May 2023 arXiv papers — page 29

Chaeeun Kim, Soyoung Yoon, Hyunji Lee, Joel Jang

Benchmarking the performance of information retrieval (IR) is mostly conducted with a fixed set of documents (static corpora). However, in realistic scenarios, this is rarely the case and the documents to be retrieved are constantly updated and added. In this paper, we focus on Generative Retrievals (GR), which apply autoregressive language models to IR prob

Mancho Manev

Almost contact complex Riemannian manifolds, known also as almost contact B-metric manifolds, are in principle equipped with a pair of mutually associated pseudo-Riemannian metrics. Each of these metrics is specialized here as a Yamabe almost soliton with a potential collinear to the Reeb vector field. The resulting manifolds are then investigated in two imp

Sai Naga Manoj Paladugu, Tao Chen, Fangzhao Alex An, Bo Yan

We explore a technique for probing energy spectra in synthetic lattices that is analogous to scanning tunneling microscopy. Using one-dimensional synthetic lattices of coupled atomic momentum states, we explore this spectroscopic technique and observe qualitative agreement between the measured and simulated energy spectra for small two- and three-site lattic

Weisong Sun, Yuchen Chen, Guanhong Tao, Chunrong Fang

Reusing off-the-shelf code snippets from online repositories is a common practice, which significantly enhances the productivity of software developers. To find desired code snippets, developers resort to code search engines through natural language queries. Neural code search models are hence behind many such engines. These models are based on deep learning

Zhengzhong Yi, Zhipeng Liang, Kaixin Zhong, Yulin Wu

We propose a new method called decoupling representation to represent Pauli operators as vectors over $GF(2)$, based on which we propose partially decoupled belief propagation and fully decoupled belief propagation decoding algorithm for quantum low density parity-check codes. These two algorithms have the capability to deal with the correlations between the

Sally Cockburn, Sarah Loeb

The derived graph of a voltage graph consisting of a single vertex and two loops of different voltages is a circulant graph with two generators. We characterize the automorphism groups of connected, two-generator circulant graphs, and give their determining and distinguishing number, and when relevant, their cost of 2-distinguishing. We do the same for the s

Laya Ghodrati, Victor M. Panaretos

We present an optimal transport framework for performing regression when both the covariate and the response are probability distributions on a compact Euclidean subset $\Omega\subset\mathbb{R}^d$, where $d>1$. Extending beyond compactly supported distributions, this method also applies when both the predictor and responses are Gaussian distributions on $\ma

Nirmal D. Wickramasinghe, Indrakshi Dey

Efficient resource allocation (RA) strategies within massive and dense Internet of Things (IoT) networks is one of the major challenges in the deployment of IoT-network based smart ecosystems involving heterogeneous power-constrained IoT devices operating in varied radio and environmental conditions. In this paper, we focus on the transmit power minimization

Jhon E. Bravo, Jean C. Cortissoz

In this short paper we show that March's criterion for the existence of a bounded non constant harmonic function on a weak model is also a necessary and sufficient condition for the solvability of the Dirichlet problem at infinity on a slight generalisation of a weak model (rotationally symmetric) metric on $\mathbb{R}^n$.

Fernando Roldán

In this article, we study two methods for solving monotone inclusions in real Hilbert spaces involving the sum of a maximally monotone operator, a monotone-Lipschitzian operator, a cocoercive operator, and a normal cone to a vector subspace. Our algorithms split and exploits the intrinsic properties of each operator involved in the inclusion. We derive our m

Linhao Dong, Zhecheng An, Peihao Wu, Jun Zhang

Speech or text representation generated by pre-trained models contains modal-specific information that could be combined for benefiting spoken language understanding (SLU) tasks. In this work, we propose a novel pre-training paradigm termed Continuous Integrate-and-Fire Pre-Training (CIF-PT). It relies on a simple but effective frame-to-token alignment: cont

Si Yi Meng, Robert M. Gower

We develop a variant of the stochastic prox-linear method for minimizing the Conditional Value-at-Risk (CVaR) objective. CVaR is a risk measure focused on minimizing worst-case performance, defined as the average of the top quantile of the losses. In machine learning, such a risk measure is useful to train more robust models. Although the stochastic subgradi

Zhuang Li, Yuyang Chai, Terry Yue Zhuo, Lizhen Qu

Textual scene graph parsing has become increasingly important in various vision-language applications, including image caption evaluation and image retrieval. However, existing scene graph parsers that convert image captions into scene graphs often suffer from two types of errors. First, the generated scene graphs fail to capture the true semantics of the ca

