December 2023 arXiv papers — page 50

Sahil Nokhwal, Manoj Chandrasekharan, Ankit Chaudhary

Various methods have been proposed to secure access to sensitive information over time, such as the many cryptographic methods in use to facilitate secure communications on the internet. But other methods like steganography have been overlooked which may be more suitable in cases where the act of transmission of sensitive information itself should remain a s

Md. Kowsher, Md. Shohanur Islam Sobuj, Asif Mahmud, Nusrat Jahan Prottasha

Efficiently fine-tuning Large Language Models (LLMs) for specific tasks presents a considerable challenge in natural language processing. Traditional methods, like prompt or prefix tuning, typically rely on arbitrary tokens for training, leading to prolonged training times and generalized token use across various class labels. To address these issues, this p

Yubai Shi, Ri He, Bingwen Zhang, Zhicheng Zhong

Lead zirconate titanate (PbZr1-xTixO3, PZT) exhibits excellent piezoelectric properties in the morphotropic phase boundary (MPB) region of its temperature-composition phase diagram. However, the microscopic origin of its high piezoelectric response remains controversial. Here, we develop a machine-learning-based deep potential (DP) model of PZT using the tra

Yuhang Li, Yuecai Han

In this paper, we consider the stochastic optimal control problem for a generalized Volterra control system. The corresponding state process is a kind of a generalized stochastic Volterra integral differential equations. We prove the existence and uniqueness of the solution of this type of equations. We obtain the stochastic maximum principle of the optimal

Marie-Chantale Pelletier, Claire Horner, Mathew Vickers, Aliya Gul

Purpose: The aim of this study was to explore the feasibility of natural capital accounting for the purpose of strengthening sustainability claims by reporting entities. The study linked riparian land improvement to ecosystem services and tested options for incorporating natural capital into financial accounting practices, specifically on the balance sheet.

Jingdong Zhang, Jiayuan Fan, Peng Ye, Bo Zhang

Multi-task dense prediction aims at handling multiple pixel-wise prediction tasks within a unified network simultaneously for visual scene understanding. However, cross-task feature interactions of current methods are still suffering from incomplete levels of representations, less discriminative semantics in feature participants, and inefficient pair-wise ta

Runze Mao, Yingrui Wang, Min Zhang, Han Li

Recent progress in artificial intelligence (AI) and high-performance computing (HPC) have brought potentially game-changing opportunities in accelerating reactive flow simulations. In this study, we introduce an open-source computational fluid dynamics (CFD) framework that integrates the strengths of machine learning (ML) and graphics processing unit (GPU) t

Jaione Tirapu Azpiroz, Ronaldo Giro, Rodrigo Neumann Barros Ferreira, Marcio Nogueira Pereira da Silva

Carbon dioxide (CO2) trapping in capillary networks of reservoir rocks is a pathway to long-term geological storage. At pore scale, the CO2 trapping potential depends on injection pressure, temperature, and the rock's interaction with the surrounding fluids. Modeling this interaction requires adequate representations of both capillary volume and surface. For

Kévin Garanger, Julie Kraus, Julian J. Rimoli

The use of machine learning techniques to homogenize the effective behavior of arbitrary microstructures has been shown to be not only efficient but also accurate. In a recent work, we demonstrated how to combine state-of-the-art micromechanical modeling and advanced machine learning techniques to homogenize complex microstructures exhibiting non-linear and

Hans-Jörg Kreowski, Aaron Lye, Aljoscha Windhorst

In this paper, we investigate the relationship between two elementary operations on derivations in the framework of graph transformation based on adhesive categories: moving a derivation along a derivation based on parallel and sequential independence on one hand and restriction of a derivation with respect to a monomorphism into the start object on the othe

Youssef Mourchid, Rim Slama

Accurate assessment of patient actions plays a crucial role in healthcare as it contributes significantly to disease progression monitoring and treatment effectiveness. However, traditional approaches to assess patient actions often rely on manual observation and scoring, which are subjective and time-consuming. In this paper, we propose an automated approac

Jingwei Yi, Yueqi Xie, Bin Zhu, Emre Kiciman

The integration of large language models with external content has enabled applications such as Microsoft Copilot but also introduced vulnerabilities to indirect prompt injection attacks. In these attacks, malicious instructions embedded within external content can manipulate LLM outputs, causing deviations from user expectations. To address this critical ye

