May 2023 arXiv papers — page 46

Marco Costanzo, Marco De Simone, Sara Federico, Ciro Natale

This paper proposes a novel method to refine the 6D pose estimation inferred by an instance-level deep neural network which processes a single RGB image and that has been trained on synthetic images only. The proposed optimization algorithm usefully exploits the depth measurement of a standard RGB-D camera to estimate the dimensions of the considered object,

Anand Mehrotra, Suraj Srivastava, Aditya K. Jagannatham, Lajos Hanzo

An orthogonal affine-precoded superimposed pilot-based architecture is developed for the cyclic prefix (CP)-aided SISO and MIMO orthogonal time frequency space systems relying on arbitrary transmitter-receiver pulse shaping. The data and pilot symbol matrices are affine-precoded and superimposed in the delay Doppler-domain followed by the development of an e

A. A. Onoprienko, V. I. Ivashchenko, V. I. Shevchenko

The review presents the results of theoretical and experimental studies of the structure, bonding between atoms, mechanical properties, thermal stability, and oxidation and corrosion resistance of films based on ternary transition metal borides.

Joseph Enguehard

Several explanation methods such as Integrated Gradients (IG) can be characterised as path-based methods, as they rely on a straight line between the data and an uninformative baseline. However, when applied to language models, these methods produce a path for each word of a sentence simultaneously, which could lead to creating sentences from interpolated wo

Niels Mündler, Jingxuan He, Slobodan Jenko, Martin Vechev

Large language models (large LMs) are susceptible to producing text that contains hallucinated content. An important instance of this problem is self-contradiction, where the LM generates two contradictory sentences within the same context. In this work, we present a comprehensive investigation into self-contradiction for various instruction-tuned LMs, cover

Rémi Bardenet, Michaël Fanuel, Alexandre Feller

DPPs were introduced by Macchi as a model in quantum optics the 1970s. Since then, they have been widely used as models and subsampling tools in statistics and computer science. Most applications require sampling from a DPP, and given their quantum origin, it is natural to wonder whether sampling a DPP on a quantum computer is easier than on a classical one.

Zhongwang Zhang, Yuqing Li, Tao Luo, Zhi-Qin John Xu

Dropout is a widely utilized regularization technique in the training of neural networks, nevertheless, its underlying mechanism and its impact on achieving good generalization abilities remain poorly understood. In this work, we derive the stochastic modified equations for analyzing the dynamics of dropout, where its discrete iteration process is approximat

Ulf-G. Meißner, Bernard Ch. Metsch, Helen Meyer

We study the dependence of the primordial nuclear abundances as a function of the electromagnetic fine-structure constant $\alpha$, keeping all other fundamental constants fixed. We update the leading nuclear reaction rates, in particular the electromagnetic contribution to the neutron-proton mass difference pertinent to $\beta$-decays, and go beyond certain

Isabel Martin-Lyons, Paul J. Truman

Skew braces are intensively studied owing to their wide ranging connections and applications. We generalize the definition of a skew brace to give a new algebraic object, which we term a skew bracoid. Our construction involves two groups interacting in a manner analogous to the compatibility condition found in the definition of a skew brace. We formulate too

Guoping Gao, Jinbo Hu, Feng Tang, Wenhui Liu

In this paper, we study the stability of nuclear magnetic resonance gyroscope (NMRG), which employs Xe nuclear spins to measure inertial rotation rate. The Xe spin polarization is sensed by an in-situ Rb-magnetometer. The Rb-magnetometer works in a parametric oscillation mode (henceforth referred to as the Rb parametric magnetometer, or Rb-PM), in which the

Zeyuan Tang, Stefan T. Bromley, Bjørk Hammer

The use of machine learning (ML) in chemical physics has enabled the construction of interatomic potentials having the accuracy of ab initio methods and a computational cost comparable to that of classical force fields. Training an ML model requires an efficient method for the generation of training data. Here we apply an accurate and efficient protocol to c

Michela Rigoselli, Davide De Grandis, Sandro Mereghetti, Christian~Malacaria

HD 49798 is a hot subdwarf of O spectral type in a 1.55 day orbit with the X-ray source RX J0648.0-4418, a compact object with spin period of 13.2 s. We use recent data from the NICER instrument, joined with archival data from XMM-Newton and ROSAT, to obtain a phase-connected timing solution spanning ~30 years. Contrary to previous works, that relied on para

