November 2024 arXiv papers — page 175

Yujin Kim, Daniel K. Park

Deterministic quantum computation with one qubit (DQC1) is of significant theoretical and practical interest due to its computational advantages in certain problems, despite its subuniversality with limited quantum resources. In this work, we introduce parameterized DQC1 as a quantum machine learning model. We demonstrate that the gradient of the measurement

Kun He, Guoliang Qiu, Xiaoming Sun

Sampling a random permutation with restricted positions, or equivalently approximating the permanent of a 0-1 matrix, is a fundamental problem in computer science, with several notable results achieved over the years. However, existing algorithms typically exhibit high computational complexity. Achieving the optimal running time remains elusive, even for non

Jordan Moradian, Michael Dubrovsky, Megha Sama, Pavel Korecky

In a study of 107 male Y Combinator founders, a surprising correlation between age-normalized testosterone and company stage was uncovered. Testosterone, a hormone associated with confidence, dominance, and drive, increased by 55.7% from pre-seed to seed funding, peaking at the Series B stage, where levels were 99.6% higher than pre-seed. After series B fund

Scott E. Meninger, Sergey K. Tolpygo

Moats in superconducting ground planes are used to trap magnetic flux away from sensitive parts of superconductor integrated circuits. We simulate the effect of magnetic flux trapped in moats on the operating margins of ac-biased SFQ shift registers (ShReg) with two ground planes for various congruent moat geometries, moat sizes, locations in the ShReg cells

Haiyan Xu, Zhigui Lin, Carlos Alberto Santos

An age-structured fish model with birth and harvesting pulses is established, where birth pulses are responsible for increasing the amount of fish due to the constant multiple placement of juveniles, and harvesting pulses describe the decrease of fish due to fishing activities. The principal eigenvalue as a threshold value depending on the harvesting and bir

Yiwei Dong, Tingjin Chu, Lele Zhang, Hadi Ghaderi

Effective models for analysing and predicting pedestrian flow are important to ensure the safety of both pedestrians and other road users. These tools also play a key role in optimising infrastructure design and geometry and supporting the economic utility of interconnected communities. The implementation of city-wide automatic pedestrian counting systems pr

Jeremy Avigad, Johan Commelin, Heather Macbeth, Adam Topaz

Interactive proof assistants make it possible for ordinary mathematicians to write definitions and theorems in a formal proof language, like a programming language, so that a computer can parse them and check them against the rules of a formal axiomatic foundation. This article describes the experience of working with a proof assistant and considers the impa

Yifan Wang, Xiaochen Yang, Fanqi Pu, Qingmin Liao

Monocular 3D object detection has attracted great attention due to simplicity and low cost. Existing methods typically follow conventional 2D detection paradigms, first locating object centers and then predicting 3D attributes via neighboring features. However, these methods predominantly rely on progressive cross-scale feature aggregation and focus solely o

Quan Long

Most methods in explainable AI (XAI) focus on providing reasons for the prediction of a given set of features. However, we solve an inverse explanation problem, i.e., given the deviation of a label, find the reasons of this deviation. We use a Bayesian framework to recover the ``true'' features, conditioned on the observed label value. We efficiently explain

Rina Bao, Erfan Darzi, Sheng He, Chuan-Heng Hsiao

Foundation models refer to artificial intelligence (AI) models that are trained on massive amounts of data and demonstrate broad generalizability across various tasks with high accuracy. These models offer versatile, one-for-many or one-for-all solutions, eliminating the need for developing task-specific AI models. Examples of such foundation models include

Noah Fleming, Yuichi Yoshida

Sensitivity measures how much the output of an algorithm changes, in terms of Hamming distance, when part of the input is modified. While approximation algorithms with low sensitivity have been developed for many problems, no sensitivity lower bounds were previously known for approximation algorithms. In this work, we establish the first polynomial lower bou

Geng Yu, Jianing Zhu, Jiangchao Yao, Bo Han

Out-of-distribution (OOD) detection is crucial for deploying reliable machine learning models in open-world applications. Recent advances in CLIP-based OOD detection have shown promising results via regularizing prompt tuning with OOD features extracted from ID data. However, the irrelevant context mined from ID data can be spurious due to the inaccurate for

R. Zhang, Guo-Jian Wang, Yuxi, Lu

Context. Open clusters (OCs) are valuable probes of stellar population characteristics. Their age and metallicity provide insights into the chemical enrichment history of the Milky Way. By studying the metallicity of OCs, we can explore the spatial distribution of composition across the Galaxy and understand stellar birth radii through chemical tagging. Howe

