May 2024 arXiv papers — page 18

Zejun Liu, Bryan K. Clark

Quantum magic, or nonstabilizerness, provides a crucial characterization of quantum systems, regarding the classical simulability with stabilizer states. In this work, we propose a novel and efficient algorithm for computing stabilizer R\'enyi entropy, one of the measures for quantum magic, in spin systems with sign-problem free Hamiltonians. This algorithm

John Bonar, John Hastings

Digital engineering practices offer significant yet underutilized potential for improving information assurance and system lifecycle management. This paper examines how capabilities like model-based engineering, digital threads, and integrated product lifecycles can address gaps in prevailing frameworks. A reference model demonstrates applying digital engine

Umar Ibrahim, Abubakar Yakubu Zandam, Fatima Muhammad Adam, Aminu Musa

Aspect-based Sentiment Analysis (ABSA) is crucial for understanding sentiment nuances in text, especially across diverse languages and cultures. This paper introduces a novel Deep Convolutional Neural Network (CNN)-based model tailored for aspect and polarity classification in Hausa movie reviews, an underrepresented language in sentiment analysis research.

Sean M. Andrews, Richard Teague, Christopher P. Wirth, Jane Huang

Using controlled injection and recovery experiments, we devised an analysis prescription to assess the quality of dynamical measurements of protoplanetary disk gas masses based on resolved (CO) spectral line data, given observational limitations (resolution, sampling, noise), measurement bias, and ambiguities in the geometry and physical conditions. With suf

Hamadi Chihaoui, Abdelhak Lemkhenter, Paolo Favaro

Image Restoration (IR) methods based on a pre-trained diffusion model have demonstrated state-of-the-art performance. However, they have two fundamental limitations: 1) they often assume that the degradation operator is completely known and 2) they alter the diffusion sampling process, which may result in restored images that do not lie onto the data manifol

Angus Alexander, Adam Rennie

We use spectral flow to present a new proof of Levinson's theorem for Schr\"{o}dinger operators on $\mathbb{R}^n$ with smooth compactly supported potential. Our proof is valid in all dimensions and in the presence of resonances. The statement is expressed in terms of the spectral shift function and the ``high energy corrected time delay'' following Guillop\'

Alexandros E. Tzikas, Jinkyoo Park, Mykel J. Kochenderfer, Ross E. Allen

Min-max problems are important in multi-agent sequential decision-making because they improve the performance of the worst-performing agent in the network. However, solving the multi-agent min-max problem is challenging. We propose a modular, distributed, online planning-based algorithm that is able to approximate the solution of the min-max objective in net

Vaibhav Vavilala, Florian Kluger, Seemandhar Jain, Bodo Rosenhahn

Describing a scene in terms of primitives -- geometrically simple shapes that offer a parsimonious but accurate abstraction of structure -- is an established and difficult fitting problem. Different scenes require different numbers of primitives, and these primitives interact strongly. Existing methods are evaluated by comparing predicted depth, normals, and

Lianlei Shan, Wenzhang Zhou, Wei Li, Xingyu Ding

The goal of incremental Few-shot Semantic Segmentation (iFSS) is to extend pre-trained segmentation models to new classes via few annotated images without access to old training data. During incrementally learning novel classes, the data distribution of old classes will be destroyed, leading to catastrophic forgetting. Meanwhile, the novel classes have only

Shenghuan Sun, Alexander Schubert, Gregory M. Goldgof, Zhiqing Sun

Vision-Language Models (VLM) can support clinicians by analyzing medical images and engaging in natural language interactions to assist in diagnostic and treatment tasks. However, VLMs often exhibit "hallucinogenic" behavior, generating textual outputs not grounded in contextual multimodal information. This challenge is particularly pronounced in the medical

Zhi Li, Muhammad Nadeem, Zengji Yue, David Cortie

In the 1960s, it was proposed that in small indirect band-gap materials, excitons can spontaneously form because the density of carriers is too low to screen the attractive Coulomb interaction between electrons and holes. The result is a novel strongly interacting insulating phase known as an excitonic insulator. Here we employ scanning tunnelling microscopy

Zijie Chen, Yuxin Geng, Xingru Chen, Feng Fu

Evolutionary game dynamics on networks typically consider the competition among simple strategies such as cooperation and defection in the Prisoner's Dilemma and summarize the effect of population structure as network reciprocity. However, it remains largely unknown regarding the evolutionary dynamics involving multiple powerful strategies typically consider

