April 2023 arXiv papers — page 121

Muhammad Bilal Khan, Muhammad Waseem, Muhammad Irfan, Asad Mehmood

Zero-photon catalysis (ZPC) introduces noiseless attenuation and can be implemented by existing technologies in quantum key distribution (QKD) protocols. In this paper, we present a ZPC-based eight-state measurement-device-independent continuous-variable QKD (MDI-CV-QKD) combined with discrete modulation and reverse reconciliation. This ZPC-involved eight-st

Hao Liu, Yuan-He Zou, Yan-Rui Liu, Shao-Zhou Jiang

The relativistic chiral Lagrangians for both spin-$\frac{1}{2}$ and spin-$\frac{3}{2}$ doubly charmed baryons are constructed up to the order $\mathcal{O}(p^{4})$. From $\mathcal{O}(p^{2})$ to $\mathcal{O}(p^{4})$, there are 19, 74, and 452 independent terms in the two-flavor case and 25, 112, and 864 independent terms in the three-flavor case. The chiral La

Qijia He, Fei Gao, Oliver Dukes, Sinead Delany-Moretlwe

In many clinical settings, an active-controlled trial design (e.g., a non-inferiority or superiority design) is often used to compare an experimental medicine to an active control (e.g., an FDA-approved, standard therapy). One prominent example is a recent phase 3 efficacy trial, HIV Prevention Trials Network Study 084 (HPTN 084), comparing long-acting cabot

Zhanqiang Bai, Jia-Jun Ma, Yutong Wang

Let $\mathfrak{g}$ be a classical complex simple Lie algebra. Let $L(\lambda)$ be a highest weight module of $\mathfrak{g}$ with highest weight $\lambda-\rho$, where $\rho$ is half the sum of positive roots. The associated variety of the annihilator ideal of $L(\lambda)$ is called the annihilator variety of $L(\lambda)$.It is known that the annihilator varie

Maksim V. Kukushkin

This paper is partly a historical survey of various approaches and methods in the fractional calculus, partly a description of the Kipriyanov extraordinary theory in comparison with the classical one. The significance and outstanding methods in constructing the independent Kipriyanov fractional calculus theory are convexly stressed, also we represent modern

Masahiro Nomura, Youhei Akimoto, Isao Ono

The covariance matrix adaptation evolution strategy (CMA-ES) is one of the most successful methods for solving black-box continuous optimization problems. One practically useful aspect of the CMA-ES is that it can be used without hyperparameter tuning. However, the hyperparameter settings still have a considerable impact, especially for difficult tasks such

Shangyu Xie, Wei Dai, Esha Ghosh, Sambuddha Roy

Prompt-tuning has received attention as an efficient tuning method in the language domain, i.e., tuning a prompt that is a few tokens long, while keeping the large language model frozen, yet achieving comparable performance with conventional fine-tuning. Considering the emerging privacy concerns with language models, we initiate the study of privacy leakage

Yilin Ning, Victor Volovici, Marcus Eng Hock Ong, Benjamin Alan Goldstein

A prediction model is most useful if it generalizes beyond the development data with external validations, but to what extent should it generalize remains unclear. In practice, prediction models are externally validated using data from very different settings, including populations from other health systems or countries, with predictably poor results. This m

Rajesh K. Malla, Julia Cen, Wilton J. M. Kort-Kamp, Avadh Saxena

We develop a framework to solve a large class of linearly driven non-Hermitian quantum systems. Such a class of models in the Hermitian scenario is commonly known as multi-state Landau-Zener models. The non-hermiticity is due to the anti-Hermitian couplings between the diabatic levels. We find that there exists a new conservation law, unique to this class of

Lu Liu, Xinlei Hu, Qingmeng Wei

In this paper, the stochastic verification theorems for stochastic control problems of reflected forward-backward stochastic differential equations are studied. We carry out the work within the frameworks of classical and viscosity solutions. The sufficient conditions of verifying the controls to be optimal are given. We also construct the feedback optimal c

Viet Nguyen, Xueyu Song

A computational approach by an implementation of the Principle Component Analysis (PCA) with K-means and Gaussian Mixture (GM) clustering methods from Machine Learning (ML) algorithms to identify structural and dynamical heterogeneities of supercooled liquids is developed. In this method, a collection of the average weighted coordination numbers ($\overline{

Xuhui Jiang, Chengjin Xu, Yinghan Shen, Yuanzhuo Wang

The flourishing of knowledge graph applications has driven the need for entity alignment (EA) across KGs. However, the heterogeneity of practical KGs, characterized by differing scales, structures, and limited overlapping entities, greatly surpasses that of existing EA datasets. This discrepancy highlights an oversimplified heterogeneity in current EA datase

Nobutaka Nakazono

In this paper, we construct higher-order generalizations of the $A_6^{(1)}$- and $A_4^{(1)}$-surface type $q$-Painlev\'e equations from the system of partial difference equations with the consistency around a cube property by periodic reduction. Moreover, we also show their extended affine Weyl group symmetries and Lax pairs.

