November 2024 arXiv papers — page 121

Hugo Alcaraz-Herrera, Michail-Antisthenis Tsompanas, Andrew Adamatzky, Igor Balaz

In medical-related tasks, soft robots can perform better than conventional robots because of their compliant building materials and the movements they are able perform. However, designing soft robot controllers is not an easy task, due to the non-linear properties of their materials. Since human expertise to design such controllers is yet not sufficiently ef

Subramanya Hegde, Ashoke Sen, P Shanmugapriya, Amitabh Virmani

We compute the supersymmetric index of half BPS black holes in N=2 supergravity with higher curvature corrections and show that the result agrees with the degeneracy of supersymmetric extremal black holes carrying the same charges. Both sides of the computation are done gravitationally.

Jake Huryn, Yifei Zhang

Let $X$ be a connected normal scheme of finite type over $\mathbf{Z}$, let $G$ be a connected reductive group over $\mathbf{Q}$, and let $\{\rho_\ell\colon\pi_1(X[1/\ell])\to G(\mathbf{Q}_\ell)\}_\ell$ be a Frobenius-compatible collection of continuous homomorphisms indexed by the primes. Assume $\mathrm{Img}(\rho_\ell)$ is Zariski-dense in $G_{\mathbf{Q}_\e

Minhan Gao, Kenneth W. Shum

A permutation code is a nonlinear code whose codewords are permutation of a set of symbols. We consider the use of permutation code in the deletion channel, and consider the symbol-invariant error model, meaning that the values of the symbols that are not removed are not affected by the deletion. In 1992, Levenshtein gave a construction of perfect single-del

Sichao Xiong, Yigit Ihlamur, Fuat Alican, Aaron Ontoyin Yin

Traditional decision tree algorithms are explainable but struggle with non-linear, high-dimensional data, limiting its applicability in complex decision-making. Neural networks excel at capturing complex patterns but sacrifice explainability in the process. In this work, we present GPTree, a novel framework combining explainability of decision trees with the

Michio Yamamoto, Yoshikazu Terada

In longitudinal data analysis, observation points of repeated measurements over time often vary among subjects except in well-designed experimental studies. Additionally, measurements for each subject are typically obtained at only a few time points. From such sparsely observed data, identifying underlying cluster structures can be challenging. This paper pr

Sungwook E. Hong, Changbom Park, Preetish K. Mishra, Juhan Kim

We investigate when and how the relations of galaxy morphology and star forming activity with clustercentric radius become evident in galaxy clusters. We identify 162 galaxy clusters with total mass $M_{\rm tot}^{\rm cl} > 5 \times 10^{13} {\rm M}_\odot$ at $z = 0.625$ in the Horizon Run 5 (HR5) cosmological hydrodynamical simulation and study how the proper

Hamed Taherkhani, Jiho Shin, Muhammad Ammar Tahir, Md Rakib Hossain Misu

Modern Large Language Model (LLM)-based programming agents often rely on test execution feedback to refine their generated code. These tests are synthetically generated by LLMs. However, LLMs may produce invalid or hallucinated test cases, which can mislead feedback loops and degrade the performance of agents in refining and improving code. This paper introd

Nishanth Kumar, William Shen, Fabio Ramos, Dieter Fox

Foundation models like Vision-Language Models (VLMs) excel at common sense vision and language tasks such as visual question answering. However, they cannot yet directly solve complex, long-horizon robot manipulation problems requiring precise continuous reasoning. Task and Motion Planning (TAMP) systems can handle long-horizon reasoning through discrete-con

Christian Cabrera, Viviana Bastidas, Jennifer Schooling, Neil D. Lawrence

Using Large Language Models (LLMs) to address critical societal problems requires adopting this novel technology into socio-technical systems. However, the complexity of such systems and the nature of LLMs challenge such a vision. It is unlikely that the solution to such challenges will come from the Artificial Intelligence (AI) community itself. Instead, th

Robin Willems, Peter Förster, Sebastian Schöps, Olaf van der Sluis

Cardio-mechanical models can be used to support clinical decision-making. Unfortunately, the substantial computational effort involved in many cardiac models hinders their application in the clinic, despite the fact that they may provide valuable information. In this work, we present a probabilistic reduced-order modeling (ROM) framework to dramatically redu

Nitish K. Chandra, Saikat Guha, Kaushik P. Seshadreesan

We study an architecture for fault-tolerant measurement-based quantum computation (FT-MBQC) over optically-networked trapped-ion modules. The architecture is implemented with a finite number of modules and ions per module, and leverages photonic interactions for generating remote entanglement between modules and local Coulomb interactions for intra-modular e