F. Q. Guo, S. L. Su, Weibin Li, X. Q. Shao

We propose a parity-controlled gate within a two-dimensional Rydberg atom array, enabling efficient discrimination between even and odd parities of virtually excited control atoms by monitoring the dynamic evolution of an auxiliary atom. This is achieved through the use of spin-exchange dipolar interactions between Rydberg states and coupling between ground

Michael Fore, Simranjit Singh, Chaehong Lee, Amritanshu Pandey

Misinformation regarding climate change is a key roadblock in addressing one of the most serious threats to humanity. This paper investigates factual accuracy in large language models (LLMs) regarding climate information. Using true/false labeled Q&A data for fine-tuning and evaluating LLMs on climate-related claims, we compare open-source models, assessing

Lucas Monteiro Paes, Dennis Wei, Flavio P. Calmon

Feature attribution methods explain black-box machine learning (ML) models by assigning importance scores to input features. These methods can be computationally expensive for large ML models. To address this challenge, there has been increasing efforts to develop amortized explainers, where a machine learning model is trained to predict feature attribution

David Pissarra, Isabel Curioso, João Alveira, Duarte Pereira

Automated clinical text anonymization has the potential to unlock the widespread sharing of textual health data for secondary usage while assuring patient privacy and safety. Despite the proposal of many complex and theoretically successful anonymization solutions in literature, these techniques remain flawed. As such, clinical institutions are still relucta

Venkat Venkatasubramanian, Arijit Chakraborty

The startling success of ChatGPT and other large language models (LLMs) using transformer-based generative neural network architecture in applications such as natural language processing and image synthesis has many researchers excited about potential opportunities in process systems engineering (PSE). The almost human-like performance of LLMs in these areas

Megan Flynn, Alexander Wang, Dean Edward Alvarez, Christopher De Sa

We present STAT: a simple algorithm to prune transformer models without any fine-tuning. STAT eliminates both attention heads and neurons from the network, while preserving accuracy by calculating a correction to the weights of the next layer. Each layer block in the network is compressed using a series of principled matrix factorizations that preserve the n

Theodorus Maria Nieuwenhuizen

A class of observers is introduced that interpolate smoothly between the Schwarzschild observer, stable at spatial infinity, and the Kerr-Schild observer, who falls into a black hole. For these observers the passing of the event and inner horizon takes a finite time, which diverges logarithmically when the interpolation parameter $\sigma$ goes to zero. In th

Mohamed Seif, Yanxi Chen

In this note, we provide a refined analysis of Mitra's algorithm \cite{mitra2008clustering} for classifying general discrete mixture distribution models. Built upon spectral clustering \cite{mcsherry2001spectral}, this algorithm offers compelling conditions for probability distributions. We enhance this analysis by tailoring the model to bipartite stochastic

Kelly N. Sanderson, Anna D. Kapińska, Moire K. M. Prescott, Audrey F. Dijeau

Jetted Active Galactic Nuclei (AGN) hosting extended photoionized nebulae provide us with a unique view of the timescales associated with AGN activity. Here, we present a new Green Bean galaxy (RGB1) at $z=0.304458\pm0.000007$ with large scale jet-induced radio emission. The Spectral Energy Distributions (SEDs) of the radio components show steep spectral ind

Anatoly V. Borisov

The power and the probability of electromagnetic radiation from an electron in a constant background tensor field violating Lorentz invariance are calculated. The case of a background field of the quasielectric type is considered. The angular distribution and the polarization of the radiation are studied. Using present experimental constraints on the backgro

Haowen Zhong, Maximiliano Isi, Katerina Chatziioannou, Will M. Farr

Tests of general relativity with gravitational waves typically introduce parameters for putative deviations and combine information from multiple events by characterizing the population distribution of these parameters through a hierarchical model. Although many tests include multiple such parameters, hierarchical tests have so far been unable to accommodate

Yutaka Iwamoto

We show that lattice isomorphisms between lattices of slowly oscillating functions on chain-connected proper metric spaces induce coarsely equivalent homeomorphisms. This result leads to a Banach-Stone-like theorem for these lattices. Furthermore, we provide a representation theorem that characterizes linear lattice isomorphisms among these lattices.

