March 2024 arXiv papers — page 14

Mikaela Aires, Geraldo Botelho

Generalizing a recent result on lineability of sets of non-injective linear operators, we prove, for quite general linear spaces $A$ of maps from an arbitraty set to a sequence space, that, for every $0 \neq f \in A$, the subset of $A$ of non-injective maps contains an infinite dimensional subspace of $A$ containing $f$. We provide aplications of the main re

Siavash Jafarzadeh, Michael Hillman

In this work, the fast-convolving reproducing kernel particle method (FC-RKPM) is introduced. This method is hundreds to millions of times faster than the traditional RKPM for 3D meshfree simulations. In this approach, the meshfree discretizations with RK approximation are expressed in terms of convolution sums. Fast Fourier transform (FFT) is then used to e

Ishfaq Aziz, Elahe Soltanaghai, Adam Watts, Mohamad Alipour

Moisture estimation of sub-surface soil and the overlaying biomass layer is pivotal in precision agriculture and wildfire risk assessment. However, the characterization of layered material is nontrivial due to the radar penetration-resolution tradeoff. Here, a waveform inversion-based method was proposed for predicting the dielectric permittivity (as a moist

Zewen Liu, Guancheng Wan, B. Aditya Prakash, Max S. Y. Lau

Since the onset of the COVID-19 pandemic, there has been a growing interest in studying epidemiological models. Traditional mechanistic models mathematically describe the transmission mechanisms of infectious diseases. However, they often suffer from limitations of oversimplified or fixed assumptions, which could cause sub-optimal predictive power and ineffi

Niklas Stoehr, Mitchell Gordon, Chiyuan Zhang, Owen Lewis

Can we localize the weights and mechanisms used by a language model to memorize and recite entire paragraphs of its training data? In this paper, we show that while memorization is spread across multiple layers and model components, gradients of memorized paragraphs have a distinguishable spatial pattern, being larger in lower model layers than gradients of

Sourabh Jain, May Hlaing, Kang Chieh Fan, Jason Midkiff

As photonic technologies continue to grow in multidimensional aspects, integrated photonics holds a unique position and continuously presents enormous possibilities to research communities. Applications span across data centers, environmental monitoring, medical diagnosis, and highly compact communication components, with further possibilities growing endles

Muhammad Faraz Ul Abrar, Nicolò Michelusi

Recently, Over-the-Air (OTA) computation has emerged as a promising federated learning (FL) paradigm that leverages the waveform superposition properties of the wireless channel to realize fast model updates. Prior work focused on the OTA device ``pre-scaler" design under \emph{homogeneous} wireless conditions, in which devices experience the same average pa

Subash Gupta, Santosh Adhikari, Arbia Hlali

Sustainable road freight transport becomes indispensable in the field of transportation and logistics. The new technological change, the environmental impacts, and social responsibility laid freight road transport in front of various challenges, which makes the sustainable practices a vital solution in the sector. This paper aims to provide a theoretical res

Lambert Dong

We examine how monetary shocks spread throughout an economic model characterized by sticky prices and general equilibrium, where the pricing strategies of firms are interlinked, fostering a mutually beneficial relationship. In this dynamic equilibrium, pricing choices of firms are influenced by overall economic factors, which are themselves affected by these

Francisco Gonzalez Montoya

We study the phase space of a particle moving in the gravitational field of a rotating black hole described by the Kerr metric from a geometrical perspective. In particular, we show the construction of a multidimensional generalization of the unstable periodic orbits, known as Normally Hyperbolic Invariant Manifold, and its stable and unstable invariant mani

Tyler Hanks, James Fairbanks, Matthew Klawonn

Cartesian reverse derivative categories (CRDCs) provide an axiomatic generalization of the reverse derivative, which allows generalized analogues of classic optimization algorithms such as gradient descent to be applied to a broad class of problems. In this paper, we show that generalized gradient descent with respect to a given CRDC induces a hypergraph fun

Sarwan Ali, Prakash Chourasia, Murray Patterson

This study introduces a novel approach, combining substruct counting, $k$-mers, and Daylight-like fingerprints, to expand the representation of chemical structures in SMILES strings. The integrated method generates comprehensive molecular embeddings that enhance discriminative power and information content. Experimental evaluations demonstrate its superiorit

Eva Schinnerer, Adam K. Leroy

Observations that resolve nearby galaxies into individual regions across multiple phases of the gas-star formation-feedback ``matter cycle'' have provided a sharp new view of molecular clouds, star formation efficiencies, timescales for region evolution, and stellar feedback. We synthesize these results, cover aspects relevant to the interpretation of observ

