May 2023 arXiv papers — page 62

Kun Qian, Yuanyuan Wang, Peter Jung, Yilei Shi

Finding sparse solutions of underdetermined linear systems commonly requires the solving of L1 regularized least squares minimization problem, which is also known as the basis pursuit denoising (BPDN). They are computationally expensive since they cannot be solved analytically. An emerging technique known as deep unrolling provided a good combination of the

Anmol Kabra, Ethan R. Elenberg

Large, general purpose language models have demonstrated impressive performance across many different conversational domains. While multi-domain language models achieve low overall perplexity, their outputs are not guaranteed to stay within the domain of a given input prompt. This paper proposes domain privacy as a novel way to quantify how likely a conditio

Jun Cen, Yizheng Wu, Kewei Wang, Xingyi Li

The Segment Anything Model (SAM) has demonstrated its effectiveness in segmenting any part of 2D RGB images. However, SAM exhibits a stronger emphasis on texture information while paying less attention to geometry information when segmenting RGB images. To address this limitation, we propose the Segment Any RGBD (SAD) model, which is specifically designed to

Nicolás F. Barrera, Patricio Fuentealba, Francisco Muñoz, Tatiana Gómez

Understanding how the $\mathrm{H}_2$ molecule is formed under the chemical conditions of the interstellar media (ISM) is critical to the whole chemistry of it. Formation of $\mathrm{H}_2$ in the ISM requires a third body acting as a reservoir of energy. Polycyclic aromatic hydrocarbons (PAH's) are excellent candidates to play that role. In this work we simul

Fantine Huot, Joshua Maynez, Chris Alberti, Reinald Kim Amplayo

Cross-lingual summarization consists of generating a summary in one language given an input document in a different language, allowing for the dissemination of relevant content across speakers of other languages. The task is challenging mainly due to the paucity of cross-lingual datasets and the compounded difficulty of summarizing and translating. This work

Andrea Sipos, Nima Fazeli

In this paper, we propose a method for estimating in-hand object poses using proprioception and tactile feedback from a bimanual robotic system. Our method addresses the problem of reducing pose uncertainty through a sequence of frictional contact interactions between the grasped objects. As part of our method, we propose 1) a tool segmentation routine that

Sara Kashiwagi, Keitaro Tanaka, Qi Feng, Shigeo Morishima

This paper presents a novel metric learning approach to address the performance gap between normal and silent speech in visual speech recognition (VSR). The difference in lip movements between the two poses a challenge for existing VSR models, which exhibit degraded accuracy when applied to silent speech. To solve this issue and tackle the scarcity of traini

Silei Xu, Shicheng Liu, Theo Culhane, Elizaveta Pertseva

While large language models (LLMs) can answer many questions correctly, they can also hallucinate and give wrong answers. Wikidata, with its over 12 billion facts, can be used to ground LLMs to improve their factuality. This paper presents WikiWebQuestions, a high-quality question answering benchmark for Wikidata. Ported over from WebQuestions for Freebase,

Tiedong Liu, Bryan Kian Hsiang Low

We introduce Goat, a fine-tuned LLaMA model that significantly outperforms GPT-4 on a range of arithmetic tasks. Fine-tuned on a synthetically generated dataset, Goat achieves state-of-the-art performance on BIG-bench arithmetic sub-task. In particular, the zero-shot Goat-7B matches or even surpasses the accuracy achieved by the few-shot PaLM-540B. Surprisin

Sida I. Wang

The striking ability of unsupervised word translation has been demonstrated with the help of word vectors / pretraining; however, they require large amounts of data and usually fails if the data come from different domains. We propose coocmap, a method that can use either high-dimensional co-occurrence counts or their lower-dimensional approximations. Freed

Stanley Burris

In Boole's famous 1854 book {\em The Laws of Thought\/} the mathematical analysis of Aristotelian logic was relegated to Chapter XV, the last chapter before his treatment of probability theory. This chapter is Boole's tour de force to show that he had a uniform method to obtain all valid syllogisms in his version of Aristotelian logic, namely he applied {\em

Cristian Giuppone, Adrián Rodríguez, Viviam Alencastro, Fernando Roig

Mean motion resonances (MMR) are a frequent phenomenon among extrasolar planetary systems. Current observations indicate that many systems have planets that are close to or inside the 2:1 MMR, when the orbital period of one of the planets is twice the other. Analytical models to describe this particular MMR can only be reduced to integrable approximations in