Sébastien Lord

A quantum tamper-evident encryption scheme is a non-interactive symmetric-key encryption scheme mapping classical messages to quantum ciphertexts such that an honest recipient of a ciphertext can detect with high probability any meaningful eavesdropping. This quantum cryptographic primitive was first introduced by Gottesman in 2003. Beyond formally defining

Jeong Rae Kim, Sandra Glotzer, Adrian Llanos, Salva Salmani-Rezaie

High temperatures promote kinetic processes which can drive crystal synthesis towards ideal thermodynamic conditions, thereby realizing samples of superior quality. While accessing very high temperatures in thin-film epitaxy is becoming increasingly accessible through laser-based heating methods, demonstrations of such utility are still emerging. Here we rea

Yonatan Kurniawan, Tracianne B. Neilsen, Benjamin L. Francis, Alex M. Stankovic

The efficacy of mathematical models heavily depends on the quality of the training data, yet collecting sufficient data is often expensive and challenging. Many modeling applications require inferring parameters only as a means to predict other quantities of interest (QoI). Because models often contain many unidentifiable (sloppy) parameters, QoIs often depe

Oleg Evnin, Dmitri Krioukov

We discover a first-order phase transition in the canonical ensemble of random unlabeled networks with a prescribed average number of links. The transition is caused by the nonconcavity of microcanonical entropy. Above the critical point coinciding with the graph symmetry phase transition, the canonical and microcanonical ensembles are equivalent and have a

Jaewoong Choi

While numerous studies have explored the field of research and development (R&D) landscaping, the preponderance of these investigations has emphasized predictive analysis based on R&D outcomes, specifically patents, and academic literature. However, the value of research proposals and novelty analysis has seldom been addressed. This study proposes a systemat

Wenrui Huang

We study the final state problem for the Hartree equation with repulsive Coulomb potential: \[i\partial_t u+\frac{1}{2}\Delta u-\frac{1}{|x|}u=((-\Delta)^{-1}|u|)^2u\] We show the work in \cite{KaMi} can be extended to the Hartree nonlinearity: Given a prescribed asymptotic profile, we construct a unique global solution scattering to the profile. In particul

Yerong Xu, Ciro Pinto, Erin Kara, Stefano Bianchi

The time lag between soft and hard X-ray photons has been observed in many active galactic nuclei (AGN) and can reveal the accretion process and geometry around supermassive black holes (SMBHs). High-frequency Fe K and soft lags are considered to originate from the light-travel distances between the corona and the accretion disk, while the propagation of the

Alexander Kleshchev

The irreducible modules over quiver Hecke superalgebras $R_\theta$ can be classified in terms of cuspidal modules. To an indivisible positive root $\alpha$ and a non-negative integer $d$, one associates a quotient $\bar R_{d\alpha}$ of $R_{d\alpha}$ called the cuspidal algebra. If the root $\alpha$ is real, the cuspidal algebra is well-understood. But if $\a

Olofin Akerele, Quadri Adeshina

We investigate a class of combinatorial sums involving reciprocals of central binomial coefficients , employing generating functions as the primary solution technique to formulate and analyze series involving the Catalan's constant. Using a direct approach, we derive new identities through integral techniques.

Yaowen Hu, Yunxiang Song, Xinrui Zhu, Xiangwen Guo

Here we show a photonic computing accelerator utilizing a system-level thin-film lithium niobate circuit which overcomes this limitation. Leveraging the strong electro-optic (Pockels) effect and the scalability of this platform, we demonstrate photonic computation at speeds up to 1.36 TOPS while consuming 0.057 pJ/OP. Our system features more than 100 thin-f

Haodong Li, Haicheng Qu, Xiaofeng Zhang

With the rapid development of large vision language models (LVLMs), these models have shown excellent results in various multimodal tasks. Since LVLMs are prone to hallucinations and there are currently few datasets and evaluation methods specifically designed for remote sensing, their performance is typically poor when applied to remote sensing tasks. To ad

Derek Perrin, José Felipe Voloch

We study $\ell$-isogeny graphs of ordinary elliptic curves defined over $\mathbb{F}_q$ with an added level structure. Given an integer $N$ coprime to $p$ and $\ell,$ we look at the graphs obtained by adding $\Gamma_0(N),$ $\Gamma_1(N),$ and $\Gamma(N)$-level structures to volcanoes. Given an order $\mathcal{O}$ in an imaginary quadratic field $K,$ we look at

Mingrui Yuan, Husain Alqattan, Dandan Hui, Mohamed Sennary

Attosecond science has leveraged the highly nonlinear interactions between intense few-cycle laser pulses and matter, allowing for unprecedented observation and control of electron motion with remarkable temporal resolution. However, most existing experiments focusing on laser-controlled attosecond dynamics have dealt with quasi-bound electrons released in t

