October 2023 arXiv papers — page 147

Doyee Byun, Nahum Arav, Mayank Sharma, Maryam Dehghanian

Context. Quasar outflows are often analyzed to determine their ability to contribute to active galactic nucleus (AGN) feedback. We identified a broad absorption line (BAL) outflow in the VLT/UVES spectrum of the quasar SDSS J1321-0041. The outflow shows troughs from Fe II, and is thus categorized as an FeLoBAL. This outflow is unusual among the population of

D. Storey, C. Zhang, P. San Miguel Claveria, G. J. Cao

Plasma Wakefield Acceleration (PWFA) provides ultrahigh acceleration gradients of 10s of GeV/m, providing a novel path towards efficient, compact, TeV-scale linear colliders and high brightness free electron lasers. Critical to the success of these applications is demonstrating simultaneously high gradient acceleration, high energy transfer efficiency, and p

Eslam Abdelrahman, Mohamed Ayman, Mahmoud Ahmed, Habib Slim

3D visual grounding is the ability to localize objects in 3D scenes conditioned by utterances. Most existing methods devote the referring head to localize the referred object directly, causing failure in complex scenarios. In addition, it does not illustrate how and why the network reaches the final decision. In this paper, we address this question Can we de

Rohin Manvi, Samar Khanna, Gengchen Mai, Marshall Burke

The application of machine learning (ML) in a range of geospatial tasks is increasingly common but often relies on globally available covariates such as satellite imagery that can either be expensive or lack predictive power. Here we explore the question of whether the vast amounts of knowledge found in Internet language corpora, now compressed within large

Daniel Persaud, Logan Ward, Jason Hattrick-Simpers

The integration of machine learning techniques in materials discovery has become prominent in materials science research and has been accompanied by an increasing trend towards open-source data and tools to propel the field. Despite the increasing usefulness and capabilities of these tools, developers neglecting to follow reproducible practices creates a sig

Huy Tran, Yating Liu, Claire Donnat

The probabilistic Latent Semantic Indexing model assumes that the expectation of the corpus matrix is low-rank and can be written as the product of a topic-word matrix and a word-document matrix. In this paper, we study the estimation of the topic-word matrix under the additional assumption that the ordered entries of its columns rapidly decay to zero. This

Laia Humbert-Vidan, Vinod Patel, Andrew P King, Teresa Guerrero Urbano

Purpose. NTCP modelling is rapidly embracing DL methods as the need to include spatial dose information is acknowledged. Finding the most appropriate way of combining radiation dose distribution images and clinical data involves technical challenges and requires domain knowledge. We propose different data fusion strategies that we hope will serve as a starti

Qinian Jin

In this paper we consider from two different aspects the proximal alternating direction method of multipliers (ADMM) in Hilbert spaces. We first consider the application of the proximal ADMM to solve well-posed linearly constrained two-block separable convex minimization problems in Hilbert spaces and obtain new and improved non-ergodic convergence rate resu

Nataliya Nechyporenko, Caleb Escobedo, Shreyas Kadekodi, Alessandro Roncone

Current motion planning approaches rely on binary collision checking to evaluate the validity of a state and thereby dictate where the robot is allowed to move. This approach leaves little room for robots to engage in contact with an object, as is often necessary when operating in densely cluttered spaces. In this work, we propose an alternative method that

Viktoria Kungel, Matteo Battisti, George Filippatos, Tobias Heibges

The Extreme Universe Space Observatory on a Super Pressure Balloon 2 (EUSO-SPB2), successfully launched from Wanaka, New Zealand on May 13, 2022, is a precursor for a space-based astroparticle observatory such as the Probe Of Extreme Multi-Messenger Astrophysics (POEMMA). EUSO-SPB2 flew two custom telescopes. Both have UV/UV-visible sensitivity and feature S

Jakob Thumm, Felix Trost, Matthias Althoff

Deep reinforcement learning (RL) has shown promising results in robot motion planning with first attempts in human-robot collaboration (HRC). However, a fair comparison of RL approaches in HRC under the constraint of guaranteed safety is yet to be made. We, therefore, present human-robot gym, a benchmark suite for safe RL in HRC. Our benchmark suite provides

D. S. Parno, A. W. P. Poon, V. Singh

There are profound connections between neutrino physics and nuclear experiments. Exceptionally precise measurements of single and double beta-decay spectra illuminate the scale and nature of neutrino mass and may finally answer the question of whether neutrinos are their own antimatter counterparts. Neutrino-nucleus scattering underpins oscillation experimen

Jorge Luis Arocha, Javier Bracho, Luis Montejano

We prove that two closed subsets of complex space $\C^n$ with corresponding complex homothetic sections (projections) are complex homothetic. The proof uses a new Helly-type theorem for cosets of closed subgroups of $\S ^1$.

