March 2024 arXiv papers — page 21

Xinyu Bian, Yuhao Liu, Yizhou Xu, Tianqi Hou

Inter-cell interference (ICI) suppression is critical for multi-cell multi-user networks. In this paper, we investigate advanced precoding techniques for coordinated multi-point (CoMP) with downlink coherent joint transmission, an effective approach for ICI suppression. Different from the centralized precoding schemes that require frequent information exchan

Qi Zhang, Guang Wang, Li Lin, Kaiwen Xia

With the advent of the era of big data, massive information, expert experience, and high-accuracy models bring great opportunities to the information cascade prediction of public emergencies. However, the involvement of specialist knowledge from various disciplines has resulted in a primarily application-specific focus (e.g., earthquakes, floods, infectious

Zhinan Hou, Feiran Zhao, Keyou You

Model predictive control (MPC) solves a receding-horizon optimization problem in real-time, which can be computationally demanding when there are thousands of constraints. To accelerate online computation of MPC, we utilize data to adaptively remove the constraints while maintaining the MPC policy unchanged. Specifically, we design the removal rule based on

Wei Jia

The Fermi sea of a metal can host exotic quantum topology, which governs its conductance quantization and is characterized by the Euler characteristic ($\chi_F$). In contrast to the well-known band topology, which is determined by the global features of wave functions, the topology of such metallic systems is intrinsically linked to the geometry of the Fermi

Jinhong Wang, Tingting Chen, Jintai Chen, Yixuan Wu

Automatic ophthalmic disease diagnosis on fundus images is important in clinical practice. However, due to complex fundus textures and limited annotated data, developing an effective automatic method for this problem is still challenging. In this paper, we present a self-supervised method via polar transformation based progressive contrastive learning, calle

Shi Jin, Nana Liu, Chuwen Ma

We introduce a simple and stable computational method for ill-posed partial differential equation (PDE) problems. The method is based on Schr\"odingerization, introduced in [S. Jin, N. Liu and Y. Yu, arXiv:2212.13969][S. Jin, N. Liu and Y. Yu, Phys. Rev. A, 108 (2023), 032603], which maps all linear PDEs into Schr\"odinger-type equations in one higher dimens

Shuo Liu, Yihui Mao, Calin A. Belta

Dynamic obstacle avoidance is a challenging topic for optimal control and optimization-based trajectory planning problems. Many existing works use Control Barrier Functions (CBFs) to enforce safety constraints for control systems. CBFs are typically formulated based on the distance to obstacles, or integrated with path planning algorithms as a safety enhance

Jin Yuan, Xuelan Qiu, Jinran Wu, Jiesi Guo

The interest in predicting online learning performance using ML algorithms has been steadily increasing. We first conducted a scientometric analysis to provide a systematic review of research in this area. The findings show that most existing studies apply the ML methods without considering learning behavior patterns, which may compromise the prediction accu

Yufan Jiang, Qiaozhi He, Xiaomin Zhuang, Zhihua Wu

We present Code Comparison Tuning (CCT), a simple and effective tuning method for code large language models (Code LLMs) to better handle subtle code errors. Specifically, we integrate the concept of comparison into instruction tuning, both at the token and sequence levels, enabling the model to discern even the slightest deviations in code. To compare the o

Sk Nayemuzzaman, Kumar Vijay Mishra, Jiawei Liu, Mohammad Saquib

As a next-generation wireless technology, the in-band full-duplex (IBFD) transmission enables simultaneous transmission and reception of signals over the same frequency, thereby doubling spectral efficiency. Further, a continuous up-scaling of wireless network carrier frequencies arising from ever-increasing data traffic is driving research on integrated sen

Jiawei Liu, Kumar Vijay Mishra, Mohammad Saquib

We address the challenge of spectral sharing between a statistical multiple-input multiple-output (MIMO) radar and an in-band full-duplex (IBFD) multi-user MIMO (MU-MIMO) communications system operating simultaneously in the same frequency band. Existing research on joint MIMO-radar-MIMO-communications (MRMC) systems has limitations, such as focusing on colo

Zeqian Chen

The Hilbert-P\'{o}lya conjecture asserts that the imaginary parts of the nontrivial zeros of the Riemann zeta function (the Riemann zeros) are the eigenvalues of a self-adjoint operator (a quantum mechanical Hamiltonian, in the physical sense), as a promising approach to prove the Riemann hypothesis (cf.\cite{SH2011}). Instead of the eigenvalues, in this pap

