December 2023 arXiv papers — page 58

Jiachen Zhao, Zhun Deng, David Madras, James Zou

As the number of large language models (LLMs) released to the public grows, there is a pressing need to understand the safety implications associated with these models learning from third-party custom finetuning data. We explore the behavior of LLMs finetuned on noisy custom data containing unsafe content, represented by datasets that contain biases, toxicit

Libo Wang, Sijun Dong, Ying Chen, Xiaoliang Meng

Semantic segmentation of remote sensing images plays a vital role in a wide range of Earth Observation applications, such as land use land cover mapping, environment monitoring, and sustainable development. Driven by rapid developments in artificial intelligence, deep learning (DL) has emerged as the mainstream for semantic segmentation and has achieved many

Ke Di, Meng Xie, Zhaoyang Wang, Renpu Li

In this paper, we propose a plasmon-induced transparency (PIT) metamaterial structure composed of Ag nanomaterials with polarization sensitivity. The metamaterial model consists of three bright modes with different resonant frequencies. The optical properties of the structure are further investigated using finite difference time domain (FDTD) method. The res

Yao-Tong Chen, Lei Du, Zhihai Wang, M. Artoni

Two interacting Rydberg atoms coupled to a waveguide realize a giant-atom platform that exhibits the controllable (phase-dependent) chirality where the direction of nonreciprocal photon scattering can be switched on demand, e.g., by the geometrical tuning of an external driving field. At variance with previous chiral setups, the simplified approach of our pr

Paolo D'Alberto

Recently, reinforcement algorithms discovered new algorithms that really jump-started a wave of excitements and a flourishing of publications. However, there is little on implementations, applications, and, especially, no absolute performance and, we show here they are not here to replace Strassen's original fast matrix multiplication yet. We present Matrix

Wen Huang, Xintao Wu

This paper studies bandit problems where an agent has access to offline data that might be utilized to potentially improve the estimation of each arm's reward distribution. A major obstacle in this setting is the existence of compound biases from the observational data. Ignoring these biases and blindly fitting a model with the biased data could even negativ

Tao Zhang, Xingye Tian, Yikang Zhou, Shunping Ji

We present the \textbf{D}ecoupled \textbf{VI}deo \textbf{S}egmentation (DVIS) framework, a novel approach for the challenging task of universal video segmentation, including video instance segmentation (VIS), video semantic segmentation (VSS), and video panoptic segmentation (VPS). Unlike previous methods that model video segmentation in an end-to-end manner

Julio Silva-Rodríguez, Sina Hajimiri, Ismail Ben Ayed, Jose Dolz

Efficient transfer learning (ETL) is receiving increasing attention to adapt large pre-trained language-vision models on downstream tasks with a few labeled samples. While significant progress has been made, we reveal that state-of-the-art ETL approaches exhibit strong performance only in narrowly-defined experimental setups, and with a careful adjustment of

Haoxing Chen, Yaohui Li, Zhangxuan Gu, Zhuoer Xu

Image harmonization is a crucial technique in image composition that aims to seamlessly match the background by adjusting the foreground of composite images. Current methods adopt either global-level or pixel-level feature matching. Global-level feature matching ignores the proximity prior, treating foreground and background as separate entities. On the othe

Yao Zhao, Zhitian Xie, Chen Liang, Chenyi Zhuang

As Large Language Models (LLMs) have made significant advancements across various tasks, such as question answering, translation, text summarization, and dialogue systems, the need for accuracy in information becomes crucial, especially for serious financial products serving billions of users like Alipay. However, for a real-world product serving millions of

Gianna Williams

Social media wields a profound influence on social and economic dynamics worldwide, people on social media began to forge a livelihood through their online presence through creative labor. This surge in social media Content Creators significantly shaped the trends and cultural landscape of the internet. While many of the social media trends we observe today

Qihang Fang, Yafei Song, Keqiang Li, Liefeng Bo

Neural radiance field (NeRF) enables the synthesis of cutting-edge realistic novel view images of a 3D scene. It includes density and color fields to model the shape and radiance of a scene, respectively. Supervised by the photometric loss in an end-to-end training manner, NeRF inherently suffers from the shape-radiance ambiguity problem, i.e., it can perfec

Xufeng Liu

The concept of entanglement is at the core of the theory of quantum information. In this paper a criterion for unentanglement of quantum states is proposed and proved. This criterion is natural, practical and easy to check.

