arXiv papers from January 2024

C. Adambukulam, J. A. Scott, S. Q. Lim, I. Aharonovich

The germanium vacancy in diamond (GeV) is a promising candidate for color center based quantum networking. Yet, like for other group-IV vacancy defects in diamond, achieving fast, high-fidelity qubit operations using traditional magnetic resonance techniques is experimentally challenging due to a weak magnetic dipole and susceptibility to thermally induced d

Hafiz Mughees Ahmad, Afshin Rahimi, Khizer Hayat

The increasing popularity of Deep Learning (DL) based Object Detection (OD) methods and their real-world applications have opened new venues in smart manufacturing. Traditional industries struck by capacity constraints after Coronavirus Disease (COVID-19) require non-invasive methods for in-depth operations' analysis to optimize and increase their revenue. I

Justin Kong, Terrence J. Moore, Fikadu T. Dagefu

This letter investigates covert routing communications in a heterogeneous network where a source transmits confidential data to a destination with the aid of relaying nodes where each transmitter judiciously chooses one modality among multiple communication modalities. We develop a novel reinforcement learning-based covert routing algorithm that finds a rout

Hojat Allah Salehi, Farhad Shirani, S. Sandeep Pradhan

This work considers the non-interactive source simulation problem (NISS). In the standard NISS scenario, a pair of distributed agents, Alice and Bob, observe a distributed binary memoryless source $(X^d,Y^d)$ generated based on joint distribution $P_{X,Y}$. The agents wish to produce a pair of discrete random variables $(U_d,V_d)$ with joint distribution $P_

Min-Seok Seo

We study the late time behavior of the scalar part of the volume modulus and the dilaton in stringy quintessence model, focusing on their contributions to the Hubble slow-roll parameter $\epsilon$ which directly measures the deviation of the spacetime geometry from de Sitter space. When only one of the moduli is allowed to move, $\epsilon$ converges to the s

Blaise Delattre, Quentin Barthélemy, Alexandre Allauzen

This paper leverages the use of \emph{Gram iteration} an efficient, deterministic, and differentiable method for computing spectral norm with an upper bound guarantee. Designed for circular convolutional layers, we generalize the use of the Gram iteration to zero padding convolutional layers and prove its quadratic convergence. We also provide theorems for b

Ao Huang, Yi Zhou, Satoshi Hattori

Network meta-analysis (NMA) is a useful tool to compare multiple interventions simultaneously in a single meta-analysis, it can be very helpful for medical decision making when the study aims to find the best therapy among several active candidates. However, the validity of its results is threatened by the publication bias issue. Existing methods to handle t

Tom Shutt, Bahrudin Trbalic, Aldo Pena-Perez, Steffen Luitz

We report on the development of a novel pixel charge readout system, Grid Activated Multi-scale pixel readout (GAMPix), which is under development for use in the GammaTPC gamma ray instrument concept. GammaTPC is being developed to optimize the use of liquid argon time projection chamber technology for gamma ray astrophysics, for which a fine grained low pow

Mohammad, Jamshidi, Dinh Thai Hoang, Diep N. Nguyen

Digital twins (DTs) are revolutionizing the biotechnology industry by enabling sophisticated digital representations of biological assets, microorganisms, drug development processes, and digital health applications. However, digital twinning at micro and nano scales, particularly in modeling complex entities like bacteria, presents significant challenges in

Ali Rabiee, Sima Ghafoori, Anna Cetera, Maryam Norouzi

This study aims to enhance BCI applications for individuals with motor impairments by comparing the effectiveness of tripolar EEG (tEEG) with conventional EEG. The focus is on interpreting and decoding various grasping movements, such as power grasp and precision grasp. The goal is to determine which EEG technology is more effective in processing and transla

Grover Lancaster-Cole, Georgiana Lyall, Thomas Malcolm, Qiyu Zhou

Following the work of Louisa and Michael Barnsley on results in tops of iterated function systems, we extend their work to graph-directed iterated function systems by investigating the relationship between top addresses and shift spaces. For the simplest overlapping interval IFS, we find a sufficient condition for the closure of its tops code space to be a s

Takashi Morita

This study reports an unintuitive finding that positional encoding enhances learning of recurrent neural networks (RNNs). Positional encoding is a high-dimensional representation of time indices on input data. Most famously, positional encoding complements the capabilities of Transformer neural networks, which lack an inherent mechanism for representing the