Tresor Y. Koffi, Youssef Mourchid, Mohammed Hindawi, Yohan Dupuis

Falls among individuals, especially the elderly population, can lead to serious injuries and complications. Detecting impact moments within a fall event is crucial for providing timely assistance and minimizing the negative consequences. In this work, we aim to address this challenge by applying thorough preprocessing techniques to the multisensor dataset, t

Jamie Vo, Naeha Sharif, Ghulam Mubashar Hassan

The early detection of Alzheimer's Disease is imperative to ensure early treatment and improve patient outcomes. There has consequently been extenstive research into detecting AD and its intermediate phase, mild cognitive impairment (MCI). However, there is very small literature in predicting the conversion to AD and MCI from normal cognitive condition. Rece

Guangyin Bao, Qi Zhang, Duoqian Miao, Zixuan Gong

In real-world scenarios, multimodal federated learning often faces the practical challenge of intricate modality missing, which poses constraints on building federated frameworks and significantly degrades model inference accuracy. Existing solutions for addressing missing modalities generally involve developing modality-specific encoders on clients and trai

Ding-Fu Shao, Evgeny Y. Tsymbal

Antiferromagnetic (AFM) spintronics has emerged as a subfield of spintronics, where an AFM N\'eel vector is used as a state variable. Efficient electric control and detection of the N\'eel vector are critical for spintronic applications. This review article features fundamental properties of AFM tunnel junctions (AFMTJs) as spintronic devices where such elec

Youssef Mourchid, Marc Donias, Yannick Berthoumieu, Mohamed Najim

This paper addresses the automatic colorization problem, which converts a gray-scale image to a colorized one. Recent deep-learning approaches can colorize automatically grayscale images. However, when it comes to different scenes which contain distinct color styles, it is difficult to accurately capture the color characteristics. In this work, we propose a

Yanyan Xu, Riccardo Di Clemente, Marta C. Gonzalez

Properly extracting patterns of individual mobility with high resolution data sources such as the one extracted from smartphone applications offers important opportunities. Potential opportunities not offered by call detailed records (CDRs), which offer resolutions triangulated from antennas, are route choices, travel modes detection and close encounters. No

Ellen M. Considine, Rachel C. Nethery, Gregory A. Wellenius, Francesca Dominici

A key strategy in societal adaptation to climate change is using alert systems to prompt preventative action and reduce the adverse health impacts of extreme heat events. This paper implements and evaluates reinforcement learning (RL) as a tool to optimize the effectiveness of such systems. Our contributions are threefold. First, we introduce a new publicly

Sarang Mittal, Kazemi Adachi, Nicholas E. Frattini, Maxwell D. Urmey

The dielectric loss of silicon nitride (Si$_3$N$_4$) limits the performance of microwave-frequency devices that rely on this material for sensing, signal processing, and quantum communication. Using superconducting resonant circuits, we measure the cryogenic loss tangent of either as-deposited or high-temperature annealed stoichiometric Si$_3$N$_4$ as a func

Bingbing Wen, Zhengyuan Yang, Jianfeng Wang, Zhe Gan

In this paper, we build a visual dialogue dataset, named InfoVisDial, which provides rich informative answers in each round even with external knowledge related to the visual content. Different from existing datasets where the answer is compact and short, InfoVisDial contains long free-form answers with rich information in each round of dialogue. For effecti

Youssef Mourchid, Rim Slama

This paper tackles the challenge of automatically assessing physical rehabilitation exercises for patients who perform the exercises without clinician supervision. The objective is to provide a quality score to ensure correct performance and achieve desired results. To achieve this goal, a new graph-based model, the Dense Spatio-Temporal Graph Conv-GRU Netwo

David Barbalas, Ralph Romero, Dipanjan Chaudhuri, Fahad Mahmood

Separating out the contributions of different scattering channels in strongly interacting metals is crucial in identifying the mechanisms that govern their properties. While momentum or current relaxation rates can be readily probed via \textit{dc} resistivity or optical/THz spectroscopy, distinguishing different kinds of inelastic scattering can be more cha

Haipeng Zhang, Ran Li, Mingyang Sun, Teng Fei

Forecast-then-optimize is a widely-used framework for decision-making problems in power systems. Traditionally, statistical losses have been employed to train forecasting models, but recent research demonstrated that improved decision utility in downstream optimization tasks can be achieved by using decision loss as an alternative. However, the implementatio

Lixu Wang, Chenxi Liu, Junfeng Guo, Jiahua Dong

In a privacy-focused era, Federated Learning (FL) has emerged as a promising machine learning technique. However, most existing FL studies assume that the data distribution remains nearly fixed over time, while real-world scenarios often involve dynamic and continual changes. To equip FL systems with continual model evolution capabilities, we focus on an imp