M. R. Perelshtein, A. I. Pakhomchik, Ar. A. Melnikov, M. Podobrii

Quantum algorithms are getting extremely popular due to their potential to significantly outperform classical algorithms. Yet, applying quantum algorithms to optimization problems meets challenges related to the efficiency of quantum algorithms training, the shape of their cost landscape, the accuracy of their output, and their ability to scale to large-size

Uri Shaham, Maor Ivgi, Avia Efrat, Jonathan Berant

We introduce ZeroSCROLLS, a zero-shot benchmark for natural language understanding over long texts, which contains only test and small validation sets, without training data. We adapt six tasks from the SCROLLS benchmark, and add four new datasets, including two novel information fusing tasks, such as aggregating the percentage of positive reviews. Using Zer

Anthony Sicilia, Jennifer C. Gates, Malihe Alikhani

While demographic factors like age and gender change the way people talk, and in particular, the way people talk to machines, there is little investigation into how large pre-trained language models (LMs) can adapt to these changes. To remedy this gap, we consider how demographic factors in LM language skills can be measured to determine compatibility with a

Woonam Lim, Miguel Moreira, Weite Pi

We prove that the cohomology rings of the moduli space $M_{d,\chi}$ of one-dimensional sheaves on the projective plane are not isomorphic for general different choices of the Euler characteristics. This stands in contrast to the $\chi$-independence of the Betti numbers of these moduli spaces. As a corollary, we deduce that $M_{d,\chi}$ are topologically diff

Daniele Barbera, Vladimir Georgiev

The work deals with the Ericksen-Leslie System for nematic liquid crystals on the whole space. In our work we suppose the initial condition of the orientation field stays on an arc connecting two fixed orthogonal vectors on the unit sphere. Thanks to this geometric assumption, we prove through energy a priori estimates the local existence and the global exis

Gábor Hegedüs

Let $A_1, \ldots ,A_m$ and $B_1, \ldots ,B_m$ be subsets of $[n]$ and let $t$ be a non-negative integer with the following property: $|A_i \cap B_i|\leq t$ for each $i$ and $|A_i\cap B_j|>t$ whenever $i< j$. Then $m\leq 2^{n-t}$. Our proof uses Lov\'asz' tensor product method. We prove the following skew version of Bollob\'as' Theorem. Let $A_1, \ldots ,A_m$

Alireza Ahmadianyazdi, Isaac J. Miller, Albert Folch

Stereolithographic 3D-printing (SLA) permits facile fabrication of high-precision microfluidic and lab-on-a-chip devices. SLA photopolymers often yield parts with low mechanical compliancy in sharp contrast to elastomers such as poly(dimethyl siloxane) (PDMS). On the other hand, SLA-printable elastomers with soft mechanical properties do not fulfill the dist

Di Wu, Christof Monz

Using a vocabulary that is shared across languages is common practice in Multilingual Neural Machine Translation (MNMT). In addition to its simple design, shared tokens play an important role in positive knowledge transfer, assuming that shared tokens refer to similar meanings across languages. However, when word overlap is small, especially due to different

Iuri Frosio, Jan Kautz

Many defenses against adversarial attacks (\eg robust classifiers, randomization, or image purification) use countermeasures put to work only after the attack has been crafted. We adopt a different perspective to introduce $A^5$ (Adversarial Augmentation Against Adversarial Attacks), a novel framework including the first certified preemptive defense against

Steven Tomsovic, Juan Diego Urbina, Klaus Richter

One major objective of controlling classical chaotic dynamical systems is exploiting the system's extreme sensitivity to initial conditions in order to arrive at a predetermined target state. In a recent letter [Phys.~Rev.~Lett. 130, 020201 (2023)], a generalization of this targeting method to quantum systems was demonstrated using successive unitary transfo

Jessica Page, Tyson Littenberg

Previous work demonstrated effective laser frequency noise (LFN) suppression for Laser Interferometer Space Antenna (LISA) data from raw phasemeter measurements using a Markov Chain Monte Carlo (MCMC) algorithm with fractional delay interpolation (FDI) techniques to estimate the spacecraft separation parameters required for time-delay interferometry (TDI) un

Fabio A. C. C. Chalub, Antonio Gómez-Corral, Martín López-García, Fátima Palacios-Rodríguez

Ordinary differential equation (ODE) models used in mathematical epidemiology assume explicitly or implicitly large populations. For the study of infections in a hospital this is an extremely restrictive assumption as typically a hospital ward has a few dozen, or even fewer, patients. This work reframes a well-known model used in the study of the spread of a

