November 2024 arXiv papers — page 18

Swee Hong Chan, Alex Kontorovich, Igor Pak

We prove the exponential growth of the cardinality of the set of numbers of spanning trees in simple (and planar) graphs on $n$ vertices, answering a question of Sedl\'a\v{c}ek from 1969. The proof uses a connection with continued fractions, ``thin orbits,'' and Zaremba's conjecture.

Ke Wang, Rufus Boyack, K. Levin

Observing the ``Higgs" or amplitude mode in superconductors has been a central challenge in condensed matter physics. Moreover, arriving at a theoretical understanding of this mode and how it is accessible in, say, conductivity experiments presents an additional challenge as here one needs to satisfy gauge invariance in the presence of disorder. In this pape

Hui Gao, Yu Min, Yupeng Wang

We systematically study relative and absolute ${\Delta}_{\mathrm{dR}}^+$-crystals on the (log-) prismatic site of a smooth (resp.~ semi-stable) formal scheme. Using explicit computation of stratifications, we classify (local) relative crystals by certain nilpotent connections, and classify (local) absolute crystals by certain enhanced connections. By using a

Robert Law, Andreas Born, Philipp Voigt, Joseph A. MacGregor

Large plume-like features within the Greenland Ice Sheet disrupt radiostratigraphy and complicate the use of isochrones in reconstructions of past ice dynamics. Here we use numerical modeling to test the hypothesis that convection is a viable mechanism for the formation of the large (>1/3 ice thickness) englacial plume-like features observed in north Greenla

Yihang Zhang, Lulu Fan, Tao An, Jun Yang

Infrared-faint radio sources (IFRSs) are believed to be a rare class of radio-loud active galactic nuclei (RL AGN) characterized by their high radio-to-infrared flux density ratios of up to several thousands. Previous studies have shown that a fraction of IFRSs are likely to be hosted in dust-obscured galaxies (DOGs). In this paper, our aim was to probe the

Alex Kemp, Dario J Fritzewski, Timothy Van Reeth, Luc IJspeert

In this work, we aim to estimate the stellar parameters of the primary (Aa) by performing asteroseismic analysis on its period-spacing pattern. We use the C-3PO neural network to perform asteroseismic modelling of the g-mode period-spacing pattern of Aa, discussing the interplay of this information with external constraints from spectroscopy ($T_{\rm eff}$ a

Anthony Etim, Jakub Szefer

Adversarial input image perturbation attacks have emerged as a significant threat to machine learning algorithms, particularly in image classification setting. These attacks involve subtle perturbations to input images that cause neural networks to misclassify the input images, even though the images remain easily recognizable to humans. One critical area wh

Angus Fung, Aaron Hao Tan, Haitong Wang, Beno Benhabib

Robotic search of people in human-centered environments, including healthcare settings, is challenging as autonomous robots need to locate people without complete or any prior knowledge of their schedules, plans or locations. Furthermore, robots need to be able to adapt to real-time events that can influence a person's plan in an environment. In this paper,

Christian Bender, Yana A. Butko, Mirko D'Ovidio, Gianni Pagnini

Anomalous diffusion is an established phenomenon but still a theoretical challenge in non-equilibrium statistical mechanics. Physical models are built incrementally, and the most recent and most general family is based on the fractional Brownian motion (fBm) with a random diffusion coefficient (superstatistical fBm) together with a time-dependent random Hurs

Jingshu Peng, Mohsen Alizadeh Noghani, Edgar Bolívar-Nieto

Accurate estimation of the whole-body center of mass (CoM) is essential for assessing human stability and postural control. However, selecting the most accurate estimation method is challenging due to the complexity of human movement, diverse nature of activities, and varying availability of equipment, such as marker-based systems and ground reaction force (

Zetai Cen, Yudong Chen, Clifford Lam

We analyze a varying-coefficient dynamic spatial autoregressive model with spatial fixed effects. One salient feature of the model is the incorporation of multiple spatial weight matrices through their linear combinations with varying coefficients, which help solve the problem of choosing the most ``correct'' one for applied econometricians who often face th

Sooahn Shin

This paper addresses one of the most prevalent problems encountered by political scientists working with difference-in-differences (DID) design: missingness in panel data. A common practice for handling missing data, known as complete case analysis, is to drop cases with any missing values over time. A more principled approach involves using nonparametric bo

Facundo Pugliese, Pablo Dmitruk

The test particle approach is a widely used method for studying the dynamics of charged particles in complex electromagnetic fields and has been successful in explaining particle energization in turbulent plasmas. However, this approach is fundamentally not self-consistent, as test particles do not generate their own electromagnetic fields and therefore do n