T. J. Hague, J. Arrington, S. Li, S. N. Santiesteban

The neutron structure function, $F_{2n}$, has historically been extracted from measurements of the deuteron structure function, but our understanding of the nuclear effects on the bound proton and neutron limits the extraction of $F_{2n}$. The MARATHON collaboration recently extracted $F_{2n}$ from the comparison of $^3$H and $^3$He targets, where the nuclea

Menghan Song, Jiarui Zhao, Zi Yang Meng, Cenke Xu

We systematically investigate the finite size scaling behavior of the R\'enyi entanglement entropy (EE) of several representative 2d quantum many-body systems between a subregion and its complement, with smooth boundaries as well as boundaries with corners. In order to reveal the subleading correction, we investigate the quantity ``subtracted EE" $S^s(l) = S

B. Gauthier, P. Rosenberg, A. Foley, M. Charlebois

We introduce a hybrid quantum-classical algorithm to compute the Green function for strongly correlated electrons on noisy intermediate-scale quantum (NISQ) devices. The technique consists in the construction of a non-orthogonal excitation basis composed of a set of single-particle excitations generated by occupation number operators. The excited sectors of

Anibal Velozo Ruiz, Renato Velozo Ruiz

In this paper, we study pointwise decay estimates in time for Vlasov fields on non-trapping asymptotically hyperbolic manifolds. We prove optimal decay estimates in time for the spatial density induced by Vlasov fields on these geometric backgrounds in dimension two. First, we show exponential decay for Vlasov fields on hyperbolic space supported away from t

Jie Han, Yixiong Zou, Haozhao Wang, Jun Wang

Few-shot intent classification and slot filling are important but challenging tasks due to the scarcity of finely labeled data. Therefore, current works first train a model on source domains with sufficiently labeled data, and then transfer the model to target domains where only rarely labeled data is available. However, experience transferring as a whole us

David Nakath, Xiangyu Weng, Mengkun She, Kevin Köser

When created faithfully from real-world data, Digital 3D representations of objects can be useful for human or computer-assisted analysis. Such models can also serve for generating training data for machine learning approaches in settings where data is difficult to obtain or where too few training data exists, e.g. by providing novel views or images in varyi

Réamonn Ó Buachalla, Petr Somberg

For the Drinfeld-Jimbo quantum enveloping algebra $U_q(\frak{sl}_{n+1})$, we show that the span of Lusztig's positive root vectors, with respect to Littlemann's nice reduced decompositions of the longest element of the Weyl group, form quantum tangent spaces for the full quantum flag manifold $\mathcal{O}_q(\mathrm{F}_{n+1})$. The associated differential cal

Umair Shahzad

Accurate transient stability assessment is a crucial prerequisite for proper power system operation and planning with various operational constraints. Transient stability assessment of modern power systems is becoming very challenging due to rising uncertainty and continuous integration of renewable energy generation. The stringent requirements of very high

Jens A. H. Nielsen, Mateusz Kicinski, Tummas N. Arge, Kannan Vijayadharan

Variational quantum algorithms (VQAs) are hybrid quantum-classical approaches used for tackling a wide range of problems on noisy intermediate-scale quantum (NISQ) devices. Testing these algorithms on relevant hardware is crucial to investigate the effect of noise and imperfections and to assess their practical value. Here, we implement a variational algorit

Léo R. Belzile, Arnab Hazra, Rishikesh Yadav

The EVA 2023 data competition consisted of four challenges, ranging from interval estimation for very high quantiles of univariate extremes conditional on covariates, point estimation of unconditional return levels under a custom loss function, to estimation of the probabilities of tail events for low and high-dimensional multivariate data. We tackle these t

Adbhut Gupta, C. Wang, S. K. Singh, K. W. Baldwin

Owing to their large effective mass, strong and tunable spin-orbit coupling, and complex band-structure, two-dimensional hole systems (2DHSs) in GaAs quantum wells provide rich platforms to probe exotic many-body physics, while also offering potential applications in ballistic and spintronics devices, and fault-tolerant topological quantum computing. We pres

Vaggos Chatziafratis, Piotr Indyk

Metric embeddings traditionally study how to map $n$ items to a target metric space such that distance lengths are not heavily distorted; but what if we only care to preserve the relative order of the distances (and not their length)? In this paper, we are motivated by the following basic question: given triplet comparisons of the form ``item $i$ is closer t

