October 2023 arXiv papers — page 22

Fabio Sozio, Francois Lallet, Antoine Perriot, Oscar Lopez-Pamies

Rubber blends are ubiquitous in countless technological applications. More often than not, rubber blends exhibit complex interpenetrating microstructures, which are thought to have a significant impact on their resulting macroscopic mechanical properties. As a first step to understand this potential impact, this paper presents a bottom-up or homogenization s

Jiaer He, Roberto Rivera

Popular investment structured products in Puerto Rico are stock market tied Individual Retirement Accounts (IRA), which offer some stock market growth while protecting the principal. The performance of these retirement strategies has not been studied. This work examines the expected return and risk of Puerto Rico stock market IRA (PRIRAs) and compares their

Rim Assouel, Pau Rodriguez, Perouz Taslakian, David Vazquez

A key aspect of human intelligence is the ability to imagine -- composing learned concepts in novel ways -- to make sense of new scenarios. Such capacity is not yet attained for machine learning systems. In this work, in the context of visual reasoning, we show how modularity can be leveraged to derive a compositional data augmentation framework inspired by

Jiahao Gong, Vaseem A. Shaik, Gwynn J. Elfring

In this paper, we explore the hydrodynamics of spheroidal active particles in viscosity gradients. This work provides a more accurate modeling approach, in comparison to spherical particles, for anisotropic organisms like Paramecium swimming through inhomogeneous environments, but more fundamentally examines the influence of particle shape on viscotaxis. We

Calvin McCarter

We report the effects of replacing the scaled dot-product (within softmax) attention with the negative-log of Euclidean distance. This form of attention simplifies to inverse distance weighting interpolation. Used in simple one hidden layer networks and trained with vanilla cross-entropy loss on classification problems, it tends to produce a key matrix conta

Hejie Cui, Xinyu Fang, Zihan Zhang, Ran Xu

Images contain rich relational knowledge that can help machines understand the world. Existing methods on visual knowledge extraction often rely on the pre-defined format (e.g., sub-verb-obj tuples) or vocabulary (e.g., relation types), restricting the expressiveness of the extracted knowledge. In this work, we take a first exploration to a new paradigm of o

Ibrahim El Shar, Daniel R. Jiang

We propose weakly coupled deep Q-networks (WCDQN), a novel deep reinforcement learning algorithm that enhances performance in a class of structured problems called weakly coupled Markov decision processes (WCMDP). WCMDPs consist of multiple independent subproblems connected by an action space constraint, which is a structural property that frequently emerges

Evan S. Gawlik, Michael Neunteufel

We construct and analyze finite element approximations of the Einstein tensor in dimension $N \ge 3$. We focus on the setting where a smooth Riemannian metric tensor $g$ on a polyhedral domain $\Omega \subset \mathbb{R}^N$ has been approximated by a piecewise polynomial metric $g_h$ on a simplicial triangulation $\mathcal{T}$ of $\Omega$ having maximum eleme

Xu Fang, Lihua Xie, Xiaolei Li

This paper studies the problem of integrated distributed network localization and formation maneuver control. We develop an integrated relative-measurement-based scheme, which only uses relative positions, distances, bearings, angles, ratio-of-distances, or their combination to achieve distributed network localization and formation maneuver control in $\math

H. Lohani, P. Mishra, Anurag Gupta, V. P. S. Awana

We present a detailed electronic structure study of the non-centrosymmetric superconductor BiPd based on our angle resolved photoemission spectroscopy (ARPES) measurements and Density Functional Theory (DFT) based calculations. We observe a high intensity distribution on the Fermi surface (FS) of this compound resulting from various electron and hole like ba

Michael V. Klibanov, Mikhail Y. Kokurin, Jingzhi Li

H\"older stability estimate and uniqueness are proven for a retrospective problem of Mean Field Games with a non-quadratic Hamiltonian. The previous result was only for the quadratic Hamiltonian. The main tool is the apparatus of Carleman estimates. The Mean Field Games theory has a broad range of applications in socio-economic sciences.

