubits, deep circuit layer

Karthik Ravichandran, Sarmistha Sarna Gomasta

This paper explores a novel technique for improving recall in cross-language information retrieval (CLIR) systems using iterative query refinement grounded in the user's lexical-semantic space. The proposed methodology combines multi-level translation, semantic embedding-based expansion, and user profile-centered augmentation to address the challenge of matc

Weile Luo, Ruibo Fan, Zeyu Li, Dayou Du

Graphics processing units (GPUs) are continually evolving to cater to the computational demands of contemporary general-purpose workloads, particularly those driven by artificial intelligence (AI) utilizing deep learning techniques. A substantial body of studies have been dedicated to dissecting the microarchitectural metrics characterizing diverse GPU gener

Zheheng Luo, Qianqian Xie, Sophia Ananiadou

Automated lay summarisation (LS) aims to simplify complex technical documents into a more accessible format to non-experts. Existing approaches using pre-trained language models, possibly augmented with external background knowledge, tend to struggle with effective simplification and explanation. Moreover, automated methods that can effectively assess the `l

Junbiao Pang, Tianyang Cai, Baochang Zhang, Jiaqi Wu

Although existing Quantization-Aware Training (QAT) methods intensively depend on knowledge distillation to guarantee performance, QAT still suffers from severe performance drop. The experiments have shown that vanilla quantization is sensitive to the perturbation from both the input and weights. Therefore, we assume that the generalization ability of QAT is

Mingyu Guan, Jack W. Stokes, Qinlong Luo, Fuchen Liu

The recent past has seen an increasing interest in Heterogeneous Graph Neural Networks (HGNNs), since many real-world graphs are heterogeneous in nature, from citation graphs to email graphs. However, existing methods ignore a tree hierarchy among metapaths, naturally constituted by different node types and relation types. In this paper, we present HetTree,

Yuying Zhao, Minghua Xu, Huiyuan Chen, Yuzhong Chen

Recommender systems (RSs) have gained widespread applications across various domains owing to the superior ability to capture users' interests. However, the complexity and nuanced nature of users' interests, which span a wide range of diversity, pose a significant challenge in delivering fair recommendations. In practice, user preferences vary significantly;

Yueqi Xie, Minghong Fang, Renjie Pi, Neil Gong

Large Language Models (LLMs) face threats from jailbreak prompts. Existing methods for detecting jailbreak prompts are primarily online moderation APIs or finetuned LLMs. These strategies, however, often require extensive and resource-intensive data collection and training processes. In this study, we propose GradSafe, which effectively detects jailbreak pro

Yipeng Wu, Audrey Farrell, Mitchell Sinclair, Chaojie Zhang

When a moderately intense, few-picoseconds long laser pulse ionizes gas to produce an underdense plasma column, a linear relativistic plasma wave or wake can be excited by the self-modulation instability that may prove useful for multi-bunch acceleration of externally injected electrons or positrons to high energies in a short distance. At the same time, due

Seiji Maekawa, Hayate Iso, Sairam Gurajada, Nikita Bhutani

While large language models (LMs) demonstrate remarkable performance, they encounter challenges in providing accurate responses when queried for information beyond their pre-trained memorization. Although augmenting them with relevant external information can mitigate these issues, failure to consider the necessity of retrieval may adversely affect overall p

Yongquan He, Zihan Wang, Peng Zhang, Zhaopeng Tu

Embedding entities and relations into continuous vector spaces has attracted a surge of interest in recent years. Most embedding methods assume that all test entities are available during training, which makes it time-consuming to retrain embeddings for newly emerging entities. To address this issue, recent works apply the graph neural network on the existin

Yuchao Wang, Yimin Wei, Guofeng Zhang, Shih Yu Chang

In a recent paper by Chen et al. [8], the authors initiated the control-theoretic study of a class of discrete-time multilinear time-invariant (MLTI) control systems, where system states, inputs, and outputs are all tensors endowed with the Einstein product. They established criteria for fundamental system-theoretic notions such as stability, reachability, a

Chen Wu, Fernando De la Torre

Text-to-image diffusion models have achieved remarkable performance in image synthesis, while the text interface does not always provide fine-grained control over certain image factors. For instance, changing a single token in the text can have unintended effects on the image. This paper shows a simple modification of classifier-free guidance can help disent