Daniel Seipt, Mathias Samuelsson, Tom Blackburn

Observing multiphoton electron-positron pair production (the nonlinear Breit-Wheeler process) requires high-energy $γ$ rays to interact with strong electromagnetic fields. In order for these observations to be as precise as possible, the $γ$ rays would ideally be both mono-energetic and highly polarized. Here we perform Monte Carlo simulations of an experime

Akira Sakurai, Kazuyuki Shudo

An effective method for suppressing intentional forks in a blockchain is the last-generated rule, which selects the most recent chain as the main chain in the event of a chain tie. This rule helps invalidate blocks that are withheld by adversaries for a certain period. However, existing last-generated rules face an issue in that their applications to the sys

Johann Gramzow

This paper shows that for certain local topological properties, given a locally quasi-finite, flat and locally finitely presented map of schemes $f\colon X\to Y$, if $Y$ has the property, then so does $X$. We also show that being locally topologically noetherian or locally connected or having locally finitely many irreducible components are examples for thes

J. Brendan Ritchie, Susan G. Wardle, Maryam Vaziri-Pashkam, Dwight J. Kravitz

A wealth of studies report evidence that occipitotemporal cortex tessellates into "category-selective" brain regions that are apparently specialized for representing ecologically important visual stimuli like faces, bodies, scenes, and tools. Here, we argue that while valuable insights have been gained through the lens of category-selectivity, a more complet

Emilio Ferrara

Generative AI (GenAI) technologies possess unprecedented potential to reshape our world and our perception of reality. These technologies can amplify traditionally human-centered capabilities, such as creativity and complex problem-solving in socio-technical contexts. By fostering human-AI collaboration, GenAI could enhance productivity, dismantle communicat

Kutay Tire, Ege Onur Taga, Muhammed Emrullah Ildiz, Samet Oymak

Retrieval-augmented generation (RAG) is a central component of modern LLM systems, particularly in scenarios where up-to-date information is crucial for accurately responding to user queries or when queries exceed the scope of the training data. The advent of time-series foundation models (TSFM), such as Chronos, and the need for effective zero-shot forecast

Jiyao Li, Mingze Ni, Yongshun Gong, Wei Liu

Deep learning underpins most of the currently advanced natural language processing (NLP) tasks such as textual classification, neural machine translation (NMT), abstractive summarization and question-answering (QA). However, the robustness of the models, particularly QA models, against adversarial attacks is a critical concern that remains insufficiently exp

Eugene Fiorini, Maxwell Fogler, Katherine Levandosky, Bryan Lu

In this paper we study a variant of the solitaire game Lights-Out, where the player's goal is to turn off a grid of lights. This variant is a two-player impartial game where the goal is to make the final valid move. This version is playable on any simple graph where each node is given an assignment of either a 0 (representing a light that is off) or 1 (repre

Kenichiro Shiraya, Kanji Suzuki, Tomohisa Yamakami

Two formulations are proposed to filter out correlations in the residuals of the multivariate GARCH model. The first approach is to estimate the correlation matrix as a parameter and transform any joint distribution to have an arbitrary correlation matrix. The second approach transforms time series data into an uncorrelated residual based on the eigenvalue d

Bennet Goeckner, Marta Pavelka

Unit interval and interval complexes are higher-dimensional generalizations of unit interval and interval graphs, respectively. We show that strongly connected unit interval complexes are shellable with shellings induced by their unit interval orders. We also show that these complexes are vertex decomposable and hence shelling completable. On the other hand,

Ruiyang Qin, Pengyu Ren, Zheyu Yan, Liu Liu

Large Language Models (LLMs) deployed on edge devices, known as edge LLMs, need to continuously fine-tune their model parameters from user-generated data under limited resource constraints. However, most existing learning methods are not applicable for edge LLMs because of their reliance on high resources and low learning capacity. Prompt tuning (PT) has rec

Khaoula Chehbouni, Jonathan Colaço Carr, Yash More, Jackie CK Cheung

In an effort to mitigate the harms of large language models (LLMs), learning from human feedback (LHF) has been used to steer LLMs towards outputs that are intended to be both less harmful and more helpful. Despite the widespread adoption of LHF in practice, the quality of this feedback and its effectiveness as a safety mitigation technique remain unclear. T