Rubing Huang, Chenhui Cui, Junlong Lian, Dave Towey

Adaptive Random Testing (ART) enhances the testing effectiveness (including fault-detection capability) of Random Testing (RT) by increasing the diversity of the random test cases throughout the input domain. Many ART algorithms have been investigated such as Fixed-Size-Candidate-Set ART (FSCS) and Restricted Random Testing (RRT), and have been widely used i

Shangyun Wang, Songbai Chen, Jiliang Jing, Jieci Wang

Quantum chaos is an intriguing topic and has attracted a great deal of interests in quantum mechanics and black hole physics. Recently, the exponential growth of out-of-time-ordered correlator (OTOC) has been proposed to diagnose quantum chaos and verify the correspondence principle. Here, good correspondence is found between the linear entanglement entropy

Konstantinos Vogiatzoglou, Costas Papadimitriou, Konstantinos Ampountolas, Michail Chatzimanolakis

Forest fires pose a natural threat with devastating social, environmental, and economic implications. The rapid and highly uncertain rate of spread of wildfires necessitates a trustworthy digital tool capable of providing real-time estimates of fire evolution and human interventions, while receiving continuous input from remote sensing. The current work aims

Sven Sandfeldt

In this paper we consider local centralizer classification and rigidity of some toral automorphisms. In low dimensions we classify up to finite index possible centralizers for volume preserving diffeomorphisms $f$ $C^{1}-$close to an ergodic irreducible toral automorphism $L$. Moreover, we show a rigidity result in the case that the centralizer of $f$ is lar

Ilia Shumailov, Zakhar Shumaylov, Yiren Zhao, Yarin Gal

Stable Diffusion revolutionised image creation from descriptive text. GPT-2, GPT-3(.5) and GPT-4 demonstrated astonishing performance across a variety of language tasks. ChatGPT introduced such language models to the general public. It is now clear that large language models (LLMs) are here to stay, and will bring about drastic change in the whole ecosystem

Animesh Mitra, Saswata Sahoo, Soumyabrata Dey

Usage data of a group of users distributed across a number of categories, such as songs, movies, webpages, links, regular household products, mobile apps, games, etc. can be ultra-high dimensional and massive in size. More often this kind of data is categorical and sparse in nature making it even more difficult to interpret any underlying hidden patterns suc

Jasivan Alex Sivakumar, Nafise Sadat Moosavi

While pre-trained language models achieve impressive performance on various NLP benchmarks, they still struggle with tasks that require numerical reasoning. Recent advances in improving numerical reasoning are mostly achieved using very large language models that contain billions of parameters and are not accessible to everyone. In addition, numerical reason

Lei Wu, Weijie J. Su

In this paper, we study the implicit regularization of stochastic gradient descent (SGD) through the lens of {\em dynamical stability} (Wu et al., 2018). We start by revising existing stability analyses of SGD, showing how the Frobenius norm and trace of Hessian relate to different notions of stability. Notably, if a global minimum is linearly stable for SGD

Zhongping Zhang, Jian Zheng, Jacob Zhiyuan Fang, Bryan A. Plummer

Diffusion models have demonstrated impressive performance in text-guided image generation. Current methods that leverage the knowledge of these models for image editing either fine-tune them using the input image (e.g., Imagic) or incorporate structure information as additional constraints (e.g., ControlNet). However, fine-tuning large-scale diffusion models

Shan-Shan Ding, Guang-Yu Ding, Kai Leong Chong, Wen-Tao Wu

We investigate the spatial distribution and dynamics of the vortices in rotating Rayleigh-B\'enard convection in a reduced Rayleigh-number range $1.3{\le}Ra/Ra_{c}{\le}166$. Under slow rotations ($Ra{\gtrsim}10Ra_{c}$), the vortices are randomly distributed. The size-distribution of the Voronoi cells of the vortex centers is well described by the standard $\

Dawei Wu, Shan-Chang Tang, Yu Shi

The behavior of accelerating Unruh-DeWitt detectors coupled with a spinor field in (3+1)-dimensional spacetime is investigated. For a single point-like detector with Gaussian switching function, the transition probability increases with the acceleration and thus the antiUnruh effect effect cannot occur. Due to the spinor structure of the Dirac field, UV dive

Charles R. Cowley, Robert E. Stencel

Properties of solar twins reported by Lehmann et al. (2023) at kiloparsec distances from the local standard of rest (LSR) are compared to solar twins within 100 pc of the Sun. These have velocity distributions closely similar to those of the nearby twins in addition to closely matching $T_{\rm eff}$, $\log{(g)}$ and $[Fe/H]$. The new twins are at slightly hi

Jorge Fandinno, Zachary Hansen, Yuliya Lierler, Vladimir Lifschitz

Refactoring is modifying a program without changing its external behavior. In this paper, we make the concept of external behavior precise for a simple answer set programming language. Then we describe a proof assistant for the task of verifying that refactoring a program in that language is performed correctly.