Wei-Wei Han, Rui Fang, William Layton

The recent 1/2-equation model of turbulence is a simplification of the standard Kolmogorov-Prandtl 1-equation URANS model. Surprisingly, initial numerical tests indicated that the 1/2-equation model produces comparable velocity statistics at reduced cost. It is also a test problem and first step for developing numerical analysis to address a full 1-equation

Holden Lee, Matheau Santana-Gijzen

We prove bounds on the variance of a function $f$ under the empirical measure of the samples obtained by the Sequential Monte Carlo (SMC) algorithm, with time complexity depending on local rather than global Markov chain mixing dynamics. SMC is a Markov Chain Monte Carlo (MCMC) method, which starts by drawing $N$ particles from a known distribution, and then

Armen E. Allahverdyan, Roger Balian, Theo M. Nieuwenhuizen

We present a graduate course on ideal measurements, analyzed as dynamical processes of interaction between the tested system S and an apparatus A, described by quantum statistical mechanics. The apparatus A=M+B involves a macroscopic measuring device M and a bath B. The requirements for ideality of the measurement allow us to specify the Hamiltonian of the i

Lucio L. Garcia, Giulio Tirabassi, Cristina Masoller, Pablo Balenzuela

The diffusion of information plays a crucial role in a society, affecting its economy and the well-being of the population. Characterizing the diffusion process is challenging because it is highly non-stationary and varies with the media type. To understand the spreading of newspaper news in Argentina, we collected data from more than 27000 articles publishe

Congchao Wang, Sean Augenstein, Keith Rush, Wittawat Jitkrittum

Reducing serving cost and latency is a fundamental concern for the deployment of language models (LMs) in business applications. To address this, cascades of LMs offer an effective solution that conditionally employ smaller models for simpler queries. Cascaded systems are typically built with independently trained models, neglecting the advantages of conside

Roan Talbut, Anthea Monod

We propose a gradient descent method for solving optimization problems arising in settings of tropical geometry - a variant of algebraic geometry that has attracted growing interest in applications such as computational biology, economics, and computer science. Our approach takes advantage of the polyhedral and combinatorial structures arising in tropical ge

Ryan Greenblatt, Fabien Roger, Dmitrii Krasheninnikov, David Krueger

To determine the safety of large language models (LLMs), AI developers must be able to assess their dangerous capabilities. But simple prompting strategies often fail to elicit an LLM's full capabilities. One way to elicit capabilities more robustly is to fine-tune the LLM to complete the task. In this paper, we investigate the conditions under which fine-tu

Tony Yue Yu, Shaowu Zhang

We construct the topological Laplace transform functor from Stokes structures of exponential type to constructible sheaves on $\mathbb C$ with vanishing cohomology. We show that it is compatible with the Fourier transform of $D$-modules, and induces an equivalence of categories. We give two applications of the construction. First, we study the Fourier transf

Mingqi Yuan, Roger Creus Castanyer, Bo Li, Xin Jin

Extrinsic rewards can effectively guide reinforcement learning (RL) agents in specific tasks. However, extrinsic rewards frequently fall short in complex environments due to the significant human effort needed for their design and annotation. This limitation underscores the necessity for intrinsic rewards, which offer auxiliary and dense signals and can enab

Yiping Wang, Yifang Chen, Wendan Yan, Alex Fang

Data selection has emerged as a core issue for large-scale visual-language model pretaining (e.g., CLIP), particularly with noisy web-curated datasets. Three main data selection approaches are: (1) leveraging external non-CLIP models to aid data selection, (2) training new CLIP-style embedding models that are more effective at selecting high-quality data tha

Toshiyuki Ohtsuka, Atsushi Okazaki, Masaki Ogura, Shunji Kotsuki

This study proposes introducing convex optimization to find initial perturbations of atmospheric states to realize specified changes in subsequent weather. In the proposed method, we formulate and solve an inverse problem to find effective perturbations in atmospheric variables so that controlled variables satisfy specified changes at a specified time. The p

F. V. Kowalski

The relaxation in a vacuum capacitor-resistor circuit is comprised of two exponential decays, one caused by surface charge and the other by the decay of energy stored between the capacitor plates. A simple phenomenological model of this relaxation is shown to be supported by measurements even though Maxwell's equations are difficult to apply in this case. Si

Xinmeng Huang, Shuo Li, Edgar Dobriban, Osbert Bastani

The growing safety concerns surrounding large language models raise an urgent need to align them with diverse human preferences to simultaneously enhance their helpfulness and safety. A promising approach is to enforce safety constraints through Reinforcement Learning from Human Feedback (RLHF). For such constrained RLHF, typical Lagrangian-based primal-dual

Ignacio García-Marco, Kolja Knauer

In 1978 Babai raised the question whether all minimal Cayley graphs have bounded chromatic number; in 1994 he conjectured a negative answer. In this paper we show that any minimal Cayley graph of a (finitely generated) generalized dihedral or nilpotent group has chromatic number at most 3, while 4 colors are sometimes necessary for soluble groups. On the oth

