May 2024 arXiv papers — page 134

Mihai I. Florea

The Optimized Gradient Method (OGM), its strongly convex extension, the Information Theoretical Exact Method (ITEM), as well as the related Triple Momentum Method (TMM) have superior convergence guarantees when compared to the Fast Gradient Method but lack adaptivity and their derivation is incompatible with composite problems. In this work we introduce a sl

Mahdi Morafah, Matthias Reisser, Bill Lin, Christos Louizos

The proliferation of edge devices has brought Federated Learning (FL) to the forefront as a promising paradigm for decentralized and collaborative model training while preserving the privacy of clients' data. However, FL struggles with a significant performance reduction and poor convergence when confronted with Non-Independent and Identically Distributed (N

Eric Evert, Benjamin Passer, Tea Štrekelj

This expository article gives a survey of matrix convex sets, a natural generalization of convex sets to the noncommutative (dimension-free) setting, with a focus on their extreme points. Mirroring the classical setting, extreme points play an important role in matrix convexity, and a natural question is, ``are matrix convex sets the (closed) matrix convex h

Zurab Berezhiani, Benedetta Belfatto

The Standard Model does not constrain the form of the Yukawa matrices and thus the origin of fermion mass hierarchies and mixing pattern remains puzzling. On the other hand, there are intriguing relations between the quark masses and their weak mixing angles, such as the well-known one $\tan \theta_C= \sqrt{m_d/m_s}$ for the Cabibbo angle, which may point to

Lars Zawallich, Renato Pajarola

When folding a 3D object from a 2D material like paper, typically only an approximation of the original surface geometry is needed. Such an approximation can effectively be created by a (progressive) mesh simplification approach, e.g. using an edge collapse technique. Moreover, when searching for an unfolding of the object, this approximation is assumed to b

Hari Chandana Kuchibhotla, Sai Srinivas Kancheti, Abbavaram Gowtham Reddy, Vineeth N Balasubramanian

Going beyond mere fine-tuning of vision-language models (VLMs), learnable prompt tuning has emerged as a promising, resource-efficient alternative. Despite their potential, effectively learning prompts faces the following challenges: (i) training in a low-shot scenario results in overfitting, limiting adaptability, and yielding weaker performance on newer cl

Ferdinand Schlatt, Maik Fröbe, Harrisen Scells, Shengyao Zhuang

Cross-encoders distilled from large language models (LLMs) are often more effective re-rankers than cross-encoders fine-tuned on manually labeled data. However, distilled models do not match the effectiveness of their teacher LLMs. We hypothesize that this effectiveness gap is due to the fact that previous work has not applied the best-suited methods for fin

Haoyu Deng, Zijing Xu, Yule Duan, Xiao Wu

Deep neural networks for image super-resolution (ISR) have shown significant advantages over traditional approaches like the interpolation. However, they are often criticized as 'black boxes' compared to traditional approaches with solid mathematical foundations. In this paper, we attempt to interpret the behavior of deep neural networks in ISR using theorie

Saeed S. I. Almishal, Jacob T. Sivak, George N. Kotsonis, Yueze Tan

We unravel the distinct roles each cation plays in phase evolution, stability, and properties within Mg1/5Co1/5Ni1/5Cu1/5Zn1/5O high-entropy oxide (HEO) by integrating experimental findings, thermodynamic analyses, and first-principles predictions. Our approach is through sequentially removing one cation at a time from the five-component high-entropy oxide t

Adriano Vogel, Sören Henning, Esteban Perez-Wohlfeil, Otmar Ertl

Parallel computing is very important to accelerate the performance of software systems. Additionally, considering that a recurring challenge is to process high data volumes continuously, stream processing emerged as a paradigm and software architectural style. Several software systems rely on stream processing to deliver scalable performance, whereas open-so

S. Norouzi, N. Azarasa, M. R. Abedi, N. Mokari

Efficient and dynamic path planning has become an important topic for urban areas with larger density of connected vehicles (CV) which results in reduction of travel time and directly contributes to environmental sustainability through reducing energy consumption. CVs exploit the cellular wireless vehicle-to-everything (C-V2X) communication technology to dis

Ziyang Zhang, Plamen Angelov, Dmitry Kangin, Nicolas Longépé

In this paper, we address two critical challenges in the domain of flood detection: the computational expense of large-scale time series change detection and the lack of interpretable decision-making processes on explainable AI (XAI). To overcome these challenges, we proposed an interpretable multi-stage approach to flood detection, IMAFD has been proposed.