Aaron L. Sarvet, Julien D. Laurendeau, Mats J. Stensrud

Policy-makers are often faced with the task of distributing a limited supply of resources. To support decision-making in these settings, statisticians are confronted with two challenges: estimands are defined by allocation strategies that are functions of features of all individuals in a cluster; and relatedly the observed data are neither independent nor id

Daniele Malitesta, Emanuele Rossi, Claudio Pomo, Fragkiskos D. Malliaros

Multimodal recommender systems work by augmenting the representation of the products in the catalogue through multimodal features extracted from images, textual descriptions, or audio tracks characterising such products. Nevertheless, in real-world applications, only a limited percentage of products come with multimodal content to extract meaningful features

Teresa Alves, Alexandre Bernardino, Plinio Moreno

In this work, we propose two cost efficient methods for object identification, using a multi-fingered robotic hand equipped with proprioceptive sensing. Both methods are trained on known objects and rely on a limited set of features, obtained during a few grasps on an object. Contrary to most methods in the literature, our methods do not rely on the knowledg

Jing Wu, Zhixin Lai, Suiyao Chen, Ran Tao

Crop management plays a crucial role in determining crop yield, economic profitability, and environmental sustainability. Despite the availability of management guidelines, optimizing these practices remains a complex and multifaceted challenge. In response, previous studies have explored using reinforcement learning with crop simulators, typically employing

Akshay Gopalkrishnan, Ross Greer, Mohan Trivedi

Vision-Language Models (VLMs) and Multi-Modal Language models (MMLMs) have become prominent in autonomous driving research, as these models can provide interpretable textual reasoning and responses for end-to-end autonomous driving safety tasks using traffic scene images and other data modalities. However, current approaches to these systems use expensive la

Ravi Mangal, Nina Narodytska, Divya Gopinath, Boyue Caroline Hu

The analysis of vision-based deep neural networks (DNNs) is highly desirable but it is very challenging due to the difficulty of expressing formal specifications for vision tasks and the lack of efficient verification procedures. In this paper, we propose to leverage emerging multimodal, vision-language, foundation models (VLMs) as a lens through which we ca

Nazanin Jafari, James Allan, Sheikh Muhammad Sarwar

Identifying the targets of hate speech is a crucial step in grasping the nature of such speech and, ultimately, in improving the detection of offensive posts on online forums. Much harmful content on online platforms uses implicit language especially when targeting vulnerable and protected groups such as using stereotypical characteristics instead of explici

Michail Tsagris

Simplicial-simplicial regression refers to the regression setting where both the responses and predictor variables lie within the simplex space, i.e. they are compositional. For this setting, constrained least squares, where the regression coefficients themselves lie within the simplex, is proposed. The model is transformation-free but the adoption of a powe

Wenbin Wang, Zhiyu He, Giuseppe Belgioioso, Saverio Bolognani

Online feedback optimization (OFO) enables optimal steady-state operations of a physical system by employing an iterative optimization algorithm as a dynamic feedback controller. When the plant consists of several interconnected sub-systems, centralized implementations become impractical due to the heavy computational burden and the need to pre-compute syste

Qijun Wang, Shichen Zhang, Kunzhe Song, Huacheng Zeng

Large language models (LLMs) have transformed the way we interact with cyber technologies. In this paper, we study the possibility of connecting LLM with wireless sensor networks (WSN). A successful design will not only extend LLM's knowledge landscape to the physical world but also revolutionize human interaction with WSN. To the end, we present ChatTracer,

Nicolas Nessi

We study the emergence of typicality in classical systems with a large number of binary state variables. We show analytically that for sufficiently large subsets of the complete state space, state functions which can be associated with macroscopic observables, such as density or energy, are sharply concentrated around a typical value, i.e., the vast majority

Doris E. M. Brown, Venkata Sriram Siddhardh Nadendla, Sajal K. Das

Stackelberg routing platforms (SRP) reduce congestion in one-shot traffic networks by proposing optimal route recommendations to selfish travelers. Traditionally, Stackelberg routing is cast as a partial control problem where a fraction of traveler flow complies with route recommendations, while the remaining respond as selfish travelers. In this paper, a no

Lawrence Liu, Jesper Lykke Jacobsen, Hubert Saleur

It was recently suggested -- based on general self-consistency arguments as well as results from the bootstrap (arXiv:2005.07708, arXiv:2007.11539, arXiv:2007.04190) -- that the CFT describing the $Q$-state Potts model is logarithmic for generic values of $Q$, with rank-two Jordan blocks for $L_0$ and ${\mkern 1.5mu\overline{\mkern-1.5mu L\mkern-1.5mu}\mkern