Jonas Iskander

Given permutations $π\in S_n$ and $σ\in S_k$, let $N_σ(π)$ denote the number of occurrences of $σ$ in $π$. While pattern avoidance and the distribution of pattern occurrences in permutations have been extensively studied, their interactions with the group structure on $S_n$ are still poorly understood. Gaetz and Ryba showed that the expected value of $χ^{λ[n

Xing Fan, Mario Reig

In this note we consider Penning trap experiments as probes of axion-mediated forces. We show that the current measurement of electron's $g$-factor already sets a new exclusion limit for monopole-dipole axion forces acting on the electron spin. We also show that the Penning trap's capability of switching an electron and a positron can isolate the effect of a

Sanja Rukavina, Vladimir D. Tonchev

In a recent paper [M. Araya, M. Harada, Some restrictions on the weight enumerators of near-extremal ternary self-dual codes and quaternary Hermitian self-dual codes, Des. Codes Cryptogr., 91 (2023), 1813--1843], Araya and Harada gave examples of self-dual near-extremal ternary codes of length 48 for $145$ distinct values of the number $A_{12}$ of codewords

Farzaneh Ramezani, Hamidreza Bolhasani

Tinnitus is a prevalent hearing disorder that can be caused by various factors such as age, hearing loss, exposure to loud noises, ear infections or tumors, certain medications, head or neck injuries, and psychological conditions like anxiety and depression. While not every patient requires medical attention, about 20% of sufferers seek clinical intervention

Yixin Wan, Fanyou Wu, Weijie Xu, Srinivasan H. Sengamedu

In this work, we propose sequence-level certainty as a common theme over hallucination in Knowledge Grounded Dialogue Generation (KGDG). We explore the correlation between the level of hallucination in model responses and two types of sequence-level certainty: probabilistic certainty and semantic certainty. Empirical results reveal that higher levels of both

Laurenz Kremeyer, Tristan L. Britt, Bradley J. Siwick, Samuel C. Huberman

Hydrodynamic phonon transport phenomena, like second sound, have been observed in liquid Helium more than 50 years ago. More recently second sound has been observed in graphite at over 200 K using transient thermal grating techniques. In this work we explore the signatures of second sound in ultrafast electron diffuse scattering (UEDS) patterns. We use densi

Mutimukwe Chantal, Han Shengnan, Viberg Olga, Cerratto-Pargman Teresa

Privacy is one of the key challenges to the adoption and implementation of online proctoring systems in higher education. To better understand this challenge, we adopt privacy as contextual integrity theory to conduct a scoping review of 17 papers. The results show different types of students' personal and sensitive information are collected and disseminated

Parv Kapoor, Simon Chu, Angela Chen

Trust has been shown to be a key factor in effective human-robot collaboration. In the context of assistive robotics, the effect of trust factors on human experience is further pronounced. Personalization of assistive robots is an orthogonal factor positively correlated with robot adoption and user perceptions. In this work, we investigate the relationship b

Wei-Shao Wei, Anthony Trubiano, Christian Sigl, Stefan Paquay

Self-assembly of complex and functional materials remains a grand challenge in soft material science. Efficient assembly depends on a delicate balance between thermodynamic and kinetic effects, requiring fine-tuning affinities and concentrations of subunits. By contrast, we introduce an assembly paradigm that allows large error-tolerance in the subunit affin

Ignacio Ponce, Federico Milano

The concept of complex frequency has been recently introduced on the IEEE Transactions on Power Systems to study bus voltage variations in magnitude and frequency and their link with complex power injections of a power system. In this paper, the complex frequency is applied to time-varying series connections, namely, RLC dynamic branches, regulating transfor

Vishal Asnani, Abhinav Kumar, Suya You, Xiaoming Liu

Previous research in $2D$ object detection focuses on various tasks, including detecting objects in generic and camouflaged images. These works are regarded as passive works for object detection as they take the input image as is. However, convergence to global minima is not guaranteed to be optimal in neural networks; therefore, we argue that the trained we

Amadeu Delshams, Albert Granados, Rodrigo G. Schaefer

In the present paper we apply the geometrical mechanism of diffusion in an \emph{a priori} unstable Hamiltonian system with 3 $+$ 1/2 degrees of freedom. This mechanism consists of combining iterations of the \emph{inner} and \emph{outer} dynamics associated to a \emph{Normallly Hyperbolic Invariant Manifold} (NHIM), to construct diffusing \emph{pseudo-orbit

Eric Price, Yihan Zhou

For some hypothesis classes and input distributions, active agnostic learning needs exponentially fewer samples than passive learning; for other classes and distributions, it offers little to no improvement. The most popular algorithms for agnostic active learning express their performance in terms of a parameter called the disagreement coefficient, but it i

Jeana Aojie Zheng, Miranda Holmes-Cerfon, David J. Pine, Sophie Marbach

Understanding the motion of particles with ligand-receptors is important for biomedical applications and material design. Yet, even among a single design, the prototypical DNA-coated colloids, seemingly similar micrometric particles hop or roll, depending on the study. We shed light on this problem by observing DNA-coated colloids diffusing near surfaces coa