Qing Zhou, Yi-Zheng Zhen, Xin-Yu Xu, Shuai Zhao

Local unitary equivalence is an important ingredient for quantifying and classifying entanglement. Verifying whether or not two quantum states are local unitary equivalent is a crucial problem, where only the case of multipartite pure states is solved. For mixed states, however, the verification of local unitary equivalence is still a challenging problem. In

Shaojie Zhang, Yinghui Wang, Jiaxing Ma, Wei Li

Feature matching is a fundamental and crucial process in visual SLAM, and precision has always been a challenging issue in feature matching. In this paper, based on a multi-level fine matching strategy, we propose a new feature matching method called KTGP-ORB. This method utilizes the similarity of local appearance in the Hamming space generated by feature d

Zhiwei Wang, Huazheng Wang, Hongning Wang

Adversarial attacks against stochastic multi-armed bandit (MAB) algorithms have been extensively studied in the literature. In this work, we focus on reward poisoning attacks and find most existing attacks can be easily detected by our proposed detection method based on the test of homogeneity, due to their aggressive nature in reward manipulations. This mot

Javier Bracho, Eric Paulí Pérez, Luis Montejano, Jorge Luis Ramírez-Alfonsín

It is known that strongly involutive polyhedra are closely related to self-dual maps where the antipodal function acts as duality isomorphism. Such a family of polyhedra appears in different combinatorial, topological and geometric contexts, and is thus attractive to be studied. In this note, we determine the 10 antipodal pairings among the classification of

Shuzhang Zhong, Zebin Yang, Meng Li, Ruihao Gong

Recent advancements in generative large language models (LLMs) have significantly boosted the performance in natural language processing tasks. However, their efficiency is hampered by the inherent limitations in autoregressive token generation. While parallel decoding with token tree verification, e.g., Medusa, has been proposed to improve decoding parallel

Anthony Carr, Tamara M. Davis, Ryan Camilleri, Chris Lidman

We present high-resolution observations of nearby ($z\lesssim 0.1$) galaxies that have hosted Type Ia supernovae to measure systemic spectroscopic redshifts using the Wide Field Spectrograph (WiFeS) instrument on the Australian National University 2.3 m telescope at Siding Spring Observatory. While most of the galaxies targeted have previous spectroscopic re

Xudong Yu, Zijian Wang, Cheng-en Liu, Yiqing Feng

We propose here a set of new methods to directly detect light mass dark matter through its scattering with abundant atmospheric muons or accelerator beams. Firstly, we plan to use the free cosmic-ray muons interacting with dark matter in a volume surrounded by tracking detectors, to trace possible interaction between dark matter and muons. Secondly, we will

Minju Seo, Jinheon Baek, James Thorne, Sung Ju Hwang

Despite large successes of recent language models on diverse tasks, they suffer from severe performance degeneration in low-resource settings with limited training data available. Many existing works tackle this problem by generating synthetic data from the training data and then training models on them, recently using Large Language Models (LLMs). However,

Liu Weiwei, Hu Wenxuan, Jing Wei, Lei Lanxin

Autonomous vehicles trained through Multi-Agent Reinforcement Learning (MARL) have shown impressive results in many driving scenarios. However, the performance of these trained policies can be impacted when faced with diverse driving styles and personalities, particularly in highly interactive situations. This is because conventional MARL algorithms usually

Anurag Bhattacharjee, Aswin Gnanaskandan

In this paper, we present a mathematical model and numerical simulation of the evaporation and drying process of a liquid droplet containing suspended solids. This type of drying is commonly encountered in manufacturing processes such as spray drying and spray pyrolysis, which have applications in industries such as food and pharmaceuticals. The proposed mod

Jingyang Cao, Mu Zhou, Jiacheng Wang, Guangyuan Liu

With the advancement of AI technology and increasing attention to deep-sea exploration, the underwater Metaverse is gradually emerging. This paper explores the concept of underwater Metaverse, emerging virtual reality systems and services aimed at simulating and enhancing virtual experience of marine environments. First, we discuss potential applications of

Mohammad Sababheh, Hamid Reza Moradi, Qingxiang Xu, Shuo Zhao

The main purpose of this paper is, in the general setting of the adjointable operators on Hilbert $C^*$-modules, to develop two new tools that can be applied to deal with the positive solutions of certain operator equations, the operator norm as well as the numerical radius, respectively. Among other things, the positivity of a $2\times 2$ block operator mat