Demircan Tas, Mine Özkar

In three-dimensional models obtained by photogrammetry of existing structures, all of the shapes that the eye can select cannot always find their equivalents in the geometric components of the model. However, the matching of meaningful parts and assemblages with the records acquired with rapid and detailed documentation methods will provide an advantage for

Samuel A. Ballas, Tom Needham, Clayton Shonkwiler

Frames in finite-dimensional vector spaces are spanning sets of vectors which provide redundant representations of signals. The Parseval frames are particularly useful and important, since they provide a simple reconstruction scheme and are maximally robust against certain types of noise. In this paper we describe a theory of frames on arbitrary vector bundl

Katarina Doctor, Mayank Kejriwal, Lawrence Holder, Eric Kildebeck

Artificial Intelligence (AI) systems, trained in controlled environments, often struggle in real-world complexities. We propose a general framework for estimating domain complexity across diverse environments, like open-world learning and real-world applications. This framework distinguishes between intrinsic complexity (inherent to the domain) and extrinsic

Yilang Zhang, Bingcong Li, Georgios B. Giannakis

Utilizing task-invariant prior knowledge extracted from related tasks, meta-learning is a principled framework that empowers learning a new task especially when data records are limited. A fundamental challenge in meta-learning is how to quickly "adapt" the extracted prior in order to train a task-specific model within a few optimization steps. Existing appr

Martin Rameš, Pavel Surynek

This paper addresses exact approaches to multi-agent collective construction problem which tasks a group of cooperative agents to build a given structure in a blocksworld under the gravity constraint. We propose a generalization of the existing exact model based on mixed integer linear programming by accommodating varying agent action durations. We refer to

Piotr M. Suder, Jason Xu, David B. Dunson

Transfer learning is a burgeoning concept in statistical machine learning that seeks to improve inference and/or predictive accuracy on a domain of interest by leveraging data from related domains. While the term "transfer learning" has garnered much recent interest, its foundational principles have existed for years under various guises. Prior literature re

Sadman Shanto, Andre Kuo, Clark Miyamoto, Haimeng Zhang

We present an open-source database of superconducting quantum device designs that may be used as the starting point for customized devices. Each design can be generated programmatically using the open-source Qiskit Metal package, and simulated using finite-element electromagnetic solvers. We present a robust workflow for achieving high accuracy on design sim

Yifei Hu, Xinge Jessie Jeng

Accurate delineation of tumor-adjacent functional brain regions is essential for planning function-preserving neurosurgery. Functional magnetic resonance imaging (fMRI) is increasingly used for presurgical counseling and planning. When analyzing presurgical fMRI data, false negatives are more dangerous to the patients than false positives because patients ar

Mengjiao Han, Jixian Li, Sudhanshu Sane, Shubham Gupta

In this paper, we present a comprehensive evaluation to establish a robust and efficient framework for Lagrangian-based particle tracing using deep neural networks (DNNs). Han et al. (2021) first proposed a DNN-based approach to learn Lagrangian representations and demonstrated accurate particle tracing for an analytic 2D flow field. In this paper, we extend

Ningyuan Nie, Yu-Cheng Chen

Flexible photonics offers the possibility to realize wearable sensors by bridging the advantages of flexible materials and photonic sensing elements. Recently, optical resonators have emerged as a tool to improve its over sensitivity by integrating with flexible photonic sensors. However, direct monitoring of multiple psychological information on human skin

Rafael Orozco, Philipp Witte, Mathias Louboutin, Ali Siahkoohi

InvertibleNetworks.jl is a Julia package designed for the scalable implementation of normalizing flows, a method for density estimation and sampling in high-dimensional distributions. This package excels in memory efficiency by leveraging the inherent invertibility of normalizing flows, which significantly reduces memory requirements during backpropagation c

Joshua Evans, Petr Šulc

We introduce allostery-mimetic building blocks model for self-assembly of 3D structures. We represent the building blocks as patchy particles, where each binding site (patch) can be irreversibly activated or deactivated by binding of the particle's other controlling patches to another particle. We show that these allostery-mimetic systems can be designed to

Shervin Safavi

Finding general principles underlying brain function has been appealing to scientists. Indeed, in some branches of science like physics and chemistry (and to some degree biology) a general theory often can capture the essence of a wide range of phenomena. Whether we can find such principles in neuroscience, and [assuming they do exist] what those principles

Simone De Reggi, Francesca Scarabel, Rossana Vermiglio

In this paper, we introduce a general numerical method to approximate the reproduction numbers of a large class of multi-group, age-structured, population models with a finite age span. To provide complete flexibility in the definition of the birth and transition processes, we propose an equivalent formulation for the age-integrated state within the extended