Zeguan Wu, Sidhant Misra, Tamás Terlaky, Xiu Yang

Solving linear systems is at the foundation of many algorithms. Recently, quantum linear system algorithms (QLSAs) have attracted great attention since they converge to a solution exponentially faster than classical algorithms in terms of the problem dimension. However, low-complexity circuit implementations of the oracles assumed in these QLSAs constitute t

I. Tsymbalov, D. Gorlova, K. Ivanov, E. Starodubtseva

We propose a novel method for changing the length of laser wakefield electron acceleration in a gas jet by a cylindrical blast wave created by a perpendicularly focused nanosecond laser pulse. The shock front destroys the wake thus stopping interaction between the laser pulse and accelerated electron bunch allowing one to directly control the interaction len

Kanishka Misra, Kyle Mahowald

Language models learn rare syntactic phenomena, but the extent to which this is attributable to generalization vs. memorization is a major open question. To that end, we iteratively trained transformer language models on systematically manipulated corpora which were human-scale in size, and then evaluated their learning of a rare grammatical phenomenon: the

Ismaila Temitayo Sanusi, Fitsum Gizachew Deriba

Noticeably, Africa is underrepresented in the computing education research (CER) community. However, there has been some effort from the researchers in the region to contribute to the growing need for computing for all. To understand the body of works that emerged from the global south region and their area of focus in computing education, we conducted a sys

Qitian Ma, Shyam Nanda Rai, Carlo Masone, Tatiana Tommasi

In the domain of computer vision, semantic segmentation emerges as a fundamental application within machine learning, wherein individual pixels of an image are classified into distinct semantic categories. This task transcends traditional accuracy metrics by incorporating uncertainty quantification, a critical measure for assessing the reliability of each se

Anirudha Sahoo, Tanguy Ropitault, Steve Blandino, Nada Golmie

Wi-Fi sensing has been used to detect and track movements in an environment, resulting in the emergence of several innovative applications. Wi-Fi sensing can detect movement and locate objects by analyzing variations in the Wi-Fi signal due to its interaction with moving objects. Until recently, Wi-Fi sensing has been primarily available through proprietary

Charlotte Trainor

A Besicovitch-Rado-Kinney (BRK) set in $\mathbb{R}^n$ is a Borel set that contains a $(n-1)$-dimensional sphere of radius $r$, for each $r>0$. It is known that such sets have Hausdorff dimension $n$ from the work of Kolasa and Wolff. In this paper, we consider an analogous problem over a finite field, $\mathbb{F}_q$. We define BRK-type sets in $\mathbb{F}_q^

Yaochen Wu

We examine the relationship between the actions of two Weyl groups on the cohomology of a smooth quiver variety: the Maffei's action of the Weyl group associated to the quiver, and the symplectic Springer action of the Namikawa-Weyl group of the affine quiver variety. We show there is a natural map from the former group to the latter, which is an embedding i

Yash Jain, David Chan, Pranav Dheram, Aparna Khare

Recent advances in machine learning have demonstrated that multi-modal pre-training can improve automatic speech recognition (ASR) performance compared to randomly initialized models, even when models are fine-tuned on uni-modal tasks. Existing multi-modal pre-training methods for the ASR task have primarily focused on single-stage pre-training where a singl

Timothy D. Brandt

This paper implements likelihood-based jump detection for detectors read out up-the-ramp, using the entire set of reads to compute likelihoods. The approach compares the $\chi^2$ value of a fit with and without a jump for every possible jump location. I show that this approach can be substantially more sensitive than one that only uses the difference between

Rafael Orozco, Abhinav Gahlot, Felix J. Herrmann

CO$_2$ sequestration is a crucial engineering solution for mitigating climate change. However, the uncertain nature of reservoir properties, necessitates rigorous monitoring of CO$_2$ plumes to prevent risks such as leakage, induced seismicity, or breaching licensed boundaries. To address this, project managers use borehole wells for direct CO$_2$ and pressu

William Koll, Corina Urdaniz, Kyungju Noh, Yujeong Bae

The adsorption and self-assembly of vanadyl phthalocyanine molecules on Ag(100) has been investigated using a combination of scanning tunneling microscopy and density functional theory. At sub-monolayer coverage, we observe two distinct adsorption configurations of isolated molecules, corresponding to the central O atom pointing toward (O-down) or away (O-up

José Bobes-Bascarán, Eduardo Mosqueira-Rey, Ángel Fernández-Leal, Elena Hernández-Pereira

