February 2024 arXiv papers — page 70

Stephan Goerttler, Fei He, Min Wu

Heat diffusion describes the process by which heat flows from areas with higher temperatures to ones with lower temperatures. This concept was previously adapted to graph structures, whereby heat flows between nodes of a graph depending on the graph topology. Here, we combine the graph heat equation with the stochastic heat equation, which ultimately yields

Shengyao Zhuang, Bevan Koopman, Xiaoran Chu, Guido Zuccon

The emergence of Vec2Text -- a method for text embedding inversion -- has raised serious privacy concerns for dense retrieval systems which use text embeddings, such as those offered by OpenAI and Cohere. This threat comes from the ability for a malicious attacker with access to embeddings to reconstruct the original text. In this paper, we investigate vario

H. Takahashi, S. Kitagawa, K. Ishida, A. Ikeda

CaSb$_2$ is the Dirac line-nodal material that exhibits a superconducting (SC) transition at 1.7 K. In spite of its conventional SC state at ambient pressure, the transition temperature $T_{\mathrm{c}}$ shows a peak structure against hydrostatic pressure. We performed ac magnetic susceptibility and $^{123}$Sb nuclear quadrupole resonance (NQR) measurements o

Angus Yang, Zehan Li, Jie Li

This study aims to explore the best practices for utilizing GenAI as a programming tool, through a comparative analysis between GPT-4 and GLM-4. By evaluating prompting strategies at different levels of complexity, we identify that simplest and straightforward prompting strategy yields best code generation results. Additionally, adding a CoT-like preliminary

Timo Keller, Mulun Yin

Let $f$ be a newform of weight $k=2r$ and level $N$ with trivial nebentypus. Let $\mathfrak{p}\nmid 2N$ be a maximal ideal of the ring of integers of the coefficient field of $f$ such that the self-dual twist of the mod-$\mathfrak{p}$ Galois representation of $f$ is reducible with constituents $\phi,\psi$. Denote a decomposition group over the rational prime

M. S. M. Farhan

This study investigates into the analysis of ferromagnetic thin films with a body centered cubic lattice and three spin layers, utilizing the solution of the fourth-order perturbed Heisenberg Hamiltonian equation with seven magnetic energy parameters. Spin-exchange interaction, magnetic dipole interaction, second-order magnetic anisotropy,fourth-order magnet

Youssef Allouah, Sadegh Farhadkhani, Rachid GuerraouI, Nirupam Gupta

The possibility of adversarial (a.k.a., {\em Byzantine}) clients makes federated learning (FL) prone to arbitrary manipulation. The natural approach to robustify FL against adversarial clients is to replace the simple averaging operation at the server in the standard $\mathsf{FedAvg}$ algorithm by a \emph{robust averaging rule}. While a significant amount of

XuDong Ling, ChaoRong Li, FengQing Qin, LiHong Zhu

Deep neural networks have made great achievements in rainfall prediction.However, the current forecasting methods have certain limitations, such as with blurry generated images and incorrect spatial positions. To overcome these challenges, we propose a Two-stage Rainfall-Forecasting Diffusion Model (TRDM) aimed at improving the accuracy of long-term rainfall

Freerk Schütt, Ana M. Valencia, Caterina Cocchi

The growing interest in tin-halide semiconductors for photovoltaic applications demands an in-depth knowledge of the fundamental properties of its constituents, starting from the smallest monomers entering the initial stages of formation. In this first-principles work based on time-dependent density-functional theory, we investigate the structural, electroni

Xinyi Wang, Shaukat Ali, Aitor Arrieta, Paolo Arcaini

Quantum Extreme Learning Machine (QELM) is an emerging technique that utilizes quantum dynamics and an easy-training strategy to solve problems such as classification and regression efficiently. Although QELM has many potential benefits, its real-world applications remain limited. To this end, we present QELM's industrial application in the context of elevat

A. M. Kamchatnov, D. V. Shaykin

We study dynamics of dark solitons in the theory of the DNLS equation by the method based on imposing the condition that this dynamics must be Hamiltonian. Combining this condition with Stokes' remark that relationships for harmonic linear waves and small-amplitude soliton tails satisfy the same linearized equations, so the corresponding solutions can be con

Alexander R. Klotz, Benjamin Estabrooks

Knots in open strands such as ropes, fibers, and polymers, cannot typically be described in the language of knot theory, which characterizes only closed curves in space. Simulations of open knotted polymer chains, often parameterized to DNA, typically perform a closure operation and calculate the Alexander polynomial to assign a knot topology. This is limite