Han Cai, Jay C. LeFebvre, Hao Li, Ethan Y. Cho

As we rapidly advance through the information age, the power consumed by computers, data centers, and networks grows exponentially. This has inspired a race to develop alternative low-power computational technologies. A new adiabatic configuration of a decades-old superconducting digital logic device has darted into the lead called quantum flux parametrons (

Michał Kijaczko

In this paper we consider fractional Sobolev spaces equipped with weights being powers of the distance to the boundary of the domain. We prove the versions of Bourgain--Brezis--Mironescu and Maz'ya--Shaposhnikova asymptotic formulae for weighted fractional Gagliardo seminorms. For $p>1$ we also provide a nonlocal characterization of classical weighted Sobole

Marten H. L. Kaas, Zoe Porter, Ernest Lim, Aisling Higham

The deployment and use of AI systems should be both safe and broadly ethically acceptable. The principles-based ethics assurance argument pattern is one proposal in the AI ethics landscape that seeks to support and achieve that aim. The purpose of this argument pattern or framework is to structure reasoning about, and to communicate and foster confidence in,

Paolo Antonelli, Boris Shakarov

We study a dissipative variant of the Gross-Pitaevskii equation with rotation. The model contains a nonlocal, nonlinear term that forces the conservation of $L^2$-norm of solutions. We are motivated by several physical experiments and numerical simulations studying the formation of vortices in Bose-Einstein condensates. We show local and global well-posednes

Antonio Giganti, Sara Mandelli, Paolo Bestagini, Marco Marcon

Biogenic Volatile Organic Compounds (BVOCs) emitted from the terrestrial ecosystem into the Earth's atmosphere are an important component of atmospheric chemistry. Due to the scarcity of measurement, a reliable enhancement of BVOCs emission maps can aid in providing denser data for atmospheric chemical, climate, and air quality models. In this work, we propo

Jiayu Yin, Jie Jiang, Ming Zhang

We investigate the kinematic topologies of light rings (LRs) and massive particle rings (PRs) encircling spherical and axisymmetric black holes. Our results demonstrate that the global topology number of LRs is consistently -1 for asymptotically flat and (Anti-)de Sitter spacetime. Additionally, we show that the global topology of PRs varies, with a value of

Tugkan Batu, Amitabh Trehan, Chhaya Trehan

We propose a simple and time-optimal algorithm for property testing a graph for its conductance in the CONGEST model. Our algorithm takes only $O(\log n)$ rounds of communication (which is known to be optimal), and consists of simply running multiple random walks of $O(\log n)$ length from a certain number of random sources, at the end of which nodes can dec

Chris Beeler, Sriram Ganapathi Subramanian, Kyle Sprague, Nouha Chatti

This paper provides a simulated laboratory for making use of Reinforcement Learning (RL) for chemical discovery. Since RL is fairly data intensive, training agents `on-the-fly' by taking actions in the real world is infeasible and possibly dangerous. Moreover, chemical processing and discovery involves challenges which are not commonly found in RL benchmarks

Zhicheng Cai

Traditionally, different types of feature operators (e.g., convolution, self-attention and involution) utilize different approaches to extract and aggregate the features. Resemblance can be hardly discovered from their mathematical formulas. However, these three operators all serve the same paramount purpose and bear no difference in essence. Hence we probe

Christian Schüßler, Marcel Hoffmann, Vanessa Wirth, Björn Eskofier

In this work a novel radar simulation concept is introduced that allows to simulate realistic radar data for Range, Doppler, and for arbitrary antenna positions in an efficient way. Further, it makes it possible to automatically annotate the simulated radar signal by allowing to decompose it into different parts. This approach allows not only almost perfect

Stefan Strohauer, Fabian Wietschorke, Lucio Zugliani, Rasmus Flaschmann

Achieving homogeneous performance metrics between nominally identical pixels is challenging for the operation of arrays of superconducting nanowire single-photon detectors (SNSPDs). Here, we utilize local helium ion irradiation to post-process and tune single-photon detection efficiency, switching current, and critical temperature of individual devices on th

Dongcheng Zhao, Guobin Shen, Yiting Dong, Yang Li

Spiking neural networks have gained significant attention due to their brain-like information processing capabilities. The use of surrogate gradients has made it possible to train spiking neural networks with backpropagation, leading to impressive performance in various tasks. However, spiking neural networks trained with backpropagation typically approximat

Ziyun Zeng, Yixiao Ge, Zhan Tong, Xihui Liu

The ultimate goal for foundation models is realizing task-agnostic, i.e., supporting out-of-the-box usage without task-specific fine-tuning. Although breakthroughs have been made in natural language processing and image representation learning, it is still challenging for video models to reach it due to the increasing uncertainty of spatiotemporal signals. T