Yiming Chen, Haiwei Wu, Jiantao Zhou

Deep Neural Networks (DNN) are susceptible to backdoor attacks where malicious attackers manipulate the model's predictions via data poisoning. It is hence imperative to develop a strategy for training a clean model using a potentially poisoned dataset. Previous training-time defense mechanisms typically employ an one-time isolation process, often leading to

Chengxiang Yin, Zhengping Che, Kun Wu, Zhiyuan Xu

Visual Question Answering (VQA) has emerged as one of the most challenging tasks in artificial intelligence due to its multi-modal nature. However, most existing VQA methods are incapable of handling Knowledge-based Visual Question Answering (KB-VQA), which requires external knowledge beyond visible contents to answer questions about a given image. To addres

Jiang-Tian Zhai, Xialei Liu, Lu Yu, Ming-Ming Cheng

Non-exemplar class incremental learning aims to learn both the new and old tasks without accessing any training data from the past. This strict restriction enlarges the difficulty of alleviating catastrophic forgetting since all techniques can only be applied to current task data. Considering this challenge, we propose a novel framework of fine-grained knowl

Chengxiang Yin, Zhengping Che, Kun Wu, Zhiyuan Xu

Video Question Answering (VideoQA) is a very attractive and challenging research direction aiming to understand complex semantics of heterogeneous data from two domains, i.e., the spatio-temporal video content and the word sequence in question. Although various attention mechanisms have been utilized to manage contextualized representations by modeling intra

Guangtao Zheng, Mengdi Huai, Aidong Zhang

Single domain generalization (SDG) aims to train a robust model against unknown target domain shifts using data from a single source domain. Data augmentation has been proven an effective approach to SDG. However, the utility of standard augmentations, such as translate, or invert, has not been fully exploited in SDG; practically, these augmentations are use

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

The process $e^{+}e^{-}\to\Sigma^{+}\bar{\Sigma}^{-}$ is studied from threshold up to 3.04 GeV/$c^2$ via the initial-state radiation technique using data with an integrated luminosity of 12.0 fb$^{-1}$, collected at center-of-mass energies between 3.773 and 4.258 GeV with the BESIII detector at the BEPCII collider. The pair production cross sections and the

Fusako Kon, Chihiro Tabata, Hiraku Saito, Taro Nakajima

We investigated the magnetic structure of the antiferromagnetic (AFM) ordered state ($T_{\rm N} \sim$ 34 K) in tetragonal UPt$_{2}$Si$_{2}$ using polarized and unpolarized neutron diffraction. Previous neutron scattering studies reported that this system possesses a simple AFM structure with a propagation vector, $Q = 0$, and the ordered magnetic moments ali

Alan J. X. Guo, Sihan Sun, Xiang Wei, Mengyi Wei

With the emergence of new storage and communication methods, the insertion, deletion, and substitution (IDS) channel has attracted considerable attention. However, many topics on the IDS channel and the associated Levenshtein distance remain open, making the invention of a novel IDS-correcting code a hard task. Furthermore, current studies on single-IDS-corr

Yunye Gong, Robik Shrestha, Jared Claypoole, Michael Cogswell

We propose a novel VQA dataset, BloomVQA, to facilitate comprehensive evaluation of large vision-language models on comprehension tasks. Unlike current benchmarks that often focus on fact-based memorization and simple reasoning tasks without theoretical grounding, we collect multiple-choice samples based on picture stories that reflect different levels of co

Vincent Pisztora, Jia Li

In this paper we propose a method for the optimal allocation of observations between an intrinsically explainable glass box model and a black box model. An optimal allocation being defined as one which, for any given explainability level (i.e. the proportion of observations for which the explainable model is the prediction function), maximizes the performanc

Jesse L. Everett, Ankit Papneja, Arindam Saha, Cameron Trainor

Optical quantum memories are essential for quantum communications and photonic quantum technologies. Ensemble optical memories based on 3-level interactions are a popular basis for implementing these memories. All such memories, however, suffer from loss due to scattering. In off-resonant 3-level interactions, such as the Raman gradient echo memory (GEM), sc

Jianheng Huang, Ante Wang, Linfeng Gao, Linfeng Song

Leveraging vast and continually updated knowledge from the Internet has been considered an important ability for a dialogue system. Therefore, the dialogue query generation task is proposed for generating search queries from dialogue histories, which will be submitted to a search engine for retrieving relevant websites on the Internet. In this regard, previo

Priyanka Chakraborty, Rachel Hemmer, Adam R. Foster, John Raymond

Accurate atomic data and plasma models are essential for interpreting the upcoming high-quality spectra from missions like XRISM and Athena. Estimating physical quantities, like temperature, abundance, turbulence, and resonance scattering factor, is highly dependent on the underlying atomic data. We use the AtomDB tool variableapec to estimate the impact of

Yupei Huang

In this paper, we study the uniformly rotating vortex patch solutions for the 2D incompressible Euler equations. Specifically, we prove that if the patch solution is close to the Rankine vortex in a certain weak topology, it is either the Kirchhoff ellipses or the Rankine vortex.

