May 2024 arXiv papers — page 54

Chi-Hua Wang, Guang Cheng

We present BadGD, a unified theoretical framework that exposes the vulnerabilities of gradient descent algorithms through strategic backdoor attacks. Backdoor attacks involve embedding malicious triggers into a training dataset to disrupt the model's learning process. Our framework introduces three novel constructs: Max RiskWarp Trigger, Max GradWarp Trigger

Kaidi Wang, Zhiguo Ding, Daniel K. C. So, Zhi Ding

This paper investigates federated learning in a wireless communication system, where random device selection is employed with non-independent and identically distributed (non-IID) data. The analysis indicates that while training deep learning networks using federated stochastic gradient descent (FedSGD) on non-IID datasets, device selection can generate grad

Ralf K. Heilmann, Alexander R. Bruccoleri, James A. Gregory, Eric M. Gullikson

The Arcus Probe mission concept has been submitted as an Astrophysics Probe Explorer candidate. It features two co-aligned high-resolution grating spectrometers: one for the soft x-ray band and one for the far UV. Together, these instruments can provide unprecedented performance to address important key questions about the structure and dynamics of our unive

Aleksandr Algasov, Ekaterina Nepovinnykh, Tuomas Eerola, Heikki Kälviäinen

Recent advancements in the automatic re-identification of animal individuals from images have opened up new possibilities for studying wildlife through camera traps and citizen science projects. Existing methods leverage distinct and permanent visual body markings, such as fur patterns or scars, and typically employ one of two strategies: local features or e

Haoyang Cao, Zhengqi Wu, Renyuan Xu

This paper introduces a novel stochastic control framework to enhance the capabilities of automated investment managers, or robo-advisors, by accurately inferring clients' investment preferences from past activities. Our approach leverages a continuous-time model that incorporates utility functions and a generic discounting scheme of a time-varying rate, tai

Zachary Metzler, Nicholas Cannady, Daniel Shy, Regina Caputo

ComPair is a prototype gamma-ray telescope for the development of key technologies for next-generation gamma-ray detectors consisting of four subsystems: a 10-layer double-sided silicon strip detector tracker, a cadmium zinc telluride calorimeter, a cesium iodide calorimeter, and a plastic anti-coincidence detector (ACD). The ACD acts as an active shield to

Xiyao Wang, Jiuhai Chen, Zhaoyang Wang, Yuhang Zhou

Large vision-language models (LVLMs) have achieved impressive results in visual question-answering and reasoning tasks through vision instruction tuning on specific datasets. However, there remains significant room for improvement in aligning visual and language modalities. Existing methods often depend on external models or data, leading to uncontrollable a

Stacey Alberts, Jianwei Lyu, Irene Shivaei, George H. Rieke

The James Webb Space Telescope (JWST) is revolutionizing our view of the Universe through unprecedented sensitivity and resolution in the infrared, with some of the largest gains realized at its longest wavelengths. We present the Systematic Mid-infrared Instrument (MIRI) Legacy Extragalactic Survey (SMILES), an eight-band MIRI survey with Near-Infrared Spec

Shubham Singh

The study examines whether fama-french equity factors can effectively explain the idiosyncratic risk and return characteristics of Bitcoin. By incorporating Fama-french factors, the explanatory power of these factors on Bitcoin's excess returns over various moving average periods is tested through applications of several statistical methods. The analysis aim

Jonathan Peck, Bart Goossens

Deep neural networks are vulnerable to so-called adversarial examples: inputs which are intentionally constructed to cause the model to make incorrect predictions or classifications. Adversarial examples are often visually indistinguishable from natural data samples, making them hard to detect. As such, they pose significant threats to the reliability of dee

Li Qiao, Zhen Gao, Mahdi Boloursaz Mashhadi, Deniz Gündüz

Over-the-air computation (AirComp) is a promising technology converging communication and computation over wireless networks, which can be particularly effective in model training, inference, and more emerging edge intelligence applications. AirComp relies on uncoded transmission of individual signals, which are added naturally over the multiple access chann

Emmet A. Francis, Justin G. Laughlin, Jørgen S. Dokken, Henrik N. T. Finsberg

Biological cells rely on precise spatiotemporal coordination of biochemical reactions to control their many functions. Such cell signaling networks have been a common focus for mathematical models, but they remain challenging to simulate, particularly in realistic cell geometries. Herein, we present our software, Spatial Modeling Algorithms for Reactions and