Jiajia Liu, Anchuan Song, David B. Jess, Jie Zhang

Power-law distributions have been studied as a significant characteristic of non-linear dissipative systems. Since discovering the power-law distribution of solar flares that was later extended to nano-flares and stellar flares, it has been widely accepted that different scales of flares share the same physical process. Here, we present the newly developed S

Regina S. Burachik, C. Yalçın Kaya, Xuemei Liu

We propose a primal--dual technique that applies to infinite dimensional equality constrained problems, in particular those arising from optimal control. As an application of our general framework, we solve a control-constrained double integrator optimal control problem and the challenging control-constrained free flying robot optimal control problem by mean

Abhishek Arora, Xinmei Yang, Shao-Yu Jheng, Melissa Dell

Many applications require linking individuals, firms, or locations across datasets. Most widely used methods, especially in social science, do not employ deep learning, with record linkage commonly approached using string matching techniques. Moreover, existing methods do not exploit the inherently multimodal nature of documents. In historical record linkage

Alexander Cao, Jean Utke, Diego Klabjan

Sequences are often not received in their entirety at once, but instead, received incrementally over time, element by element. Early predictions yielding a higher benefit, one aims to classify a sequence as accurately as possible, as soon as possible, without having to wait for the last element. For this early sequence classification, we introduce our novel

Arsalan Motamedi, Hadi Zadeh-Haghighi, Christoph Simon

We explore the power of reservoir computing with a single oscillator in learning time series using quantum and classical models. We demonstrate that this scheme learns the Mackey--Glass (MG) chaotic time series, a solution to a delay differential equation. Our results suggest that the quantum nonlinear model is more effective in terms of learning performance

Yong Zheng, Haozong Zhong, Haisu Zhang, Rongbo Wu

Programmable photonic circuits performing universal linear-optical transformations underpin vital functions in photonic quantum information processing, quantum-enhanced sensor networks, machine learning and many other intriguing applications. Recent advances in photonic integrated circuits facilitate monolithic integration of externally controlled Mach-Zehnd

D. -S. Wang

In this work, we develop universal quantum computing models that form a family of quantum von Neumann architecture, with modular units of memory, control, CPU, internet, besides input and output. This family contains three generations characterized by dynamical quantum resource theory, and it also circumvents no-go theorems on quantum programming and control

Zeraoulia Rafik

The first part of this paper is about Consequences resulting from Yitang Zhang's latest claimed results on Landau-Siegel zero posted by some mathematicians in Mathoverflow ,For the second part we are able to derive new Chaotic dynamics for Yitang Zhang on Landau-Siegel zero such that the behavior of the new dynamics has been discussed ,Lyaponove Exponents ha

Henglai Wei, Guangyuan Li, Yang Lu, Hui Zhang

This paper considers the integrated motion control and energy management problems of the series hybrid electric vehicles (SHEV) with constraints. We propose a multi-objective model predictive control (MOMPC)-based energy management approach, which is embedded with the motion control to guarantee driving comfort. In addition, due to the slow response of the e

Jinwei Zhang, Thanh D. Nguyen, Eddy Solomon, Chao Li

Purpose: To develop a method for rapid sub-millimeter T1, T2, T2* and QSM mapping in a single scan using multi-contrast Learned Acquisition and Reconstruction Optimization (mcLARO). Methods: A pulse sequence was developed by interleaving inversion recovery and T2 magnetization preparations and single-echo and multi-echo gradient echo acquisitions, which sens

Fucai Lin, Qiyun Wu

Assume that $\mathcal{P}$ is a topological property of a space $X$, then we say that $X$ is {\it dense-$\mathcal{P}$} if each dense subset of $X$ has the property $\mathcal{P}$. In this paper, we mainly discuss dense subsets of a space $X$, and we prove that: (1) if $X$ is Tychonoff space, then $X$ is dense-pseudocompact iff the range of each continuous real