Aleksandar Armacki, Pranay Sharma, Gauri Joshi, Dragana Bajovic

We study high-probability convergence guarantees of learning on streaming data in the presence of heavy-tailed noise. In the proposed scenario, the model is updated in an online fashion, as new information is observed, without storing any additional data. To combat the heavy-tailed noise, we consider a general framework of nonlinear stochastic gradient desce

Lixing Zhu, Runcong Zhao, Lin Gui, Yulan He

Narrative understanding involves capturing the author's cognitive processes, providing insights into their knowledge, intentions, beliefs, and desires. Although large language models (LLMs) excel in generating grammatically coherent text, their ability to comprehend the author's thoughts remains uncertain. This limitation hinders the practical applications o

H. Mahrouch, A. Jabar, S. Idrissi, L. Bahmad

In this work, we used the density functional calculation (DFT) implemented in the Quantum Espresso software, using the approximations (GGA, GGA+U) to illustrate the electronic and magnetic properties of the perovskite CaMnO$_3$. It has been found that the CaMnO$_3$ perovskite is stable in the G-AFM phase. When expecting the total and partial DOSs, a strong c

Md Golam Morshed, Laura Rehm, Ankit Shukla, Yunkun Xie

True random number generators (TRNGs) are fundamental building blocks for many applications, such as cryptography, Monte Carlo simulations, neuromorphic computing, and probabilistic computing. While perpendicular magnetic tunnel junctions (pMTJs) based on low-barrier magnets (LBMs) are natural sources of TRNGs, they tend to suffer from device-to-device varia

Stefano Massaroli, Michael Poli, Daniel Y. Fu, Hermann Kumbong

Recent advances in attention-free sequence models rely on convolutions as alternatives to the attention operator at the core of Transformers. In particular, long convolution sequence models have achieved state-of-the-art performance in many domains, but incur a significant cost during auto-regressive inference workloads -- naively requiring a full pass (or c

Laura Rehm, Md Golam Morshed, Shashank Misra, Ankit Shukla

Nanoscale magnetic tunnel junction (MTJ) devices can efficiently convert thermal energy in the environment into random bitstreams for computational modeling and cryptography. We recently showed that perpendicular MTJs activated by nanosecond pulses can generate true random numbers at high data rates. Here, we explore the dependence of probability bias-the de

Abdellah El Mekki, Muhammad Abdul-Mageed, ElMoatez Billah Nagoudi, Ismail Berrada

Bilingual Lexicon Induction (BLI), where words are translated between two languages, is an important NLP task. While noticeable progress on BLI in rich resource languages using static word embeddings has been achieved. The word translation performance can be further improved by incorporating information from contextualized word embeddings. In this paper, we

Yi Ren, Samuel Lavoie, Mikhail Galkin, Danica J. Sutherland

Compositional generalization, the ability of an agent to generalize to unseen combinations of latent factors, is easy for humans but hard for deep neural networks. A line of research in cognitive science has hypothesized a process, ``iterated learning,'' to help explain how human language developed this ability; the theory rests on simultaneous pressures tow

Matthew Nice, Matt Bunting, Alex Richardson, Gergely Zachar

We demonstrate a new capability of automated vehicles: mixed autonomy traffic control. With this new capability, automated vehicles can shape the traffic flows composed of other non-automated vehicles, which has the promise to improve safety, efficiency, and energy outcomes in transportation systems at a societal scale. Investigating mixed autonomy mobile tr

Milena Dimova, Natalia Kolkovska, Nikolai Kutev

In this paper, we study the initial boundary value problem for the nonlinear wave equation with combined power-type nonlinearities with variable coefficients. The global behavior of the solutions with non-positive and sub-critical energy is completely investigated. The threshold between the nonexistence of global in time weak solutions and non-blowing up sol

Martin Chak, Pierre Monmarché

Contraction in Wasserstein 1-distance with explicit rates is established for generalized Hamiltonian Monte Carlo with stochastic gradients under possibly nonconvex conditions. The algorithms considered include splitting schemes of kinetic Langevin diffusion commonly used in molecular dynamics simulations. To accommodate the degenerate noise structure corresp

Taiki Miyanishi, Fumiya Kitamori, Shuhei Kurita, Jungdae Lee

City-scale 3D point cloud is a promising way to express detailed and complicated outdoor structures. It encompasses both the appearance and geometry features of segmented city components, including cars, streets, and buildings, that can be utilized for attractive applications such as user-interactive navigation of autonomous vehicles and drones. However, com