Aritra Bandyopadhyay, Snigdha Ghosh, Ricardo L. S. Farias, Sabyasachi Ghosh

We have estimated parallel and perpendicular components of electrical conductivity and shear viscosity of quark matter at finite magnetic field and temperature by using their one-loop Kubo expressions in the framework of Nambu--Jona-Lasinio (NJL) model. At finite magnetic field, a non-trivial medium dependence of those quantities can be found. Previously the

Jay Chiehen Liao, Cheng-Te Li

This work presents a novel approach to tabular data prediction leveraging graph structure learning and graph neural networks. Despite the prevalence of tabular data in real-world applications, traditional deep learning methods often overlook the potentially valuable associations between data instances. Such associations can offer beneficial insights for clas

Nicola Messina, Jan Sedmidubsky, Fabrizio Falchi, Tomáš Rebok

Due to recent advances in pose-estimation methods, human motion can be extracted from a common video in the form of 3D skeleton sequences. Despite wonderful application opportunities, effective and efficient content-based access to large volumes of such spatio-temporal skeleton data still remains a challenging problem. In this paper, we propose a novel conte

Dibakar Roychowdhury

We perform nonrelativistic expansions of type IIB $ AdS_5 \times S^5 $ Green-Schwarz (GS) superstrings upto quadratic order in fermionic excitation. We carry out a systematic $ 1/c $ expansion both for the bosonic as well as the fermionic sector of the type IIB model and explore the LO theory in detail. We explore various global as well as local symmetries o

Freja Vandeputte, Noël Hallemans, Jishnu Ayyangatu Kuzhiyil, Nessa Fereshteh Saniee

The impedance of a Li-ion battery contains information about its state of charge (SOC), state of health (SOH) and remaining useful life (RUL). Commonly, electrochemical impedance spectroscopy (EIS) is used as a nonparametric data-driven technique for estimating this impedance from current and voltage measurements. In this article, however, we propose a consi

Tjeerd Jan Heeringa, Len Spek, Felix Schwenninger, Christoph Brune

The approximation properties of infinitely wide shallow neural networks heavily depend on the choice of the activation function. To understand this influence, we study embeddings between Barron spaces with different activation functions. These embeddings are proven by providing push-forward maps on the measures $\mu$ used to represent functions $f$. An activ

Lianet De la Cruz Toranzo, Ricardo Abreu Blaya, Swanhild Bernstein

In this paper we solve the problem on finding a sectionally Clifford algebra-valued harmonic function, zero at infinity and satisfying certain boundary value condition related to higher order Lipschitz functions. Our main tool are the Hardy projections related to a singular integral operator arising in bimonogenic function theory, which turns out to be an in

Lorenzo Torricelli

We introduce a notion of geometric tempering using exponentially-dampened Mittag-Leffler tempering functions and closely investigate the univariate case. Characteristic exponents and cumulants are calculated, as well as spectral densities. Absolute continuity relations are shown, and short and long time scaling limits of the associated L\'evy processes analy

Daniel Köhler, Maurice Quach, Michael Ulrich, Frank Meinl

Architectures that first convert point clouds to a grid representation and then apply convolutional neural networks achieve good performance for radar-based object detection. However, the transfer from irregular point cloud data to a dense grid structure is often associated with a loss of information, due to the discretization and aggregation of points. In t

Xin Qin, Jindong Wang, Shuo Ma, Wang Lu

Human activity recognition (HAR) is a time series classification task that focuses on identifying the motion patterns from human sensor readings. Adequate data is essential but a major bottleneck for training a generalizable HAR model, which assists customization and optimization of online web applications. However, it is costly in time and economy to collec

Yige Yuan, Bingbing Xu, Bo Lin, Liang Hou

The generalization of neural networks is a central challenge in machine learning, especially concerning the performance under distributions that differ from training ones. Current methods, mainly based on the data-driven paradigm such as data augmentation, adversarial training, and noise injection, may encounter limited generalization due to model non-smooth

Moritz Flaschel, Huitian Yu, Nina Reiter, Jan Hinrichsen

We propose an automated computational algorithm for simultaneous model selection and parameter identification for the hyperelastic mechanical characterization of human brain tissue. Following the motive of the recently proposed computational framework EUCLID (Efficient Unsupervised Constitutive Law Identitication and Discovery) and in contrast to conventiona

T. Yu. Magakian, T. A. Movsessian, H. R. Andreasyan, A. V. Moiseev

The aim of the present study was the spectral analysis of an unusual pre-main-sequence star in the cometary nebula RNO 54, which was suspected by several researchers as a FUori-like object. We performed long-slit spectroscopy of the star on the 6-m telescope with the SCORPIO-2 multi-mode focal reducer. We discover a short ($\sim4$ arcsec or $\sim6000$ AU) an