Zexu Li, Suraj P. Prabhu, Zachary T. Popp, Shubhi S. Jain

Biomedical research requires large, diverse samples to produce unbiased results. Automated methods for matching variables across datasets can accelerate this process. Research in this area has been limited, primarily focusing on lexical matching and ontology based semantic matching. We aimed to develop new methods, leveraging large language models (LLM) and

Zijian Zhang, Linhua Jiang, Weiyang Liu, Luis C. Ho

James Webb Space Telescope (JWST) has revealed a population of red and compact sources at $z \gtrsim 5$ known as ``Little Red Dots'' (LRDs) that are likely active galactic nuclei (AGNs). Here we present a comprehensive study of the variability of 314 LRDs with multi-epoch JWST observations in five deep fields: UDS, GOODS-S, GOODS-N, Abell 2744, and COSMOS. O

Manuel Gloeckler, Shoji Toyota, Kenji Fukumizu, Jakob H. Macke

Amortized simulation-based inference (SBI) methods train neural networks on simulated data to perform Bayesian inference. While this strategy avoids the need for tractable likelihoods, it often requires a large number of simulations and has been challenging to scale to time series data. Scientific simulators frequently emulate real-world dynamics through tho

ICARUS Collaboration, F. Abd Alrahman, P. Abratenko, N. Abrego-Martinez

We present a search for long-lived particles (LLPs) produced from kaon decay that decay to two muons inside the ICARUS neutrino detector. This channel would be a signal of hidden sector models that can address outstanding issues in particle physics such as the strong CP problem and the microphysical origin of dark matter. The search is performed with data co

Imen Ayadi, Florent Bouchard, Frédéric Pascal

This paper deals with Elliptical Wishart distributions - which generalize the Wishart distribution - in the context of signal processing and machine learning. Two algorithms to compute the maximum likelihood estimator (MLE) are proposed: a fixed point algorithm and a Riemannian optimization method based on the derived information geometry of Elliptical Wisha

Ismael Villegas Molina, Audria Montalvo, Benjamin Ochoa, Paul Denny

Computer science has historically presented barriers for non-native English speaking (NNES) students, often due to language and terminology challenges. With the rise of large language models (LLMs), there is potential to leverage this technology to support NNES students more effectively. Recent implementations of LLMs as tutors in classrooms have shown promi

Xiang Li, Mingsi Liu, Lixin Duan

Purpose: Automatic and accurate segmentation of fundus vessel images has become an essential prerequisite for computer-aided diagnosis of ophthalmic diseases such as diabetes mellitus. The task of high-precision retinal vessel segmentation still faces difficulties due to the low contrast between the branch ends of retinal vessels and the background, the long

Minsoo L. Kim, Jeongmin Shim, H. -S. Sim, Donghoon Kim

When a local impurity spin interacts with conduction electrons whose density of states (DOS) has a (pseudo)gap or diverges at the Fermi energy, a local moment (LM) phase can be favored over a Kondo phase. Theoretically studying quantum entanglement between the impurity and conduction electrons, we demonstrate that conduction electrons form an ''LM spin cloud

Shuo Yang, Siwen Luo, Soyeon Caren Han

Existing Multimodal Large Language Models (MLLMs) and Visual Language Pretrained Models (VLPMs) have shown remarkable performances in the general Visual Question Answering (VQA). However, these models struggle with VQA questions that require external commonsense knowledge due to the challenges in generating high-quality prompts and the high computational cos

Hao Chen, Cunsheng Ding

Binary self-dual cyclic codes have been studied since the classical work of Sloane and Thompson published in IEEE Trans. Inf. Theory, vol. 29, 1983. Twenty five years later, an infinite family of binary self-dual cyclic codes with lengths $n_i$ and minimum distances $d_i \geq \frac{1}{2} \sqrt{n_i+2}$ was presented in a paper of IEEE Trans. Inf. Theory, vol.

Sichen Qian

This paper investigates a fully distributed adaptive Nash equilibrium (NE) seeking algorithm for constrained noncooperative games with prescribed-time stability. On the one hand, prescribed-time stability for the proposed NE seeking algorithm is obtained by using an adaptive penalty technique, a time-varying control gain and a cosine-related time conversion

Laifa Tao, Haifei Liu, Guoao Ning, Wenyan Cao

Accurately diagnosing bearing faults is crucial for maintaining the efficient operation of rotating machinery. However, traditional diagnosis methods face challenges due to the diversification of application environments, including cross-condition adaptability, small-sample learning difficulties, and cross-dataset generalization. These challenges have hinder

Alexander Kleshchev

We explicitly compute the dimensions of certain idempotent truncations of RoCK blocks of cyclotomic quiver Hecke superalgebras. Equivalently, this amounts to a computation of the value of the Shapovalov form on certain explicit vectors in the basic representations of twisted affine Kac-Moody Lie algebras of type $A$.