Yi-Zheng Fan, Yi-Min Song, Yi Wang

We give a decomposition of the Laplace operator (in matrix form) of a covering simplicial complex as a direct sum of several matrices, one of which is the Laplace operator of the base complex. It follows that the spectrum of a covering simplicial complex is a multiset union of the spectrum of the base simplicial complex and the spectra of other relevant matr

Miracle Chibuzor Marcel, Jorbedom Leelabari Gerald, Bauleni Bvumbwe, Idris Abubakar Sani

WDS 03286+2523 BRT 133, is a double-star system that has been under observation since 1896. In this study, we present new measurements of the position angle and separation of the system, utilizing data obtained from a web telescope with a Charged Couple Device (CCD) camera, Gaia EDR3, and historical records. We determined that the position angle and separati

Chendi Xie, Adam D. Smith, Haoran Yan, Wei-Chih Chen

Metallic hydrogen and hydride materials stand as promising avenues to achieve room-temperature superconductivity. Characterized by their high phonon frequencies and moderate coupling strengths, several high-pressure hydrides were theoretically predicted to exhibit transition temperatures ($T_c$) exceeding 250\,K, a claim further substantiated by experimental

Sajal Dash, Isaac Lyngaas, Junqi Yin, Xiao Wang

Large language models (LLMs) have demonstrated remarkable success as foundational models, benefiting various downstream applications through fine-tuning. Recent studies on loss scaling have demonstrated the superior performance of larger LLMs compared to their smaller counterparts. Nevertheless, training LLMs with billions of parameters poses significant cha

Hao Tong, Shengyan Liu, Kejie Fang

Mechanical bound states in the continuum (BICs) present an alternative avenue for developing high-frequency, high-Q mechanical resonators, distinct from the conventional band structure engineering method. While symmetry-protected mechanical BICs have been realized in phononic crystals, the observation of accidental mechanical BICs -- whose existence is indep

Yang Lu, Lin Chen, Yonggang Zhang, Yiliang Zhang

Federated learning (FL) has shown remarkable success in cooperatively training deep models, while typically struggling with noisy labels. Advanced works propose to tackle label noise by a re-weighting strategy with a strong assumption, i.e., mild label noise. However, it may be violated in many real-world FL scenarios because of highly contaminated clients,

Chunmei Liu, Hongsheng Zhang

Exact solutions of spherically symmetric black hole and gravitational wave are explored in $f(R)$ gravity in arbitrary dimension. We find two exact solutions for the radiation and absorption of null dust. In the framework of general relativity, the Birkhoff theorem strictly forbids the existence of spherical gravitational waves in vacuum space. We find spher

Bichen Wu, Ching-Yao Chuang, Xiaoyan Wang, Yichen Jia

In this paper, we introduce Fairy, a minimalist yet robust adaptation of image-editing diffusion models, enhancing them for video editing applications. Our approach centers on the concept of anchor-based cross-frame attention, a mechanism that implicitly propagates diffusion features across frames, ensuring superior temporal coherence and high-fidelity synth

Alec J. Linot, Peter J. Schmid, Kunihiko Taira

We study the effect of acceleration and deceleration on the stability of channel flows. To do so, we derive an exact solution for laminar profiles of channel flows with arbitrary, time-varying wall motion and pressure gradient. This solution then allows us to investigate the stability of any unsteady channel flow. In particular, we restrict our investigation

Mehmet S. Ismail

In this note, I introduce Estimated Performance Rating (PR$^e$), a novel system for evaluating player performance in sports and games. PR$^e$ addresses a key limitation of the Tournament Performance Rating (TPR) system, which is undefined for zero or perfect scores in a series of games. PR$^e$ is defined as the rating that solves an optimization problem rela

Zhuoqi Liu, Shuaibin Gao, Chenggui Yuan, Qian Guo

This paper focuses on the numerical stability of stochastic McKean-Vlasov equations (SMVEs) via the stochastic particle method. Firstly, the long-time propagation of chaos in the mean-square sense is obtained, and the almost sure propagation in infinite horizon is also proved. Next, when the coefficients satisfy linear growth conditions, the mean-square and

