December 2023 arXiv papers — page 107

Mi Kyoung Kim

This study aims to develop a data driven system to enhance the analysis and improvement of user experiences in interior spaces, acknowledging the significant impact of design on individuals health, productivity, and quality of life.

Florian U. Bernlochner, Michele Papucci, Dean J. Robinson

We derive the $\Lambda_b \to \Lambda_c$ form factors for the Standard Model and beyond at second order in Heavy Quark Effective Theory (HQET), applying the recently-proposed Residual Chiral Expansion (RCE) to reduce the set of unknown subsubleading hadronic functions to a single, highly-constrained function, that is fully determined by hadron mass parameters

Augusto Nieto-Barthaburu

We present a model of a forecaster who must predict the future value of a variable that depends on an exogenous state and on the intervention of a policy-maker. We investigate the incentives of the forecaster to acquire costly private information to use in his forecasting exercise. We show that the policy-making environment plays a crucial role in determinin

Sofia Z. Sheikh, Wael Farah, Alexander W. Pollak, Andrew

FRB 20220912A is a repeating Fast Radio Burst (FRB) that was discovered in Fall 2022 and remained highly active for several months. We report the detection of 35 FRBs from 541 hours of follow-up observations of this source using the recently refurbished Allen Telescope Array, covering 1344 MHz of bandwidth primarily centered at 1572 MHz. All 35 FRBs were det

Sidong Feng, Mingyue Yuan, Jieshan Chen, Zhenchang Xing

Wireframing is a critical step in the UI design process. Mid-fidelity wireframes offer more impactful and engaging visuals compared to low-fidelity versions. However, their creation can be time-consuming and labor-intensive, requiring the addition of actual content and semantic icons. In this paper, we introduce a novel solution WireGen, to automatically gen

Liudmyla Kryvonos

This paper collects open problems that were presented at the ``Hausdorff Geometry of Polynomials" workshop held on July 10-14, 2023 in Sofia, Bulgaria.

Jayoung Kim, Yehjin Shin, Jeongwhan Choi, Hyowon Wi

Structured data, which constitutes a significant portion of existing data types, has been a long-standing research topic in the field of machine learning. Various representation learning methods for tabular data have been proposed, ranging from encoder-decoder structures to Transformers. Among these, Transformer-based methods have achieved state-of-the-art p

Claire E. Murray, Sten Hasselquist, Joshua E. G. Peek, Christina Willecke Lindberg

The structure and dynamics of the star-forming disk of the Small Magellanic Cloud (SMC) have long confounded us. The SMC is widely used as a prototype for galactic physics at low metallicity, and yet we fundamentally lack an understanding of the structure of its interstellar medium (ISM). In this work, we present a new model for the SMC by comparing the kine

François Michel, Olivier Bonaventure

The SSH protocol was designed in the late nineties to cope with the security problems of the telnetf family of protocols. It brought authentication and confidentiality to remote access protocols and is now widely used. Almost 30 years after the initial design, we revisit SSH in the light of recent protocols including QUIC, TLS 1.3 and HTTP/3. We propose, imp

Leandro Candido, Marek Cúth, Ondřej Smetana

We exhibit a new approach to the proofs of the existence of a large family of almost isometric ideals in nonseparable Banach spaces and existence of a large family of almost isometric local retracts in metric spaces. Our approach also implies the existence of a large family of nontrivial projections on every dual of a nonseparable Banach space. We prove thre

David Cifuentes-Núñez, Pedro Montealegre, Ivan Rapaport

Augustine et al. [DISC 2022] initiated the study of distributed graph algorithms in the presence of Byzantine nodes in the congested clique model. In this model, there is a set $B$ of Byzantine nodes, where $|B|$ is less than a third of the total number of nodes. These nodes have complete knowledge of the network and the state of other nodes, and they conspi

Harry C. P. Kendell, Giacomo Ferranti, Carrie A. Weidner

We demonstrate a method for encoding Gottesman-Kitaev-Preskill (GKP) error-correcting qubits with single ultracold atoms trapped in individual sites of a deep optical lattice. Using quantum optimal control protocols, we demonstrate the generation of GKP qubit states with 10 dB squeezing, which is the current minimum allowable squeezing level for use in surfa

Jehan Yang, Kent Shibata, Douglas Weber, Zackory Erickson

High-density electromyography (HDEMG) can detect myoelectric activity as control inputs to a variety of electronically-controlled devices. Furthermore, HDEMG sensors may be built into a variety of clothing, allowing for a non-intrusive myoelectric interface that is integrated into a user's routine. In our work, we introduce an easily-producible HDEMG device