Jae-Hun Lee, Doyoung Yoon, ByeongMoon Ji, Kyungyul Kim

Linear probing (LP) (and $k$-NN) on the upstream dataset with labels (e.g., ImageNet) and transfer learning (TL) to various downstream datasets are commonly employed to evaluate the quality of visual representations learned via self-supervised learning (SSL). Although existing SSL methods have shown good performances under those evaluation protocols, we obse

Chuan Liu, Fucai Lin

For a space $X$, let $(CL(X), \tau_V)$, $(CL(X), \tau_{locfin})$ and $(CL(X), \tau_F)$ be the set $CL(X)$ of all nonempty closed subsets of $X$ which are endowed with Vietoris topology, locally finite topology and Fell topology respectively. We prove that $(CL(X), \tau_V)$ is quasi-metrizable if and only if $X$ is a separable metrizable space and the set of

Yongjun Zhang, Sijia Liu, Yi Wang, Xinguang Fan

The outbreak of COVID-19 has led to a global surge of Sinophobia partly because of the spread of misinformation, disinformation, and fake news on China. In this paper, we report on the creation of a novel classifier that detects whether Chinese-language social media posts from Twitter are related to fake news about China. The classifier achieves an F1 score

Yuri A. Godin, Boris Vainberg

We investigate the behavior of waves in a periodic medium containing small soft inclusions or cavities of arbitrary shape, such that the homogeneous Dirichlet conditions are satisfied at the boundary. The leading terms of Bloch waves, their dispersion relations, and cutoff frequencies are rigorously derived. Our approach reveals the existence of exceptional

Shibo Yao

To leverage machine learning in any decision-making process, one must convert the given knowledge (for example, natural language, unstructured text) into representation vectors that can be understood and processed by machine learning model in their compatible language and data format. The frequently encountered difficulty is, however, the given knowledge is

B. Acharya, C. Adams, A. A. Aleksandrova, K. Alfonso

This whitepaper presents the research priorities decided on by attendees of the 2022 Town Meeting for Fundamental Symmetries, Neutrons and Neutrinos, which took place December 13-15, 2022 in Chapel Hill, NC, as part of the Nuclear Science Advisory Committee (NSAC) 2023 Long Range Planning process. A total of 275 scientists registered for the meeting. The whi

Yvonne Chua, Sankha Cooray, Juan Pablo Forero Cortes, Paul Denny

Technology integration in educational settings has led to the development of novel sensor-based tools that enable students to measure and interact with their environment. Although reports from using such tools can be positive, evaluations are often conducted under controlled conditions and short timeframes. There is a need for longitudinal data collected in

Jin Jia, Pin Yu

In the framework of the nonlinear stability of Minkowski spacetime, we show that if the radiation field of the curvature tensor vanishes, the spacetime must be flat.

Anne Broadbent, Arthur Mehta, Yuming Zhao

Given that reliable cloud quantum computers are becoming closer to reality, the concept of delegation of quantum computations and its verifiability is of central interest. Many models have been proposed, each with specific strengths and weaknesses. Here, we put forth a new model where the client trusts only its classical processing, makes no computational as

Xuwen Chen, Shunlin Shen, Zhifei Zhang

We study the mean-field and semiclassical limit of the quantum many-body dynamics with a repulsive $\delta$-type potential $N^{3\beta}V(N^{\beta}x)$ and a Coulomb potential, which leads to a macroscopic fluid equation, the Euler-Poisson equation with pressure. We prove quantitative strong convergence of the quantum mass and momentum densities up to the first

Hongyang Du, Ruichen Zhang, Dusit Niyato, Jiawen Kang

Driven by advances in generative artificial intelligence (AI) techniques and algorithms, the widespread adoption of AI-generated content (AIGC) has emerged, allowing for the generation of diverse and high-quality content. Especially, the diffusion model-based AIGC technique has been widely used to generate content in a variety of modalities. However, the rea

Devamardeep Hayatpur, Haijun Xia, Daniel Wigdor

Program visualizations help to form useful mental models of how programs work, and to reason and debug code. But these visualizations exist at a fixed level of abstraction, e.g., line-by-line. In contrast, programmers switch between many levels of abstraction when inspecting program behavior. Based on results from a formative study of hand-designed program v

Woongbae Park

The conformal heat flow of harmonic maps is a system of evolution equations combined with harmonic map flow with metric evolution in conformal direction. It is known that global weak solution of the flow exists and smooth except at mostly finitely many singular points. In this paper, we show that no finite time singularity occurs, unlike the usual harmonic m