Yuchen Lou, Alex M. Ganose

Anisotropy in crystals plays a pivotal role in many technological applications. For example, anisotropic electronic and thermal transport are thought to be beneficial for thermoelectric applications, while anisotropic mechanical properties are of interest for emerging metamaterials, and anisotropic dielectric materials have been suggested as a novel platform

Arnab Bhattacharyya, Sutanu Gayen, Philips George John, Sayantan Sen

This work establishes a novel link between the problem of PAC-learning high-dimensional graphical models and the task of (efficient) counting and sampling of graph structures, using an online learning framework. We observe that if we apply the exponentially weighted average (EWA) or randomized weighted majority (RWM) forecasters on a sequence of samples from

Nick Stracke, Stefan Andreas Baumann, Joshua M. Susskind, Miguel Angel Bautista

Text-to-image generative models have become a prominent and powerful tool that excels at generating high-resolution realistic images. However, guiding the generative process of these models to consider detailed forms of conditioning reflecting style and/or structure information remains an open problem. In this paper, we present LoRAdapter, an approach that u

A. Garrett Lisi

Discrete charge, parity, and time symmetries (C, P, and T) of quantized fermion states are extended by a triality symmetry (t), producing the CPTt Group, transforming between three generations of fermions.

L. G. S. Duarte, H. S. Ferreira, L. A. C. P. da Mota

The Prelle-Singer method allows determining an elementary first integral admitted by a polynomial vector field in the plane. It is a semi-algorithm whose nonlinear step consists of determining the Darboux polynomials of the vector field. In this article we construct a linear procedure to determine the Darboux polynomials present in the integrating factor of

Laura Ciobanu, Georg Zetzsche

Indexed languages are a classical notion in formal language theory. As the language equivalent of second-order pushdown automata, they have received considerable attention in higher-order model checking. Unfortunately, counting properties are notoriously difficult to decide for indexed languages: So far, all results about non-regular counting properties show

S M Toufiqul Huq Sowrov

International trade has been in the forefront of economic development and growth debates. Trade openness, its definition, scope, and impacts have also been studied numerously. Tariff has been dubbed as negative influencer of economic growth as per conventional wisdom and most empirical studies. This paper empirically examines relationships among trade openne

Honghyok Kim

When exposure measurement error (EME), confounder measurement error (CME), or both are present, health effect estimates regarding exposure mixtures and critical exposure time-window may not represent the true effects. For example, in air pollution epidemiology, modeled estimates for multiple air pollutants and meteorological factors may serve as surrogates f

Guillaume Thekkadath, Martin Houde, Duncan England, Philip Bustard

Strongly-driven nonlinear optical processes such as spontaneous parametric down-conversion and spontaneous four-wave mixing can produce multiphoton nonclassical beams of light which have applications in quantum information processing and sensing. In contrast to the low-gain regime, new physical effects arise in a high-gain regime due to the interactions betw

Zili Tang, Yuming Feng, Meng Guo

Pushing is a simple yet effective skill for robots to interact with and further change the environment. Related work has been mostly focused on utilizing it as a non-prehensile manipulation primitive for a robotic manipulator. However, it can also be beneficial for low-cost mobile robots that are not equipped with a manipulator. This work tackles the general

Shah Saad Alam, Victor E. Colussi, John Drew Wilson, Jarrod T. Reilly

The performance of a quantum sensor is fundamentally limited by noise. This noise is particularly damaging when it becomes correlated with the readout of a target signal, caused by fluctuations of the sensor's operating parameters. These uncertainties limit sensitivity in a way that can be understood with multiparameter estimation theory. We develop a new ap

Sajad Daei, Mikael Skoglund, Gabor Fodor

In this work, we address the challenge of accurately obtaining channel state information at the transmitter (CSIT) for frequency division duplexing (FDD) multiple input multiple output systems. Although CSIT is vital for maximizing spatial multiplexing gains, traditional CSIT estimation methods often suffer from impracticality due to the substantial training

Dinkar Juyal, Harshith Padigela, Chintan Shah, Daniel Shenker

Pathology is the study of microscopic inspection of tissue, and a pathology diagnosis is often the medical gold standard to diagnose disease. Pathology images provide a unique challenge for computer-vision-based analysis: a single pathology Whole Slide Image (WSI) is gigapixel-sized and often contains hundreds of thousands to millions of objects of interest

Gabriele Perna, Simone Mastrogiovanni, Angelo Ricciardone

With the advent of future-generation interferometers a huge number of Gravitational Wave (GW) signals is expected to be measured without an electromagnetic counterpart. Although these signals do not allow a simultaneous measurement of the redshift and the luminosity distance, it is still possible to infer cosmological parameters. In this paper, we focus on t