Adam Heins, Angela P. Schoellig

We consider a nonprehensile manipulation task in which a mobile manipulator must balance objects on its end effector without grasping them -- known as the waiter's problem -- and move to a desired location while avoiding static and dynamic obstacles. In constrast to existing approaches, our focus is on fast online planning in response to new and changing env

Yuguang Yang, Runtang Guo, Sheng Wu, Yimi Wang

Interpretation of deep learning remains a very challenging problem. Although the Class Activation Map (CAM) is widely used to interpret deep model predictions by highlighting object location, it fails to provide insight into the salient features used by the model to make decisions. Furthermore, existing evaluation protocols often overlook the correlation bet

Meysam Mohseni, Péter Udvarhelyi, Gergő Thiering, Adam Gali

Certain intrinsic point defects in silicon carbide are promising quantum systems with efficient spin-photon interface. Despite carbon vacancy in silicon carbide is an elementary and relatively abundant intrinsic defect, no optical signal has been reported associated with it. Here, we revisit the positively charged carbon vacancy defects in the 4H polytype of

Song Bian, Zhao Song, Junze Yin

Many convex optimization problems with important applications in machine learning are formulated as empirical risk minimization (ERM). There are several examples: linear and logistic regression, LASSO, kernel regression, quantile regression, $p$-norm regression, support vector machines (SVM), and mean-field variational inference. To improve data privacy, fed

Hiroyuki Sagawa, Tomoya Naito, Xavier Roca-Maza, Tetsuo Hatsuda

An approach is proposed to link the charge symmetry breaking (CSB) nuclear interaction and the low-energy constants in quantum chromodynamics (QCD) by matching the CSB effect in nuclear matter. The resulting CSB interaction is applied to study the Okamoto-Nolen-Schiffer anomaly, still lacking a satisfactory microscopic understanding, on the energy difference

Naveen Badathala, Abisek Rajakumar Kalarani, Tejpalsingh Siledar, Pushpak Bhattacharyya

Hyperbole and metaphor are common in day-to-day communication (e.g., "I am in deep trouble": how does trouble have depth?), which makes their detection important, especially in a conversational AI setting. Existing approaches to automatically detect metaphor and hyperbole have studied these language phenomena independently, but their relationship has hardly,

Taicheng Guo, Kehan Guo, Bozhao Nan, Zhenwen Liang

Large Language Models (LLMs) with strong abilities in natural language processing tasks have emerged and have been applied in various kinds of areas such as science, finance and software engineering. However, the capability of LLMs to advance the field of chemistry remains unclear. In this paper, rather than pursuing state-of-the-art performance, we aim to e

Shishir Adhikari, Elena Zheleva

Causal inference in networks should account for interference, which occurs when a unit's outcome is influenced by treatments or outcomes of peers. Heterogeneous peer influence (HPI) occurs when a unit's outcome is influenced differently by different peers based on their attributes and relationships, or when each unit has a different susceptibility to peer in

Guilherme Pombo, Robert Gray, Amy P. K. Nelson, Chris Foulon

Causal mapping of the functional organisation of the human brain requires evidence of \textit{necessity} available at adequate scale only from pathological lesions of natural origin. This demands inferential models with sufficient flexibility to capture both the observable distribution of pathological damage and the unobserved distribution of the neural subs

Nikita Alutis, Egor Chistov, Mikhail Dremin, Dmitriy Vatolin

This paper discusses the challenges of evaluating deblurring-methods quality and proposes a reduced-reference metric based on machine learning. Traditional quality-assessment metrics such as PSNR and SSIM are common for this task, but not only do they correlate poorly with subjective assessments, they also require ground-truth (GT) frames, which can be diffi

Chenyu Zheng, Guoqiang Wu, Chongxuan Li

Generative data augmentation, which scales datasets by obtaining fake labeled examples from a trained conditional generative model, boosts classification performance in various learning tasks including (semi-)supervised learning, few-shot learning, and adversarially robust learning. However, little work has theoretically investigated the effect of generative

Avijit Dhara, Pritam Das, Devarshi Chakrabarty, Kritika Ghosh

Anisotropy endows topological aspects in optical systems and furnishes a platform to explore non-Hermitian physics, which can be harnessed for the polarization-selective amplification of light. Here, we show a zero-threshold Raman laser can be achieved in an anisotropic optical microcavity via polarization-controlled optical pumping. A loss-gain mechanism be