This paper presents a comprehensive study on the evaluation of explanatory capabilities of machine learning models, with a focus on Decision Trees, Random Forest and XGBoost models using a pancreatic cancer dataset. We use Human-in-the-Loop related techniques and medical guidelines as a source of domain knowledge to establish the importance of the different

Abhinav Prakash Gahlot, Haoyun Li, Ziyi Yin, Rafael Orozco

We present an uncertainty-aware Digital Twin (DT) for geologic carbon storage (GCS), capable of handling multimodal time-lapse data and controlling CO2 injectivity to mitigate reservoir fracturing risks. In GCS, DT represents virtual replicas of subsurface systems that incorporate real-time data and advanced generative Artificial Intelligence (genAI) techniq

Marie-Christine Düker, Vladas Pipiras

This work proposes a novel procedure to test for common structures across two high-dimensional factor models. The introduced test allows to uncover whether two factor models are driven by the same loading matrix up to some linear transformation. The test can be used to discover inter-individual relationships between two datasets. In addition, it can be appli

Tomislav Petrović

Whether Kolmogorov-Loveland randomness (KLR) is the same as Martin-L\"of randomness (MLR) is a major open problem in the study of algorithmic randomness. More general classes of betting strategies than Kolmogorov-Loveland ones have been studied in \cite{MMS, Rute, TP}. In each case it was proven that the class induces a notion of randomness equivalent to MLR

Gaurav Shinde, Rohan Mohapatra, Pooja Krishan, Harish Garg

Lithium-ion batteries (Li-ion) have revolutionized energy storage technology, becoming integral to our daily lives by powering a diverse range of devices and applications. Their high energy density, fast power response, recyclability, and mobility advantages have made them the preferred choice for numerous sectors. This paper explores the seamless integratio

Shahed Rezaei, Reza Najian Asl, Shirko Faroughi, Mahdi Asgharzadeh

To obtain fast solutions for governing physical equations in solid mechanics, we introduce a method that integrates the core ideas of the finite element method with physics-informed neural networks and concept of neural operators. This approach generalizes and enhances each method, learning the parametric solution for mechanical problems without relying on d

Hui Ma, Jiaxu Ma, Mingxuan Yang

We investigate anisotropic capillary hypersurfaces within a wedge in Euclidean space. In this study, we generalize the Minkowski norm \(F\), traditionally employed to define the anisotropic surface energy, to a gauge on the unit sphere \(S^n\). This generalization helps to illuminate a significant relationship between capillary hypersurfaces and hypersurface

Andreas Krug, Erik Nikolov

Given an action of a finite group on a triangulated category with a suitable strong exceptional collection, a construction of Elagin produces an associated strong exceptional collection on the equivariant category. We prove that the endomorphism algebra of the induced exceptional collection is the basic reduction of the skew group algebra of the endomorphism

Anna Kh. Balci, Ho-Sik Lee

We establish Zaremba problem for Laplacian and $p$-Laplacian with degenerate weights when the Dirichlet condition is only imposed in a set of positive weighted capacity. We prove weighted Sobolev-Poincar\'{e} inequality with sharp scaling-invariant constants involving weighted capacity. Then we show higher integrability of the gradient of the solution (Meyer

Alexander Schlaich, Matthieu Vandamme, Marie Plazanet, Benoit Coasne

In the field of nanoconfined fluids, there are striking examples of deformation/transport coupling in which mechanical solicitation of the confining host and dynamics of the confined fluid impact each other. While this intriguing behavior can be potentially used for practical applications (e.g. energy storage, phase separation, catalysis), the underlying mec

Anna Kukleva, Fadime Sener, Edoardo Remelli, Bugra Tekin

Lately, there has been growing interest in adapting vision-language models (VLMs) to image and third-person video classification due to their success in zero-shot recognition. However, the adaptation of these models to egocentric videos has been largely unexplored. To address this gap, we propose a simple yet effective cross-modal adaptation framework, which

Ángel Crespo-Blanco

The connection between monotonicity formulas and the (S$_+$)-property is that, for some popular differential operators, the former is used to prove the latter. The purpose of this paper is to explore this connection, remark how in the past both the monotonicity formulas and the (S$_+$)-property were focused on power-law growth, and prove the same type of res

N. Pulido-Mateo, H. Mendpara, M. Duwe, T. Dubielzig

We report on the implementation of arbitrary circuits on a universal two-qubit register that can act as the computational module in a trapped-ion quantum computer based on the quantum charge-coupled device architecture. A universal set of quantum gates is implemented on a two-ion Coulomb crystal of $^9$Be$^+$ ions using only chip-integrated microwave address