Tongxin Yin, Jean-François Ton, Ruocheng Guo, Yuanshun Yao

In critical applications, it is vital for classifiers to defer decision-making to humans. We propose a post-hoc method that makes existing classifiers selectively abstain from predicting certain samples. Our abstaining classifier is incentivized to maintain the original accuracy for each sub-population (i.e. no harm) while achieving a set of group fairness d

Avijit Thawani, Jay Pujara, Ashwin Kalyan

Despite recent successes in language models, their ability to represent numbers is insufficient. Humans conceptualize numbers based on their magnitudes, effectively projecting them on a number line; whereas subword tokenization fails to explicitly capture magnitude by splitting numbers into arbitrary chunks. To alleviate this shortcoming, alternative approac

Milan Bašić

In this paper, we aim to address the open questions raised in various recent papers regarding characterization of circulant graphs with three or four distinct eigenvalues in their spectra. Our focus is on providing characterizations and constructing classes of graphs falling under this specific category. We present a characterization of circulant graphs with

Saranya Venkatraman, Adaku Uchendu, Dongwon Lee

The Uniform Information Density (UID) principle posits that humans prefer to spread information evenly during language production. We examine if this UID principle can help capture differences between Large Language Models (LLMs)-generated and human-generated texts. We propose GPT-who, the first psycholinguistically-inspired domain-agnostic statistical detec

Yucheng Li, Bo Dong, Chenghua Lin, Frank Guerin

Large language models (LLMs) achieved remarkable performance across various tasks. However, they face challenges in managing long documents and extended conversations, due to significantly increased computational requirements, both in memory and inference time, and potential context truncation when the input exceeds the LLM's fixed context length. This paper

Justin Lee, Tuomas Oikarinen, Arjun Chatha, Keng-Chi Chang

Recent advances have greatly increased the capabilities of large language models (LLMs), but our understanding of the models and their safety has not progressed as fast. In this paper we aim to understand LLMs deeper by studying their individual neurons. We build upon previous work showing large language models such as GPT-4 can be useful in explaining what

Mark Loveland, Jessica Meixner, Eirik Valseth, Clint Dawson

During hurricanes, coupled wave-circulation models are critical tools for public safety. The standard approach is to use a high fidelity circulation model coupled with a wave model which uses the most advanced source terms. As a result, the models can be highly computationally expensive and so this study investigates the potential consequences of using highl

Dibyendu Das, Yuanjie Lu, Erion Plaku, Xuesu Xiao

When facing a new motion-planning problem, most motion planners solve it from scratch, e.g., via sampling and exploration or starting optimization from a straight-line path. However, most motion planners have to experience a variety of planning problems throughout their lifetimes, which are yet to be leveraged for future planning. In this paper, we present a

Ilijas Farah, David Jekel, Jennifer Pi

We provide a complete characterization of theories of tracial von Neumann algebras that admit quantifier elimination. We also show that the theory of a separable tracial von Neumann algebra $\mathcal{N}$ is never model complete if its direct integral decomposition contains $\mathrm{II}_1$ factors $\mathcal{M}$ such that $M_2(\mathcal{M})$ embeds into an ultr

Shakeeb Murtaza, Soufiane Belharbi, Marco Pedersoli, Aydin Sarraf

Self-supervised vision transformers (SSTs) have shown great potential to yield rich localization maps that highlight different objects in an image. However, these maps remain class-agnostic since the model is unsupervised. They often tend to decompose the image into multiple maps containing different objects while being unable to distinguish the object of in

Joel Barnett, Wen Li, Elena Resmerita, Luminita Vese

Recovering images corrupted by multiplicative noise is a well known challenging task. Motivated by the success of multiscale hierarchical decomposition methods (MHDM) in image processing, we adapt a variety of both classical and new multiplicative noise removing models to the MHDM form. On the basis of previous work, we further present a tight and a refined

Eric Xu, Soummya Kar, Guannan Qu

We study the problem of distributed online control of networked systems with time-varying cost functions and disturbances, where each node only has local information of the states and forecasts of the costs and disturbances. We develop a distributed truncated predictive control (DTPC) algorithm, where each node solves a ``truncated'' predictive optimal contr