Domenico Giordano, Pierluigi Amodio, Felice Iavernaro, Francesca Mazzia

We take up the investigation we left in the future-work stack in Giordano \textit{et al.} [``Fluid statics of a self-gravitational isothermal sphere of van der Waals' gas,'' Phys. Fluids \textbf{36}, 056127 (2024)], in which we pointed out the obvious necessity to inquire about the existence or absence of values of the characteristic numbers \itm{\alphay} an

B. P. Pandey, Mark Wardle

In weak magnetic fields ($\lesssim 50 \,\mbox{G}$), parallel and perpendicular viscosities, mainly from neutrals, may exceed magnetic diffusivities (Ohm, Hall, ambipolar) in the middle and upper chromosphere. Ion-driven gyroviscosity may dominate in the upper chromosphere and transition region. In strong fields ($\gtrsim 100\, \mbox{G}$), viscosities primari

Mohak Shah

Rapid developments in AI and its adoption across various domains have necessitated a need to build robust guardrails and risk containment plans while ensuring equitable benefits for the betterment of society. The current technology-centered approach has resulted in a fragmented, reactive, and ineffective policy apparatus. This paper highlights the immediate

Alex Laguë, Mathew S. Madhavacheril, Kendrick M. Smith, Simone Ferraro

The cosmic velocity field is an unbiased probe of the total matter distribution but is challenging to measure directly at intermediate and high redshifts. The large-scale velocity field imprints a signal in the cosmic microwave background (CMB) through the kinetic Sunyaev-Zeldovich (kSZ) effect. We perform the first 3d reconstruction of the large-scale veloc

Quanling Deng

Building on previous work that provided analytical solutions to generalised matrix eigenvalue problems arising from numerical discretisations, this paper develops exact eigenvalues and eigenvectors for a broader class of $n$-dimensional matrices, focusing on non-symmetric and non-persymmetric matrices. These matrices arise in one-dimensional Laplacian eigenv

Joanna Kaleta, Paweł Skierś, Jan Dubiński, Przemysław Korzeniowski

We introduce Mediffusion -- a new method for semi-supervised learning with explainable classification based on a joint diffusion model. The medical imaging domain faces unique challenges due to scarce data labelling -- insufficient for standard training, and critical nature of the applications that require high performance, confidence, and explainability of

Zhehao Yi, Rahul Bhadani

We find that using neural networks to generate quantum states can effectively alleviate the barren plateau phenomenon present in random variational quantum circuits.

Sarah J Zou, Irene Lim, Jackson W Foster, Garry Chinn

In this study, we explore the use of Co-55 as a radioisotope for multiplexed PET (mPET) by utilizing its emission of a prompt gamma-ray in cascade with a positron during decay. We leverage the prompt-gamma signal to generate triple coincidences for a Co-55-labeled tracer, allowing us to distinguish it from a tracer labeled with a pure positron emitter, such

Deniz Kurdak, Patrick R. Banner, Yaxin Li, Sean R. Muleady

Experimental control over the strength and angular dependence of interactions between atoms is a key capability for advancing quantum technologies. Here, we use microwave dressing to manipulate and enhance Rydberg-Rydberg interactions in an atomic ensemble. By varying the cloud length relative to the blockade radius and measuring the statistics of the light

T. Danilovich, A. M. S. Richards, M. Van de Sande, C. A. Gottlieb

We analyse continuum and molecular emission, observed with ALMA, from the dust-enshrouded intermediate-mass AGB star OH 30.1 -0.7. We find a secondary peak in the continuum maps, "feature B", separated by 4.6" from the AGB star, which corresponds to a projected separation of $1.8 \times 10^4$ au, placing a lower limit on the physical separation. This feature

Kelvin L Walls, Iran R Roman, Kelsey Van Ert, Colter Harper

This study examines the pitch content in traditional Ghanaian seperewa (Akan harp-lute) songs, utilizing a unique dataset from field recordings of the mid-twentieth century. We selected 71 songs and used Demucs to isolate vocals from instrumental tracks. We then retrieved the F0 content from these isolated tacks and applied Gaussian Mixture Models (GMM) to a

Gabriel Sac Himelfarb, Moshe Schwartz

We study skew-tolerant Gray codes, which are Gray codes in which changes in consecutive codewords occur in adjacent positions. We present the first construction of asymptotically non-vanishing skew-tolerant Gray codes, offering an exponential improvement over the known construction. We also provide linear-time encoding and decoding algorithms for our codes.