Wenda Fang

In this paper, we define and study the classical $R$-matrix for vertex Lie algebra, based on which we propose to construct a new vertex Lie algebra. We give a systematic way to construct the $R$-matrix for affine Kac-Moody vertex Lie algebra and study the universal vertex algebra associated with the new vertex Lie algebra that we obtained by $R$-matrix. As a

Liyao Tang, Zhe Chen, Shanshan Zhao, Chaoyue Wang

Pseudo-labels are widely employed in weakly supervised 3D segmentation tasks where only sparse ground-truth labels are available for learning. Existing methods often rely on empirical label selection strategies, such as confidence thresholding, to generate beneficial pseudo-labels for model training. This approach may, however, hinder the comprehensive explo

Giuseppe Gaeta, Miguel Angel Rodriguez

In a recent paper we have classified scalar Ito equations which admits a standard symmetry; these are also directly integrable by the Kozlov substitution. In the present work, we consider the diffusion (Fokker-Planck) equations associated to such symmetric Ito equations.

Minaya Allahverdiyeva, Narmin Huseynova, Shahin Mamedov, Jannat Samadov

The deuteron is a spin1 particle and due to current conservation and the P and C invariance of the EM interaction, it has three EM form factors in the one photon exchange (OPE) approximation, which include the charge GC(Q2), quadrupole GQ(Q2) and magnetic GM(Q2) form factors and was calculated in [1, 2, 3] at a zero temperature within soft-wall and hard-wall

Shuo Yang, Jiachi Chen, Zibin Zheng

Recently, the birth of non-fungible tokens (NFTs) has attracted great attention. NFTs are capable of representing users' ownership on the blockchain and have experienced tremendous market sales due to their popularity. Unfortunately, the high value of NFTs also makes them a target for attackers. The defects in NFT smart contracts could be exploited by attack

Aleksandr Lobanov, Alexander Gasnikov, Fedor Stonyakin

In this paper, we study the black box optimization problem under the Polyak--Lojasiewicz (PL) condition, assuming that the objective function is not just smooth, but has higher smoothness. By using "kernel-based" approximation instead of the exact gradient in Stochastic Gradient Descent method, we improve the best known results of convergence in the class of

Katarina Boos, Sang Kyu Kim, Thomas Bracht, Friedrich Sbresny

The interaction of a resonant light field with a quantum two-level system is of key interest both for fundamental quantum optics and quantum technological applications employing resonant excitation. While emission under resonant continuous-wave excitation has been well-studied, the more complex emission spectrum of dynamically dressed states, a quantum two-l

Marta Varela, Anil A Bharath

Supervised deep learning methods typically rely on large datasets for training. Ethical and practical considerations usually make it difficult to access large amounts of healthcare data, such as medical images, with known task-specific ground truth. This hampers the development of adequate, unbiased and robust deep learning methods for clinical tasks. Magnet

Jesus Cañas, Anjali Harikumar, Stephen T. Purcell, Nevine Rochat

Conventional ultraviolet (UV) lamps for disinfection emit radiation in the 255-270 nm range, which poses a high risk of causing cancer and cataracts. To address these concerns, solid-state far UV-C sources emitting below 240 nm are gaining attention as a safe and sustainable disinfection solution for occupied spaces. Here, we delve into the extension of the

D. Rossi, J. Motruk, L. Rademaker, D. A. Abanin

Schwinger boson mean field theory is a powerful approach to study frustrated magnetic systems which allows to distinguish long range magnetic orders from quantum spin liquid phases, where quantum fluctuations remain strong up to zero temperature. In this work, we use this framework to study the Heisenberg model on the Kagome lattice with up to third nearest

Sédrick Stassin, Alexandre Englebert, Géraldin Nanfack, Julien Albert

EXplainable Artificial Intelligence (XAI) aims to help users to grasp the reasoning behind the predictions of an Artificial Intelligence (AI) system. Many XAI approaches have emerged in recent years. Consequently, a subfield related to the evaluation of XAI methods has gained considerable attention, with the aim to determine which methods provide the best ex

G. I. Kokhirova, A. I. Zhonmuhammadi, U. H. Khamroev, T. J. Jopek

The Virginid meteoroid streams produce a series of meteor showers active annually during February-May. A certain parent comet is not found but a related association of some showers with near-Earth asteroids was previously established and a cometary origin of these asteroids was suggested. We performed a new search for NEAs belonging to the Virginid asteroid-