Henry Collis, Shahab Mirjalili, Ali Mani

A general approach for transforming phase field equations into generalized curvilinear coordinates is proposed in this work. The proposed transformation can be applied to isotropic, non-isotropic, and curvilinear grids without adding any ambiguity in determining the phase field parameters. Moreover, it accurately adapts the interface thickness to the local g

Grant Merz, Xin Liu, Samuel Schmidt, Alex I. Malz

Photometric redshifts will be a key data product for the Rubin Observatory Legacy Survey of Space and Time (LSST) as well as for future ground and space-based surveys. The need for photometric redshifts, or photo-zs, arises from sparse spectroscopic coverage of observed galaxies. LSST is expected to observe billions of objects, making it crucial to have a ph

Yubo Zhang, Anton M. Graf, Alhun Aydin, Joonas Keski-Rahkonen

We find that Anderson localization ceases to exist when a random medium begins to move, but another type of fundamental quantum effect, Planckian diffusion $D = \alpha\hbar/m$, rises to replace it, with $\alpha $ of order of unity. Planckian diffusion supercedes the Planckian speed limit $\tau= \alpha \hbar/k_B T,$ as it not only implies this relation in the

Sangwook Kim, Aly Khalifa, Thomas G. Purdie, Chris McIntosh

Deep learning-based automated contouring and treatment planning has been proven to improve the efficiency and accuracy of radiotherapy. However, conventional radiotherapy treatment planning process has the automated contouring and treatment planning as separate tasks. Moreover in deep learning (DL), the contouring and dose prediction tasks for automated trea

Mahmoud Abdelgalil, Tryphon T. Georgiou

We consider the problem of steering a collection of n particles that obey identical n-dimensional linear dynamics via a common state feedback law towards a rearrangement of their positions, cast as a controllability problem for a dynamical system evolving on the space of matrices with positive determinant. We show that such a task is always feasible and, mor

Anders Aamand, Allen Liu, Shyam Narayanan

In the trace reconstruction problem our goal is to learn an unknown string $x\in \{0,1\}^n$ given independent traces of $x$. A trace is obtained by independently deleting each bit of $x$ with some probability $\delta$ and concatenating the remaining bits. It is a major open question whether the trace reconstruction problem can be solved with a polynomial num

Xiaoyu Deng, Zhengjian Kang, Xintao Li, Yongzhe Zhang

Graphic visual content helps in promoting information communication and inspiration divergence. However, the interpretation of visual content currently relies mainly on humans' personal knowledge background, thereby affecting the quality and efficiency of information acquisition and understanding. To improve the quality and efficiency of visual information t

Tao-Chung Ching, Carl Heiles, Di Li, Timothy Robishaw

We present the polarization calibration of the 19-beam receiver at 1420 MHz within the full illumination of the Five-hundred-meter Aperture Spherical Telescope from October 2018 to March 2023. We perform spider observations to characterize the on-axis Mueller matrix of the central beam. The calibrated polarization percentage and polarization angle of a sourc

Thomas Oliver de Jong, Mircea Lazar

This paper presents a kernelized offset-free data-driven predictive control scheme for nonlinear systems. Traditional model-based and data-driven predictive controllers often struggle with inaccurate predictors or persistent disturbances, especially in the case of nonlinear dynamics, leading to tracking offsets and stability issues. To overcome these limitat

Nicholas Dale, Omar A. Ashour, Marc Vila, Resham B. Regmi

Nonrelativistic spin splitting (NRSS) challenges conventional wisdom about antiferromagnets by allowing spin-split electronic bands even in collinear orders with zero net magnetization. This sub-class of antiferromagnets, recently dubbed "altermagnets," enforces distinctive spin textures via spin-group symmetries in the crystal. However, direct experimental

L. F. Alves da Silva, L. M. R. Rocha, M. H. Y. Moussa

We present a protocol based on the interplay between superradiance and superabsorption to achieve the coherent deflection of an atomic sample due to the momentum transfer from the atoms to a cavity field. The coherent character of this momentum transfer, causing the atomic sample to deflect as a whole, follows from the collective nature of the atomic superra

Dheeman Saha, Aaron Segura, Biraj Tiwari

Analyzing large datasets and summarizing it into useful information is the heart of the data mining process. In healthcare, information can be converted into knowledge about patient historical patterns and possible future trends. During the COVID-19 pandemic, data mining COVID-19 patient information poses an opportunity to discover patterns that may signal t