Rihui Jin, Yu Li, Guilin Qi, Nan Hu

Table understanding (TU) has achieved promising advancements, but it faces the challenges of the scarcity of manually labeled tables and the presence of complex table structures.To address these challenges, we propose HGT, a framework with a heterogeneous graph (HG)-enhanced large language model (LLM) to tackle few-shot TU tasks.It leverages the LLM by align

Ce Jin, Hongxun Wu

An important area of research in exact algorithms is to solve Subset-Sum-type problems faster than meet-in-middle. In this paper we study Pigeonhole Equal Sums, a total search problem proposed by Papadimitriou (1994): given $n$ positive integers $w_1,\dots,w_n$ of total sum $\sum_{i=1}^n w_i < 2^n-1$, the task is to find two distinct subsets $A, B \subseteq

Che Guan, Mengyu Huang, Peng Zhang

In today's fast-paced industry, professionals face the challenge of summarizing a large number of documents and extracting vital information from them on a daily basis. These metrics are frequently hidden away in tables and/or their nested hyperlinks. To address this challenge, the approach of Table Question Answering (QA) has been developed to extract the r

Kechi Zhang, Ge Li, Huangzhao Zhang, Zhi Jin

Addressing the limitation of context length in large language models for code-related tasks is the primary focus of this paper. Existing LLMs are constrained by their pre-trained context lengths, leading to performance issues in handling long complex code sequences. Inspired by how human programmers navigate code, we introduce Hierarchical Rotary Position Em

Chunqiu Steven Xia, Yinlin Deng, Lingming Zhang

LLMs have become the go-to choice for code generation tasks, with an exponential increase in the training, development, and usage of LLMs specifically for code generation. To evaluate the ability of LLMs on code, both academic and industry practitioners rely on popular handcrafted benchmarks. However, prior benchmarks contain only a very limited set of probl

Vipula Rawte, S. M Towhidul Islam Tonmoy, Krishnav Rajbangshi, Shravani Nag

The widespread adoption of Large Language Models (LLMs) has facilitated numerous benefits. However, hallucination is a significant concern. In response, Retrieval Augmented Generation (RAG) has emerged as a highly promising paradigm to improve LLM outputs by grounding them in factual information. RAG relies on textual entailment (TE) or similar methods to ch

Shuo Yang, Jiachi Chen, Mingyuan Huang, Zibin Zheng

Reentrancy, a notorious vulnerability in smart contracts, has led to millions of dollars in financial loss. However, current smart contract vulnerability detection tools suffer from a high false positive rate in identifying contracts with reentrancy vulnerabilities. Moreover, only a small portion of the detected reentrant contracts can actually be exploited

Hao Shen, Lu Shi, Wanru Xu, Yigang Cen

Video Anomaly Detection (VAD), aiming to identify abnormalities within a specific context and timeframe, is crucial for intelligent Video Surveillance Systems. While recent deep learning-based VAD models have shown promising results by generating high-resolution frames, they often lack competence in preserving detailed spatial and temporal coherence in video

Pranav Chakravarthy, Jordan Payette, Martin Pinsonnault

We give a complete and self-contained exposition of the $J$-tame inflation lemma: Given any tame almost complex structure $J$ on a symplectic $4$-manifold $(M,\omega)$, and given any compact, embedded, $J$-holomorphic submanifold $Z$, it is always possible to construct a deformation of symplectic forms $\omega_t$ in classes $[\omega_t]=[\omega]+t\mathrm{PD}{

Yichi Zhang, Zhuo Chen, Lingbing Guo, Yajing Xu

Multi-modal knowledge graph completion (MMKGC) aims to automatically discover the unobserved factual knowledge from a given multi-modal knowledge graph by collaboratively modeling the triple structure and multi-modal information from entities. However, real-world MMKGs present challenges due to their diverse and imbalanced nature, which means that the modali

Mohammed Alromema, Mohammed A. Makarem

The study explores the optimization of evolutionary solver parameters for minimizing total tardiness in single machine scheduling, an NP-hard problem with zero ready times included. It investigates various parameter combinations, including population sizes, mutation rates, and a constant convergence rate, both above and below default values. The aim is to en

Robert Schippa

We show local smoothing estimates in $L^p$-spaces for solutions to the Hermite wave equation. For this purpose, we obtain a parametrix given by a Fourier Integral Operator, which we linearize. This leads us to analyze local smoothing estimates for solutions to Klein-Gordon equations. We show $\ell^2$-decoupling estimates adapted to the mass parameter to obta