Goran Petrevski

This paper surveys the empirical literature of inflation targeting. The main findings from our review are the following: there is robust empirical evidence that larger and more developed countries are more likely to adopt the IT regime; the introduction of this regime is conditional on previous disinflation, greater exchange rate flexibility, central bank in

Farhad Mortezapour Shiri, Thinagaran Perumal, Norwati Mustapha, Raihani Mohamed

Deep learning (DL) has emerged as a powerful subset of machine learning (ML) and artificial intelligence (AI), outperforming traditional ML methods, especially in handling unstructured and large datasets. Its impact spans across various domains, including speech recognition, healthcare, autonomous vehicles, cybersecurity, predictive analytics, and more. Howe

Devarshi Chakrabarty, Avijit Dhara, Pritam Das, Kritika Ghosh

Non-Hermitian systems with parity-time (PT) symmetry have been realized using optical constructs in the classical domain, leading to a plethora of non-intuitive phenomena. However, PT-symmetry in purely quantum non-Hermitian systems like microcavity exciton-polaritons has not been realized so far. Here we show how a pair of nearly orthogonal sets of anisotro

Hao Yan, Haixing Miao, Shun Wang, Yiqiu Ma

Spaceborne gravitational wave detection mission has a demanding requirement for the precision of displacement sensing, which is conducted by the interaction between the laser field and test mass. However, due to the roughness of the reflecting surface of the test mass, the displacement measurement along the sensitive axis suffers a coupling error caused by t

Debasish Bhattacharjee, Dipam Das, Santanu Acharjee, Tarini Kumar Dutta

In ecology, foraging requires animals to expend energy in order to obtain resources. The cost of foraging can be reduced through kleptoparasitism, the theft of a resource that another individual has expended effort to acquire. Thus, kleptoparasitism is one of the most significant feeding techniques in ecology. In this study, we investigate a two predator one

Junhyeok Jang, Miryeong Kwon, Donghyun Gouk, Hanyeoreum Bae

We present GraphTensor, a comprehensive open-source framework that supports efficient parallel neural network processing on large graphs. GraphTensor offers a set of easy-to-use programming primitives that appreciate both graph and neural network execution behaviors from the beginning (graph sampling) to the end (dense data processing). Our framework runs di

Xiaochen Wang, Lei Zhou, Alex McAvoy, Aming Li

Imitation is an important learning heuristic in animal and human societies. Previous explorations report that the fate of individuals with cooperative strategies is sensitive to the protocol of imitation, leading to a conundrum about how different styles of imitation quantitatively impact the evolution of cooperation. Here, we take a different perspective on

Julián Haddad, Dylan Langharst, Eli Putterman, Michael Roysdon

Schneider introduced an inter-dimensional difference body operator on convex bodies and proved an associated inequality. In the prequel to this work, we showed that this concept can be extended to a rich class of operators from convex geometry and proved the associated isoperimetric inequalities. The role of cosine-like operators, which generate convex bodie

Xiaofan Lu, Linan Zhang, Hongjin He

Automated model selection is an important application in science and engineering. In this work, we develop a learning approach for identifying structured dynamical systems from undersampled and noisy spatiotemporal data. The learning is performed by a sparse least-squares fitting over a large set of candidate functions via a nonconvex $\ell_1-\ell_2$ sparse

Vyacheslav Yu. Shaprynskiǐ

The semigroup $B_0$ is the only, up to isomorphism, 4-element subsemigroup of the 5-element Brandt semigroup $B_2$. Being an inverse semigroup, the semigroup $B_2$ can naturally be considered an additively idempotent semiring and $B_0$ is its subsemiring. We show that the semiring $B_0$ has a finite basis of identities.

Sanyam Jain

This paper implements and investigates popular adversarial attacks on the YOLOv5 Object Detection algorithm. The paper explores the vulnerability of the YOLOv5 to adversarial attacks in the context of traffic and road sign detection. The paper investigates the impact of different types of attacks, including the Limited memory Broyden Fletcher Goldfarb Shanno

Benjamin Laufer, Thomas Krendl Gilbert, Helen Nissenbaum

Optimization is offered as an objective approach to resolving complex, real-world decisions involving uncertainty and conflicting interests. It drives business strategies as well as public policies and, increasingly, lies at the heart of sophisticated machine learning systems. A paradigm used to approach potentially high-stakes decisions, optimization relies