Yiruo Cheng, Kelong Mao, Zhicheng Dou

Conversational dense retrieval has shown to be effective in conversational search. However, a major limitation of conversational dense retrieval is their lack of interpretability, hindering intuitive understanding of model behaviors for targeted improvements. This paper presents CONVINV, a simple yet effective approach to shed light on interpretable conversa

Yoon Seong Heo, Tae Wan Kim, Wooseok Lee, Jungseok Choi

Mesoscopic-scale stacking reconfigurations are investigated when van der Waals films are stacked. We have developed a method to visualize complicated stacking structures and mechanical distortions simultaneously in stacked atom-thick films using Raman spectroscopy. In the rigid limit, we found that the distortions originate from the transfer process, which c

Ru Wang, Zach Potter, Yun Ho, Daniel Killough

Reading is a challenging task for low vision people. While conventional low vision aids (e.g., magnification) offer certain support, they cannot fully address the difficulties faced by low vision users, such as locating the next line and distinguishing similar words. To fill this gap, we present GazePrompt, a gaze-aware reading aid that provides timely and t

Tatsuya Miura, Glen Wheeler

For an old problem of Euler's elastica we prove the novel global property that every planar elastica with non-constant monotone curvature is uniquely minimal subject to the clamped boundary condition. We also partly extend this unique minimality to the length-penalised case; this result is new even in view of local minimality. As an application we prove uniq

Zi Haur Pang, Yahui Fu, Divesh Lala, Keiko Ochi

In the realm of human-AI dialogue, the facilitation of empathetic responses is important. Validation is one of the key communication techniques in psychology, which entails recognizing, understanding, and acknowledging others' emotional states, thoughts, and actions. This study introduces the first framework designed to engender empathetic dialogue with vali

Yuhang Guan, Runzheng Wang, Lei Fu, Huanle Zhang

The global patent application count has steadily increased, achieving eight consecutive years of growth.The global patent industry has shown a general trend of expansion. This is attributed to the increasing innovation activities, particularly in the fields of technology, healthcare, and biotechnology. Some emerging market countries, such as China and India,

Vladimir Dotsenko, Evgeny Feigin, Piotr Kucharski, Markus Reineke

In earlier work of three of the authors of the present paper, a supercommutative quadratic algebra was associated to each symmetric quiver, and a new proof of positivity of motivic Donaldson-Thomas invariants of symmetric quivers was given using the so called numerical Koszul property of these algebras. It was furthermore conjectured that for each symmetric

Zijian Li, Ruichu Cai, Zhenhui Yang, Haiqin Huang

As environments evolve, temporal distribution shifts can degrade time series forecasting performance. A straightforward solution is to adapt to nonstationary changes while preserving stationary dependencies. Hence, some methods disentangle stationary and nonstationary components by assuming uniform distribution shifts, but it is impractical since when the di

E. G. Mishchenko

Hot electrons formed in a graphene crystal by high-intensity short-duration laser pulses can exist for a time that is less than an electron-phonon energy relaxation time. During that time, electron-electron collisions cause the electrons to thermalize to a local effective temperature that propagates (diffuses) through graphene. The non-uniform nature of the

Qi Bi, Beichen Zhou, Jingjun Yi, Wei Ji

Oriented object detection has been rapidly developed in the past few years, but most of these methods assume the training and testing images are under the same statistical distribution, which is far from reality. In this paper, we propose the task of domain generalized oriented object detection, which intends to explore the generalization of oriented object

Taran Driver, Miles Mountney, Jun Wang, Lisa Ortmann

The photoelectric effect is not truly instantaneous, but exhibits attosecond delays that can reveal complex molecular dynamics. Sub-femtosecond duration light pulses provide the requisite tools to resolve the dynamics of photoionization. Accordingly, the past decade has produced a large volume of work on photoionization delays following single photon absorpt

Qingyao Tian, Huai Liao, Xinyan Huang, Bingyu Yang

Localizing the bronchoscope in real time is essential for ensuring intervention quality. However, most existing methods struggle to balance between speed and generalization. To address these challenges, we present BronchoTrack, an innovative real-time framework for accurate branch-level localization, encompassing lumen detection, tracking, and airway associa

Lirui Liu, Joe Suzuki

We introduce a novel Information Criterion (IC), termed Learning under Singularity (LS), designed to enhance the functionality of the Widely Applicable Bayes Information Criterion (WBIC) and the Singular Bayesian Information Criterion (sBIC). LS is effective without regularity constraints and demonstrates stability. Watanabe defined a statistical model or a