H. T. Jense, I. Harrison, E. Calabrese, A. Spurio Mancini

We present a coherent, re-usable python framework which further builds on the cosmological emulator code CosmoPower. In the current era of high-precision cosmology, we require high-accuracy calculations of cosmological observables with Einstein-Boltzmann codes. For detailed statistical analyses, such codes often incur high costs in terms of computing power,

Léopold Van Brandt, Jean-Charles Delvenne

Bistable autonomous systems can be found inmany areas of science. When the intrinsic noise intensity is large, these systems exhibits stochastic transitions from onemetastable steady state to another. In electronic bistable memories, these transitions are failures, usually simulated in a Monte-Carlo fashion at a high CPU-time price. Existing closed-form form

James Foster, Stephen McCrory, Christian DeBuys, Sylvain Bertrand

The ability to accomplish manipulation and locomotion tasks in the presence of significant time-varying external loads is a remarkable skill of humans that has yet to be replicated convincingly by humanoid robots. Such an ability will be a key requirement in the environments we envision deploying our robots: dull, dirty, and dangerous. External loads constit

Andrew D. Santos

Super-Kamiokande is the world's largest water Cherenkov experiment with its 50-kton tank of ultrapure water, recently doped with gadolinium to enhance neutron capture identification. It is a highly versatile, multi-purpose experiment in the MeV-TeV range, and here we will summarize the latest results and advancements in the atmospheric-$\nu$, solar-$\nu$, an

Louis Brezin, Kirill S. Korolev

The mechanisms leading cells to acquire a fitness advantage and establish themselves in a population are paramount to understanding the development and growth of cancer. Although there are many works that study separately either the evolutionary dynamics or the mechanics of cancer, little has been done to couple evolutionary dynamics to mechanics. To address

Kylee Santos, Stan Moore, Tomas Oppelstrup, Amirali Sharifian

Molecular dynamics (MD) simulations have transformed our understanding of the nanoscale, driving breakthroughs in materials science, computational chemistry, and several other fields, including biophysics and drug design. Even on exascale supercomputers, however, runtimes are excessive for systems and timescales of scientific interest. Here, we demonstrate s

Zikang Deng

This paper investigates an extremely classic NP-complete problem: How to determine if a graph G, where each vertex has a degree of at most 4, can be 3-colorable(The research in this paper focuses on graphs G that satisfy the condition where the degree of each vertex does not exceed 4. To conserve space, it is assumed throughout the paper that graph G meets t

Stanislav DeLaurentiis, Zoltan Haiman, John Ryan Westernacher-Schneider, Luke Major Krauth

Recent hydrodynamical simulations have shown that circumbinary gas disks drive the orbits of binary black holes to become eccentric, even when general relativistic corrections to the orbit are significant. Here, we study the general relativistic (GR) apsidal precession of eccentric equal-mass binary black holes in circumbinary disks (CBDs) via two-dimensiona

Sajad Daei, Gabor Fodor, Mikael Skoglund

This work considers an uplink wireless communication system where multiple users with multiple antennas transmit data frames over dynamic channels. Previous studies have shown that multiple transmit and receive antennas can substantially enhance the sum-capacity of all users when the channel is known at the transmitter and in the case of uncorrelated transmi

Debajit Bose, Rohan Pramanick, Tirtha Sankar Ray

Sub-GeV neutrinos produced in a stellar core may emerge from main sequence stars, white dwarfs and brown dwarfs producing possible observable signals of dark matter capture. A distribution of these stars near the Milky Way galactic center will produce a neutrino flux that can be probed at Earth based neutrino observatories like Super-Kamiokande and Hyper-Kam

Daryl Mupupuni, Anupama Guntu, Liang Hong, Kamrul Hasan

The expanding role of Artificial Intelligence (AI) in diverse engineering domains highlights the challenges associated with deploying AI models in new operational environments, involving substantial investments in data collection and model training. Rapid application of AI necessitates evaluating the feasibility of utilizing pre-trained models in unobserved

Shilong Wang, Hao Wu, Yifan Duan, Guibin Zhang

The ever-designed Graph Neural Networks, though opening a promising path for the modeling of the graph-structure data, unfortunately introduce two daunting obstacles to their deployment on devices. (I) Most of existing GNNs are shallow, due mostly to the over-smoothing and gradient-vanish problem as they go deeper as convolutional architectures. (II) The vas

Cristian Vega, Stanislav Boldyrev, Vadim Roytershteyn

Strong magnetically dominated Alfv\'enic turbulence is an efficient engine of non-thermal particle acceleration in a relativistic collisionless plasma. We argue that in the limit of strong magnetization, the type of energy distribution attained by accelerated particles depends on the relative strengths of turbulent fluctuations $\delta B_0$ and the guide fie