Yongwei Yuan, Zhe Zhou, Julia Belyakova, Suresh Jagannathan

We consider the formulation of a symbolic execution (SE) procedure for functional programs that interact with effectful, opaque libraries. Our procedure allows specifications of libraries and abstract data type (ADT) methods that are expressed in Linear Temporal Logic over Finite Traces (LTLf), interpreting them as symbolic finite automata (SFAs) to enable i

Jinchao Ge, Bowen Zhang, Akide Liu, Minh Hieu Phan

Class-incremental semantic segmentation (CSS) requires that a model learn to segment new classes without forgetting how to segment previous ones: this is typically achieved by distilling the current knowledge and incorporating the latest data. However, bypassing iterative distillation by directly transferring outputs of initial classes to the current learnin

Seyed Hossein Alavi, Weijia Xu, Nebojsa Jojic, Daniel Kennett

We introduce GamePlot, an LLM-powered assistant that supports game designers in crafting immersive narratives for turn-based games, and allows them to test these games through a collaborative game play and refine the plot throughout the process. Our user study with 14 game designers shows high levels of both satisfaction with the generated game plots and sen

Alexander Kleshchev

Let $\k$ be a characteristic zero PID, $S$ be a $\k$-algebra and $T\subseteq S$ be a full rank subalgebra. Suppose the algebra $T$ is symmetric. It is important to know when $T$ is a {\em maximal symmetric subalgebra} of $S$, i.e. no $\k$-subalgebra $C$ satisfying $T\subsetneq C\subseteq S$ is symmetric. In this note we establish a useful sufficient conditio

Yuxi Xie, Guanzhen Li, Xiao Xu, Min-Yen Kan

Large vision-language models (LVLMs) suffer from hallucination, resulting in misalignment between the output textual response and the input visual content. Recent research indicates that the over-reliance on the Large Language Model (LLM) backbone, as one cause of the LVLM hallucination, inherently introduces bias from language priors, leading to insufficien

Julia Wilkins, Sivan Ding, Magdalena Fuentes, Juan Pablo Bello

Self-supervised learning (SSL) offers a powerful way to learn robust, generalizable representations without labeled data. In music, where labeled data is scarce, existing SSL methods typically use generated supervision and multi-view redundancy to create pretext tasks. However, these approaches often produce entangled representations and lose view-specific i

Edward Kendall, Paraham Hajishafiezahramini, Matthew Hamilton, Gregory Doyle

Breast cancer presents the second largest cancer risk in the world to women. Early detection of cancer has been shown to be effective in reducing mortality. Population screening programs schedule regular mammography imaging for participants, promoting early detection. Currently, such screening programs require manual reading. False-positive errors in the rea

H. Wang, Y. Pang, D. Shang

In this study, the novel hybrid machine learning approach is proposed in carbon price fluctuation prediction. Specifically, a research framework integrating DILATED Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) neural network algorithm is proposed. The advantage of the combined framework is that it can make feature extraction more eff

Yunkai Dang, Mengxi Gao, Yibo Yan, Xin Zou

Multimodal large language models (MLLMs) have recently achieved state-of-the-art performance on tasks ranging from visual question answering to video understanding. However, existing studies have concentrated mainly on visual-textual misalignment, leaving largely unexplored the MLLMs' ability to preserve an originally correct answer when confronted with misl

Linzhe Huang, Chunlan Jiang, Zhengwei Liu, Jinsong Wu

In this paper, we study the quantum channel on a von Neuamnn algebra $\mathcal{M}$ preserving a von Neumann subalgebra $\mathcal{N}$, namely an $\mathcal{N}$-$\mathcal{N}$-bimodule unital completely positive map. By introducing the relative irreducibility of a bimodule quantum channel, we show that its eigenvalues with modulus 1 form a finite cyclic group, c

Aliasghar Arab, Milad Khaleghi, Alireza Partovi, Alireza Abbaspour

This paper presents a comprehensive hazard analysis, risk assessment, and loss evaluation for an Evasive Minimum Risk Maneuvering (EMRM) system designed for autonomous vehicles. The EMRM system is engineered to enhance collision avoidance and mitigate loss severity by drawing inspiration from professional drivers who perform aggressive maneuvers while mainta

G. Araujo-Pardo, C. De la Cruz, M. Matamala, M. A. Pizaña

Cages ($r$-regular graphs of girth $g$ and minimum order) and their variants have been studied for over seventy years. Here we propose a new variant, "weighted cages". We characterize their existence; for cases $g=3,4$ we determine their order; we give Moore-like bounds and present some computational results.