John R. McNulty, Lee Kho, Alexandria L. Case, Charlie Fornaca

In medical imaging, access to data is commonly limited due to patient privacy restrictions and the issue that it can be difficult to acquire enough data in the case of rare diseases.[1] The purpose of this investigation was to develop a reusable open-source synthetic image generation pipeline, the GAN Image Synthesis Tool (GIST), that is easy to use as well

Samantha M. Lawler, Hanno Rein, Aaron C. Boley

On 8 April 2024, tens of millions of people across North America will be able to view a total solar eclipse. Such astronomical events have been important throughout history, but with nearly 10,000 satellites in orbit, we question whether total eclipses will now reveal a sky full of satellites, fundamentally changing this experience for humanity. Using the cu

Patrick Chao, Edoardo Debenedetti, Alexander Robey, Maksym Andriushchenko

Jailbreak attacks cause large language models (LLMs) to generate harmful, unethical, or otherwise objectionable content. Evaluating these attacks presents a number of challenges, which the current collection of benchmarks and evaluation techniques do not adequately address. First, there is no clear standard of practice regarding jailbreaking evaluation. Seco

Reiji Murakami

Kobayashi-Pevzner discovered in [Selecta Math., 2016] that the failure of the multiplicity-one property in the fusion rule of Verma modules of sl2 occurs exactly when the Rankin-Cohen bracket vanishes, and 1classified all the corresponding parameters. In this paper we provide yet another characterization for these parameters, and give a precise description o

Yoonseong Kang, Hyowoon Seo, Wan Choi

This paper addresses the intricate task of hybrid-field channel estimation in extremely large-scale MIMO (XL-MIMO) systems, critical for the progression of 6G communications. Within these systems, comprising a line-of-sight (LoS) channel component alongside far-field and near-field scattering channel components, our objective is to tackle the channel estimat

Lingjun Zhao, Jingyu Song, Katherine A. Skinner

In the field of 3D object detection for autonomous driving, LiDAR-Camera (LC) fusion is the top-performing sensor configuration. Still, LiDAR is relatively high cost, which hinders adoption of this technology for consumer automobiles. Alternatively, camera and radar are commonly deployed on vehicles already on the road today, but performance of Camera-Radar

Yutong He, Alexander Robey, Naoki Murata, Yiding Jiang

Prompt engineering is an effective but labor-intensive way to control text-to-image (T2I) generative models. Its time-intensive nature and complexity have spurred the development of algorithms for automated prompt generation. However, these methods often struggle with transferability across T2I models, require white-box access to the underlying model, or pro

Joyce Xin-Yan Lim, Quang-Cuong Pham

The rise in additive manufacturing comes with unique opportunities and challenges. Massive part customization and rapid design changes are made possible with additive manufacturing, however, manufacturing industries that desire the implementation of robotics automation to improve production efficiency could face challenges in the gripper design and grasp pla

Benhao Huang

The emergence of text-to-image models marks a significant milestone in the evolution of AI-generated images (AGIs), expanding their use in diverse domains like design, entertainment, and more. Despite these breakthroughs, the quality of AGIs often remains suboptimal, highlighting the need for effective evaluation methods. These methods are crucial for assess

Michael S. Turner

The age of the Universe at a given redshift is a fundamental relationship in cosmology. For many years, the uncertainties in it were dauntingly large, close to a factor of 2. In this age of precision cosmology, they are now at the percent level and dominated by the uncertainty in the Hubble constant. The uncertainties due to the parameters that describe the

Changwon Lee, Israel F. Araujo, Dongha Kim, Junghan Lee

Quantum convolutional neural networks (QCNNs) represent a promising approach in quantum machine learning, paving new directions for both quantum and classical data analysis. This approach is particularly attractive due to the absence of the barren plateau problem, a fundamental challenge in training quantum neural networks (QNNs), and its feasibility. Howeve

Yunpeng Zhang, Deheng Qian, Ding Li, Yifeng Pan

Modeling complicated interactions among the ego-vehicle, road agents, and map elements has been a crucial part for safety-critical autonomous driving. Previous works on end-to-end autonomous driving rely on the attention mechanism for handling heterogeneous interactions, which fails to capture the geometric priors and is also computationally intensive. In th

Stephen Y Zhang, Michael P H Stumpf, Tom Needham, Agnese Barbensi

Topological data analysis is a powerful tool for describing topological signatures in real world data. An important challenge in topological data analysis is matching significant topological signals across distinct systems. In geometry and probability theory, optimal transport formalises notions of distance and matchings between distributions and structured