Advaith Narayanan

The rapidly advancing fields of statistical modeling and machine learning have significantly enhanced data-driven design and optimization. This paper focuses on leveraging these design algorithms to optimize a medical walker, an integral part of gait rehabilitation and physiological therapy of the lower extremities. To achieve the desirable qualities of a wa

Moses C. Nah, Johannes Lachner, Neville Hogan

Motor primitives are fundamental building blocks of a controller which enable dynamic robot behavior with minimal high-level intervention. By treating motor primitives as basic "modules," different modules can be sequenced or superimposed to generate a rich repertoire of motor behavior. In robotics, two distinct approaches have been proposed: Dynamic Movemen

Yunshan Ma, Xiaohao Liu, Yinwei Wei, Zhulin Tao

Automatic bundle construction is a crucial prerequisite step in various bundle-aware online services. Previous approaches are mostly designed to model the bundling strategy of existing bundles. However, it is hard to acquire large-scale well-curated bundle dataset, especially for those platforms that have not offered bundle services before. Even for platform

Luke McDermott, Daniel Cummings

With the rise in interest of sparse neural networks, we study how neural network pruning with synthetic data leads to sparse networks with unique training properties. We find that distilled data, a synthetic summarization of the real data, paired with Iterative Magnitude Pruning (IMP) unveils a new class of sparse networks that are more stable to SGD noise o

Kaijian Zou, Xinliang Frederick Zhang, Winston Wu, Nick Beauchamp

News media is expected to uphold unbiased reporting. Yet they may still affect public opinion by selectively including or omitting events that support or contradict their ideological positions. Prior work in NLP has only studied media bias via linguistic style and word usage. In this paper, we study to which degree media balances news reporting and affects c

Arman Zarei, Bingzhao Zhu, Mahsa Shoaran

Epilepsy is one of the most prevalent brain disorders that disrupts the lives of millions worldwide. For patients with drug-resistant seizures, there exist implantable devices capable of monitoring neural activity, promptly triggering neurostimulation to regulate seizures, or alerting patients of potential episodes. Next-generation seizure detection systems

Nynke M. D. Niezink

This review discusses the paper ''A Tale of Two Datasets: Representativeness and Generalisability of Inference for Samples of Networks'' by Krivitsky, Coletti, and Hens, published in the Journal of the American Statistical Association in 2023.

Liang Yan, Gengchen Wei, Chen Yang, Shengzhong Zhang

This paper introduces a new approach to address the issue of class imbalance in graph neural networks (GNNs) for learning on graph-structured data. Our approach integrates imbalanced node classification and Bias-Variance Decomposition, establishing a theoretical framework that closely relates data imbalance to model variance. We also leverage graph augmentat

Muhammad Arslan Raza, Muhammad Shoaib Farooq, Adel Khelifi, Atif Alvi

Emotions, as a fundamental ingredient of any social interaction, lead to behaviors that represent the effectiveness of the interaction through facial expressions and gestures in humans. Hence an agent must possess the social and cognitive abilities to understand human social parameters and behave accordingly. However, no such emotion-oriented behavior model

Sixue Zheng, Zhengwu Fang, Scott X. Mao

Much scientific and practical interest is currently focused on the atomic-scale mechanical behaviors of metallic nanocrystals with different crystal structures at room temperature, while the high-temperature plastic deformation in tungsten nanocrystals remains not well understood, due to the technical difficulty in elevating the experimental temperature duri

Nikita Kornilov, Ohad Shamir, Aleksandr Lobanov, Darina Dvinskikh

In this paper, we consider non-smooth stochastic convex optimization with two function evaluations per round under infinite noise variance. In the classical setting when noise has finite variance, an optimal algorithm, built upon the batched accelerated gradient method, was proposed in (Gasnikov et. al., 2022). This optimality is defined in terms of iteratio

Boya Zhang, Weijian Luo, Zhihua Zhang

Adversarial attacks can mislead neural network classifiers. The defense against adversarial attacks is important for AI safety. Adversarial purification is a family of approaches that defend adversarial attacks with suitable pre-processing. Diffusion models have been shown to be effective for adversarial purification. Despite their success, many aspects of d

Konstantin Warawa, Yassine Agarmani, Harald Schubert, Martin Dressel

We investigate experimentally the ultrafast changes in the spectral response of the Mott insulator $\kappa$-(BEDT-TTF)$_2$Cu[N(CN)$_2$]Cl ($\kappa$-Cl) upon photodoping with intense excitation at 1.6 eV and probing with continuum pulses simultaneously covering both the terahertz and infrared (IR) ranges (from 0 to 0.6 eV). A quantitative analysis of the diff