Haoyu Han, Xiaorui Liu, Feng Shi, MohamadAli Torkamani

Graph Neural Networks (GNNs) have emerged as a powerful tool for semi-supervised node classification tasks. However, recent studies have revealed various biases in GNNs stemming from both node features and graph topology. In this work, we uncover a new bias - label position bias, which indicates that the node closer to the labeled nodes tends to perform bett

Hong Guo, Jianwu Lin, Fanlin Huang

Market making (MM) is an important research topic in quantitative finance, the agent needs to continuously optimize ask and bid quotes to provide liquidity and make profits. The limit order book (LOB) contains information on all active limit orders, which is an essential basis for decision-making. The modeling of evolving, high-dimensional and low signal-to-

Sudip Shekhar, Wim Bogaerts, Lukas Chrostowski, John E. Bowers

Silicon photonics has developed into a mainstream technology driven by advances in optical communications. The current generation has led to a proliferation of integrated photonic devices from thousands to millions - mainly in the form of communication transceivers for data centers. Products in many exciting applications, such as sensing and computing, are a

Gaofeng Xu, Krish Patel, Igor Zutic

Lasers with injected spin-polarized carriers show an outstanding performance in both static and dynamic operation. In addition to the intensity response of conventional lasers, without spin-polarized carriers, both intensity and polarization of light can be exploited for optical communication in spin-lasers. However, the polarization dynamics of spin-lasers

Gopal Sharma, Hao Guo, Chen Shen, Jun Tanimoto

Cooperation plays a crucial role in both nature and human society, and the conundrum of cooperation attracts the attention from interdisciplinary research. In this study, we investigated the evolution of cooperation in optional prisoner's dilemma games by introducing simple bots. We focused on one-shot and anonymous games, where the bots could be programmed

Yun Yue, Jiadi Jiang, Zhiling Ye, Ning Gao

Deep Neural Networks (DNNs) generalization is known to be closely related to the flatness of minima, leading to the development of Sharpness-Aware Minimization (SAM) for seeking flatter minima and better generalization. In this paper, we revisit the loss of SAM and propose a more general method, called WSAM, by incorporating sharpness as a regularization ter

Ha-Yeong Choi, Sang-Hoon Lee, Seong-Whan Lee

Diffusion-based generative models have exhibited powerful generative performance in recent years. However, as many attributes exist in the data distribution and owing to several limitations of sharing the model parameters across all levels of the generation process, it remains challenging to control specific styles for each attribute. To address the above pr

Riku Toshimitsu, Hiroshi Isakari

This study builds on a recent paper by Lai et al [Appl. Comput. Harmon. Anal., 2018] in which a novel boundary integral formulation is presented for scalar wave scattering analysis in two-dimensional layered and half-spaces. The seminal paper proposes a hybrid integral representation that combines the Sommerfeld integral and layer potential to efficiently de

Yash Madhani, Mitesh M. Khapra, Anoop Kunchukuttan

We create publicly available language identification (LID) datasets and models in all 22 Indian languages listed in the Indian constitution in both native-script and romanized text. First, we create Bhasha-Abhijnaanam, a language identification test set for native-script as well as romanized text which spans all 22 Indic languages. We also train IndicLID, a

Karthick Prasad Gunasekaran

Lung cancer poses a significant global public health challenge, emphasizing the importance of early detection for improved patient outcomes. Recent advancements in deep learning algorithms have shown promising results in medical image analysis. This study aims to explore the application of object detection particularly YOLOv5, an advanced object identificati

Ju Liu, Jiashen Guan

We continue our investigation of finite deformation linear viscoelastodynamics by focusing on constructing accurate and reliable numerical schemes. The concrete thermomechanical foundation developed in the previous study paves the way for pursuing discrete formulations with critical physical and mathematical structures preserved. Energy stability, momentum c

Xiao Yang, Xuejiao Zhao, Zhiqi Shen

Heterogeneous Graph Neural Networks (HGNNs) are a class of powerful deep learning methods widely used to learn representations of heterogeneous graphs. Despite the fast development of HGNNs, they still face some challenges such as over-smoothing, and non-robustness. Previous studies have shown that these problems can be reduced by using gradient regularizati

Jakub Iwański, Mateusz Tokarczyk, Aleksandra K. Dąbrowska, Jan Pawłowski

The versatile range of applications for two-dimensional (2D) materials has encouraged scientists to further engineer the properties of these materials. This is often accomplished by stacking layered materials into more complex van der Waals heterostructures. A much less popular but technologically promising approach is the alloying of 2D materials with diffe