Hamideh. Sadat Fazael Ardakani, Sajad Daei, Arash Amini, Mikael Skoglund

In this work, we consider the matrix completion problem, where the objective is to reconstruct a low-rank matrix from a few observed entries. A commonly employed approach involves nuclear norm minimization. For this method to succeed, the number of observed entries needs to scale at least proportional to both the rank of the ground-truth matrix and the coher

Ha S. Nguyen, Jared Vanasse

The Gamow-Teller and Fermi matrix elements, ${\left<\mathbf{GT}\right>}$ and ${\left<\mathbf{F}\right>}$, respectively, for tritium $\beta$-decay are calculated to next-to-leading order (NLO) in pionless effective field theory in the absence of Coulomb and isospin violation giving the leading order predictions ${\left<\mathbf{GT}\right>}_{0}=0.9807$ and ${\l

Francesca La Piana, Gerardo Morsella

We provide an explicit expression for the modular hamiltonian of the von Neumann algebras associated to the unit double cone for the (fermionic) quantum field theories of the 2-component Weyl (helicity 1/2) field, and of the 4-component massless Dirac and Majorana fields. To this end, we represent the one particle spaces of these theories in terms of solutio

Victor Medina, Ruben Garvi, Javier Granizo, Pedro Amaral

One of the limitations of conventional VCO-ADCs is the restriction to first-order noise shaping. True-VCO architectures have been proposed to increase the noise-shaping order by cascading several VCO integrators, but without requiring analog feedback loops. A high noise shaping order allows to reduce the input VCO frequency compared to a conventional VCO-ADC

Alena Tsanda, Elena Bruches

The paper discusses the creation of a multimodal dataset of Russian-language scientific papers and testing of existing language models for the task of automatic text summarization. A feature of the dataset is its multimodal data, which includes texts, tables and figures. The paper presents the results of experiments with two language models: Gigachat from SB

Sivaprasad Omanakuttan

This dissertation explores quantum computation using qudits encoded into large spins, emphasizing the concept of quantum co-design to harness the unique capabilities of physical platforms for enhanced quantum information processing. First, we delve into the generation of high-fidelity universal gate sets for quantum computation with qudits. Leveraging princi

YuFei Lai

In the field of machine learning, traditional regularization methods tend to directly add regularization terms to the loss function. This paper introduces the "Lai loss", a novel loss design that integrates the regularization terms (specifically, gradients) into the traditional loss function through straightforward geometric concepts. This design penalizes t

Benjamin Minixhofer, Edoardo Maria Ponti, Ivan Vulić

Language models (LMs) are bound to their tokenizer, which maps raw text to a sequence of vocabulary items (tokens). This restricts their flexibility: for example, LMs trained primarily on English may still perform well in other natural and programming languages, but have vastly decreased efficiency due to their English-centric tokenizer. To mitigate this, we

Tamal Mukhopadhyay, Banadipa Chakraborty, Anamika Kotal, Ujjal Debnath

In this research, we have reconstructed the extended $f(\mathcal{P})$ cubic gravity and symmetric $f(\mathcal{Q})$ teleparallel gravity from the $(m,n)$-type Barrow Holographic Dark Energy (BHDE) model. We have derived the unknown functions $f(\mathcal{P})$ and $f(\mathcal{Q})$ in terms of $\mathcal{P}$ and $\mathcal{Q}$, assuming a flat, homogeneous, and is

Sajad Daei, Gabor Fodor, Mikael Skoglund

This work investigates a multi-user, multi-antenna uplink wireless system, in which multiple users transmit signals to a base station. Prior research has explored the potential for linear growth in spectral efficiency by employing multiple transmit and receive antennas. This gain depends heavily on the quality of channel state information and the number of u

Kristina Rannu

We present a five-dimensional twice anisotropic holographic model for heavy quarks supported by Einstein-dilaton-three-Maxwell action. A special feature of the model is the presence of $z^5$-term in the metric strain coefficient (warp factor). It's influence on the model properties, mainly on the confinement/deconfinement phase transition, is considered. Con

Jiří J. L. Vaníček, Zhan Tong Zhang

Hagedorn functions are carefully constructed generalizations of Hermite functions to the setting of many-dimensional squeezed and coupled harmonic systems. Wavepackets formed by superpositions of Hagedorn functions have been successfully used to solve the time-dependent Schr\"{o}dinger equation exactly in harmonic systems and variationally in anharmonic syst

