October 2023 arXiv papers — page 81

Sara Bonacina, Daniele Zago, Giovanna Capizzi, Bianca Maria Colosimo

Traditional Statistical Process Control methodologies face several challenges when monitoring defects in complex geometries, such as those of products obtained via Additive Manufacturing techniques. Many approaches cannot be applied in these settings due to the high dimensionality of the data and the lack of parametric and distributional assumptions on the o

Cecilia Chirenti, Simone Dichiara, Amy Lien, M. Coleman Miller

The gamma-ray burst (GRB) GRB~211211A is believed to have occurred due to the merger of two neutron stars or a neutron star and a black hole, despite its duration of more than a minute. Subsequent analysis has revealed numerous interesting properties including the possible presence of a $\sim 22$~Hz quasiperiodic oscillation (QPO) during precursor emission.

Cheng Jiayang, Lin Qiu, Tsz Ho Chan, Tianqing Fang

Analogy-making between narratives is crucial for human reasoning. In this paper, we evaluate the ability to identify and generate analogies by constructing a first-of-its-kind large-scale story-level analogy corpus, \textsc{StoryAnalogy}, which contains 24K story pairs from diverse domains with human annotations on two similarities from the extended Structur

Shengmin Zhang

Let $G$ be a finite group and $H$ be a subgroup of $G$. Then $H$ is said to be a $p$-$CAP$-subgroup of $G$, if $H$ covers or avoids any $pd$-chief factor of $G$. Furthermore, $H$ is said to be a strong $p$-$CAP$-subgroup of $G$, if for any $H \leq K \leq G$, $H$ is a $p$-$CAP$-subgroup of $K$. A subgroup $L$ is called an $ICSPC$-subgroup of $G$, if $[L,G] \c

A. Dammak, F. Raouafi, A. Cavanna, P. Rudolf

Functionalized graphene offers great potential in the field of rapid detection of gases at room temperature. We performed first-principles calculations to study the suitability of 4-sulfobenzenediazonium salts (4SBD) as bandgap modifier in graphene. The signature of unpaired spins is evidenced near the Fermi level owing to the symmetry breaking of graphene s

Ian Lundberg, Rachel Brown-Weinstock, Susan Clampet-Lundquist, Sarah Pachman

Why are life trajectories difficult to predict? We investigated this question through in-depth qualitative interviews with 40 families sampled from a multi-decade longitudinal study. Our sampling and interviewing process were informed by the earlier efforts of hundreds of researchers to predict life outcomes for participants in this study. The qualitative ev

Simon Lemaire, Julien Moatti

We are interested in the high-order approximation of anisotropic, potential-driven advection-diffusion models on general polytopal partitions. We study two hybrid schemes, both built upon the Hybrid High-Order technology. The first one hinges on exponential fitting and is linear, whereas the second is nonlinear. The existence of solutions is established for

Han Zhang, Rayehe Karimi Mahabadi, Cynthia Rudin, Johann Guilleminot

This paper studies the utility of techniques within uncertainty quantification, namely spectral projection and polynomial chaos expansion, in reducing sampling needs for characterizing acoustic metamaterial dispersion band responses given stochastic material properties and geometric defects. A novel method of encoding geometric defects in an interpretable, r

Zheyuan Zhang, Lanhong Yao, Bin Wang, Debesh Jha

Large-scale, big-variant, high-quality data are crucial for developing robust and successful deep-learning models for medical applications since they potentially enable better generalization performance and avoid overfitting. However, the scarcity of high-quality labeled data always presents significant challenges. This paper proposes a novel approach to add

William J. Fischer, Cara Battersby, Doug Johnstone, Rachel Lee

Evidence abounds that young stellar objects undergo luminous bursts of intense accretion that are short compared to the time it takes to form a star. It remains unclear how much these events contribute to the main-sequence masses of the stars. We demonstrate the power of time-series far-infrared (far-IR) photometry to answer this question compared to similar

Jack Breen, Katie Allen, Kieran Zucker, Geoff Hall

For many patients, current ovarian cancer treatments offer limited clinical benefit. For some therapies, it is not possible to predict patients' responses, potentially exposing them to the adverse effects of treatment without any therapeutic benefit. As part of the automated prediction of treatment effectiveness in ovarian cancer using histopathological imag

A. M. Baldini, H. Benmansour, G. Boca, G. Cavoto

The cylindrical drift chamber is the most innovative part of the MEG~II detector, the upgraded version of the MEG experiment. The MEG~II chamber differs from the MEG one because it is a single volume cylindrical structure, instead of a segmented one, chosen to improve its resolutions and efficiency in detecting low energy positrons from muon decays at rest.