Cihan Okay, Walker H. Stern

We introduce a theory of twisted simplicial distributions on simplicial principal bundles, which allow us to capture Bell's non-locality, and the more general notion of quantum contextuality. We leverage the classical theory of simplicial principal bundles, as well as structures on categories of such bundles, to provide powerful computational tools for analy

Dylan S. Small

For learning about the causal effect of a treatment, a randomized controlled trial (RCT) is considered the gold standard. However, randomizing treatment is sometimes unethical or infeasible, and instead an observational study may be conducted. While some aspects of a well designed RCT cannot be replicated in an observational study, one aspect that can is to

Debdipta Goswami

This paper proposes a novel and interpretable recurrent neural-network structure using the echo-state network (ESN) paradigm for time-series prediction. While the traditional ESNs perform well for dynamical systems prediction, it needs a large dynamic reservoir with increased computational complexity. It also lacks interpretability to discern contributions f

Ömer Akin, Yuning Wu

This paper explores the paradoxical nature of computational creativity, focusing on the inherent limitations of closed digital systems in emulating the open-ended, dynamic process of human creativity. Through a comprehensive analysis, we delve into the concept of the State Space Paradox (SSP) in computational research on creativity, which arises from the att

Wejdene Haouari, Abdelhakim Senhaji Hafid, Marios Fokaefs

Ethereum smart contracts are highly powerful, immutable, and able to retain massive amounts of tokens. However, smart contracts keep attracting attackers to benefit from smart contract flaws and Ethereum unexpected behavior. Thus, methodologies and tools have been proposed to help implement secure smart contracts and to evaluate the security of smart contrac

Ulysses Alvarez, Kyungyong Lee

It is an open problem to find cell decompositions of quiver Grassmannians associated to each cluster variable. We initiate a new approach to this problem by giving an explicit description for each individual subrepresentation. In this paper, we illustrate our approach for the Kronecker quiver.

Louis-Pierre Arguin, Emma Bailey

Let $\delta>0$ and $\sigma=\frac{1}{2}+\tfrac{\delta}{\log T}$. We prove that, for any $\alpha>0$ and $V\sim \alpha\log \log T$ as $T\to\infty$, $\frac{1}{T}\text{meas}\big\{t\in [T,2T]: \log|\zeta(\sigma+\rm{i} \tau)|>V\big\}\geq C_\alpha(\delta)\int_V^\infty \frac{e^{-y^2/\log\log T}}{\sqrt{\pi\log\log T}} \rm{d} y,$ where $\delta$ is large enough dependin

Niall Taylor, Dan Schofield, Andrey Kormilitzin, Dan W Joyce

Pre-trained Large Language Models (LLMs) often struggle on out-of-domain datasets like healthcare focused text. We explore specialized pre-training to adapt smaller LLMs to different healthcare datasets. Three methods are assessed: traditional masked language modeling, Deep Contrastive Learning for Unsupervised Textual Representations (DeCLUTR), and a novel

Andrew Kolmer Forbes, Philip Daniel Blocher, Ivan H. Deutsch

We show how the decoherence that occurs in an entangling atomic spin-light interface can be simply modeled as the dynamics of a bosonic mode. Although one seeks to control the collective spin of the atomic system in the permutationally invariant (symmetric) subspace, diffuse scattering and optical pumping are local, making an exact description of the many-bo

Jhon A. Castro-Correa, Jhony H. Giraldo, Mohsen Badiey, Fragkiskos D. Malliaros

Reconstructing time-varying graph signals (or graph time-series imputation) is a critical problem in machine learning and signal processing with broad applications, ranging from missing data imputation in sensor networks to time-series forecasting. Accurately capturing the spatio-temporal information inherent in these signals is crucial for effectively addre

Santiago Varona, Markus Müller, Alejandro Bermudez

We introduce Lindblad-like quantum tomography (L$\ell$QT) as a quantum characterization technique of time-correlated noise in quantum information processors. This approach enables the estimation of time-local master equations, including their possible negative decay rates, by maximizing a likelihood function subject to dynamical constraints. We discuss L$\el

Gayathri Eknath

In this thesis, we examine the interplay of interstellar dust and gas in the Andromeda galaxy (M31). In Chapter 2, we use $^{12}$CO CARMA observations of M31 and dust mass surface density measurements from the Bayesian algorithm PPMAP to investigate whether the dust emissivity index ($\beta$) is different inside and outside molecular clouds. We find little d