Xiaotong Zhang, Jinger Chong, Kamal Youcef-Toumi

Perception serves as a critical component in the functionality of autonomous agents. However, the intricate relationship between perception metrics and robotic metrics remains unclear, leading to ambiguity in the development and fine-tuning of perception algorithms. In this paper, we introduce a methodology for quantifying this relationship, taking into acco

Thomas Randall, Tyler Allen, Rong Ge

Word2Vec remains one of the highly-impactful innovations in the field of Natural Language Processing (NLP) that represents latent grammatical and syntactical information in human text with dense vectors in a low dimension. Word2Vec has high computational cost due to the algorithm's inherent sequentiality, intensive memory accesses, and the large vocabularies

Issifu Iddrisu, Sinan Gezici

The position estimation problem based on received power measurements is investigated for visible light systems in the presence of luminous flux degradation of light emitting diodes (LEDs). When the receiver is unaware of this degradation and performs position estimation accordingly, there exists a mismatch between the true model and the assumed model. For th

Francisco Zelaya-Arrazabal, Hector Pulgar-Painemal, Jingzi Liu, Horacio Silva-Saravia

This paper explores the novel concept of damping controller coordination, which aims to minimize the Total Action metric by identifying an optimal switching combination (on/off) of these controllers. The metric is rooted in power system physics, capturing oscillation energy associated with all synchronous generators in the grid. While coordination has shown

Metod Saniga, Frédéric Holweck, Colm Kelleher, Axel Muller

Split Cayley hexagons of order two are distinguished finite geometries living in the three-qubit symplectic polar space in two different forms, called classical and skew. Although neither of the two yields observable-based contextual configurations of their own, {\it classically}-embedded copies are found to fully encode contextuality properties of the most

Can Chen, Xu-Wen Wang, Yang-Yu Liu

The stability of ecological systems is a fundamental concept in ecology, which offers profound insights into species coexistence, biodiversity, and community persistence. In this article, we provide a systematic and comprehensive review on the theoretical frameworks for analyzing the stability of ecological systems. Notably, we survey various stability notio

Daniela Pérez, Mário Raia Neto

We find a new exact solution to Einstein field equations that represents a cosmological wormhole embedded in a flat Friedmann-Lema\^itre-Robertson-Walker universe. The new metric is a generalization of a previous cosmological wormhole solution found by Kim. We explicitly show that the flaring out condition is satisfied at the throat at all cosmic times; in a

Satoru Hayami, Ryota Yambe, Hiroaki Kusunose

We analyze the condition of photo-induced atomic-scale chirality and magnetic toroidal moment. By performing a high-frequency expansion in the Floquet formalism, we derive an effective static model Hamiltonian from the spinful $s$-$p$ hybridized model of a single atom interacting with an electromagnetic wave with a particular polarization. The lowest-order a

Pablo Ochoa, Ariel Salort

In this paper, we consider different versions of the classical Hopf's boundary lemma in the setting of the fractional $p-$Laplacian for $p \geq 2$. We start by providing for a new proof to a Hopf's lemma based on comparison principles. Afterwards, we give a Hopf's result for sign-changing potential describing the behavior of the fractional normal derivative

Mike Ludkovski, Saad Mouti, Glen Swindle

We consider the construction of renewable portfolios targeting specified carbon-free (CFE) hourly performance scores. We work in a probabilistic framework that uses a collection of simulation scenarios and imposes probability constraints on achieving the desired CFE score. In our approach there is a fixed set of available CFE generators and a given load cust

Greta Galliani, Piercesare Secchi, Francesca Ieva

Accurately estimating Origin-Destination (OD) matrices is a topic of increasing interest for efficient transportation network management and sustainable urban planning. Traditionally, travel surveys have supported this process; however, their availability and comprehensiveness can be limited. Moreover, the recent COVID-19 pandemic has triggered unprecedented

Zabir Al Nazi, Wei Peng

The deployment of large language models (LLMs) within the healthcare sector has sparked both enthusiasm and apprehension. These models exhibit the remarkable capability to provide proficient responses to free-text queries, demonstrating a nuanced understanding of professional medical knowledge. This comprehensive survey delves into the functionalities of exi

Shawn Shan, Stanley Wu, Haitao Zheng, Ben Y. Zhao

Recent work proposed a new mechanism to remove protective perturbation added by Glaze in order to again enable mimicry of art styles from images protected by Glaze. Despite promising results shown in the original paper, our own tests with the authors' code demonstrated several limitations of the proposed purification approach. The main limitations are 1) pur