Jiaping Xiao, Mir Feroskhan

Safe navigation of drones in the presence of adversarial physical attacks from multiple pursuers is a challenging task. This paper proposes a novel approach, asynchronous multi-stage deep reinforcement learning (AMS-DRL), to train adversarial neural networks that can learn from the actions of multiple evolved pursuers and adapt quickly to their behavior, ena

Joon Sung Park, Joseph C. O'Brien, Carrie J. Cai, Meredith Ringel Morris

Believable proxies of human behavior can empower interactive applications ranging from immersive environments to rehearsal spaces for interpersonal communication to prototyping tools. In this paper, we introduce generative agents--computational software agents that simulate believable human behavior. Generative agents wake up, cook breakfast, and head to wor

Yulin Yu, Longqi Yang, Siân Lindley, Mengting Wan

The COVID-19 pandemic has accelerated digital transformations across industries, but also introduced new challenges into workplaces, including the difficulties of effectively socializing with colleagues when working remotely. This challenge is exacerbated for new employees who need to develop workplace networks from the outset. In this paper, by analyzing a

Takuro Kutsuna

Distribution shifts are problems where the distribution of data changes between training and testing, which can significantly degrade the performance of a model deployed in the real world. Recent studies suggest that one reason for the degradation is a type of overfitting, and that proper regularization can mitigate the degradation, especially when using hig

Xuyang Li, Jianwu Fang, Kai Du, Kuizhi Mei

This paper focuses on the continuous control of the unmanned aerial vehicle (UAV) based on a deep reinforcement learning method for a large-scale 3D complex environment. The purpose is to make the UAV reach any target point from a certain starting point, and the flying height and speed are variable during navigation. In this work, we propose a deep reinforce

Hanmeng Liu, Ruoxi Ning, Zhiyang Teng, Jian Liu

Harnessing logical reasoning ability is a comprehensive natural language understanding endeavor. With the release of Generative Pretrained Transformer 4 (GPT-4), highlighted as "advanced" at reasoning tasks, we are eager to learn the GPT-4 performance on various logical reasoning tasks. This report analyses multiple logical reasoning datasets, with popular b

Gustavo S. Orozco-Galvan, Amador Garcia-Fuente, Salvador Barraza-Lopez

We study a 4-orbital tight-binding (TB) model for ZrSiS from the square sublattice generated by the Si atoms. After studying three other alternatives, we endow such model with a new effective spin-orbit coupling (SOC) consistent with {\em ab initio} dispersions around the Fermi energy ($E_F$) in four systematic steps: (1) We calculate the electronic dispersi

Haoyu Wang, Junpeng Di, Yuegu Xie

We extract cyclic information in turnover and find it can explain the momentum echo. The reversal in recent month momentum is the key factor that cancels out the recent month momentum and excluding it makes the echo regress to a damped shape. Both rational and behavioral theories can explain the reversal. This study is the first explanation of the momentum e

Avijoy Chakma, Abu Zaher Md Faridee, Indrajeet Ghosh, Nirmalya Roy

Machine learning-based wearable human activity recognition (WHAR) models enable the development of various smart and connected community applications such as sleep pattern monitoring, medication reminders, cognitive health assessment, sports analytics, etc. However, the widespread adoption of these WHAR models is impeded by their degraded performance in the

Bikramaditya Datta, Rajiv Sethi

The scale and terms of aggregate borrowing in an economy depend on the manner in which wealth is distributed across potential creditors with heterogeneous beliefs about the future. This distribution evolves over time as uncertainty is resolved, in favour of optimists if loans are repaid in full, and in favour of pessimists if there is widespread default. We

Taeho Kil, Seonghyeon Kim, Sukmin Seo, Yoonsik Kim

Sequence generation models have recently made significant progress in unifying various vision tasks. Although some auto-regressive models have demonstrated promising results in end-to-end text spotting, they use specific detection formats while ignoring various text shapes and are limited in the maximum number of text instances that can be detected. To overc

Tuğrulcan Elmas, İlker Gül

Opinion mining plays a critical role in understanding public sentiment and preferences, particularly in the context of political elections. Traditional polling methods, while useful, can be expensive and less scalable. Social media offers an alternative source of data for opinion mining but presents challenges such as noise, biases, and platform limitations