Ankit Gupta, George Saon, Brian Kingsbury

The emergence of industrial-scale speech recognition (ASR) models such as Whisper and USM, trained on 1M hours of weakly labelled and 12M hours of audio only proprietary data respectively, has led to a stronger need for large scale public ASR corpora and competitive open source pipelines. Unlike the said models, large language models are typically based on T

Shreya Rajagopal, Jae Ho Sohn, Hari Subramonyam, Shiwali Mohan

Generative AI systems such as ChatGPT and Claude are built upon language models that are typically evaluated for accuracy on curated benchmark datasets. Such evaluation paradigms measure predictive and reasoning capabilities of language models but do not assess if they can provide information that is useful to people. In this paper, we take some initial step

Óscar Pedreira, Félix García, Mario Piattini, Alejandro Cortiñas

Gamification has been applied in software engineering to improve quality and results by increasing people's motivation and engagement. A systematic mapping has identified research gaps in the field, one of them being the difficulty of creating an integrated gamified environment comprising all the tools of an organization, since most existing gamified tools a

Alex Grzankowski

The present paper looks at one of the most thorough articles on the intelligence of GPT, research conducted by engineers at Microsoft. Although there is a great deal of value in their work, I will argue that, for familiar philosophical reasons, their methodology, !Blackbox Interpretability"#is wrongheaded. But there is a better way. There is an exciting and

Ruixue Lian, William A. Sethares, Junjie Hu

Supervised contrastive learning (SCL) frameworks treat each class as independent and thus consider all classes to be equally important. This neglects the common scenario in which label hierarchy exists, where fine-grained classes under the same category show more similarity than very different ones. This paper introduces a family of Label-Aware SCL methods (

Filippo Baroni

We describe an algorithm which, given two essential curves on a surface $S$, computes their distance in the curve graph of $S$, up to multiplicative and additive errors. As an application, we present an algorithm to decide the Nielsen-Thurston type (periodic, reducible, or pseudo-Anosov) of a mapping class of $S$. The novelty of our algorithms lies in the fa

Antoine Gansemer, Andrew Hassell

We study pseudodifferential operators on a hyperbolic surface using `Zelditch quantization'. We motivate and study the trace of $A_2^* A_1(t)$, where $A_2$ is a fixed operator and the Zelditch symbol of $A_1(t)$ evolves by geodesic flow. We find conditions under which the trace decays exponentially as $t \to \pm \infty$.

Chi Cheuk Tsang

We show that a transitive Anosov flow with orientable stable and unstable foliations that either (i) admits a Birkhoff section whose first return map is a Penner type pseudo-Anosov map, or (ii) is totally periodic admits a genus one Birkhoff section. This provides evidence for a conjecture of Fried and Ghys. The proof utilizes a result of the author on the h

Ali Rabiee, Sima Ghafoori, Anna Cetera, Reza Abiri

This research aims to decode hand grasps from Electroencephalograms (EEGs) for dexterous neuroprosthetic development and Brain-Computer Interface (BCI) applications, especially for patients with motor disorders. Particularly, it focuses on distinguishing two complex natural power and precision grasps in addition to a neutral condition as a no-movement condit

Sota Moriyama, Koji Watanabe, Katsumi Inoue, Akihiro Takemura

We introduce MOD-CL, a multi-label object detection framework that utilizes constrained loss in the training process to produce outputs that better satisfy the given requirements. In this paper, we use $\mathrm{MOD_{YOLO}}$, a multi-label object detection model built upon the state-of-the-art object detection model YOLOv8, which has been published in recent

Aleksander Bogucki, Aleksandra Łopion, Karolina Ewa Połczyńska, Wojciech Pacuski

This study examines the spin-lattice relaxation rate of Mn$^{2+}$ ions in strained diluted magnetic semiconductor (Cd,Mn)Te/(Cd,Mg)Te quantum wells using the optically detected magnetic resonance (ODMR) technique. By adjusting the magnesium (Mg) content in the buffer layer, we created samples with different strain levels. Our time-resolved ODMR results show

Jonas Mayer Martins, Svetlana V. Gurevich, Julien Javaloyes

We study the dynamics of an optoelectronic circuit composed of an excitable nanoscale resonant-tunneling diode (RTD) driving a nanolaser diode (LD) coupled via time-delayed feedback. Using a combination of numerical path-continuation methods and time simulations, we demonstrate that this RTD-LD system can serve as an artificial neuron, generating pulses in t

John Krueger, Sarka Stejskalova

We introduce an abstract framework for forcing over a free Suslin tree with suborders of products of forcings which add some structure to the tree using countable approximations. The main ideas of this framework are consistency, separation, and the Key Property. We give three applications of this framework: specializing derived trees of a free Suslin tree, a