Lihu Chen, Gaël Varoquaux, Fabian M. Suchanek

Positional Encodings (PEs) are used to inject word-order information into transformer-based language models. While they can significantly enhance the quality of sentence representations, their specific contribution to language models is not fully understood, especially given recent findings that various positional encodings are insensitive to word order. In

Anders Bredahl Kock, David Preinerstorfer

In this article, we study the critical growth rates of dimension below which Gaussian critical values can be used for hypothesis testing but beyond which they cannot. We are particularly interested in how these growth rates depend on the number of moments that the observations possess.

Orr Krupnik, Elisei Shafer, Tom Jurgenson, Aviv Tamar

Adaptable models could greatly benefit robotic agents operating in the real world, allowing them to deal with novel and varying conditions. While approaches such as Bayesian inference are well-studied frameworks for adapting models to evidence, we build on recent advances in deep generative models which have greatly affected many areas of robotics. Harnessin

Hongjie Li, Jun Zou

The current study is motivated by the paper [Z. Liu, et al., {\it Science}, 289(5485), 2000], which investigates the incorporation of hard inclusions within a soft elastic matrix (HISE). The objective is to attain a negative mass density, which is caused by sub-wavelength dipolar resonances. This paper offers a comprehensive and mathematically rigorous under

Sarthak Roy, Ashish Harshavardhan, Animesh Mukherjee, Punyajoy Saha

Recently efforts have been made by social media platforms as well as researchers to detect hateful or toxic language using large language models. However, none of these works aim to use explanation, additional context and victim community information in the detection process. We utilise different prompt variation, input information and evaluate large languag

Joann Jones, Craig J. Copi, Glenn D. Starkman, Yashar Akrami

The standard cosmological model predicts statistically isotropic cosmic microwave background (CMB) fluctuations characterized by the CMB temperature coefficients $a_{\ell m}$ being independent Gaussian random variables with zero mean and with variance that depends only on $\ell$. However, several summary statistics of CMB isotropy have anomalous values, incl

Francesco Paissan, Luca Della Libera, Zhepei Wang, Mirco Ravanelli

In this paper, we explore audio-editing with non-rigid text edits. We show that the proposed editing pipeline is able to create audio edits that remain faithful to the input audio. We explore text prompts that perform addition, style transfer, and in-painting. We quantitatively and qualitatively show that the edits are able to obtain results which outperform

Zhenhua Wang

We initiate the study of Lie-Trotter means in JB-algebras, which is an extension of Lie-Trotter formulas in JB-algebras. We show that two-variable Lie-Trotter means include the weighted arithmetic mean, weighted harmonic mean, weighted geometric mean, and weighted spectral geometric mean. Consequently, several generalized Lie-Trotter formulas in JB-algebras

Jian-He Zheng, Xiang-Yu Wang, Ruo-Yu Liu, Bing Zhang

The TeV afterglow of the BOAT GRB 221009A was interpreted as arising from a narrow jet while the radio to X-ray afterglows were interpreted as arising from a wide structured jet. However, there is no model explaining the TeV and lower-energy multi-wavelength afterglows simultaneously. We here investigate a two-component jet model, including a narrow uniform

Petri J. Käpylä

(abridged) Context: Rotation is thought to influence the size of convective eddies and the efficiency of convective energy transport in the deep convection zones of stars. Rotationally constrained convection has been invoked to explain the lack of large-scale power in observations of solar flows. Aims: The main aims are to quantify the effects of rotation on

Zhangjie Qin, Daniel Azses, Eran Sela, Robert Raussendorf

Computational power in measurement-based quantum computing stems from the symmetry-protected topological (SPT) order of entangled resource states. However, resource states are prone to preparation errors. We introduce a quantum error correction approach using redundant nonlocal symmetry of the resource state. We demonstrate it within a teleportation protocol

Markus Schweighofer, Luis Felipe Vargas

In 1995, Reznick showed an important variant of the obvious fact that any positive semidefinite (real) quadratic form is a sum of squares of linear forms: If a form (of arbitrary even degree) is positive definite then it becomes a sum of squares of forms after being multiplied by a sufficiently high power of the sum of its squared variables. If the form is j

Chandeepa Dissanayake

The Closest String Problem is an NP-complete problem which appears more commonly in bioinformatics and coding theory. Less surprisingly, classical approaches have been pursued with two prominent algorithms being the genetic algorithm and simulated annealing. Latest improvements to quantum computing devices with a specialization in optimization tasks such as