Takahiro Yokoyama, Yusei Kobayashi, Noriyoshi Arai, Arash Nikoubashman

We investigate the self-assembly of amphiphilic nanocubes under rest and shear using molecular dynamics (MD) simulations and kinetic Monte Carlo (KMC) calculations. These particles combine both interaction and shape anisotropy, making them valuable models for studying folded proteins and DNA-functionalized nanoparticles. The nanocubes can self-assemble into

Afra Amini, Massimiliano Ciaramita

Large language models are able to learn new tasks in context, where they are provided with instructions and a few annotated examples. However, the effectiveness of in-context learning is dependent on the provided context, and the performance on a downstream task can vary considerably, depending on the instruction. Importantly, such dependency on the context

Qianghui Guo, Yinglie Jin, Lisa H. Sun, Shina Xu

Combinatorial enumeration of various RNA secondary structures and protein contact maps, is of great interest for both combinatorists and computational biologists. Enumeration of protein contact maps has considerable difficulties due to the significant higher vertex degree than that of RNA secondary structures. The state of art maximum vertex degree in previo

Naihao Deng, Yikai Liu, Mingye Chen, Winston Wu

The performance of current supervised AI systems is tightly connected to the availability of annotated datasets. Annotations are usually collected through annotation tools, which are often designed for specific tasks and are difficult to customize. Moreover, existing annotation tools with an active learning mechanism often only support limited use cases. To

Zizhuo Wang, Ziyang Xu, Xingxing Jia

Visual Cryptography Schemes (VCS) based on the "XOR" operation (XVCS) exhibit significantly smaller pixel expansion and higher contrast compared to those based on the "OR" operation. Moreover, the "XOR" operation appears to possess superior qualities, as it effectively operates within a binary field, while the "OR" operation merely functions as a ring with i

Renjie Pi, Jiahui Gao, Shizhe Diao, Rui Pan

In recent years, the field of computer vision has seen significant advancements thanks to the development of large language models (LLMs). These models have enabled more effective and sophisticated interactions between humans and machines, paving the way for novel techniques that blur the lines between human and machine intelligence. In this paper, we introd

Carolin M. Geitner, Lea J. Köglmeier, Inéz Frerichs, Patrick Langguth

We present a novel computational model for the dynamics of alveolar recruitment/derecruitment (RD), which reproduces the underlying characteristics typically observed in injured lungs. The basic idea is a pressure- and time-dependent variation of the stress-free reference volume in reduced dimensional viscoelastic elements representing the acinar tissue. We

Itamar J. Allali, Fabrizio Rompineve, Mark P. Hertzberg

Interacting dark sectors may undergo changes in the number of their relativistic species during the early universe, due to a mass threshold $m$ (similar to changes in the Standard Model bath), and in doing so affect the cosmic history. When such changes occur close to recombination, i.e., for $m\sim (0.1-10)~\text{eV}$, the stringent bound on the effective n

Chengyin Hu, Weiwen Shi, Chao Li, Jialiang Sun

Deep neural networks (DNNs) have made remarkable strides in various computer vision tasks, including image classification, segmentation, and object detection. However, recent research has revealed a vulnerability in advanced DNNs when faced with deliberate manipulations of input data, known as adversarial attacks. Moreover, the accuracy of DNNs is heavily in

Francesco Pedrotti, Jan Maas, Marco Mondelli

Score-based generative models (SGMs) are powerful tools to sample from complex data distributions. Their underlying idea is to (i) run a forward process for time $T_1$ by adding noise to the data, (ii) estimate its score function, and (iii) use such estimate to run a reverse process. As the reverse process is initialized with the stationary distribution of t

David Dukić, Kiril Gashteovski, Goran Glavaš, Jan Šnajder

Event detection is a crucial information extraction task in many domains, such as Wikipedia or news. The task typically relies on trigger detection (TD) -- identifying token spans in the text that evoke specific events. While the notion of triggers should ideally be universal across domains, domain transfer for TD from high- to low-resource domains results i

Davide Piccioni, Christian Apostoli, Federico Becca, Guglielmo Mazzola

We show that a two-body Jastrow wave function is able to capture the ground-state properties of the $S=1$ antiferromagnetic Heisenberg chain with the single-ion anisotropy term, in both the topological and trivial phases. Here, the optimized Jastrow pseudo potential assumes a very simple form in Fourier space, i.e., $v_{q} \approx 1/q^2$, which is able to gi