Jacopo Giordano, Angelo Cenedese, Andrea Serrani

The work considers the design of an indirect adaptive controller for a satellite equipped with a robotic arm manipulating an object. Uncertainty on the manipulated object can considerably impact the overall behavior of the system. In addition, the dynamics of the actuators of the base satellite are non-linear and can be affected by malfunctioning. Neglecting

Paul Fay, Glenn Hurlbert, Maya Tennant

Here we introduce a new game on graphs, called cup stacking, following a line of what can be considered as $0$-, $1$-, or $2$-person games such as chip firing, percolation, graph burning, zero forcing, cops and robbers, graph pebbling, and graph pegging, among others. It can be more general, but the most basic scenario begins with a single cup on each vertex

Karan Taneja, Xiaolong He, Chung-Hao Lee, John Hodgson

Measuring the forces of individual muscles in a muscle group around a joint is non-trivial, and researchers have suggested using surrogates for individual muscle forces instead. Traditionally, experimentalists have shown that the force output of the skeletal muscle tissue can be correlated to the intra-muscular pressure (IMP) generated by the muscle belly. H

Feng Liu, Alessandro Principi

Pair spin-orbit interaction can emerge in strongly-interacting systems characterized by a large spin-orbit coupling. Here we study the role of this interaction in stabilizing ordered and unconventional superconducting phases. We find that, if the system avoids superconductivity, the order realized is a combination of charge-density and spin-vorticity waves.

Charles Frohman, Joanna Kania-Bartoszynska, Thang Lê

The sliced skein algebra of a closed surface of genus $g$ with $m$ punctures, $\mathfrak{S}=\Sigma_{g,m}$, is the quotient of the Kauffman bracket skein algebra $\mathcal{S}_\xi(\mathfrak{S})$ corresponding to fixing the scalar values of its peripheral curves. We show that the sliced skein algebra of a finite type surface is a domain if the ground ring is a

Rupak Timalsina, Haohan Wang, Bharat Giri, Adam Erickson

Spin waves, collective dynamic magnetic excitations, offer crucial insights into magnetic material properties. Rare-earth iron garnets offer an ideal spin-wave (SW) platform with long propagation length, short wavelength, gigahertz frequency, and applicability to magnon spintronic platforms. Of particular interest, thulium iron garnet (TmIG) has attracted a

M. Clarke, J. Dick, Q. T. Le Gia, K. Mustapha

We explore a linear inhomogeneous elasticity equation with random Lam\'e parameters. The latter are parameterized by a countably infinite number of terms in separated expansions. The main aim of this work is to estimate expected values (considered as an infinite dimensional integral on the parametric space corresponding to the random coefficients) of linear

Eric Bahuaud, Sharmila Gunasekaran, Hari K Kunduri, Eric Woolgar

We investigate deformations of the Kerr-(A)dS near horizon geometry and derive partial infinitesimal rigidity results for it. The proof comprises two parts. First, we follow the analysis of Jezierski and Kami\'nski [Gen Rel Grav 45 (2013) 987--1004] to eliminate all but a finite number of Fourier modes of linear perturbations. In the second part, we give an

Marius Costandin, Beniamin Costandin

In this paper we study the problem of maximizing the distance to a given point $C_0$ over a polytope $\mathcal{P}$. Assuming that the polytope is circumscribed by a known ball we construct an intersection of balls which preserves the vertices of the polytope on the boundary of this ball, and show that the intersection of balls approximates the polytope arbit

Christopher A. Grome, Wei Ji

Radiation hardening of the MOSFET is of the highest priority for sustaining high-power systems in the space radiation environment. SiC-based power electronics are being looked at as a strong alternative for high power spaceborne power electronic systems. The SiC MOSFET has been shown to be most prone to SEB of the radiation effects. The knowledge of SiC MOSF

R. Moriya, R. Bates, M. Bullough, N. Cooke

Low Gain Avalanche Detectors (LGADs) are silicon semiconductor sensors with an implanted thin p-doped multiplication layer that is designed to provide low gain. Most importantly, LGADs are specifically engineered to provide excellent spatial and temporal resolution simultaneously. The technology shows promising prospects of fulfilling the 4D tracking require

Jiancong Xiao, Ruoyu Sun, Zhi- Quan Luo

Deep neural networks (DNNs) are vulnerable to adversarial attacks. It is found empirically that adversarially robust generalization is crucial in establishing defense algorithms against adversarial attacks. Therefore, it is interesting to study the theoretical guarantee of robust generalization. This paper focuses on norm-based complexity, based on a PAC-Bay