Sajal Kumar Giri, George C. Schatz

This study examines the Raman scattering and charge transfer properties of molecules adsorbed on the surface of a tetrahedral Au$_{120}$ nanoparticle based on the time-dependent density functional tight-binding (TD-DFTB) method. We study Raman scattering (SERS) enhancements for pyridine where the molecule is adsorbed either on the tip (V complex) or surface

Jiawen Shan, Zexin Wang, Dancheng Lu

Let $S=K[x_1,\ldots,x_n]$ and let $I$ be the $t$-path ideal of the line graph $L_n$ with $n$-vertices. It is shown that the set of associated prime ideals of $I^s$ is equal to the set of minimal prime ideals of $I$ for all $s\geq 1$, and we provide an explicit description of these prime ideals. Additionally, as the main contribution of this paper, we derive

Yuting Liu, Masamune Oguri

The Hubble constant, $H_0$, which is a crucial parameter in astrophysics and cosmology, is under significant tension. We explore an independent technique to measure $H_0$ based on the time-delay cosmography with strong gravitational lensing of a supernova lensed by a galaxy cluster, focusing on SN Refsdal in MACS J1149.5+2223, the first gravitationally lense

Xikai Yang, Jian Wu, Xi Wang, Yuchen Yuan

Glaucoma is one of the major eye diseases that leads to progressive optic nerve fiber damage and irreversible blindness, afflicting millions of individuals. Glaucoma forecast is a good solution to early screening and intervention of potential patients, which is helpful to prevent further deterioration of the disease. It leverages a series of historical fundu

Zizi Wang

In the field of modeling the dynamics of oncolytic viruses, researchers often face the challenge of using specialized mathematical terms to explain uncertain biological phenomena. This paper introduces a basic framework for an oncolytic virus dynamics model with a general growth rate $\mathcal{F}$ and a general nonlinear incidence term $\mathcal{G}$. The con

Chong Zhang, Jiapeng Sheng, Tingguang Li, He Zhang

Learning highly dynamic behaviors for robots has been a longstanding challenge. Traditional approaches have demonstrated robust locomotion, but the exhibited behaviors lack diversity and agility. They employ approximate models, which lead to compromises in performance. Data-driven approaches have been shown to reproduce agile behaviors of animals, but typica

Yuxi Wang, Xingxing Zhang, Wujuan Yan, Nianjie Liang

Two-dimensional (2D) crystals proved revolutionary soon after graphene was discovered in 2004. However, 2D amorphous materials only became accessible in 2020 and remain largely unexplored. In particular, the thermophysical properties of amorphous materials are of great interest upon transition from 3D to 2D. Here, we probe thermal transport in 2D amorphous c

Nathan G. Welch, Hana Ševčíková, Adrian E. Raftery

The link between age and migration propensity is long established, but existing models of country-level net migration ignore the effect of population age distribution on past and projected migration rates. We propose a method to estimate and forecast international net migration rates for the 200 most populous countries, taking account of changes in populatio

Aviv Brokman, Ramakanth Kavuluru

Cutting edge techniques developed in the general NLP domain are often subsequently applied to the high-value, data-rich biomedical domain. The past few years have seen generative language models (LMs), instruction finetuning, and few-shot learning become foci of NLP research. As such, generative LMs pretrained on biomedical corpora have proliferated and biom

Liyi Li, Le Chang, Rance Cleaveland, Mingwei Zhu

This paper develops a model of quantum behavior that is intended to support the abstract yet accurate design and functional verification of quantum communication protocols. The work is motivated by the need for conceptual tools for the development of quantum-communication systems that are usable by non-specialists in quantum physics while also correctly capt

Nathan Beck, Adithya Iyer, Rishabh Iyer

As supervised fine-tuning of pre-trained models within NLP applications increases in popularity, larger corpora of annotated data are required, especially with increasing parameter counts in large language models. Active learning, which attempts to mine and annotate unlabeled instances to improve model performance maximally fast, is a common choice for reduc

Ruprecht Machleidt, Francesca Sammarruca

During the past two decades, chiral effective field theory has evolved into a powerful tool to derive nuclear forces from first principles. Nearly all two-nucleon interactions have been worked out up to sixth order of chiral perturbation theory, while, with few exceptions, three-nucleon forces, which play a subtle, but crucial role in microscopic nuclear str

Xin-Yu He, Pei-Pei Zhang, Qiang Yuan, Yi-Qing Guo

Cosmic rays travel throughout the Galaxy, leaving traces from radio to ultra-high-energy gamma rays due to interactions with the interstellar gas, radiation field and magnetic field. Therefore, it is necessary to utilize multi-wavelength investigations on the Galactic diffuse emission to shed light on the physics of CR production and propagation. In this wor