Zesen Huang, Marco Velli, Chen Shi, Yingjie Zhu

Alfv\'en waves, considered one of the primary candidates for heating and accelerating the fast solar wind, are ubiquitous in spacecraft observations, yet their origin remains elusive. In this study, we analyze data from the first 19 encounters of the Parker Solar Probe (PSP) and report dominance of 2-minute oscillations near the Alfv\'en surface. The frequen

Francesco Nobili, Davide Parise

We study quantitative stability results for different classes of Sobolev inequalities on general compact Riemannian manifolds. We prove that, up to constants depending on the manifold, a function that nearly saturates a critical Sobolev inequality is quantitatively $W^{1,2}$-close to a non-empty set of extremal functions, provided that the corresponding opti

Haiyu Wu, Sicong Tian, Aman Bhatta, Jacob Gutierrez

Reported face verification accuracy has reached a plateau on current well-known test sets. As a result, some difficult test sets have been assembled by reducing the image quality or adding artifacts to the image. However, we argue that test sets can be challenging without artificially reducing the image quality because the face recognition (FR) models suffer

Weijie Xu, Richard Futrell

The effect of syntactic priming exhibits three well-documented empirical properties: the lexical boost, the inverse frequency effect, and the asymmetrical decay. We aim to show how these three empirical phenomena can be reconciled in a general learning framework, the hierarchical Bayesian model (HBM). The model represents syntactic knowledge in a hierarchica

Sean Ku

We study the essential self-adjointness of semi-bounded Schr\"{o}dinger operators on birth-death chains. First, we offer a general characterization which originates from studying a second order linear recurrence with variational coefficients which comes from the Schr\"{o}dinger operator. As this characterization is algebraically complicated, we present an ad

Julien Dallot, Maciej Pacut, Marcin Bienkowski, Darya Melnyk

In the well-known Minimum Linear Arrangement problem (MinLA), the goal is to arrange the nodes of an undirected graph into a permutation so that the total stretch of the edges is minimized. This paper studies an online (learning) variant of MinLA where the graph is not given at the beginning, but rather revealed piece-by-piece. The algorithm starts in a fixe

Haoran Duan, Shidong Wang, Varun Ojha, Shizheng Wang

While traditional feature engineering for Human Activity Recognition (HAR) involves a trial-anderror process, deep learning has emerged as a preferred method for high-level representations of sensor-based human activities. However, most deep learning-based HAR requires a large amount of labelled data and extracting HAR features from unlabelled data for effec

Yiran Luo, Joshua Feinglass, Tejas Gokhale, Kuan-Cheng Lee

Domain Generalization (DG) is a challenging task in machine learning that requires a coherent ability to comprehend shifts across various domains through extraction of domain-invariant features. DG performance is typically evaluated by performing image classification in domains of various image styles. However, current methodology lacks quantitative understa

Carlos Toxtli

The rapid advancement of Generative Artificial Intelligence (AI), such as Large Language Models (LLMs) and Multimodal Large Language Models (MLLM), has the potential to revolutionize the way we work and interact with digital systems across various industries. However, the current state of software automation, such as Robotic Process Automation (RPA) framewor

Ranthony A. Clark, Tom Needham, Thomas Weighill

Dimension reduction techniques typically seek an embedding of a high-dimensional point cloud into a low-dimensional Euclidean space which optimally preserves the geometry of the input data. Based on expert knowledge, one may instead wish to embed the data into some other manifold or metric space in order to better reflect the geometry or topology of the poin

Aitor Calvo-Fernández, Manish Kumar, Diego Soler-Polo, Asier Eiguren

The mechanism of Kondo screening in strongly correlated molecules with several unpaired electrons on a metal surface is still under debate. Here, we provide a theoretical framework that rationalizes the emergence of Kondo screening involving several extended molecular orbitals with unpaired electrons. We introduce a perturbative model, which provides simple

Rafael López, Marian Ioan Munteanu

Translators in the special linear group $SL(2,\mathbb{R})$ are surfaces whose mean curvature $H$ and unit normal vector $N$ satisfy $H=\langle N,X\rangle$, where $X$ is a fixed Killing vector field. In this paper we study and classify those translators that are invariant by a one-parameter group of isometries. By the Iwasawa decomposition, there are three ty

Sopam Dasgupta, Joaquín Arias, Elmer Salazar, Gopal Gupta

Machine learning models that automate decision-making are increasingly used in consequential areas such as loan approvals, pretrial bail approval, and hiring. Unfortunately, most of these models are black boxes, i.e., they are unable to reveal how they reach these prediction decisions. A need for transparency demands justification for such predictions. An af