Hanan Hamza, Fiza Gafoor, Fathima Sithara, Gayathri Anil

In the era of advanced artificial intelligence and human-computer interaction, identifying emotions in spoken language is paramount. This research explores the integration of deep learning techniques in speech emotion recognition, offering a comprehensive solution to the challenges associated with speaker diarization and emotion identification. It introduces

Shihao Zhu, Juefei Wu, Peng Zhu, Cuiying Pei

Topological transition metal dichalcogenides (TMDCs) have attracted much attention due to its potential applications in spintronics and quantum computations. In this work, we systematically investigate the structural and electronic properties of topological TMDCs candidate ZrTe2 under high pressure. A pressure-induced Lifshitz transition is evidenced by the

Chao Xiong, Cuiying Pei, Qi Wang, Yi Zhao

Lu5Rh6Sn18 is one of the caged-type quasiskutterudite superconductors with superconducting transition temperature Tc = 4.12 K. Here, we investigate the effect of pressure on the superconductivity in Lu5Rh6Sn18 by combining high pressure electrical transport, synchrotron x-ray diffraction (XRD) and chemical doping. Application of high pressure can enhance bot

Mingde Yao, Ruikang Xu, Yuanshen Guan, Jie Huang

Existing methods have demonstrated effective performance on a single degradation type. In practical applications, however, the degradation is often unknown, and the mismatch between the model and the degradation will result in a severe performance drop. In this paper, we propose an all-in-one image restoration network that tackles multiple degradations. Due

Yu Fu, Zhong-Bo Kang, Farid Salazar, Xin-Nian Wang

The Color Glass Condensate (CGC) effective theory and the collinear factorization at high-twist (HT) are two well-known frameworks describing perturbative QCD multiple scatterings in nuclear media. It has long been recognized that these two formalisms have their own domain of validity in different kinematics regions. Taking direct photon production in proton

Jiasheng Chen, Juan Tang, Ming Yan, Shuai Lai

As is well known, differential algebraic equations (DAEs), which are able to describe dynamic changes and underlying constraints, have been widely applied in engineering fields such as fluid dynamics, multi-body dynamics, mechanical systems and control theory. In practical physical modeling within these domains, the systems often generate high-index DAEs. Cl

Hans-Otto Walther

Differential equations with state-dependent delays define a semiflow of continuously differentiable solution operators in general only on an associated submanifold of the Banach space $C^1([-h,0],\mathbb{R}^n)$. We extend a recent result on simplicity of these {\it solution manifolds} to systems where the delay is given by the state only implicitly in an ext

Ramesh Chandra, Pooja Devi, P. F. Chen, Brigitte Schmieder

Extreme-ultraviolet (EUV) waves are one of the large-scale phenomena on the Sun. They are defined as large propagating fronts in the low corona with speeds ranging from a few tens km/s to a multiple of 1000 km/s. They are often associated with solar filament eruptions, flares, or coronal mass ejections (CMEs). EUV waves show different features, such as, wave

Paul Marriott, Weinan Qi, Yi Shen

In this paper we examine isotropic Gaussian random fields defined on $\mathbb R^N$ satisfying certain conditions. Specifically, we investigate the type of a critical point situated within a small vicinity of another critical point, with both points surpassing a given threshold. It is shown that the Hessian of the random field at such a critical point is equa

Danny Wood, Theodore Papamarkou, Matt Benatan, Richard Allmendinger

In order to trust the predictions of a machine learning algorithm, it is necessary to understand the factors that contribute to those predictions. In the case of probabilistic and uncertainty-aware models, it is necessary to understand not only the reasons for the predictions themselves, but also the reasons for the model's level of confidence in those predi

Lukas Conrads, Luis Schüler, Konstantin G. Wirth, Matthias Wuttig

Tailoring light-matter interaction is essential to realize nanophotonic components. It can be achieved with surface phonon polaritons (SPhPs), an excitation of photons coupled with phonons of polar crystals, which also occur in 2d materials such as hexagonal boron nitride or anisotropic crystals. Ultra-confined resonances are observed by restricting the SPhP

Naoko Kurahashi

As IceCube surpasses a decade of operation in the full detector configuration, results that drive forward the fields of neutrino astronomy, cosmic ray physics, multi-messenger astronomy, particle physics, and beyond continue to emerge at an accelerated pace. IceCube data is dominated by background events, and thus teasing out the signal is the common challen

Rajgowrav Cheenikundil

The thesis discusses micromagnetic simulation studies on high-frequency magnetic dynamics in three-dimensional ferromagnetic nanoarchitectures made of interconnected magnetic nanowire networks. Such artificial magnetic materials with nanoscale features have recently emerged as a vivid topic of research, as their geometry has a decisive impact on their magnet