Hanbit Oh, Takamitsu Matsubara

Interactive imitation learning is an efficient, model-free method through which a robot can learn a task by repetitively iterating an execution of a learning policy and a data collection by querying human demonstrations. However, deploying unmatured policies for clearance-limited tasks, like industrial insertion, poses significant collision risks. For such t

Chandan Kumar, Jansel Herrera-Gerena, John Just, Matthew Darr

Training image-based object detectors presents formidable challenges, as it entails not only the complexities of object detection but also the added intricacies of precisely localizing objects within potentially diverse and noisy environments. However, the collection of imagery itself can often be straightforward; for instance, cameras mounted in vehicles ca

Nur Yildirim, Susanna Zlotnikov, Aradhana Venkat, Gursimran Chawla

Clinical practice guidelines, care pathways, and protocols are designed to support evidence-based practices for clinicians; however, their adoption remains a challenge. We set out to investigate why clinicians deviate from the ``Wake Up and Breathe'' protocol, an evidence-based guideline for liberating patients from mechanical ventilation in the intensive ca

Jianhao Yan, Yun Luo, Yue Zhang

The application scope of large language models (LLMs) is increasingly expanding. In practical use, users might provide feedback based on the model's output, hoping for a responsive model that can complete responses according to their feedback. Whether the model can appropriately respond to users' refuting feedback and consistently follow through with executi

Jianhao Yan, Futing Wang, Yafu Li, Yue Zhang

Large language models (LLMs) trained on vast corpora suffer from inevitable stereotype biases. Mitigating these biases with fine-tuning could be both costly and data-hungry. Model editing methods, which focus on modifying LLMs in a post-hoc manner, are of great potential to address debiasing. However, it lacks a comprehensive study that facilitates both inte

Yan Tang, Shiqing Zhang

In this work the minimization problem for the difference of convex (DC) functions is studied by using Moreau envelopes and the descent method with Moreau gradient is employed to approximate the numerical solution. The main regularization idea in this work is inspired by Hiriart-Urruty [14], Moudafi[17], regularize the components of the DC problem by adapting

Takeshi Suzuki, Yigui Zhong, Kecheng Liu, Teruto Kanai

We describe a setup for time- and angle-resolved photoemission spectroscopy with wavelength-tunable excitation and extreme ultraviolet probe. It is enabled by using the 10 kHz twin Ti:sapphire amplifiers seeded by the common Ti:sapphire oscillator. The typical probe energy is 21.7 eV, and the wavelength of the pump excitation is tuned between 2400 and 1200 n

Xiangyu Zhou, Yao Qiang, Saleh Zare Zade, Mohammad Amin Roshani

The advent of Large Language Models (LLMs) has marked significant achievements in language processing and reasoning capabilities. Despite their advancements, LLMs face vulnerabilities to data poisoning attacks, where the adversary inserts backdoor triggers into training data to manipulate outputs. This work further identifies additional security risks in LLM

Misak M Sargsian

Electro-disintegration of the deuteron at large $Q^2$ currently represents on of the most promising reactions which allows to probe the bound nuclear state at internal momenta comparable to the rest mass of the nucleon. Large internal momentum in this case makes non-nuncleonic states energetically more feasible and the question that we address is what are th

Hyeon-Ju Jeon, Jeon-Ho Kang, In-Hyuk Kwon, O-Joun Lee

The impact of meteorological observations on weather forecasting varies with sensor type, location, time, and other environmental factors. Thus, quantitative analysis of observation impacts is crucial for effective and efficient development of weather forecasting systems. However, the existing impact analysis methods are difficult to be widely applied due to

Zihao Xu, Yi Liu, Gelei Deng, Yuekang Li

Large Language Models (LLMS) have increasingly become central to generating content with potential societal impacts. Notably, these models have demonstrated capabilities for generating content that could be deemed harmful. To mitigate these risks, researchers have adopted safety training techniques to align model outputs with societal values to curb the gene

Virgile Thiévenaz, Jochem G. Meijer, Detlef Lohse, Alban Sauret

Water usually contains dissolved gases, and because freezing is a purifying process these gases must be expelled for ice to form. Bubbles appear at the freezing front and are then trapped in ice, making pores. These pores come in a range of sizes from microns to millimeters and their shapes are peculiar; never spherical but elongated, and usually fore-aft as