Renzi Wang, Flavia Sofia Acerbo, Tong Duy Son, Panagiotis Patrinos

This paper presents a novel approach to imitation learning from observations, where an autoregressive mixture of experts model is deployed to fit the underlying policy. The parameters of the model are learned via a two-stage framework. By leveraging the existing dynamics knowledge, the first stage of the framework estimates the control input sequences and he

Ankit Shaw

This paper introduces a cost effective localization system combining monocular visual odometry , augmented reality (AR) poses, and integrated INS-GPS data. We address monocular VO scale factor issues using AR poses and enhance accuracy with INS and GPS data, filtered through an Extended Kalman Filter . Our approach, tested using manually annotated trajectori

Anthony John Bracken

The description of a closed quantum system is extended with the identification of an underlying substructure enabling an expanded formulation of dynamics in the Heisenberg picture. Between measurements a ``state point" moves in an underlying multi-dimensional complex projective space with constant velocity determined by the quantum state vector. Following a

Atieh Taheri, Arthur Caetano, Misha Sra

Many people often take walking for granted, but for individuals with mobility disabilities, this seemingly simple act can feel out of reach. This reality can foster a sense of disconnect from the world since walking is a fundamental way in which people interact with each other and the environment. Advances in virtual reality and its immersive capabilities ha

Atieh Taheri, Purav Bhardwaj, Arthur Caetano, Alice Zhong

Advances in artificial intelligence have transformed the paradigm of human-computer interaction, with the development of conversational AI systems playing a pivotal role. These systems employ technologies such as natural language processing and machine learning to simulate intelligent and human-like conversations. Driven by the personal experience of an indi

Shawn Li, Huixian Gong, Hao Dong, Tiankai Yang

Out-of-distribution (OOD) detection is essential for ensuring the robustness of machine learning models by identifying samples that deviate from the training distribution. While traditional OOD detection has primarily focused on single-modality inputs, such as images, recent advances in multimodal models have demonstrated the potential of leveraging multiple

Sophia Burger, Stella Stopkowicz, Jürgen Gauss

The computation of magnetizability tensors using gauge-including atomic orbitals is discussed in the context of Cholesky decomposition for the two-electron repulsion integrals with a focus on the involved doubly differentiated integrals. Three schemes for their handling are suggested: the first exploits the DF aspect of Cholesky decomposition, the second use

Si Suo, Deheng Wei, Budi Zhao, Chongpu Zhai

Controversy exists regarding whether grain morphology reduces or enhances the drag of a single grain in creeping flows; further complication occurs when orientation dependence for aspherical grains comes into play. To quantify influences of shape irregularity and orientation, this study numerically investigates the drag in creeping flows for fractally rough

Paweł Skierś, Kamil Deja

In this work, we introduce JDCL - a new method for continual learning with generative rehearsal based on joint diffusion models. Neural networks suffer from catastrophic forgetting defined as abrupt loss in the model's performance when retrained with additional data coming from a different distribution. Generative-replay-based continual learning methods try

Sushant K. Singh, Radoslaw Ryblewski, Wojciech Florkowski

The equations of perfect spin hydrodynamics are solved for the first time using a realistic (3+1)-dimensional hydrodynamic background, calibrated to reproduce a comprehensive set of hadronic observables, including rapidity distributions, transverse momentum spectra, and elliptic flow coefficients for Au+Au collisions at the beam energy of $\sqrt{s_{\rm NN}}

Matias Doradau, Ramiro Tomas Martinez, Rodolfo Sassot, Marco Stratmann

We revisit the notion of nuclear parton-to-pion fragmentation functions at next-to-leading order accuracy as an effective description of hadroproduction in nuclear environments such as in semi-inclusive lepton-nucleus deep-inelastic scattering and in single inclusive proton-nucleus collisions. We assess their viability in the face of very precise data collec

Parsa Delavari, Ipek Oruc, Timothy H Murphy

The availability of large-scale neuronal population datasets necessitates new methods to model population dynamics and extract interpretable, scientifically translatable insights. Existing deep learning methods often overlook the biological mechanisms underlying population activity and thus exhibit suboptimal performance with neuronal data and provide little

Krzysztof Bogdan, Damian Fafuła, Paweł Sztonyk

We construct a strong Markov process corresponding to the Dirichlet form of Servadei and Valdinoci and use the process to solve the corresponding Neumann boundary problem for the fractional Laplacian and the half-line.