Bangyu Lan, Stefano Stramigioli, Kenan Niu

Accurate bone tracking is crucial for kinematic analysis in orthopedic surgery and prosthetic robotics. Traditional methods (e.g., skin markers) are subject to soft tissue artifacts, and the bone pins used in surgery introduce the risk of additional trauma and infection. For electromyography (EMG), its inability to directly measure joint angles requires comp

Shuhong Liu, Nozomi Akashi, Qingyao Huang, Yasuo Kuniyoshi

Chaos presents complex dynamics arising from nonlinearity and a sensitivity to initial states. These characteristics suggest a depth of expressivity that underscores their potential for advanced computational applications. However, strategies to effectively exploit chaotic dynamics for information processing have largely remained elusive. In this study, we r

Raphaël Cerf

We define the pivotal set of a Boolean function and we prove a fundamental inequality on its expected size, when the inputs are independent random coins of parameter~$p$. We give two complete proofs of this inequality. Along the way, we obtain the classical Margulis--Russo formula. We give a short proof of the classical Hoeffding inequality for i.i.d. Bernou

Samuel Sokota, Dylan Sam, Christian Schroeder de Witt, Spencer Compton

Minimum-entropy coupling (MEC) -- the process of finding a joint distribution with minimum entropy for given marginals -- has applications in areas such as causality and steganography. However, existing algorithms are either computationally intractable for large-support distributions or limited to specific distribution types and sensitive to hyperparameter c

Alice Barbara Tumpach, Gabriel Larotonda

This paper is a self-contained exposition of the geometry of symmetric positive-definite real $n\times n$ matrices $\operatorname{SPD}(n)$, including necessary and sufficent conditions for a submanifold $\mathcal{N} \subset\operatorname{SPD}(n)$ to be totally geodesic for the affine-invariant Riemannian metric. A non-linear projection $x\mapsto \pi(x)$ on a

Claire Donnat, Elena Tuzhilina

Canonical Correlation Analysis (CCA) is a widespread technique for discovering linear relationships between two sets of variables $X \in \mathbb{R}^{n \times p}$ and $Y \in \mathbb{R}^{n \times q}$. In high dimensions however, standard estimates of the canonical directions cease to be consistent without assuming further structure. In this setting, a possible

Pierre Chambon, Jean-Benoit Delbrouck, Thomas Sounack, Shih-Cheng Huang

Since the release of the original CheXpert paper five years ago, CheXpert has become one of the most widely used and cited clinical AI datasets. The emergence of vision language models has sparked an increase in demands for sharing reports linked to CheXpert images, along with a growing interest among AI fairness researchers in obtaining demographic data. To

Robert B. Griffiths

The article `Bell Nonlocality in Classical Systems Coexisting with Other System Types' by Chiribella et al. defines `classical' in a quantum context in a way that ignores noncommuting quantum projectors, and is hence inconsistent with Hilbert-space quantum theory.

Gabriel Moreira, Manuel Marques, João Paulo Costeira

A cornerstone of geometric reconstruction, rotation averaging seeks the set of absolute rotations that optimally explains a set of measured relative orientations between them. In addition to being an integral part of bundle adjustment and structure-from-motion, the problem of synchronizing rotations also finds applications in visual simultaneous localization

Guilherme Ilário Correr, Pedro C. Azado, Diogo O. Soares-Pinto, Gabriel Carlo

Parameterized quantum circuits play a key role for the development of quantum variational algorithms in the realm of the NISQ era. Knowing their actual capability of performing different kinds of tasks is then of the utmost importance. By comparing them with a prototypical class of universal random circuits we have found that their approach to the asymptotic

Muhammad Miskeen Khan, Edwin Chaparro, Bhuvanesh Sundar, Allison Carter

We propose the use of phonon$\unicode{x2013}$mediated interactions as an entanglement resource to engineer Einstein$\unicode{x2013}$Podolsky$\unicode{x2013}$Rosen (EPR) correlations and to perform teleportation of collective spin states in two$\unicode{x2013}$dimensional ion crystals. We emulate continuous variable quantum teleportation protocols between sub

Tânia Tomè, Mário J. de Oliveira

We investigate the thermodynamics as well as the population dynamics of ecosystems based on a stochastic approach in which the number of individuals of the several species of the ecosystem are treated as stochastic variables. The several species are connected by feeding relationships that are understood as unidirectional processes in which a certain amount o