Fuyu Li, Changhui Rao, Xinhua Zhao, Yang Guo

The formation mechanism of light bridges (LBs) is strongly related to the dynamic evolution of solar active regions (ARs). To study the relationship between LB formation and AR evolution phases, we employ 109 LB samples from 69 ARs in 2014 using observational data from the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory (HMI/SDO). LB

Nathan Beck, Truong Pham, Rishabh Iyer

With increasing volume of data being used across machine learning tasks, the capability to target specific subsets of data becomes more important. To aid in this capability, the recently proposed Submodular Mutual Information (SMI) has been effectively applied across numerous tasks in literature to perform targeted subset selection with the aid of a exemplar

Hidekazu Yoshioka, Motoh Tsujimura, Yumi Yoshioka

The classical logit dynamic on a continuous action space for decision-making un-der uncertainty is generalized to the dynamic where the exponential function for the softmax part has been replaced by a rational one that includes the former as a special case. We call the new dynamic as the rational logit dynamic. The use of the rational logit function implies

Vijeta Deshpande, Minhwa Lee, Zonghai Yao, Zihao Zhang

Prior research on Twitter (now X) data has provided positive evidence of its utility in developing supplementary health surveillance systems. In this study, we present a new framework to surveil public health, focusing on mental health (MH) outcomes. We hypothesize that locally posted tweets are indicative of local MH outcomes and collect tweets posted from

Mumtaz Hussain, Johannes Schleischitz, Benjamin Ward

We study the Folklore set of Dirichlet improvable matrices in $\mathbb R^{m\times n}$ which are neither singular nor badly approximable. We prove the non-emptiness for all positive integer pairs $m,n$ apart from $\{m,n\}=\{ 1,1\}$ and $\{m,n\}=\{ 2,3\}$ in a constructive manner. For a wide range of integer pairs $(m,n)$ we construct subsets of the Folklore s

Shashikant Kumar, Xin Jing, John E. Pask, Phanish Suryanarayana

We develop a framework for on-the-fly machine learned force field (MLFF) molecular dynamics (MD) simulations of warm dense matter (WDM). In particular, we employ an MLFF scheme based on the kernel method and Bayesian linear regression, with the training data generated from Kohn-Sham density functional theory (DFT) using the Gauss Spectral Quadrature method,

Zexue He, Leonid Karlinsky, Donghyun Kim, Julian McAuley

Large Language Models (LLMs) struggle to handle long input sequences due to high memory and runtime costs. Memory-augmented models have emerged as a promising solution to this problem, but current methods are hindered by limited memory capacity and require costly re-training to integrate with a new LLM. In this work, we introduce an associative memory module

Amit Dhurandhar, Rahul Nair, Moninder Singh, Elizabeth Daly

Evaluation and ranking of large language models (LLMs) has become an important problem with the proliferation of these models and their impact. Evaluation methods either require human responses which are expensive to acquire or use pairs of LLMs to evaluate each other which can be unreliable. In this paper, we provide a novel perspective where, given a datas

Liwen Sun, Abhineet Agarwal, Aaron Kornblith, Bin Yu

In the emergency department (ED), patients undergo triage and multiple laboratory tests before diagnosis. This time-consuming process causes ED crowding which impacts patient mortality, medical errors, staff burnout, etc. This work proposes (time) cost-effective diagnostic assistance that leverages artificial intelligence systems to help ED clinicians make e

Kaiyuan Wang, Yunlong Li, Tiange Wu, Deming Liu

On-chip optical neural networks (ONNs) have recently emerged as an attractive hardware accelerator for deep learning applications, characterized by high computing density, low latency, and compact size. As these networks rely heavily on massive matrix multiplication, photonic computing cores for matrix computation become crucial components for on-chip ONNs,

Zhen Tan, Dawei Li, Song Wang, Alimohammad Beigi

Data annotation and synthesis generally refers to the labeling or generating of raw data with relevant information, which could be used for improving the efficacy of machine learning models. The process, however, is labor-intensive and costly. The emergence of advanced Large Language Models (LLMs), exemplified by GPT-4, presents an unprecedented opportunity

Alessandra Corsi, Lisa Barsotti, Emanuele Berti, Matthew Evans

The ground-based gravitational wave (GW) detectors LIGO and Virgo have enabled the birth of multi-messenger GW astronomy via the detection of GWs from merging stellar-mass black holes (BHs) and neutron stars (NSs). GW170817, the first binary NS merger detected in GWs and all bands of the electromagnetic spectrum, is an outstanding example of the impact that