Xin-Cheng Wen, Cuiyun Gao, Shuzheng Gao, Yang Xiao

Recently, there has been a growing interest in automatic software vulnerability detection. Pre-trained model-based approaches have demonstrated superior performance than other Deep Learning (DL)-based approaches in detecting vulnerabilities. However, the existing pre-trained model-based approaches generally employ code sequences as input during prediction, a

Xiao Tan, Wei Xu, Chaoran Wang

Voice, the discursive construction of the writer's identity, has been extensively studied and theorized in composition studies. In multimodal writing, students are able to mobilize both linguistic and non linguistic resources to express their real or imagined identities. But at the same time, when students are limited to choose from available online resource

Yuxuan Yao, Han Wu, Zhijiang Guo, Biyan Zhou

Large language models (LLMs) have demonstrated outstanding performance across various tasks, yet they still exhibit limitations such as hallucination, unfaithful reasoning, and toxic content. One potential approach to mitigate these issues is learning from human or external feedback (e.g. tools). In this paper, we introduce an intrinsic self-correct reasonin

Yishuai Cai, Shaowu Yang, Minglong Li, Xinglin Chen

Optimizing the morphologies and the controllers that adapt to various tasks is a critical issue in the field of robot design, aka. embodied intelligence. Previous works typically model it as a joint optimization problem and use search-based methods to find the optimal solution in the morphology space. However, they ignore the implicit knowledge of task-to-mo

Wei Zhang, Jin-Ke Huang, Ting-Ting Sun, Jing Peng

The inner fission barriers of the even-even uranium isotopes from the proton to the neutron drip line are studied with the deformed relativistic Hartree-Bogoliubov theory in continuum. A periodic evolution for the ground state shapes is shown with the neutron number, i.e., spherical shapes at shell closures $N=$126, 184, 258, and prolate dominated shapes bet

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

By analyzing $e^+e^-$ annihilation data corresponding to an integrated luminosity of 2.93 $\rm fb^{-1}$ collected at a center-of-mass energy of 3.773 GeV with the \text{BESIII} detector, the first observation of the semileptonic decays $D^0\rightarrow K_S^0\pi^-\pi^0 e^+ \nu_e$ and $D^+\rightarrow K_S^0\pi^+\pi^- e^+ \nu_e$ is reported. With a dominant hadro

Yuling Jiao, Yuhui Liu, Jerry Zhijian Yang, Cheng Yuan

In this article, we propose a novel Stabilized Physics Informed Neural Networks method (SPINNs) for solving wave equations. In general, this method not only demonstrates theoretical convergence but also exhibits higher efficiency compared to the original PINNs. By replacing the $L^2$ norm with $H^1$ norm in the learning of initial condition and boundary cond

Sergey G. Bobkov, Devraj Duggal

Covariance representations are developed for the uniform distributions on the Euclidean spheres in terms of spherical gradients and Hessians. They are applied to derive a number of Sobolev type inequalities and to recover and refine the concentration of measure phenomenon, including second order concentration inequalities. A detail account is also given in t

Matteo Marchi, Jonathan Bunton, João Pedro Silvestre, Paulo Tabuada

Optimization algorithms have a rich and fundamental relationship with ordinary differential equations given by its continuous-time limit. When the cost function varies with time -- typically in response to a dynamically changing environment -- online optimization becomes a continuous-time trajectory tracking problem. To accommodate these time variations, one

Kulaga Anton, Borysova Olga, Karmazin Alexey, Koval Maria

Genomic data has become increasingly accessible to the general public with the advent of companies offering whole genome sequencing at a relatively low cost. However, their reports are not verifiable due to a lack of crucial details and transparency: polygenic risk scores do not always mention all the polymorphisms involved. Simultaneously, tackling the manu

Gilles Carron, Bo-Yong Chen, Yuanpu Xiong

In this paper, we study the relationship between the type problem and the asymptotic behaviour of the first (Dirichlet) eigenvalues $\lambda_1(B_r)$ of ``balls'' $B_r:=\{\rho<r\}$ on a complete Riemannian manifold $M$ as $r\rightarrow +\infty$, where $\rho$ is a Lipschitz continuous exhaustion function with $|\nabla\rho|\leq1$ a.e. on $M$. We obtain several

S. M. Kayser Mehbub Siam, Khadiza Islam Sumaiya, Md Rakib Al-Amin, Tamim Hasan Turjo

Rapid urbanization and improved living standards have led to a substantial increase in the number of vehicles on the road, consequently resulting in a rise in the frequency of accidents. Among these accidents, motorbike accidents pose a particularly high risk, often resulting in serious injuries or deaths. A significant number of these fatalities occur due t