Alireza AmaniHamedani, Ali Aouad, Tristan Pollner, Amin Saberi

We study stationary online bipartite matching, where both types of nodes--offline and online--arrive according to Poisson processes. Offline nodes wait to be matched for some random time, determined by an exponential distribution, while online nodes need to be matched immediately. This model captures scenarios such as deceased organ donation and time-sensiti

Saif Mahmud, Kian Mahmoodi, Chi-Jung Lee, Francois Guimbretiere

Hand-face interactions play a key role in many everyday tasks, providing insights into user habits, behaviors, intentions, and expressions. However, existing wearable sensing systems often struggle to track these interactions in daily settings due to their reliance on multiple sensors or privacy-sensitive, vision-based approaches. To address these challenges

Jiacheng Sun, Hsiang-Wei Huang, Cheng-Yen Yang, Zhongyu Jiang

Multi-object tracking in sports scenarios has become one of the focal points in computer vision, experiencing significant advancements through the integration of deep learning techniques. Despite these breakthroughs, challenges remain, such as accurately re-identifying players upon re-entry into the scene and minimizing ID switches. In this paper, we propose

Patricio Pérez-Piña

We prove the equidistribution of some cycles of S-arithmetic nature that are related to RM points and Stark-Heegner points. We also prove the equidistribution of Picard orbits of ATR cycles as defined by Darmon, Rotger and Zhao.

Joseph A. Smiga, Gabriel T. Landi

The outcome of continuously measuring a quantum system is a string of data whose intricate correlation properties reflect the underlying quantum dynamics. In this paper we study the role of these correlation in reconstructing the probabilities of finite sequences of outcomes, the so-called empirical distributions. Our approach is cast in terms of generic qua

Kjell Janke, Andrés Emilio Wedenig, Peter Schwerdtfeger, Konstantin Gaul

Vacuum polarisation (VP) and electron self energy (SE) are implemented and evaluated as quantum electrodynamic (QED) corrections in a (quasi-relativistic) two-component zeroth order regular approximation (ZORA) framework. For VP, the Uehling potential is considered, and for SE, the effective potentials proposed by Flambaum and Ginges as well as the one propo

Yilun Liu, Yunpu Ma, Shuo Chen, Zifeng Ding

The Mixture-of-Experts (MoE) paradigm has emerged as a powerful approach for scaling transformers with improved resource utilization. However, efficiently fine-tuning MoE models remains largely underexplored. Inspired by recent works on Parameter-Efficient Fine-Tuning (PEFT), we present a unified framework for integrating PEFT modules directly into the MoE m

S. Dalla, A. Hutchinson, R. A. Hyndman, K. Kihara

Context. Solar energetic particles (SEPs) are detected in interplanetary space in association with solar flares and coronal mass ejections (CMEs). The magnetic connection between the observing spacecraft and the solar active region (AR) source of the event is a key parameter in determining whether SEPs are observed and the particle event's properties. Aims.

Pingchuan Ma, Zhengqi Gao, Amir Begovic, Meng Zhang

Nanophotonic device design aims to optimize photonic structures to meet specific requirements across various applications. Inverse design has unlocked non-intuitive, high-dimensional design spaces, enabling the discovery of high-performance devices beyond heuristic or analytic methods. The adjoint method, which calculates gradients for all variables using ju

Eirik F. Kjønstad, Henrik Koch

The geometric phase effect arises from the dependence on the nuclear coordinates in the electronic Hamiltonian, leading to sign changes of the electronic wave functions upon traversal of certain paths in nuclear configuration space. The geometric phase effect can have important consequences for the electronic structure problem, but this fact has largely gone

Charalampos Evangelou

Let $G=GL_n(K)$ be the general linear group defined over an infinite field $K$ of positive characteristic $p$ and let $\Delta(\lambda)$ be the Weyl module of $G$ which corresponds to a partition $\lambda$. In this paper we classify all homomorphisms $\Delta(\lambda) \to \Delta(\mu)$ when $\lambda=(a,b,1^d)$ and $\mu=(a+d,b)$, $d>1$. In particular, we show th

Sara Angelico, Eirik F. Kjønstad, Henrik Koch

Minimum energy conical intersections can be used to rationalize photochemical processes. In this Letter, we examine an algorithm to locate these structures that does not require the evaluation of nonadiabatic coupling vectors, showing that it minimizes the energy on hypersurfaces that envelop the intersection seam. By constraining the states to be separated

Berwyn Hoyt

Elegance of a database API matters. Frequently, database APIs suit the database designer, rather than the programmer's desire for elegance and efficiency. This article pursues both: firstly, by comparing the Lua APIs for two separate databases, Redis and YottaDB. Secondly, it looks under the API covers at how object orientation can help to retain API efficie

Ricardo F. Ferreira, Matheus E. Pacola, Vitor G. Schiavone, Rodrigo F. O. Pena

We address the problem of identifying functional interactions among stochastic neurons with variable-length memory from their spiking activity. The neuronal network is modeled by a stochastic system of interacting point processes with variable-length memory. Each chain describes the activity of a single neuron, indicating whether it spikes at a given time. O