Qijia Shen, Guangrun Wang

Generating accurate 3D models is a challenging problem that traditionally requires explicit learning from 3D datasets using supervised learning. Although recent advances have shown promise in learning 3D models from 2D images, these methods often rely on well-structured datasets with multi-view images of each instance or camera pose information. Furthermore,

Irshad A. Meer, Karl-Ludwig Besser, Mustafa Ozger, Dominic Schupke

In modern cell-less wireless networks, mobility management is undergoing a significant transformation, transitioning from single-link handover management to a more adaptable multi-connectivity cluster reconfiguration approach, including often conflicting objectives like energy-efficient power allocation and satisfying varying reliability requirements. In thi

P. Aseev, Ž. Gačević, J. M. Mánuel, J. J. Jiménez

This work presents an experimental and theoretical insight into formation mechanisms of single crystalline wurtzite InN quantum dots (QDs) fabricated via metal droplet epitaxy (DE) by employing plasma assisted molecular beam epitaxy. The applied procedure consists of two fabrication stages. During the first stage, the cold substrate (T = 15 {\deg}C) is expos

Peng Luo, Di Zhu

Urban spaces, though often perceived as discrete communities, are shared by various functional and social groups. Our study introduces a graph-based physics-aware deep learning framework, illuminating the intricate overlapping nature inherent in urban communities. Through analysis of individual mobile phone positioning data at Twin Cities metro area (TCMA) i

P. Edera, M. Brizioli, M. Madani, E. Ngouamba

We study the yielding transition in soft jammed materials under oscillatory shear, employing a novel methodology that combines rheological measurements with detailed dynamical observations. This method provides a comprehensive view of the intricate interactions between macroscopic mechanical behavior, mesoscopic deformation patterns, and microscopic dynamics

Ertem Nusret Tas, David Tse, Yifei Wang

Since the creation of Bitcoin 15 years ago, there has been an explosion in the number of permissionless blockchains. Each of these blockchains provides an open ledger that anyone can read from and write to. In this multi-chain world, an important question emerges: how can we build a more secure overlay blockchain by reading from and writing to a given set of

Aaron Baughman, Stephen Hammer, Rahul Agarwal, Gozde Akay

We address the problem of scaling up the production of media content, including commentary and personalized news stories, for large-scale sports and music events worldwide. Our approach relies on generative AI models to transform a large volume of multimodal data (e.g., videos, articles, real-time scoring feeds, statistics, and fact sheets) into coherent and

Hongpeng Guo, Haotian Gu, Xiaoyang Wang, Bo Chen

Federated learning (FL) is a machine learning paradigm that allows multiple clients to collaboratively train a shared model while keeping their data on-premise. However, the straggler issue, due to slow clients, often hinders the efficiency and scalability of FL. This paper presents FedCore, an algorithm that innovatively tackles the straggler problem via th

Steve Reeves

We propose here to look at how abstract a model of a usable system can be, but still say something useful and interesting, so this paper is an exercise in abstraction and formalisation, with usability-of-design as an example target use. We take the view that when we claim to be designing a usable system we have, at the very least, to give assurances about it

Kaarthik Sundar, Andrew Mastin, Manuel Garcia, Russell Bent

The article introduces the stochastic N-k interdiction problem for power grid operations and planning that aims to identify a subset of k components (out of N components) that maximizes the expected damage, measured in terms of load shed. Uncertainty is modeled through a fixed set of outage scenarios, where each scenario represents a subset of components rem

Ž. Gačević, J. Grandal, Q. Guo, R. Kirste

Self assembled AlN nanowires (NWs) are grown by plasma assisted molecular beam epitaxy (PAMBE) on SiO2 / Si (111) substrates. Using a combination of in-situ reflective high energy electron diffraction and ex situ X ray diffraction (XRD), we show that the NWs grow nearly strain free, preferentially perpendicular to the amorphous SiO2 interlayer and without ep

Artur Avila, David Damanik, Zhenghe Zhang

We consider discrete one-dimensional Schr\"odinger operators whose potentials are generated by H\"older continuous sampling along the orbits of a uniformly hyperbolic transformation. For any ergodic measure satisfying a suitable bounded distortion property, we establish a uniform large deviation estimate in a large energy region provided that the sampling fu