Angelica Chen, Sadhika Malladi, Lily H. Zhang, Xinyi Chen

Preference learning algorithms (e.g., RLHF and DPO) are frequently used to steer LLMs to produce generations that are more preferred by humans, but our understanding of their inner workings is still limited. In this work, we study the conventional wisdom that preference learning trains models to assign higher likelihoods to more preferred outputs than less p

Jennifer Berg, Beth Malmskog, Mckenzie West

In this article, we construct codes with hierarchical locality using natural geometric structures in Artin-Schreier surfaces of the form $y^p-y=f(x,z)$. Our main theorem describes the codes, their hierarchical structure and recovery algorithms, and gives parameters. We also develop a family of examples using codes defined over $\mathbb{F}_{p^2}$ on the surfa

Zoe Piran, Michal Klein, James Thornton, Marco Cuturi

Learning meaningful representations of complex objects that can be seen through multiple ($k\geq 3$) views or modalities is a core task in machine learning. Existing methods use losses originally intended for paired views, and extend them to $k$ views, either by instantiating $\tfrac12k(k-1)$ loss-pairs, or by using reduced embeddings, following a \textit{on

Yechen Xu, Xinhao Kong, Tingjun Chen, Danyang Zhuo

The complexity of large language model (LLM) serving workloads has substantially increased due to the integration with external tool invocations, such as ChatGPT plugins. In this paper, we identify a new opportunity for efficient LLM serving for requests that trigger tools: tool partial execution alongside LLM decoding. To this end, we design Conveyor, an ef

Mingqi Yuan, Huijiang Wang, Kai-Fung Chu, Fumiya Iida

Advances in robotics have been driving the development of human-robot interaction (HRI) technologies. However, accurately perceiving human actions and achieving adaptive control remains a challenge in facilitating seamless coordination between human and robotic movements. In this paper, we propose a hierarchical procedural framework to enable dynamic robot-a

Volodymyr Zhuravlov

The article explores the arithmetic of multiplication as a model of many valued projective logic. It is demonstrated that closed numerical intervals within this framework constitute Heyting algebras. The conditions for these algebras to be Boolean are identified. The article claims have undergone numerical verification. Paths for generalization to normed lin

Matteo Buzzegoli, Kirill Tuchin

We consider the Dirac field uniformly rotating with angular velocity $\Omega$ and also subject to the constant magnetic field $B$ directed along the rotation axis. The causal states are constrained to the interior of the light cylinder of radius $c/\Omega$. When this radius is smaller than the system size, as in the quark-gluon plasma, the effect of the boun

Ariel E. Rosenfield

We investigate how change of enriching base category via a faithful, conservative right adjoint functor interacts with enriched coverages and sheaves on a given enriched category. We prove that change of base via such a functor gives rise both to an injective mapping on subobjects in enriched presheaf categories, and to an injective mapping on enriched cover

Zhitian Zhang, Anjian Li, Angelica Lim, Mo Chen

Long-term human trajectory prediction is a challenging yet critical task in robotics and autonomous systems. Prior work that studied how to predict accurate short-term human trajectories with only unimodal features often failed in long-term prediction. Reinforcement learning provides a good solution for learning human long-term behaviors but can suffer from

Siwei Hu, Michael F. Hyland, Ritun Saha, Jacob J. Berkel

The integration of traditional fixed-route transit (FRT) and more flexible microtransit has been touted as a means of improving mobility and access to opportunity, increasing transit ridership, and promoting environmental sustainability. To help evaluate integrated FRT and microtransit public transit (PT) system (henceforth ``integrated fixed-flex PT system'

Eddye Alejandro Bustamante, Jose Manuel Jiménez, Alexander Muñoz

This work is devoted to study the relation between regularity and decay for solutions of the two-dimensional modified Zakharov-Kuznetsov equation in the weighted Sobolev spaces $Z_{s,(r_1,r_2)}:=H^s(\R^2)\cap L^2((1+|x|^{2r_1}+|y|^{2r_2})dxdy)$.

