April 2024 arXiv papers — page 141

Wenxiong Xie, Chris T Fairhall, Ricardo García-Mayoral

We study the effect of surface texture on an overlying turbulent flow for textures made of an alternating slip/no-slip pattern, a common model for superhydrophobic surfaces, but also a particularly simple form of texture. For texture sizes $L^+ \gtrsim 25$, the texture effectively imposes homogeneous slip boundary conditions on the overlying, background turb

Osamu Iyama, Yuta Kimura, Kenta Ueyama

Tilting theory is one of the central tools in modern representation theory, in particular in the study of Cohen-Macaulay representations. We study Cohen-Macaulay representations of $\mathbb N$-graded Artin-Schelter Gorenstein algebras $A$ of dimension one, without assuming the connectedness condition. This framework covers a broad class of noncommutative Gor

Luna Hiron, Martha C. Schönau, Keshav J. Raja, Eric P. Chassignet

Internal tide generation and breaking play a primary role in the vertical transport and mixing of heat and other properties in the ocean interior, thereby influencing climate regulation. Additionally, internal tides increase sound speed variability in the ocean, consequently impacting underwater acoustic propagation. With advancements in large-scale ocean mo

Hsun-Yi Hsieh, Yu-Chun Kao

We present a convenient trick for computing the sizes of clusters within a network. The rationale relies on the mathematics of the geometric series and the fundamental matrix of a Markov Chain.

Dino Habibović, Kathryn R. Hamilton, Ofer Neufeld, Laura Rego

Ultrashort laser pulses pose unique tools to trigger and probe the fastest charge dynamics in matter, allowing the investigation of fundamental physical phenomena with unprecedented resolution in space, time, and energy. One of the most fascinating opportunities that ultrashort pulses offer is the possibility of modulating and investigating symmetries by tai

Chang Li, Ruisheng Yang, Xinchao Huang, Quanhong Fu

Non-Hermiticity has recently emerged as a rapidly developing field due to its exotic characteristics related to open systems, where the dissipation plays a critical role. In the presence of balanced energy gain and loss with environment, the system exhibits parity-time (PT) symmetry, meanwhile as the conjugate counterpart, anti-PT symmetry can be achieved wi

Haoran Ma, Liao Ye, Fanjie Ruan, Zichao Zhao

Generative adversarial networks (GANs) have achieved remarkable success with realistic tasks such as creating realistic images, texts, and audio. Combining GANs and quantum computing, quantum GANs are thought to have an exponential advantage over their classical counterparts due to the stronger expressibility of quantum circuits. In this research, a two-qubi

Emani Dotch, Vitica Arnold

We explore ideas and inclusive practices for designing and testing child-centered artificially intelligent technologies for neurodivergent children. AI is promising for supporting social communication, self-regulation, and sensory processing challenges common for neurodivergent children. The authors, both neurodivergent individuals and related to neurodiverg

Sai Aparna Aketi, Abolfazl Hashemi, Kaushik Roy

Decentralized learning is crucial in supporting on-device learning over large distributed datasets, eliminating the need for a central server. However, the communication overhead remains a major bottleneck for the practical realization of such decentralized setups. To tackle this issue, several algorithms for decentralized training with compressed communicat

Joás Venâncio, Lameque Filho, Herondy Mota, Azadeh Mohammadi

This work investigates the thermal Casimir effect associated with a massive spinor field defined on a four-dimensional flat space with a circularly compactified spatial dimension whose periodicity is oriented along a vector in $xy$-plane. We employ the generalized zeta function method to establish a finite definition for the vacuum free energy density. This

Kejun Li, Vsevolod D. Dergachev, Ilya D. Dergachev, Shimin Zhang

Optically detected magnetic resonance (ODMR) is an efficient and reliable method that enables initialization and readout of spin states through spin-photon interface. In general, high quantum efficiency and large spin-dependent photoluminescence contrast are desirable for reliable quantum information readout. However, reliable prediction of the ODMR contrast

Sekeun Kim, Hui Ren, Peng Guo, Abder-Rahman Ali

Echocardiography segmentation for cardiac analysis is time-consuming and resource-intensive due to the variability in image quality and the necessity to process scans from various standard views. While current automated segmentation methods in echocardiography show promising performance, they are trained on specific scan views to analyze corresponding data.