Alexander R. Klotz

This manuscript discusses gravity tunnels formed by connecting two vertical shafts by a constant-radius tunnel within the Earth, which featured in a dream I had in September 2024. The total travel time through such a tunnel can be minimized with respect to the radius at which the shafts are connected. I derive this minimal radius and minimum time given two a

Rosário D. Laureano

After the fundamental work of Livschitz in [1; 2], various research directions emerged, among which the following stand out: (i) the study of cocycles with values in groups and semigroups beyond R, as well as the investigation of corresponding regularity results; (ii) the analysis of how a certain degree of regularity of the cocycle can confer corresponding

Margherita Taddei, Hannah Contreras, Hai-Nam Doan, Declan P. McCarthy

We show that the use of 1,3-diaminopropane (DAP) as a chemical modifier at the perovskite/electron-transport layer (ETL) interface enhances the power conversion efficiency (PCE) of 1.7 eV bandgap FACs mixed-halide perovskite single-junction cells, primarily by boosting the open-circuit voltage (VOC) from 1.06 V to 1.15 V. Adding a post-processing annealing s

Amit Agarwal, Hitesh Patel, Priyaranjan Pattnayak, Srikant Panda

The development of robust Document AI models has been constrained by limited access to high-quality, labeled datasets, primarily due to data privacy concerns, scarcity, and the high cost of manual annotation. Traditional methods of synthetic data generation, such as text and image augmentation, have proven effective for increasing data diversity but often fa

Weiqiu You, Youngja Park

Understanding the attack patterns associated with a cyberattack is crucial for comprehending the attacker's behaviors and implementing the right mitigation measures. However, majority of the information regarding new attacks is typically presented in unstructured text, posing significant challenges for security analysts in collecting necessary information. I

Saverio Capolongo, Axel Kleinschmidt, Hannes Malcha, Hermann Nicolai

We propose a new approach to studying hyperbolic Kac-Moody algebras, focussing on the rank-3 algebra $\mathfrak{F}$ first investigated by Feingold and Frenkel. Our approach is based on the concrete realization of this Lie algebra in terms of a Hilbert space of transverse and longitudinal physical string states, which are expressed in a basis using DDF operat

Faraz Salimnezhad, Hasret Turkeri, Iskender Gokalp, Metin Muradoglu

A hybrid sharp-interface immersed-boundary/front-tracking (IB/FT) method is developed for interface-resolved simulation of evaporating droplets in incompressible multiphase flows. A one-field formulation is used to solve the flow, species mass fraction and energy equations in the entire computational domain with appropriate jump conditions at the interface.

Jiaojiao Zhang, Linglingzhi Zhu, Dominik Fay, Mikael Johansson

We introduce a locally differentially private (LDP) algorithm for online federated learning that employs temporally correlated noise to improve utility while preserving privacy. To address challenges posed by the correlated noise and local updates with streaming non-IID data, we develop a perturbed iterate analysis that controls the impact of the noise on th

Mehrdad Phoroutan-Mehr, Hai-Bo Yu

The supermassive black hole at the center of M87 could redistribute dark matter particles within its sphere of influence, creating a high-density region known as a density spike. This spike can significantly enhance dark matter annihilation signals, making M87 a critical target for deriving stringent constraints on annihilation cross sections. In this work,

Julie A. Adams, Christopher A. Sanchez, Vivek Mallampati, Joshua Bhagat Smith

The common theory is that human pilot's performance degrades when responsible for an increased number of uncrewed aircraft systems (UAS). This theory was developed in the early 2010's for ground robots and not highly autonomous UAS. It has been shown that increasing autonomy can mitigate some performance impacts associated with increasing the number of UAS.

Bas Lodewijks

We study a family of Crump--Mode--Jagers branching processes in random environment that explode, i.e. that grow infinitely large in finite time with positive probability. Building on recent work of the author and Iyer (``On the structure of genealogical trees associated with explosive Crump--Mode--Jagers branching processes", arXiv:2311.14664, 2023), we weak

C. E. Ferreira Lopes, L. A. Gutiérrez-Soto, V. S. Ferreira Alberice, N. Monsalves

Context. Spectroscopic surveys like APOGEE, GALAH, and LAMOST have significantly advanced our understanding of the Milky Way by providing extensive stellar parameters and chemical abundances. Complementing these, photometric surveys with narrow/medium-band filters, such as the Southern Photometric Local Universe Survey (S-PLUS), offer the potential to estima

Sahil Bhola, Karthik Duraisamy

Modern computer architectures support low-precision arithmetic, which present opportunities for the adoption of mixed-precision algorithms to achieve high computational throughput and reduce energy consumption. As a growing number of scientific computations leverage specialized hardware accelerators, the risk of rounding errors increases, potentially comprom