Ida Egendal, Rasmus Froberg Brøndum, Marta Pelizzola, Asger Hobolth

The aim of this study is to provide a foundation to understand the relationship between non-negative matrix factorization (NMF) and non-negative autoencoders enabling proper interpretation and understanding of autoencoder-based alternatives to NMF. Since its introduction, NMF has been a popular tool for extracting interpretable, low-dimensional representatio

Roberto Riganti, Yilin Zhu, Wei Cai, Salvatore Torquato

In this article, we employ multiscale physics-informed neural networks (MscalePINNs) for the inverse design of finite-size photonic materials with stealthy hyperuniform (SHU) disordered geometries. Specifically, we show that MscalePINNs can capture the fast spatial variations of complex fields scattered by arrays of dielectric nanocylinders arranged accordin

Chi-en Amy Tai, Alexander Wong

Breast cancer is a significant cause of death from cancer in women globally, highlighting the need for improved diagnostic imaging to enhance patient outcomes. Accurate tumour identification is essential for diagnosis, treatment, and monitoring, emphasizing the importance of advanced imaging technologies that provide detailed views of tumour characteristics

Richard B. Lehoucq, Michael Weylandt, Jonathan W. Berry

We show that certain Graph Laplacian linear sets of equations exhibit optimal accuracy, guaranteeing that the relative error is no larger than the norm of the relative residual and that optimality occurs for carefully chosen right-hand sides. Such sets of equations arise in PageRank and Markov chain theory. We establish new relationships among the PageRank t

Joseph D. Lykken, Daniel Jafferis, Alexander Zlokapa, David K. Kolchmeyer

We extend the protocol of Gao and Jafferis arXiv:1911.07416 to allow wormhole teleportation between two entangled copies of the Sachdev-Ye-Kitaev (SYK) model communicating only through a classical channel. We demonstrate in finite $N$ simulations that the protocol exhibits the characteristic holographic features of wormhole teleportation discussed and summar

Michela Lorandi, Anya Belz

Rerunning a metric-based evaluation should be more straightforward, and results should be closer, than in a human-based evaluation, especially where code and model checkpoints are made available by the original authors. As this report of our efforts to rerun a metric-based evaluation of a set of single-attribute and multiple-attribute controllable text gener

Althea V. Moorhead, Denis Vida, Peter G. Brown, Margaret D. Campbell-Brown

The rate at which meteors pass through Earth's atmosphere has been measured or estimated many times over; existing flux measurements span at least 12 astronomical magnitudes, or roughly five decades in mass. Unfortunately, the common practice of scaling flux to a universal reference magnitude of +6.5 tends to collapse the magnitude or mass dimension. Further

Yuki Imamura

We introduce a bicategory that refines the localization of the category of dg categories with respect to quasi-equivalences and investigate its properties via formal category theory. Concretely, we first introduce the bicategory of dg categories $\mathsf{DBimod}$, whose Hom categories are given by the derived categories of dg bimodules, and then define the d

Luisa F. Rodríguez Díaz, Cis Lagae, Anish M. Amarsi, Lionel Bigot

Context: Traditional one-dimensional (1D) hydrostatic model atmospheres introduce systematic modelling errors into spectroscopic analyses of FGK-type stars. Aims: We present an updated version of the STAGGER-grid of 3D model atmospheres, and explore the accuracy of post-processing methods in preparation for spectral synthesis. Methods: New and old models wer

Thomas Chuna, Michael Murillo

A considerable number of semi-empirical and first-principles models have been created to describe the dynamic response of a collisionally damped charged-particle system. However, known challenges persist for established dynamic structure factors (DSF), dielectric functions, and conductivities. For instance, the semi-empirical Drude-Smith conductivity [N.M. S

Prajindra Sankar Krishnan, Chai Phing Chen, Gamal Alkawsi, Sieh Kiong Tiong

The COVID-19 pandemic and the implementation of social distancing policies have rapidly changed people's visiting patterns, as reflected in mobility data that tracks mobility traffic using location trackers on cell phones. However, the frequency and duration of concurrent occupancy at specific locations govern the transmission rather than the number of custo

Chi-en Amy Tai, Alexander Wong

In 2020, prostate cancer saw a staggering 1.4 million new cases, resulting in over 375,000 deaths. The accurate identification of clinically significant prostate cancer is crucial for delivering effective treatment to patients. Consequently, there has been a surge in research exploring the application of deep neural networks to predict clinical significance

Edward Gaibor, Shruti Varade, Rohini Deshmukh, Tim Meyer

Can web-based image processing and visualization tools easily integrate into existing websites without significant time and effort? Our Boostlet.js library addresses this challenge by providing an open-source, JavaScript-based web framework to enable additional image processing functionalities. Boostlet examples include kernel filtering, image captioning, da