Yoshikazu Suzumura, Takao Tsumuraya, Masao Ogata

The Seebeck coefficient is examined for two-dimensional Dirac electrons in the three-quarter filled organic conductor alpha-(BEDT-TTF)_2I_3 under hydrostatic pressure, where the Seebeck coefficient is proportional to the ratio of the thermoelectric conductivity to the electrical conductivity. We present an improved tight-binding model in two dimensions with

Lillian Muyama, Antoine Neuraz, Adrien Coulet

Background: Clinical diagnosis is typically reached by following a series of steps recommended by guidelines authored by colleges of experts. Accordingly, guidelines play a crucial role in rationalizing clinical decisions but suffer from limitations as they are built to cover the majority of the population and fail at covering patients with uncommon conditio

Cheongho Han, Andrzej Udalski, Ian A. Bond, Chung-Uk Lee

We undertake a project to reexamine microlensing data gathered from high-cadence surveys. The aim of the project is to reinvestigate lensing events with light curves exhibiting intricate anomaly features associated with caustics, yet lacking prior proposed models to explain these features. Through detailed reanalyses considering higher-order effects, we iden

Ebtihal J. Alwadee, Xianfang Sun, Yipeng Qin, Frank C. Langbein

Early-stage 3D brain tumor segmentation from magnetic resonance imaging (MRI) scans is crucial for prompt and effective treatment. However, this process faces the challenge of precise delineation due to the tumors' complex heterogeneity. Moreover, energy sustainability targets and resource limitations, especially in developing countries, require efficient an

Ian Doust, Anthony Weston

Nontrivial $p$-polygonal equalities impose certain conditions on the geometry of a metric space $(X,d)$ and so it is of interest to be able to identify the values of $p \in [0,\infty)$ for which such equalities exist. Following work of Li and Weston, Kelleher, Miller, Osborn and Weston established that if a metric space $(X,d)$ is of $p$-negative type, then

Ruo-Chen Wang, Zhuo-Chen Li, Xing-Yan Fan, Xiang-Ru Xie

In quantum information, the Werner state is a benchmark to test the boundary between quantum mechanics and classical models. There have been three well-known critical values for the two-qubit Werner state, i.e., $V_{\rm c}^{\rm E}=1/3$ characterizing the boundary between entanglement and separable model, $V_{\rm c}^{\rm B}=1/K_G(3)$ characterizing the bounda

T. R. Routray, S. Sahoo, X. Viñas, D. N. Basu

The equation of state of hot neutron star matter of n+p+e+$\mu$ composition in $\beta$-equilibrium is studied for both neutrino-free isothermal and neutrino-trapped isentropic conditions, using the formalism where the thermal evolution is built upon its zero-temperature predictions in a self-consistent manner. The accuracy of the parabolic approximation, oft

Guilherme Seidyo Imai Aldeia, Fabricio Olivetti de Franca, William G. La Cava

Parent selection plays an important role in evolutionary algorithms, and many strategies exist to select the parent pool before breeding the next generation. Methods often rely on average error over the entire dataset as a criterion to select the parents, which can lead to an information loss due to aggregation of all test cases. Under epsilon-lexicase selec

Guilherme Seidyo Imai Aldeia, Fabricio Olivetti de Franca

In some situations, the interpretability of the machine learning models plays a role as important as the model accuracy. Interpretability comes from the need to trust the prediction model, verify some of its properties, or even enforce them to improve fairness. Many model-agnostic explanatory methods exists to provide explanations for black-box models. In th

Tonghua Liu, Marek Biesiada, Shuxun Tian, Kai Liao

The measurement of the parametrized post-Newtonian parameter $\gamma_{\rm{PPN}}$ is a robust test of general relativity (GR). In some modified theories of gravity, $\gamma_{\rm{PPN}}$ may evolve with the redshift and deviate from one at high redshifts. This means that precise constraints on $\gamma_{\rm{PPN}}$ acquired in the solar system experiments could n

Gennaro D'Angelo, Peter Bodenheimer

We compute the accretion efficiency of small solids, with radii 1 cm $\le$ Rs $\le$ 10 m, on planets embedded in gaseous disks. Planets have masses 3 $\le$ Mp $\le$ 20 Earth masses (Me) and orbit within 10 AU of a solar-mass star. Disk thermodynamics is modeled via three-dimensional radiation-hydrodynamic calculations that typically resolve the planetary env

Bo Lin, Yangzheng Zhong, Weiqing Ren

Understanding the transition events between metastable states in complex systems is an important subject in the fields of computational physics, chemistry and biology. The transition pathway plays an important role in characterizing the mechanism underlying the transition, for example, in the study of conformational changes of bio-molecules. In fact, computi

Giwon Hong, Aryo Pradipta Gema, Rohit Saxena, Xiaotang Du

Large Language Models (LLMs) have transformed the Natural Language Processing (NLP) landscape with their remarkable ability to understand and generate human-like text. However, these models are prone to ``hallucinations'' -- outputs that do not align with factual reality or the input context. This paper introduces the Hallucinations Leaderboard, an open init