JunJie Wee, Jiahui Chen, Kelin Xia, Guo-Wei Wei

Protein mutations can significantly influence protein solubility, which results in altered protein functions and leads to various diseases. Despite of tremendous effort, machine learning prediction of protein solubility changes upon mutation remains a challenging task as indicated by the poor scores of normalized Correct Prediction Ratio (CPR). Part of the c

Nikita Markarian, Alexander Polishchuk

We prove that a pair of Feigin-Odesskii Poisson brackets on ${\mathbb P}^4$ associated with elliptic curves given as linear sections of the Grassmannian $G(2,5)$ are compatible if and only if this pair of elliptic curves is contained in a del Pezzo surface obtained as a linear section of $G(2,5)$.

Joshua Flynn, Nguyen Lam, Guozhen Lu

Firstly, this paper establishes useful forms of the remainder term of Hardy-type inequalities on general domains where the weights are functions of the distance to the boundary. For weakly mean convex domains we use the resulting identities to establish nonexistence of extremizers for and improve known sharp Hardy inequalities. Secondly, we establish geometr

Shashank Shekhar Pandey, Ashadul Halder, A. S. Majumdar

We explore the 21-cm signal in our Universe containing inhomogeneous matter distribution at considerably large scales. Employing Buchert's averaging procedure in the context of a model of spacetime with multiple inhomogeneous domains, we evaluate the effect of our model parameters on the observable 21-cm signal brightness temperature. Our model parameters ar

K. Raghavan, A. Lovato

Nuclear quantum many-body methods rely on integral transform techniques to infer properties of electroweak response functions from ground-state expectation values. Retrieving the energy dependence of these responses is highly non-trivial, especially for quantum Monte Carlo methods, as it requires inverting the Laplace transform -- a notoriously ill-posed pro

Kang Gao, Stephen Weston, Perukrishnen Vytelingum, Namid R. Stillman

We propose the Chiarella-Heston model, a new agent-based model for improving the effectiveness of deep hedging strategies. This model includes momentum traders, fundamental traders, and volatility traders. The volatility traders participate in the market by innovatively following a Heston-style volatility signal. The proposed model generalises both the exten

Valeri V. Makarov, Alexey Goldin

The transiting planet K2-18 b is one of the best candidates for a relatively nearby world harboring biological life. The long-term orbital evolution of this planet is investigated using theoretical and purely numerical techniques for two possible configurations: a single planet orbiting the host star, and a two-planet system including the proposed inner plan

Baha Zarrouki, Chenyang Wang, Johannes Betz

Employing Stochastic Nonlinear Model Predictive Control (SNMPC) for real-time applications is challenging due to the complex task of propagating uncertainties through nonlinear systems. This difficulty becomes more pronounced in high-dimensional systems with extended prediction horizons, such as autonomous vehicles. To enhance closed-loop performance in and

Guanghu Sui, Zhishuai Li, Ziyue Li, Sun Yang

The previous state-of-the-art (SOTA) method achieved a remarkable execution accuracy on the Spider dataset, which is one of the largest and most diverse datasets in the Text-to-SQL domain. However, during our reproduction of the business dataset, we observed a significant drop in performance. We examined the differences in dataset complexity, as well as the

Maxime Fairon

Any multiplicative quiver variety is endowed with a Poisson structure constructed by Van den Bergh through reduction from a Hamiltonian quasi-Poisson structure. The smooth locus carries a corresponding symplectic form defined by Yamakawa through quasi-Hamiltonian reduction. In this note, we include the Poisson structure as part of a pencil of compatible Pois

Maria Kulikova

This paper suggests a few novel Cholesky-based square-root algorithms for the maximum correntropy criterion Kalman filtering. In contrast to the previously obtained results, new algorithms are developed in the so-called {\it condensed} form that corresponds to the {\it a priori} filtering. Square-root filter implementations are known to possess a better cond

Qingyue Zhang, Qing Liu, You Zhou

Predicting properties of large-scale quantum systems is crucial for the development of quantum science and technology. Shadow estimation is an efficient method for this task based on randomized measurements, where many-qubit random Clifford circuits are used for estimating global properties like quantum fidelity. Here we introduce the minimal Clifford measur

Stepan G. Margaryan

This is the latest in a series of articles aimed at exploring the relationship between the complexity classes of P and NP. In the previous papers, we have proved that the sat CNF problem is polynomially reduced to the problem of finding a special covering for a set under the special decomposition of this set and vice versa. That is, these problems are polino