The H. E. S. S. Collaboration, :, F. Aharonian, F. Ait Benkhali

Gamma-ray observations have established energetic isolated pulsars as outstanding particle accelerators and antimatter factories in the Galaxy. There is, however, no consensus regarding the acceleration mechanisms and the radiative processes at play, nor the locations where these take place. The spectra of all observed gamma-ray pulsars to date show strong c

Tan-Phat Nguyen, Mykhailo Klymenko, Gary Beane, Mitko Oldfield

The remarkable electrical, optical and mechanical properties of graphene make it a desirable material for electronics, optoelectronics and quantum applications. A fundamental understanding of the electrical conductivity of graphene across a wide frequency range is required for the development of such technologies. In this study, we use terahertz (THz) time-d

Zihao Zhou, Rose Yu

Learning continuous-time point processes is essential to many discrete event forecasting tasks. However, integration poses a major challenge, particularly for spatiotemporal point processes (STPPs), as it involves calculating the likelihood through triple integrals over space and time. Existing methods for integrating STPP either assume a parametric form of

Saeed Maleki

AI models are increasing in size and recent advancement in the community has shown that unlike HPC applications where double precision datatype are required, lower-precision datatypes such as fp8 or int4 are sufficient to bring the same model quality both for training and inference. Following these trends, GPU vendors such as NVIDIA and AMD have added hardwa

Vignesh Ram Somnath, Pier Giuseppe Sessa, Maria Rodriguez Martinez, Andreas Krause

Protein interactions and assembly formation are fundamental to most biological processes. Predicting the assembly structure from constituent proteins -- referred to as the protein docking task -- is thus a crucial step in protein design applications. Most traditional and deep learning methods for docking have focused mainly on binary docking, following eithe

Jihwan Jeong, Yinlam Chow, Guy Tennenholtz, Chih-Wei Hsu

Recommender systems (RSs) play a central role in connecting users to content, products, and services, matching candidate items to users based on their preferences. While traditional RSs rely on implicit user feedback signals, conversational RSs interact with users in natural language. In this work, we develop a comPelling, Precise, Personalized, Preference-r

Gaia Collaboration, M. Schultheis, H. Zhao, T. Zwitter

Diffuse interstellar bands (DIBs) are absorption features seen in optical and infrared spectra of stars that are probably caused by large and complex molecules in the ISM. Here we investigate the Galactic distribution and properties of two DIBs identified in almost six million stellar spectra collected by the Gaia Radial Velocity Spectrometer. These measurem

Navapat Nananukul, Khanin Sisaengsuwanchai, Mayank Kejriwal

Entity Resolution (ER) is the problem of semi-automatically determining when two entities refer to the same underlying entity, with applications ranging from healthcare to e-commerce. Traditional ER solutions required considerable manual expertise, including domain-specific feature engineering, as well as identification and curation of training data. Recentl

J. I. Katz

A microshot from FRB 20220912A \citep{H23} satisfies the uncertainty relation $\Delta \omega \Delta t \ge 1$ by a factor of only $\lessapprox 3$. A Crab pulsar nanoshot \citep{HE07} exceeds this bound by a similar factor. The number of orthogonal plasma modes contributing to the coherent radiation is also $\approx \Delta \omega \Delta t$, placing constraints

Benjamin Gammage, Justin Hilburn

We define 2-categories of microlocal perverse (resp. coherent) sheaves of categories on the skeleton of a hypertoric variety and show that the generators of these 2-categories lift the projectives (resp. simples) in hypertoric category $\mathcal{O}$. We then establish equivalences of 2-categories categorifying the Koszul duality between Gale dual hypertoric

Kaustubh Sridhar, Souradeep Dutta, Dinesh Jayaraman, James Weimer

Imitation learning considerably simplifies policy synthesis compared to alternative approaches by exploiting access to expert demonstrations. For such imitation policies, errors away from the training samples are particularly critical. Even rare slip-ups in the policy action outputs can compound quickly over time, since they lead to unfamiliar future states

Dakota Hamilton, Loraine Navarro, Dionysios Aliprantis

The objective of this paper is to improve the accuracy and robustness of optimal power flow (OPF) formulations for distribution systems modeled down to the low-voltage point of connection of individual buildings. An approach for addressing the uncertain switching behavior of building loads(e.g., air conditioners, water heaters, or pool pumps) and variable re

Maegan Tucker, Kejun Li, Aaron D. Ames

Successfully achieving bipedal locomotion remains challenging due to real-world factors such as model uncertainty, random disturbances, and imperfect state estimation. In this work, we propose a novel metric for locomotive robustness -- the estimated size of the hybrid forward invariant set associated with the step-to-step dynamics. Here, the forward invaria