Fengwei Tian, Ravi Tandon

Ensuring privacy during inference stage is crucial to prevent malicious third parties from reconstructing users' private inputs from outputs of public models. Despite a large body of literature on privacy preserving learning (which ensures privacy of training data), there is no existing systematic framework to ensure the privacy of users' data during inferen

Zheyan Zhang, Diego Klabjan, Renee CB Manworren

In this work, we address a challenge in video inpainting: reconstructing occluded regions in dynamic, real-world scenarios. Motivated by the need for continuous human motion monitoring in healthcare settings, where facial features are frequently obscured, we propose a diffusion-based video-level inpainting model, DiffMVR. Our approach introduces a dynamic du

J. N. Graham, S. S. Islam, V. Sazgari, Y. Li

The experimental realisation of unconventional superconductivity and charge order in kagome systems \textit{A}V$_3$Sb$_5$ is of critical importance. We conducted a highly systematic study of Cs(V$_{1-x}$Nb$_x$)$_3$Sb$_5$ with $x$=0.07 (Nb$_{0.07}$-CVS) by employing a unique combination of tuning parameters such as doping, hydrostatic pressure, magnetic field

Danni Peng, Zhifei Yan

Finding near-rainbow Hamilton cycles in properly edge-coloured graphs was first studied by Andersen, who proved in 1989 that every proper edge colouring of the complete graph on $n$ vertices contains a Hamilton cycle with at least $n-\sqrt{2n}$ distinct colours. This result was improved to $n-O(\log^2 n)$ by Balogh and Molla in 2019. In this paper, we consid

Raju S. Khatiwada, C. Ortiz, Basanta R. Giri

This paper investigates the influence of nonlinear dissipative forces, specifically Gravitational Friction (GF), on the precession of celestial bodies within the framework of general relativity. We derive a modified line element by introducing a density-dependent term to model interactions between planetary bodies and the low-density interplanetary medium, p

C M Hull

Dirac's formulation of magnetic monopoles is shown to be equivalent to Maxwell theory coupled to 2-form gauge fields so that it has a local 1-form symmetry, with the 2-form gauge fields given in terms of the 2-form current densities associated with the Dirac strings. The field equations of Dirac's theory do not depend on the positions of the Dirac strings pr

Ilya Shesterikov

This work explores a novel approach to mitigating turbulence in fusion plasmas through spatially modulated plasma profiles. By imposing a harmonic modulation on plasma parameters, we introduce conditions that alter the propagation characteristics of turbulent and MHD waves, a primary source of transport and instabilities in fusion devices. This modulation ap

Jefferson Delgado-Quesada, David Barral, Kamel Bencheikh, Edgar A. Rojas-González

The arrays of nonlinear waveguides are a powerful integrated photonics platform for studying and manipulating quantum states of light. Also, they are a valuable resource for various quantum technologies. In this work, we employed a supermode approach to obtain an analytic solution to the evolution of degenerate biphoton states in arrays of nonlinear waveguid

Pragati Shuddhodhan Meshram, Swetha Karthikeyan, Bhavya Bhavya, Suma Bhat

Multi-modal Large Language Models (MLLMs) are gaining significant attention for their ability to process multi-modal data, providing enhanced contextual understanding of complex problems. MLLMs have demonstrated exceptional capabilities in tasks such as Visual Question Answering (VQA); however, they often struggle with fundamental engineering problems, and t

Saurabh Bhandari, Michael J. Daniels, Maria Josefsson, Donald M. Lloyd-Jones

Causal mediation analysis of observational data is an important tool for investigating the potential causal effects of medications on disease-related risk factors, and on time-to-death (or disease progression) through these risk factors. However, when analyzing data from a cohort study, such analyses are complicated by the longitudinal structure of the risk

Lindsay Fuller, Enrique Lopez-Rodriguez, Ismael Garcia-Bernete, Cristina Ramos Almeida

We present a 19.7 - 214 $\mu$m imaging atlas of local (4 - 181 Mpc; median 43 Mpc) active galactic nuclei (AGN) observed with FORCAST and HAWC+ on board the SOFIA telescope with angular resolutions ~ 3"- 20". This atlas comprises 22 Seyferts (17 Type 2 and 5 Type 1) with a total of 69 images, 41 of which have not been previously published. The AGN span a ran

Vasileios Basiouris

In this article we study the significance of string loop corrections, in a perturbative moduli stabilization scenario, focusing on their role in unraveling the origin of dark radiation in the late cosmological epoch and their correlation to dark matter. More specifically, a detailed analysis is provided in which the mass hierarchy of the normalized fields in