Rounak Meyur, Sumit Purohit, Braden K. Webb

The abundance of cyber-physical components in modern day power grid with their diverse hardware and software vulnerabilities has made it difficult to protect them from advanced persistent threats (APTs). An attack graph depicting the propagation of potential cyber-attack sequences from the initial access point to the end objective is vital to identify critic

Parker Johnson, Michael Zingale, Eric T. Johnson, Alexander Smith

Simulating complex astrophysical reacting flows is computationally expensive -- reactions are stiff and typically require implicit integration methods. The reaction update is often the most expensive part of a simulation, which motivates the exploration of more economical methods. In this research note, we investigate how the explicit Runge--Kutta--Chebyshev

Sergey K. Tolpygo, Ravi Rastogi, Terence Weir, Evan B. Golden

To increase integration scale of superconductor electronics, we are developing a new, SFQ7ee, node of the fabrication process at MIT Lincoln Laboratory. In comparison to the existing SFQ5ee node, we increased the number of fully planarized superconducting layers to ten and utilized NbN and NbN/Nb kinetic inductors to increase the inductor number density abov

Marc Salinas, Jorge Piekarewicz

The recent pioneering campaigns conducted by the Lead Radius Experiment (PREX) and the Calcium Radius Experiment (CREX) collaborations have uncovered major deficiencies in the theoretical description of some fundamental properties of atomic nuclei. Following a recent refinement to the isovector sector of covariant energy density functionals [1], we present h

Leonardo Banchi

Many inference scenarios rely on extracting relevant information from known data in order to make future predictions. When the underlying stochastic process satisfies certain assumptions, there is a direct mapping between its exact classical and quantum simulators, with the latter asymptotically using less memory. Here we focus on studying whether such quant

Gavin Zheng

Mixed-integer nonlinear programmings (MINLPs) are powerful formulation tools for task planning. However, it suffers from long solving time especially for large scale problems. In this work, we first formulate the task planning problem for item stowing into a mixed-integer nonlinear programming problem, then solve it using Alternative Direction Method of Mult

Jens Naumann, Binbin Xu, Stefan Leutenegger, Xingxing Zuo

We introduce a novel monocular visual odometry (VO) system, NeRF-VO, that integrates learning-based sparse visual odometry for low-latency camera tracking and a neural radiance scene representation for fine-detailed dense reconstruction and novel view synthesis. Our system initializes camera poses using sparse visual odometry and obtains view-dependent dense

Chen Li, Tingwei Ye, Tongyu Zong, Liyang Sun

While live 360 degree video streaming delivers immersive viewing experience, it poses significant bandwidth and latency challenges for content delivery networks. Edge servers are expected to play an important role in facilitating live streaming of 360 degree videos. In this paper, we propose a novel predictive edge caching algorithm (Coffee) for live 360 deg

Sudharshan Suresh, Haozhi Qi, Tingfan Wu, Taosha Fan

To achieve human-level dexterity, robots must infer spatial awareness from multimodal sensing to reason over contact interactions. During in-hand manipulation of novel objects, such spatial awareness involves estimating the object's pose and shape. The status quo for in-hand perception primarily employs vision, and restricts to tracking a priori known object

Ben Hambly, Philipp Jettkant

We introduce a novel class of semilinear nonlocal backward stochastic partial differential equations (BSPDE) on half-spaces driven by an infinite-dimensional c\`adl\`ag martingale. The equations exhibit a degeneracy and have no explicit condition at the boundary of the half-space. To treat the existence and uniqueness of such BSPDEs we establish a generalisa

Brendan Rhyno, Ivan Velkovsky, Peter Adshead, Bryce Gadway

Inspired by recent advances in observational astrophysics and continued explorations in the field of analog gravity, we discuss the prospect of simulating models of cosmology within the context of synthetic mechanical lattice experiments. We focus on the physics of expanding Universe scenarios described by the Friedmann-Lema\^itre-Robertson-Walker (FLRW) met

Kevin A. Interiano-Alberto, Peter K. Morse, Robert S. Hoy

Using hybrid molecular dynamics/SWAP Monte Carlo (MD/SMC) simulations, we show that the terminal relaxation times $\tau$ for FIRE energy minimization of soft-sphere glasses exhibit thermal onset as samples become increasingly well-equilibrated. Although $\tau(\phi)$ can decrease by orders of magnitude as equilibration proceeds and the jamming density $\phi_{