Ruixiang Fei, Li Yang

The phenomena of crystal size changes and structural phase transitions induced by light irradiation have garnered significant interest due to their potential for tuning and controlling a wide range of material properties through highly cooperative interactions. However, a microscopic theory that can comprehensively explain these phenomena in correlation with

Lexin Zhou, Pablo A. Moreno-Casares, Fernando Martínez-Plumed, John Burden

We introduce the fundamental ideas and challenges of Predictable AI, a nascent research area that explores the ways in which we can anticipate key validity indicators (e.g., performance, safety) of present and future AI ecosystems. We argue that achieving predictability is crucial for fostering trust, liability, control, alignment and safety of AI ecosystems

Xiaoqi Tan, Siyuan Yu, Raouf Boutaba, Alberto Leon-Garcia

This paper provides threshold policies with tight guarantees for online selection with convex cost (OSCC). In OSCC, a seller wants to sell some asset to a sequence of buyers with the goal of maximizing her profit. The seller can produce additional units of the asset, but at non-decreasing marginal costs. At each time, a buyer arrives and offers a price. The

Karel D'Oosterlinck, Semere Kiros Bitew, Brandon Papineau, Christopher Potts

State-of-the-art coreference resolutions systems depend on multiple LLM calls per document and are thus prohibitively expensive for many use cases (e.g., information extraction with large corpora). The leading word-level coreference system (WL-coref) attains 96.6% of these SOTA systems' performance while being much more efficient. In this work, we identify a

Mohammad Peivandi, Jason Zhang, Michael Lu, Dongxiao Zhu

Brain tumor segmentation presents a formidable challenge in the field of Medical Image Segmentation. While deep-learning models have been useful, human expert segmentation remains the most accurate method. The recently released Segment Anything Model (SAM) has opened up the opportunity to apply foundation models to this difficult task. However, SAM was prima

Bhavya Vasudeva, Kameron Shahabi, Vatsal Sharan

Neural networks (NNs) are known to exhibit simplicity bias where they tend to prefer learning 'simple' features over more 'complex' ones, even when the latter may be more informative. Simplicity bias can lead to the model making biased predictions which have poor out-of-distribution (OOD) generalization. To address this, we propose a framework that encourage

Muhammad Farhan Ahmed, Matteo Maragliano, Vincent Frémont, Carmine Tommaso Recchiuto

Autonomous exploration in unknown environments remains a fundamental challenge in robotics, particularly for applications such as search and rescue, industrial inspection, and planetary exploration. Multi-robot active SLAM presents a promising solution by enabling collaborative mapping and exploration while actively reducing uncertainty. However, existing ap

Cong Ma, Xingyu Xu, Tian Tong, Yuejie Chi

Many problems encountered in science and engineering can be formulated as estimating a low-rank object (e.g., matrices and tensors) from incomplete, and possibly corrupted, linear measurements. Through the lens of matrix and tensor factorization, one of the most popular approaches is to employ simple iterative algorithms such as gradient descent (GD) to reco

Aram Vajdi, Lee W. Cohnstaedt, Caterina M. Scoglio

Dengue is a vector-borne disease transmitted by Aedes mosquitoes. The worldwide spread of these mosquitoes and the increasing disease burden have emphasized the need for a spatio-temporal risk map capable of assessing dengue outbreak conditions and quantifying the outbreak risk. Given that the life cycle of Aedes mosquitoes is strongly influenced by habitat

Daniel Kelshaw, Luca Magri

Manifolds discovered by machine learning models provide a compact representation of the underlying data. Geodesics on these manifolds define locally length-minimising curves and provide a notion of distance, which are key for reduced-order modelling, statistical inference, and interpolation. In this work, we propose a model-based parameterisation for distanc

Florian Peißker, Michal Zajaček, Lauritz Thomkins, Andreas Eckart

A detailed analysis of the Nuclear Stellar Cluster (NSC) concedes not only the existence of the Scluster with its fast-moving stars and the supermassive black hole (SMBH) Sgr A*. It also reveals an embedded region of gas and dust with an exceptionally high stellar density called IRS 13. The IRS 13 cluster can be divided into the northern and the eastern coun

Yiren Liu, Si Chen, Haocong Cheng, Mengxia Yu

Developing novel research questions (RQs) often requires extensive literature reviews, especially in interdisciplinary fields. To support RQ development through human-AI co-creation, we leveraged Large Language Models (LLMs) to build an LLM-based agent system named CoQuest. We conducted an experiment with 20 HCI researchers to examine the impact of two inter