Jinyu Cai, Yunhe Zhang, Zhoumin Lu, Wenzhong Guo

Graph anomaly detection (GAD) aims to identify anomalous graphs that significantly deviate from other ones, which has raised growing attention due to the broad existence and complexity of graph-structured data in many real-world scenarios. However, existing GAD methods usually execute with centralized training, which may lead to privacy leakage risk in some

Nailei Hei, Qianyu Guo, Zihao Wang, Yan Wang

Well-designed prompts have demonstrated the potential to guide text-to-image models in generating amazing images. Although existing prompt engineering methods can provide high-level guidance, it is challenging for novice users to achieve the desired results by manually entering prompts due to a discrepancy between novice-user-input prompts and the model-pref

Anjali Gupta, Shreyans J. Nagori, Abhijnan Chakraborty, Rohit Vaish

Social commerce platforms are emerging businesses where producers sell products through re-sellers who advertise the products to other customers in their social network. Due to the increasing popularity of this business model, thousands of small producers and re-sellers are starting to depend on these platforms for their livelihood; thus, it is important to

Hiroto Adachi, Yusuke Kato, Jun-ichiro Ohe, Masanori Ichioka

We develop a time-dependent Ginzburg-Landau theory of the vortex spin Hall effect, i.e., a spin Hall effect that is driven by the motion of superconducting vortices. For the direct vortex spin Hall effect in which an input charge current drives the transverse spin current accompanying the vortex motion, we start from the well-known Schmid-Caroli-Maki solutio

Jin-Li Zhang, Jun Wu

The Nambu--Jona-Lasinio model is utilized to investigate the pion and kaon photon leading-twist transition distribution amplitudes using proper time regularization. Separately, the properties of the vector and axial vector pion photon transition distribution amplitudes are examined, and the results meet the desired properties. Our study involves sum rule and

Zhe Tang, Ruocheng Gu, Sihao Li, Kyeong Soo Kim

Wi-Fi fingerprinting has emerged as the most popular approach to indoor localization. The use of ML algorithms has greatly improved the localization performance of Wi-Fi fingerprinting, but its success depends on the availability of fingerprint databases composed of a large number of RSSIs, the MAC addresses of access points, and the other measurement inform

Xuefan Yin, Ye Chen, Xiaoyu Zhang, Zixuan Zhang

Berry curvature that describes local geometrical properties of energy bands can elucidate many fascinating phenomena in solid-state, photonic, and phononic systems, given its connection to global topological invariants such as the Chern number. Despite its significance, the observation of Berry curvature poses a substantial challenging since wavefunctions ar

Haozhe Liu, Wentian Zhang, Feng Liu, Haoqian Wu

The vulnerability of automated fingerprint recognition systems (AFRSs) to presentation attacks (PAs) promotes the vigorous development of PA detection (PAD) technology. However, PAD methods have been limited by information loss and poor generalization ability, resulting in new PA materials and fingerprint sensors. This paper thus proposes a global-local mode

Guangzong Xiao, Tengfang Kuang, Yutong He, Xinlin Chen

Phonon lasers, as mechanical analogues of optical lasers, are unique tools for not only fundamental studies of phononics but also diverse applications such as acoustic imaging and force sensing. Very recently, by levitating a micro-size sphere in an optical tweezer, higher-order mechanical harmonics were observed in the phonon-lasing regime, as the first ste

Sean McCurdy

The main results of this paper provide VMO-type estimates for the quadratic tilt-excess on varifolds with critical generalized mean curvature. These estimates apply to varifolds with "almost-integral" density which are close to a multiplicity one $m$-disc in a ball in the usual senses. The class of almost-integral varifolds allows for varifolds with non-perp

Deniz Yılmaz

We extend the notion of a p-permutation equivalence to an equivalence between direct products of block algebras. We prove that a p-permutation equivalence between direct products of blocks gives a bijection between the factors and induces a p-permutation equivalence between corresponding blocks.

Chi Chen, Yiyang Du, Zheng Fang, Ziyue Wang

Recent developments in Multimodal Large Language Models (MLLMs) have shown rapid progress, moving towards the goal of creating versatile MLLMs that understand inputs from various modalities. However, existing methods typically rely on joint training with paired multimodal instruction data, which is resource-intensive and challenging to extend to new modaliti

Qianqian Xie, Qingyu Chen, Aokun Chen, Cheng Peng

Recent advancements in large language models (LLMs) like ChatGPT and LLaMA show promise in medical applications, yet challenges remain in medical language comprehension. This study presents Me-LLaMA, a new medical LLM family based on open-source LLaMA models, optimized for medical text analysis and diagnosis by leveraging large-scale, domain-specific dataset