Ali Hamed Safwan, Raditya Weda Bomantara

Majorana fermions and their generalizations to $\mathbb{Z}_n$ parafermions are considered promising building blocks of fault-tolerant quantum computers for their ability to encode quantum information nonlocally. In such topological quantum computers, highly robust quantum gates are obtained by braiding pairs of these quasi-particles. However, it is well-know

Alexandre Audibert, Aurélien Gauffre, Massih-Reza Amini

Multi-label classification, which involves assigning multiple labels to a single input, has emerged as a key area in both research and industry due to its wide-ranging applications. Designing effective loss functions is crucial for optimizing deep neural networks for this task, as they significantly influence model performance and efficiency. Traditional los

Jiayi Liu, Xudong Sun, Peter W. Schuck, Sarah A. Jaeggli

Quiet-Sun regions cover most of the Sun's surface; its magnetic fields contribute significantly to the solar chromospheric and coronal heating. However, characterizing the magnetic fields of the quiet Sun is challenging due to their weak polarization signal. The 4-m \textit{Daniel K. Inouye Solar Telescope} (\textit{DKIST}) is expected to improve our underst

Jiuda Wu, Behrooz Yousefzadeh

Materials and devices subject to spatiotemporal modulation of their effective properties have a demonstrated ability to support nonreciprocal transmission of waves. Most notably, spatiotemporally modulated systems can restrict wave transmission to only one direction; i.e. a very large difference in the energy transmitted between two points in opposite direct

Qile Jiang, Marc Salvadori, Dale Ota, Vijaya Shankar

Maxwell's equations, a system of linear partial differential equations (PDEs), describe the behavior of electric and magnetic fields in time and space and are essential for many important electromagnetic applications. Although numerical methods have been applied successfully in the past, the primary challenge in solving these equations arises from the freque

A. Rososhek, E. S. Lavine, B. R. Kusse, W. M. Potter

In this paper, we present the first direct experimental evidence supported by numerical modeling of a turbulent plasma column formed during a gas-puff z-pinch implosion generated by COBRA current. Utilizing an imaging refractometer, we show a significant decrease in spatial autocorrelation of the laser field and the appearance of a laser speckle pattern shor

Saroj Gopali, Sima Siami-Namini, Faranak Abri, Akbar Siami Namin

As an intriguing case is the goodness of the machine and deep learning models generated by these LLMs in conducting automated scientific data analysis, where a data analyst may not have enough expertise in manually coding and optimizing complex deep learning models and codes and thus may opt to leverage LLMs to generate the required models. This paper invest

Magdalini Paschali, Zhihong Chen, Louis Blankemeier, Maya Varma

Recent advances in artificial intelligence have witnessed the emergence of large-scale deep learning models capable of interpreting and generating both textual and imaging data. Such models, typically referred to as foundation models, are trained on extensive corpora of unlabeled data and demonstrate high performance across various tasks. Foundation models h

Feng Xiong, Runxi Cheng, Wang Chen, Zhanqiu Zhang

Model merging has recently gained attention as an economical and scalable approach to incorporate task-specific weights from various tasks into a unified multi-task model. For example, in Task Arithmetic (TA), adding the fine-tuned weights of different tasks can enhance the model's performance on those tasks, while subtracting them leads to task forgetting.

Daniel Morales-Brotons, Grigorios Chrysos, Stratis Tzoumas, Volkan Cevher

Supervised deep learning requires massive labeled datasets, but obtaining annotations is not always easy or possible, especially for dense tasks like semantic segmentation. To overcome this issue, numerous works explore Unsupervised Domain Adaptation (UDA), which uses a labeled dataset from another domain (source), or Semi-Supervised Learning (SSL), which tr

Yael Vinker

Visual communication, dating back to prehistoric cave paintings, is the use of visual elements to convey ideas and information. In today's visually saturated world, effective design demands an understanding of graphic design principles, visual storytelling, human psychology, and the ability to distill complex information into clear visuals. This dissertation

Luis Fernandez, Manuel Rivera, Thomas Tradler

For any simplicial complex $X$ with a total ordering of its vertices, one can construct a chain complex $\mathbb{L}_\bullet(X)$ generated by necklaces of simplices in $X$, which computes the homology of the free loop space of the geometric realization of $X$. Motivated by string topology, we describe two explicit chain maps $C_\bullet(X) \to \mathbb{L}_\bull

Yi Chung

The comparable abundances of dark matter and baryons imply a deep connection between the dark sector and the QCD sector. In models of asymmetric dark matter, the number densities of both sectors are ensured to be similar. However, a complete solution should also include a mechanism for comparable masses. In this letter, we present a solution based on a stron