Heiko Koziolek, Sten Gruener, Virendra Ashiwal

Large language models (LLMs) providing generative AI have become popular to support software engineers in creating, summarizing, optimizing, and documenting source code. It is still unknown how LLMs can support control engineers using typical control programming languages in programming tasks. Researchers have explored GitHub CoPilot or DeepMind AlphaCode fo

Yiqi Lin, Hao Wu, Ruichen Wang, Haonan Lu

Generating and editing a 3D scene guided by natural language poses a challenge, primarily due to the complexity of specifying the positional relations and volumetric changes within the 3D space. Recent advancements in Large Language Models (LLMs) have demonstrated impressive reasoning, conversational, and zero-shot generation abilities across various domains

Evgenii Chzhen, Christophe Giraud, Zhen Li, Gilles Stoltz

We consider contextual bandit problems with knapsacks [CBwK], a problem where at each round, a scalar reward is obtained and vector-valued costs are suffered. The learner aims to maximize the cumulative rewards while ensuring that the cumulative costs are lower than some predetermined cost constraints. We assume that contexts come from a continuous set, that

M Prévost

Generalized Stieltjes constants $\gamma$ n (a) are the coecients in the Laurent series for the Hurwitz-zeta function $\zeta$(s, a) at the pole s = 1. Many authors proved formulas for these constants. In this paper, using a recurrence between ($\zeta$(s + j, a)) j and proved by the author, we prove a general result which contains some of these formulas as par

Sotiris Anagnostidis, Dario Pavllo, Luca Biggio, Lorenzo Noci

Autoregressive Transformers adopted in Large Language Models (LLMs) are hard to scale to long sequences. Despite several works trying to reduce their computational cost, most of LLMs still adopt attention layers between all pairs of tokens in the sequence, thus incurring a quadratic cost. In this study, we present a novel approach that dynamically prunes con

Xi Chen, Chenghao Guo, Emmanouil-Vasileios Vlatakis-Gkaragkounis, Mihalis Yannakakis

We give the first quasipolynomial upper bound $\phi n^{\text{polylog}(n)}$ for the smoothed complexity of the SWAP algorithm for local Graph Partitioning (also known as Bisection Width), where $n$ is the number of nodes in the graph and $\phi$ is a parameter that measures the magnitude of perturbations applied on its edge weights. More generally, we show tha

R. Kadono, M. Miyazaki, M. Hiraishi, H. Okabe

X-ray absorption spectroscopy (XAS) and magnetic circular dichroism (XMCD) measurements at the oxygen (O) $K$-edge were performed to investigate the magnetic polarization of ligand O atoms in the weak ferromagnetic (WFM) phase of the Ir perovskite compound Sr$_2$IrO$_4$. With the onset of the WFM phase below $T_{\rm N}\simeq240$ K, XMCD signals corresponding

Jinhyung Park

We establish precise nonvanishing results for asymptotic syzygies of smooth projective varieties. This refines Ein-Lazarsfeld's asymptotic nonvanishing theorem. Combining with the author's previous asymptotic vanishing result, we completely determine the asymptotic shapes of the minimal free resolutions of the graded section modules of a line bundle on a smo

Vasileios Moschopoulos, Pantelis Kyriakidis, Aristotelis Lazaridis, Ioannis Vlahavas

A successful tactic that is followed by the scientific community for advancing AI is to treat games as problems, which has been proven to lead to various breakthroughs. We adapt this strategy in order to study Rocket League, a widely popular but rather under-explored 3D multiplayer video game with a distinct physics engine and complex dynamics that pose a si

Francesco Di Filippo, Naoki Ogawa, Shinji Mukohyama, Takahiro Waki

Understanding the dynamics of soft hair might shine some light on the information loss paradox. In this paper, we introduce a new coordinate system, dressed coordinates, in order to analyze the quantum states of Hawking radiation as a first step toward understanding the connection between soft hair and the information paradox. Dressed coordinates can be intr

Yuxin Huang, Hao Wang, Zhaoran Liu, Licheng Pan

Accurate estimation of multiple quality variables is critical for building industrial soft sensor models, which have long been confronted with data efficiency and negative transfer issues. Methods sharing backbone parameters among tasks address the data efficiency issue; however, they still fail to mitigate the negative transfer problem. To address this issu

Zhicheng Bao, Haotai Liang, Chen Dong, Xiaodong Xu

In this paper, we propose a new wireless video communication scheme to achieve high-efficiency video transmission over noisy channels. It exploits the idea of model division multiple access (MDMA) and extracts common semantic features across video frames. Besides, deep joint source-channel coding (JSCC) is applied to overcome the distortion caused by noisy c