T. Kato, H. Sugama, T. -H. Watanabe, M. Nunami

Microturbulence in magnetic confined plasmas contributes to energy exchange between particles of different species as well as the particle and heat fluxes. Although the effect of turbulent energy exchange has not been considered significant in previous studies, it is anticipated to have a greater impact than collisional energy exchange in low collisional pla

Chi Jin, Zheng Chang, Fengye Hu, Hsiao-Hwa Chen

Due to the constraints on power supply and limited encryption capability, data security based on physical layer security (PLS) techniques in backscatter communications has attracted a lot of attention. In this work, we propose to enhance PLS in a full-duplex symbiotic radio (FDSR) system with a proactive eavesdropper, which may overhear the information and i

James Y. S. Tan, Shawn Xie Wu, Salih Yanikgonul, Chao Li

Silicon photonic integrated circuit (PIC) builds on the demand for a low cost approach from established silicon-based manufacturing infrastructure traditionally built for electronics. Besides its natural abundance, silicon has desirable properties such as optically low loss (at certain critical wavelengths), and small form factor to enable high density scale

Yanan Chen, Zihao Cui, Yingying Gao, Junlan Feng

The expectation to deploy a universal neural network for speech enhancement, with the aim of improving noise robustness across diverse speech processing tasks, faces challenges due to the existing lack of awareness within static speech enhancement frameworks regarding the expected speech in downstream modules. These limitations impede the effectiveness of st

Hao Wang, Chenyi Zhang, Tongyang Li

The problem of minimizing the maximum of $N$ convex, Lipschitz functions plays significant roles in optimization and machine learning. It has a series of results, with the most recent one requiring $O(N\epsilon^{-2/3} + \epsilon^{-8/3})$ queries to a first-order oracle to compute an $\epsilon$-suboptimal point. On the other hand, quantum algorithms for optim

Tina Torabi, Yangshuai Wang, Christoph Ortner

The temperature-dependent behavior of defect densities within a crystalline structure is intricately linked to the phenomenon of vibrational entropy. Traditional methods for evaluating vibrational entropy are computationally intensive, limiting their practical utility. We show that total entropy can be decomposed into atomic site contributions and rigorously

Lei Yu, Abir Ray

Recognizing the imperative to address the reliability and transparency issues of Large Language Models (LLM), this work proposes an LLM maturity model tailored for text-to-query applications. This maturity model seeks to fill the existing void in evaluating LLMs in such applications by incorporating dimensions beyond mere correctness or accuracy. Moreover, t

Nan Xiao, Bo Lang, Ting Wang, Yikai Chen

Threat actor attribution is a crucial defense strategy for combating advanced persistent threats (APTs). Cyber threat intelligence (CTI), which involves analyzing multisource heterogeneous data from APTs, plays an important role in APT actor attribution. The current attribution methods extract features from different CTI perspectives and employ machine learn

Mohammad Ewais, Paul Chow

Datacenters of today have maintained the same architecture for decades using the server as the primary building block. However, this traditional approach suffers from under-utilization of its resources, often caused by over-allocating these resources when deploying applications to accommodate worst-case scenarios. Specifically, servers can quickly drain thei

Hyolim Jeon, Dongje Yoo, Daeun Lee, Sejung Son

Despite the increasing demand for AI-based mental health monitoring tools, their practical utility for clinicians is limited by the lack of interpretability.The CLPsych 2024 Shared Task (Chim et al., 2024) aims to enhance the interpretability of Large Language Models (LLMs), particularly in mental health analysis, by providing evidence of suicidality through

Sen Li, Ruochen Wang, Cho-Jui Hsieh, Minhao Cheng

Existing text-to-image models still struggle to generate images of multiple objects, especially in handling their spatial positions, relative sizes, overlapping, and attribute bindings. To efficiently address these challenges, we develop a training-free Multimodal-LLM agent (MuLan), as a human painter, that can progressively generate multi-object with intric

Dai Aoki

Various single crystal growth techniques are presented for the unconventional superconductor UTe2. The molten salt flux liquid transport (MSFLT) method is employed to grow high-quality and large single crystals, exhibiting a high residual resistivity ratio (RRR = 200-800). On the other hand, the Te self-flux and chemical vapor transport (CVT) method produces

Xuan Pang, Bao-Hua Sun, Li-Hua Zhu, Guang-Hong Lu

Photonuclear reactions using a laser Compton scattering (LCS) gamma source provide a new method for producing radioisotopes for medical applications. Compared with the conventional method, this method has the advantages of a high specific activity and less heat. Initiated by the Shanghai Laser Electron Gamma Source (SLEGS), we conducted a survey of potential