Antonio J. G. Busson, Rafael Rocha, Rennan Gaio, Rafael Miceli

This work proposes the Two-headed DragoNet, a Transformer-based model for hierarchical multi-label classification of financial transactions. Our model is based on a stack of Transformers encoder layers that generate contextual embeddings from two short textual descriptors (merchant name and business activity), followed by a Context Fusion layer and two outpu

Juan Alvarado, Jan Ramon, Yuyi Wang

This work has two contributions. The first one is extending the Large Deviation Principle for uniform hyper-graphons from Lubetzky and Zhao \cite{lubetzky2015replica} to the multi-relational setting where each hyper-graphon can have different arities. This extension enables the formulation of the most typical possible world in Relational Probabilistic Logic

Joseph Sobek, Jose R. Medina Inojosa, Betsy J. Medina Inojosa, S. M. Rassoulinejad-Mousavi

Artificial intelligence-enhanced identification of organs, lesions, and other structures in medical imaging is typically done using convolutional neural networks (CNNs) designed to make voxel-accurate segmentations of the region of interest. However, the labels required to train these CNNs are time-consuming to generate and require attention from subject mat

Rüdiger Schack

In the QBist approach to quantum mechanics, a measurement is an action an agent takes on the world external to herself. A measurement device is an extension of the agent and both measurement outcomes and their probabilities are personal to the agent. According to QBism, nothing in the quantum formalism implies either that the quantum state assignments of two

Chi Zhang, Peijun Sang, Yingli Qin

We propose a novel test procedure for comparing mean functions across two groups within the reproducing kernel Hilbert space (RKHS) framework. Our proposed method is adept at handling sparsely and irregularly sampled functional data when observation times are random for each subject. Conventional approaches, which are built upon functional principal componen

Alexander Cicchino, Siva Nadarajah

Provable nonlinear stability bounds the discrete approximation and ensures that the discretization does not diverge. For high-order methods, discrete nonlinear stability and entropy stability, have been successfully implemented for discontinuous Galerkin (DG) and residual distribution schemes, where the stability proofs depend on properties of L2-norms. In t

Kristen Such, Harel Biggie, Christoffer Heckman

Degraded rangelands undergo continual shifts in the appearance and distribution of plant life. The nature of these changes however is subtle: between seasons seedlings sprout up and some flourish while others perish, meanwhile, over multiple seasons they experience fluctuating precipitation volumes and can be grazed by livestock. The nature of these conditio

Chen Yang, Jeremy Forest, Matthew Einhorn, Thomas A. Cleland

Animal behavior analysis plays a crucial role in various fields, such as life science and biomedical research. However, the scarcity of available data and the high cost associated with obtaining a large number of labeled datasets pose significant challenges. In this research, we propose a novel approach that leverages instance segmentation-based transfer lea

Yimin Zhong, Kui Ren, Olof Runborg, Richard Tsai

The implicit boundary integral method (IBIM) provides a framework to construct quadrature rules on regular lattices for integrals over irregular domain boundaries. This work provides a systematic error analysis for IBIMs on uniform Cartesian grids for boundaries with different degree of regularities. We first show that the quadrature error gains an addition

Antonio J. G. Busson, Rennan Gaio, Rafael H. Rocha, Francisco Evangelista

Saturn is an innovative platform that assists Foundation Model (FM) building and its integration with IT operations (Ops). It is custom-made to meet the requirements of data scientists, enabling them to effectively create and implement FMs while enhancing collaboration within their technical domain. By offering a wide range of tools and features, Saturn stre

R. R. Marcelo Gregorio, N. J. C. Spooner, F. Dastgiri, A. C. Ezeribe

In the field of directional dark matter experiments, SF6 has emerged as an ideal target gas. A critical challenge with this gas, and with other proposed gases, is the effective removal of contaminant gases. This includes radon which produce unwanted background events, but also common pollutants such as water, oxygen, and nitrogen, which can capture ionisatio

Sergi Burniol Clotet

We study the closure of horocycles on rank 1 nonpositively curved surfaces with finitely generated fundamental group. Each horocycle is closed or dense on a certain subset of the unit tangent bundle. In fact, we classify each half-horocycle in terms of the associated geodesic rays. We also determine the nonwandering set of the horocyclic flow and characteriz