Jung-Shen B. Tai, Andrew J. Hess, Jin-Sheng Wu, Ivan I. Smalyukh

Magnetic monopoles, despite their ongoing experimental search as elementary particles, have inspired the discovery of analogous excitations in condensed matter systems. In chiral condensed matter systems, emergent monopoles are responsible for the onset of transitions between topologically distinct states and phases, like in the case of transitions from heli

David Thorne, Brett T. Lopez

Executing time-sensitive multi-robot missions involves two distinct problems: Multi-Robot Task Assignment (MRTA) and Multi-Agent Path Finding (MAPF). Computing safe paths that complete every task and minimize the time to mission completion, or makespan, is a significant computational challenge even for small teams. In many missions, tasks can be generated du

Silvana M. Pesenti, Pietro Millossovich, Andreas Tsanakas

Differential sensitivity measures provide valuable tools for interpreting complex computational models used in applications ranging from simulation to algorithmic prediction. Taking the derivative of the model output in direction of a model parameter can reveal input-output relations and the relative importance of model parameters and input variables. Noneth

Zehui Li, Yuhao Ni, Tim August B. Huygelen, Akashaditya Das

The harnessing of machine learning, especially deep generative models, has opened up promising avenues in the field of synthetic DNA sequence generation. Whilst Generative Adversarial Networks (GANs) have gained traction for this application, they often face issues such as limited sample diversity and mode collapse. On the other hand, Diffusion Models are a

Samuel W. Coles, Benjamin J. Morgan, Benjamin Rotenberg

RevelsMD is a new open source Python library, which uses reduced variance force sampling based estimators to calculate 3D particle densities and radial distribution functions from molecular dynamics simulations. This short note describes the scientific background of the code, its utility and how it fits within the current zeitgeist in computational chemistry

Mike Huisman, Aske Plaat, Jan N. van Rijn

Deep neural networks can yield good performance on various tasks but often require large amounts of data to train them. Meta-learning received considerable attention as one approach to improve the generalization of these networks from a limited amount of data. Whilst meta-learning techniques have been observed to be successful at this in various scenarios, r

Hao Sun

Recent advancements in Large Language Models (LLMs) have garnered wide attention and led to successful products such as ChatGPT and GPT-4. Their proficiency in adhering to instructions and delivering harmless, helpful, and honest (3H) responses can largely be attributed to the technique of Reinforcement Learning from Human Feedback (RLHF). In this paper, we

D. M. van Egmond

We analyze different BRST invariant solutions for the introduction of a mass term in Yang-Mills (YM) theories. First, we analyze the non-local composite gauge-invariant field $A^h_{\mu}(x)$, which can be localized by the Stueckelberg-like field $\xi^a(x)$. This enables us to introduce a mass term in the $SU(N)$ YM model, a feature that has been indicated at

Wailing Chen, Yuhao Yan, Chunning Ji, Md. Mahbub Alam

As one of the most intriguing wake patterns of two side-by-side circular cylinders at an intermediate gap spacing, the flip-flopping (FF) flow has attracted great attention of fundamental research interest. This FF flow is featured by the intermittently and randomly switching gap flow with correspondingly changing forces of the two cylinders. In this paper,

Zhaohui Xing, Qing Du, Peiyuan Pang, Guangrong Jin

Metal halide perovskites, a class of cost-effective semiconductor materials, are of great interest for modern and upcoming display technologies that prioritize the light-emitting diodes (LEDs) with high efficiency and excellent color purity. The prevailing approach to achieving efficient luminescence from pervoskites is enhancing exciton binding effect and c

Veli Safak

Kardemir Karabuk Iron Steel Industry Trade & Co. Inc., ranked as the 24th largest industrial company in Turkey, offers three distinct stocks listed on the Borsa Istanbul: KRDMA, KRDMB, and KRDMD. These stocks, sharing the sole difference in voting power, have exhibited significant price divergence over an extended period. This paper conducts an in-depth anal

Şaban Öztürk, M. Yiğit Turalı, Tolga Çukur

Chest X-ray is an essential diagnostic tool in the identification of chest diseases given its high sensitivity to pathological abnormalities in the lungs. However, image-driven diagnosis is still challenging due to heterogeneity in size and location of pathology, as well as visual similarities and co-occurrence of separate pathology. Since disease-related re

Jiaxuan Wang, Ruynet. L. de Matos Filho, Girish S. Agarwal, Luiz Davidovich

Quantum estimation of parameters defining open-system dynamics may be enhanced by using ancillas that are entangled with the probe but are not submitted to the dynamics. Here we consider the important problem of estimation of transmission of light by a sample, with losses due to absorption and scattering. We show, through the determination of the quantum Fis