Sergio Moreschini, Elham Younesian, David Hästbacka, Michele Albano

Edge-to-cloud computing is an emerging paradigm for distributing computational tasks between edge devices and cloud resources. Different approaches for orchestration, offloading, and many more purposes have been introduced in research. However, it is still not clear what has been implemented in the industry. This work aims to merge this gap by mapping the ex

Yueh-Cheng Huang, Chen-Tao Hsu, Jen-Hui Chuang

In computer vision, finding correct point correspondence among images plays an important role in many applications, such as image stitching, image retrieval, visual localization, etc. Most of the research works focus on the matching of local feature before a sampling method is employed, such as RANSAC, to verify initial matching results via repeated fitting

Mandira Das, Himanshu Murari, Subhradip Ghosh, Biplab Sanyal

Investigation of structure-property relations in chemically and magnetically disordered materials can give rise to interesting physical phenomena. The potential of two-dimensional MXenes as electrodes in supercapacitor applications have been studied extensively. However, the role of chemical and magnetic disorder on their electrochemical parameters like the

Rui-Di Wang, Shu-Min Wu, Xiao-Li Huang

It is well known that nonlocal coherence reflects nonclassical correlations better than quantum entanglement. Here, we analyze nonlocal coherence harvesting from the quantum vacuum to particle detectors adiabatically interacting with a quantum scalar field in Minkowski spacetime. We find that the harvesting-achievable separation range of nonlocal coherence i

Xikun Li, Błażej Jaworowski, Masudul Haque, Anne E. B. Nielsen

We study quench dynamics of bosonic fractional quantum Hall systems in small lattices with cylindrical boundary conditions and low particle density. The states studied have quasiholes or quasiparticles relative to the bosonic Laughlin state at half filling. Pinning potentials are placed at edge sites (or sites close to the edges) and are then turned off. Bec

Xiyan Peng, Zhaoxiang Qi, Tianmeng Zhang, Zhenyu Wu

We present the astrometric calibration of the Beijing-Arizona Sky Survey (BASS). The BASS astrometry was tied to the International Celestial Reference Frame via the \emph{Gaia} Data Release 2 reference catalog. For effects that were stable throughout the BASS observations, including differential chromatic refraction and the low charge transfer efficiency of

Lena Patterer, Sabrina Kollmann, Teresa de los Arcos, Leonie Jende

Polycarbonate (PC) and protective (Ti,Al)N coatings exhibit extremely different material properties, specifically crystal structure, thermal stability, elastic and plastic behavior as well as thermal expansion coefficients. These differences present formidable challenges for the deposition process development as low-temperature synthesis routes have to be ex

Jonathan H. Jiang, Anamaria Berea, Heather Bowden, Prithwis Das

In this first part of our series, we delve into the foundational aspects of the "Message in a Bottle" (henceforth referred to as MIAB). This study stands as a continuation of the legacy set by the Voyager Golden Records launched aboard Voyager 1 and 2 in 1977, which aimed to communicate with intelligent species beyond our world. These Records continue to ser

Fangqi Zhu, Lin Zhang, Jun Gao, Bing Qin

Event skeleton generation, aiming to induce an event schema skeleton graph with abstracted event nodes and their temporal relations from a set of event instance graphs, is a critical step in the temporal complex event schema induction task. Existing methods effectively address this task from a graph generation perspective but suffer from noise-sensitive and

Elias Zavitsanos, Dimitris Mavroeidis, Konstantinos Bougiatiotis, Eirini Spyropoulou

In this work, we examine the evaluation process for the task of detecting financial reports with a high risk of containing a misstatement. This task is often referred to, in the literature, as ``misstatement detection in financial reports''. We provide an extensive review of the related literature. We propose a new, realistic evaluation framework for the tas

Satoshi Takahashi, Atushi Yoshikawa

In data science education, the importance of learning to solve real-world problems has been argued. However, there are two issues with this approach: (1) it is very costly to prepare multiple real-world problems (using real data) according to the learning objectives, and (2) the learner must suddenly tackle complex real-world problems immediately after learn

Lucas Fidon

Despite the recent success of deep learning methods at achieving new state-of-the-art accuracy for medical image segmentation, some major limitations are still restricting their deployment into clinics. One major limitation of deep learning-based segmentation methods is their lack of robustness to variability in the image acquisition protocol and in the imag

Dachuan Shi, Chaofan Tao, Anyi Rao, Zhendong Yang

Recent vision-language models have achieved tremendous advances. However, their computational costs are also escalating dramatically, making model acceleration exceedingly critical. To pursue more efficient vision-language Transformers, this paper introduces Cross-Guided Ensemble of Tokens (CrossGET), a general acceleration framework for vision-language Tran