Maria Francesca Abbate, Thomas Dupic, Emmanuelle Vigne, Melody A. Shahsavarian

B cell receptors (BCRs) play a crucial role in recognizing and fighting foreign antigens. High-throughput sequencing enables in-depth sampling of the BCRs repertoire after immunization. However, only a minor fraction of BCRs actively participate in any given infection. To what extent can we accurately identify antigen-specific sequences directly from BCRs re

Daniel Corey, Michael Joswig, Julien Schanz, Marcel Wack

Motivated by the vast literature of quantum automorphism groups of graphs, we define and study quantum automorphism groups of matroids. A key feature of quantum groups is that there are many quantizations of a classical group, and this phenomenon manifests in the cryptomorphic characterizations of matroids. Our primary goals are to understand, using theoreti

Gunnar Kudrjavets, Aditya Kumar, Jeff Thomas, Ayushi Rastogi

Engineers build large software systems for multiple architectures, operating systems, and configurations. A set of inconsistent or missing compiler flags generates code that catastrophically impacts the system's behavior. In the authors' industry experience, defects caused by an undesired combination of compiler flags are common in nontrivial software projec

Gunnar Kudrjavets, Aditya Kumar, Jeff Thomas, Ayushi Rastogi

An accepted practice to decrease applications' memory usage is to reduce the amount and frequency of memory allocations. Factors such as (a) the prevalence of out-of-memory (OOM) killers, (b) memory allocations in modern programming languages done implicitly, (c) overcommitting being a default strategy in the Linux kernel, and (d) the rise in complexity and

Grant Wilkins, Sheng Di, Jon C. Calhoun, Zilinghan Li

With the promise of federated learning (FL) to allow for geographically-distributed and highly personalized services, the efficient exchange of model updates between clients and servers becomes crucial. FL, though decentralized, often faces communication bottlenecks, especially in resource-constrained scenarios. Existing data compression techniques like grad

Julien St-Pierre, Karim Oualkacha, Julien St-Pierre

Interactions between genes and environmental factors may play a key role in the etiology of many common disorders. Several regularized generalized linear models (GLMs) have been proposed for hierarchical selection of gene by environment interaction (GEI) effects, where a GEI effect is selected only if the corresponding genetic main effect is also selected in

Djuna Croon, Jeremy Sakstein

Using high-resolution simulations of black hole formation from the direct collapse of massive stars undergoing pulsational pair-instability supernovae (PPISN), we find a new phenomenon which significantly affects the explosion and leads to two peaks in the resulting black hole mass function (BHMF). Lighter stars experiencing the pair-instability can form a n

Francesco Di Colandrea, Nazanin Dehghan, Alessio D'Errico, Ebrahim Karimi

The characterization of a quantum device is a crucial step in the development of quantum experiments. This is accomplished via Quantum Process Tomography, which combines the outcomes of different projective measurements to deliver a possible reconstruction of the underlying process. The tomography is typically performed by processing an overcomplete set of m

Federico Herrero-Hervás, Angiolo Farina

A new model is considered to describe the evolution of tissue temperature and water concentration during a thermal ablation process. The aim of this study is to expand the results obtained in Blauth et. al. 2020, where the tissue water concentration is considered as a known function, experimentally obtained. The model consists of two coupled equations: a par

Paris A. Karakasis, Nicholas D. Sidiropoulos

Canonical correlation analysis (CCA) is a classic statistical method for discovering latent co-variation that underpins two or more observed random vectors. Several extensions and variations of CCA have been proposed that have strengthened our capabilities in terms of revealing common random factors from multiview datasets. In this work, we first revisit the

Yixuan Li, Archer Y. Yang, Ariane Marelli, Yue Li

Survival models can help medical practitioners to evaluate the prognostic importance of clinical variables to patient outcomes such as mortality or hospital readmission and subsequently design personalized treatment regimes. Electronic Health Records (EHRs) hold the promise for large-scale survival analysis based on systematically recorded clinical features

Farid Arthaud

We study the amount of entropy players asymptotically need to play a repeated normal-form game in a Nash equilibrium. Hub\'a\v{c}ek, Naor, and Ullman (SAGT'15, TCSys'16) gave sufficient conditions on a game for the minimal amount of randomness required to be $O(1)$ or $\Omega(n)$ for all players, where $n$ is the number of repetitions. We provide a complete

Michele Fava, Jesper Lykke Jacobsen, Adam Nahum

We study the 1D quantum Heisenberg chain with randomly ferromagnetic or antiferromagnetic couplings (a model previously studied by approximate strong-disorder RG). We find that, at least for sufficiently large spin $S$, the ground state has ``spin glass'' order. The spin waves on top of this state have the dynamical exponent ${z=3/2}$, intermediate between t