Michimasa Inaba, Mariko Ukiyo, Keiko Takamizo

Mental health care poses an increasingly serious challenge to modern societies. In this context, there has been a surge in research that utilizes information technologies to address mental health problems, including those aiming to develop counseling dialogue systems. However, there is a need for more evaluations of the performance of counseling dialogue sys

Marton Havasi, Sonali Parbhoo, Finale Doshi-Velez

Interpretability methods that utilise local surrogate models (e.g. LIME) are very good at describing the behaviour of the predictive model at a point of interest, but they are not guaranteed to extrapolate to the local region surrounding the point. However, overfitting to the local curvature of the predictive model and malicious tampering can significantly l

Feng Chen

Achieving a universally high accuracy in object detection is quite challenging, and the mainstream focus in the industry currently lies on detecting specific classes of objects. However, deploying one or multiple object detection networks requires a certain amount of GPU memory for training and storage capacity for inference. This presents challenges in term

Aytaç Özkan, Elena Stoykova, Thomas Sikora, Violeta Madjarova

In Optical Coherence Tomography (OCT), speckle noise significantly hampers image quality, affecting diagnostic accuracy. Current methods, including traditional filtering and deep learning techniques, have limitations in noise reduction and detail preservation. Addressing these challenges, this study introduces a novel denoising algorithm, Block-Matching Stee

Tsubasa Harada

In the online facility assignment on a line (OFAL) with a set $S$ of $k$ servers and a capacity $c:S\to\mathbb{N}$, each server $s\in S$ with a capacity $c(s)$ is placed on a line and a request arrives on a line one-by-one. The task of an online algorithm is to irrevocably assign a current request to one of the servers with vacancies before the next request

Wei Zhao, Zhitao Hou, Siyuan Wu, Yan Gao

Writing formulas on spreadsheets, such as Microsoft Excel and Google Sheets, is a widespread practice among users performing data analysis. However, crafting formulas on spreadsheets remains a tedious and error-prone task for many end-users, particularly when dealing with complex operations. To alleviate the burden associated with writing spreadsheet formula

Weixin Li, Yuhao Wu, Yang Liu, Weike Pan

In real recommendation scenarios, users often have different types of behaviors, such as clicking and buying. Existing research methods show that it is possible to capture the heterogeneous interests of users through different types of behaviors. However, most multi-behavior approaches have limitations in learning the relationship between different behaviors

Jingqi Chen, Yu Wang, Wenjie Dou

In this paper, we introduce the Floquet Ehrenfest and Floquet surface hopping approaches to study the nonadiabatic dynamics in the laser-dressed solid systems. We demonstrate that these two approaches can be formulated in both real and reciprocal spaces. Using these approaches, we are able to simulate the interaction between electronic carriers and phonons u

Masae Katai, Parisa Edalati, Jacqueline Hidalgo-Jimenez, Yu Shundo

Photocatalytic CO2 conversion is a clean technology to deal with CO2 emissions, and titanium oxide (TiO2) polymorphs are the most investigated photocatalysts for such an application. In this study, black TiO2 brookite is produced by a high-pressure torsion (HPT) method and employed as an active photocatalyst for CO2 conversion. Black brookite with a large co

Shubhashis Roy Dipta, Sai Vallurupalli

The aim of SemEval-2024 Task 1, "Semantic Textual Relatedness for African and Asian Languages" is to develop models for identifying semantic textual relatedness (STR) between two sentences using multiple languages (14 African and Asian languages) and settings (supervised, unsupervised, and cross-lingual). Large language models (LLMs) have shown impressive pe

Zhanglu Yan, Weiran Chu, Yuhua Sheng, Kaiwen Tang

N-terminal coding sequence (NCS) influences gene expression by impacting the translation initiation rate. The NCS optimization problem is to find an NCS that maximizes gene expression. The problem is important in genetic engineering. However, current methods for NCS optimization such as rational design and statistics-guided approaches are labor-intensive yie

Zhongzhi Li, Jingqi Tu, Jiacheng Zhu, Jianliang Ai

Deep transfer learning (DTL) is a fundamental method in the field of Intelligent Fault Detection (IFD). It aims to mitigate the degradation of method performance that arises from the discrepancies in data distribution between training set (source domain) and testing set (target domain). Considering the fact that fault data collection is challenging and certa