Sahil Mishra, Sanjaya Kumar Panda

Cloud computing is one of the innovative computing, which deals with storing and accessing data and programs over the Internet [1]. It is the delivery of computing resources and services, such as storing of data on servers and databases, providing networking facilities and software development platforms over the Internet. It provides the flexibility of resou

Kaiyang Gao, Kexuan Zhou, Jiyu Shen, Zeyi Lu

In this study, the sol-gel method synthesized the magnetic measurement and analysis of single-phase polycrystalline perovskite DyFe1-xCrxO3 (DFCO). The experimental data were fitted and calculated by a four-sublattice molecular field model. Unlike previous studies, we found that in DyFe1-xCrxO3, the spin of the A-site rare earth ion Dy3+ also changed simulta

Shubham Mishra, Sourav Pal, Aditya Srivastav, Anurag Tripathi

Scattering amplitudes involving multiple partons are plagued with infrared singularities. The soft singularities of the amplitude are captured by the soft function which is defined as the vacuum expectation value of Wilson line correlators. Renormalization properties of soft function allows us to write it as an exponential of the finite soft anomalous dimens

Kaiyang Gao, Jiyu Shen, Zeyi Lu, Jiajun Mo

To investigate the influence of ion spin on the coupling between ferromagnetism and ferroelectricity in type II multiferroic perovskite, we prepared the multiferroic perovskite Er0.9La0.1Cr0.8Fe0.2O3 (ELCFO) using the sol-gel method, and explored the macroscopic magnetic properties of ELCFO through M\"ossbauer spectrum and magnetic testing. The thermal magne

Lina Achaji, Julien Moreau, François Aioun, François Charpillet

Anticipating human actions in front of autonomous vehicles is a challenging task. Several papers have recently proposed model architectures to address this problem by combining multiple input features to predict pedestrian crossing actions. This paper focuses specifically on using images of the pedestrian's context as an input feature. We present several spa

Alexander Kruchkov

We consider a non-Hermitian yet $\mathcal{PT}$-symmetric Fermi liquid ($\mathcal{PT}$-FL) in external electric fields. Due to $\mathcal{PT}$-symmetry, the system exhibits real spectrum, Fermi surface and electric conductivity are well-defined through propagators. We find that, in contrast to the conventional Fermi liquids (FL), the $\mathcal{PT}$-FL can exhi

Munkhjargal Gochoo, Munkh-Erdene Otgonbold, Erkhembayar Ganbold, Jun-Wei Hsieh

With the advance of AI, road object detection has been a prominent topic in computer vision, mostly using perspective cameras. Fisheye lens provides omnidirectional wide coverage for using fewer cameras to monitor road intersections, however with view distortions. To our knowledge, there is no existing open dataset prepared for traffic surveillance on fishey

Ting Xu, Huiyun Yang, Zhen Wu, Jiaze Chen

Aspect Sentiment Triplet Extraction (ASTE) is widely used in various applications. However, existing ASTE datasets are limited in their ability to represent real-world scenarios, hindering the advancement of research in this area. In this paper, we introduce a new dataset, named DMASTE, which is manually annotated to better fit real-world scenarios by provid

Jingwei Hu, Ansgar Jüngel, Nicola Zamponi

The global-in-time existence of weak solutions to a spatially homogeneous multispecies Fokker-Planck-Landau system for plasmas in the three-dimensional whole space is shown. The Fokker-Planck-Landau system is a simplification of the Landau equations assuming a linearized, velocity-independent, and isotropic kernel. The resulting equations depend nonlocally a

Zhong Zhang, Bang Liu, Junming Shao

Pre-trained language models (PLMs) are known to be overly parameterized and have significant redundancy, indicating a small degree of freedom of the PLMs. Motivated by the observation, in this paper, we study the problem of re-parameterizing and fine-tuning PLMs from a new perspective: Discovery of intrinsic task-specific subspace. Specifically, by exploitin

Julia Kaiwen Lau, Kelvin Kai Wen Kong, Julian Hao Yong, Per Hoong Tan

Recent studies have proposed the use of Text-To-Speech (TTS) systems to automatically synthesise speech test cases on a scale and uncover a large number of failures in ASR systems. However, the failures uncovered by synthetic test cases may not reflect the actual performance of an ASR system when it transcribes human audio, which we refer to as false alarms.