Yusuf Oluwatoki Yusuf, Astrid Dufaure, Liisa-Ida Sorsa, Christelle Eyraud

This study conducts a quantitative distinguishability analysis using quasi-monostatic experimental radar data to find a topographic and backpropagated tomographic reconstruction for an analogue of asteroid Itokawa (25143). In particular, we consider a combination of travel-time and wavefield backpropagation tomography using the time-frequency representation

GERDA Collaboration, M. Agostini, A. Alexander, G. R. Araujo

A search for full energy depositions from bosonic keV-scale dark matter candidates of masses between 65 keV and 1021 keV has been performed with data collected during Phase II of the GERmanium Detector Array (GERDA) experiment. Our analysis includes direct dark matter absorption as well as dark Compton scattering. With a total exposure of 105.5 kg yr, no evi

Abdullah Nazhat Abdullah, Tarkan Aydin

The transformer architecture has driven many successes in a variety of tasks within the field of deep learning, in particular the recent advances in natural language processing (NLP) culminating with large language models (LLM). Adding to that success, transformer architecture has found widespread interest from computer vision (CV) researchers and practition

Philippe Gagnon, Florian Maire

Lifted samplers form a class of Markov chain Monte Carlo methods which has drawn a lot attention in recent years due to superior performance in challenging Bayesian applications. A canonical example of lifted samplers is the one that is derived from a random walk Metropolis algorithm for a totally-ordered state space such as the integers or the real numbers.

Yabo Li, Mikhail Litvinov

We explicitly realize the Rep($Q_8$) non-invertible symmetry-protected topological (SPT) state as a 1+1d cluster state on a tensor product Hilbert space of qubits. Using the Kramers-Wannier operator, we construct the lattice models for the phases of all the symmetries in the Rep($Q_8$) duality web. We further show that we can construct a class of lattice mod

Hwiyoung Lee, Shuo Chen

Machine learning models for continuous outcomes often yield systematically biased predictions, particularly for values that largely deviate from the mean. Specifically, predictions for large-valued outcomes tend to be negatively biased (underestimating actual values), while those for small-valued outcomes are positively biased (overestimating actual values).

Matias Araya Satriani, Felipe Barra

The reduced state of a small system strongly coupled to a charger in thermal equilibrium may be athermal and used as a small battery once disconnected. By harnessing the battery-charger correlations, the battery's extractable energy can increase above the ergotropy. We introduce a protocol that uses a quantum system as a memory that measures the charger and

Hanxuan Ye, Hongzhe Li

Traditional statistical and machine learning methods typically assume that the training and test data follow the same distribution. However, this assumption is frequently violated in real-world applications, where the training data in the source domain may under-represent specific subpopulations in the test data of the target domain. This paper addresses tar

Zhenfei Jiang, Tian Li, Matthew L. Boone, Zhenhuan Yi

Strong quantum-correlated sources are essential but delicate resources for quantum information science and engineering protocols. Decoherence and loss are the two main disruptive processes that lead to the loss of nonclassical behavior in quantum correlations. In quantum systems, scattering can contribute to both decoherence and loss. In this work, we presen

Jash Desai, Gabriel Herczeg, David McNutt, Max Pezzelle

It was recently conjectured that a certain vacuum Kerr-Schild spacetime, which may be regarded as a self-dual analog of the Kerr metric, is equivalent to the self-dual Taub-NUT instanton. We confirm this conjecture by applying the Cartan-Karlhede algorithm to each metric and showing that for suitable choices of null tetrad, the algorithm leads to the same in

Alexandre Mauroy, Igor Mezic

We develop a novel EDMD-type algorithm that captures the spectrum of the Koopman operator defined on a reproducing kernel Hilbert space of analytic functions. This method, which we call analytic EDMD, relies on an orthogonal projection on polynomial subspaces, which is equivalent to a data-driven Taylor approximation. In the case of dynamics with a hyperboli

Benedetta Veronesi, Cristiano Longarini, Giuseppe Lodato, Guillaume Laibe

The mass of protoplanetary discs sets the amount of material available for planet formation, determines the level of coupling between gas and dust, and possibly sets gravitational instabilities. Measuring mass of discs is challenging, since it is not possible to directly detect H$_2$, and CO-based estimates remain poorly constrained. An alternative method th

Adam S. Shai, Sarah E. Marzen, Lucas Teixeira, Alexander Gietelink Oldenziel

What computational structure are we building into large language models when we train them on next-token prediction? Here, we present evidence that this structure is given by the meta-dynamics of belief updating over hidden states of the data-generating process. Leveraging the theory of optimal prediction, we anticipate and then find that belief states are l