Rishabh Saraswat, Miroslav Kolos, Rekha Verma, František Karlický

n this study, we utilize a rigorous ab initio-based finite momentum Bethe-Salpeter equation to investigate the photoluminescence emission in two-dimensional hexagonal tungsten carbide (h-WC). This thermodynamically stable monolayer exhibits an indirect optical gap, resulting in phonon-assisted emission. We observe that light absorption is a direct process ce

Suvrajit Bhattacharjee, Debashish Goswami

We discuss notions of almost complex, complex and K\"{a}hler structures in the realm of non-commutative geometry and investigate them for a class of finite dimensional spectral triples on the three-point space. We classify all the almost complex structures on this non-commutative manifold, which also turn out to be complex structures, but none of them are K\

Daniel Bogdoll, Iramm Hamdard, Lukas Namgyu Rößler, Felix Geisler

The scale-up of autonomous vehicles depends heavily on their ability to deal with anomalies, such as rare objects on the road. In order to handle such situations, it is necessary to detect anomalies in the first place. Anomaly detection for autonomous driving has made great progress in the past years but suffers from poorly designed benchmarks with a strong

Thijs Stockmans, Naor Scheinowitz, Erwoud van der Linden, Irina Malysheva

Polarization imaging can yield crucial information in multiple applications of remote sensing, such as characterization of clouds, aerosols, and the Aurora Borealis. Some applications require sub-percent polarimetric sensitivity and accuracy in determining the Stokes parameters, which can be a challenge to attain. In 2018, Sony released a low-cost CMOS-based

Hanze Dong, Wei Xiong, Bo Pang, Haoxiang Wang

We present the workflow of Online Iterative Reinforcement Learning from Human Feedback (RLHF) in this technical report, which is widely reported to outperform its offline counterpart by a large margin in the recent large language model (LLM) literature. However, existing open-source RLHF projects are still largely confined to the offline learning setting. In

Rubens E. G. Machado, Kenzo R. Sakamoto, Andressa Wille, Gustavo F. Gonçalves

Barred galaxies often develop a box/peanut pseudobulge, but they can also host a nearly spherical classical bulge, which is known to gain rotation due to the bar. We aim to explore how the presence of gas impacts the rotation of classical bulges. We carried out a comprehensive set of hydrodynamical N-body simulations with different combinations of bulge mass

Chi-en Amy Tai, Alexander Wong

Breast cancer was diagnosed for over 7.8 million women between 2015 to 2020. Grading plays a vital role in breast cancer treatment planning. However, the current tumor grading method involves extracting tissue from patients, leading to stress, discomfort, and high medical costs. A recent paper leveraging volumetric deep radiomic features from synthetic corre

Suvrajit Bhattacharjee, Marzieh Forough

Let $G$ be a locally compact, Hausdorff, second countable groupoid and $A$ be a separable, $C_0(G^{(0)})$-nuclear, $G$-$C^*$-algebra. We prove the existence of quasi-invariant, completely positive and contractive lifts for equivariant, completely positive and contractive maps from $A$ into a separable, quotient $C^*$-algebra. Along the way, we construct the

Prajwal Ghimire, Ben Kinnersley, Golestan Karami, Prabhu Arumugam

Immunotherapy is an effective precision medicine treatment for several cancers. Imaging signatures of the underlying genome (radiogenomics) in glioblastoma patients may serve as preoperative biomarkers of the tumor-host immune apparatus. Validated biomarkers would have the potential to stratify patients during immunotherapy clinical trials, and if trials are

Mingyu Kim, Jun-Seong Kim, Se-Young Yun, Jin-Hwa Kim

The multi-plane representation has been highlighted for its fast training and inference across static and dynamic neural radiance fields. This approach constructs relevant features via projection onto learnable grids and interpolating adjacent vertices. However, it has limitations in capturing low-frequency details and tends to overuse parameters for low-fre

Hillary Diane Andales, Ananda Santos Figueiredo, Casey Gordon Fienberg, Mohammad K. Mardini

We present a detailed chemical abundance and kinematic analysis of six extremely metal-poor ($-4.2 \leq$ [Fe/H] $\leq-$2.9) halo stars with very low neutron-capture abundances ([Sr/H] and [Ba/H]) based on high-resolution Magellan/MIKE spectra. Three of our stars have [Sr/Ba] and [Sr/H] ratios that resemble those of metal-poor stars in ultra-faint dwarf galax