Deokjae Lee, JunYeong Lee, Jung-Woo Ha, Jin-Hwa Kim

The deployment of large-scale generative models is often restricted by their potential risk of causing harm to users in unpredictable ways. We focus on the problem of black-box red teaming, where a red team generates test cases and interacts with the victim model to discover a diverse set of failures with limited query access. Existing red teaming methods co

Marc Cheong, Raula Gaikovina Kula, Christoph Treude

A key drawback to using a Open Source third-party library is the risk of introducing malicious attacks. In recently times, these threats have taken a new form, when maintainers turn their Open Source libraries into protestware. This is defined as software containing political messages delivered through these libraries, which can either be malicious or benign

Dawei Zhu, Xiaoyu Shen, Marius Mosbach, Andreas Stephan

Weakly supervised learning is a popular approach for training machine learning models in low-resource settings. Instead of requesting high-quality yet costly human annotations, it allows training models with noisy annotations obtained from various weak sources. Recently, many sophisticated approaches have been proposed for robust training under label noise,

Thomas Maunder, Bernhard Müller, Fionntan Callan, Stuart Sim

Spectroscopy is an important tool for providing insights into the structure of core-collapse supernova explosions. We use the Monte Carlo radiative transfer code ARTIS to compute synthetic spectra and light curves based on a two-dimensional explosion model of an ultra-stripped supernova. These calculations are designed both to identify observable fingerprint

Xuanjie Fang, Sijie Cheng, Yang Liu, Wei Wang

Pre-trained language models (PLMs) have been widely used to underpin various downstream tasks. However, the adversarial attack task has found that PLMs are vulnerable to small perturbations. Mainstream methods adopt a detached two-stage framework to attack without considering the subsequent influence of substitution at each step. In this paper, we formally m

Gian Maria Dall'Ara, Samuele Mongodi

We investigate a few aspects of the notion of Levi core, introduced by the authors in a previous work: a basic finiteness question, the connections with Kohn's algorithm and with Catlin's property (P).

Bhawani Sandeep, Surjeet Singh, Sumit Kumar

In this work, geometry optimization of mechanical truss using computer-aided finite element analysis is presented. The shape of the truss is a dominant factor in determining the capacity of load it can bear. At a given parameter space, our goal is to find the parameters of a hull that maximize the load-bearing capacity and also don't yield to the induced str

Xiao Li, Hang Chen, Xiaolin Hu

Object detection is a critical component of various security-sensitive applications, such as autonomous driving and video surveillance. However, existing object detectors are vulnerable to adversarial attacks, which poses a significant challenge to their reliability and security. Through experiments, first, we found that existing works on improving the adver

Xin Xiong, Furao Shen, Xiangyu Wang, Jian Zhao

Graph contrastive learning (GCL) shows great potential in unsupervised graph representation learning. Data augmentation plays a vital role in GCL, and its optimal choice heavily depends on the downstream task. Many GCL methods with automated data augmentation face the risk of insufficient information as they fail to preserve the essential information necessa

Yusheng Tian, Guangyan Zhang, Tan Lee

This paper is about developing personalized speech synthesis systems with recordings of mildly impaired speech. In particular, we consider consonant and vowel alterations resulted from partial glossectomy, the surgical removal of part of the tongue. The aim is to restore articulation in the synthesized speech and maximally preserve the target speaker's indiv

Elad Romanov

The randomized singular value decomposition (R-SVD) is a popular sketching-based algorithm for efficiently computing the partial SVD of a large matrix. When the matrix is low-rank, the R-SVD produces its partial SVD exactly; but when the rank is large, it only yields an approximation. Motivated by applications in data science and principal component analysis

Kento Sasaki, Yuki Nakamura, Tokuyuki Teraji, Takashi Oka

Geometric effects can play a pivotal role in streamlining quantum manipulation. We demonstrate a geometric diabatic control, that is, perfect tunneling between spin states in a diamond by a quadratic sweep of a driving field. The field sweep speed for the perfect tunneling is determined by the geometric amplitude factor and can be tuned arbitrarily. Our resu

Mauajama Firdaus, Avinash Madasu, Asif Ekbal

Natural Language Understanding (NLU) and Natural Language Generation (NLG) are the two critical components of every conversational system that handles the task of understanding the user by capturing the necessary information in the form of slots and generating an appropriate response in accordance with the extracted information. Recently, dialogue systems in

Yushan Zhang, Johan Edstedt, Bastian Wandt, Per-Erik Forssén

We tackle the task of scene flow estimation from point clouds. Given a source and a target point cloud, the objective is to estimate a translation from each point in the source point cloud to the target, resulting in a 3D motion vector field. Previous dominant scene flow estimation methods require complicated coarse-to-fine or recurrent architectures as a mu