Xiao Li, Lei Zhao, Daoli Zhu, Anthony Man-Cho So

The subgradient method is one of the most fundamental algorithmic schemes for nonsmooth optimization. The existing complexity and convergence results for this method are mainly derived for Lipschitz continuous objective functions. In this work, we first extend the typical iteration complexity results for the subgradient method to cover non-Lipschitz convex a

Lean Wang, Lei Li, Damai Dai, Deli Chen

In-context learning (ICL) emerges as a promising capability of large language models (LLMs) by providing them with demonstration examples to perform diverse tasks. However, the underlying mechanism of how LLMs learn from the provided context remains under-explored. In this paper, we investigate the working mechanism of ICL through an information flow lens. O

Mar Canet Solà, Antonina Korepanova, Ksenia Mukhina, Maximilian Schich

Museum collection strategies are governed by a variety of factors, including topical focus, acquisition funds, availability of works in the art market, donations and specific coincidental opportunities. Yet, it remains unclear if more fundamental collection patterns emerge, exist, and are shared between museums, which could for example allow an established a

Jan Kára, Sergey Zharikov, Marek Wolf, Ainash Amantayeva

We present a new study of the Z~Cam-type eclipsing cataclysmic variable AY~Piscium with the aim of determining the fundamental parameters of the system and the structure of the accretion flow therein. We use time-resolved photometric observations supplemented by spectroscopy in the standstill, to which we applied our light-curve modeling techniques and the D

Betul Gokkaya, Leonardo Aniello, Basel Halak

The software product is a source of cyber-attacks that target organizations by using their software supply chain as a distribution vector. As the reliance of software projects on open-source or proprietary modules is increasing drastically, SSC is becoming more and more critical and, therefore, has attracted the interest of cyber attackers. While existing st

M. B. Weissman, J. M. Robins

A paper evaluating the effects of lessons intended to encourage high school students to continue physics studies made some important errors. One was to underestimate the width of confidence intervals by failing to use standard cluster randomization analysis. Another was to use a missing-data imputation program that inappropriately assumes that data are missi

Károly Bezdek

Let ${\mathbb E}^d$ denote the $d$-dimensional Euclidean space. The $r$-ball body generated by a given set in ${\mathbb E}^d$ is the intersection of balls of radius $r$ centered at the points of the given set. The author [Discrete Optimization 44/1 (2022), Paper No. 100539] proved the following Blaschke-Santal\'o-type inequality for $r$-ball bodies: for all

Oswin So, Chuchu Fan

Tasks for autonomous robotic systems commonly require stabilization to a desired region while maintaining safety specifications. However, solving this multi-objective problem is challenging when the dynamics are nonlinear and high-dimensional, as traditional methods do not scale well and are often limited to specific problem structures. To address this issue

Kamil Kowol, Stefan Bracke, Hanno Gottschalk

In this study, we propose a novel approach to enrich the training data for automated driving by using a self-designed driving simulator and two human drivers to generate safety-critical corner cases in a short period of time, as already presented in~\cite{kowol22simulator}. Our results show that incorporating these corner cases during training improves the r

Lukas-Paul Rausch, Maik Schünemann, Eric Drebitz, Daniel Harnack

When selective attention is devoted to one of multiple stimuli within receptive fields of neurons in visual area V4, cells respond as if only the attended stimulus was present. The underlying neural mechanisms are still debated, but computational studies suggest that a small rate advantage for neural populations passing the attended signal to V4 suffices to

Jeonghoon Kim, Jung Hyun Lee, Sungdong Kim, Joonsuk Park

Large language models (LLMs) face the challenges in fine-tuning and deployment due to their high memory demands and computational costs. While parameter-efficient fine-tuning (PEFT) methods aim to reduce the memory usage of the optimizer state during fine-tuning, the inherent size of pre-trained LLM weights continues to be a pressing concern. Even though qua

Vladimir Onoochin

In this article, it is analyzed a problem of existence of a function which is able to transform electromagnetic potentials defined in one gauge to corresponding potentials defined in the other gauge. It is shown that such a function cannot exist.

Marcos Dajczer, Theodoros Vlachos

We investigate the topology of the compact submanifolds in round spheres that satisfy a lower bound on the Ricci curvature depending only on the length of the mean curvature vector of the immersion. Just in special cases, the limited strength of the assumption allows some strong additional information on the extrinsic geometry of the submanifold.