Quentin Bramas, Hirotsugu Kakugawa, Sayaka Kamei, Anissa Lamani

We consider a strong variant of the crash fault-tolerant gathering problem called stand-up indulgent gathering (SUIG), by robots endowed with limited visibility sensors and lights on line-shaped networks. In this problem, a group of mobile robots must eventually gather at a single location, not known beforehand, regardless of the occurrence of crashes. Diffe

Aritra Bhowmick, Mert Kosan, Zexi Huang, Ambuj Singh

Graph clustering is a fundamental and challenging task in the field of graph mining where the objective is to group the nodes into clusters taking into consideration the topology of the graph. It has several applications in diverse domains spanning social network analysis, recommender systems, computer vision, and bioinformatics. In this work, we propose a n

Chun-Hao Hsieh, Jun-Yu Huang, Yuh-Renn Wu

Presented here is the optimization of a planar two-terminal perovskite/silicon tandem solar cell with a texture structure. The developed simulation model is fitted to published experimental results, and the importance of current matching in the two-terminal structure is discussed. With the texture structure optimized and considering current matching, the opt

Au-Chen Lee, D. Baillie, P. B. Blakie

We consider the ground states, excitations and dynamics of a quasi-two-dimensional binary dipolar Bose-Einstein condensate. Our focus is on the transition to a spin-stripe ground state in which the translational invariance is spontaneously broken by a striped immiscible pattern of the alternating components. We develop a ground state phase diagram showing th

Daniel Wines, Kamal Choudhary

The observation of superconductivity in hydride-based materials under ultrahigh pressures (for example, H$_3$S and LaH$_{10}$) has fueled the interest in a more data-driven approach to discovering new high-pressure hydride superconductors. In this work, we performed density functional theory (DFT) calculations to predict the critical temperature ($T_c$) of o

Qingping Zhou, Guixian Xu, Zhexin Wen, Hongqiao Wang

Physics-guided deep learning is an important prevalent research topic in scientific machine learning, which has tremendous potential in various complex applications including science and engineering. In these applications, data is expensive to acquire and high accuracy is required for making decisions. In this work, we introduce an efficient physics-guided d

Cuneyd Ozturk, Randall A. Berry, Dongning Guo, Michael L. Honig

This paper proposes the transmission of beacon signals to alert potential interferers of an ongoing or impending passive sensing measurement. We focus on the interference from Low-Earth Orbiting (LEO) satellites to a radio-telescope. We compare the beacon approach with two versions of Radio Quiet Zones (RQZs): fixed quiet zones on the ground and in the sky,

Shichong Peng, Alireza Moazeni, Ke Li

Deep generative models, such as diffusion models, GANs, and IMLE, have shown impressive capability in tackling inverse problems. However, the validity of model-generated solutions w.r.t. the forward problem and the reliability of associated uncertainty estimates remain understudied. This study evaluates recent diffusion-based, GAN-based, and IMLE-based metho

Martin Weyssow, Claudio Di Sipio, Davide Di Ruscio, Houari Sahraoui

Motivated by recent work on lifelong learning applications for language models (LMs) of code, we introduce CodeLL, a lifelong learning dataset focused on code changes. Our contribution addresses a notable research gap marked by the absence of a long-term temporal dimension in existing code change datasets, limiting their suitability in lifelong learning scen

Gyeong-Geon Lee, Seonyeong Mun, Myeong-Kyeong Shin, Xiaoming Zhai

This research aims to demonstrate that AI can function not only as a tool for learning, but also as an intelligent agent with which humans can engage in collaborative learning (CL) to change epistemic practices in science classrooms. We adopted a design and development research approach, following the Analysis, Design, Development, Implementation and Evaluat

Kohei Noda

In this note, we study the determinantal structure of the $k$-th conditional expectation of the overlap for induced spherical unitary ensemble. We will show the universality for the scaling limits of the $k$-the conditional expectation of the overlap in the three regimes, strongly non-unitary, weakly non-unitary, and the singular origin regimes. As a consequ

GuoDong Du, HaoJian Deng, JiaHao Su, Yuan Huang

In this work we address the problem of rain streak removal with RAW images. The general approach is firstly processing RAW data into RGB images and removing rain streak with RGB images. Actually the original information of rain in RAW images is affected by image signal processing (ISP) pipelines including none-linear algorithms, unexpected noise, artifacts a