Akshay Bansal, Atul Singh Arora, Thomas Van Himbeeck, Jamie Sikora

Self-testing is the task where spatially separated Alice and Bob cooperate to deduce the inner workings of untrusted quantum devices by interacting with them in a classical manner. We examine the task above where Alice and Bob do not trust each other which we call adversarial self-testing. We show that adversarial self-testing implies secure sampling -- a si

Hsinyu Chang, Yicheng Hsu, Mingsian R. Bai

Recent research advances in deep neural network (DNN)-based beamformers have shown great promise for speech enhancement under adverse acoustic conditions. Different network architectures and input features have been explored in estimating beamforming weights. In this paper, we propose a deep beamformer based on an efficient convolutional recurrent network (C

Jinheon Baek, Soyeong Jeong, Minki Kang, Jong C. Park

Recent Language Models (LMs) have shown impressive capabilities in generating texts with the knowledge internalized in parameters. Yet, LMs often generate the factually incorrect responses to the given queries, since their knowledge may be inaccurate, incomplete, and outdated. To address this problem, previous works propose to augment LMs with the knowledge

Victoria Palhares, Gian Marti, Oscar Castañeda, Christoph Studer

All-digital massive multiuser (MU) multiple-input multiple-output (MIMO) at millimeter-wave (mmWave) frequencies is a promising technology for next-generation wireless systems. Low-resolution analog-to-digital converters (ADCs) can be utilized to reduce the power consumption of all-digital basestation (BS) designs. However, simultaneously transmitting user e

Hossein Lamei Ramandi

We show it is consistent with $\ZFC$ that there is an everywhere Kurepa line which is order isomorphic to all of its dense $\aleph_2$-dense suborders. Moreover, this Kurepa line does not contain any Aronszajn suborder. We also show it is consistent with $\ZFC$ that there is a minimal Kurepa line which does not contain any Aronszajn suborder.

Robert Logan, Amy W. Wehe, Dori C. Woods, Jon Tilly

Levenshtein distance is a commonly used edit distance metric, typically applied in language processing, and to a lesser extent, in molecular biology analysis. Biological nucleic acid sequences are often embedded in longer sequences and are subject to insertion and deletion errors that introduce frameshift during sequencing. These frameshift errors are due to

Jayde Sylvie Massmann, Adrian Wang Kwon

This paper serves to define an extension, which we call dimensional Veblen, of Oswald Veblen's system of ordinal functions below the large Veblen ordinal. This is facilitated by iterating derivatives of ordinal functions along multidimensional array structures, and can be viewed as the "maximal" natural extension of the Veblen functions. We then construct an

Rodrigo Pérez-Dattari, Cosimo Della Santina, Jens Kober

Imitation Learning (IL) is a powerful technique for intuitive robotic programming. However, ensuring the reliability of learned behaviors remains a challenge. In the context of reaching motions, a robot should consistently reach its goal, regardless of its initial conditions. To meet this requirement, IL methods often employ specialized function approximator

Jonathan Beall, Jordan Elm, Mathew W Semler, Li Wang

Multi-Arm, Multi-Stage (MAMS) clinical trial designs allow for multiple therapies to be compared across a spectrum of clinical trial phases. MAMS designs can be categorized into several overarching design groups, including adaptive designs (AD) and multi-arm (MA) designs. Factorial clinical trials designs represent an additional group of designs which can pr

Richard Lee Lai

We develop a microfluidic platform for the long-term cultivation and observation of both THP-1 cells under different physiological conditions. First, we determine optimal seeding conditions and microwell geometry. Next, we observe changes in cell size and circularity. Results show that gradient magnetic forces on the order of 102 T/m results in stunted growt

Liding Zhang, Zhenshan Bing, Kejia Chen, Lingyun Chen

In path planning, anytime almost-surely asymptotically optimal planners dominate the benchmark of sampling-based planners. A notable example is Batch Informed Trees (BIT*), where planners iteratively determine paths to batches of vertices within the exploration area. However, utilizing a consistent batch size is inefficient for initial pathfinding and optima

Jeremy Seeman, William Sexton, David Pujol, Ashwin Machanavajjhala

We consider the problem of the private release of statistics (like aggregate payrolls) where it is critical to preserve the contribution made by a small number of outlying large entities. We propose a privacy formalism, per-record zero concentrated differential privacy (PzCDP), where the privacy loss associated with each record is a public function of that r

Chunheng Zhao, Jacob Maarek, Seyed Mohammadamin Taleghani, Stephane Zaleski

We propose a hybrid continuum surface force (CSF) formulation to model the interface interaction within the three-phase volume of fluid (VOF) method. Instead of employing the height function globally, we compute the curvature based on a smooth fraction function near the region of the triple contact line. In addition, we apply the isotropic finite difference