Bo-Kyeong Kim, Hyoung-Kyu Song, Thibault Castells, Shinkook Choi

Text-to-image (T2I) generation with Stable Diffusion models (SDMs) involves high computing demands due to billion-scale parameters. To enhance efficiency, recent studies have reduced sampling steps and applied network quantization while retaining the original architectures. The lack of architectural reduction attempts may stem from worries over expensive ret

Vladimir Vorobyev, Alexey Bobtsov, Nikolay Nikolaev, Anton Pyrkin

The article investigates an algorithm for identifying an unknown constant parameter for a scalar regression model using a nonlinear operator that allows us to obtain a new regression equation (with an expanded number of unknown parameters) for which the influence of interference in measurement or disturbance will be minimal.

Yu Ding, Yirui Cong, Xiangke Wang

In this paper, we focus on the distributed set-membership filtering (SMFing) problem for a multi-agent system with absolute (taken from agents themselves) and relative (taken from neighbors) measurements. In the literature, the relative measurements are difficult to deal with, and the SMFs highly rely on specific set descriptions. As a result, establishing t

George Haller, Bálint Kaszás, Aihui Liu, Joar Axås

A primary spectral submanifold (SSM) is the unique smoothest nonlinear continuation of a nonresonant spectral subspace $E$ of a dynamical system linearized at a fixed point. Passing from the full nonlinear dynamics to the flow on an attracting primary SSM provides a mathematically precise reduction of the full system dynamics to a very low-dimensional, smoot

Christian A. Schroth, Christian Eckrich, Ibrahim Kakouche, Stefan Fabian

The large number and scale of natural and man-made disasters have led to an urgent demand for technologies that enhance the safety and efficiency of search and rescue teams. Semi-autonomous rescue robots are beneficial, especially when searching inaccessible terrains, or dangerous environments, such as collapsed infrastructures. For search and rescue mission

J. Glorius, Yu. A. Litvinov, M. Aliotta, F. Amjad

Low-energy investigations on rare ion beams are often limited by the available intensity and purity of the ion species in focus. Here, we present the first application of a technique that combines in-flight production at relativistic energies with subsequent secondary beam storage, accumulation and finally deceleration to the energy of interest. Using the FR

C. Malhaire, M. Granata, D. Hofman, A. Amato

In this work, optical profilometry and finite-element simulations are applied on buckled micro-machined membranes for the stress analysis of ion-beam-sputtered $\mathrm{Ta_{2}O_{5}}$ and $\mathrm{SiO_{2}}$ thin films. Layers with different thicknesses are grown on silicon substrates, then several membranes with different geometries are manufactured with stan

Gergely Hanczár, Marcell Stippinger, Dávid Hanák, Marcell T. Kurbucz

In recent years, numerous screening methods have been published for ultrahigh-dimensional data that contain hundreds of thousands of features; however, most of these features cannot handle data with thousands of classes. Prediction models built to authenticate users based on multichannel biometric data result in this type of problem. In this study, we presen

Shuchang Tao, Qi Cao, Huawei Shen, Yunfan Wu

Despite the success of graph neural networks (GNNs), their vulnerability to adversarial attacks poses tremendous challenges for practical applications. Existing defense methods suffer from severe performance decline under unseen attacks, due to either limited observed adversarial examples or pre-defined heuristics. To address these limitations, we analyze th

Geesara Kulathunga, Hany Hamed, Alexandr Klimchik

This paper presents a technique to cope with the gap between high-level planning, e.g., reference trajectory tracking, and low-level controlling using a learning-based method in the plan-based control paradigm. The technique improves the smoothness of maneuvering through cluttered environments, especially targeting low-speed velocity profiles. In such a prof

Rishi Advani, Paolo Papotti, Abolfazl Asudeh

The detection of fake news has received increasing attention over the past few years, but there are more subtle ways of deceiving one's audience. In addition to the content of news stories, their presentation can also be made misleading or biased. In this work, we study the impact of the ordering of news stories on audience perception. We introduce the probl

Massimo Moccia, Giuseppe Castaldi, Andrea Alù, Vincenzo Galdi

Time-harmonic electromagnetic plane waves in anisotropic media can exhibit complex-valued wavevectors (with nonzero real and imaginary parts) even in the absence of material dissipation. These peculiar modes, usually referred to as "ghost waves," hybridize the typical traits of conventional propagating and evanescent waves, displaying both phase accumulation

Marco Aiello, Ilche Georgievski

The paper speculates about how ChatGPT-like systems can support the field of automated service composition and identifies new research areas to explore in order to take advantage of such tools in the field of service-oriented composition.