Islam Osman, Mohamed S. Shehata

Current state-of-the-art video object segmentation models have achieved great success using supervised learning with massive labeled training datasets. However, these models are trained using a single source domain and evaluated using videos sampled from the same source domain. When these models are evaluated using videos sampled from a different target doma

Xiangyu Qi, Yangsibo Huang, Yi Zeng, Edoardo Debenedetti

The exposure of security vulnerabilities in safety-aligned language models, e.g., susceptibility to adversarial attacks, has shed light on the intricate interplay between AI safety and AI security. Although the two disciplines now come together under the overarching goal of AI risk management, they have historically evolved separately, giving rise to differi

Julia Jansson, Ottmar Cronie

Recently, Cronie et al. (2024) introduced the notion of cross-validation for point processes and a new statistical methodology called Point Process Learning (PPL). In PPL one splits a point process/pattern into a training and a validation set, and then predicts the latter from the former through a parametrised Papangelou conditional intensity. The model para

Benjamin A. Jasperson, Ilia Nikiforov, Amit Samanta, Fei Zhou

A simulation can stand its ground against experiment only if its prediction uncertainty is known. The unknown accuracy of interatomic potentials (IPs) is a major source of prediction uncertainty, severely limiting the use of large-scale classical atomistic simulations in a wide range of scientific and engineering applications. Here we explore covariance betw

Nestor Maslej, Loredana Fattorini, Raymond Perrault, Vanessa Parli

The 2024 Index is our most comprehensive to date and arrives at an important moment when AI's influence on society has never been more pronounced. This year, we have broadened our scope to more extensively cover essential trends such as technical advancements in AI, public perceptions of the technology, and the geopolitical dynamics surrounding its developme

Barbora Vénosová, František Karlický

Two-dimensional transition metal carbides and nitrides (MXenes) are a perspective group of materials with a broad palette of applications. Surface terminations are a product of the MXene preparation, and post-processing can also lead to partial coverage. Despite applicability and fundamental properties being driven by termination patterns, it is not fully cl

Sudeshna Das, Yao Ge, Yuting Guo, Swati Rajwal

The increasing use of social media to share lived and living experiences of substance use presents a unique opportunity to obtain information on side effects, use patterns, and opinions on novel psychoactive substances. However, due to the large volume of data, obtaining useful insights through natural language processing technologies such as large language

Dhruvit Patel, Troy Arcomano, Brian Hunt, Istvan Szunyogh

This paper explores the potential of a hybrid modeling approach that combines machine learning (ML) with conventional physics-based modeling for weather prediction beyond the medium range. It extends the work of Arcomano et al. (2022), which tested the approach for short- and medium-range weather prediction, and the work of Arcomano et al. (2023), which inve

Juyeon Won, Rong Zhang, Cheng Peng, Ravhi Kumar

Recent band structure calculations have suggested the potential for band tuning in a chiral semiconductor, Ag$_3$AuTe$_2$, to zero upon application of negative strain. In this study, we report on the synthesis of polycrystalline Ag$_3$AuTe$_2$ and investigate its transport, optical properties, and pressure compatibility. Transport measurements reveal the sem

Haowen Lai, Gaoxiang Luo, Yifei Liu, Mingmin Zhao

This paper introduces PanoRadar, a novel RF imaging system that brings RF resolution close to that of LiDAR, while providing resilience against conditions challenging for optical signals. Our LiDAR-comparable 3D imaging results enable, for the first time, a variety of visual recognition tasks at radio frequency, including surface normal estimation, semantic

A. Pathak, O. Napoly, J. -F. Ostiguy

The upcoming Proton Improvement Plan-II (PIP-II), designated for enhancements to the Fermilab accelerator complex, features a new 800 MeV superconducting linac and a Beam Transfer Line (BTL) to transport the beam to the existing Booster synchrotron. To mitigate the space charge tune shift associated with a high intensity accumulated beam, the low emittance l

Blake Pelton, Adam Sapek, Ken Eguro, Daniel Lo

Digital systems are growing in importance and computing hardware is growing more heterogeneous. Hardware design, however, remains laborious and expensive, in part due to the limitations of conventional hardware description languages (HDLs) like VHDL and Verilog. A longstanding research goal has been programming hardware like software, with high-level languag

Tomas Ortega, Hamid Jafarkhani

We consider a decentralized optimization problem for networks affected by communication delays. Examples of such networks include collaborative machine learning, sensor networks, and multi-agent systems. To mimic communication delays, we add virtual non-computing nodes to the network, resulting in directed graphs. This motivates investigating decentralized o

C. D. Cruz, J. C. Stephenson, J. K. Wahlstrand

The nonlinear response of materials, an increasingly important aspect of light-matter interaction, can be challenging to measure in highly absorbing materials. Here, we introduce an interferometric technique that enables a direct measurement of the nonlinear complex permittivity in a bulk medium from reflectivity alone. We demonstrate the utility of pump-pro