Pei-Syuan Wang, Hung-Hsuan Chen

This paper details an empirical investigation into using Graph Contrastive Learning (GCL) to generate mathematical equation representations, a critical aspect of Mathematical Information Retrieval (MIR). Our findings reveal that this simple approach consistently exceeds the performance of the current leading formula retrieval model, TangentCFT. To support on

Hassan Jardali, Mahmoud Ali, Lantao Liu

We propose a new method for autonomous navigation in uneven terrains by utilizing a sparse Gaussian Process (SGP) based local perception model. The SGP local perception model is trained on local ranging observation (pointcloud) to learn the terrain elevation profile and extract the feasible navigation subgoals around the robot. Subsequently, a cost function,

Peter Schaldenbrand, Gaurav Parmar, Jun-Yan Zhu, James McCann

Prior robot painting and drawing work, such as FRIDA, has focused on decreasing the sim-to-real gap and expanding input modalities for users, but the interaction with these systems generally exists only in the input stages. To support interactive, human-robot collaborative painting, we introduce the Collaborative FRIDA (CoFRIDA) robot painting framework, whi

Neelesh Gupta, Pengmiao Zhang, Rajgopal Kannan, Viktor Prasanna

Deep neural networks (DNNs) have proven to be effective models for accurate Memory Access Prediction (MAP), a critical task in mitigating memory latency through data prefetching. However, existing DNN-based MAP models suffer from the challenges such as significant physical storage space and poor inference latency, primarily due to their large number of param

Chitra Subramanian, Miao Liu, Naweed Khan, Jonathan Lenchner

Multi-agent reinforcement learning (MARL) is well-suited for runtime decision-making in optimizing the performance of systems where multiple agents coexist and compete for shared resources. However, applying common deep learning-based MARL solutions to real-world problems suffers from issues of interpretability, sample efficiency, partial observability, etc.

Zakary Rodrigue Diakité

This paper investigates the demand for lamb, beef, pork, and poultry in Canada, both at the national level and in disaggregated provinces, to identify meat consumption patterns in different provinces. Meat consumption plays a significant role in Canada's economy and is an important source of calories for the population. However, meat demand faces several con

William B. Johnson, Gideon Schechtman

The main result is that the only non trivial closed ideal in the Banach algebra $L(L^p)$ of bounded linear operators on $L^p(0,1)$, $1\le p < \infty$, that has a left approximate identity is the ideal of compact operators. The algebra $L(L^1)$ has at least one non trivial closed ideal that has a contractive right approximate identity as well as many, includi

Nur Yildirim, Susanna Zlotnikov, Deniz Sayar, Jeremy M. Kahn

Advances in artificial intelligence (AI) have enabled unprecedented capabilities, yet innovation teams struggle when envisioning AI concepts. Data science teams think of innovations users do not want, while domain experts think of innovations that cannot be built. A lack of effective ideation seems to be a breakdown point. How might multidisciplinary teams i

K. S. Vikrant, D. Dadkhah, S. O. Reza Moheimani

Planar electromagnetic actuators based on the principle of linear motors are widely employed for micro and nano positioning applications. These actuators usually employ a planar magnetic platform driven by a co-planar electromagnetic coil. While these actuators offer a large motion range and high positioning resolution, their actuation bandwidth is limited d

Jianqiang Shen, Yuchin Juan, Shaobo Zhang, Ping Liu

Web-scale search systems typically tackle the scalability challenge with a two-step paradigm: retrieval and ranking. The retrieval step, also known as candidate selection, often involves extracting standardized entities, creating an inverted index, and performing term matching for retrieval. Such traditional methods require manual and time-consuming developm

Didi Ahmad Mulya, Roni Muslim

We investigate the phenomena of order-disorder phase transition and the universality of the majority-rule model defined on three complex networks, namely the Barabasi-Albert, Watts-Strogatz, and Erdos-Renyi networks. Assume each agent holds two possible opinions distributed randomly across the networks' nodes. Agents adopt anticonformity and independence beh

Freda Shi, Kevin Gimpel, Karen Livescu

We present the structured average intersection-over-union ratio (STRUCT-IOU), a similarity metric between constituency parse trees motivated by the problem of evaluating speech parsers. STRUCT-IOU enables comparison between a constituency parse tree (over automatically recognized spoken word boundaries) with the ground-truth parse (over written words). To co