Junnan Dong, Qinggang Zhang, Huachi Zhou, Daochen Zha

Knowledge-based visual question answering (KVQA) has been extensively studied to answer visual questions with external knowledge, e.g., knowledge graphs (KGs). While several attempts have been proposed to leverage large language models (LLMs) as an implicit knowledge source, it remains challenging since LLMs may generate hallucinations. Moreover, multiple kn

Shivam Gupta, Ajil Jalal, Aditya Parulekar, Eric Price

Diffusion models are a remarkably effective way of learning and sampling from a distribution $p(x)$. In posterior sampling, one is also given a measurement model $p(y \mid x)$ and a measurement $y$, and would like to sample from $p(x \mid y)$. Posterior sampling is useful for tasks such as inpainting, super-resolution, and MRI reconstruction, so a number of

J. Nieves, A. Feijoo, M. Albaladejo, Meng-Lin Du

We present a detailed study of the lowest-lying ${\frac{1}{2}}^-$ and ${\frac{3}{2}}^-$ $\Lambda_{Q}$ resonances both in the heavy quark (bottom and charm) and the strange sectors. We have paid special attention to the interplay between the constituent quark-model and chiral baryon-meson degrees of freedom, which are coupled using a unitarized scheme consist

R. Z. E. Alsaberi, M. D. Filipovic, H. Sano, P. Kavanagh

We present the first and deepest Australia Telescope Compact Array radio continuum images of the Honeycomb Nebula at 2000 and 5500 MHz solely from archival data. The resolutions of these images are 3.6 x 2.8 arcsec2 and 1.3 x 1.2 arcsec2 at 2000 and 5500 MHz. We find an average radio spectral index for the remnant of -0.76 +- 0.07. Polarisation maps at 5500

Zhaomeng Chen, Zihuai He, Benjamin B. Chu, Jiaqi Gu

Identifying which variables do influence a response while controlling false positives pervades statistics and data science. In this paper, we consider a scenario in which we only have access to summary statistics, such as the values of marginal empirical correlations between each dependent variable of potential interest and the response. This situation may a

Danny C. Price

In radio astronomy, the science output of a telescope is often limited by computational resources. This is especially true for transient and technosignature surveys that need to search high-resolution data across a large parameter space. The tremendous data volumes produced by modern radio array telescopes exacerbate these processing challenges. Here, we int

Zijie Pan, Yushan Jiang, Dongjin Song, Sahil Garg

Recent studies in multivariate time series (MTS) forecasting reveal that explicitly modeling the hidden dependencies among different time series can yield promising forecasting performance and reliable explanations. However, modeling variable dependencies remains underexplored when MTS is continuously accumulated under different regimes (stages). Due to the

Jinsung Jeon, Hyundong Jin, Jonghyun Choi, Sanghyun Hong

A standard practice in developing image recognition models is to train a model on a specific image resolution and then deploy it. However, in real-world inference, models often encounter images different from the training sets in resolution and/or subject to natural variations such as weather changes, noise types and compression artifacts. While traditional

Fangqi Li, Haodong Zhao, Wei Du, Shilin Wang

To trace the copyright of deep neural networks, an owner can embed its identity information into its model as a watermark. The capacity of the watermark quantify the maximal volume of information that can be verified from the watermarked model. Current studies on capacity focus on the ownership verification accuracy under ordinary removal attacks and fail to

Luca Maestrini, Francis K. C. Hui, Alan H. Welsh

Restricted maximum likelihood (REML) estimation is a widely accepted and frequently used method for fitting linear mixed models, with its principal advantage being that it produces less biased estimates of the variance components. However, the concept of REML does not immediately generalize to the setting of non-normally distributed responses, and it is not

Hossein Jamali, Sergiu M. Dascalu, Frederick C. Harris

In today's world, where moving forward hinges on innovation and working together, this article introduces a new global online platform that is all about sparking teamwork to come up with new ideas. This platform goes beyond borders and barriers between different fields, creating an exciting space where people from all over the world can swap ideas, get helpf

Colin Defant, Andrew Sack

Laplante-Anfossi associated to each rooted plane tree a polytope called an operahedron. He also defined a partial order on the vertex set of an operahedron and asked if the resulting poset is a lattice. We answer this question in the affirmative, motivating us to name Laplante-Anfossi's posets operahedron lattices. The operahedron lattice of a chain with $n+

Ziqiang Wang, Xuewei Feng, Qi Li, Kun Sun

In this paper, we unveil a fundamental side channel in Wi-Fi networks, specifically the observable frame size, which can be exploited by attackers to conduct TCP hijacking attacks. Despite the various security mechanisms (e.g., WEP and WPA2/WPA3) implemented to safeguard Wi-Fi networks, our study reveals that an off path attacker can still extract sufficient