Tomohiro Matsuda

The whole picture of gauge theory is described by manifolds, while the field equation provides only a part (a section) of the manifold. Just as a three-dimensional object is reconstructed from two planar images, a monopole is constructed by combining two solutions. The Schwinger and the Unruh effects and the Hawking radiation are the production of particles

Ziqiang Yu, Xiaohui Yu, Tao Zhou, Yueting Chen

We study the problem of processing continuous k nearest neighbor (CkNN) queries over moving objects on road networks, which is an essential operation in a variety of applications. We are particularly concerned with scenarios where the object densities in different parts of the road network evolve over time as the objects move. Existing methods on CkNN query

Ziqiang Yu, Xiaohui Yu, Nick Koudas, Yueting Chen

The problem of identifying the k-shortest paths KSPs for short in a dynamic road network is essential to many location-based services. Road networks are dynamic in the sense that the weights of the edges in the corresponding graph constantly change over time, representing evolving traffic conditions. Very often such services have to process numerous KSP quer

Erik Johansson

This paper presents a gentle introduction to cohomology vanishing theorems, largely based on the paper work of Hongshan Li. It offers an insightful exploration of unitary local systems on complex manifolds, particularly focusing on their characteristics near normal crossing divisors. The Main Vanishing Theorem, demonstrating the vanishing of specific cohomol

Timothy Duff, Viktor Korotynskiy, Tomas Pajdla, Margaret Regan

Galois/monodromy groups attached to parametric systems of polynomial equations provide a method for detecting the existence of symmetries in solution sets. Beyond the question of existence, one would like to compute formulas for these symmetries, towards the eventual goal of solving the systems more efficiently. We describe and implement one possible approac

Zifei Shen, Shuijin Zhang

This paper studies the nonlinear fractional Helmholtz equation \begin{equation}\label{main} (-\Delta)^{s} u-k^{2} u=Q(x)|u|^{p-2}u, ~~\mathrm{in}~~\mathbb{R}^{N},~~N\geq3, \end{equation} where $\frac{N}{N+1}<s<\frac{N}{2}$, $\frac{2(N+1)}{N-1}<p<\frac{2N}{N-2s}$ are two real exponents, and the coefficient $Q$ is bounded continuous, nonnegative and satisfies

F. W. Nijhoff, D. J. Zhang

The lattice Boussinesq (lBSQ) equation is a member of the lattice Gel'fand-Dikii (lGD) hierarchy, introduced in \cite{NijPapCapQui1992}, which is an infinite family of integrable systems of partial difference equations labelled by an integer $N$, where $N=2$ represents the lattice Korteweg-de Vries (KdV) system, and $N=3$ the Boussinesq system. In \cite{Hiet

Tannon Kew, Florian Schottmann, Rico Sennrich

The vast majority of today's large language models (LLMs) are English-centric, having been pretrained predominantly on English text. Yet, in order to meet user expectations, models need to be able to respond appropriately in multiple languages once deployed in downstream applications. This requires strong cross-lingual transfer abilities. In this work, we in

Tim Valicenti, Justice Vidal, Ritik Patnaik

In AI research, the optimization of Large Language Models (LLMs) remains a significant challenge, crucial for advancing the field's practical applications and sustainability. Building upon the foundational work of Professor Song Han's lab at MIT, this paper introduces a novel approach in developing Mini-GPTs via contextual pruning. Our methodology strategica

Guozhong Zheng, Weiran Cai, Guanxiao Qi, Jiqiang Zhang

Efficient allocation is important in nature and human society, where individuals frequently compete for limited resources. The Minority Game (MG) is perhaps the simplest toy model to address this issue. However, most previous solutions assume that the strategies are provided a priori and static, failing to capture their adaptive nature. Here, we introduce th

Hen Emuna, Nadav Borenstein, Xin Qian, Hyeonsu Kang

Biologically Inspired Design (BID), or Biomimicry, is a problem-solving methodology that applies analogies from nature to solve engineering challenges. For example, Speedo engineers designed swimsuits based on shark skin. Finding relevant biological solutions for real-world problems poses significant challenges, both due to the limited biological knowledge e

Abdulkadhem A. Abdulkadhem

In this paper, we introduce an innovative approach for extracting trajectories from a camera sensor in GPS-denied environments, leveraging visual odometry. The system takes video footage captured by a forward-facing camera mounted on a vehicle as input, with the output being a chain code representing the camera's trajectory. The proposed methodology involves