K. -J. Chuang, C. Jäger, J. C. Santos, Th. Henning

The solid-state C$_2$H$_2$ chemistry in interstellar H$_2$O-rich ice has been proposed to explain astronomically observed complex organic molecules (COMs), including ketene (CH$_2$CO), acetaldehyde (CH$_3$CHO), and ethanol (CH$_3$CH$_2$OH), toward early star-forming regions. This formation mechanism is supported by recent laboratory studies and theoretical c

Chi-en Amy Tai, Alexander Wong

In 2020, 685,000 deaths across the world were attributed to breast cancer, underscoring the critical need for innovative and effective breast cancer treatment. Neoadjuvant chemotherapy has recently gained popularity as a promising treatment strategy for breast cancer, attributed to its efficacy in shrinking large tumors and leading to pathologic complete res

E. Uykur, A. A. Tsirlin, F. Long, M. Wenzel

We report temperature-dependent reflectivity spectra of the layered van der Waals magnet CrSBr in the far-infrared region. Polarization-dependent measurements resolve the vibrational modes along the E$\|a$- and $b$-axes and reveal the clear structural anisotropy. While the $a$-axis phonons notably harden on cooling, the $b$-axis phonon frequencies are almost

Hengchao Chen

Manifold data analysis is challenging due to the lack of parametric distributions on manifolds. To address this, we introduce a series of Riemannian radial distributions on Riemannian symmetric spaces. By utilizing the symmetry, we show that for many Riemannian radial distributions, the Riemannian $L^p$ center of mass is uniquely given by the location parame

Edan Lerner, Eran Bouchbinder

Understanding the statistical mechanics of low-energy excitations in structural glasses has been the focus of extensive research efforts in the past decades due to their key roles in determining the low-temperature mechanical and transport properties of these intrinsically nonequilibrium materials. While it is established that glasses feature low-energy nonp

Kashif Mehmood, Katina Kralevska, David Palma

This paper presents a novel approach to network management by integrating intent-based networking (IBN) with knowledge graphs (KGs), creating a more intuitive and efficient pipeline for service orchestration. By mapping high-level business intents onto network configurations using KGs, the system dynamically adapts to network changes and service demands, ens

Atsushi Shiho

In this paper, we prove the blow-up invariance for Hodge-Witt sheaves with modulus, which is a generalization of a result of Koizumi for Witt sheaves and that of Kelly-Miyazaki and Koizumi for Hodge sheaves. As a consequence, we obtain the representability of Hodge-Witt sheaves with modulus in the category of motives with modulus under the assumption of reso

Hector A. Valdez, Sean McPherson

We use positional-unigram byte models along with maximum likelihood for generalized TLS fingerprinting and empirically show that it is robust to cipher stunting. Our approach creates a set of positional-unigram byte models from client hello messages. Each positional-unigram byte model is a statistical model of TLS client hello traffic created by a client app

Yijun Yuan, Michael Bleier, Andreas Nüchter

We present SceneFactory, a workflow-centric and unified framework for incremental scene modeling, that conveniently supports a wide range of applications, such as (unposed and/or uncalibrated) multi-view depth estimation, LiDAR completion, (dense) RGB-D/RGB-L/Mono/Depth-only reconstruction and SLAM. The workflow-centric design uses multiple blocks as the bas

Istvan Gabor Marian, Andrea Trombettoni, Istvan Nandori

Time-evolution of the Universe as described by the Friedmann equation can be coupled to equations of motion of matter fields. Quantum effects may be incorporated to improve these classical equations of motion by the renormalization group (RG) running of their couplings. Since temporal and thermal evolutions are linked to each other, astrophysical and cosmolo

Shulei Qu, Zhenguo Gao, Xiaowei Chen, Na Li

In driving scenarios, automobile active safety systems are increasingly incorporating deep learning technology. These systems typically need to handle multiple tasks simultaneously, such as detecting fatigue driving and recognizing the driver's identity. However, the traditional parallel-style approach of combining multiple single-task models tends to waste

Niseem Magdy, Mariam Hegazy, Aliaa Rafaat, Wenliang Li

Understanding the substructure of atomic nuclei, particularly the clustering of nucleons inside them, is essential for comprehending nuclear dynamics. Various cluster configurations can emerge depending on excitation energy, the number and types of core clusters, and the presence of excess neutrons. Despite the prevalence of tightly bound cluster formations

Andrey Kupavskii

For any $\epsilon>0$ and $n>(1+\epsilon)t$, $n>n_0(\epsilon)$ we determine the size of the largest $t$-intersecting family of permutations, as well as give a sharp stability result. This resolves a conjecture of Ellis, Friedgut and Pilpel (2011) and shows the validity of conjectures of Frankl and Deza (1977) and Cameron (1988) for $n>(1+\epsilon )t$. We note