Brice Romuald Gueyap Kounga

This paper studies a nonseparable model for dyadic outcomes, such as trade flows between pairs of countries, in which the outcome depends on the two agents' observed characteristics and on a scalar unobservable through an unknown function that is strictly increasing in the unobservable. I establish identification of a normalized representative of the str

Weizheng Cao, Juefei Wu, Yongkai Li, Cuiying Pei

Layered SnAs-based Zintl compounds exhibit a distinctive electronic structure, igniting extensive research efforts in areas of superconductivity, topological insulators and quantum magnetism. In this paper, we systematically investigate the crystal structures and electronic properties of the Zintl compound SrSn2As2 under high-pressure. At approximately 20.8

Aohan Zeng, Mingdao Liu, Rui Lu, Bowen Wang

Open large language models (LLMs) with great performance in various tasks have significantly advanced the development of LLMs. However, they are far inferior to commercial models such as ChatGPT and GPT-4 when acting as agents to tackle complex tasks in the real world. These agent tasks employ LLMs as the central controller responsible for planning, memoriza

Emilio Carrizosa, Jasone Ramírez-Ayerbe, Dolores Romero Morales

Due to the increasing use of Machine Learning models in high stakes decision making settings, it has become increasingly important to have tools to understand how models arrive at decisions. Assuming a trained Supervised Classification model, explanations can be obtained via counterfactual analysis: a counterfactual explanation of an instance indicates how t

Xin Zeng, Xiaoyu Wang, Tengxiang Zhang, Chun Yu

Existing gesture interfaces only work with a fixed set of gestures defined either by interface designers or by users themselves, which introduces learning or demonstration efforts that diminish their naturalness. Humans, on the other hand, understand free-form gestures by synthesizing the gesture, context, experience, and common sense. In this way, the user

Lin Zhu, Bruno Leonardi, Aboutaleb Haddadi, Sudipta Dutta

This paper proposes a centralized multi-plant reactive power and voltage controller to support voltage control in the interconnected onshore power system. This controller utilizes a hierarchical control structure consisting of a master controller and multiple slave controllers. To validate the proposed method, a realistic planning case of the New York State

Kalle Kujanpää, Joni Pajarinen, Alexander Ilin

Solving complex planning problems has been a long-standing challenge in computer science. Learning-based subgoal search methods have shown promise in tackling these problems, but they often suffer from a lack of completeness guarantees, meaning that they may fail to find a solution even if one exists. In this paper, we propose an efficient approach to augmen

Weize Chen, Xiaoyue Xu, Xu Han, Yankai Lin

Parameter-shared pre-trained language models (PLMs) have emerged as a successful approach in resource-constrained environments, enabling substantial reductions in model storage and memory costs without significant performance compromise. However, it is important to note that parameter sharing does not alleviate computational burdens associated with inference

Cheng-Kun Yang, Min-Hung Chen, Yung-Yu Chuang, Yen-Yu Lin

We present a Multimodal Interlaced Transformer (MIT) that jointly considers 2D and 3D data for weakly supervised point cloud segmentation. Research studies have shown that 2D and 3D features are complementary for point cloud segmentation. However, existing methods require extra 2D annotations to achieve 2D-3D information fusion. Considering the high annotati

Saray Bakker, Luzia Knoedler, Max Spahn, Wendelin Böhmer

In this paper, we address the problem of real-time motion planning for multiple robotic manipulators that operate in close proximity. We build upon the concept of dynamic fabrics and extend them to multi-robot systems, referred to as Multi-Robot Dynamic Fabrics (MRDF). This geometric method enables a very high planning frequency for high-dimensional systems

Yupei Liu, Yuqi Jia, Runpeng Geng, Jinyuan Jia

A prompt injection attack aims to inject malicious instruction/data into the input of an LLM-Integrated Application such that it produces results as an attacker desires. Existing works are limited to case studies. As a result, the literature lacks a systematic understanding of prompt injection attacks and their defenses. We aim to bridge the gap in this work

Bishal Sonar, Satyam Guragain, Ravi Srivastava

Given two signed graphs $\Gamma_1$ with nodes $\{u_1,u_2,\cdots,u_n\}$ and $\Gamma_2$, the neighbourhood corona, $\Gamma_1*\Gamma_2$ is the signed graph obtained by taking one copy of $\Gamma_1$ and $n_1$ copies of $\Gamma_2$, and joining every neighbour of the $i^{th}$ node with each nodes of the $i^{th}$ copy of $\Gamma_2$ by a new signed edge. In this pap