Hancheng Min, René Vidal

The implicit bias of gradient-based training algorithms has been considered mostly beneficial as it leads to trained networks that often generalize well. However, Frei et al. (2023) show that such implicit bias can harm adversarial robustness. Specifically, they show that if the data consists of clusters with small inter-cluster correlation, a shallow (two-l

Peter Richtárik, Abdurakhmon Sadiev, Yury Demidovich

This paper presents a comprehensive analysis of a broad range of variations of the stochastic proximal point method (SPPM). Proximal point methods have attracted considerable interest owing to their numerical stability and robustness against imperfect tuning, a trait not shared by the dominant stochastic gradient descent (SGD) algorithm. A framework of assum

Li Ping, Liao Wen-Ping, Sun Qi-Bin, Li Min-Yu

We model the TESS light curve of the binary system RX Dra, and also first calculate a lot of theoretical models to fit the g-mode frequencies previously detected from the TESS series of this system. The mass ratio is determined to be $q$=0.9026$^{+0.0032}_{-0.0032}$. We newly found that there are 16 frequencies (F1-F7, F11-F20) identified as dipole g-modes,

Yi-Ting Shen, Hyungtae Lee, Heesung Kwon, Shuvra S. Bhattacharyya

Synthetic data generation has emerged as a promising solution to the data scarcity issue in aerial-view human detection. However, creating datasets that accurately reflect varying real-world human appearances, particularly diverse poses, remains challenging and labor-intensive. To address this, we propose SynPoseDiv, a novel framework that diversifies human

Hajrudin Bešić, Andreas Deutschmann-Olek, Kenan Mešić, Kostas Kanellopulos

We present an advanced thermal response model for micro- and nanomechanical systems in photothermal sensing, designed to balance speed and precision. Our model considers the two time constants of the nanomechanical element and the supporting chip, triggered by photothermal heating, enabling precise photothermal input signal estimation through Kalman filterin

A. Belhaj, H. Belmahi, A. Bouhouch, S. E. Ennadifi

In this work, we reconsider the study of 5D black branes in M-theory compactifications by means of $\mathcal{N}=2$ supergravity formalism. Precisely, we provide a model relaying on a three parameter Calabi-Yau manifold in the $\mathbb{P}^{1}\times\mathbb{P}^{1}\times\mathbb{P}^{2}$ projective space factorization, referred to as economical model. First, we in

Sergio Rojas-Galeano

This paper investigates the application of pre-trained large language models (LLMs) for spam email classification using zero-shot prompting. We evaluate the performance of both open-source (Flan-T5) and proprietary LLMs (ChatGPT, GPT-4) on the well-known SpamAssassin dataset. Two classification approaches are explored: (1) truncated raw content from email su

David Hunn, Willie K. Harrison

Recent theoretical developments in coset coding theory have provided continuous-valued functions which give the equivocation and maximum likelihood (ML) decoding probability of coset secrecy codes. In this work, we develop a method for incorporating these functions, along with a complex set of constraints, into a gradient descent optimization algorithm. This

Gabriel Buginga, Edmundo de Souza e Silva

Survival analysis aims to predict the timing of future events across various fields, from medical outcomes to customer churn. However, the integration of clustering into survival analysis, particularly for precision medicine, remains underexplored. This study introduces SurvMixClust, a novel algorithm for survival analysis that integrates clustering with sur

Andreas Crivellin

Indications for new Higgs bosons at 95\,GeV and 152\,GeV with significance of 3.8$\sigma$ and $4.3\sigma$, respectively, have been obtained. While the former contains the inclusive $\gamma\gamma$ channel, the latter is obtained by combining several modes of associated di-photon production, i.e. $\gamma\gamma+X$ with $X=\ell,\,\ell b,\,{\rm MET},\,\tau,\,...$

Soumyabrata Kundu, Risi Kondor

We introduce Steerable Transformers, an extension of the Vision Transformer mechanism that maintains equivariance to the special Euclidean group $\mathrm{SE}(d)$. We propose an equivariant attention mechanism that operates on features extracted by steerable convolutions. Operating in Fourier space, our network utilizes Fourier space non-linearities. Our expe

Byungjoo Kim, Seongjin Hong, Yong-Su Kim, Kyunghwan Oh

Recently, remote-controlled quantum information processing has been proposed for its applications in secure quantum processing protocols and distributed quantum networks. For remote-controlled quantum gates, the experimental realization of controlled unitary (CU) gates between any quantum gates is an essential task. Here, we propose and experimentally demons

Arman Irani, Michalis Faloutsos, Kevin Esterling

How can we model arguments and their dynamics in online forum discussions? The meteoric rise of online forums presents researchers across different disciplines with an unprecedented opportunity: we have access to texts containing discourse between groups of users generated in a voluntary and organic fashion. Most prior work so far has focused on classifying