Maggie D. Bailey, Douglas W. Nychka, Manajit Sengupta, Soutir Bandyopadhyay

Bias correction is a common pre-processing step applied to climate model data before it is used for further analysis. This article introduces an efficient adaptation of a well-established bias-correction method - quantile mapping - for global horizontal irradiance (GHI) that ensures corrected data is physically plausible through incorporating measurements of

Tiger Lu, Qier An, Gongjie Li, Sarah C. Millholland

The two planets of the HAT-P-11 system represent fascinating dynamical puzzles due to their significant eccentricities and orbital misalignments. In particular, HAT-P-11 b is on a close-in orbit that tides should have circularized well within the age of the system. Here we propose a two-step dynamical process that can reproduce all intriguing aspects of the

Qier An, Tiger Lu, G. Mirek Brandt, Timothy D Brandt

Planet-star obliquity and planet-planet ]mutual inclination encode a planetary system's dynamical history, but both of their values are hard to measure for misaligned systems with close-in companions. HAT-P-11 is a K4 star with two known planets: a close-in, misaligned super-Neptune with a $\approx$5-day orbit, and an outer super-Jupiter with a $\approx$10-y

Aditya Ramamoorthy, Ruoyu Meng, Vrinda S. Girimaji

Within distributed learning, workers typically compute gradients on their assigned dataset chunks and send them to the parameter server (PS), which aggregates them to compute either an exact or approximate version of $\nabla L$ (gradient of the loss function $L$). However, in large-scale clusters, many workers are slower than their promised speed or even fai

Lawford Hatcher

We give an elementary new proof of the hot spots conjecture for L-shaped domains. This result, in addition to a new eigenvalue inequality, allows us to locate the hot spots in Swiss cross translation surfaces. We then prove, in several cases, that first mixed Dirichlet-Neumann eigenfunctions of the Laplacian on L-shaped domains also have no interior critical

Maxine Perroni-Scharf, Zachary Ferguson, Thomas Butrille, Carlos Portela

Triply periodic minimal surfaces (TPMS) are a class of metamaterials with a variety of applications and well-known primitive morphologies. We present a new method for discovering novel microscale TPMS structures with exceptional energy-dissipation capabilities, achieving double the energy absorption of the best existing TPMS primitive structure. Our approach

Agustin J. Lapi, Blas J. Irigoyen Gimenez, Miqueas E. Gamero, Claudio R. Chavez Blanco

We present a candidate sensor for future spectroscopic applications, such as a Stage-5 Spectroscopic Survey Experiment or the Habitable Worlds Observatory. This type of charge-coupled device (CCD) sensor features multiple in-line amplifiers at its output stage allowing multiple measurements of the same charge packet, either in each amplifier or in the differ

Grégoire Pichard, Desiree Lim, Etienne Bloch, Julien Vaneecloo

We report on the trapping of single rubidium atoms in large arrays of optical tweezers comprising up to 2088 sites in a cryogenic environment at 6 K. Our approach relies on the use of microscope objectives that are in-vacuum but at room temperature, in combination with windowless thermal shields into which the objectives are protruding to ensure a cryogenic

Yuri V. Grats, Pavel Spirin

We develop precise formulation for the effects of vacuum polarization near a pointlike source with a zero-range ($\delta$-like) potential in three spatial dimensions. There are different ways of introducing $\delta$-interaction in the framework of quantum theory. We discuss the approach based on the concept of self-adjoint extensions of densely defined symme

Polezhaev Ignat, Goncharenko Igor, Iurina Natalya

In this paper, we present a novel methodology we call MDS-ViTNet (Multi Decoder Saliency by Vision Transformer Network) for enhancing visual saliency prediction or eye-tracking. This approach holds significant potential for diverse fields, including marketing, medicine, robotics, and retail. We propose a network architecture that leverages the Vision Transfo

Mamuli Zakradze, Zaza Tabagari, Nana Koblishvili, Tinatin Davitashvili

In the present paper, an algorithm for the numerical solution of the external Dirichlet generalized harmonic problem for a sphere by the method of probabilistic solution (MPS) is given, where generalized indicates that a boundary function has a finite number of first kind discontinuity curves. The algorithm consists of the following main stages: (1) the tran

Han Wang, Sihong He, Zhili Zhang, Fei Miao

We explore a Federated Reinforcement Learning (FRL) problem where $N$ agents collaboratively learn a common policy without sharing their trajectory data. To date, existing FRL work has primarily focused on agents operating in the same or ``similar" environments. In contrast, our problem setup allows for arbitrarily large levels of environment heterogeneity.