Koen Devesse, Luca Lanzilao, Johan Meyers

As wind farms continue to grow in size, mesoscale effects such as blockage and gravity waves become increasingly important. Allaerts & Meyers (J. Fluid Mech., 2019) proposed an atmospheric perturbation model (APM) that can simulate the interaction of wind farms and the atmospheric boundary layer while keeping computational costs low. The model resolves the m

L. M. Abreu, R. O. Magalhães, F. S. Navarra, H. P. L. Vieira

We investigate the interactions of the charged exotic state $Z_c(3900)$ in a hadronic medium composed of light mesons. We study processes such as $Z_c \pi \to D\bar{D}$, $Z_c \pi \to D^*\bar{D}^*$, $Z_c \pi \to D\bar{D}^*$ and the inverse ones. Using effective Lagrangians and form factors calculated with QCD sum rules (treating the $Z_c(3900)$ as a tetraquar

Vladimir Lisy, Jana Tothova

Recently, Lad, Patel, and Pratap (LP&P) [Phys. Rev. E 105, 064107 (2022)] revisited a microscopic theory of molecular motion in liquids, proposed by Glass and Rice [Phys. Rev. 176, 239 (1968)]. Coming from this theory, LP&P derived a new equation of motion for the velocity autocorrelation function (VAF) and argued that the friction coefficient of particles i

Yohan Potaux, Debajyoti Sarkar, Sergey N. Solodukhin

The classical black hole spacetime is modified semiclassically, depending strongly on the choice of the quantum states. In particular, for the Boulware state the spacetime often takes a wormhole structure mimicking closely a spacetime with a horizon. In this paper, in the context of the two-dimensional dilaton RST model, we consider all possible important in

Yuichi Itto

The weak correlation between spatiotemporal fluctuations in nonequilibrium complex systems is shown to govern the fluctuation distribution, maximizing the conditional entropy associated with such fluctuations. The result is illustrated in diffusion phenomena observed in living cells. A generic feature of the weak correlation is briefly mentioned.

Sumedh Gupte, Prashanth L. A., Sanjay P. Bhat

We consider the problems of estimation and optimization of utility-based shortfall risk (UBSR), which is a popular risk measure in finance. In the context of UBSR estimation, we derive a non-asymptotic bound on the mean-squared error of the classical sample average approximation (SAA) of UBSR. Next, in the context of UBSR optimization, we derive an expressio

Zezhou Huang, Pavan Kalyan Damalapati, Eugene Wu

Text-to-SQL allows experts to use databases without in-depth knowledge of them. However, real-world tasks have both query and data ambiguities. Most works on Text-to-SQL focused on query ambiguities and designed chat interfaces for experts to provide clarifications. In contrast, the data management community has long studied data ambiguities, but mainly addr

Fady Rezk

Meta-learning that uses implicit gradient have provided an exciting alternative to standard techniques which depend on the trajectory of the inner loop training. Implicit meta-learning (IML), however, require computing $2^{nd}$ order gradients, particularly the Hessian which is impractical to compute for modern deep learning models. Various approximations fo

Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu

Root Cause Analysis (RCA) is becoming increasingly crucial for ensuring the reliability of microservice systems. However, performing RCA on modern microservice systems can be challenging due to their large scale, as they usually comprise hundreds of components, leading significant human effort. This paper proposes TraceDiag, an end-to-end RCA framework that

Goo Ishikawa, Yoshinori Machida

A distribution of rank $4$ on a $7$-dimensional manifold is called a $(4, 7)$-distribution if its local sections generate the whole tangent space by taking Lie brackets once. Singular curves of $(4, 7)$-distributions are studied in this paper. In particular the class of hyperbolic $(4, 7)$-distributions of type $C_3$ is introduced and singular curves are com

Yangjun Wu, Kebin Fang, Dongxiang Zhang, Han Wang

Structured dropout approaches, such as attention dropout and DropHead, have been investigated to regularize the multi-head attention mechanism in Transformers. In this paper, we propose a new regularization scheme based on token-level rather than structure-level to reduce overfitting. Specifically, we devise a novel Token-Level Masking (TLM) training strateg

Maryam Haghighat, Peyman Moghadam, Shaheer Mohamed, Piotr Koniusz

Masked Image Modeling (MIM) is a powerful self-supervised strategy for visual pre-training without the use of labels. MIM applies random crops to input images, processes them with an encoder, and then recovers the masked inputs with a decoder, which encourages the network to capture and learn structural information about objects and scenes. The intermediate