Justin Hom, Jennifer Patience, Christine H. Chen, Gaspard Duchêne

Spatially-resolved images of debris disks are necessary to determine disk morphological properties and the scattering phase function (SPF) which quantifies the brightness of scattered light as a function of phase angle. Current high-contrast imaging instruments have successfully resolved several dozens of debris disks around other stars, but few studies have

Piu Rajak, Mahabul Islam, J. J. Jiménez, J. M. Mánuel

InN quantum dots (QDs) are considered to be promising nanostructures for different device applications. For any hexagonal AB stacking semiconductor system, polarity is an important feature which affects the electronic properties. Therefore, the determination of this characteristic on any wurtzite (semi)polar III nitride compound or alloy is essential for def

Juan Garcia-Bellido, Michael Hawkins

The recent astrometric data of hundreds of millions of stars from Gaia DR3 has allowed a precise determination of the Milky Way rotation curve up to $28$~kpc. The data suggests a rapid decline in the density of dark matter beyond $19$~kpc. We fit the whole rotation curve with four components (gas, disk, bulge and halo) and compute the microlensing optical de

Luca Donati, Christof Schutte, Marcus Weber

We have investigated how Langevin dynamics is affected by the friction coefficient using the novel algorithm ISOKANN, which combines the transfer operator approach with modern machine learning techniques. ISOKANN describes the dynamics in terms of an invariant subspace projection of the Koopman operator defined in the entire state space, avoiding approximati

Manuel Enrique Cuesta, D. J. McComas, L. Y. Khoo, R. Bandyopadhyay

Solar energetic particle (SEP) events have been observed by the Parker Solar Probe (PSP) spacecraft since its launch in 2018. These events include sources from solar flares and coronal mass ejections (CMEs). Onboard PSP is the IS\(\odot\)IS instrument suite measuring ions over energies from ~ 20 keV/nucleon to 200 MeV/nucleon and electrons from ~ 20 keV to 6

Stefan Frei, Tobias Knoke, Marc C. Steinbach, Anne-Kathrin Wenske

We present a monolithic finite element formulation for (nonlinear) fluid-structure interaction in Eulerian coordinates. For the discretization we employ an unfitted finite element method based on inf-sup stable finite elements. So-called ghost penalty terms are used to guarantee the robustness of the approach independently of the way the interface cuts the f

Joana Tirana, Spyros Lalis, Dimitris Chatzopoulos

Federated Learning (FL) stands out as a widely adopted protocol facilitating the training of Machine Learning (ML) models while maintaining decentralized data. However, challenges arise when dealing with a heterogeneous set of participating devices, causing delays in the training process, particularly among devices with limited resources. Moreover, the task

Lucas Machado Moschen, María Soledad Aronna

This study presents a mathematical model for optimal vaccination strategies in interconnected metropolitan areas, considering commuting patterns. It is a compartmental model with a vaccination rate for each city, acting as a control function. The commuting patterns are incorporated through a weighted adjacency matrix and a parameter that selects day and nigh

Benjamin Merlin Bumpus, James Fairbanks, Martti Karvonen, Wilmer Leal

What is a time-varying graph, a time-varying topological space, or, more generally, a mathematical structure that evolves over time? In this work, we lay the foundations for a general theory of temporal data by introducing categories of narratives. These are sheaves on posets of time intervals that encode snapshots of a temporal object along with the relatio

Congyu Fang, Adam Dziedzic, Lin Zhang, Laura Oliva

Machine Learning (ML) has demonstrated its great potential on medical data analysis. Large datasets collected from diverse sources and settings are essential for ML models in healthcare to achieve better accuracy and generalizability. Sharing data across different healthcare institutions is challenging because of complex and varying privacy and regulatory re

Zackary Rackauckas

Infineon has identified a need for engineers, account managers, and customers to rapidly obtain product information. This problem is traditionally addressed with retrieval-augmented generation (RAG) chatbots, but in this study, I evaluated the use of the newly popularized RAG-Fusion method. RAG-Fusion combines RAG and reciprocal rank fusion (RRF) by generati

Jenny M. Rodríguez-Gómez, Christoph Kuckein, Sergio J. Gonzalez Manrique, Jonas Saqri

A joint campaign of several space-borne and ground-based observatories, such as the GREGOR solar telescope, the Extreme-ultraviolet Imaging Spectrometer (EIS), and the Interface Region Imaging Spectrograph (Hinode Observing Plan 381, 11-22 October 2019) was conducted to investigate the plasma $\beta$ in quiet sun regions. In this work, we focus on October 13

Ian D. Kretz, Clare C. Parran, John D. Ramsdell, Paul D. Rowe

In distributed systems, trust decisions are made on the basis of integrity evidence generated via remote attestation. Examples of the kinds of evidence that might be collected are boot time image hash values; fingerprints of initialization files for userspace applications; and a comprehensive measurement of a running kernel. In layered attestations, evidence

Neil Dey, Ryan Martin, Jonathan P. Williams

A common goal in statistics and machine learning is estimation of unknowns. Point estimates alone are of little value without an accompanying measure of uncertainty, but traditional uncertainty quantification methods, such as confidence sets and p-values, often require distributional or structural assumptions that may not be justified in modern applications.