George Tang, Krishna Murthy Jatavallabhula, Antonio Torralba

We tackle the problem of learning an implicit scene representation for 3D instance segmentation from a sequence of posed RGB images. Towards this, we introduce 3DIML, a novel framework that efficiently learns a neural label field which can render 3D instance segmentation masks from novel viewpoints. Opposed to prior art that optimizes a neural field in a sel

Georgi B. Mitev, Jonathan Tennyson, Sergey N. Yurchenko

Multi-reference configuration interaction (MRCI) potential energy curves (PECs) and spin-orbit couplings for the X $^2\Pi$, A $^2 \Sigma^+$, 1 $^2 \Sigma^-$, 1 $^4 \Sigma^-$, and 1 $^4 \Pi$ states of OH are computed and refined against empirical energy levels and transitions to produce a spectroscopic model. Predissociation lifetimes are determined by discre

Austin Narcomey, Nathan Tsoi, Ruta Desai, Marynel Vázquez

Preference learning has long been studied in Human-Robot Interaction (HRI) in order to adapt robot behavior to specific user needs and desires. Typically, human preferences are modeled as a scalar function; however, such a formulation confounds critical considerations on how the robot should behave for a given task, with desired -- but not required -- robot

Anselm Vossen

This contribution highlights some topics addressed by current and future experiments in Semi-Inclusive Deep-Inelastic Scattering. We concentrate on the programs at 12 and 22 GeV at Jefferson Lab using the CLAS detector, and at the future Electron-Ion Collider.

B. I. Shklovskii

Efros-Shklovskii (ES) Coulomb gap in density of localized states and ES law of the variable range hopping conductivity were coined 50 years ago. The theory and its first confirmations were reviewed in our monograph 40-years ago. This paper reviews the subsequent experimental evidence, theoretical advancements, and novel applications. Out of hundreds of exper

Sayak Mukherjee, Andrea Simonetto, Hadi Jamali-Rad

Effective collaboration among heterogeneous clients in a decentralized setting is a rather unexplored avenue in the literature. To structurally address this, we introduce Model Agnostic Peer-to-peer Learning (coined as MAPL) a novel approach to simultaneously learn heterogeneous personalized models as well as a collaboration graph through peer-to-peer commun

Xia-Ming Zheng, Mehdi Kargarian

Motivated by recent experimental observations of Kondo resonances in cobalt atoms on single layer 1T-TaSe$_{2}$, we theoretically investigate the effect of coupling a U(1) quantum spin liquid with a spinon Fermi surface to a lattice of Anderson impurities. Within the slave-rotor formalism, we find that above a critical coupling strength between the spin liqu

Niall Taylor, Andrey Kormilitzin, Isabelle Lorge, Alejo Nevado-Holgado

Contemporary large language models (LLMs) may have utility for processing unstructured, narrative free-text clinical data contained in electronic health records (EHRs) -- a particularly important use-case for mental health where a majority of routinely-collected patient data lacks structured, machine-readable content. A significant problem for the the United

Ya. S. Derbenev

A "Figure 8" shaped synchrotron is suggested for use in accelerating polarized protons in the energy range below ~ 20 GeV. The spin tune in such an accelerator does not ramp with energy and is equal to zero . Then the intrinsic spin resonances will not appear. A partial Siberian snake (solenoid) is proposed to be inserted into the ring in order to stabilize

Christopher Caruvana, Steven Clontz, Jared Holshouser

We present a comprehensive report on the relationships between variations of the Menger and Rothberger selection properties with respect to $\omega$-covers and $k$-covers in the most general topological setting and address the finite productivity of some of these properties. We collect various examples that separate certain properties and we carefully identi

Jian-Cheng Feng, Xuepeng Chen, Yang Su, Li Sun

Aims. We aim to investigate the molecular environment of the supernova remnant (SNR) G150.3+4.5, and explore its association with ambient molecular clouds (MCs). Methods. We present large-field CO (J=1-0) molecular line observations toward SNR G150.3+4.5, using the 13.7 m millimeter telescope of the Purple Mountain Observatory. The observations have an angul

Gabriele Berton, Gabriele Trivigno, Barbara Caputo, Carlo Masone

Visual Place Recognition aims at recognizing previously visited places by relying on visual clues, and it is used in robotics applications for SLAM and localization. Since typically a mobile robot has access to a continuous stream of frames, this task is naturally cast as a sequence-to-sequence localization problem. Nevertheless, obtaining sequences of label