Bo Tang, Elias B. Khalil

The end-to-end predict-then-optimize framework, also known as decision-focused learning, has gained popularity for its ability to integrate optimization into the training procedure of machine learning models that predict the unknown cost (objective function) coefficients of optimization problems from contextual instance information. Naturally, most of the pr

Yi Huang, Sankar Das Sarma

Motivated by recent experiments on Si/SiGe quantum wells with a co-design of high electron mobility and large valley splitting [B. Paquelet Wuetz, et al., Nature Communications 14, 1385 (2023); D. D. Esposti, et al., arXiv:2309.02832], suitable for a Si-based spin qubit quantum computing platform, we examine the role of disorder by theoretically calculating

Christian Bean, Émile Nadeau, Jay Pantone, Henning Ulfarsson

We prove that any class of permutations defined by avoiding a partially ordered pattern (POP) with height at most two has a regular insertion encoding and thus has a rational generating function. Then, we use Combinatorial Exploration to find combinatorial specifications and generating functions for hundreds of other permutation classes defined by avoiding a

Julio A. do Nascimento, Adam Kerrigan, S. A. Cavill, Phil J. Hasnip

We present a methodology based on the calculation of the inelastic scattering from magnons via the spin scattering function in confined geometries such as thin films using a second quantization formalism, for both ferromagnetic and antiferromagnetic materials. The case studies are chosen with an aim to demonstrate the effects of film thickness and crystal or

Dimitrios Mitsotakis

Considered herein is a modified Newton method for the numerical solution of nonlinear equations where the Jacobian is approximated using a complex-step derivative approximation. We show that this method converges for sufficiently small complex-step values, which need not be infinitesimal. Notably, when the individual derivatives in the Jacobian matrix are ap

V. V. Gorokhovik

The paper deals with partial and weak preference relations defined on infinite-dimensional vector spaces and compatible with algebraic operations. By a partial preference we mean an asymmetric and transitive binary relation, while a weak preference is such a partial preference for which the indifference relation corresponding it is transitive (an indifferenc

Daniel Dominguez-Vazquez, Sergio A. Castiblanco-Ballesteros, Gustaaf B. Jacobs, Daniel M. Tartakovsky

Eulerian-Lagrangian models of particle-laden (multiphase) flows describe fluid flow and particle dynamics in the Eulerian and Lagrangian frameworks respectively. Regardless of whether the flow is turbulent or laminar, the particle dynamics is stochastic because the suspended particles are subjected to random forces. We use a polynomial chaos expansion (PCE),

Shawn Westerdale

As liquid argon (LAr) detectors are made at progressively larger sizes, accurate models of LAr optical properties become increasingly important for simulating light transport, understanding signals, and developing analyses. The refractive index, group velocity, and Rayleigh scattering length are particularly important for vacuum ultraviolet (VUV) and visible

Alejandro Duque, Abdullah Syed, Kastan V. Day, Matthew J. Berry

The recent development of large language models (LLMs) with multi-billion parameters, coupled with the creation of user-friendly application programming interfaces (APIs), has paved the way for automatically generating and executing code in response to straightforward human queries. This paper explores how these emerging capabilities can be harnessed to faci

Juanita Duque-Rosero, Rachel Pries

Suppose $C$ is a cyclic Galois cover of the projective line branched at the three points $0$, $1$, and $\infty$. Under a mild condition on the ramification, we determine the structure of the graded Lie algebra of the lower central series of the fundamental group of $C$ in terms of a basis which is well-suited to studying the action of the absolute Galois gro

Yanni Georghiades, Rajesh Mishra, Karl Kreder, Sriram Vishwanath

We study the incentives behind double-spend attacks on Nakamoto-style Proof-of-Work cryptocurrencies. In these systems, miners are allowed to choose which transactions to reference with their block, and a common strategy for selecting transactions is to simply choose those with the highest fees. This can be problematic if these transactions originate from an

Wenchao Xu, Tamara Šumarac, Emily H. Qiu, Matthew L. Peters

Attempts to create quantum degenerate gases without evaporative cooling have been pursued since the early days of laser cooling, with the consensus that polarization gradient cooling (PGC, also known as "optical molasses") alone cannot reach condensation. In the present work, we report that simple PGC can generate a small Bose-Einstein condensate (BEC) insid

Wenjie Mei, Muhammad Nadeem, MirSaleh Bahavarnia, Ahmad F. Taha

System identification through learning approaches is emerging as a promising strategy for understanding and simulating dynamical systems, which nevertheless faces considerable difficulty when confronted with power systems modeled by differential-algebraic equations (DAEs). This paper introduces a neural network (NN) framework for effectively learning and sim