Bo Zhou, Qianglong Chen, Tianyu Wang, Xiaomi Zhong

To fully evaluate the overall performance of different NLP models in a given domain, many evaluation benchmarks are proposed, such as GLUE, SuperGLUE and CLUE. The fi eld of natural language understanding has traditionally focused on benchmarks for various tasks in languages such as Chinese, English, and multilingua, however, there has been a lack of attenti

Roman Andriushchenko, Alexander Bork, Milan Češka, Sebastian Junges

This paper marries two state-of-the-art controller synthesis methods for partially observable Markov decision processes (POMDPs), a prominent model in sequential decision making under uncertainty. A central issue is to find a POMDP controller - that solely decides based on the observations seen so far - to achieve a total expected reward objective. As findin

Pablo Andres-Martinez, Tim Forrer, Daniel Mills, Jun-Yi Wu

We consider a heterogeneous network of quantum computing modules, sparsely connected via Bell states. Operations across these connections constitute a computational bottleneck and they are likely to add more noise to the computation than operations performed within a module. We introduce several techniques for transforming a given quantum circuit into one im

J. N. Wieland, A. L. Romanov, A. Valishev, G. Stancari

Nonlinear integrable optics (NIO) are a promising novel approach at improving the stability of high intensity beams. Implementations of NIO based on specialized magnetic elements are being tested at the Integrable Optics Test Accelerator (IOTA) at Fermilab. One method of verifying proper implementation of these solutions is by measuring the analytic invarian

Qunying Song, Emelie Engström, Per Runeson

Testing autonomous driving systems for safety and reliability is extremely complex. A primary challenge is identifying the relevant test scenarios, especially the critical ones that may expose hazards or risks of harm to autonomous vehicles and other road users. There are several proposed methods and tools for critical scenario identification, while the indu

Kiumars Aryana, Hyun Jung Kim, Cosmin-Constantin Popescu, Steven Vitale

Reconfigurable or programmable photonic devices are rapidly growing and have become an integral part of many optical systems. The ability to selectively modulate electromagnetic waves through electrical stimuli is crucial in the advancement of a variety of applications from data communication and computing devices to environmental science and space explorati

Daniel de Florian, Lucas Palma Conte

We discuss the effect of QED corrections in the evolution of polarized parton distributions. We solve the corresponding evolution equations exactly to ${\cal O}(\alpha )$ and ${\cal O}(\alpha_s^2)$ in Mellin $N$-space, extending the available techniques for pure QCD evolution. To accomplish this, we introduce, for the first time, the Altarelli-Parisi polariz

Thijs Havinga, Xianjun Jiao, Wei Liu, Ingrid Moerman

Field-Programmable Gate Array (FPGA)-based Software-Defined Radio (SDR) is well-suited for experimenting with advanced wireless communication systems, as it allows to alter the architecture promptly while obtaining high performance. However, programming the FPGA using a Hardware Description Language (HDL) is a time-consuming task for FPGA developers and diff

Anne-Solène Bornens, Michel Nowak

Variational Quantum Algorithms (VQA) have emerged with a wide variety of applications. One question to ask is either they can efficiently be implemented and executed on existing architectures. Current hardware suffers from uncontrolled noise that can alter the expected results of one calculation. The nature of this noise is different from one technology to a

Xianjie Liu, Hongwei Shi

Postoperative infection diagnosis is a common and serious complication that generally poses a high diagnostic challenge. This study focuses on PJI, a type of postoperative infection. X-ray examination is an imaging examination for suspected PJI patients that can evaluate joint prostheses and adjacent tissues, and detect the cause of pain. Laboratory examinat

Antonio Pasqua, Surajit Chattopadhyay, Irina Radinschi, Azzah Aziz Alshehri

In this work, we examine the cosmological characteristics of the Power Law Entropy Corrected Holographic Dark Energy (PLECHDE) model with infrared (IR) cut-off, which is determined by the curvature parameter $k$, the time derivative of $H$, and the average radius of the Ricci scalar curvature $R$, which varies with the Hubble parameter $H$ squared. We obtain

Shitian He, Huanxin Zou, Yingqian Wang, Boyang Li

Pointly Supervised Object Detection (PSOD) has attracted considerable interests due to its lower labeling cost as compared to box-level supervised object detection. However, the complex scenes, densely packed and dynamic-scale objects in Remote Sensing (RS) images hinder the development of PSOD methods in RS field. In this paper, we make the first attempt to

Juan Manuel Márquez, Diego Portillo-Sánchez, Pablo Roig

A novel method to differentiate the effects of Dirac and Majorana (D-M) neutrinos in four-body decays has been discussed in arXiv:2106.11785. There, it is concluded that the back-to-back kinematic scenario seems to avoid the constraint imposed by the "practical Dirac-Majorana confusion theorem", as one does not need to fully integrate over neutrino and antin