Wenqian Ye, Luyang Jiang, Eric Xie, Guangtao Zheng

Back in the early 20th century, a horse named Hans appeared to perform arithmetic and other intellectual tasks during exhibitions in Germany, while it actually relied solely on involuntary cues in the body language from the human trainer. Modern machine learning models are no different. These models are known to be sensitive to spurious correlations between

Rui Jiao, Xiangzhe Kong, Li Zhang, Ziyang Yu

Pretraining on a large number of unlabeled 3D molecules has showcased superiority in various scientific applications. However, prior efforts typically focus on pretraining models in a specific domain, either proteins or small molecules, missing the opportunity to leverage cross-domain knowledge. To mitigate this gap, we introduce Equivariant Pretrained Trans

Satvik Tripathi, Liam Mutter, Meghana Muppuri, Suhani Dheer

This study introduces and evaluates the PRECISE framework, utilizing OpenAI's GPT-4 to enhance patient engagement by providing clearer and more accessible chest X-ray reports at a sixth-grade reading level. The framework was tested on 500 reports, demonstrating significant improvements in readability, reliability, and understandability. Statistical analyses

Yuhang Zhou, Yuchen Ni, Yunhui Gan, Zhangyue Yin

Large Language Models (LLMs) are increasingly adopted in financial analysis for interpreting complex market data and trends. However, their use is challenged by intrinsic biases (e.g., risk-preference bias) and a superficial understanding of market intricacies, necessitating a thorough assessment of their financial insight. To address these issues, we introd

Shitao Tang, Jiacheng Chen, Dilin Wang, Chengzhou Tang

This paper presents a neural architecture MVDiffusion++ for 3D object reconstruction that synthesizes dense and high-resolution views of an object given one or a few images without camera poses. MVDiffusion++ achieves superior flexibility and scalability with two surprisingly simple ideas: 1) A ``pose-free architecture'' where standard self-attention among 2

Junyan Liu, Yunfan Li, Ruosong Wang, Lin F. Yang

Existing metrics for reinforcement learning (RL) such as regret, PAC bounds, or uniform-PAC (Dann et al., 2017), typically evaluate the cumulative performance, while allowing the agent to play an arbitrarily bad policy at any finite time t. Such a behavior can be highly detrimental in high-stakes applications. This paper introduces a stronger metric, uniform

Daniel Li, Lincoln Murr

In 2023, we are using the latest models of GPT-4 to advance program synthesis. The large language models have significantly improved the state-of-the-art for this purpose. To make these advancements more accessible, we have created a repository that connects these models to Huamn Eval. This dataset was initally developed to be used with a language model call

Hongtao Zhu, Sizhe Zhang, Yang Su, Zhenyu Zhao

In the domain of causal inference research, the prevalent potential outcomes framework, notably the Rubin Causal Model (RCM), often overlooks individual interference and assumes independent treatment effects. This assumption, however, is frequently misaligned with the intricate realities of real-world scenarios, where interference is not merely a possibility

Maohao Shen, Subhro Das, Kristjan Greenewald, Prasanna Sattigeri

We consider the issue of calibration in large language models (LLM). Recent studies have found that common interventions such as instruction tuning often result in poorly calibrated LLMs. Although calibration is well-explored in traditional applications, calibrating LLMs is uniquely challenging. These challenges stem as much from the severe computational req

Yusheng Luo, Yongquan Zhang

This paper studies quasiconformal non-equivalence of Julia sets and limit sets. We proved that any Julia set is quasiconformally different from the Apollonian gasket. We also proved that any Julia set of a quadratic rational map is quasiconformally different from the gasket limit set of a geometrically finite Kleinian group.

Shoukuan Zhao, Diandong Tang, Xiaoxiao Xiao, Ruixia Wang

Conical intersections (CIs) are pivotal in many photochemical processes. Traditional quantum chemistry methods, such as the state-average multi-configurational methods, face computational hurdles in solving the electronic Schr\"odinger equation within the active space on classical computers. While quantum computing offers a potential solution, its feasibilit

Mohammad Rowshan, Vlad-Florin Drăgoi, Jinhong Yuan

The structure of a linear block code is pivotal in defining fundamental properties, particularly weight distribution, and code design. In this study, we characterize the Type II structure of polar codewords with weights less than twice the minimum weight $w_{min}$, utilizing the lower triangular affine (LTA) transform. We present a closed-form formula for th