Hui Li, Jian Ni, Fangzhu Yang

The rise of generative artificial intelligence (AI) has facilitated automated product design but often neglects valuable consumer preference data within companies' internal datasets. Additionally, external sources such as social media and user-generated content (UGC) platforms contain substantial untapped information on product design and consumer preference

Dea Gogishvili, Emmanuel Minois-Genin, Jan van Eck, Sanne Abeln

Hydrophobic patches on protein surfaces play important functional roles in protein-protein and protein-ligand interactions. Large hydrophobic surfaces are also involved in the progression of aggregation diseases. Predicting exposed hydrophobic patches from a protein sequence has been shown to be a difficult task. Fine-tuning foundation models allows for adap

MHD Anas Alsakkal, Runze Wang, Jayawan Wijekoon, Huajin Tang

Spike-based encoders represent information as sequences of spikes or pulses, which are transmitted between neurons. A prevailing consensus suggests that spike-based approaches demonstrate exceptional capabilities in capturing the temporal dynamics of neural activity and have the potential to provide energy-efficient solutions for low-power applications. The

Lorenzo Tiberi, Francesca Mignacco, Kazuki Irie, Haim Sompolinsky

Despite the remarkable empirical performance of Transformers, their theoretical understanding remains elusive. Here, we consider a deep multi-head self-attention network, that is closely related to Transformers yet analytically tractable. We develop a statistical mechanics theory of Bayesian learning in this model, deriving exact equations for the network's

Chunyu Yuan, Dongfang Zhao, Sos S. Agaian

Skin lesion segmentation is key for early skin cancer detection. Challenges in automatic segmentation from dermoscopic images include variations in color, texture, and artifacts of indistinct lesion boundaries. Deep learning methods like CNNs and U-Net have shown promise in addressing these issues. To further aid early diagnosis, especially on mobile devices

Kun Zhao, Bohao Yang, Chen Tang, Chenghua Lin

The long-standing one-to-many problem of gold standard responses in open-domain dialogue systems presents challenges for automatic evaluation metrics. Though prior works have demonstrated some success by applying powerful Large Language Models (LLMs), existing approaches still struggle with the one-to-many problem, and exhibit subpar performance in domain-sp

MHD Anas Alsakkal, Jayawan Wijekoon

This paper presents a novel FPGA-based neuromorphic cochlea, leveraging the general-purpose spike-coding algorithm, Spiketrum. The focus of this study is on the development and characterization of this cochlea model, which excels in transforming audio vibrations into biologically realistic auditory spike trains. These spike trains are designed to withstand n

Marco Guarise, Mirco Andreotti, Anna Balboni, Roberto Calabrese

Burst effect of Silicon Photomultiplier (SiPM) at cryogenic temperatures have been discovered few years ago looking at the dark count rate of SiPMs at liquid nitrogen temperatures. Bursts are trains of single signals that happen randomly and are clearly distinguishable from the primary DCR and correlated noise because of their particular time distribution. I

P. Jameson Graber

In this expository article, we give an overview of the concept of potential mean field games of first order. We give a new proof that minimizers of the potential are equilibria by using a Lagrangian formulation. We also provide criteria to determine whether or not a game has a potential. Finally, we discuss in some depth the selection problem in mean field g

Shuai Zhang, Heshan Devaka Fernando, Miao Liu, Keerthiram Murugesan

This paper studies the transfer reinforcement learning (RL) problem where multiple RL problems have different reward functions but share the same underlying transition dynamics. In this setting, the Q-function of each RL problem (task) can be decomposed into a successor feature (SF) and a reward mapping: the former characterizes the transition dynamics, and

Elad Liebman

Fault detection is a key challenge in the management of complex systems. In the context of SparkCognition's efforts towards predictive maintenance in large scale industrial systems, this problem is often framed in terms of anomaly detection - identifying patterns of behavior in the data which deviate from normal. Patterns of normal behavior aren't captured s

Mikkel N. Lund, Sarbani Basu, Allyson Bieryla, Luca Casagrande

The KEYSTONE project aims to enhance our understanding of solar-like oscillators by delivering a catalogue of global asteroseismic parameters (${\Delta\nu}$ and ${\nu_{\rm max}}$) for 173 stars, comprising mainly dwarfs and subgiants, observed by the K2 mission in its short-cadence mode during campaigns 6-19. We derive atmospheric parameters and luminosities

Justin H. Porter, Matthew R. W. Brake

Modern engineering structures exhibit nonlinear vibration behavior as designs are pushed to reduce weight and energy consumption. Of specific interest here, joints in assembled structures introduce friction, hysteresis, and unilateral contact resulting in nonlinear vibration effects. In many cases, it is impractical to remove jointed connections necessitatin

Vít Musil, S. Spektor

We study the convergence of the Hermite series of measurable functions on the real line. We characterize the norm convergence of truncated partial Hermite sums in rearrangement invariant spaces provided that the truncations vanish sufficiently slowly. Moreover, we provide the necessary and sufficient conditions for convergence in the Orlicz modular.