Joachim Gudmundsson, Sampson Wong

Low density graphs are considered to be a realistic graph class for modelling road networks. It has advantages over other popular graph classes for road networks, such as planar graphs, bounded highway dimension graphs, and spanners. We believe that low density graphs have the potential to be a useful graph class for road networks, but until now, its usefuln

Jacopo Tagliabue, Ryan Curtin, Ciro Greco

Data pre-processing pipelines are the bread and butter of any successful AI project. We introduce a novel programming model for pipelines in a data lakehouse, allowing users to interact declaratively with assets in object storage. Motivated by real-world industry usage patterns, we exploit these new abstractions with a columnar and differential cache to maxi

Anouar Moustaj, Lumen Eek, Malte Rontgen, Cristiane Morais Smith

Latent symmetries, which materialize after performing isospectral reductions, have recently been shown to be instrumental in revealing novel topological phases in one-dimensional systems, among many other applications. In this work, we explore how to construct a family of seemingly complicated two-dimensional models that result in energy-dependent Haldane mo

David Beers, Heather A Harrington, Jacob Leygonie, Uzu Lim

Persistent homology (PH) studies the topology of data across multiple scales by building nested collections of topological spaces called filtrations, computing homology and returning an algebraic object that can be vizualised as a barcode--a multiset of intervals. The barcode is stable and interpretable, leading to applications within mathematics and data sc

Sandra Sajan, Haojie Guo, Tarushi Agarwal, Irián Sánchez-Ramírez

The emergence of superconductivity in the octahedrally coordinated (1T) phase of TaS2 is preceded by the intriguing loss of long-range order in the charge density wave (CDW). Such decoherence, attainable by different methods, results in the formation of nm-sized coherent CDW domains bound by a two-dimensional network of domain walls (DW) - mosaic phase -, wh

Mohamad Mahdi Rajaei Rizi, Jeyanandh Paramesh, Kamran Entesari

This paper conducts a comprehensive system-level analysis of mm-Wave full-duplex transceivers, focusing on a receiver employing a four-stage self-interference cancellation (SIC) process. The analysis aims to optimize the noise and linearity performance requirements of each transceiver block, ensuring that the self-interference (SI) signal does not compromise

Frederick Tsz-Ho Fong

In this article, we establish some uniqueness and symmetry results of self-similar solutions to curvature flows by some homogeneous speed functions of principal curvatures in some warped product spaces. In particular, we proved that any compact star-shaped self-similar solution to any parabolic flow with homogeneous degree $-1$ (including the inverse mean cu

Sarah E. Harvey, David Lipshutz, Alex H. Williams

Neural responses encode information that is useful for a variety of downstream tasks. A common approach to understand these systems is to build regression models or ``decoders'' that reconstruct features of the stimulus from neural responses. Popular neural network similarity measures like centered kernel alignment (CKA), canonical correlation analysis (CCA)

Zitao Shuai, Chenwei Wu, Zhengxu Tang, Bowen Song

Diffusion Transformers (DiTs) have recently achieved remarkable success in text-guided image generation. In image editing, DiTs project text and image inputs to a joint latent space, from which they decode and synthesize new images. However, it remains largely unexplored how multimodal information collectively forms this joint space and how they guide the se

Mohsin Ali, Haider Raza, John Q Gan, Ariel Pokhojaev

Objectives: Age and gender estimation is crucial for various applications, including forensic investigations and anthropological studies. This research aims to develop a predictive system for age and gender estimation in living individuals, leveraging dental measurements such as Coronal Height (CH), Coronal Pulp Cavity Height (CPCH), and Tooth Coronal Index

Owen R. Dunton, Tom Arbaugh, Francis W. Starr

Phase change materials such as Ge$_{2}$Sb$_{2}$Te$_{5}$ (GST) are ideal candidates for next-generation, non-volatile, solid-state memory due to the ability to retain binary data in the amorphous and crystal phases, and rapidly transition between these phases to write/erase information. Thus, there is wide interest in using molecular modeling to study GST. Re

Arshia M. Jacob, Meera Nandakumar, Nirupam Roy, Karl M. Menten

Ever since they were first detected in the interstellar medium, the radio wavelength (3.3 GHz) hyperfine-structure splitting transitions in the rotational ground state of CH have been observed to show anomalous excitation. Astonishingly, this behaviour has been uniformly observed towards a variety of different sources probing a wide range of physical conditi