Soroush Nasiriany, Sean Kirmani, Tianli Ding, Laura Smith

We explore how intermediate policy representations can facilitate generalization by providing guidance on how to perform manipulation tasks. Existing representations such as language, goal images, and trajectory sketches have been shown to be helpful, but these representations either do not provide enough context or provide over-specified context that yields

Huibin Zhao, Weipeng Guan, Peng Lu

3D Gaussian Splatting (3DGS) has shown its ability in rapid rendering and high-fidelity mapping. In this paper, we introduce LVI-GS, a tightly-coupled LiDAR-Visual-Inertial mapping framework with 3DGS, which leverages the complementary characteristics of LiDAR and image sensors to capture both geometric structures and visual details of 3D scenes. To this end

Michael Jaber, Shachar Lovett, Anthony Ostuni

We improve the best known upper bounds on the density of corner-free sets over quasirandom groups from inverse poly-logarithmic to quasi-polynomial. We make similarly substantial improvements to the best known lower bounds on the communication complexity of a large class of permutation functions in the 3-player Number-on-Forehead model. Underpinning both res

Mikihiro Fujii, Keiichi Watanabe

We consider the compressible Navier--Stokes system with the Coriolis force on the $3$D whole space. In this model, the Coriolis force causes the linearized solution to behave like a $4$th order dissipative semigroup $\{ e^{-t\Delta^2} \}_{t>0}$ with slower time decay rates than the heat kernel, which creates difficulties in nonlinear estimates in the low-fre

Baoguo Liu, Huiguang Zhang, Wei Feng, Zongyao Liu

The achievement of spectral super-resolution sensing is critically important for a variety of applications, such as radar, remote sensing, and wireless communication. However, in compressed spectrum sensing, challenges such as spectrum leakage and the picket-fence effect significantly complicate the accurate extraction of super-resolution signal components.

Joshua J. Ziegler, Paul R. Shapiro, Taha Dawoodbhoy, Paz Beniamini

Dispersion measures (DM) of fast radio bursts (FRBs) probe the density of electrons in the intergalactic medium (IGM) along their lines-of-sight, including the average density versus distance to the source and its variations in direction. While previous study focused on low-redshift, FRBs are potentially detectable out to high redshift, where their DMs can,

Shunsaku Kitagawa, Kousuke Nakanishi, Hiroki Matsumura, Yuki Takahashi

We report the re-measurement of the $a$-axis spin susceptibility component in an early-stage sample of the spin-triplet superconductor UTe$_2$ with the transition temperature of $T_{\rm SC}$ = 1.6 K. Using Knight-shift measurements along the $b$ axis and at a 10-degree tilt from the $b$ axis towards the $a$ axis, we accurately determined the $a$-axis compone

Minho Choi, Jinlin Xiang, Anna Wirth-Singh, Seung-Hwan Baek

Artificial neural networks (ANNs) have fundamentally transformed the field of computer vision, providing unprecedented performance. However, these ANNs for image processing demand substantial computational resources, often hindering real-time operation. In this paper, we demonstrate an optical encoder that can perform convolution simultaneously in three colo

Shilei Fan, Mamateli Kadir, Peishan Li

In this paper, we provide a geometric characterization of tiles in the finite abelian groups \( \mathbb{Z}_{p^n} \times \mathbb{Z}_q \) and \( \mathbb{Z}_{p^n} \times \mathbb{Z}_p \) using the concept of a \( p \)-homogeneous tree, which provides an intuitively visualizable criterion.

Wanying Ding, Vinay K. Chaudhri, Naren Chittar, Krishna Konakanchi

Knowledge Graphs have emerged as a compelling abstraction for capturing key relationship among the entities of interest to enterprises and for integrating data from heterogeneous sources. JPMorgan Chase (JPMC) is leading this trend by leveraging knowledge graphs across the organization for multiple mission critical applications such as risk assessment, fraud

Xiuyuan Cheng, Tingnan Gong, Yao Xie

Point processes are widely used statistical models for continuous-time discrete event data, such as medical records, crime reports, and social network interactions, to capture the influence of historical events on future occurrences. In many applications, however, event times are not observed exactly, motivating the need to incorporate time uncertainty into