Laxman Dhulipala, George Z. Li, Quanquan C. Liu

Recent work by Dhulipala et al. \cite{DLRSSY22} initiated the study of the $k$-core decomposition problem under differential privacy via a connection between low round/depth distributed/parallel graph algorithms and private algorithms with small error bounds. They showed that one can output differentially private approximate $k$-core numbers, while only incu

Reese Kneeland, Jordyn Ojeda, Ghislain St-Yves, Thomas Naselaris

The release of large datasets and developments in AI have led to dramatic improvements in decoding methods that reconstruct seen images from human brain activity. We evaluate the prospect of further improving recent decoding methods by optimizing for consistency between reconstructions and brain activity during inference. We sample seed reconstructions from

I. Seidu, E. Nyarko, S. Iddi, E. Ranganai

In this article, a heuristic approach is used to determined the best approximate distribution of $\dfrac{Y_1}{Y_1 + Y_2}$, given that $Y_1,Y_2$ are independent, and each of $Y_1$ and $Y$ is distributed as the $\mathcal{F}$-distribution with common denominator degrees of freedom. The proposed approximate distribution is subject to graphical comparisons and di

Tiziano De Angelis, Fabien Gensbittel, Stéphane Villeneuve

We construct Nash equilibria in feedback form for a class of two-person stochastic games of singular control with absorption, arising from a stylized model for corporate finance. More precisely, the paper focusses on a strategic dynamic game in which two financially-constrained firms operate in the same market. The firms distribute dividends and are faced wi

Tattwamasi Amrutam, Ilan Hirshberg, Apurva Seth

Given a unital $C(X)$-algebra $A$ discrete group $\Gamma$ and an action $\alpha: \Gamma\to \text{aut}(A)$ which leaves $C(X)$ invariant and such that $C(X)\rtimes_{\alpha,r} \Gamma$ is simple, and a $2$-cocycle $\omega$, we obtain a bijective correspondence between maximal $\Gamma$-invariant ideals of $A$ and maximal ideals in $A\rtimes_{\alpha,\omega,r} \Ga

Amelia Drew, Tomasz Kinowski, E. P. S. Shellard

In this paper, we perform an investigation into the effect of the string radius of curvature $R_\mathrm{\,Gaussian}$ on the magnitude and relative magnitude of the massive and massless radiation from axion (global) string configurations, motivated by qualitative observations from string network simulations. We construct initial conditions from travelling wav

Alexis Tzelilis

When do droplets merge and when do they bounce? Over the last 10 years, advances in experimental techniques, such as high-speed cameras, have enabled us to make important discoveries on how the dynamics of thin gas films can influence the behaviour of liquid droplets. However, the exact physical mechanisms governing these phenomena are a subject of debate, l

Kostantinos Papadamou, Jay Patel, Jeremy Blackburn, Philipp Jovanovic

This paper presents a large-scale analysis of the cryptocurrency community on Reddit, shedding light on the intricate relationship between the evolution of their activity, emotional dynamics, and price movements. We analyze over 130M posts on 122 cryptocurrency-related subreddits using temporal analysis, statistical modeling, and emotion detection. While /r/

Paula Benaglia

In this article, I will present some figures and milestones of the written production of the Instituto Argentino de Radioastronomia (IAR), as well as a personal review of the scientific achievements carried out in recent years by the researchers working at the IAR. I will also briefly describe the scientific objectives of the IAR's flagship project, the Mult

Sulong Zhou

This dissertation will combine new tools and methodologies to answer pressing questions regarding inundation area and hurricane events in complex, heterogeneous changing environments. In addition to remote sensing approaches, citizen science and machine learning are both emerging fields that harness advancing technology to answer environmental management and

Marcel Rodekamp, Michael Engelhardt, Jeremy R. Green, Stefan Krieg

The second Mellin moments $\langle x\rangle$ of the nucleon's unpolarized, polarized, and transversity parton distribution functions (PDFs) are computed. Two lattice QCD ensembles at the physical pion mass are used: these were generated using a tree-level Symanzik-improved gauge action and 2+1 flavour tree-level improved Wilson Clover fermions coupling via 2

Tianyu Zhan

When designing confirmatory Phase 3 studies, one usually evaluates one or more efficacious and safe treatment option(s) based on data from previous studies. However, several retrospective research articles reported the phenomenon of ``diminished treatment effect in Phase 3'' based on many case studies. Even under basic assumptions, it was shown that the comm