Yanyong Wang, Manuel Engel, Christopher Lane, Henrique Miranda

Electron-phonon coupling (EPC) is key for understanding many properties of materials such as superconductivity and electric resistivity. Although first principles density-functional-theory (DFT) based EPC calculations are used widely, their efficacy is limited by the accuracy and efficiency of the underlying exchange-correlation functionals. These limitation

Abra Geiger, James M. Cordes, Michael T. Lam, Stella Koch Ocker

Free electrons in the interstellar medium refract and diffract radio waves along multiple paths, resulting in angular and temporal broadening of radio pulses that limits pulsar timing precision. We determine multifrequency, multi-epoch scattering times for the large dispersion measure millisecond pulsar J1903+0327 by developing a three component model for th

Karolina Schmidt, Luis Rodrigues

The main contribution of this paper is a methodology for multiple non-cooperating swarms of unmanned aerial vehicles to independently cover a common area. In contrast to previous research on coverage control involving more than one swarm, this paper does not assume cooperation between distinct groups but considers them as entirely independent units following

F. Aharonian, F. Ait Benkhali, J. Aschersleben, H. Ashkar

Owing to their rapid cooling rate and hence loss-limited propagation distance, cosmic-ray electrons and positrons (CRe) at very high energies probe local cosmic-ray accelerators and provide constraints on exotic production mechanisms such as annihilation of dark matter particles. We present a high-statistics measurement of the spectrum of CRe candidate event

Gabriel Rodriguez-Rondon, Jean-Marie Dufour

We present the R package MSTest, which implements hypothesis testing procedures to identify the number of regimes in Markov switching models. These models have wide-ranging applications in economics, finance, and numerous other fields. The MSTest package includes the Monte Carlo likelihood ratio test procedures proposed by Rodriguez-Rondon and Dufour (2024),

Anoushkrit Goel, Bipanjit Singh, Ankita Joshi, Ranjeet Ranjan Jha

White matter tract segmentation is crucial for studying brain structural connectivity and neurosurgical planning. However, segmentation remains challenging due to issues like class imbalance between major and minor tracts, structural similarity, subject variability, symmetric streamlines between hemispheres etc. To address these challenges, we propose Tracto

Javier M. Magan, Qingyue Wu

There are two types of quantum chaos: eigenbasis chaos and spectral chaos. The first type controls the early-time physics, e.g. the thermal relaxation and the sensitivity of the system to initial conditions. It can be traced back to the Eigenstate Thermalization Hypothesis (ETH), a statistical hypothesis about the eigenvectors of the Hamiltonian. The second

Parmita Mondal, Allison Shields, Mohammad Mahdi Shiraz Bhurwani, Kyle A Williams

This study aims to mitigate these biases and enhance QA analysis by applying a path-length correction (PLC) correction, followed by singular value decomposition (SVD)-based deconvolution, to angiograms obtained through both in-silico and in-vitro methods. We utilized DSA data from in-silico and in-vitro patient-specific intracranial aneurysm models. To remov

Gabriel Coutinho, Thomás Jung Spier, Shengtong Zhang

In this paper we prove a conjecture by Wocjan, Elphick and Anekstein (2018) which upper bounds the sum of the squares of the positive (or negative) eigenvalues of the adjacency matrix of a graph by an expression that behaves monotonically in terms of the vector chromatic number. One of our lemmas is a strengthening of the Cauchy-Schwarz inequality for Hermit

Daniel M. Kane, Anthony Ostuni, Kewen Wu

We characterize the power of constant-depth Boolean circuits in generating uniform symmetric distributions. Let $f\colon\{0,1\}^m\to\{0,1\}^n$ be a Boolean function where each output bit of $f$ depends only on $O(1)$ input bits. Assume the output distribution of $f$ on uniform input bits is close to a uniform distribution $D$ with a symmetric support. We sho

Raul Oliveira Ribeiro

This research thesis presents a novel higher-order spectral element method (SEM) formulated in cylindrical coordinates for analyzing electromagnetic fields in waveguides filled with complex anisotropic media. In this study, we consider a large class of cylindrical waveguides: radially-bounded and radially-unbounded domains; homogeneous and inhomogeneous wave

Ecenaz Erdemir, Kyuhong Park, Michael J. Morais, Vianne R. Gao

As businesses increasingly adopt cloud technologies, they also need to be aware of new security challenges, such as server-side script attacks, to ensure the integrity of their systems and data. These scripts can steal data, compromise credentials, and disrupt operations. Unlike executables with standardized formats (e.g., ELF, PE), scripts are plaintext fil

Nishan Pantha, Muthukumaran Ramasubramanian, Iksha Gurung, Manil Maskey

The rapid development in large language models (LLMs) has transformed the landscape of natural language processing and understanding (NLP/NLU), offering significant benefits across various domains. However, when applied to scientific research, these powerful models exhibit critical failure modes related to scientific integrity and trustworthiness. Existing g

Jon Chaika, Donald Robertson

We show that there is a rank 1 transformation that is mildly mixing but does not have minimal self-joinings, answering a question of Thouvenot.