Chao Zhang, Zaichen Xiang, Lingyong Zeng, Peifeng Yu

The trade-off effect of the oxygen permeability and stability of oxygen transport membranes (OTMs) still exists in working atmospheres containing CO2. Herein, we reported a new series of 60 wt%Ce0.9Ln0.1O2-{\delta}-40wt%Ln0.6Sr0.4Fe0.9Ti0.1O3-{\delta} (CLnO-LnSFTO, Ln = La, Pr, Nd, Sm, Gd, Tb) dual-phase OTMs by selecting different Ln elements based on the r

Wanying Ding, Manoj Cherukumalli, Santosh Chikoti, Vinay K. Chaudhri

Knowledge graphs have gained popularity for their ability to organize and analyze complex data effectively. When combined with graph embedding techniques, such as graph neural networks (GNNs), knowledge graphs become a potent tool in providing valuable insights. This study explores the application of graph embedding in identifying competitors from a financia

Eunyoung Park, Sinwoo Kim, Melody M. Wang, Junha Hwang

Metallic glass is a frozen liquid with structural disorder that retains degenerate free energy without spontaneous symmetry breaking to become a solid. For over half a century, this puzzling structure has raised fundamental questions about how structural disorder impacts glass-liquid phase transition kinetics, which remain elusive without direct evidence. In

P. H. F. Oliveira, W. P. Lima

With the advent of Albert Einstein's theory of special relativity, Klein and Gordon made the first attempt to elevate time to the status of a coordinate in the Schr\"odinger equation. In this study, we graphically discuss the eigenfunctions and eigenenergies of the Klein-Gordon equation with a Yukawa-type potential (YP), within a position-dependent mass (PDM

Jinzhuan Cai, Jin Guo, Alexander L. Gavrilyuk, Ilia Ponomarenko

The coherent configuration $\mathsf{WL}(X)$ of a graph $X$ is the smallest coherent configuration on the vertices of $X$ that contains the edge set of $X$ as a relation. The aim of the paper is to study $\mathsf{WL}(X)$ when $X$ is a Cartesian product of graphs. The example of a Hamming graph shows that, in general, $\mathsf{WL}(X)$ does not coincide with th

Yihan Wang, Andrew Bai, Nanyun Peng, Cho-Jui Hsieh

Pre-trained Large Language Models (LLMs) require post-training methods such as supervised fine-tuning (SFT) on instruction-response pairs to enable instruction following. However, this process can potentially harm existing capabilities learned during pre-training. In this paper, we investigate the loss of context awareness after SFT, where context awareness

Zachary R. Wiethorn, Kye E. Hunter, Andrés Montoya-Castillo, Tim J. Zuehlsdorff

Porphyrins offer a malleable and cost-efficient platform to sculpt bioinspired technologies with tunable charge transfer, energy conversion, and photocatalytic properties. Yet, despite decades of research, the physical mechanisms that determine their electronic spectra remain elusive. Even for metal-free porphyrins, no consensus exists on the origin of the s

Damian R Sowinski, Gourab Ghoshal, Adam Frank

The Daisy World model has long served as a foundational framework for understanding the self-regulation of planetary biospheres, providing insights into the feedback mechanisms that may govern inhabited exoplanets. In this study, we extend the classic Daisy World model through the lens of Semantic Information Theory (SIT), aiming to characterize the informat

MZ Naser, Ahmed Z Naser

This article introduces a new symbolic regression algorithm based on the SPINEX (Similarity-based Predictions with Explainable Neighbors Exploration) family. This new algorithm (SPINEX_SymbolicRegression) adopts a similarity-based approach to identifying high-merit expressions that satisfy accuracy- and structural similarity metrics. We conducted extensive b

Amy H. Knight, Lauren Rhodes, Douglas J. K. Buisson, James H. Matthews

The neutron star low-mass X-ray binary, EXO 0748--676, recently returned to outburst after a $\sim$ 16 year-long quiescence. Since its return, there has been a global effort to capture the previously unseen rise of the source and to understand its somewhat early return to outburst, as it is typical for a source to spend longer in quiescence than in outburst.