Jinkun Zhang, Edmund Yeh

Collaborative edge computing (CEC) is an emerging paradigm for heterogeneous devices to collaborate on edge computation jobs. For congestible links and computing units, delay-optimal forwarding and offloading for service chain tasks (e.g., DNN with vertical split) in CEC remains an open problem. In this paper, we formulate the service chain forwarding and of

Jianyu Xu, Hanwen Zhang, Ling Liang, Lei Deng

We study the optimal order (or sequence) of contracting a tensor network with a minimal computational cost. We conclude 2 different versions of this optimal sequence: that minimize the operation number (OMS) and that minimize the time complexity (CMS). Existing results only shows that OMS is NP-hard, but no conclusion on CMS problem. In this work, we firstly

Zenon Gniazdowski

The article attempts to find an algebraic formula describing the correlation coefficients between random variables and the principal components representing them. As a result of the analysis, starting from selected statistics relating to individual random variables, the equivalents of these statistics relating to a set of random variables were presented in t

Haichao Zhang, Yi Xu, Hongsheng Lu, Takayuki Shimizu

Trajectory prediction plays a vital role in understanding pedestrian movement for applications such as autonomous driving and robotics. Current trajectory prediction models depend on long, complete, and accurately observed sequences from visual modalities. Nevertheless, real-world situations often involve obstructed cameras, missed objects, or objects out of

Kevin Costello, Owen Gwilliam

Factorization algebras are local-to-global objects living on manifolds, and they arise naturally in mathematics and physics. Their local structure encompasses examples like associative algebras and vertex algebras; in these examples, their global structure encompasses Hochschild homology and conformal blocks. In the setting of quantum field theory, factoriza

Kosmas Pinitas, David Renaudie, Mike Thomsen, Matthew Barthet

This paper introduces a large scale multimodal corpus collected for the purpose of analysing and predicting player engagement in commercial-standard games. The corpus is solicited from 25 players of the action role-playing game Tom Clancy's The Division 2, who annotated their level of engagement using a time-continuous annotation tool. The cleaned and proces

William R. Cannon, Samuel Britton, Mikahl Banwarth-Kuhn, Mark Alber

We demonstrate and characterize a first-principles approach to modeling the mass action dynamics of metabolism. Starting from a basic definition of entropy expressed as a multinomial probability density using Boltzmann probabilities with standard chemical potentials, we derive and compare the free energy dissipation and the entropy production rates. We expre

Rugved Pund, Anže Slosar, Aaron Parsons

We expect the monopole signal at the lowest frequencies below $100\,$MHz to be composed of two components: the deep Rayleigh-Jeans tail of the cosmic microwave background and two distinct features: the dark ages trough at $\sim 17\,$MHz and the cosmic dawn trough at $\sim 75\,$Mhz. These are hidden under orders of magnitude brighter foregrounds whose emissio

Gavin Brown, Michael Wemyss

This paper determines the full derived deformation theory of certain smooth rational curves C in Calabi-Yau 3-folds, by determining all higher A_\infty-products in its controlling DG-algebra. This geometric setup includes very general cases where C does not contract, cases where the curve neighbourhood is not rational, all known simple smooth 3-fold flops, a

Johan Helsing, Shidong Jiang, Anders Karlsson

This work concerns the design of perfectly conducting objects that are invisible to an incident transverse magnetic plane wave. The object in question is a finite planar waveguide with a finite periodic array of barriers. By optimizing this array, the amplitude of the scattered field is reduced to less than $10^{-9}$ times the amplitude of the incident plane

Tycho F. A. van der Ouderaa, Alexander Immer, Mark van der Wilk

Convolutions encode equivariance symmetries into neural networks leading to better generalisation performance. However, symmetries provide fixed hard constraints on the functions a network can represent, need to be specified in advance, and can not be adapted. Our goal is to allow flexible symmetry constraints that can automatically be learned from data usin

Ioannis Papastathopoulos, Lambert de Monte, Ryan Campbell, Haavard Rue

We use a functional analogue of the quantile function for probability measures on $\mathbb{R}^d$ to characterize a novel limit Poisson point process for radially recentred and rescaled random vectors under a radial-directional decomposition. This limit process yields new multivariate distributions, including \textit{radial generalised Pareto distributions},