Kartik Gokhale, Amit Kumar Mallik, Ankit Kumar Misra, Swaprava Nath

Stable marriage of a two-sided market with unit demand is a classic problem that arises in many real-world scenarios. In addition, a unique stable marriage in this market simplifies a host of downstream desiderata. In this paper, we explore a new set of sufficient conditions for unique stable matching (USM) under this setup. Unlike other approaches that also

Zheng Zhang, Junxiang Wang, Liang Zhao

Graph Neural Networks (GNNs) have achieved great success in representing data with dependencies by recursively propagating and aggregating messages along the edges. However, edges in real-world graphs often have varying degrees of difficulty, and some edges may even be noisy to the downstream tasks. Therefore, existing GNNs may lead to suboptimal learned rep

Reza Roshanghias, Reza Saadat

The issue of diminished spectral efficiency (SE) of the downlink (DL) transmission in distributed cell-free massive MIMO (CF-mMIMO) systems poses a significant challenge in terms of user equipment (UE) performance when compared to their centralized CF-mMIMO counterparts. The primary root cause of this issue can be attributed to the reduced efficacy of distri

Graciela Boente, Florencia Leonardi, Daniela Rodriguez, Mariela Sued

Linear regression models have been extensively considered in the literature. However, in some practical applications they may not be appropriate all over the range of the covariate. In this paper, a more flexible model is introduced by considering a regression model $Y=r(X)+\varepsilon$ where the regression function $r(\cdot)$ is assumed to be linear for lar

Shijian Li, Xu-Ri Yao, Wei Zhang, Yeliang Wang

Recently, several single-pixel imaging (SPI) schemes have emerged for imaging fast-moving objects and have shown dramatic results. However, fast image reconstruction of a moving object with high quality is still challenging for SPI, thereby limiting its practical application. In this paper, we present a simultaneous tracking and imaging method that incorpora

Jumpei Kawakami

We consider the 3-dimensional nonlinear Schr\"{o}dinger equation (NLS) with average nonlinearity. This is a limiting model of NLS with strong magnetic confinement and a generalized model of the resonant system of NLS with a partial harmonic oscillator in terms of nonlinear power. We provide a new proof for the conservation law of kinetic energy and remove th

Giovanni Eugenio Comi, Virginia De Cicco, Giovanni Scilla

A new notion of pairing between measure vector fields with divergence measure and scalar functions, which are not required to be weakly differentiable, is introduced. In particular, in the case of essentially bounded divergence-measure fields, the functions may not be of bounded variation. This naturally leads to the definition of $BV$-like function classes

S. P. Baranov, A. V. Lipatov, M. A. Malyshev, A. M. Snigirev

A novel observable, the double nuclear modification factor, is proposed to probe simultaneously the initial and final state effects in nucleus-nucleus collisions. An interesting competition between the combinatorial enhancement in the double parton scattering and the suppression due to parton energy loss can be observed in the production rate of two hard par

Jakub Drápal, Hannes Westermann, Jaromir Savelka

Thematic analysis and other variants of inductive coding are widely used qualitative analytic methods within empirical legal studies (ELS). We propose a novel framework facilitating effective collaboration of a legal expert with a large language model (LLM) for generating initial codes (phase 2 of thematic analysis), searching for themes (phase 3), and class

Hao Wang, Zhi-Qi Cheng, Jingdong Sun, Xin Yang

Multi-view or even multi-modal data is appealing yet challenging for real-world applications. Detecting anomalies in multi-view data is a prominent recent research topic. However, most of the existing methods 1) are only suitable for two views or type-specific anomalies, 2) suffer from the issue of fusion disentanglement, and 3) do not support online detecti

Huan Qing

The latent class model is a powerful tool for identifying latent classes within populations that share common characteristics for categorical data in social, psychological, and behavioral sciences. In this article, we propose two new algorithms to estimate a latent class model for categorical data. Our algorithms are developed by using a newly defined regula

Xuan Qi, Yi Wei

It is commonly recognized that the expressiveness of deep neural networks is contingent upon a range of factors, encompassing their depth, width, and other relevant considerations. Currently, the practical performance of the majority of deep neural networks remains uncertain. For ReLU (Rectified Linear Unit) networks with piecewise linear activations, the nu

Elliott Ash, Naman Goel, Nianyun Li, Claudia Marangon

Machine learning based decision-support tools in criminal justice systems are subjects of intense discussions and academic research. There are important open questions about the utility and fairness of such tools. Academic researchers often rely on a few small datasets that are not sufficient to empirically study various real-world aspects of these questions