Jian-Bo Cheng, Bo-Lin Huang, Zi-Yang Lin, Shi-Lin Zhu

We investigate the $Z_b$, $Z_c$ and $Z_{cs}$ states within the chiral effective field theory framework and the $S$-wave single channel molecule picture. With the complex scaling method, we accurately solve the Schr\"odinger equation in momentum space. Our analysis reveals that the $Z_b(10610)$, $Z_b(10650)$, $Z_c(3900)$ and $Z_c(4020)$ states are the resonan

Hilaf Hasson, Danielle C. Maddix, Yuyang Wang, Gaurav Gupta

Ensembling is among the most popular tools in machine learning (ML) due to its effectiveness in minimizing variance and thus improving generalization. Most ensembling methods for black-box base learners fall under the umbrella of "stacked generalization," namely training an ML algorithm that takes the inferences from the base learners as input. While stackin

Helen Au-Yang, Jacques H. H. Perk

We present our personal histories with Michael Fisher. We describe how each one of us first came to Cornell University. We also discuss our many subsequent interactions and successful collaborations with him on various physics projects.

Lawrence Stewart, Francis S Bach, Felipe Llinares López, Quentin Berthet

We introduce a differentiable clustering method based on stochastic perturbations of minimum-weight spanning forests. This allows us to include clustering in end-to-end trainable pipelines, with efficient gradients. We show that our method performs well even in difficult settings, such as data sets with high noise and challenging geometries. We also formulat

Sin Kit Lo, Yue Liu, Guangsheng Yu, Qinghua Lu

Distributed trust is a nebulous concept that has evolved from different perspectives in recent years. While one can attribute its current prominence to blockchain and cryptocurrency, the distributed trust concept has been cultivating progress in federated learning, trustworthy and responsible AI in an ecosystem setting, data sharing, privacy issues across or

Flavien Mabilat

In this article, we study the classification of some natural numbers related to the combinatorics of congruence subgroups of the modular group. More precisely, we will focus here on the notion of minimal monomial solutions. These are the solutions of a matrix equation (also appearing in the study of Coxeter friezes), modulo an integer $N$, whose components a

Zi-Qing Xiao, Run-Qiu Yang

This paper gives concrete examples to exhibit how to use the replica trick to calculate the quantum (quasi-)distances holographically. First, we consider the fidelity and relative entropy between thermal states that are dual to the Schwarzschild-AdS black holes. Then we generalize our method into the RN-AdS black holes by adding a U(1) gauge field. We also i

Gilles Dowek, Thérèse Hardin, Claude Kirchner

We define an extension of predicate logic, called Binding Logic, where variables can be bound in terms and in propositions. We introduce a notion of model for this logic and prove a soundness and completeness theorem for it. This theorem is obtained by encoding this logic back into predicate logic and using the classical soundness and completeness theorem th

Zhiwei Hao, Jianyuan Guo, Kai Han, Han Hu

The tremendous success of large models trained on extensive datasets demonstrates that scale is a key ingredient in achieving superior results. Therefore, the reflection on the rationality of designing knowledge distillation (KD) approaches for limited-capacity architectures solely based on small-scale datasets is now deemed imperative. In this paper, we ide

Gilles Dowek

Deduction modulo is a way to express a theory using computation rules instead of axioms. We present in this paper an extension of deduction modulo, called Polarized deduction modulo, where some rules can only be used at positive occurrences, while others can only be used at negative ones. We show that all theories in propositional calculus can be expressed i

Jooyoung Choi, Yunjey Choi, Yunji Kim, Junho Kim

Text-to-image diffusion models can generate diverse, high-fidelity images based on user-provided text prompts. Recent research has extended these models to support text-guided image editing. While text guidance is an intuitive editing interface for users, it often fails to ensure the precise concept conveyed by users. To address this issue, we propose Custom

Yinfang Chen, Huaibing Xie, Minghua Ma, Yu Kang

Ensuring the reliability and availability of cloud services necessitates efficient root cause analysis (RCA) for cloud incidents. Traditional RCA methods, which rely on manual investigations of data sources such as logs and traces, are often laborious, error-prone, and challenging for on-call engineers. In this paper, we introduce RCACopilot, an innovative o

Xinyue Xu, Yuhan Hsi, Haonan Wang, Xiaomeng Li

Medical image data are often limited due to the expensive acquisition and annotation process. Hence, training a deep-learning model with only raw data can easily lead to overfitting. One solution to this problem is to augment the raw data with various transformations, improving the model's ability to generalize to new data. However, manually configuring a ge