Jinyoung Lee, Duc Trung Dinh, Hyeonsik Yeom, Si-Hyeon Lee

This work studies a covert communication scheme for an uplink multi-user scenario in which some users are opportunistically selected to help a covert user. In particular, the selected users emit interfering signals via an orthogonal resource dedicated to the covert user together with signals for their own communications using orthogonal resources allocated t

Yifan Wu, Ziyang Guo, Michails Mamakos, Jason Hartline

Understanding how helpful a visualization is from experimental results is difficult because the observed performance is confounded with aspects of the study design, such as how useful the information that is visualized is for the task. We develop a rational agent framework for designing and interpreting visualization experiments. Our framework conceives two

Gauri Gupta, Ritvik Kapila, Keshav Gupta, Ramesh Raskar

Unsupervised approaches for learning representations invariant to common transformations are used quite often for object recognition. Learning invariances makes models more robust and practical to use in real-world scenarios. Since data transformations that do not change the intrinsic properties of the object cause the majority of the complexity in recogniti

STAR Collaboration, M. I. Abdulhamid, B. E. Aboona, J. Adam

The deconfined quark-gluon plasma (QGP) created in relativistic heavy-ion collisions enables the exploration of the fundamental properties of matter under extreme conditions. Non-central collisions can produce strong magnetic fields on the order of $10^{18}$ Gauss, which offers a probe into the electrical conductivity of the QGP. In particular, quarks and an

Hu Yan-Chao, Zhou Wen-Feng, Tang Ming-Zhi, Wang Gang

This paper investigates the bistability of curved compression ramp (CCR) flows. It reports that both separated and attached states can be stably established even for the same boundary conditions, revealing that the ultimate stable states of CCR flows also depend on the initial conditions and evolutionary history. Firstly, we design a thought experiment invol

Shaoyu Chen, Tianheng Cheng, Jiemin Fang, Qian Zhang

Small object detection requires the detection head to scan a large number of positions on image feature maps, which is extremely hard for computation- and energy-efficient lightweight generic detectors. To accurately detect small objects with limited computation, we propose a two-stage lightweight detection framework with extremely low computation complexity

Haotian Jiang, Yin Tat Lee, Zhao Song, Lichen Zhang

Given a convex function $f$ on $\mathbb{R}^n$ with an integer minimizer, we show how to find an exact minimizer of $f$ using $O(n^2 \log n)$ calls to a separation oracle and $O(n^4 \log n)$ time. The previous best polynomial time algorithm for this problem given in [Jiang, SODA 2021, JACM 2022] achieves $O(n^2\log\log n/\log n)$ oracle complexity. However, t

Ran Li, Jin Wang

The present study focuses on analyzing the generalized free energy function of the $D$-dimensional charged Gauss-Bonnet AdS black holes. We examine the fluctuating black holes that are in contact with thermal baths at an arbitrary ensemble temperature, resulting in the corresponding Euclidean geometry with a conical singularity at the event horizon. By prope

Yiwen Zhu, Rathijit Sen, Robert Horton, John Mark

The dynamic nature of resource allocation and runtime conditions on Cloud can result in high variability in a job's runtime across multiple iterations, leading to a poor experience. Identifying the sources of such variation and being able to predict and adjust for them is crucial to cloud service providers to design reliable data processing pipelines, provis

Davide Romano

The paper explains why the de Broglie-Bohm theory reduces to Newtonian mechanics in the macroscopic classical limit. The quantum-to-classical transition is based on three steps: (i) interaction with the environment produces effectively factorized states, leading to the formation of effective wave functions and hence decoherence; (ii) the effective wave funct

Maor Farid

Data-Driven Response Regime Exploration and Identification (DR$^2$EI) is a novel and fully data-driven method for identifying and classifying response regimes of a dynamical system without requiring human intervention. This approach is a valuable tool for exploring and discovering response regimes in complex dynamical systems, especially when the governing e

Nathan P. Lawrence, Philip D. Loewen, Shuyuan Wang, Michael G. Forbes

We propose a framework for the design of feedback controllers that combines the optimization-driven and model-free advantages of deep reinforcement learning with the stability guarantees provided by using the Youla-Kucera parameterization to define the search domain. Recent advances in behavioral systems allow us to construct a data-driven internal model; th

Peter Sun, John A. Marohn

Simulation has become an essential component of designing and developing scientific experiments. The conventional procedural approach to coding simulations of complex experiments is often error-prone, hard to interpret, and inflexible, making it hard to incorporate changes such as algorithm updates, experimental protocol modifications, and looping over exper