Farhad Arpanaei, Mahdi Ranjbar Zefreh, Yanchao Jiang, Pierluigi Poggiolini

The extension of elastic optical networks (EON) to multi-band transmission (MB-EON) shows promise in enhancing spectral efficiency, throughput, and long-term cost-effectiveness for telecom operators. However, designing MB-EON networks introduces complex challenges, notably the optimization of physical parameters like optical power and quality of transmission

Sebin Oh, Junho Song, Taeyong Kim

This study proposes a modularized deep learning-based loading protocol for optimal parameter estimation of Bouc-Wen (BW) class models. The protocol consists of two key components: optimal loading history construction and CNN-based rapid parameter estimation. Each component is decomposed into independent sub-modules tailored to distinct hysteretic behaviors-b

Therese Biedl, Prosenjit Bose, Babak Miraftab

The $d$-independence number of a graph $G$ is the largest possible size of an independent set $I$ in $G$ where each vertex of $I$ has degree at least $d$ in $G$. Upper bounds for the $d$-independence number in planar graphs are well-known for $d=3,4,5$, and can in fact be matched with constructions that actually have minimum degree $d$. In this paper, we exp

Xiaoxuan Lei, Takuya Ito, Pouya Bashivan

Working memory is a central cognitive ability crucial for intelligent decision-making. Recent experimental and computational work studying working memory has primarily used categorical (i.e., one-hot) inputs, rather than ecologically relevant, multidimensional naturalistic ones. Moreover, studies have primarily investigated working memory during single or fe

Shakiba Davari, Daniel Stover, Alexander Giovannelli, Cory Ilo

Recent advancements in Augmented Reality (AR) research have highlighted the critical role of context awareness in enhancing interface effectiveness and user experience. This underscores the need for intelligent AR (iAR) interfaces that dynamically adapt across various contexts to provide optimal experiences. In this paper, we (a) propose a comprehensive fram

Donny R. Pearson, Ashwith Prabhu, Selvin Tobar, Jack D'Amelio

Rare-earth emitters in solids are well-suited for implementing efficient, long-lived quantum memory coupled to integrated photonics for scalable quantum technologies. They are typically introduced as dopants in a solid-state host, but this introduces disorder and limits the available density of emitters. Stoichiometric materials can offer high densities with

Matthias Zach

We extend the circle of ideas from a previous paper on hypersurfaces to functions $f \colon (\mathbb C^n, 0) \to (\mathbb C^k, 0)$ with an isolated singularity in a stratified sense on an arbitrary, but fixed complex analytic germ $(X, 0)$. An extension of Tib{\u a}r's Bouquet Theorem to this setup allows for a topological definition of Milnor numbers $\mu(\

Dorian Rudolph

$\mathsf{QMA}_1$ is $\mathsf{QMA}$ with perfect completeness, i.e., the prover must accept with a probability of exactly $1$ in the YES-case. Whether $\mathsf{QMA}_1$ and $\mathsf{QMA}$ are equal is still a major open problem. It is not even known whether $\mathsf{QMA}_1$ has a universal gateset; Solovay-Kitaev does not apply due to perfect completeness. Hen

Ronald Orozco López

In this paper, we introduce the Rogers-Szeg\"o deformed $q$-differential operators g$_{n}(bD_{q}|u)$ based on $q$-differential operator $D_{q}$. The motivation for introducing the operators g$_{n}(bD_{q})$ is that their limit turns out to be the $q$-exponential operator T$(bD_{q})$ given by Chen. The deformed homogeneous Al-Salam-Carlitz polynomials $\Psi_{m

Yuri Fukaya, Kazuki Maeda, Keiji Yada, Jorge Cayao

We consider Josephson junctions formed by coupling two conventional superconductors via an unconventional magnet and investigate the formation of Andreev bound states, their impact on the Josephson effect, and the emergent superconducting correlations. We focus on unconventional magnets known as $d$-wave altermagnets and $p$-wave magnets. We find that the An

Yunkai Wang, Eric Chitambar

In quantum-enhanced astronomical imaging, multiple distant apertures work together by utilizing quantum resources distributed from a central server. Our findings suggest that pre-processing the stellar light received by all telescopes can improve imaging performance without increasing resource consumption. The pre-processing leverages weak quantum measuremen

Gaurav Kumar, Aditya G. Nair

This work introduces a novel adaptive mesh refinement (AMR) method that utilizes dominant balance analysis (DBA) for efficient and accurate grid adaptation in computational fluid dynamics (CFD) simulations. The proposed method leverages a Gaussian mixture model (GMM) to classify grid cells into active and passive regions based on the dominant physical intera

Benedikt P. Klein, Matthew A. Stoodley, Joel Deyerling, Luke A. Rochford

Chemical vapour deposition enables large-domain growth of ideal graphene, yet many applications of graphene require the controlled inclusion of specific defects. We present a one-step chemical vapour deposition procedure aimed at retaining the precursor topology when incorporated into the grown carbonaceous film. When azupyrene, the molecular analogue of the

Apoorva Lal

We examine the challenges in ranking multiple treatments based on their estimated effects when using linear regression or its popular double-machine-learning variant, the Partially Linear Model (PLM), in the presence of treatment effect heterogeneity. We demonstrate by example that overlap-weighting performed by linear models like PLM can produce Weighted Av