Chang Liu, Danny Laghi, Nicola Tamanini

Extreme mass-ratio inspirals (EMRIs), namely binary systems composed of a massive black hole and a compact stellar-mass object, are anticipated to be among the gravitational wave (GW) sources detected by the Laser Interferometer Space Antenna (LISA). Similarly to compact binary mergers detected by current GW detectors, EMRIs can be used as cosmic rulers to p

Hadrien Notarantonio, Sergey Yurkevich

In this article, we study systems of $n \geq 1$, not necessarily linear, discrete differential equations (DDEs) of order $k \geq 1$ with one catalytic variable. We provide a constructive and elementary proof of algebraicity of the solutions of such equations. This part of the present article can be seen as a generalization of the pioneering work by Bousquet-

Konstantin V. Getman, Ágnes Kóspál, Nicole Arulanantham, Dmitry A. Semenov

DQ Tau is a unique young high-eccentricity binary system that exhibits regular magnetic reconnection flares and pulsed accretion near periastron. We conducted NuSTAR, Swift, and Chandra observations during the July 30, 2022 periastron to characterize X-ray, near-ultraviolet (NUV), and optical flaring emissions. Our findings confirm the presence of X-ray supe

Yilong Wang, Bifang Liu, Erlin Qiao, Huaqing Cheng

It is commonly believed that accretion discs are truncated and their inner regions are described by advection dominated accretion flows (ADAFs) in the hard spectral state of black hole X-ray binaries. However, the increasing occurrence of a relativistically blurred Fe K$\alpha$ line together with a hard continuum points to the existence of a thin disc locate

Olivier Sprangers, Wander Wadman, Sebastian Schelter, Maarten de Rijke

Existing hierarchical forecasting techniques scale poorly when the number of time series increases. We propose to learn a coherent forecast for millions of time series with a single bottom-level forecast model by using a sparse loss function that directly optimizes the hierarchical product and/or temporal structure. The benefit of our sparse hierarchical los

Nico Daheim, Thomas Möllenhoff, Edoardo Maria Ponti, Iryna Gurevych

Models trained on different datasets can be merged by a weighted-averaging of their parameters, but why does it work and when can it fail? Here, we connect the inaccuracy of weighted-averaging to mismatches in the gradients and propose a new uncertainty-based scheme to improve the performance by reducing the mismatch. The connection also reveals implicit ass

Nikolay Khabarov, Alexey Smirnov, Michael Obersteiner

The "Hotelling rule" (HR) called to be "the fundamental principle of the economics of exhaustible resources" has a logical deficiency which was never paid a sufficient attention to. This deficiency should be taken into account before attempting to explain discrepancies between the price prediction provided by the HR and historically observed prices. Our anal

Jana Gauss, Fabian Scheipl, Moritz Herrmann

Whether class labels in a given data set correspond to meaningful clusters is crucial for the evaluation of clustering algorithms using real-world data sets. This property can be quantified by separability measures. The central aspects of separability for density-based clustering are between-class separation and within-class connectedness, and neither classi

Moses Openja, Gabriel Laberge, Foutse Khomh

The cause-to-effect analysis can help us decompose all the likely causes of a problem, such as an undesirable business situation or unintended harm to the individual(s). This implies that we can identify how the problems are inherited, rank the causes to help prioritize fixes, simplify a complex problem and visualize them. In the context of machine learning

Rachel E. C. Smith, Inês Ochoa, Rúben Inácio, Jonathan Shoemaker

We propose a differentiable vertex fitting algorithm that can be used for secondary vertex fitting, and that can be seamlessly integrated into neural networks for jet flavour tagging. Vertex fitting is formulated as an optimization problem where gradients of the optimized solution vertex are defined through implicit differentiation and can be passed to upstr

Amir Feder, Yoav Wald, Claudia Shi, Suchi Saria

The reliance of text classifiers on spurious correlations can lead to poor generalization at deployment, raising concerns about their use in safety-critical domains such as healthcare. In this work, we propose to use counterfactual data augmentation, guided by knowledge of the causal structure of the data, to simulate interventions on spurious features and t

Lluís Arola-Fernández, Lucas Lacasa

Unraveling the emergence of collective learning in systems of coupled artificial neural networks points to broader implications for machine learning, neuroscience, and society. Here we introduce a minimal model that condenses several recent decentralized algorithms by considering a competition between two terms: the local learning dynamics in the parameters