Mana Masuda, Ryo Hachiuma, Ryo Fujii, Hideo Saito

In this paper, we present an end-to-end unsupervised anomaly detection framework for 3D point clouds. To the best of our knowledge, this is the first work to tackle the anomaly detection task on a general object represented by a 3D point cloud. We propose a deep variational autoencoder-based unsupervised anomaly detection network adapted to the 3D point clou

Iván Alvarez-Ríos, Francisco S. Guzmán, Paul R. Shapiro

We illustrate the effect of boundary conditions on the evolution of structure in Fuzzy Dark Matter. Scenarios explored include the evolution of single, ground-state equilibrium solutions of the Schr\"odinger-Poisson system, the relaxation of a Gaussian density fluctuation, mergers of two equilibrium configurations, and the random merger of many solitons. For

Young Jin Kim, Leanne Duffy, Igor Savukov, Ping-Han Chu

An optical quantum sensor (OQS) based on lasers and alkali-metal atoms is a sensitive ambient-temperature magnetometer that can be used in axion dark matter search with an inductor-capacitor (LC) circuit at kHz and MHz frequencies. We have previously investigated the sensitivity of an LC circuit-OQS axion detector to ultralight axion dark matter that could b

Michael Nickerson, Bowen Song, Jim Brookhyser, Gregory Erwin

A 16-channel optical phased array is fabricated on a gallium arsenide photonic integrated circuit platform with a low-complexity process. Tested with a 1064 nm external laser, the array demonstrates 0.92{\deg} beamwidth, 15.3{\deg} grating-lobe-free steering range, and 12 dB sidelobe level. Based on a reverse biased p-i-n structure, component phase modulator

Yashas Malur Saidutta, Rakshith Sharma Srinivasa, Ching-Hua Lee, Chouchang Yang

Keyword spotting systems continuously process audio streams to detect keywords. One of the most challenging tasks in designing such systems is to reduce False Alarm (FA) which happens when the system falsely registers a keyword despite the keyword not being uttered. In this paper, we propose a simple yet elegant solution to this problem that follows from the

Yoonbok Lee

We investigate the joint distribution of $L$-functions on the line $ \sigma= \frac12 + \frac1{G(T)}$ and $ t \in [ T, 2T]$, where $ \log \log T \leq G(T) \leq \frac{ \log T}{ ( \log \log T)^2 } $. We obtain an upper bound on the discrepancy between the joint distribution of $L$-functions and that of their random models. As an application we prove an asymptot

Sihao Chen, William Bruno, Dan Roth

News sources undergo the process of selecting newsworthy information when covering a certain topic. The process inevitably exhibits selection biases, i.e. news sources' typical patterns of choosing what information to include in news coverage, due to their agenda differences. To understand the magnitude and implications of selection biases, one must first di

Jinwon Lee, Jae Whan Park, Gil-Young Cho, Han Woong Yeom

Kinks, point-like geometrical defects along dislocations, domain walls, and DNA, are stable and mobile, as solutions of a sine-Gordon wave equation. While they are widely investigated for crystal deformations and domain wall motions, electronic properties of individual kinks have received little attention. In this work, electronically and topologically disti

Abhinau K. Venkataramanan, Cosmin Stejerean, Ioannis Katsavounidis, Alan C. Bovik

The Visual Multimethod Assessment Fusion (VMAF) algorithm has recently emerged as a state-of-the-art approach to video quality prediction, that now pervades the streaming and social media industry. However, since VMAF requires the evaluation of a heterogeneous set of quality models, it is computationally expensive. Given other advances in hardware-accelerate

Jing Shi, Wei Xiong, Zhe Lin, Hyun Joon Jung

Recent advances in personalized image generation allow a pre-trained text-to-image model to learn a new concept from a set of images. However, existing personalization approaches usually require heavy test-time finetuning for each concept, which is time-consuming and difficult to scale. We propose InstantBooth, a novel approach built upon pre-trained text-to

Sijie Zhu, Linjie Yang, Chen Chen, Mubarak Shah

Visual Place Recognition (VPR) estimates the location of query images by matching them with images in a reference database. Conventional methods generally adopt aggregated CNN features for global retrieval and RANSAC-based geometric verification for reranking. However, RANSAC only employs geometric information but ignores other possible information that coul