Jianing Qian, Anastasios Panagopoulos, Dinesh Jayaraman

Generic re-usable pre-trained image representation encoders have become a standard component of methods for many computer vision tasks. As visual representations for robots however, their utility has been limited, leading to a recent wave of efforts to pre-train robotics-specific image encoders that are better suited to robotic tasks than their generic count

Elisabeth Gliott, Adam Rançon, Nicolas Cherroret

We explore the emergence of universal dynamic scaling in an interacting Bose gas around the condensation transition, under the combined influence of an external driving force and spatial disorder. As time progresses, we find that the Bose gas crosses over three distinct dynamical regimes: (i) an inverse turbulent cascade where interactions dominate the drive

Yumin Zhang, Xingyu Miao, Haoran Duan, Bo Wei

Text-to-3D content creation is a rapidly evolving research area. Given the scarcity of 3D data, current approaches often adapt pre-trained 2D diffusion models for 3D synthesis. Among these approaches, Score Distillation Sampling (SDS) has been widely adopted. However, the issue of over-smoothing poses a significant limitation on the high-fidelity generation

Ryan McKenna

Correlated noise mechanisms such as DP Matrix Factorization (DP-MF) have proven to be effective alternatives to DP-SGD in large-epsilon few-epoch training regimes. Significant work has been done to find the best correlated noise strategies, and the current state-of-the-art approach is DP-BandMF, which optimally balances the benefits of privacy amplification

Ramya Ramalingam, Sangdon Park, Osbert Bastani

Machine learning has become an effective tool for automatically annotating unstructured data (e.g., images) with structured labels (e.g., object detections). As a result, a new programming paradigm called neurosymbolic programming has emerged where users write queries against these predicted annotations. However, due to the intrinsic fallibility of machine l

Deepak Bhoriya, Dinshaw S. Balsara, Vladimir Florinski, Harish Kumar

We present a new numerical model for solving the Chew-Goldberger-Low system of equations describing a bi-Maxwellian plasma in a magnetic field. Heliospheric and geospace environments are often observed to be in an anisotropic state with distinctly different parallel and perpendicular pressure components. The CGL system represents the simplest leading order c

Izhar, M. M. A. Fiagbenu, S. Yao, X. Du

Bulk Acoustic Wave (BAW) filters find applications in radio frequency (RF) communication systems for Wi-Fi, 3G, 4G, and 5G networks. In the beyond-5G (potential 6G) era, high frequency bands (>8 GHz) are expected to require resonators with high-quality factor (Q) and electromechanical coupling (k_t^2) to form filters with low insertion loss and high selectiv

Maria-Florina Balcan, Dravyansh Sharma

Decision trees and their ensembles are popular in machine learning as easy-to-understand models. Several techniques have been proposed in the literature for learning tree-based classifiers, with different techniques working well for data from different domains. In this work, we develop approaches to design tree-based learning algorithms given repeated access

Florent Baudier, Audrey Fovelle

In this note, we introduce and study the notions of asymptotic B-convexity and asymptotic infratype $p$, and we prove asymptotic analogs of a series of results due to Giesy \cite{Giesy66} and Pisier \cite{Pisier74}. In particular, we give a simplified proof of an asymptotic version of Pisier's $\ell_1$-theorem that was originally proven by Causey, Draga, and

Giovani H. Vicentin, Grzegorz Kowal, Elisabete M. de Gouveia Dal Pino, Alex Lazarian

We investigate the impact of turbulence on magnetic reconnection through high-resolution 3D magnetohydrodynamical (MHD) simulations, spanning Lundquist numbers from $S=10^3$ to $10^6$. Building on Lazarian and Vishniac's (1999) theory, which asserts reconnection rate independence from Ohmic resistivity, we introduce small-scale perturbations until $t=0.1\, t

Yuanliang Li, Hanzheng Dai, Jun Yan

Automated penetration testing (AutoPT) based on reinforcement learning (RL) has proven its ability to improve the efficiency of vulnerability identification in information systems. However, RL-based PT encounters several challenges, including poor sampling efficiency, intricate reward specification, and limited interpretability. To address these issues, we p