Utsav Akhaury, Pascale Jablonka, Jean-Luc Starck, Frédéric Courbin

As ground-based all-sky astronomical surveys will gather millions of images in the coming years, a critical requirement emerges for the development of fast deconvolution algorithms capable of efficiently improving the spatial resolution of these images. By successfully recovering clean and high-resolution images from these surveys, the objective is to deepen

Vinod Kumar Chauhan, Lei Clifton, Achille Salaün, Huiqi Yvonne Lu

While machine learning algorithms hold promise for personalised medicine, their clinical adoption remains limited, partly due to biases that can compromise the reliability of predictions. In this paper, we focus on sample selection bias (SSB), a specific type of bias where the study population is less representative of the target population, leading to biase

Aleksei Turobov, Diane Coyle, Verity Harding

The utilisation of AI-driven tools, notably ChatGPT, within academic research is increasingly debated from several perspectives including ease of implementation, and potential enhancements in research efficiency, as against ethical concerns and risks such as biases and unexplained AI operations. This paper explores the use of the GPT model for initial coding

Tiago M. Crispim, Milko Estrada, C. R. Muniz, G. Alencar

We provide a way for embedding a 4-dimensional geometry corresponding to the Simpson Visser (SV) spacetime which is capable of representing a traversable wormhole, a one-way wormhole, or a regular black hole into a Randall-Sundrum setup. To achieve this, we linearly deform the bulk geometry and the bulk matter distribution concerning a coupling constant. The

Xiaoyu Chen, Changde Du, Che Liu, Yizhe Wang

Decoding language information from brain signals represents a vital research area within brain-computer interfaces, particularly in the context of deciphering the semantic information from the fMRI signal. However, many existing efforts concentrate on decoding small vocabulary sets, leaving space for the exploration of open vocabulary continuous text decodin

Haoyang Zheng, Hengrong Du, Qi Feng, Wei Deng

Replica exchange stochastic gradient Langevin dynamics (reSGLD) is an effective sampler for non-convex learning in large-scale datasets. However, the simulation may encounter stagnation issues when the high-temperature chain delves too deeply into the distribution tails. To tackle this issue, we propose reflected reSGLD (r2SGLD): an algorithm tailored for co

Arushi Jain, Josiah P. Hanna, Doina Precup

General Value Functions (GVFs) (Sutton et al., 2011) represent predictive knowledge in reinforcement learning. Each GVF computes the expected return for a given policy, based on a unique reward. Existing methods relying on fixed behavior policies or pre-collected data often face data efficiency issues when learning multiple GVFs in parallel using off-policy

Troy Shinbrot, Wise Young

Many vertebrate motor and sensory systems decussate, or cross the midline to the opposite side of the body. The successful crossing of millions of axons during development requires a complex of tightly controlled regulatory processes. Because these processes have evolved in many distinct systems and organisms, it seems reasonable to presume that decussation

Moo K. Chung, Ji Bi Che, Veena A. Nair, Camille Garcia Ramos

We introduce a novel, data-driven topological data analysis (TDA) approach for embedding brain networks into a lower-dimensional space in quantifying the dynamics of temporal lobe epilepsy (TLE) obtained from resting-state functional magnetic resonance imaging (rs-fMRI). This embedding facilitates the orthogonal projection of 0D and 1D topological features,

Ayodeji O. Abioye, Aleksandra Landowska, William Hunt, Horia Maior

One of the challenges of human-swarm interaction (HSI) is how to manage the operator's workload. In order to do this, we propose a novel neurofeedback technique for the real-time measurement of workload using functional near-infrared spectroscopy (fNIRS). The objective is to develop a baseline for workload measurement in human-swarm interaction using fNIRS a

Martin Fasser, Laurin Ostermann, Helmut Ritsch, Christoph Hotter

In contrast to free space, in waveguides the dispersive and dissipative dipole-dipole interactions among quantum emitters exhibit a periodic behavior over remarkably long distances. We propose a novel setup exploiting this long-range periodicity in order to create highly excited subradiant states and facilitate fast controlled collective energy transport amo

Marc Joosten, Michal Repisky, Marius Kadek, Pekka Pyykkö

We present an all-electron, four-component relativistic implementation of electric field gradients (EFGs) at the nuclei using Gaussian-type orbitals and periodic boundary conditions. This allows us to include relativistic effects variationally, which is important for compounds containing heavy elements and for a property dependent the electronic structure cl