Jingdi Chen, Hanhan Zhou, Yongsheng Mei, Gina Adam

Many cybersecurity problems that require real-time decision-making based on temporal observations can be abstracted as a sequence modeling problem, e.g., network intrusion detection from a sequence of arriving packets. Existing approaches like reinforcement learning may not be suitable for such cybersecurity decision problems, since the Markovian property ma

Daniel F. Urrego, Gerard J. Machado, Juan P. Torres

We demonstrate in a {\it proof-of-concept} experiment spectral-domain optical coherence tomography where steering of the optical beam that probes the sample in a transverse scan does not make use of any mechanical element. Steering is done with the help of a spatial light modulator, that introduces a spatially-dependent phase between the two orthogonal polar

Amin Yousefpour, Zahra Zanjani Foumani, Mehdi Shishehbor, Carlos Mora

In this paper we introduce GP+, an open-source library for kernel-based learning via Gaussian processes (GPs) which are powerful statistical models that are completely characterized by their parametric covariance and mean functions. GP+ is built on PyTorch and provides a user-friendly and object-oriented tool for probabilistic learning and inference. As we d

Henrik Axelsen, Sebastian Axelsen, Valdemar Licht, Jason Potts

Managing rapidly growing decentralized gaming communities brings unique challenges at the nexus of cultural economics and technology. This paper introduces a streamlined analytical framework that utilizes Large Language Models (LLMs), in this instance open-access generative pre-trained transformer (GPT) models, offering an efficient solution with deeper insi

Samuel Bartlett-Tisdall, Christopher P. Herzog, Vladimir Schaub

We use the numerical conformal bootstrap to study boundary quantum electrodynamics, the theory of a four dimensional photon in a half space coupled to charged conformal matter on the boundary. This system is believed to be a boundary conformal field theory with an exactly marginal coupling corresponding to the strength of the interaction between the photon a

Muqing Zheng, Bo Peng, Ang Li, Xiu Yang

Hybrid quantum-classical approaches offer potential solutions to quantum chemistry problems, yet they often manifest as constrained optimization problems. Here, we explore the interconnection between constrained optimization and generalized eigenvalue problems through the Unitary Coupled Cluster (UCC) excitation generators. Inspired by the generator coordina

Pedro C. S. Costa, Dong An, Ryan Babbush, Dominic Berry

The solution of linear systems of equations is the basis of many other quantum algorithms, and recent results provided an algorithm with optimal scaling in both the condition number $\kappa$ and the allowable error $\epsilon$ [PRX Quantum \textbf{3}, 040303 (2022)]. That work was based on the discrete adiabatic theorem, and worked out an explicit constant fa

Md Abrar Jahin, Subrata Talapatra

This research delves into Musculoskeletal Disorder (MSD) risk factors, using a blend of Natural Language Processing (NLP) and mode-based ranking. The aim is to refine understanding, classification, and prioritization for focused prevention and treatment. Eight NLP models are evaluated, combining pre-trained transformers, cosine similarity, and distance metri

Helge Kristian Jenssen

We consider one-dimensional self-similar solutions to the isentropic Euler system when the initial data are at vacuum to the left of the origin. For $x>0$ the initial velocity and sound speed are of form $u_0(x)=u_+x^{1-\lambda}$ and $c_0(x)=c_+x^{1-\lambda}$, for constants $u_+\in\RR$, $c_+>0$, $\lambda\in\RR$. We analyze the resulting solutions in terms of

Christian Velten, Brett Bell, Wolfgang A. Tomé

We investigated cellular distribution of a tumor-specific gadolinium chelate in 4T1 and U87 cancer cells with the goal to generate more realistic geometries for Monte Carlo simulations of radiation interaction with nanoparticles in cells. Cells were exposed to the agent in-vitro for 30 minutes to 72 hours before being fixed and imaged using transmission elec

Christian Velten, Megi Gjini, N. Patrik Brodin, Wolfgang A. Tomé

Purpose: To derive kinetic equations for multi-compartmental contrast agent distribution from first principles and apply it to two and three compartments for Gd-EOB-DTPA using low time resolution human liver DCE-MRI data. Methods: The continuity and diffusion equation were combined and used to derive a general form for differential equations governing multi-

M. A. Rodríguez-García, F. E. Becerra

Phase estimation plays a central role in communications, sensing, and information processing. Quantum correlated states, such as squeezed states, enable phase estimation beyond the shot-noise limit, and in principle approach the ultimate quantum limit in precision, when paired with optimal quantum measurements. However, physical realizations of optimal quant