Mikhail N. Semeikin, Kay Joerg Wiese

A $d$-dimensional elastic manifold at depinning is described by a renormalized field theory, based on the Functional Renormalization Group (FRG). Here we analyze this theory to 3-loop order, equivalent to third order in $\epsilon=4-d$, where $d$ is the internal dimension. The critical exponent reads $\zeta = \frac \epsilon3 + 0.04777 \epsilon^2 -0.068354 \ep

Mann Khatri, Mirza Yusuf, Yaman Kumar, Rajiv Ratn Shah

The burdensome impact of a skewed judges-to-cases ratio on the judicial system manifests in an overwhelming backlog of pending cases alongside an ongoing influx of new ones. To tackle this issue and expedite the judicial process, the proposition of an automated system capable of suggesting case outcomes based on factual evidence and precedent from past cases

Zeyu Jin, Ruo Li

A promising approach to investigating high-dimensional problems is to identify their intrinsically low-dimensional features, which can be achieved through recently developed techniques for effective low-dimensional representation of functions such as machine learning. Based on available finite-dimensional approximate solution manifolds, this paper proposes a

Zhiyuan Liu, Sihang Li, Yanchen Luo, Hao Fei

Language Models (LMs) have demonstrated impressive molecule understanding ability on various 1D text-related tasks. However, they inherently lack 2D graph perception - a critical ability of human professionals in comprehending molecules' topological structures. To bridge this gap, we propose MolCA: Molecular Graph-Language Modeling with Cross-Modal Projector

Lena Chatziastros, Bertram Bitsch, Aaron David Schneider

The chemical fingerprint of a planet reveals information about its formation history regarding when and where it formed. The water content of a planet can help to constrain its formation pathway: If the planet formed in the outer regions of the disk and migrated inward, it will be water-rich due to the accretion of water-ice-rich solids. Conversely, formatio

Devanshu Shekhar, Pragya Shukla

We analyze the subsystem size scaling of the entanglement entropy of a non-ergodic pure state that can be described by a multi-parametric Gaussian ensemble of complex matrices in a bipartite basis. Our analysis indicates, for a given set of global constraints, the existence of infinite number of universality classes of local complexity, characterized by the

Yifei Li, Xin Wang, Jian Sun, Gang Wang

This paper considers self-triggered consensus control of unknown linear multi-agent systems (MASs). Self-triggering mechanisms (STMs) are widely used in MASs, thanks to their advantages in avoiding continuous monitoring and saving computing and communication resources. However, existing results require the knowledge of system matrices, which are difficult to

Ningyu Xu, Qi Zhang, Jingting Ye, Menghan Zhang

Large language models (LLMs) have exhibited considerable cross-lingual generalization abilities, whereby they implicitly transfer knowledge across languages. However, the transfer is not equally successful for all languages, especially for low-resource ones, which poses an ongoing challenge. It is unclear whether we have reached the limits of implicit cross-

Lin Li, Yifei Wang, Chawin Sitawarin, Michael Spratling

Existing works have made great progress in improving adversarial robustness, but typically test their method only on data from the same distribution as the training data, i.e. in-distribution (ID) testing. As a result, it is unclear how such robustness generalizes under input distribution shifts, i.e. out-of-distribution (OOD) testing. This omission is conce

Zhimeng Gao, Sariel Har-Peled

$ \newcommand{\Re}{\mathbb{R}} \newcommand{\reals}{\mathbb{R}} \newcommand{\SetX}{\mathsf{X}} \newcommand{\rad}{r} \newcommand{\Eps}{\Mh{\mathcal{E}}} \newcommand{\p}{\Mh{p}} \newcommand{\q}{\Mh{q}} \newcommand{\Mh}[1]{#1} \newcommand{\query}{q} \newcommand{\eps}{\varepsilon} \newcommand{\VorX}[1]{\mathcal{V} \pth{#1}} \newcommand{\Polygon}{\mathsf{P}} \newc

Aditya Gupta, Asheesh Singh

Agriculture, as the cornerstone of human civilization, constantly seeks to integrate technology for enhanced productivity and sustainability. This paper introduces $\textit{Agri-GNN}$, a novel Genotypic-Topological Graph Neural Network Framework tailored to capture the intricate spatial and genotypic interactions of crops, paving the way for optimized predic

Ayoub Arraji, Saad Benjelloun, Salma Lahbabi

We study the stability of the one electron atom Schr\"odinger model with self-generated magnetic field in two dimensions. The magnetic energy is taken of the general form $K\int_{\mathbb{R}^2} |B|^p$ and we study the stability of the model as a function of the power $p$ and the coupling constant $K$. We show that for $p>3/2$, the model is always stable, and

Jiawen Zhu, Choubo Ding, Yu Tian, Guansong Pang

Open-set supervised anomaly detection (OSAD) - a recently emerging anomaly detection area - aims at utilizing a few samples of anomaly classes seen during training to detect unseen anomalies (i.e., samples from open-set anomaly classes), while effectively identifying the seen anomalies. Benefiting from the prior knowledge illustrated by the seen anomalies, c

Silouanos Brazitikos, Anthony Carbery, Finlay McIntyre

We consider some integral-geometric quantities that have recently arisen in harmonic analysis and elsewhere, derive some sharp geometric inequalities relating them, and place them in a wider context.