Hui Sun, Bo Pan, Zhening Yang, Adam M. Krajewski

Assembly of dissimilar metals can be achieved by different methods, for example, casting, welding, and additive manufacturing (AM). However, undesired phases formed in liquid-phase assembling processes due to solute segregation during solidification diminish mechanical and other properties of the processed parts. In the present work, an open-source software

Pei Xi, Lin

With recent advancements in the development of artificial intelligence applications using theories and algorithms in machine learning, many accurate models can be created to train and predict on given datasets. With the realization of the importance of imaging interpretation in cancer diagnosis, this article aims to investigate the theory behind Deep Learnin

A. A. Balinsky, A. D. Balinsky

Conformal Prediction (CP) serves as a robust framework that quantifies uncertainty in predictions made by Machine Learning (ML) models. Unlike traditional point predictors, CP generates statistically valid prediction regions, also known as prediction intervals, based on the assumption of data exchangeability. Typically, the construction of conformal predicti

Rundong Fan, Shili Wei, Huiru JI, Zhuang Qian

The surface errors of freeform surfaces reflect the manufacturing complexities and significantly impact the feasibility of processing designed optical systems. With multiple degrees of freedom, freeform surfaces pose challenges in surface tolerance analysis in the field. Nevertheless, current research has neglected the influence of surface slopes on the dire

Yanting Wang, Hongye Fu, Wei Zou, Jinyuan Jia

Different from a unimodal model whose input is from a single modality, the input (called multi-modal input) of a multi-modal model is from multiple modalities such as image, 3D points, audio, text, etc. Similar to unimodal models, many existing studies show that a multi-modal model is also vulnerable to adversarial perturbation, where an attacker could add s

Junjie Wen, Jinqiang Cui, Benyun Zhao, Bingxin Han

In recent years, significant progress has been made in the field of underwater image enhancement (UIE). However, its practical utility for high-level vision tasks, such as underwater object detection (UOD) in Autonomous Underwater Vehicles (AUVs), remains relatively unexplored. It may be attributed to several factors: (1) Existing methods typically employ UI

Liangjian Wen, Xiasi Wang, Jianzhuang Liu, Zenglin Xu

Self-supervised learning aims to learn representation that can be effectively generalized to downstream tasks. Many self-supervised approaches regard two views of an image as both the input and the self-supervised signals, assuming that either view contains the same task-relevant information and the shared information is (approximately) sufficient for predic

Vijay Mohan, Peyman Khezr

In this paper, we take a close look at a problem labeled maximal extractable value (MEV), which arises in a blockchain due to the ability of a block producer to manipulate the order of transactions within a block. Indeed, blockchains such as Ethereum have spent considerable resources addressing this issue and have redesigned the block production process to a

Ji Lin, Ligeng Zhu, Wei-Ming Chen, Wei-Chen Wang

Tiny Machine Learning (TinyML) is a new frontier of machine learning. By squeezing deep learning models into billions of IoT devices and microcontrollers (MCUs), we expand the scope of AI applications and enable ubiquitous intelligence. However, TinyML is challenging due to hardware constraints: the tiny memory resource makes it difficult to hold deep learni

Ryota Maruo, Hisashi Kashima

We study the problem of achieving high efficiency in iterative combinatorial auctions (ICAs). ICAs are a kind of combinatorial auction where the auctioneer interacts with bidders to gather their valuation information using a limited number of queries, aiming for efficient allocation. Preference elicitation, a process that incrementally asks bidders to value

Didier Aussel, Daniel Lasluisa, David Salas

This work explores bilevel problems in the context of cardinality constraints. More specifically Single-Leader-Multi-Follower games (SLMFG) involving cardinality constraints are considered in two different configurations: one with the cardinality constraint at the leader's level and a mixed structure in which the cardinality constraint is split between leade

Harsh Sharma, Gaurav Narang, Janardhan Rao Doppa, Umit Ogras

Processing-in-memory (PIM) has emerged as an enabler for the energy-efficient and high-performance acceleration of deep learning (DL) workloads. Resistive random-access memory (ReRAM) is one of the most promising technologies to implement PIM. However, as the complexity of Deep convolutional neural networks (DNNs) grows, we need to design a manycore architec

Setu Kumar Basak, K. Virgil English, Ken Ogura, Vitesh Kambara

GitGuardian monitored secrets exposure in public GitHub repositories and reported that developers leaked over 12 million secrets (database and other credentials) in 2023, indicating a 113% surge from 2021. Despite the availability of secret detection tools, developers ignore the tools' reported warnings because of false positives (25%-99%). However, each sec