Alexander I. Bobenko, Tim Hoffmann, Andrew O. Sageman-Furnas

In 1883, Darboux gave a local classification of isothermic surfaces with one family of planar curvature lines using complex analytic methods. His choice of real reduction cannot contain tori. We classify isothermic tori with one family of planar curvature lines. They are found in the second real reduction of Darboux's description. We give explicit theta func

Yinmin Zhang, Jie Liu, Chuming Li, Yazhe Niu

Offline-to-online Reinforcement Learning (O2O RL) aims to improve the performance of offline pretrained policy using only a few online samples. Built on offline RL algorithms, most O2O methods focus on the balance between RL objective and pessimism, or the utilization of offline and online samples. In this paper, from a novel perspective, we systematically s

Noah Glennon, Nathan Musoke, Ethan O. Nadler, Chanda Prescod-Weinstein

We explore how dynamical friction in an ultralight dark matter (ULDM) background is affected by dark matter self-interactions. We calculate the force of dynamical friction on a point mass moving through a uniform ULDM background with self-interactions, finding that the force of dynamical friction vanishes for sufficiently strong repulsive self-interactions.

Matias D. Cattaneo, Fang Han, Zhexiao Lin

In two influential contributions, Rosenbaum (2005, 2020) advocated for using the distances between component-wise ranks, instead of the original data values, to measure covariate similarity when constructing matching estimators of average treatment effects. While the intuitive benefits of using covariate ranks for matching estimation are apparent, there is n

Rene Richard, Nabil Belacel

In scenarios where obtaining real-time labels proves challenging, conventional approaches may result in sub-optimal performance. This paper presents an optimal strategy for streaming contexts with limited labeled data, introducing an adaptive technique for unsupervised regression. The proposed method leverages a sparse set of initial labels and introduces an

Huong Luu, Marek Chrobak

Analysis of pipe networks involves computing flow rates and pressure differences on pipe segments in the network, given the external inflow/outflow values. This analysis can be conducted using iterative methods, among which the algorithms of Hardy Cross and Newton-Raphson have historically been applied in practice. In this note, we address the mathematical a

R. Teal Witter, Lucas Rosenblatt

The open streets initiative "opens" streets to pedestrians and bicyclists by closing them to cars and trucks. The initiative, adopted by many cities across North America, increases community space in urban environments. But could open streets also make cities safer and less congested? We study this question by framing the choice of which streets to open as a

Sam Showalter, Alex Boyd, Padhraic Smyth, Mark Steyvers

Given a pre-trained classifier and multiple human experts, we investigate the task of online classification where model predictions are provided for free but querying humans incurs a cost. In this practical but under-explored setting, oracle ground truth is not available. Instead, the prediction target is defined as the consensus vote of all experts. Given t

Z. Haba

We study the quantum field theory (QFT) of a scalar field in the Schr\"odinger picture in the functional formulation. We derive a formula for the evolution kernel in a flat expanding metric. We discuss a transition between Riemannian and pseudoRiemannian metrics (signature inversion). We express the real time Schr\"odinger evolution by the Brownian motion .

R. Lucchesi, P. Jablonka, Á. Skúladóttir, C. Lardo

We present our analysis of VLT/UVES and X-shooter observations of six very metal-poor stars, including four stars at [Fe/H]$\approx$$-3$ in the Fornax and Carina dwarf spheroidal (dSph) galaxies. To date, this metallicity range in these two galaxies has not yet been investigated fully, or at all in some cases. The chemical abundances of 25 elements are prese

F. A. da Silva Barbosa, J. M. Hoff da Silva

Guided by a conservative formulation in investigating the physical content of quantum fields, we explore non-standard Wigner classes of particles that could provide the basis for self-interaction models to dark matter. We critically contrast the analysis with long-standing constraints to non-standard Wigner classes in the literature to discuss the model's vi

Saskia Demulder, Dieter Lust, Thomas Raml

The distance conjecture diagnoses viable low-energy effective realisation of consistent theories of quantum gravity by examining their breakdown at infinite distance in their parameter space. At the same time, infinite distance points in parameter space are naturally intertwined with string dualities. We explore the implications of the distance conjecture wh

Domnique Monnet, Dominique Orban

We propose a multi-precision extension of the Quadratic Regularization (R2) algorithm that enables it to take advantage of low-precision computations, and by extension to decrease energy consumption during the solve. The lower the precision in which computations occur, the larger the errors induced in the objective value and gradient, as well as in all other