L. Fellay, M. -A. Dupret

In close binary systems, tidal interactions and rotational effects can strongly influence stellar evolution as a result of mass-transfer, common envelope phases, ... All these aspects can only be treated following improvements of theoretical models, taking into account the breaking of spherical symmetry occurring in close binaries. Current models of binary s

Gaia Grosso, Marco Letizia, Maurizio Pierini, Andrea Wulzer

The Neyman-Pearson strategy for hypothesis testing can be employed for goodness of fit if the alternative hypothesis is selected from data by exploring a rich parametrised family of models, while controlling the impact of statistical fluctuations. The New Physics Learning Machine (NPLM) methodology has been developed as a concrete implementation of this idea

Jesús Dueñas, Iacopo P. Longo, Rafael Obaya

This paper investigates biological models that represent the transition equation from a system in the past to a system in the future. It is shown that finite-time Lyapunov exponents calculated along a locally pullback attractive solution are efficient indicators (early-warning signals) of the presence of a tipping point. Precise time-dependent transitions wi

Tianhong Li, Vibhaalakshmi Sivaraman, Pantea Karimi, Lijie Fan

Packet loss during video conferencing often results in poor quality and video freezing. Retransmitting lost packets is often impractical due to the need for real-time playback, and using Forward Error Correction (FEC) for packet recovery is challenging due to the unpredictable and bursty nature of Internet losses. Excessive redundancy leads to inefficiency a

Genqian Liu

In this note, we shall point out that all ``numerically calculations'' and figures in \cite{CaFrLeVa-23} are wrong because these calculations are based on some incorrect formulas. Furthermore, by pointing out several serious errors in \cite{CaFrLeVa-23} and especially by Section 7, Proposition 7.1, Remarks 7.2--7.3, and Section 8 (a result of A. Pierzchalski

Mhairi Dunion, Trevor McInroe, Kevin Sebastian Luck, Josiah P. Hanna

Reinforcement Learning (RL) environments can produce training data with spurious correlations between features due to the amount of training data or its limited feature coverage. This can lead to RL agents encoding these misleading correlations in their latent representation, preventing the agent from generalising if the correlation changes within the enviro

David Conlon, Jacob Fox, Huy Tuan Pham, Yufei Zhao

For positive integers $n,r,s$ with $r > s$, the set-coloring Ramsey number $R(n;r,s)$ is the minimum $N$ such that if every edge of the complete graph $K_N$ receives a set of $s$ colors from a palette of $r$ colors, then there is a subset of $n$ vertices where all of the edges between them receive a common color. If $n$ is fixed and $\frac{s}{r}$ is less tha

A. Theocharous, G. G. Gregoriou, P. Sapountzis, I. Kontoyiannis

We consider the problem of detecting causal relationships between discrete time series, in the presence of potential confounders. A hypothesis test is introduced for identifying the temporally causal influence of $(x_n)$ on $(y_n)$, causally conditioned on a possibly confounding third time series $(z_n)$. Under natural Markovian modeling assumptions, it is s

Milad Bader, Robert G. Clapp, Kurt T. Nihei, Biondo Biondi

Source footprints represent an inherent problem to full-waveform inversion (FWI). They are caused by the high data sensitivity to the model parameters in the vicinity of the seismic sources and can be exacerbated by source-related errors in the modeling operator. We propose a simple, effective, and efficient method to remove source footprints in FWI when sou

Priyanshu Gupta, Avishree Khare, Yasharth Bajpai, Saikat Chakraborty

Developers expend a significant amount of time in editing code for a variety of reasons such as bug fixing or adding new features. Designing effective methods to predict code edits has been an active yet challenging area of research due to the diversity of code edits and the difficulty of capturing the developer intent. In this work, we address these challen

Man Luo, Xin Xu, Zhuyun Dai, Panupong Pasupat

In-context learning (ICL), teaching a large language model (LLM) to perform a task with few-shot demonstrations rather than adjusting the model parameters, has emerged as a strong paradigm for using LLMs. While early studies primarily used a fixed or random set of demonstrations for all test queries, recent research suggests that retrieving semantically simi

Rui Li, Xu Chen, Chaozhuo Li, Yanming Shen

Embedding models have shown great power in knowledge graph completion (KGC) task. By learning structural constraints for each training triple, these methods implicitly memorize intrinsic relation rules to infer missing links. However, this paper points out that the multi-hop relation rules are hard to be reliably memorized due to the inherent deficiencies of