Mingxing Rao, Yinhong Qin, Soheil Kolouri, Jie Ying Wu

Purpose: In order to produce a surgical gesture recognition system that can support a wide variety of procedures, either a very large annotated dataset must be acquired, or fitted models must generalize to new labels (so called "zero-shot" capability). In this paper we investigate the feasibility of latter option. Methods: Leveraging the Bridge-Prompt framew

Hao Guo, Henk Wymeersch, Behrooz Makki, Hui Chen

Future generations of mobile networks call for concurrent sensing and communication functionalities in the same hardware and/or spectrum. Compared to communication, sensing services often suffer from limited coverage, due to the high path loss of the reflected signal and the increased infrastructure requirements. To provide a more uniform quality of service,

Vinayvivian Rodrigues, Bingbin Yu, Christoph Stoeffler, Shivesh Kumar

Parallel Continuum Robots (PCR) are closed-loop mechanisms but use elastic kinematic links connected in parallel between the end-effector (EE) and the base platform. PCRs are actuated primarily through large deflections of the interconnected elastic links unlike by rigid joints in rigid parallel mechanisms. In this paper, Cosserat rod theory-based forward an

Akshat Chaudhari, Chakradhar Guntuboina, Hongshuo Huang, Amir Barati Farimani

The pursuit of novel alloys tailored to specific requirements poses significant challenges for researchers in the field. This underscores the importance of developing predictive techniques for essential physical properties of alloys based on their chemical composition and processing parameters. This study introduces AlloyBERT, a transformer encoder-based mod

Seyed Rasoul Hosseini, Hamid Taheri, Mohammad Teshnehlab

Lane detection for autonomous vehicles is an important concept, yet it is a challenging issue of driver assistance systems in modern vehicles. The emergence of deep learning leads to significant progress in self-driving cars. Conventional deep learning-based methods handle lane detection problems as a binary segmentation task and determine whether a pixel be

Zhiyuan Yao, Zheng Li, Matthew Thomas, Ionut Florescu

Investors and regulators can greatly benefit from a realistic market simulator that enables them to anticipate the consequences of their decisions in real markets. However, traditional rule-based market simulators often fall short in accurately capturing the dynamic behavior of market participants, particularly in response to external market impact events or

Marco Cannici, Davide Scaramuzza

Neural Radiance Fields (NeRFs) have shown great potential in novel view synthesis. However, they struggle to render sharp images when the data used for training is affected by motion blur. On the other hand, event cameras excel in dynamic scenes as they measure brightness changes with microsecond resolution and are thus only marginally affected by blur. Rece

Rongge Xu, Holiverse Yang

We classify $E_2$ condensable algebras in a modular tensor category $\mathcal{C}$ up to 2-Morita equivalence. From a physical perspective, this is equivalent to providing a criterion for when different $E_2$ condensable algebras result in the same condensed topological phase in a 2d anyon condensation process. By considering the left and right centers of $E_

Maria Flors Mor-Ruiz, Julius Wallnöfer, Wolfgang Dür

Entanglement-based quantum networks exhibit a unique flexibility in the choice of entangled resource states that are then locally manipulated by the nodes to fulfill any request in the network. Furthermore, this manipulation is not uniquely defined and thus can be optimized. We tailor the adaptation of the resource state or pre-established entanglement to ac

Carlos Heredia, Josep Llosa

We prove that higher-derivative and genuinely nonlocal Lagrangian systems can be Lyapunov-stable even when their Hamiltonians lack a lower bound. Explicit free and coupled Pais-Uhlenbeck oscillators, together with a genuine nonlocal model, are analysed to identify the precise conditions under which stability holds. These counterexamples point out the logical

Tuna Han Salih Meral, Enis Simsar, Federico Tombari, Pinar Yanardag

Low-Rank Adaptation (LoRA) has emerged as a powerful and popular technique for personalization, enabling efficient adaptation of pre-trained image generation models for specific tasks without comprehensive retraining. While employing individual pre-trained LoRA models excels at representing single concepts, such as those representing a specific dog or a cat,

Serhii Shafraniuk

When the external electromagnetic field (EF) with the frequency $\omega $ acts on the discrete levels in the quantum dot (QD), it induces the mixed quantum state characterized by Rabi flops (RF). The RF process involves the oscillations in the level population $\delta n_{\alpha \beta }\left( t\right) $ accompanied by the cyclical absorption and re-emission o

Jose Henrique Rodrigues da Rocha, Julio Gabriel de Falco Manuel, Antonio Jose Faria Bombard