Daniel Bramblett, Siddharth Srivastava

Planning in real-world settings often entails addressing partial observability while aligning with users' requirements. We present a novel framework for expressing users' constraints and preferences about agent behavior in a partially observable setting using parameterized belief-state query (BSQ) policies in the setting of goal-oriented partially observable

Achuth Chandrasekhar, Jonathan Chan, Francis Ogoke, Olabode Ajenifujah

Generalized large language models (LLMs) such as GPT-4 may not provide specific answers to queries formulated by materials science researchers. These models may produce a high-level outline but lack the capacity to return detailed instructions on manufacturing and material properties of novel alloys. Enhancing a smaller model with specialized domain knowledg

O. J. Franca, Stefan Yoshi Buhmann

Our study delves into the modifications observed in Vavilov-Cherenkov radiation when its generating charged particle moves parallel to an interface formed by two generic magnetoelectric media, focusing on topological insulators. We compute the electromagnetic field through the Green's function. Applying the far-field approximation and the steepest descent me

Mahdi Moghaddam, Mario Dzemidzic, Daniel Guerrero, Mintao Liu

Human brain function dynamically adjusts to ever-changing stimuli from the external environment. Studies characterizing brain functional reconfiguration are nevertheless scarce. Here we present a principled mathematical framework to quantify brain functional reconfiguration when engaging and disengaging from a stop signal task (SST). We apply tangent space p

Deepak Bal, Patrick Bennett, Emily Heath, Shira Zerbib

Given a $k$-uniform hypergraph $G$ and a set of $k$-uniform hypergraphs $\mathcal{H}$, the generalized Ramsey number $f(G,\mathcal{H},q)$ is the minimum number of colors needed to edge-color $G$ so that every copy of every hypergraph $H\in \mathcal{H}$ in $G$ receives at least $q$ different colors. In this note we obtain bounds, some asymptotically sharp, on

Nan Huang, Christian Kümmerle, Xiang Zhang

Normalization techniques are crucial for enhancing Transformer models' performance and stability in time series analysis tasks, yet traditional methods like batch and layer normalization often lead to issues such as token shift, attention shift, and sparse attention. We propose UnitNorm, a novel approach that scales input vectors by their norms and modulates

Matheus Valentim, Jeanette Falk, Nanna Inie

The recent leaps in complexity and fluency of Large Language Models (LLMs) mean that, for the first time in human history, people can interact with computers using natural language alone. This creates monumental possibilities of automation and accessibility of computing, but also raises severe security and safety threats: When everyone can interact with LLMs

Seungeon Lee, Nina Corvelo Benz, Suhas Thejaswi, Manuel Gomez-Rodriguez

Resettlement agencies have started to adopt data-driven algorithmic matching to match refugees to locations using employment rate as a measure of utility. Given a pool of refugees, data-driven algorithmic matching utilizes a classifier to predict the probability that each refugee would find employment at any given location. Then, it uses the predicted probab

Fatma Hmissi, Sofiane Ouni

Nowadays, all sectors utilize devices that are part of the Internet of Things (IoT) for the purpose of connecting and exchanging information with other devices and systems over the Internet. This increases the diversity of devices and their working environments, which, in turn, creates new challenges, such as real-time interaction, security, interoperability

Vsevolod A. Afanasev

We study the class of idempotent-generated pseudo-composition algebras, which is a subclass of the family of axial algebras. More specifically, we utilise the group-algebra correspondence, natural to the axial framework in order to study some automorphism subgroups of such pseudo-composition algebras.

Pratikkumar Raje, Karthik Duraisamy

A wall-modeled large eddy simulation approach is proposed in a Discontinuous Galerkin (DG) setting, building on the slip-wall concept of Bae et al. (JFM'19) and the universal scaling relationship by Pradhan and Duraisamy (JFM'23). The effect of the order of the DG approximation is introduced via the length scales in the formulation. The level of under-resolu

Damien K. J. Lee, Zeyu Deng, Gopalakrishnan Sai Gautam, Pieremanuele Canepa

Metals, such as tin, antimony, and lead (Pb) have garnered renewed attention for their potential use as alloyant-negative electrode materials in sodium (Na)-ion batteries (NIBs). Despite Pb's toxicity and its high molecular weight, lead is one of the most commonly recycled metals, positioning Pb as a promising candidate for a cost-effective, high-capacity an