Raisa Islam, Subhasish Mazumdar, Rakibul Islam

In supervised machine learning, feature selection plays a very important role by potentially enhancing explainability and performance as measured by computing time and accuracy-related metrics. In this paper, we investigate a method for feature selection based on the well-known L1 and L2 regularization strategies associated with logistic regression (LR). It

William A. Sirignano

A new unsteady flamelet model is developed to be used for sub-grid modeling and coupling with the resolved flow description for turbulent combustion. Difficulties with prior unsteady flamelet models are identified. The model extends the quasi-steady rotational flamelet model which differs from prior models in several critical ways. (i) The effects of shear s

Wen Fan, Haoran Li, Weiyong Si, Shan Luo

Tactile sensing is significant for robotics since it can obtain physical contact information during manipulation. To capture multimodal contact information within a compact framework, we designed a novel sensor called ViTacTip, which seamlessly integrates both tactile and visual perception capabilities into a single, integrated sensor unit. ViTacTip features

Samuel Yuan, S. V. Dordevic

Effective computational search holds great potential for aiding the discovery of High-Temperature Superconductors (HTSs), especially given the lack of systematic methods for their discovery. Recent progress has been made in this area with machine learning, especially with deep generative models, which have been able to outperform traditional manual searches

Vishnu Jayaprakash, Jae Bem You, Chiranjeevi Kanike, Jinfeng Liu

Accurate detection and analysis of traces of persistent organic pollutants in water is important in many areas, including environmental monitoring and food quality control, due to their long environmental stability and potential bioaccumulation. While conventional analysis of organic pollutants requires expensive equipment, surface enhanced Raman spectroscop

Nikolay Moshchevitin, Anurag Rao, Uri Shapira

For an m by n real matrix A, we investigate the set of badly approximable targets for A as a subset of the m-torus. It is well known that this set is large in the sense that it is dense and has full Hausdorff dimension. We investigate the relationship between its measure and Diophantine properties of A. On the one hand, we give the first examples of a non-si

Junaid Iqbal Khan

The current trend in data regulation requirements and privacy-preserving machine learning has emphasized the importance of machine unlearning. The naive approach to unlearning training data by retraining over the complement of the forget samples is susceptible to computational challenges. These challenges have been effectively addressed through a collection

Nicolae Suciu, Florin A. Radu, Jakob S. Stokke, Emil Cătinaş

Numerical solutions for flows in partially saturated porous media pose challenges related to the non-linearity and elliptic-parabolic degeneracy of the governing Richards' equation. Iterative methods are therefore required to manage the complexity of the flow problem. Norms of successive corrections in the iterative procedure form sequences of positive numbe

James P. Lavine

Suppose an initial state is coupled to a continuum of energy states. The population of the initial state is expected to decrease with time, but is the decrease monotonic? The occupation probability of the initial state is the survival probability and the question is equivalent to asking if there are intervals of time where the survival probability increases.

Sergei Bazylik, Magne Mogstad, Joseph Romano, Azeem Shaikh

It is common to rank different categories by means of preferences that are revealed through data on choices. A prominent example is the ranking of political candidates or parties using the estimated share of support each one receives in surveys or polls about political attitudes. Since these rankings are computed using estimates of the share of support rathe

Marco Di Mauro, Salvatore Esposito, Adele Naddeo

The onset and the development of the concept of exchange force in quantum physics are historically reconstructed, starting from Heisenberg's seminal contributions in 1926 and going through the great developments in nuclear physics, which allowed the emergence of the idea of force mediating virtual quanta. Although most of such work was performed in Europe, t

Kate Candon, Nicholas C. Georgiou, Helen Zhou, Sidney Richardson

Recent work in Human-Robot Interaction (HRI) has shown that robots can leverage implicit communicative signals from users to understand how they are being perceived during interactions. For example, these signals can be gaze patterns, facial expressions, or body motions that reflect internal human states. To facilitate future research in this direction, we c