Charilaos Efthymiou

This work establishes novel optimum mixing bounds for the Glauber dynamics on the Hard-core and Ising models. These bounds are expressed in terms of the local connective constant of the underlying graph $G$. This is a notion of effective degree for $G$. Our results have some interesting consequences for bounded degree graphs: (a) They include the max-degree

Tao Hong, Zhaoyi Xu, Jason Hu, Jeffrey A. Fessler

Model-based iterative reconstruction plays a key role in solving inverse problems. However, the associated minimization problems are generally large-scale, nonsmooth, and sometimes even nonconvex, which present challenges in designing efficient iterative solvers. Preconditioning methods can significantly accelerate the convergence of iterative methods. In so

Mert Gökduman, Hanwen Yao, Henry D. Pfister

Quantum low-density parity-check (LDPC) codes are a promising family of quantum error-correcting codes for fault tolerant quantum computing with low overhead. Decoding quantum LDPC codes on quantum erasure channels has received more attention recently due to advances in erasure conversion for various types of qubits including neutral atoms, trapped ions, and

Kays Haddad, Gustav Uhre Jakobsen, Gustav Mogull, Jan Plefka

Worldline quantum field theory (WQFT) has proven itself a powerful tool for classical two-body scattering calculations in general relativity. In this paper we develop a new worldline action involving bosonic oscillators, which enables the use of the WQFT formalism to describe massive compact bodies to all orders in their spins. Inspired by bosonic string the

Sudeb Majee, Åke Brännström, Niklas L. P. Lundström

In this paper, we present a telegraph diffusion model with variable exponents for image despeckling. Moving beyond the traditional assumption of a constant exponent in the telegraph diffusion framework, we explore three distinct variable exponents for edge detection. All of these depend on the gray level of the image or its gradient. We rigorously prove the

Anna Perfilyeva, Vittal Raghavendra Miskin, Ryan Aven, Craig Drohan

This study sought to better understand the causes of price disparity in cesarean sections, using newly released hospital data. Beginning January 1, 2021, Centers for Medicare and Medicaid Services (CMS) requires hospitals functioning in the United States to publish online pricing information for items and services these hospitals provide in a machine-readabl

Feng Zhao, Yizhou Wu, Mingzhe Hu, Chih-Wei Chang

Medical imaging has played a pivotal role in advancing and refining digital twin technology, allowing for the development of highly personalized virtual models that represent human anatomy and physiological functions. A key component in constructing these digital twins is the integration of high-resolution imaging data, such as MRI, CT, PET, and ultrasound,

Mohammad Mehdi Morovati, Amin Nikanjam, Foutse Khomh

Over the past decade, Deep Learning (DL) has become an integral part of our daily lives. This surge in DL usage has heightened the need for developing reliable DL software systems. Given that fault localization is a critical task in reliability assessment, researchers have proposed several fault localization techniques for DL-based software, primarily focusi

Aditya V. Jonnalagadda, Hashim A. Hashim, Andrew Harris

Contemporary Artificial Intelligence (AI) and Machine Learning (ML) research places a significant emphasis on transfer learning, showcasing its transformative potential in enhancing model performance across diverse domains. This paper examines the efficiency and effectiveness of transfer learning in the context of wildfire detection. Three purpose-built mode

Daniel Reichart, Nickolas Solomey

The Solar Neutrino and Astro-Particle PhYsics (SNAPPY) Cubesat is expected to launch in 2025 and it will carry into a polar orbit a prototype test detector for solar neutrino background studies while over the Earth's poles for the neutrino Solar Orbiting Laboratory future project ($\nu$SOL). During this flight it is possible to do other science measurements.

Jackson M. Steinkamp, Laura J. Brattain, Conor J. Walsh, Robert D. Howe

Controlling hand exoskeletons for assisting impaired patients in grasping tasks is challenging because it is difficult to infer user intent. We hypothesize that majority of daily grasping tasks fall into a small set of categories or modes which can be inferred through real-time analysis of environmental geometry from 3D point clouds. This paper presents a lo