Pei Huang, Haoze Wu, Yuting Yang, Ieva Daukantas

Quantization replaces floating point arithmetic with integer arithmetic in deep neural network models, providing more efficient on-device inference with less power and memory. In this work, we propose a framework for formally verifying properties of quantized neural networks. Our baseline technique is based on integer linear programming which guarantees both

Eric Rawls, Bryan Andrews, Kelvin Lim, Erich Kummerfeld

Designing studies that apply causal discovery requires navigating many researcher degrees of freedom. This complexity is exacerbated when the study involves fMRI data. In this paper we (i) describe nine challenges that occur when applying causal discovery to fMRI data, (ii) discuss the space of decisions that need to be made, (iii) review how a recent case s

Ivan Klianev

We demonstrate a deterministic Byzantine consensus algorithm with synchronous operation in partial synchrony. It is naturally leaderless, tolerates any number of $ f<n/2 $ Byzantine processes with 2 rounds of exchange of originator-only signed messages, and terminates within a bounded interval of time. The algorithm is resilient to transient faults and async

Jack Sandberg, Niklas Åkerblom, Morteza Haghir Chehreghani

We consider the combinatorial volatile Gaussian process (GP) semi-bandit problem. Each round, an agent is provided a set of available base arms and must select a subset of them to maximize the long-term cumulative reward. We study the Bayesian setting and provide novel Bayesian cumulative regret bounds for three GP-based algorithms: GP-UCB, GP-BayesUCB and G

Kristofer D. Kusano, John M. Scanlon, Yin-Hsiu Chen, Timothy L. McMurry

This paper examines the safety performance of the Waymo Driver, an SAE level 4 automated driving system (ADS) used in a rider-only (RO) ride-hailing application without a human driver, either in the vehicle or remotely. ADS crash data was derived from NHTSA's Standing General Order (SGO) reporting over 7.14 million RO miles through the end of October 2023 in

Oscar Chang, Dung N. Tran, Kazuhito Koishida

Generalization remains a major problem in supervised learning of single-channel speech enhancement. In this work, we propose learnable loss mixup (LLM), a simple and effortless training diagram, to improve the generalization of deep learning-based speech enhancement models. Loss mixup, of which learnable loss mixup is a special variant, optimizes a mixture o

Todd A. Oliver, Craig Michoski, Samuel Langendorf, Andrew LaJoie

This paper introduces a novel approach for automated estimation of plasma temperature and density using emission spectroscopy, integrating Bayesian inference with sophisticated physical models. We provide an in-depth examination of Bayesian methods applied to the complexities of plasma diagnostics, supported by a robust framework of physical and measurement

Byron Chin

We study the typical structure of a sparse Erd\H{o}s--R\'enyi random graph conditioned on the lower tail subgraph count event. We show that in certain regimes, a typical graph sampled from the conditional distribution resembles the entropy minimizer of the mean field approximation in the sense of both subgraph counts and cut norm. The main ingredients are an

Alistair Moffat

Search engine results pages are usually abstracted as binary relevance vectors and hence are categorical data, meaning that only a limited set of operations is permitted, most notably tabulation of occurrence frequencies, with determination of medians and averages not possible. To compare retrieval systems it is thus usual to make use of a categorical-to-num

Rafael A. Bilbao, Marlon Oliveira, Eduardo Santana

In this work, based on Pinheiro for deterministic systems, we extend the notion of zooming systems to the random context and based on the technique of Arbieto-Matheus-Oliveira we prove the existence of equilibrium states for which we call random zooming potentials, that include the hyperbolic ones, possibly with the presence of a critical set. With a mild co

Gabriel M. Carral, Iñaki Garay, Francesca Vidotto

The definition of a quantum system requires a Hilbert space, a way to define the dynamics, and an algebra of observables. The structure of the observable algebra is related to a tensor product decomposition of the Hilbert space and represents the composition of the system by subsystems. It has been remarked that the Hamiltonian may determine this tensor prod

Yi-Ting Chuang, Liang-Yan Hsu

Cavity quantum electrodynamics (CQED) and its extensions are widely used for the description of exciton-polariton systems. However, the exciton-polariton models based on CQED vary greatly within different contexts. One of the most significant discrepancies among these CQED models is whether one should include direct intermolecular interactions in the CQED Ha

Ekaterina Amerik, Frédéric Campana

We investigate algebraically coisotropic submanifolds $X$ in a holomorphic symplectic projective manifold $M$. Motivated by our results in the hypersurface case, we raise the following question: when $X$ is not uniruled, is it true that up to a finite étale cover, the pair $(X,M)$ is a product $(Z\times Y, N\times Y)$ where $N, Y$ are holomorphic symplectic