Aida Abiad, Afrouz Jabal Ameli, Luuk Reijnders

The exact distance $t$-power of a graph $G$, $G^{[\sharp t]}$, is a graph which has the same vertex set as $G$, with two vertices adjacent in $G^{[\sharp t]}$ if and only if they are at distance exactly $t$ in the original graph $G$. We study the clique number of this graph, also known as the $t$-equidistant number. We show that it is NP-hard to determine th

Lee M Gunderson, Gecia Bravo-Hermsdorff, Peter Orbanz

In this work, we describe a method that determines an exact map from a finite set of subgraph densities to the parameters of a stochastic block model (SBM) matching these densities. Given a number $K$ of blocks, the subgraph densities of a finite number of stars and bistars uniquely determines a single element of the class of all degree-separated stochastic

Andrea Chiavassa, Kateryna Kravchenko, Jared A. Goldberg

Evolved cool stars of various masses are major cosmic engines, delivering substantial mechanical and radiative feedback to the interstellar medium through strong stellar winds and supernova ejecta. These stars play a pivotal role in enriching the interstellar medium with vital chemical elements that constitute the essential building blocks for forming subseq

Kshitij Goel, Wennie Tabib

This paper describes continuous-space methodologies to estimate the collision probability, Euclidean distance and gradient between an ellipsoidal robot model and an environment surface modeled as a set of Gaussian distributions. Continuous-space collision probability estimation is critical for uncertainty-aware motion planning. Most collision detection and a

Atul Mohan, Surajit Mondal, Sven Wedemeyer, Natchimuthuk Gopalswamy

AD Leo is a young and active M dwarf with high flaring rates across the X-ray to radio bands. Flares accelerate particles in the outer coronal layers and often impact exospace weather. Wide-band radio dynamic spectra let us explore the evolution of particle acceleration activity across the corona. Identifying the emission features and modelling the mechanism

Connor R. Forsythe, Cristian Arteaga, John P. Helveston

This paper introduces the Mixed Aggregate Preference Logit (MAPL, pronounced "maple'') model, a novel class of discrete choice models that leverages machine learning to model unobserved heterogeneity in discrete choice analysis. The traditional mixed logit model (also known as "random parameters logit'') parameterizes preference heterogeneity through assumpt

Philipp Otto, Alessandro Fassò, Paolo Maranzano

This review article focuses on regularised estimation procedures applicable to geostatistical and spatial econometric models. These methods are particularly relevant in the case of big geospatial data for dimensionality reduction or model selection. To structure the review, we initially consider the most general case of multivariate spatiotemporal processes

Roberto Bomfin, Marwa Chafii

This paper considers an integrated sensing and communication (ISAC) system with monostatic radar functionality using a zero-padding orthogonal frequency division multiplexing (ZP-OFDM) downlink transmission. We focus on ISAC's sensing aspect, employing an energy-detection (ED) method. The ZP-OFDM transmission is motivated by the fact that sensing can be perf

S. Autti, A. Casey, N. Eng, N. Darvishi

We report the results of radioactivity assays and heat leak calculations for a range of common cryogenic materials, considered for use in the QUEST-DMC superfluid 3He dark matter detector. The bolometer, instrumented with nanomechanical resonators, will be sensitive to energy deposits from dark matter interactions. Events from radioactive decays and cosmic r

Tony Feng, Jonathan Wang

We study conjectures of Ben-Zvi--Sakellaridis--Venkatesh that categorify the relationship between automorphic periods and $L$-functions in the context of the Geometric Langlands equivalence. We provide evidence for these conjectures in some low-rank examples, by using derived Fourier analysis and the theory of chiral algebras to categorify the Rankin-Selberg

Atiquer Rahman Sarkar, Yao-Shun Chuang, Noman Mohammed, Xiaoqian Jiang

For sharing privacy-sensitive data, de-identification is commonly regarded as adequate for safeguarding privacy. Synthetic data is also being considered as a privacy-preserving alternative. Recent successes with numerical and tabular data generative models and the breakthroughs in large generative language models raise the question of whether synthetically g

Luigi Chierchia, Isabella Fascitiello

We review Kolmogorov's 1954 fundamental paper {\sl On the Conservation of Conditionally Periodic Motions under Small Perturbation of the Hamiltonian} (Dokl. akad. nauk SSSR,1954, vol. {\bf 98}, pp.527--530), both from the historical and the mathematical point of view. In particular, we discuss Theorem~2 (which deals with the measure in phase space of persist