L. J. A. Scott, C. S. Jeffery, C. M. Byrne, M. Dorsch

Heavy metal subdwarfs are a class of hot subdwarfs with very high abundances of heavy elements, typically around 10 000 times solar. They include stars which are strongly enhanced in either lead or zirconium, as well as other elements. Vertical stratification of the enhanced elements, where the element is concentrated in a thin layer of the atmosphere, has b

Tomasz Dębiec, Piotr Gwiazda, Błażej Miasojedow, Zuzanna Szymańska

We consider a macroscopic model for the growth of living tissues incorporating pressure-driven dispersal and pressure-modulated proliferation. Assuming a power-law relation between the mechanical pressure and the cell density, the model can be expressed as the porous medium equation with a growth term. We prove H\"older continuous dependence of the solutions

Romy Loehnert, Artur Braun, Debajeet K. Bora

The objective of this work was to investigate the photoelectrochemical (PEC) performance of nanoparticulate hematite thin film photoelectrodes prepared by a soft-chemistry route. Two cost-effective thin film fabrication techniques were employed to deposit the hematite film. First, the film was deposited on conducting glass substrates by dip coating of the or

David T. Stephen, Xie Chen

Finite depth quantum circuits provide an equivalence relation between gapped phases. Moreover, there can be nontrivial domain walls either within the same gapped phase or between different gapped phases, whose equivalence relations are given by finite depth quantum circuits in one lower dimension. In this paper, we use such unitary equivalence relations to s

Wei Dong, Xing Zhang, Bihui Chen, Dawei Yan

Parameter-efficient fine-tuning for pre-trained Vision Transformers aims to adeptly tailor a model to downstream tasks by learning a minimal set of new adaptation parameters while preserving the frozen majority of pre-trained parameters. Striking a balance between retaining the generalizable representation capacity of the pre-trained model and acquiring task

Vishal Purohit, Junjie Luo, Yiheng Chi, Qi Guo

The astonishing development of single-photon cameras has created an unprecedented opportunity for scientific and industrial imaging. However, the high data throughput generated by these 1-bit sensors creates a significant bottleneck for low-power applications. In this paper, we explore the possibility of generating a color image from a single binary frame of

Debajeet K. Bora, Romy Loehnart, Artur Braun

In this study, the use of X-Ray reflectometry technique signifies the types of rough and smooth surfaces of hematite film prepared from different fatty acid derivatives of the iron salt. Followed by this, the film morphology and crystallographic properties concerning different fatty acid chain length have been discussed.

Simone Marchesi, Alicia Tocino

In this survey we recognize Enrique Arrondo's contributions over the whole of its career, recalling his professional history and collecting the results of his mathematical production.

Mingdai Yang, Zhiwei Liu, Liangwei Yang, Xiaolong Liu

Pretraining has been widely explored to augment the adaptability of graph learning models to transfer knowledge from large datasets to a downstream task, such as link prediction or classification. However, the gap between training objectives and the discrepancy between data distributions in pretraining and downstream tasks hinders the transfer of the pretrai

Gunda Kipp, Hope M Bretscher, Benedikt Schulte, Dorothee Herrmann

Van der Waals (vdW) heterostructures host many-body quantum phenomena that can be tuned in situ using electrostatic gates. These gates are often microstructured graphite flakes that naturally form plasmonic cavities, confining light in discrete standing waves of current density due to their finite size. Their resonances typically lie in the GHz - THz range,

Vuong Bui

In this short note, we prove Hadwiger&#39;s conjecture for strongly monotypic polytopes.

Mike Perkins, Jasper Roe, Binh H. Vu, Darius Postma

This study investigates the efficacy of six major Generative AI (GenAI) text detectors when confronted with machine-generated content that has been modified using techniques designed to evade detection by these tools (n=805). The results demonstrate that the detectors&#39; already low accuracy rates (39.5%) show major reductions in accuracy (17.4%) when face

Federico Mazza, Sounak Biswas, Xinlin Yan, Andrey Prokofiev

A strange metal is an exotic state of correlated quantum matter; intensive efforts are ongoing to decipher its nature. Here we explore whether the quantum Fisher information (QFI), a concept from quantum metrology, can provide new insight. We use inelastic neutron scattering and quantum Monte Carlo simulations to study a Kondo destruction quantum critical po