Zheng Xie, Yu Liu, Ming Li

Weakly supervised learning aims to empower machine learning when the perfect supervision is unavailable, which has drawn great attention from researchers. Among various types of weak supervision, one of the most challenging cases is to learn from multiple unlabeled (U) datasets with only a little knowledge of the class priors, or U$^m$ learning for short. In

Jinyung Hong, Keun Hee Park, Theodore P. Pavlic

Many interpretable AI approaches have been proposed to provide plausible explanations for a model's decision-making. However, configuring an explainable model that effectively communicates among computational modules has received less attention. A recently proposed shared global workspace theory showed that networks of distributed modules can benefit from sh

Adrià Gómez-Valent, Nick E. Mavromatos, Joan Solà Peracaula

We discuss the potential alleviation of both the Hubble and the growth of galactic structure data tensions observed in the current epoch of Cosmology in the context of the so-called Stringy Running Vacuum Model (RVM) of Cosmology. This is a gravitational field theory coupled to matter, which, at early eras, contains gravitational (Chern-Simons (CS) type) ano

Saisai Ding, Juncheng Li, Jun Wang, Shihui Ying

The multi-scale information among the whole slide images (WSIs) is essential for cancer diagnosis. Although the existing multi-scale vision Transformer has shown its effectiveness for learning multi-scale image representation, it still cannot work well on the gigapixel WSIs due to their extremely large image sizes. To this end, we propose a novel Multi-scale

Yuri Lucas Direbieski, Hiroki Tanioka, Kenji Matsuura, Hironori Takeuchi

Modern information communications use cryptography to keep the contents of communications confidential. RSA (Rivest-Shamir-Adleman) cryptography and elliptic curve cryptography, which are public-key cryptosystems, are widely used cryptographic schemes. However, it is known that these cryptographic schemes can be deciphered in a very short time by Shor's algo

Karthik Valmeekam, Matthew Marquez, Sarath Sreedharan, Subbarao Kambhampati

Intrigued by the claims of emergent reasoning capabilities in LLMs trained on general web corpora, in this paper, we set out to investigate their planning capabilities. We aim to evaluate (1) the effectiveness of LLMs in generating plans autonomously in commonsense planning tasks and (2) the potential of LLMs in LLM-Modulo settings where they act as a source

Wei Wang, Yang Liu, Hao Sun

Time series prediction is a prevalent issue across various disciplines, such as meteorology, traffic surveillance, investment, and energy production and consumption. Many statistical and machine-learning strategies have been developed to tackle this problem. However, these approaches either lack explainability or exhibit less satisfactory performance when th

Zi Liang, Pinghui Wang, Ruofei Zhang, Nuo Xu

The drastic increase in language models' parameters has led to a new trend of deploying models in cloud servers, raising growing concerns about private inference for Transformer-based models. Existing two-party privacy-preserving techniques, however, only take into account natural language understanding (NLU) scenarios. Private inference in natural language

Ujjwala Anantheswaran, Himanshu Gupta, Mihir Parmar, Kuntal Kumar Pal

Event detection refers to identifying event occurrences in a text and comprises of two subtasks; event identification and classification. We present EDM3, a novel approach for Event Detection that formulates three generative tasks: identification, classification, and combined detection. We show that EDM3 helps to learn transferable knowledge that can be leve

Jian-Nan Su, Min Gan, Guang-Yong Chen, Wenzhong Guo

Recent developments in the field of non-local attention (NLA) have led to a renewed interest in self-similarity-based single image super-resolution (SISR). Researchers usually used the NLA to explore non-local self-similarity (NSS) in SISR and achieve satisfactory reconstruction results. However, a surprising phenomenon that the reconstruction performance of

Mengyu Zhang, Xiangyu Ren, Guanglei Xi, Zhenxing Zhang

Quantum error-correcting codes (QECCs) can eliminate the negative effects of quantum noise, the major obstacle to the execution of quantum algorithms. However, realizing practical quantum error correction (QEC) requires resolving many challenges to implement a high-performance real-time decoding system. Many decoding algorithms have been proposed and optimiz

Kei Yuen Chan, Kayue Daniel Wong

We construct an exact functor from the category of Harish-Chandra modules of $\mathrm{GL}_n(\mathbb C)$ to the category of finite-dimensional modules of graded Hecke algebras of type A. We show that the functor preserves parabolically induced modules, standard modules, irreducible modules, unitary modules and Dirac series. We also use the functor to connect