Denis Michel

The exponential factor of Arrhenius satisfactorily quantifies the energetic restriction of chemical reactions but is still awaiting a rigorous basis. Assuming that the Arrhenius equation should be based on statistical mechanics and is probabilistic in nature, two structures for this equation are compared, depending on whether the reactant energies are viewed

Hui Zheng, Eric Sivonxay, Max Gallant, Ziyao Luo

Amorphous materials exhibit unique properties that make them suitable for various applications in science and technology, ranging from optical and electronic devices and solid-state batteries to protective coatings. However, data-driven exploration and design of amorphous materials is hampered by the absence of a comprehensive database covering a broad chemi

Petros Georgiou, Sharu Theresa Jose, Osvaldo Simeone

In a manner analogous to their classical counterparts, quantum classifiers are vulnerable to adversarial attacks that perturb their inputs. A promising countermeasure is to train the quantum classifier by adopting an attack-aware, or adversarial, loss function. This paper studies the generalization properties of quantum classifiers that are adversarially tra

Tao Sheng, Tejas Sudharshan Mathai, Alexander Shieh, Ronald M. Summers

The skeletal region is one of the common sites of metastatic spread of cancer in the breast and prostate. CT is routinely used to measure the size of lesions in the bones. However, they can be difficult to spot due to the wide variations in their sizes, shapes, and appearances. Precise localization of such lesions would enable reliable tracking of interval c

Santiago Jockwich, Sourav Tarafder, Giorgio Venturi

In this paper, we unify the study of classical and non-classical algebra-valued models of set theory, by studying variations of the interpretation functions for identity and set-membership. Although, these variations coincide with the standard interpretation in Boolean-valued constructions, nonetheless they extend the scope of validity of ZF to new algebra-v

Fernando Argentieri, Luigi Chierchia

In this short note, we discuss the topology of Diophantine numbers, giving simple explicit examples of Diophantine isolated numbers (among those with same Diophantine constatnts), showing that, Diophantine sets are not always Cantor sets. General properties of isolated Diophantine numbers are also briefly discussed.

Simona Sanfelici, Giacomo Toscano

This paper presents the Fourier-Malliavin Volatility (FMVol) estimation library for MATLAB. This library includes functions that implement Fourier- Malliavin estimators (see Malliavin and Mancino (2002, 2009)) of the volatility and co-volatility of continuous stochastic volatility processes and second-order quantities, like the quarticity (the squared volati

Gaston Giribet, Olivera Mišković, Nahuel Yazbek, Jorge Zanelli

AdS supergravity admits supersymmetric solutions that describe BPS defects. Here, we investigate such solutions in AdS$_3$ supergravity, which is formulated as a Chern-Simons theory on $\mathrm{OSp}(2|1)\,\times\, \mathrm{OSp}(2|1)$. We compute the Killing spinor equation on the BTZ geometry in different ways, looking for BPS solutions on the entire space of

Sureka Thiruchittampalam, Bikram Pratap Banerjee, Nancy F Glenn, Simit Raval

Minor errors in the spoil deposition process, such as placing stronger materials with higher shear strength over weaker ones, can lead to potential dump failure. Irregular deposition and inadequate compaction complicate coal spoil behaviour, necessitating a robust methodology for temporal monitoring. This study explores using unmanned aerial vehicles (UAV) e

Francesca Bianchi, Enis Kaya, J. Steffen Müller

In this paper, we develop an algorithm for computing Coleman--Gross (and hence Nekov\'a\v{r}) $p$-adic heights on hyperelliptic curves over number fields with arbitrary reduction type above $p$. This height is defined as a sum of local heights at each finite place and we use algorithms for Vologodsky integrals, developed by Katz and the second-named author,

René Zuñiga, Carlos Vasconcellos, Baptiste Darbois Texier, Francisco Melo

Several locomotion strategies are based on the anisotropic nature of the forces experienced by the moving body with its environment. We report experiments on the anisotropy of the frictional force experienced by a cylinder moving in a granular medium as a function of the orientation $\alpha$ between the cylinder and its velocity. The component of the force i

Zhenghao Zeng, David Arbour, Avi Feller, Raghavendra Addanki

In many real-world causal inference applications, the primary outcomes (labels) are often partially missing, especially if they are expensive or difficult to collect. If the missingness depends on covariates (i.e., missingness is not completely at random), analyses based on fully observed samples alone may be biased. Incorporating surrogates, which are fully