Sebastian Ali Sacasa-Cespedes

Introduction: the Navier-Stokes equations are essential in fluid dynamics, describing the motion of fluids like liquids and gases. Solving these equations, especially in complex flows and high-Reynolds-number regimes, is a significant challenge. Numerical simulations provide some insights, but often under restrictive assumptions that limit applicability. Rec

Ángel A. Soní, Irene Cruz-González, Martín Herrera-Endoqui, Erika Benítez

Spectrum analysis at 3 mm of the central region ($r\sim$800 pc) of NGC\,4303 showed molecular gas lines of both dense gas tracers (HCN, HNC, HCO$^+$, and C$_2$H) and diffuse gases ($^{13}$CO and C$^{18}$O). Molecular gas derived parameters: $H_2$ mass $M_{H_2}$=(1.75$\pm$0.32)$\times10^{8}$ M$_{\odot}$; radial velocity, V$_{dense}=$178$\pm$60 km\,s$^{-1}$, a

Neil Majithia, Elena Simperl

Governments typically collect and steward a vast amount of high-quality data on their citizens and institutions, and the UK government is exploring how it can better publish and provision this data to the benefit of the AI landscape. However, the compositions of generative AI training corpora remain closely guarded secrets, making the planning of data sharin

Zetao Zheng, Guoan Li, Deqiang Ouyang, Decui Liang

Limit order book (LOB) is a dynamic, event-driven system that records real-time market demand and supply for a financial asset in a stream flow. Event stream prediction in LOB refers to forecasting both the timing and the type of events. The challenge lies in modeling the time-event distribution to capture the interdependence between time and event type, whi

Yuhao Li, Robert E. Smith

Theoretical frameworks based on Press-Schechter formalism and excursion set arguments suggest that the abundance of dark matter haloes exhibits universal behaviour when expressed in terms of peak height. If true, this implies that a single high-accuracy cosmological simulation could serve as a basis for constructing an emulator applicable to any other cosmol

Yuanlong Zheng, Connor Blake, Layla Mravac, Fengxue Zhang

The integration of machine learning and robotics into thin film deposition is transforming material discovery and optimization. However, challenges remain in achieving a fully autonomous cycle of deposition, characterization, and decision-making. Additionally, the inherent sensitivity of thin film growth to hidden parameters such as substrate conditions and

Todd Darcie, J. Stewart Aitchison

The responsivity of perturbation sensing can be effectively enhanced by using higher-order exceptional points (HOEPs) due to their nonlinear response to frequency perturbations. However, experimental realization can be difficult due to the stringent parameter conditions associated with these points. In this work, we study an EP laser composed of two coupled

Shrey Ganatra, Spandan Anaokar, Pushpak Bhattacharyya

The proliferation of IoT devices generates vast interaction data, offering insights into user behaviour. While prior work predicts what actions users perform, the timing of these actions -- critical for enabling proactive and efficient smart systems -- remains relatively underexplored. Addressing this gap, we focus on predicting the time of the next user act

Mason Ng, Jeremy Hare, Gaurava K. Jaisawal, Christian Malacaria

We report on follow-up observations of the recently discovered transient by the Einstein Probe, EP240709A, with the Neutron star Interior Composition Explorer (NICER). We also incorporated archival multiwavelength survey data from the Neil Gehrels Swift Observatory (X-ray), Gaia (optical), the Fermi Gamma-ray Space Telescope (gamma-ray), and the Wide-field I

Vahid Attari, Raymundo Arroyave

Materials data, especially those related to high-temperature properties, pose significant challenges for machine learning models due to extreme skewness, wide feature ranges, modality, and complex relationships. While traditional models like tree-based ensembles (e.g., XGBoost, LightGBM) are commonly used for tabular data, they often struggle to fully captur

Yixiong Wang, Maria Paskevich, Hui Wang

The mobile gaming industry, particularly the free-to-play sector, has been around for more than a decade, yet it still experiences rapid growth. The concept of games-as-service requires game developers to pay much more attention to recommendations of content in their games. With recommender systems (RS), the inevitable problem of bias in the data comes hand

Malick A. Gaye, Dylan Albrecht, Steve Young, Tameem Albash

Qubit performance is often reported in terms of a variety of single-value metrics, each providing a facet of the underlying noise mechanism limiting performance. However, the value of these metrics may drift over long time-scales, and reporting a single number for qubit performance fails to account for the low-frequency noise processes that give rise to this