Marco Maculan

Let $A$ be an abelian variety over a complete non-Archimedean field $K$. The universal cover of the Berkovich space attached to $A$ reflects the reduction behaviour of $A$. In this paper the universal cover of the universal vector extension $E(A)$ of $A$ is described. In a forthcoming paper ( arXiv:2007.04659), this will be one of the crucial tools to show t

Hsin-Jung Yang, Joe Beck, Md Zahid Hasan, Ekin Beyazit

In the rapidly evolving field of autonomous systems, the safety and reliability of the system components are fundamental requirements. These components are often vulnerable to complex and unforeseen environments, making natural edge-case generation essential for enhancing system resilience. This paper presents GENESIS-RL, a novel framework that leverages sys

Roni Con, Ryan Gabrys, Eitan Yaakobi

In this work we consider a generalization of the well-studied problem of coding for ``stuck-at'' errors, which we refer to as ``strong stuck-at'' codes. In the traditional framework of stuck-at codes, the task involves encoding a message into a one-dimensional binary vector. However, a certain number of the bits in this vector are 'frozen', meaning they are

Yuning Wu, Jiaying Wei, Jean Oh, Daniel Cardoso Llach

In the dynamic construction industry, traditional robotic integration has primarily focused on automating specific tasks, often overlooking the complexity and variability of human aspects in construction workflows. This paper introduces a human-centered approach with a "work companion rover" designed to assist construction workers within their existing pract

Beatriz Dias, Robert Koenig

We propose efficient classical algorithms which (strongly) simulate the action of bosonic linear optics circuits applied to superpositions of Gaussian states. Our approach relies on an augmented covariance matrix formalism to keep track of relative phases between individual terms in a linear combination. This yields an exact simulation algorithm whose runtim

Yuta Takahashi, Akira Masuoka

We describe the structure of the quotient $\mathfrak{G}/\mathfrak{H}$ of a formal supergroup $\mathfrak{G}$ by its formal sub-supergroup $\mathfrak{H}$. This is a consequence which arises as a continuation of the authors' work (partly with M. Hashi) on algebraic/analytic supergoups.The results are presented and proved in terms of super-cocommutative Hopf sup

Zainab Al-Zanbouri, Gauri Sharma, Shaina Raza

This study investigates how machine learning (ML) models can predict hospital readmissions for diabetic patients fairly and accurately across different demographics (age, gender, race). We compared models like Deep Learning, Generalized Linear Models, Gradient Boosting Machines (GBM), and Naive Bayes. GBM stood out with an F1-score of 84.3% and accuracy of 8

Amin Abolghasemi, Zhaochun Ren, Arian Askari, Mohammad Aliannejadi

An important unexplored aspect in previous work on user satisfaction estimation for Task-Oriented Dialogue (TOD) systems is their evaluation in terms of robustness for the identification of user dissatisfaction: current benchmarks for user satisfaction estimation in TOD systems are highly skewed towards dialogues for which the user is satisfied. The effect o

Paul Hege, Massimo Moscolari, Stefan Teufel

We show how the spectrum of normal discrete short-range infinite-volume operators can be approximated with two-sided error control using only data from finite-sized local patches. As a corollary, we prove the computability of the spectrum of such infinite-volume operators with the additional property of finite local complexity and provide an explicit algorit

Nils Dencker

In this paper we show that condition $\operatorname{Sub_r}(\Psi)$ on the subprincipal symbol is sufficient for local solvability of linear pseudodifferential operators of real subprincipal type. These are the operators having real principal symbol, which is of principal type and vanishes of second order on an involutive manifold where the subprincipal symbol

Ericson D. Lopez, Bryan A. Ubillus, Ariel A. Meza

The ionosphere affects radio signals by altering their speed, direction, and trajectory, causing a temporary delay known as ionospheric delay, which is directly related to the total electron content (TEC). Although research in other equatorial locations has explored TEC implications, qualitative research is required to predict its behavior in the ionosphere.