Cameron T. Pratt, Zhijie Qu, Joel N. Bregman, Christopher J. Miller

All-sky maps of the thermal Sunyaev-Zel'dovich effect (SZ) tend to suffer from systematic features arising from the component separation techniques used to extract the signal. In this work, we investigate one of these methods known as needlet internal linear combination (NILC) and test its performance on simulated data. We show that NILC estimates are strong

Kartikey Sharma, Deborah Hendrych, Mathieu Besançon, Sebastian Pokutta

We tackle the network design problem for centralized traffic assignment, which can be cast as a mixed-integer convex optimization (MICO) problem. For this task, we propose different formulations and solution methods in both a deterministic and a stochastic setting in which the demand is unknown in the design phase. We leverage the recently proposed Boscia fr

Kim Klinger-Logan, Tian An Wong

We show that the values of elliptic Dedekind sums, after normalization, are equidistributed mod 1. The key ingredient is a non-trivial bound on generalized Selberg-Kloosterman sums for discrete subgroups of $\PSL_2(\mathbb C)$ using Poincar\'e series.

Yuchen Chen, Jing Lei

In some high-dimensional and semiparametric inference problems involving two populations, the parameter of interest can be characterized by two-sample U-statistics involving some nuisance parameters. In this work we first extend the framework of one-step estimation with cross-fitting to two-sample U-statistics, showing that using an orthogonalized influence

Karolina Seweryn, Gabriel Chęć, Szymon Łukasik, Anna Wróblewska

This study explores the potential of super-resolution techniques in enhancing object detection accuracy in football. Given the sport's fast-paced nature and the critical importance of precise object (e.g. ball, player) tracking for both analysis and broadcasting, super-resolution could offer significant improvements. We investigate how advanced image process

Adrien Bolland, Gaspard Lambrechts, Damien Ernst

In order to compute near-optimal policies with policy-gradient algorithms, it is common in practice to include intrinsic exploration terms in the learning objective. Although the effectiveness of these terms is usually justified by an intrinsic need to explore environments, we propose a novel analysis with the lens of numerical optimization. Two criteria are

Javier Rivera-Dean, Anna Steffinlongo, Neil Parker-Sánchez, Antonio Acín

Device-Independent Quantum Key Distribution (DIQKD) aims to generate secret keys between two parties without relying on trust in their employed devices, imposing strict noise constraints for key generation. This study explores the resilience of high-dimensional quantum systems in DIQKD, focusing on a comparison between qubits and qutrits. Lower bounds on ach

Ivan Y. Tyukin, Tatiana Tyukina, Daniel van Helden, Zedong Zheng

We present a new methodology for handling AI errors by introducing weakly supervised AI error correctors with a priori performance guarantees. These AI correctors are auxiliary maps whose role is to moderate the decisions of some previously constructed underlying classifier by either approving or rejecting its decisions. The rejection of a decision can be us

Simon A. Lee, Sujay Jain, Alex Chen, Kyoka Ono

In this work, we introduce the Multiple Embedding Model for EHR (MEME), an approach that serializes multimodal EHR tabular data into text using pseudo-notes, mimicking clinical text generation. This conversion not only preserves better representations of categorical data and learns contexts but also enables the effective employment of pretrained foundation m

Luca Soldaini, Rodney Kinney, Akshita Bhagia, Dustin Schwenk

Information about pretraining corpora used to train the current best-performing language models is seldom discussed: commercial models rarely detail their data, and even open models are often released without accompanying training data or recipes to reproduce them. As a result, it is challenging to conduct and advance scientific research on language modeling

Lien Cartaya, Stephen Griffeth

We propose a generalization of Haiman's conjecture on the diagonal coinvariant rings of real reflection groups to the context of irreducible quaternionic reflection groups (also known as symplectic reflection groups). For a reflection group $W$ acting on a quaternionic vector space $V$, by regarding $V$ as a complex vector space we consider the scheme-theore

Janice Ahn, Rishu Verma, Renze Lou, Di Liu

Mathematical reasoning serves as a cornerstone for assessing the fundamental cognitive capabilities of human intelligence. In recent times, there has been a notable surge in the development of Large Language Models (LLMs) geared towards the automated resolution of mathematical problems. However, the landscape of mathematical problem types is vast and varied,

Hyeok Kim, Yea-Seul Kim, Jessica Hullman

Data sonification-mapping data variables to auditory variables, such as pitch or volume-is used for data accessibility, scientific exploration, and data-driven art (e.g., museum exhibitions) among others. While a substantial amount of research has been made on effective and intuitive sonification design, software support is not commensurate, limiting researc