Henry Antonio Palasciano, Marina I. Knight, Guy P. Nason

This article introduces the class of continuous time locally stationary wavelet processes. Continuous time models enable us to properly provide scale-based time series models for irregularly-spaced observations for the first time, while also permitting a spectral representation of the process over a continuous range of scales. We derive results for both the

Jan Maurycy Uszko, Stephen J. Eichhorn, Avinash J. Patil, Simon R. Hall

Fulminating gold, the first high-explosive compound to be discovered, disintegrates in a mysterious cloud of purple smoke, the nature of which has been speculated upon since its discovery in 1585. In this work, we show that the colour of the smoke is due to the presence of gold nanoparticles.

David Liu, Zhengkun Li, Zihao Wu, Changying Li

Robotic crop phenotyping has emerged as a key technology to assess crops' morphological and physiological traits at scale. These phenotypical measurements are essential for developing new crop varieties with the aim of increasing productivity and dealing with environmental challenges such as climate change. However, developing and deploying crop phenotyping

Marta Navarro, Josué Feliu, Salvador Petit, María E. Gómez

Simultaneous multithreading processors improve throughput over single-threaded processors thanks to sharing internal core resources among instructions from distinct threads. However, resource sharing introduces inter-thread interference within the core, which has a negative impact on individual application performance and can significantly increase the turna

Hua Tang, Lu Cheng, Ninghao Liu, Mengnan Du

While the accuracy-fairness trade-off has been frequently observed in the literature of fair machine learning, rigorous theoretical analyses have been scarce. To demystify this long-standing challenge, this work seeks to develop a theoretical framework by characterizing the shape of the accuracy-fairness trade-off Pareto frontier (FairFrontier), determined b

Zhuang Xiong

Let $\Gamma = (G, \sigma)$ be a signed graph, where $G = (V(G),E(G))$ is an (unsigned) graph, called the underlying graph. The net Laplacian matrix of $\Gamma$ is defined as $L^{\pm}(\Gamma) = D^{\pm}(\Gamma) - A(\Gamma)$, where $D^{\pm}(\Gamma)$ and $A(\Gamma)$ are the diagonal matrix of net-degrees and the adjacency matrix of $\Gamma$, respectively. The nu

Myroslav Holovko, Vojko Vlachy, Dung di Caprio

In this paper, starting from the Bogoliubov-Born-Green-Yvon equations of the liquid-state theory, we formulate two equivalent approaches for the calculation of the total density profile and of the charge density profile of ionic fluids near nonplanar charged surfaces. In the framework of these approaches, we establish exact conditions, that a particular poin

Robert-Jan Slager, Adrien Bouhon, F. Nur Ünal

We comment on the recent paper ``Floquet non-Abelian topological insulator and multifold bulk-edge correspondence" by Tianyu Li and Haiping Hu, Nat. Comm. {\bf 14}, 6418 (2023). Apart from the fact that the authors unjustly imply to study multi-gap topology in Floquet systems for the first time, only known homotopic relations are presented. While such insigh

Yifei Xiong, Nianqiao Phyllis Ju, Sanguo Zhang

Many modern statistical analysis and machine learning applications require training models on sensitive user data. Under a formal definition of privacy protection, differentially private algorithms inject calibrated noise into the confidential data or during the data analysis process to produce privacy-protected datasets or queries. However, restricting acce

Shraddha Singh, Mina Doosti, Natansh Mathur, Mahshid Delavar

We present a framework for the unification and standardization of quantum network protocols, making their realization easier and expanding their use cases to a broader range of communities interested in quantum technologies. Our framework is available as an open-source repository, the Quantum Protocol Zoo. We follow a modular approach by identifying two key

Debabrata Mondal, K. Sengupta, Subhasis Sinha

In an atom-photon interacting system described by Tavis Cummings Hubbard (TCH) model, we demonstrate the emergence of a quasi-steady state in a dissipative environment that exhibits intriguing ergodic behavior. The TCH model undergoes a dissipative transition from normal to superradiant phase hosting a gapped Higgs and gapless Goldstone modes. However, in a