Ya. S. Greenberg, A. A. Shtygashev, A. G. Moiseev

In this paper, we study the spatiotemporal distribution of the photon electric field produced by the scattering of a single photon narrow pulse from a system of two identical qubits coupled to continuum modes in a one-dimensional (1D) open waveguide. We derive the time-dependent dynamical equations for qubits' and photon amplitudes which allow the calculatio

Volodymyr Denysiuk, Olena Hryshko

This article focuses on trigonometric Riemann B-splines and Riemann kernels of trigonometric interpolation splines of arbitrary order; it is shown that trigonometric interpolation splines are a convolution of trigonometric B-splines with corresponding kernels. Theoretical statements are followed by examples, and the results are applicable in many practical a

Veljko Toljić

In this paper, we try to minimize the scope of possible unique metric spectra up to equivalence. While it is well known that every spectra $S\subseteq \mathbb{R}^+$ is equivalent to a spectra $T\subseteq \mathbb{N}$, it has remained open if $T$ could also maintain a desirable combinatorial form. Conant questioned if $T= \{t_1,...,t_n \}_{<}$ could be taken s

Maurice H. ter Beek, Clemens Dubslaff

The analysis of configurable systems, i.e., systems those behaviors depend on parameters or support various features, is challenging due to the exponential blowup arising in the number of configuration options. This volume contains the post-proceedings of TiCSA 2023, the first workshop on Trends in Configurable Systems Analysis, where current challenges and

Maya Stein

Which conditions ensure that a digraph contains all oriented paths of some given length, or even a all oriented trees of some given size, as a subgraph? One possible condition could be that the host digraph is a tournament of a certain order. In arbitrary digraphs and oriented graphs, conditions on the chromatic number, on the edge density, on the minimum ou

Henrik Claßen, Jonas Thierfeldt, Julian Tochman-Szewc, Philipp Wiesner

While the environmental impact of digitalization is becoming more and more evident, the climate crisis has become a major issue for society. For instance, data centers alone account for 2.7% of Europe's energy consumption today. A considerable part of this load is accounted for by cloud-based services for automated software development, such as continuous in

Mohamed El Amine Seddik, Maxime Guillaud, Alexis Decurninge, José Henrique de Morais Goulart

This work introduces an asymptotic study of Hotelling-type tensor deflation in the presence of noise, in the regime of large tensor dimensions. Specifically, we consider a low-rank asymmetric tensor model of the form $\sum_{i=1}^r \beta_i{\mathcal{A}}_i + {\mathcal{W}}$ where $\beta_i\geq 0$ and the ${\mathcal{A}}_i$'s are unit-norm rank-one tensors such tha

Jiangyan Ma, Yifei Wang, Yisen Wang

Spectral embedding is a powerful graph embedding technique that has received a lot of attention recently due to its effectiveness on Graph Transformers. However, from a theoretical perspective, the universal expressive power of spectral embedding comes at the price of losing two important invariance properties of graphs, sign and basis invariance, which also

Jin Zhu, Runzhe Wan, Zhengling Qi, Shikai Luo

This paper endeavors to augment the robustness of offline reinforcement learning (RL) in scenarios laden with heavy-tailed rewards, a prevalent circumstance in real-world applications. We propose two algorithmic frameworks, ROAM and ROOM, for robust off-policy evaluation and offline policy optimization (OPO), respectively. Central to our frameworks is the st

Quanlong Guan, Tong Zhu, Liangda Fang, Junming Qiu

Belief revision and update, two significant types of belief change, both focus on how an agent modify her beliefs in presence of new information. The most striking difference between them is that the former studies the change of beliefs in a static world while the latter concentrates on a dynamically-changing world. The famous AGM and KM postulates were prop

Jiayi Shen, Xiantong Zhen, Qi, Wang

This paper focuses on the data-insufficiency problem in multi-task learning within an episodic training setup. Specifically, we explore the potential of heterogeneous information across tasks and meta-knowledge among episodes to effectively tackle each task with limited data. Existing meta-learning methods often fail to take advantage of crucial heterogeneou

Felix Gotti, Henrick Rabinovitz

A commutative cancellative monoid is atomic if every non-invertible element factors into irreducibles (also called atoms), while an integral domain is atomic if its multiplicative monoid is atomic. Back in the eighties, Gilmer posed the question of whether the fact that a torsion-free monoid $M$ and an integral domain $R$ are both atomic implies that the mon