Robert Johansson

This paper introduces an interdisciplinary framework called Machine Psychology, which merges principles from operant learning psychology with a specific Artificial Intelligence model, the Non-Axiomatic Reasoning System (NARS), to enhance Artificial General Intelligence (AGI) research. The core premise of this framework is that adaptation is crucial to both b

Eloi Moliner, Sebastian Braun, Hannes Gamper

Audio domain transfer is the process of modifying audio signals to match characteristics of a different domain, while retaining the original content. This paper investigates the potential of Gaussian Flow Bridges, an emerging approach in generative modeling, for this problem. The presented framework addresses the transport problem across different distributi

W. Asztalos, Y. Torun, S. Bidhar, F. Pellemoine

Increase of primary beam power for neutrino beam-lines leads to a reduced lifespan for production targets. New concepts for robust targets are emerging from the field of High Power Targetry (HPT); one idea being investigated by the HPT R&D Group at Fermilab is an electrospun nanofiber target. As part of their evaluation, samples with different densities were

Nicolas Dupuis, Luca Buratti, Sanjay Vishwakarma, Aitana Viudes Forrat

Code Large Language Models (Code LLMs) have emerged as powerful tools, revolutionizing the software development landscape by automating the coding process and reducing time and effort required to build applications. This paper focuses on training Code LLMs to specialize in the field of quantum computing. We begin by discussing the unique needs of quantum com

P. Djorwé, A. H. Abdel-Aty, K. S. Nisar, S. G. Nana Engo

We propose a scheme to generate robust optomechanical entanglement. This scheme is based on a Backward Stimulated Brillouin Scattering (BSBS) process, which is hosted within an optomechanical structure. Our benchmark system consists of an acoustic (mechanical) mode coupled to two optical modes through the BSBS (radiation pressure) process. For a moderate val

Vincent A. Cicirello

We report on experiments with the ziggurat algorithm for generating Gaussian distributed random numbers. The study utilizes our open source Java implementation that was introduced originally for Java 11 at a time when the Java API only provided the much slower polar method. Our Java implementation of the ziggurat algorithm is a port of the GNU Scientific Lib

Tugba Akinci D'Antonoli, Lucas K. Berger, Ashraya K. Indrakanti, Nathan Vishwanathan

Since the introduction of TotalSegmentator CT, there is demand for a similar robust automated MRI segmentation tool that can be applied across all MRI sequences and anatomic structures. In this retrospective study, a nnU-Net model (TotalSegmentator) was trained on MRI and CT examinations to segment 80 anatomic structures relevant for use cases such as organ

Adem B. Dalkılıç

The problem of modeling ropes arises in many applications, including providing haptic feedback to surgeons who are using surgical robots to realign the distal and proximal ends of split bones. Here, we consider a simplified, 2D variant of the haptic feedback estimation problem and discuss how visibility decompositions greatly simplify the problem. Then, we i

Jonas Heinzmann, Pietro Carrara, Chenyi Luo, Manav Manav

In the context of the Damage Mechanics Challenge, we adopt a phase-field model of brittle fracture to blindly predict the behavior up to failure of a notched three-point-bending specimen loaded under mixed-mode conditions. The beam is additively manufactured using a geo-architected gypsum based on the combination of bassanite and a water-based binder. The ca

W. Asztalos, Y. Torun, S. Bidhar, F. Pellemoine

High Power Targetry (HPT) R&D is critical in the context of increasing beam intensity and energy for next generation accelerators. Many target concepts and novel materials are being developed and tested for their ability to withstand extreme beam environments; the HPT R&D Group at Fermilab is developing an electrospun nanofiber material for this purpose. The

Srdan Milic, Igor Krois

A method for increasing efficiency of radio frequency (RF) amplifier employing laterally diffused metal oxide semiconductor (LDMOS) transistors coupled to an RF exciter depending on the emission mode of modulated RF input signals generated by exciter, if exciter output signal is of a type where modulated RF signals do not have continuously varying envelope,

Aleksei Gorshkov

In the paper we study the 2D div-curl problem in the exterior domain which models the flow with given vorticity, divergency, boundary condition at infinity, and Dirichlet condition on the solid surface. We will find the relations on vorticity and divergence for uniqueness solvability of the problem and deduce the explicit formula.

Peng Wang, Songshuo Lu, Yaohua Tang, Sijie Yan

We present a generative dialogue system capable of operating in a full-duplex manner, allowing for seamless interaction. It is based on a large language model (LLM) carefully aligned to be aware of a perception module, a motor function module, and the concept of a simple finite state machine (called neural FSM) with two states. The perception and motor funct