Soumia Siyoucef, Rose Al-Aslani, Mourad Adnane, Muhammad Mahboob Ur Rahman

Dehydration occurs when the body loses more water than it takes in. Mild dehydration can lead to fatigue, cognitive impairments, and physical complications, while severe dehydration can cause life-threatening conditions like heat stroke, kidney damage, and hypovolemic shock. Traditional bio chemistry-based clinical gold standard methods are expensive, time-c

Jeremy Schofield, Raymond Kapral

The dynamics of a quantum system coupled to a classical environment and subject to constraints that drive it out of equilibrium is described. The evolution of the system is governed by the quantum-classical Liouville equation. Rather than evaluating the evolution of the mixed quantum-classical density operator, we derive exact equations of motion for the non

Oscar J. C. Dias, Prahar Mitra, Jorge E. Santos

Using a mix of analytical and numerical methods, we construct new rotating, charged "hairy" black hole solutions of $D=5$, ${\cal N}=8$ gauged supergravity that are dual, via the AdS/CFT correspondence, to thermal states in $D=4$, ${\cal N}=4$ SYM at finite chemical and angular potential, thereby complementing and extending the results of [arXiv:1005.1287, a

Mohamed Aghzal, Xiang Yue, Erion Plaku, Ziyu Yao

Despite their promise to perform complex reasoning, large language models (LLMs) have been shown to have limited effectiveness in end-to-end planning. This has inspired an intriguing question: if these models cannot plan well, can they still contribute to the planning framework as a helpful plan evaluator? In this work, we generalize this question to conside

Sabri Bensid

We present a variational framework for studying the existence of solutions of a class of elliptic free boundary problems on stratified Lie groups. Using the important monotonicity result in a Non-Euclidean setup, we prove that our solution is the limit of mountain pass points of a sequence of C1-functionals approximating the energy

Dibyajyoti Mallick, Priya Chakraborty, Sayantari Ghosh

Addiction to internet-based social media has increasingly emerged as a critical social problem, especially among young adults and teenagers. Based on multiple research studies, excessive usage of social media may have detrimental psychological and physical impacts. In this study, we are going to explore mathematically the dynamics of social media addiction b

Tiffany Zhu, Kexun Zhang, William Yang Wang

The impressive essay writing and problem-solving capabilities of large language models (LLMs) like OpenAI's ChatGPT have opened up new avenues in education. Our goal is to gain insights into the widespread use of LLMs among secondary students to inform their future development. Despite school restrictions, our survey of over 300 middle and high school studen

Rabeeya Hamid, Demeng Feng, Pournima Narayanan, Justin S. Edwards

Frequency upconversion, which converts low-energy photons into higher-energy ones, typically requires intense coherent illumination to drive nonlinear processes or the use of externally driven optoelectronic devices. Here, we demonstrate an upconversion system that converts low-intensity (down to ~10-7 W/cm$^2$) incoherent near-infrared (NIR) light into the

Eleanor Stuart, Kris Pardo

Dark matter (DM) models with a non-zero DM-baryon interaction cross section imply energy transfer between DM and baryons. We present a new method of constraining the DM-baryon interaction cross section and DM particle mass for velocity-independent interactions using the thermodynamics of galaxy clusters. If the baryonic gas in these clusters is in thermodyna

Latika Joshi, Ines Belkhodja, Livneh Naaman, Thomas Burbine

This study uses the Pearson's chi-square test to analyze the VNIR reflectance spectra of seven asteroids and look for spectral matches among approximately 11,000 laboratory spectra of meteoritic, terrestrial, synthetic, Apollo, and Luna samples. First, we use the chi-square method to analyze three well-studied asteroids - (4) Vesta, (6) Hebe, and (19) Fortun

Daniel Morales-Brotons, Thijs Vogels, Hadrien Hendrikx

Weight averaging of Stochastic Gradient Descent (SGD) iterates is a popular method for training deep learning models. While it is often used as part of complex training pipelines to improve generalization or serve as a `teacher' model, weight averaging lacks proper evaluation on its own. In this work, we present a systematic study of the Exponential Moving A

Hongzheng Liu, Zhiyue Wu

This paper revisits Brownian motion from the perspective of Information Theory, aiming to explore the connections between Information Theory, Thermodynamics, and Complex Science. First, we propose a single-particle discrete Brownian motion model (SPBM). Within the framework of the maximum entropy principle and Bayesian inference, we demonstrate the equivalen

Xiaopo Cheng, Dell Zimmerman, Elizabeth Iffrig, Wilbur A. Lam

Recent clinical results indicate that aberrant erythrocyte aggregation in hematological disorders is accompanied by endothelial damage and glycocalyx disruption, but the underlying biophysical mechanisms remain unclear. This study uses direct computational modeling to explore how red blood cell (RBC) aggregation impacts shear stress in small blood vessels, h