Jacob Herbert, Artur Wolek

This paper describes the design of a miniature uncrewed underwater vehicle (MiniUUV) and related instrumentation for indoor experimentation. The MiniUUV was developed using 3D printed components and low-cost, off-the-shelf electronics. The vehicle uses a propeller differential propulsion drive and a peristaltic pump with a syringe for buoyancy control. A wat

Chunmei Xu, Shengheng Liu, Yongming Huang, Bjorn Ottersten

At present, there is a trend to deploy ubiquitous artificial intelligence (AI) applications at the edge of the network. As a promising framework that enables secure edge intelligence, federated learning (FL) has received widespread attention, and over-the-air computing (AirComp) has been integrated to further improve the communication efficiency. In this pap

Yanis Labrak, Adrien Bazoge, Oumaima El Khettari, Mickael Rouvier

The biomedical domain has sparked a significant interest in the field of Natural Language Processing (NLP), which has seen substantial advancements with pre-trained language models (PLMs). However, comparing these models has proven challenging due to variations in evaluation protocols across different models. A fair solution is to aggregate diverse downstrea

Sunanda Prabhu Gaunkar, Denise Fischer, Filip Rozpędek, Umang Bhatia

In this paper, we explain the conceptual development of the STAGE Lab Quantum Casino (a.k.a. the STAGE Lab Quantum Arcade), one of the Lab's most recent artistic endeavors about quantum physics. This work consists of a series of card and digital games and an interactive experience, exposing the public to quantum physics and minimizing learning barriers. Furt

Ping Liu, Haichao Wei, Xiaochen Hou, Jianqiang Shen

We present LinkSAGE, an innovative framework that integrates Graph Neural Networks (GNNs) into large-scale personalized job matching systems, designed to address the complex dynamics of LinkedIns extensive professional network. Our approach capitalizes on a novel job marketplace graph, the largest and most intricate of its kind in industry, with billions of

Zhaoyuan Su, Ammar Ahmed, Zirui Wang, Ali Anwar

As the number of pre-trained machine learning (ML) models is growing exponentially, data reduction tools are not catching up. Existing data reduction techniques are not specifically designed for pre-trained model (PTM) dataset files. This is largely due to a lack of understanding of the patterns and characteristics of these datasets, especially those relevan

Catalina Mourgues-Codern, David Benrimoh, Jay Gandhi, Emily A. Farina

Hallucinations and delusions are often grouped together within the positive symptoms of psychosis. However, recent evidence suggests they may be driven by distinct computational and neural mechanisms. Examining the time course of their emergence may provide insights into the relationship between these underlying mechanisms. Participants from the second (N =

X. San Liang, Dake Chen, Renhe Zhang

It has been said, arguably, that causality analysis should pave a promising way to interpretable deep learning and generalization. Incorporation of causality into artificial intelligence (AI) algorithms, however, is challenged with its vagueness, non-quantitiveness, computational inefficiency, etc. During the past 18 years, these challenges have been essenti

Xiangci Li, Jessica Ouyang

Due to the rapid pace of research publications, keeping up to date with all the latest related papers is very time-consuming, even with daily feed tools. There is a need for automatically generated, short, customized literature reviews of sets of papers to help researchers decide what to read. While several works in the last decade have addressed the task of

Wun-Hao Kang, Michael Barth, Aitor Garcia-Ruiz, Alina Mreńca-Kolasińska

Graphene-based van der Waals heterostructures take advantage of tailoring spin-orbit coupling (SOC) in the graphene layer by proximity effect. At long-wavelength -- saddled by the electronic states near the Dirac points -- the proximitized features can be effectively modelled by the Hamiltonian involving novel SOC terms and allow for an admixture of the tang

Maria Axenovich, Christian Winter

A poset $(Q,\le_Q)$ contains an induced copy of a poset $(P,\le_P)$ if there exists an injective mapping $\phi\colon P\to Q$ such that for any two elements $X,Y\in P$, $X\le_P Y$ if and only if $\phi(X)\le_Q \phi(Y)$. By $Q_n$ we denote the Boolean lattice $(2^{[n]},\subseteq)$. The poset Ramsey number $R(P,Q)$ for posets $P$ and $Q$ is the least integer $N$

Hari Sadhan Ghosh, Soumyadeep Halder, Subrata Das, Sonjoy Majumder

Motivated by the recent realization of space-borne Bose-Einstein Condensates (BECs) under micro-gravity conditions, we extend the understanding of ultracold dipolar bosonic gases by exploring their behavior in a novel trapping configuration known as the ``bubble trap" topology. Utilizing the three-dimensional numerical simulations within the extended Gross-P