Yuke Li, Guangyi Chen, Ben Abramowitz, Stefano Anzellott

Few-shot action recognition aims at quickly adapting a pre-trained model to the novel data with a distribution shift using only a limited number of samples. Key challenges include how to identify and leverage the transferable knowledge learned by the pre-trained model. We therefore propose CDTD, or Causal Domain-Invariant Temporal Dynamics for knowledge tran

Hao-Yuan Chen, Yen-Jui Chang, Shih-Wei Liao, Ching-Ray Chang

Quantum embedding with transformers is a novel and promising architecture for quantum machine learning to deliver exceptional capability on near-term devices or simulators. The research incorporated a vision transformer (ViT) to advance quantum significantly embedding ability and results for a single qubit classifier with around 3 percent in the median F1 sc

Yuanhang Liu, Donghui Yang, Xingwu Zeng, Can Zhang

In this paper, a quantitative estimate of unique continuation for the stochastic heat equation with bounded potentials on the whole Euclidean space is established. This paper generalizes the earlier results in [29] and [17] from a bounded domain to an unbounded one. The proof is based on the locally parabolic-type frequency function method. An observability

Sai Krishna Revanth Vuruma, Ashley Margetts, Jianhai Su, Faez Ahmed

Generative Artificial Intelligence (AI) has shown tremendous prospects in all aspects of technology, including design. However, due to its heavy demand on resources, it is usually trained on large computing infrastructure and often made available as a cloud-based service. In this position paper, we consider the potential, challenges, and promising approaches

Hanchen Xia, Feng Jiang, Naihao Deng, Cunxiang Wang

Large Language Models (LLMs) have demonstrated strong performance on various tasks. To unleash their power on the Text-to-SQL task, we propose $R^3$ (Review-Rebuttal-Revision), a consensus-based multi-agent system for Text-to-SQL tasks. $R^3$ outperforms the existing single LLM Text-to-SQL systems as well as the multi-agent Text-to-SQL systems by $1.3\%$ to

Jinghang Li, Tales Santini, Yuanzhe Huang, Joseph M. Mettenburg

White matter hyperintensity (WMH) remains the top imaging biomarker for neurodegenerative diseases. Robust and accurate segmentation of WMH holds paramount significance for neuroimaging studies. The growing shift from 3T to 7T MRI necessitates robust tools for harmonized segmentation across field strengths and artifacts. Recent deep learning models exhibit p

Liqin Zhou, Fazhi Yang, Shuai Zhang, Tiantian Zhang

We study the chemical rules for predicting and understanding topological states in stacked kagome and honeycomb lattices in both analytical and numerical ways. Starting with a minimal five-band tight-binding model, we sort out all the topological states into five groups, which are determined by the interlayer and intralayer hopping parameters. Combined with

Niuchang Ouyang, Dongyi Shen, Chen Wang, Ruihuan Cheng

The phonon transport mechanisms and the anomalous temperature-dependent lattice thermal conductivities (kL) in Ag-based argyrodites have not been fully understood. Herein, we systematically study the phonon thermal transport of five Ag-based crystalline argyrodites Ag7PS6, Ag7AsS6, Ag8SnS6, Ag8GeS6 and Ag9GaS6 utilizing perturbation theory and the unified th

Jin-Jun Chen, Kai Xu, Li-Hang Ren, Yu-Ran Zhang

We demonstrate that the dynamics of quantum coherence serves as an effective probe for identifying dephasing, which is a distinctive signature of many-body localization (MBL). Quantum coherence can be utilized to measure both the local coherence of specific subsystems and the total coherence of the whole system in a consistent manner. Our results reveal that

Utkarsh Patil, Stephen Merriman, Shubhendu Kumar, Ali Dhinojwala

Understanding the extent of contact between objects is crucial for surface-driven phenomena in the natural world. While interactions are known to occur at subnanometer-length scales, the techniques needed to measure such small-length scales are lacking. We combined two complementary techniques of attenuated total reflectance infrared (ATR-IR) spectroscopy co

Yue Sun, Chris H. Rycroft

We present a fully-integrated lattice Boltzmann (LB) method for fluid--structure interaction (FSI) simulations that efficiently models deformable solids in complex suspensions and active systems. Our Eulerian method (LBRMT) couples finite-strain solids to the LB fluid on the same fixed computational grid with the reference map technique (RMT). An integral pa

Brian Alspach, Aditya Joshi

A graph $X$ is $k$-spanning cyclable if for any subset $S$ of $k$ distinct vertices there is a 2-factor of $X$ consisting of $k$ cycles such that each vertex in $S$ belongs to a distinct cycle. In this paper we examine the $k$-spanning cyclability of 4-valent Cayley graphs on Abelian groups.