Federico Echenique, Gerelt Tserenjigmid

We study the testable implications of models of dynamically inconsistent choices when planned choices are unobservable, and thus only "on path" data is available. First, we discuss the approach in Blow, Browning and Crawford (2021), who characterize first-order rationalizability of the model of quasi-hyperbolic discounting. We show that the first-order appro

Christos Baziotis, Biao Zhang, Alexandra Birch, Barry Haddow

Multilingual machine translation (MMT), trained on a mixture of parallel and monolingual data, is key for improving translation in low-resource language pairs. However, the literature offers conflicting results on the performance of different methods of including monolingual data. To resolve this, we examine how denoising autoencoding (DAE) and backtranslati

Saman Moniri, Yao Yang, Yakun Yuan, Jihan Zhou

Medium- and high-entropy alloys (M/HEAs) mix multiple principal elements with near-equiatomic composition and represent a paradigm-shift strategy for designing new materials for metallurgy, catalysis, and other fields. One of the core hypotheses of M/HEAs is lattice distortion. However, experimentally determining the 3D local lattice distortion in M/HEAs rem

Daiki Chijiwa

Training deep neural networks (DNNs) is computationally expensive, which is problematic especially when performing duplicated or similar training runs in model ensemble or fine-tuning pre-trained models, for example. Once we have trained one DNN on some dataset, we have its learning trajectory (i.e., a sequence of intermediate parameters during training) whi

Sora Shiratani, Synge Todo

The quantum phase transition of the one-dimensional long-range transverse-field Ising model is explored by combining the quantum Monte Carlo method and stochastic parameter optimization, specifically achieved by tuning correlation ratios so that space and imaginary time are isotropic. In our simulations, the simulator automatically determines the parameters

Julien Martinelli, Ayush Bharti, Armi Tiihonen, S. T. John

Contextual Bayesian Optimization (CBO) efficiently optimizes black-box functions with respect to design variables, while simultaneously integrating contextual information regarding the environment, such as experimental conditions. However, the relevance of contextual variables is not necessarily known beforehand. Moreover, contextual variables can sometimes

Hiroto Kasai, Yuki Takeuchi, Yuichiro Matsuzaki, Yasuhiro Tokura

A quantum sensing network is used to simultaneously detect and measure physical quantities, such as magnetic fields, at different locations. However, there is a risk that the measurement data is leaked to the third party during the communication. Many theoretical and experimental efforts have been made to realize a secure quantum sensing network where a high

Ambarish Chattopadhyay, Jose R. Zubizarreta

Comparison and contrast are the basic means to unveil causation and learn which treatments work. To build good comparison groups, randomized experimentation is key, yet often infeasible. In such non-experimental settings, we illustrate and discuss diagnostics to assess how well the common linear regression approach to causal inference approximates desirable

Anfeng Xu, Rajat Hebbar, Rimita Lahiri, Tiantian Feng

Speech processing techniques are useful for analyzing speech and language development in children with Autism Spectrum Disorder (ASD), who are often varied and delayed in acquiring these skills. Early identification and intervention are crucial, but traditional assessment methodologies such as caregiver reports are not adequate for the requisite behavioral p

Sophie Engineer, Ana C. S. Costa, Alexandre C. Orthey, Xiaogang Qiang

Verifying entanglement between parties is essential for creating secure quantum communication. However, finite statistics can lead to false positive outcomes in any tests for entanglement. Here, we introduce a one-sided device-independent protocol that corrects for apparent signaling effects in experimental probability distributions, caused by statistical fl

I. I. Musiienko, S. O. Lebedynskyi, R. I. Kholodov

The potential barrier model considering an additional current that can lead to the high-gradient breakdowns in accelerating structures is proposed. An oscillatory resonance feature of the field emission current from a double-layer metal system with a nanoscale coating is shown. The double potential barrier was used for calculations of the field emission curr

Eloy Anguiano Batanero, Ángela Fernández Pascual, Álvaro Barbero Jiménez

Data quality or data evaluation is sometimes a task as important as collecting a large volume of data when it comes to generating accurate artificial intelligence models. In fact, being able to evaluate the data can lead to a larger database that is better suited to a particular problem because we have the ability to filter out data obtained automatically of

Sebastiano Battisti, Giorgio De Simoni, Luca Chirolli, Alessandro Braggio

Thermoelectric effects in normal metals and superconductors are usually very small due to the presence of electron-hole symmetry. Here, we show that superconducting junctions brought out of equilibrium manifest a sizable bipolar thermoelectric effect that stems from a strong violation of the detailed balance. To fully control the effect, we consider a therma