K. Matsuura, M. Roppongi, M. Qiu, Q. Sheng

Iron-chalcogenide superconductors FeSe$_{1-x}$S$_x$ possess unique electronic properties such as non-magnetic nematic order and its quantum critical point. The nature of superconductivity with such nematicity is important for understanding the mechanism of unconventional superconductivity. A recent theory suggested the possible emergence of a fundamentally n

Blake Bordelon, Cengiz Pehlevan

We analyze the dynamics of finite width effects in wide but finite feature learning neural networks. Starting from a dynamical mean field theory description of infinite width deep neural network kernel and prediction dynamics, we provide a characterization of the $O(1/\sqrt{\text{width}})$ fluctuations of the DMFT order parameters over random initializations

Soumya Sai Vanka, Maryam Safi, Jean-Baptiste Rolland, George Fazekas

The integration of artificial intelligence (AI) technology in the music industry is driving a significant change in the way music is being composed, produced and mixed. This study investigates the current state of AI in the mixing workflows and its adoption by different user groups. Through semi-structured interviews, a questionnaire-based study, and analyzi

Xiangyi Yan, Junayed Naushad, Chenyu You, Hao Tang

Recent advancements in self-supervised learning have demonstrated that effective visual representations can be learned from unlabeled images. This has led to increased interest in applying self-supervised learning to the medical domain, where unlabeled images are abundant and labeled images are difficult to obtain. However, most self-supervised learning appr

Sajad Meisami, William Edward Bodell

In this work, we provide a comprehensive survey of smart contract upgradability patterns using proxies. A primary characteristic of smart contracts on the Ethereum blockchain is that they are immutable once implemented, no changes can be made. Taking human error into account, as well as technology improvements and newly discovered vulnerabilities, there has

Tri Nguyen, Elia Merzari

Single-phase natural circulation thermosiphon loops have been attracting increased interest as they represent the prototype of passive safety systems. However, the stability properties of thermosiphon loops, which can affect and compromise their functionality, are still actively investigated. Traditionally, the stability analysis of thermosiphon loops has be

Haonan Zhang

The existing buildings and building construction sectors together are responsible for over one-third of the total global energy consumption and nearly 40% of total greenhouse gas (GHG) emissions. GHG emissions from the building sector are made up of embodied emissions and operational emissions. Recognizing the importance of reducing energy use and emissions

Eiji Konishi

In the Lorentzian classicalized holographic tensor network (cHTN), we derive its relativistic on-shell equation from its Lorentzian action in the presence of a relativistic massive particle in the bulk spacetime: $-\sigma \hbar \theta=Mc^2$. Here, $\sigma$ is the von Neumann entropy of the cHTN per site in nats, $\theta$ is the real-proper-time expansion of

Daniel Campos, ChengXiang Zhai, Alessandro Magnani

The success of contextual word representations and advances in neural information retrieval have made dense vector-based retrieval a standard approach for passage and document ranking. While effective and efficient, dual-encoders are brittle to variations in query distributions and noisy queries. Data augmentation can make models more robust but introduces o

Tu Bui, Shruti Agarwal, Ning Yu, John Collomosse

Data hiding such as steganography and invisible watermarking has important applications in copyright protection, privacy-preserved communication and content provenance. Existing works often fall short in either preserving image quality, or robustness against perturbations or are too complex to train. We propose RoSteALS, a practical steganography technique l

Alaa Shaker, Alaa Aldarf, Igor Bessmertny

Named entity recognition (NER) is a natural language processing task (NLP), which aims to identify named entities and classify them like person, location, organization, etc. In the Arabic language, we can find a considerable size of unstructured data, and it needs to different preprocessing tool than languages like (English, Russian, German...). From this po

Sangwoo Park, Osvaldo Simeone

In this work, we aim at augmenting the decisions output by quantum models with "error bars" that provide finite-sample coverage guarantees. Quantum models implement implicit probabilistic predictors that produce multiple random decisions for each input through measurement shots. Randomness arises not only from the inherent stochasticity of quantum measuremen

Felix Hufnagel, Anne Broadbent, Ebrahim Karimi

Certified deletion is a protocol which allows two parties to share information, from Alice to Bob, in such a way that if Bob chooses to delete the information, he can prove to Alice that the deletion has taken place by providing a verification key. It is not possible for Bob to both provide this verification, and gain information about the message that was s

Lola Etievant, Mitchell H. Gail

The case-cohort design obtains complete covariate data only on cases and on a random sample (the subcohort) of the entire cohort. Subsequent publications described the use of stratification and weight calibration to increase efficiency of estimates of Cox model log relative hazards, and there has been some work estimating pure risk. Yet there are few example

Tewodros Amdeberhan, David Callan

We prove some special cases of Bergeron's inequality involving two Gaussian polynomials (or $q$-binomials).