Kanoy Kumar Das, Amit Roy, Kamalesh Saha

In this article, we give combinatorial formulas for the regularity and the projective dimension of $3$-path ideals of chordal graphs, extending the well-known formulas for the edge ideals of chordal graphs given in terms of the induced matching number and the big height, respectively. As a consequence, we get that the $3$-path ideal of a chordal graph is Coh

Jayr Pereira, Francisco Rodrigues, Jaylton Pereira, Cleber Zanchettin

This paper presents an approach to enhancing Augmentative and Alternative Communication (AAC) systems by integrating Colourful Semantics (CS) with transformer-based language models specifically tailored for Brazilian Portuguese. We introduce an adapted BERT model, BERTptCS, which incorporates the CS framework for improved prediction of communication cards. T

Pranshu Malviya, Jerry Huang, Aristide Baratin, Quentin Fournier

Determining the optimal model for a given task often requires training multiple models from scratch, which becomes impractical as dataset and model sizes grow. A more efficient alternative is to expand smaller pre-trained models, but this approach is underutilized due to a limited understanding of its impact on the training dynamics. Existing methods for qua

Franck Iutzeler, Edouard Pauwels, Samuel Vaiter

We consider stochastic optimization problems where the objective depends on some parameter, as commonly found in hyperparameter optimization for instance. We investigate the behavior of the derivatives of the iterates of Stochastic Gradient Descent (SGD) with respect to that parameter and show that they are driven by an inexact SGD recursion on a different o

Rezaur Rashid, Joshua Melton, Ouldouz Ghorbani, Siddharth Krishnan

In today's digital age, social media platforms play a crucial role in shaping public opinion. This study explores how discussions led by influencers on Twitter, now known as 'X', affect public sentiment and contribute to online polarization. We developed a counterfactual framework to analyze the polarization scores of conversations in scenarios both with and

Julian Gass, Michael Levin

Recently, it was proposed that the chiral central charge of a gapped, two-dimensional quantum many-body system is proportional to a bulk ground state entanglement measure known as the modular commutator. While there is significant evidence to support this relation, we show in this paper that it is not universal. We give examples of lattice systems that have

Artem Lukoianov, Haitz Sáez de Ocáriz Borde, Kristjan Greenewald, Vitor Campagnolo Guizilini

While 2D diffusion models generate realistic, high-detail images, 3D shape generation methods like Score Distillation Sampling (SDS) built on these 2D diffusion models produce cartoon-like, over-smoothed shapes. To help explain this discrepancy, we show that the image guidance used in Score Distillation can be understood as the velocity field of a 2D denoisi

Rafael Lopez, Marian Ioan Munteanu

We introduce the notion of conformal trajectories in three-dimensional Riemannian manifolds $M^3$. Given a conformal vector field $V\in\mathfrak{X}(M^3)$, a conformal trajectory of $V$ is a regular curve $\gamma$ in $M^3$ satisfying $\nabla_{\gamma'}\gamma'=q\, V\times\gamma'$, for some fixed non-zero constant $q\in {\mathbb{R}}$. In this paper, we study con

Changhong Li, Junrong Lai, Jinjie Xiang, Chaofan Wu

We explore a dual scenario of generalized inflation and bounce cosmologies, producing a scale-invariant curvature perturbation spectrum. Bayesian analysis with pulsar timing array data identifies, for the first time, viable regions from inflation and bounce that simultaneously explain stochastic gravitational wave background (SGWB) signals and CMB anisotropi

Mingming Nie, Jonathan Musgrave, Shu-Wei Huang

This paper explores the integration of cross-polarized stimulated Brillouin scattering (XP-SBS) with Kerr and quadratic nonlinearities in lithium niobate (LN) to enhance photonic device performance. Three novel applications are demonstrated: (i) a reconfigurable stimulated Brillouin laser (SBL) with 0.7-Hz narrow linewidth and 40-nm tunability, enabled by XP

Vydhourie Thiyageswaran, Tyler McCormick, Jennifer Brennan

Randomized controlled trials often suffer from interference, a violation of the Stable Unit Treatment Values Assumption (SUTVA) in which a unit's treatment assignment affects the outcomes of its neighbors. This interference causes bias in naive estimators of the average treatment effect (ATE). A popular method to achieve unbiasedness is to pair the Horvitz-T

Parth Padalkar, Natalia Ślusarz, Ekaterina Komendantskaya, Gopal Gupta

Recent efforts in interpreting Convolutional Neural Networks (CNNs) focus on translating the activation of CNN filters into a stratified Answer Set Program (ASP) rule-sets. The CNN filters are known to capture high-level image concepts, thus the predicates in the rule-set are mapped to the concept that their corresponding filter represents. Hence, the rule-s