Zicheng Zhang, Bonan Li, Xuecheng Nie, Congying Han

Existing works have advanced Text-to-Image (TTI) diffusion models for video editing in a one-shot learning manner. Despite their low requirements of data and computation, these methods might produce results of unsatisfied consistency with text prompt as well as temporal sequence, limiting their applications in the real world. In this paper, we propose to add

Lena Patterer, Pavel Ondračka, Dimitri Bogdanovski, Stanislav Mráz

Interfacial bond formation during sputter deposition of metal oxide thin films onto polycarbonate (PC) is investigated by ab initio molecular dynamics simulations and X-ray photoelectron spectroscopy (XPS) analysis of PC | X interfaces (X = Al$_2$O$_3$, TiO$_2$, TiAlO$_2$). Generally, the predicted bond formation is consistent with the experimental data. For

Richeek Das, Aaron Jerry Ninan, Adithya Bhaskar, Ajit Rajwade

Group testing is a technique which avoids individually testing $n$ samples for a rare disease and instead tests $n < p$ pools, where a pool consists of a mixture of small, equal portions of a subset of the $p$ samples. Group testing saves testing time and resources in many applications, including RT-PCR, with guarantees for the recovery of the status of the

Smitha Milli, Emma Pierson, Nikhil Garg

Many recommender systems optimize a linear weighting of different user behaviors, such as clicks, likes, and shares. We analyze the optimal choice of weights from the perspectives of both users and content producers who strategically respond to the weights. We consider three aspects of each potential behavior: value-faithfulness (how well a behavior indicate

Dag Evensberget, Stephen C. Marsden, Bradley D. Carter, Raquel Salmeron

Solar-type stars, which shed angular momentum via magnetised stellar winds, enter the main sequence with a wide range of rotational periods $P_\text{rot}$. This initially wide range of rotational periods contracts and has mostly vanished by a stellar age $t\sim0.6$ Gyr, after which Solar-type stars spin according to the Skumanich relation $P_\text{rot}\propt

X. Gao, F. Z. K. Li, L. Wan, J. Y. X. Yang

The elements in the hyperoctahedral group $\mathfrak{B}_n$ can be treated as signed permutations with the natural order $\cdots<-2<-1<0<1<2<\cdots$, or as colored permutations with the $r$-order $-1<_r-2<_r\cdots<_r0<_r1<_r2<_r\cdots$. For any $\pi\in\mathfrak{B}_n$, let $\operatorname{des}^B(\pi)$ and $\operatorname{ides}^B(\pi)$ be the number of descents a

Olivier Bernard, Pierre-Alain Fouque, Andrea Lesavourey

We describe several algorithms for computing $e$-th roots of elements in a number field $K$, where $e$ is an odd prime-power integer. In particular we generalize Couveignes' and Thom\'e's algorithms originally designed to compute square-roots in the Number Field Sieve algorithm for integer factorization. Our algorithms cover most cases of $e$ and $K$ and all

Yonglin Yu, Shuo Xu, Lei Zhang, Ziqian Shang

In this paper, we present the design and implementation of a two-element interferometer working in the millimeter wave band (39.5 GHz - 40 GHz) for observing solar radio emissions through nulling interference. The system is composed of two 50 cm aperture Cassegrain antennas mounted on a common equatorial mount, with a separation of 230 wavelengths. The cross

Zihao Yu, Haoyang Li, Fangcheng Fu, Xupeng Miao

Due to the recent success of diffusion models, text-to-image generation is becoming increasingly popular and achieves a wide range of applications. Among them, text-to-image editing, or continuous text-to-image generation, attracts lots of attention and can potentially improve the quality of generated images. It's common to see that users may want to slightl

Gabriel Roccabruna, Seyed Mahed Mousavi, Giuseppe Riccardi

The valence analysis of speakers' utterances or written posts helps to understand the activation and variations of the emotional state throughout the conversation. More recently, the concept of Emotion Carriers (EC) has been introduced to explain the emotion felt by the speaker and its manifestations. In this work, we investigate the natural inter-dependency

Marawan Elbatel, Robert Martí, Xiaomeng Li

Representational transfer from publicly available models is a promising technique for improving medical image classification, especially in long-tailed datasets with rare diseases. However, existing methods often overlook the frequency-dependent behavior of these models, thereby limiting their effectiveness in transferring representations and generalizations

Rui-Yang Ju, Yu-Shian Lin, Jen-Shiun Chiang, Chih-Chia Chen

To efficiently extract textual information from color degraded document images is a significant research area. The prolonged imperfect preservation of ancient documents has led to various types of degradation, such as page staining, paper yellowing, and ink bleeding. These types of degradation badly impact the image processing for features extraction. This p