Caesnan M. G. Leditto, Angus Southwell, Behnam Tonekaboni, Gregory A. L. White

Predicting and analyzing global behaviour of complex systems is challenging due to the intricate nature of their component interactions. Recent work has started modelling complex systems using networks endowed with multiway interactions among nodes, known as higher-order networks. Simplicial complexes are a class of higher-order networks that have received s

Ali Ghadami, Mohammadreza Taghimohammadi, Mohammad Mohammadzadeh, Mohammad Hosseinipour

Robots' acceptability among humans and their sociability can be significantly enhanced by incorporating human-like reactions. Humans can react to environmental events very quickly and without thinking. An instance where humans show natural reactions is when they encounter a sudden and loud sound that startles or frightens them. During such moments, individua

Tao Han, Da Liu, Ian Low, Xing Wang

It has long been recognized that the scattering of electroweak particles at very high energies is dominated by vector boson fusion, which probes the origin of electroweak symmetry breaking and offers a unique window into the ultraviolet regime of the SM. Previous studies assume SM-like couplings and rely on the effective $W$ approximation (or electroweak par

Paivi Torma

Quantum geometry defines the phase and amplitude distances between quantum states. The phase distance is characterized by the Berry curvature and thus relates to topological phenomena. The significance of the full quantum geometry, including the amplitude distance characterized by the quantum metric, has started to receive attention in the last few years. Va

Yibo Xia, Lizhen Wang, Xiang Deng, Xiaoyan Luo

Synthesizing high-fidelity and emotion-controllable talking video portraits, with audio-lip sync, vivid expressions, realistic head poses, and eye blinks, has been an important and challenging task in recent years. Most existing methods suffer in achieving personalized and precise emotion control, smooth transitions between different emotion states, and the

Matija Tečer, Marco Di Liberto, Pietro Silvi, Simone Montangero

One dimensional confinement in waveguide Quantum Electrodynamics (QED) plays a crucial role to enhance light-matter interactions and to induce a strong quantum nonlinear optical response. In two or higher dimensional settings, this response is reduced since photons can be emitted within a larger phase space, opening the question whether strong photon-photon

Sergio Benvenuti, Riccardo Comi, Sara Pasquetti

We consider 3d N=2 non-abelian Hanany-Witten brane setups with chiral flavor symmetry. We propose that the associated field theories are quivers with improved bifundamentals, instead of standard bifundamentals. The improved bifundamental is a strongly coupled SCFT that carries one more U(1) global symmetry than the standard bifundamental. As a consequence, o

Renata Ferrero, Sebastián A. Franchino-Viñas, Markus B. Fröb, William C. C. Lima

We show that there exists a generalized, universal notion of the trace anomaly for theories which are not conformally invariant at the classical level. The definition is suitable for any regularization scheme and clearly states to what extent the classical equations of motion should be used, thus resolving existing controversies surrounding previous proposal

T. Cridge, L. A. Harland-Lang, R. S. Thorne

We present the MSHT20qed_an3lo parton distribution functions (PDFs). These result from the first global PDF analysis to combine QED and approximate N${}^3$LO (aN${}^3$LO) QCD corrections in the theoretical calculation of the PDF evolution and cross sections entering the fit. We examine the PDF impact, and find that the effect of QED is relatively mild in com

Danny Horta, Adrian M. Price-Whelan, David W. Hogg, Kathryn V. Johnston

Under the assumption of a simple and time-invariant gravitational potential, many Galactic dynamics techniques infer the Milky Way's mass and dark matter distribution from stellar kinematic observations. These methods typically rely on parameterized potential models of the Galaxy and must take into account non-trivial survey selection effects, because they m

S. Veronese, C. Vignali, P. Severgnini, G. A. Matzeu

We present a thorough study of the Changing-Look Active Galactic Nucleus (CL-AGN) Mrk 1018, utilizing an extensive dataset spanning optical, UV, and X-ray spectro-photometric data from 2005 to 2019. We analysed X-ray spectra and broad-band photometry, and performed optical-to-X-ray spectral energy distribution (SED) fitting to comprehend the observed changin

Noi Shitrit, Iair Arcavi

Massive (>~ 8 solar masses) stars are the progenitors of many astrophysical systems, yet key aspects of their structure and evolution are poorly understood. Asteroseismology has the potential to solve these open puzzles, however, sampling both the short period pulsations and long period beat patterns of massive stars poses many observational challenges. Grou