Anna Koufakou

Student opinions for a course are important to educators and administrators, regardless of the type of the course or the institution. Reading and manually analyzing open-ended feedback becomes infeasible for massive volumes of comments at institution level or online forums. In this paper, we collected and pre-processed a large number of course reviews public

Zhe Zhou, Ashish Mishra, Benjamin Delaware, Suresh Jagannathan

Test input generators are an important part of property-based testing (PBT) frameworks, and a key expectation is that they be capable of producing all acceptable elements that satisfy both the function's input type and the generator-provided constraints. However, it is not readily apparent how to validate whether a particular generator's output satisfies thi

Aneta Lisowska, Szymon Wilk, Mor Peleg

We are developing a virtual coaching system that helps patients adhere to behavior change interventions (BCI). Our proposed system predicts whether a patient will perform the targeted behaviour and uses counterfactual examples with feature control to guide personalisation of BCI. We use simulated patient data with varying levels of receptivity to interventio

Jae Myung Kim, A. Sophia Koepke, Cordelia Schmid, Zeynep Akata

Cross-modal retrieval methods are the preferred tool to search databases for the text that best matches a query image and vice versa. However, image-text retrieval models commonly learn to memorize spurious correlations in the training data, such as frequent object co-occurrence, instead of looking at the actual underlying reasons for the prediction in the i

Roberto Lange, Gabriel M. Magalhães, Franciane F. Rocha, Pedro V. S. Coimbra

A new OpenFOAM application to simulate multiphase flows in porous media is formulated and tested. The proposed solver combines the Eulerian multi-fluid formulation for a system of phase fractions with Darcy's law for flows through porous media. It is based on the multiphaseEulerFoam and includes models for reservoir simulation of the porousMultiphaseFoam, ta

Todd Eisworth, James Cummings, Justin Tatch Moore

The purpose of this article is to give new constructions of linear orders which are minimal with respect to being non-$\sigma$-scattered. Specifically, we will show that Jensen's principle $\diamondsuit$ implies that there is a minimal Countryman line, answering a question of Baumgartner. We also produce the first consistent examples of minimal non-$\sigma$-

Gennady Medvinsky, Ben Livshits

While cryptocurrencies have been rapidly gaining adoption, secure wallet interactions are still elusive for many users, which frequently leads to loss of funds. Here we propose an approach to securing interactions with cryptocurrency wallets for end-users. The approach called FailSafe consists of several defence-in-depth measures that can be applied near-ter

Johannes Teutsch, Sebastian Ellmaier, Sebastian Kerz, Dirk Wollherr

The fundamental lemma from behavioral systems theory yields a data-driven non-parametric system representation that has shown great potential for the data-efficient control of unknown linear and weakly nonlinear systems, even in the presence of measurement noise. In this work, we strive to extend the applicability of this paradigm to more strongly nonlinear

Luís Carvalho, João Lopes Costa, José Mourão, Gonçalo Oliveira

Recent developments in applications of artificial neural networks with over $n=10^{14}$ parameters make it extremely important to study the large $n$ behaviour of such networks. Most works studying wide neural networks have focused on the infinite width $n \to +\infty$ limit of such networks and have shown that, at initialization, they correspond to Gaussian

Tianyi Zhang, Matthew Johnson-Roberson

Underwater imagery often exhibits distorted coloration as a result of light-water interactions, which complicates the study of benthic environments in marine biology and geography. In this research, we propose an algorithm to restore the true color (albedo) in underwater imagery by jointly learning the effects of the medium and neural scene representations.

Christian Iliadis

Both nonresonant and resonance reaction data are subject to laboratory electron screening effects. For nonresonant reactions, such effects are well documented and the measured cross sections can be corrected to find the unscreened ones. Frequently, the procedure and expression to calculate laboratory electron screening factors for nonresonant reactions are a

Francesco Montagna, Nicoletta Noceti, Lorenzo Rosasco, Kun Zhang

This paper demonstrates how to discover the whole causal graph from the second derivative of the log-likelihood in observational non-linear additive Gaussian noise models. Leveraging scalable machine learning approaches to approximate the score function $\nabla \log p(\mathbf{X})$, we extend the work of Rolland et al. (2022) that only recovers the topologica