Aleksandra Pachalieva, Jeffrey D. Hyman, Daniel O'Malley, Hari Viswanathan

We perform a set of flow and reactive transport simulations within three-dimensional fracture networks to learn the factors controlling mineral reactions. CO$_2$ mineralization requires CO$_2$-laden water, dissolution of a mineral that then leads to precipitation of a CO$_2$-bearing mineral. Our discrete fracture networks (DFN) are partially filled with quar

Fabian JE Telschow, Samuel Davenport

The Gaussian Kinematic Formula (GKF) is a powerful and computationally efficient tool to perform statistical inference on random fields and became a well-established tool in the analysis of neuroimaging data. Using realistic error models, recent articles show that GKF based methods for \emph{voxelwise inference} lead to conservative control of the familywise

Jiawei Yao, Xiaochao Pan, Tong Wu, Xiaofeng Zhang

Detecting lane lines from sensors is becoming an increasingly significant part of autonomous driving systems. However, less development has been made on high-definition lane-level mapping based on aerial images, which could automatically build and update offline maps for auto-driving systems. To this end, our work focuses on extracting fine-level detailed la

Christian P. Fries

By its nature, the so-called social cost of carbon (SCC(t)) will likely not cover the cost induced by climate change (damage cost and abatement cost) if it is used as a CO$_2$-price. It is a marginal price only. We define an implied CO$_2$-price that covers the climate change-induced costs. The price can be interpreted as a \textit{polluter pays principle}.

Iosif Bena, Raphaël Dulac

The entropy of the supersymmetric D2-D4-P black hole comes at weak coupling from D2-brane strips stretched between parallel D4 branes and carrying momentum waves. We use the DBI action of D4 branes to construct two pieces of plumbing that enter in the construction of these microstates. The first is a semi-infinite D2 brane ending on a D4 brane and carrying a

T. J. Christiansen, K. Datchev

The behavior of the resolvent at low energies has implications for many kinds of asymptotics, including for the scattering matrix and phase, for the Dirichlet-to-Neumann map, and for wave evolution. In this paper we present a robust method, based in part on resolvent identity arguments following Vodev and boundary pairing arguments following Melrose, for der

Louis Lapp, Sahara Ali, Jianwu Wang

Arctic sea ice plays integral roles in both polar and global environmental systems, notably ecosystems, communities, and economies. As sea ice continues to decline due to climate change, it has become imperative to accurately predict the future of sea ice extent (SIE). Using datasets of Arctic meteorological and SIE variables spanning 1979 to 2021, we propos

Hui-Yu Yang, Hai-Long Shi, Qing-Kun Wan, Kun Zhang

The Tavis-Cummings (TC) model, which serves as a natural physical realization of a quantum battery, comprises $N_b$ atoms as battery cells that collectively interact with a shared photon field, functioning as the charger, initially containing $n_0$ photons. In this study, we introduce the invariant subspace method to effectively represent the quantum dynamic

Diego A. Mejía, Andrés F. Uribe-Zapata

The notion of $\theta$-FAM-linkedness, introduced in the second author's master thesis, is a formalization of the notion of strong FAM limits for intervals, whose initial form and applications have appeared in the work of Saharon Shelah, Jakob Kellner, and Anda T\u{a}nasie, for controlling cardinals characteristics of the continuum in ccc forcing extensions.

Sean O'Neill, Katrina Hay, Justin deMattos

Two moons of Saturn, Janus and Epimetheus, are in co-orbital motion, exchanging orbits approximately every four Earth years as the inner moon approaches the outer moon and they gravitationally interact. The orbital radii of these moons differ by only 50 km (less than the moons' mean physical radii), and it is this slight difference in their orbits that enabl

Emmanuel Ortiz-Pacheco, Sara Collins, Luka Leskovec, M. Padmanath

We perform a lattice simulation to investigate the doubly charmed tetraquark $T^+_{cc}$ observed by the LHCb collaboration, slightly below the $D^{*+}D^0$ threshold, with flavor content $cc\bar{u}\bar{d}$ and isospin-$0$. Two-meson interpolators are implemented to explore the isospin quantum numbers $I=0$ and $I=1$. We observe attraction near the $DD^*$ thre

Tonmoy Hossain, Miaomiao Zhang

Geometric transformations have been widely used to augment the size of training images. Existing methods often assume a unimodal distribution of the underlying transformations between images, which limits their power when data with multimodal distributions occur. In this paper, we propose a novel model, Multimodal Geometric Augmentation (MGAug), that for the