Takumi Sannomiya, Taeko Matsukata, Naoki Yamamoto

Control of the angular momentum of light is a key technology for next-generation nano-optical devices and optical communications, including quantum communication and encoding. We propose an approach to controllably generate circularly polarized light from a circular hole in a metal film using an electron beam by coherently exciting transition radiation and l

Jianhui Sun, Xidong Wu, Heng Huang, Aidong Zhang

Federated Averaging (FedAvg) is known to experience convergence issues when encountering significant clients system heterogeneity and data heterogeneity. Server momentum has been proposed as an effective mitigation. However, existing server momentum works are restrictive in the momentum formulation, do not properly schedule hyperparameters and focus only on

Hanse Kim, Jitendra Pal, Chanyong Park

We study general correlation functions of various quantum field theories in the holographic setup. Following the holographic proposal, we investigate correlation functions via a geodesic length connecting boundary operators. We show that this holographic description can reproduce the known two- and three-point functions of conformal field theory. Using this

Andreas Papachristodoulou, Christos Kyrkou, Stelios Timotheou, Theocharis Theocharides

The Forward-Forward (FF) Algorithm has been recently proposed to alleviate the issues of backpropagation (BP) commonly used to train deep neural networks. However, its current formulation exhibits limitations such as the generation of negative data, slower convergence, and inadequate performance on complex tasks. In this paper, we take the main ideas of FF a

Chenzhong Yin, Hantang Zhang, Mingxi Cheng, Xiongye Xiao

Malware represents a significant security concern in today's digital landscape, as it can destroy or disable operating systems, steal sensitive user information, and occupy valuable disk space. However, current malware detection methods, such as static-based and dynamic-based approaches, struggle to identify newly developed (``zero-day") malware and are limi

Guimin Dong, Lihua Cai, Mingyue Tang, Laura E. Barnes

Mobile sensing appears as a promising solution for health inference problem (e.g., influenza-like symptom recognition) by leveraging diverse smart sensors to capture fine-grained information about human behaviors and ambient contexts. Centralized training of machine learning models can place mobile users' sensitive information under privacy risks due to data

A. L. Love, S. A. Bateman, W. Belardi, F. Yu

Optical fibers have emerged as a transformative platform for building better and more robust solid state lasers. However, the wavelengths available to these lasers are limited. Using hollow core optical fibers allows us to add gases as new potential gain media for fiber lasers, and also liberates the gas laser from the limits normally imposed by diffraction.

Bumsoo Kim, Taeho Choi, Jaewoo Kang, Hyunwoo J. Kim

Recent advances in deep neural networks have achieved significant progress in detecting individual objects from an image. However, object detection is not sufficient to fully understand a visual scene. Towards a deeper visual understanding, the interactions between objects, especially humans and objects are essential. Most prior works have obtained this info

Manuel Laguna, Rafael Martí, Anna Martinez-Gavara, Sergio Perez-Peló

This is a comprehensive review of the Greedy Randomized Adaptive Search Procedure (GRASP) metaheuristic and its hybridization with Path Relinking (PR) over the past two decades. GRASP with PR has become a widely adopted approach for solving hard optimization problems since its proposal in 1999. The paper covers the historical development of GRASP with PR and

Michael S. Jolly, Djoko Wirosoetisno

We consider the behaviour of a passive tracer $\theta$ governed by $\partial_t\theta + u\cdot\nabla\theta = \Delta\theta + g$ in two space dimensions with prescribed smooth random incompressible velocity $u(x,t)$ and source $g(x)$. In 1959, Batchelor, Howells and Townsend (J.\ Fluid Mech.\ 5:113) predicted that the tracer (power) spectrum should then scale a

Bumsoo Kim, Yeonsik Jo, Jinhyung Kim, Seung Hwan Kim

Contrastive Language-Image Pretraining has emerged as a prominent approach for training vision and text encoders with uncurated image-text pairs from the web. To enhance data-efficiency, recent efforts have introduced additional supervision terms that involve random-augmented views of the image. However, since the image augmentation process is unaware of its

Silvia Terribile

Time efficiency is paramount for the localisation industry, which demands ever-faster turnaround times. However, translation speed is largely underresearched, and there is a lack of clarity about how language service providers (LSPs) can evaluate the performance of their post-editing (PE) and human translation (HT) services. This study constitutes the first