Chicago Y. Park, Michael T. McCann, Cristina Garcia-Cardona, Brendt Wohlberg

We present a concise derivation for several influential score-based diffusion models that relies on only a few textbook results. Diffusion models have recently emerged as powerful tools for generating realistic, synthetic signals -- particularly natural images -- and often play a role in state-of-the-art algorithms for inverse problems in image processing. W

Dan Yang, Stephan G. Heinemann, Robert H. Cameron, Laurent Gizon

Maps of the magnetic field at the Sun's surface are commonly used as boundary conditions in space-weather modeling. However, continuous observations are only available from the Sun's Earth-facing side. One commonly used approach to mitigate the lack of far-side information is to apply a surface flux transport (SFT) model to model the evolution of the magneti

Miles Q. Li, Benjamin C. M. Fung, Shih-Chia Huang

Training large language models is a computationally intensive process that often requires substantial resources to achieve state-of-the-art results. Incremental layer-wise training has been proposed as a potential strategy to optimize the training process by progressively introducing layers, with the expectation that this approach would lead to faster conver

Ted Kwartler, Nataliia Bagan, Ivan Banny, Alan Aqrawi

The Single-Turn Crescendo Attack (STCA), first introduced in Aqrawi and Abbasi [2024], is an innovative method designed to bypass the ethical safeguards of text-to-text AI models, compelling them to generate harmful content. This technique leverages a strategic escalation of context within a single prompt, combined with trust-building mechanisms, to subtly d

Hossein Rokni, Patrick Singleton, Yuanlong Zheng, Connor Blake

The inherent trade-off between ultra-low thermal conductivity and high mechanical rigidity in natural materials limits their utility in advanced applications. Inspired by the unique architecture of layered honeycomb structures, this study introduces a new class of metamaterials designed to overcome these constraints. By systematically exploring unit cell con

Yuanche Liu, Antonela Matijašić, Julian Miczajka, Yingxuan Xu

We evaluate the three-loop five-point pentagon-box-box massless integral family in the dimensional regularization scheme, via canonical differential equation. We use tools from computational algebraic geometry to enable the necessary integral reductions. The boundary values of the differential equation are determined analytically in the Euclidean region. To

Yifei He

We study Kac operators (e.g. energy operator) in percolation and self-avoiding walk bulk CFTs with central charge $c=0$. The proper normalizations of these operators can be deduced at generic $c$ by requiring the finiteness and reality of the three-point constants in cluster and loop model CFTs. At $c=0$, Kac operators become zero-norm states and the bottom

Maitreya Patel, Song Wen, Dimitris N. Metaxas, Yezhou Yang

Diffusion models (DMs) excel in photorealism, image editing, and solving inverse problems, aided by classifier-free guidance and image inversion techniques. However, rectified flow models (RFMs) remain underexplored for these tasks. Existing DM-based methods often require additional training, lack generalization to pretrained latent models, underperform, and

Eon Lee, Andrés R. Vindas-Meléndez, Zhi Wang

Building from the work of von Bell et al.~(2022), we study the Ehrhart theory of order polytopes arising from a special class of distributive lattices, known as generalized snake posets. We present arithmetic properties satisfied by the Ehrhart polynomials of order polytopes of generalized snake posets along with a computation of their Gorenstein index. Then

A. Melo, R. Cañameras, S. Schuldt, S. H. Suyu

In the past, researchers have mostly relied on single-resolution images from individual telescopes to detect gravitational lenses. We propose a search for galaxy-scale lenses that, for the first time, combines high-resolution single-band images (in our case the Hubble Space Telescope, HST) with lower-resolution multi-band images (in our case Legacy survey, L

Mriganka Dutta, Banibrata Mukhopadhyay

The theory of general relativity is often considered under the framework of modified Einstein gravity to explain different phenomena under strong curvature. The strong curvature effect plays a main role near black holes, where the gravitational field is strongest. The idea of black hole thermodynamics is to describe the strong field curvature properties of a

Zenia Zuraiq, Achal Kumar, Alexander J. Hackett, Surendra Bhattarai

Over the last few decades, there has been considerable interest in the violation of the sacred "Chandrasekhar" mass limit of white dwarfs (WDs). Peculiar over-luminous type Ia supernovae (such as SNLS-03D3bb) lend observational support to the idea that these super-Chandrasekhar WDs exist. Our group, for more than a decade, has been actively working on the th