Annika Bonerath, Martin Nöllenburg, Soeren Terziadis, Markus Wallinger

Boundary labeling is a well-known method for displaying short textual labels for a set of point features in a figure alongside the boundary of that figure. Labels and their corresponding points are connected via crossing-free leaders. We propose orbital boundary labeling as a new variant of the problem, in which (i) the figure is enclosed by a circular conto

Shahin Heidari, Shannon Hashemi, Mohammad-Soroush Khorsand, Alireza Daneshfar

The rise of digital currency and the public ledger Block Chain has led to the development of a new type of electronic contract known as "smart contracts." For these contracts to be considered valid, they must adhere to traditional contract rules and be concluded without any impediments. Once written, encrypted, and signed, smart contracts are recorded in the

Saeid Asgari Taghanaki, Joseph Lambourne

The advent of generative AI models has revolutionized digital content creation, yet it introduces challenges in maintaining copyright integrity due to generative parroting, where models mimic their training data too closely. Our research presents a novel approach to tackle this issue by employing an overfitted Masked Autoencoder (MAE) to detect such parroted

David Gray Widder, Laura Dabbish, James Herbsleb, Nikolas Martelaro

Past work has sought to design AI ethics interventions--such as checklists or toolkits--to help practitioners design more ethical AI systems. However, other work demonstrates how these interventions may instead serve to limit critique to that addressed within the intervention, while rendering broader concerns illegitimate. In this paper, drawing on work exam

Daniel O-Campa, Erik Díaz-Bautista

In this paper, we examine the electron interaction within tilted anisotropic Dirac materials when subjected to external electric and magnetic fields possessing translational symmetry. Specifically, we focus on a distinct non-zero electric field magnitude, enabling the decoupling of the differential equation system inherent in the eigenvalue problem. Subseque

Yann Sakref, Olivier Rivoire

The apparent difficulty of designing simple autocatalysts that grow exponentially in the absence of enzymes, external drives or ingenious internal mechanisms severely constrains scenarios for the emergence of evolution by natural selection in chemical and physical systems. Here, we systematically analyze these difficulties in the context of one of the simple

De-An Huang, Shijia Liao, Subhashree Radhakrishnan, Hongxu Yin

There has been tremendous progress in multimodal Large Language Models (LLMs). Recent works have extended these models to video input with promising instruction following capabilities. However, an important missing piece is temporal localization. These models cannot accurately answer the "When?" questions. We identify three key aspects that limit their tempo

Daniel Menges, Adil Rasheed

In the current data-intensive era, big data has become a significant asset for Artificial Intelligence (AI), serving as a foundation for developing data-driven models and providing insight into various unknown fields. This study navigates through the challenges of data uncertainties, storage limitations, and predictive data-driven modeling using big data. We

Jean-Christophe Pain

The polynomials introduced by Sister Celine cover different usual orthogonal polynomials as special cases. Among them, the Jacobi and discrete Hahn polynomials are of particular interest for the quantum theory of angular momentum. In this note, we show that characters of irreducible representations of the rotation group as well as Wigner rotation "d" matrice

Mengjiang Sun, Peng Chen, Zhenxin Cao, Fei Shen

With the leaping advances in autonomous vehicles and transportation infrastructure, dual function radar-communication (DFRC) systems have become attractive due to the size, cost and resource efficiency. A frequency modulated continuous waveform (FMCW)-based radar-communication system (FRaC) utilizing both sparse multiple-input and multiple-output (MIMO) arra

Jorgen Cani, Ioannis Mademlis, Adamantia Anna Rebolledo Chrysochoou, Georgios Th. Papadopoulos

Illicit object detection is a critical task performed at various high-security locations, including airports, train stations, subways, and ports. The continuous and tedious work of examining thousands of X-ray images per hour can be mentally taxing. Thus, Deep Neural Networks (DNNs) can be used to automate the X-ray image analysis process, improve efficiency

Marco Barletta, Marcello Cinque, Davide De Vita

The adoption of cloud computing technologies in the industry is paving the way to new manufacturing paradigms. In this paper we propose a model to optimize the orchestration of workloads with differentiated criticality levels on a cloud-enabled factory floor. Preliminary results show that it is possible to optimize the guarantees to deployed jobs without pen

Seppo Hassi, Henk de Snoo

A semibounded operator or relation $S$ in a Hilbert space with lower bound $m \in {\mathbb R}$ has a symmetric extension $S_{\rm f}=S {\, \widehat + \,} (\{0\} \times {\rm mul\,} S^*)$, the weak Friedrichs extension of $S$, and a selfadjoint extension $S_{\rm F}$, the Friedrichs extension of $S$, that satisfy $S \subset S_{\rm f} \subset S_{\rm F}$. The Frie

M. Krivoš, Z. Tang, N. Floyd, C. L. Morris

The upcoming UCNProBe experiment at Los Alamos National Laboratory will measure the $\beta$-decay rate of free neutrons with different systematic uncertainties than previous beam-based neutron lifetime experiments. We have developed a new $^{10}$B-coated YAP:Ce scintillator whose properties are presented. The advantage of the YAP:Ce scintillator is its high