Tom Kelly, John Femiani, Peter Wonka

We present WinSyn, a unique dataset and testbed for creating high-quality synthetic data with procedural modeling techniques. The dataset contains high-resolution photographs of windows, selected from locations around the world, with 89,318 individual window crops showcasing diverse geometric and material characteristics. We evaluate a procedural model by tr

Ibuki Terashima, Tetsuo Hyodo

To study the internal structure of the exotic hadron $X(3872)$, considering the coupled-channel potential between quarks and hadrons is necessary because $X(3872)$ is regarded as a mixture state of $c\bar{c}$ and $D^0\bar{D}^{*0}$. In this work, we construct the hadron-hadron potentials coupled to the quark channel and study the properties of the $D^0\bar{D}

Bruno Bertini, Cecilia De Fazio, Juan P. Garrahan, Katja Klobas

We study the nonequilibrium dynamics of the Floquet quantum East model (a Trotterized version of the kinetically constrained quantum East spin chain) at its "deterministic point", where evolution is defined in terms of CNOT permutation gates. We solve exactly the thermalization dynamics for a broad class of initial product states by means of "space evolution

Georgios Vacalis, Atsushi Higuchi, Robert Bingham, Gianluca Gregori

We investigate the connection between the classical Larmor formula and the quantum Unruh effect by computing the emitted power by a uniformly accelerated charged particle and its angular distribution in the coaccelerated frame. We consider a classical particle accelerated with nonzero charge only for a finite period and then take the infinite-time limit afte

R. Dengler

This work examines field theories for RNA-like polymers with single strand and double strand polymers and a periodic base sequence. These field theories originate from lattice models, describe polymers in a good solvent, and in principle exactly describe the critical behavior. A central role is played by the conventional one-component branched polymer and th

William Ravenscroft, Stefan Goetze, Thomas Hain

Speech separation remains an important topic for multi-speaker technology researchers. Convolution augmented transformers (conformers) have performed well for many speech processing tasks but have been under-researched for speech separation. Most recent state-of-the-art (SOTA) separation models have been time-domain audio separation networks (TasNets). A num

Yash Garg, Nebiyou Yismaw, Rakib Hyder, Ashley Prater-Bennette

Multi-task and multi-domain learning methods seek to learn multiple tasks/domains, jointly or one after another, using a single unified network. The primary challenge and opportunity lie in leveraging shared information across these tasks and domains to enhance the efficiency of the unified network. The efficiency can be in terms of accuracy, storage cost, c

Chen Qiu, Xingyu Li, Chaithanya Kumar Mummadi, Madan Ravi Ganesh

Prompt learning for vision-language models, e.g., CoOp, has shown great success in adapting CLIP to different downstream tasks, making it a promising solution for federated learning due to computational reasons. Existing prompt learning techniques replace hand-crafted text prompts with learned vectors that offer improvements on seen classes, but struggle to

Genoveva Vargas-Solar, Mirian Halfeld Ferrari Alves, Anne-Lyse Minard Forst

This paper highlights the challenges, current trends, and open issues related to the representation, querying and analytics of content extracted from texts. The internet contains vast text-based information on various subjects, including commercial documents, medical records, scientific experiments, engineering tests, and events that impact urban and natural

Jan-Christoph Kassing, Florian Frohn, Jürgen Giesl

There are many evaluation strategies for term rewrite systems, but proving termination automatically is usually easiest for innermost rewriting. Several syntactic criteria exist when innermost termination implies full termination. We adapt these criteria to the probabilistic setting, e.g., we show when it suffices to analyze almost-sure termination (AST) w.r

J. van Marrewijk, L. Di Mascolo, A. S. Gill, N. Battaglia

How protoclusters evolved from sparse galaxy overdensities to mature galaxy clusters is still not well understood. In this context, detecting and characterizing the hot ICM at high redshifts (z~2) is key to understanding how the continuous accretion from and mergers along the filamentary large-scale structure impact the first phases of cluster formation. We

Zezhi Shao, Fei Wang, Yongjun Xu, Wei Wei

Multivariate Time Series (MTS) analysis is crucial to understanding and managing complex systems, such as traffic and energy systems, and a variety of approaches to MTS forecasting have been proposed recently. However, we often observe inconsistent or seemingly contradictory performance findings across different studies. This hinders our understanding of the

E. Nieto-Valeiras, E. Ganju, N. Chawla, J. LLorca

Slip transfer and blocking across grain boundaries was studied in a Ti foil with a strong rolling texture deformed in tension. Prior to deformation, the shape of the grains and the orientation of the grain boundaries were quantified through laboratory scale diffraction contrast tomography (LabDCT). Mechanical deformation led to the activation of <a> prismati