Xin Zhang, Victor S. Sheng

We propose an innovative token representation and update method in a new ultra-small language model: the Wave network. Specifically, we use a complex vector to represent each token, encoding both global and local semantics of the input text. A complex vector consists of two components: a magnitude vector representing the global semantics of the input text, a

Shih-Yu Chang

This paper explores operators with countable, continuous, and hybrid spectra, focusing on both finite dimensional and infinite dimensional cases, particularly in non-Hermitian systems. For finite dimensional operators, a novel concept of analogous matrices is introduced. Here, matrices are considered analogous if they share the same projector and nilpotent s

Yang Gao, Po-Chien Luan, Alexandre Alahi

The ability of intelligent systems to predict human behaviors is crucial, particularly in fields such as autonomous vehicle navigation and social robotics. However, the complexity of human motion have prevented the development of a standardized dataset for human motion prediction, thereby hindering the establishment of pre-trained models. In this paper, we a

Quang Luong Nhat Nguyen, Ruiming Cao, Laura Waller

Image registration techniques usually assume that the images to be registered are of a certain type (e.g. single- vs. multi-modal, 2D vs. 3D, rigid vs. deformable) and there lacks a general method that can work for data under all conditions. We propose a registration method that utilizes neural networks for image representation. Our method uses untrained net

Karuna Bhaila, Minh-Hao Van, Kennedy Edemacu, Chen Zhao

The emerging in-context learning (ICL) ability of large language models (LLMs) has prompted their use for predictive tasks in various domains with different data types, including tabular data, facilitated by serialization methods. However, with increasing applications in high-stakes domains, it has been shown that LLMs can inherit social bias and discriminat

Maraz Mia, Mir Mehedi A. Pritom, Tariqul Islam, Kamrul Hasan

Intrusion detection has been a commonly adopted detective security measures to safeguard systems and networks from various threats. A robust intrusion detection system (IDS) can essentially mitigate threats by providing alerts. In networks based IDS, typically we deal with cyber threats like distributed denial of service (DDoS), spoofing, reconnaissance, bru

Xiaojun Jia, Sensen Gao, Qing Guo, Ke Ma

Vision-language pre-training (VLP) models excel at interpreting both images and text but remain vulnerable to multimodal adversarial examples (AEs). Advancing the generation of transferable AEs, which succeed across unseen models, is key to developing more robust and practical VLP models. Previous approaches augment image-text pairs to enhance diversity with

Victor Cardenas

The document addresses the essential role of financial regulatory frameworks in mitigating climate-related risks within the financial sector. The assessment evaluates Canada's efforts to establish a regulatory framework for financial climate risk disclosure, compares it to international standards, and identifies areas for improvement. The regulatory framewor

Amal Alofi, Mark Dukes

The lacking polynomial is a graph polynomial introduced by Chan, Marckert, and Selig in 2013 that is closely related to the Tutte polynomial of a graph. It arose by way of a generalization of the Abelian sandpile model and is essentially the generating function of the level statistic on the set of recurrent configurations, called stochastically recurrent sta

Kangrui Ruan, Xinyang Wang, Xuan Di

Social media has become an important platform for people to express their opinions towards transportation services and infrastructure, which holds the potential for researchers to gain a deeper understanding of individuals' travel choices, for transportation operators to improve service quality, and for policymakers to regulate mobility services. A significa

Shigeng Sun, Jorge Nocedal

This paper introduces a modified Byrd-Omojokun (BO) trust region algorithm to address the challenges posed by noisy function and gradient evaluations. The original BO method was designed to solve equality constrained problems and it forms the backbone of some interior point methods for general large-scale constrained optimization. A key strength of the BO me

Aahlad Puli, Nhi Nguyen, Rajesh Ranganath

Feature attributions attempt to highlight what inputs drive predictive power. Good attributions or explanations are thus those that produce inputs that retain this predictive power; accordingly, evaluations of explanations score their quality of prediction. However, evaluations produce scores better than what appears possible from the values in the explanati

Vladimir V. Skokov

These lecture notes, prepared for the 2024 XQCD PhD, provide an introduction to the analytic structure of an equation of state near a second-order phase transition and its most prominent landmark: the Yang-Lee edge singularity. In addition to discussing general properties, the notes review recent theoretical progress in locating the QCD critical point by tra

Marie Muehlhaus, Jürgen Steimle

Generative Artificial Intelligence (Generative AI) holds significant promise in reshaping interactive systems design, yet its potential across the four key phases of human-centered design remains underexplored. This article addresses this gap by investigating how Generative AI contributes to requirements elicitation, conceptual design, physical design, and e