Magnetorheological fluids (MRF) are smart composite materials that, under an external magnetic field, show a reversible solid-liquid transition in less than 10 ms. This study aimed to evaluate which organoclays would jellify a synthetic oil for the formulation of MRF. Three dispersant additives for carbonyl iron powder were evaluated. Fifteen different gelli

Rolandos Alexandros Potamias, Michail Tarasiou, Stylianos Ploumpis, Stefanos Zafeiriou

In the realm of 3D computer vision, parametric models have emerged as a ground-breaking methodology for the creation of realistic and expressive 3D avatars. Traditionally, they rely on Principal Component Analysis (PCA), given its ability to decompose data to an orthonormal space that maximally captures shape variations. However, due to the orthogonality con

Noah Pursell, Anindya Maiti

We present a novel framework to advance generative artificial intelligence (AI) applications in the realm of printed art products, specifically addressing large-format products that require high-resolution artworks. The framework consists of a pipeline that addresses two major challenges in the domain: the high complexity of generating effective prompts, and

Mathieu Anel, Reid Barton

We introduce homotopical variants of the axioms of countable and dependent choice for infinity-topoi and use them to give criteria for Postnikov completeness, revisiting a result of Mondal and Reinecke.

Patrick Daniels, Pol van Hoften, Dongryul Kim, Mingjia Zhang

We prove a conjecture of Pappas and Rapoport about the existence of ''canonical'' integral models of Shimura varieties of Hodge type with quasi-parahoric level structure at a prime $p$. For these integral models, we moreover show uniformization of isogeny classes by integral local Shimura varieties, and prove a conjecture of Kisin and Pappas on local model d

Mingyu Cai, Karankumar Patel, Soshi Iba, Songpo Li

In human-robot collaboration, shared control presents an opportunity to teleoperate robotic manipulation to improve the efficiency of manufacturing and assembly processes. Robots are expected to assist in executing the user's intentions. To this end, robust and prompt intention estimation is needed, relying on behavioral observations. The framework presents

Jonas Hall, Christos G. Cassandras, Sean B. Andersson

We consider the problem of using an autonomous agent to persistently monitor a collection of dynamic targets distributed in an environment. We generalize existing work by allowing the agent's dynamics to vary throughout the environment, leading to a hybrid dynamical system. This introduces an additional layer of complexity towards the planning portion of the

Kevin Barkevich, Reynold Bailey, Gabriel J. Diaz

Algorithms for the estimation of gaze direction from mobile and video-based eye trackers typically involve tracking a feature of the eye that moves through the eye camera image in a way that covaries with the shifting gaze direction, such as the center or boundaries of the pupil. Tracking these features using traditional computer vision techniques can be dif

Tenio Popmintchev, Ming-Chang Chen, Alon Bahabad, Michael Gerrity

We show how bright, fully coherent, hard x-ray beams can be generated through nonlinear upconversion of femtosecond laser light. By using longer-wavelength mid-infrared driving lasers of moderate peak intensity, full phase matching of the high harmonic generation process can extend, in theory, into the hard x-ray region of the spectrum. We identify the domin

Yi-Wen Liu, Yu-Chen Zhuang, Ya-Ning Ren, Chao Yan

Phase singularities are phase-indeterminate points where wave amplitudes are zero, which manifest as phase vertices or wavefront dislocations. In the realm of optical and electron beams, the phase singularity has been extensively explored, demonstrating a profound connection to orbital angular momentum. Direct local imaging of the impact of orbital angular m

Dennis F. Gardner, Michael Tanksalvala, Elisabeth R. Shanblatt, Xiaoshi Zhang

Coherent diffractive imaging is unique as the only route for achieving diffraction-limited spatial resolution in the extreme ultraviolet and X-ray regions, limited only by the wavelength of the light. Recently, advances in coherent short wavelength light sources, coupled with progress in algorithm development, have significantly enhanced the power of x-ray i

Arnob Kumar Ghosh, Arijit Saha, Tanay Nag

We consider a non-Hermitian (NH) analog of a second-order topological insulator, protected by chiral symmetry, in the presence of next-nearest neighbor hopping elements to theoretically investigate the interplay beyond the first nearest neighbor hopping amplitudes and topological order away from Hermiticity. In addition to the four zero-energy corner modes p

Tenio Popmintchev, Ming-Chang Chen, Oren Cohen, Michael E. Grisham

We demonstrate that phase-matched frequency upconversion of ultrafast laser light can be extended to shorter wavelengths by using longer driving laser wavelengths. Experimentally, we show that the phase-matching cutoff for harmonic generation in argon increases from 45 to 100 eV when the driving laser wavelength is increased from 0.8 to 1.3 micrometers. Phas