Wenhao Ding, Yulong Cao, Ding Zhao, Chaowei Xiao

Simulation plays a crucial role in the development of autonomous vehicles (AVs) due to the potential risks associated with real-world testing. Although significant progress has been made in the visual aspects of simulators, generating complex behavior among agents remains a formidable challenge. It is not only imperative to ensure realism in the scenarios ge

Bumsoo Kim, Jinhyung Kim, Yeonsik Jo, Seung Hwan Kim

Recent advances in vision language pretraining (VLP) have been largely attributed to the large-scale data collected from the web. However, uncurated dataset contains weakly correlated image-text pairs, causing data inefficiency. To address the issue, knowledge distillation have been explored at the expense of extra image and text momentum encoders to generat

Debasish Dutta, Jakub Klencki

The majority of massive stars are formed in multiple systems and will interact with companions via mass transfer. This interaction typically leads to the primary shedding its envelope and the formation of a "stripped star". Classically, stripped stars are expected to quickly contract to become hot UV-bright helium stars. Surprisingly, recent optical surveys

Tolga Ergen, Mert Pilanci

Due to the non-convex nature of training Deep Neural Network (DNN) models, their effectiveness relies on the use of non-convex optimization heuristics. Traditional methods for training DNNs often require costly empirical methods to produce successful models and do not have a clear theoretical foundation. In this study, we examine the use of convex optimizati

Marcel Gievers, Thomas Müller, Heinrich Fröml, Sebastian Diehl

We investigate the transport properties of a quantum wire of weakly interacting fermions in the presence of local particle loss. We calculate current and conductance in this system due to applied external chemical potential bias that can be measured in experimental realizations of ultracold fermions in quasi one-dimensional traps. Using a Keldysh field theor

Ryan Campbell, Emma Guo, Evan Hu, Reya Vir

In-context learning (ICL) has revolutionized the capabilities of transformer models in NLP. In our project, we extend the understanding of the mechanisms underpinning ICL by exploring whether transformers can learn from sequential, non-textual function class data distributions. We introduce a novel sliding window sequential function class and employ toy-size

Dipankar Sarkar

Ethereum has emerged as a leading platform for decentralized applications (dApps) due to its robust smart contract capabilities. One of the critical issues in the Ethereum ecosystem is Maximal Extractable Value (MEV), a concept that has gained significant attention in the blockchain community. However, MEV has remained a major challenge with significant impl

Fahim Ahmed Zaman, Wahidul Alam, Tarun Kanti Roy, Amanda Chang

Researchers have shown significant correlations among segmented objects in various medical imaging modalities and disease related pathologies. Several studies showed that using hand crafted features for disease prediction neglects the immense possibility to use latent features from deep learning (DL) models which may reduce the overall accuracy of differenti

Evolene Premillieu, Antonio M. Caravaca-Aguirre, Simon Labouesse, Kristina Irsch

Diffraction-limited imaging in epi-fluorescence microscopy remains a challenge when sample aberrations are present or when the region of interest rests deep within an inhomogeneous medium. Adaptive optics is an attractive solution albeit with limited field of view and requiring relatively complicated systems. Alternatively, reconstruction algorithms have bee

Gianluca Nogara, Francesco Pierri, Stefano Cresci, Luca Luceri

Proprietary public APIs play a crucial and growing role as research tools among social scientists. Among such APIs, Google's machine learning-based Perspective API is extensively utilized for assessing the toxicity of social media messages, providing both an important resource for researchers and automatic content moderation. However, this paper exposes an i

Michela Esposito, Nicole Schieber, Alessandro Olivo, Yannick Schwab

We propose an imaging system and methodology for mapping soft-tissue samples in three dimensions, with micron-scale and isotropic spatial resolution, with low-concentrations as well as in the absence of heavy metal staining. We used hard X-ray phase-contrast imaging for the X-ray ability to non-destructively probe the internal structure of opaque specimens a

Fahim Ahmed Zaman, Mathews Jacob, Amanda Chang, Kan Liu

Diffusion models have shown impressive performance for image generation, often times outperforming other generative models. Since their introduction, researchers have extended the powerful noise-to-image denoising pipeline to discriminative tasks, including image segmentation. In this work we propose a conditional score-based generative modeling framework fo

Hongyi He, Longjun Liu, Haonan Zhang, Nanning Zheng

Among existing Neural Architecture Search methods, DARTS is known for its efficiency and simplicity. This approach applies continuous relaxation of network representation to construct a weight-sharing supernet and enables the identification of excellent subnets in just a few GPU days. However, performance collapse in DARTS results in deteriorating architectu