Adebamigbe Fasanmade, Ali H. Al-Bayatti, Jarrad Neil Morden, Fabio Caraffini

Risk mitigation techniques are critical to avoiding accidents associated with driving behaviour. We provide a novel Multi-Class Driver Distraction Risk Assessment (MDDRA) model that considers the vehicle, driver, and environmental data during a journey. MDDRA categorises the driver on a risk matrix as safe, careless, or dangerous. It offers flexibility in ad

Ivan Landjev, Konstantin Vorob'ev

We consider the problem of finding $A_2(n,\{d_1,d_2\})$ defined as the maximal size of a binary (non-linear) code of length $n$ with two distances $d_1$ and $d_2$. Binary codes with distances $d$ and $d+2$ of size $\sim\frac{n^2}{\frac{d}{2}(\frac{d}{2}+1)}$ can be obtained from $2$-packings of an $n$-element set by blocks of cardinality $\frac{d}{2}+1$. Thi

Zhiyu An, Xianzhong Ding, Wan Du

Recent years have seen an emerging interest in the trustworthiness of machine learning-based agents in the wild, especially in robotics, to provide safety assurance for the industry. Obtaining behavioral guarantees for these agents remains an important problem. In this work, we focus on guaranteeing a model-based planning agent reaches a goal state within a

Zhen Tan, Chengshuai Zhao, Raha Moraffah, Yifan Li

Due to their unprecedented ability to process and respond to various types of data, Multimodal Large Language Models (MLLMs) are constantly defining the new boundary of Artificial General Intelligence (AGI). As these advanced generative models increasingly form collaborative networks for complex tasks, the integrity and security of these systems are crucial.

Zhiqiang Zhong, Davide Mottin

Predicting protein properties is paramount for biological and medical advancements. Current protein engineering mutates on a typical protein, called the wild-type, to construct a family of homologous proteins and study their properties. Yet, existing methods easily neglect subtle mutations, failing to capture the effect on the protein properties. To this end

Tao Chen, Siqi Zuo, Cheng Li, Mingyang Zhang

In business and marketing, analyzing the reasons behind buying is a fundamental step towards understanding consumer behaviors, shaping business strategies, and predicting market outcomes. Prior research on purchase reason has relied on surveys to gather data from users. However, this method is limited in scalability, often focusing on specific products or br

Alexander Guterman, Bojan Kuzma, Sushil Singla, Svetlana Zhilina

For an arbitrary normed space $\mathcal X$ over a field $\mathbb F \in \{ \mathbb R, \mathbb C \}$, we define the directed graph $\Gamma(\mathcal X)$ induced by Birkhoff-James orthogonality on the projective space $\mathbb P(\mathcal X)$, and also its nonprojective counterpart $\Gamma_0(\mathcal X)$. We show that, in finite-dimensional normed spaces, $\Gamma

Kewei Cheng, Nesreen K. Ahmed, Theodore Willke, Yizhou Sun

Although Large Language Models (LLMs) excel at addressing straightforward reasoning tasks, they frequently struggle with difficulties when confronted by more complex multi-step reasoning due to a range of factors. Firstly, natural language often encompasses complex relationships among entities, making it challenging to maintain a clear reasoning chain over l

Zhiqiang Zhong, Kuangyu Zhou, Davide Mottin

As Machine Learning (ML) models grow in size and demand higher-quality training data, the expenses associated with re-training and fine-tuning these models are escalating rapidly. Inspired by recent impressive achievements of Large Language Models (LLMs) in different fields, this paper delves into the question: can LLMs efficiently improve an ML's performanc

Rostislav Arkhipov, Mikhail Arkhipov, Nikolay Rosanov

Recently, the possibility of the generation and interaction of unipolar half-cycle electromagnetic pulses with quantum systems has been the subject of active research. Such pulses can have many different and interesting applications. They are able to excite quantum systems very fast. Based on the numerical solution of Maxwell-Bloch equations, this paper theo

Nithin Chalapathi, Yiheng Du, Aditi Krishnapriyan

Imposing known physical constraints, such as conservation laws, during neural network training introduces an inductive bias that can improve accuracy, reliability, convergence, and data efficiency for modeling physical dynamics. While such constraints can be softly imposed via loss function penalties, recent advancements in differentiable physics and optimiz