Denis Boyer, Satya N. Majumdar

The study of diffusion with preferential returns to places visited in the past has attracted an increased attention in recent years. In these highly non-Markov processes, a standard diffusive particle intermittently resets at a given rate to previously visited positions. At each reset, a position to be revisited is randomly chosen with a probability proporti

Shubhangi Ghosh, Luigi Gresele, Julius von Kügelgen, Michel Besserve

Independent Mechanism Analysis (IMA) seeks to address non-identifiability in nonlinear Independent Component Analysis (ICA) by assuming that the Jacobian of the mixing function has orthogonal columns. As typical in ICA, previous work focused on the case with an equal number of latent components and observed mixtures. Here, we extend IMA to settings with a la

Alexander Braylan, Madalyn Marabella, Omar Alonso, Matthew Lease

Human annotations are vital to supervised learning, yet annotators often disagree on the correct label, especially as annotation tasks increase in complexity. A strategy to improve label quality is to ask multiple annotators to label the same item and aggregate their labels. Many aggregation models have been proposed for categorical or numerical annotation t

Santiago Cabrera, Edson D. Leonel, Arturo C. Marti

The double coplanar pendulum is an example of the coexistence of regular and chaotic dynamics for equal energy values but different initial conditions. Regular trajectories predominate for low energies; as the energy is increased, the system passes through values where chaotic trajectories are abundant, and then, increasing the energy further, it is again do

Ilias Tsingenopoulos, Vera Rimmer, Davy Preuveneers, Fabio Pierazzi

Despite considerable efforts on making them robust, real-world AI-based systems remain vulnerable to decision based attacks, as definitive proofs of their operational robustness have so far proven intractable. Canonical robustness evaluation relies on adaptive attacks, which leverage complete knowledge of the defense and are tailored to bypass it. This work

Weibo Gao, Qi Liu, Hao Wang, Linan Yue

Cognitive diagnosis seeks to estimate the cognitive states of students by exploring their logged practice quiz data. It plays a pivotal role in personalized learning guidance within intelligent education systems. In this paper, we focus on an important, practical, yet often underexplored task: domain-level zero-shot cognitive diagnosis (DZCD), which arises d

Kevin Garner, Christos Tsolakis, Polykarpos Thomadakis, Nikos Chrisochoides

This paper presents the foundational elements of a distributed memory method for mesh generation that is designed to leverage concurrency offered by large-scale computing. To achieve this goal, meshing functionality is separated from performance aspects by utilizing a separate entity for each - a shared memory mesh generation code called CDT3D and PREMA for

Michael Callen, Miguel Fajardo-Steinhäuser, Michael G. Findley, Tarek Ghani

Can digital payments systems help reduce extreme hunger? Humanitarian needs are at their highest since 1945, aid budgets are falling behind, and hunger is concentrating in fragile states where repression and aid diversion present major obstacles. In such contexts, partnering with governments is often neither feasible nor desirable, making private digital pla

Ema Dimastrogiovanni, Matteo Fasiello, Jacob M. Leedom, Margherita Putti

We consider inflationary models with multiple spectator axions coupled to dark gauge sectors via Chern-Simons (CS) terms. The energy injection into Abelian gauge fields from the axions engenders a multi-peak profile for scalar and tensor spectra. We highlight the constraining power of CMB spectral distortions on the scalar signal and discuss the conditions u

Ruochen Huang, Yoonkyung Lee

Multivariate count data with many zeros frequently occur in a variety of application areas such as text mining with a document-term matrix and cluster analysis with microbiome abundance data. Exponential family PCA (Collins et al., 2001) is a widely used dimension reduction tool to understand and capture the underlying low-rank structure of count data. It pr

Karl Lackner, Rainer Burhenn, Sina Fietz, Alexander von Müller

Nanostructured solid boron-hydrogen compounds have been suggested as target and fuel for laser fusion, offering improved laser-plasma coupling, avoiding cryogenic fuel handling and fuel pre-compression and ultimately allowing a transit from DT- to aneutronic pB- fusion power production. We describe the scaling of the different energy loss channels ({\alpha}-

Ian McDougall, Shayne Wadle, Harish Batchu, Karthikeyan Sankaralingam

Modern chip designs are increasingly complex, making it difficult for developers to glean meaningful insights about hardware behavior while real workloads are running. Hardware introspection aims to solve this by enabling the hardware itself to observe and report on its internal operation - especially in the field, where the chip is executing real-world soft