Hadi Fanaee-T

We introduce Natural Learning (NL), a novel algorithm that elevates the explainability and interpretability of machine learning to an extreme level. NL simplifies decisions into intuitive rules, like "We rejected your loan because your income, employment status, and age collectively resemble a rejected prototype more than an accepted prototype." When applied

Michael Lutz, Arth Bohra, Manvel Saroyan, Artem Harutyunyan

In the realm of web agent research, achieving both generalization and accuracy remains a challenging problem. Due to high variance in website structure, existing approaches often fail. Moreover, existing fine-tuning and in-context learning techniques fail to generalize across multiple websites. We introduce Wilbur, an approach that uses a differentiable rank

Shaozhi Li, M Sabbir Salek, Yao Wang, Mashrur Chowdhury

Driven by the significant advantages offered by quantum computing, research in quantum machine learning has increased in recent years. While quantum speed-up has been demonstrated in some applications of quantum machine learning, a comprehensive understanding of its underlying mechanisms for improved performance remains elusive. Our study address this proble

Qing Jin, Angelos Georghiou, Phebe Vayanos, Grani A. Hanasusanto

We study two-stage distributionally robust optimization (DRO) problems with decision-dependent information discovery (DDID) wherein (a portion of) the uncertain parameters are revealed only if an (often costly) investment is made in the first stage. This class of problems finds many important applications in selection problems (e.g., in hiring, project portf

Daichi Hayashi

Feferman (1975) defines an impredicative system $\mathsf{T}_0$ of explicit mathematics, which is proof-theoretically equivalent to the subsystem $\Delta^1_2$-$\mathsf{CA} + \mathsf{BI}$ of second-order arithmetic. In this paper, we propose several systems of Frege structure with the same proof-theoretic strength as $\mathsf{T}_0$. To be precise, we first con

Guilherme Seidyo Imai Aldeia, Fabricio Olivetti de Franca, William G. La Cava

Symbolic regression (SR) searches for parametric models that accurately fit a dataset, prioritizing simplicity and interpretability. Despite this secondary objective, studies point out that the models are often overly complex due to redundant operations, introns, and bloat that arise during the iterative process, and can hinder the search with repeated explo

Lee Mason, Blánaid Hicks, Jonas S. Almeida

Spatial cluster analysis, the detection of localized patterns of similarity in geospatial data, has a wide-range of applications for scientific discovery and practical decision making. One way to detect spatial clusters is by using local indicators of spatial association, such as Local Moran's I or Getis-Ord Gi*. However, different indicators tend to produce

Pascale Garaud, Greg P. Chini, Laura Cope, Kasturi Shah

Recent theoretical progress using multiscale asymptotic analysis has revealed various possible regimes of stratified turbulence. Notably, buoyancy transport can either be dominated by advection or diffusion, depending on the effective P\'eclet number of the flow. Two types of asymptotic models have been proposed, which yield measurably different predictions

Mamady Delamou, El Mehdi Amhoud

The advancement of wireless communication systems toward 5G and beyond is spurred by the demand for high data rates, exceedingly dependable low-latency communication, and extensive connectivity that aligns with sensing requisites such as advanced high-resolution sensing and target detection. Consequently, embedding sensing into communication has gained consi

Andrew Holliday, Ahmed El-Geneidy, Gregory Dudek

Planning a network of public transit routes is a challenging optimization problem. Metaheuristic algorithms search through the space of possible transit networks by applying heuristics that randomly alter routes in a network. Existing algorithms almost exclusively use heuristics that modify the network in purely random ways. In this work, we explore whether

Sowmya S. Sundaram, Benjamin Solomon, Avani Khatri, Anisha Laumas

Metadata play a crucial role in ensuring the findability, accessibility, interoperability, and reusability of datasets. This paper investigates the potential of large language models (LLMs), specifically GPT-4, to improve adherence to metadata standards. We conducted experiments on 200 random data records describing human samples relating to lung cancer from

Bo Peng, Daniel Goldstein, Quentin Anthony, Alon Albalak

We present Eagle (RWKV-5) and Finch (RWKV-6), sequence models improving upon the RWKV (RWKV-4) architecture. Our architectural design advancements include multi-headed matrix-valued states and a dynamic recurrence mechanism that improve expressivity while maintaining the inference efficiency characteristics of RNNs. We introduce a new multilingual corpus wit

Maryam Ahang, Mostafa Abbasi, Todd Charter, Homayoun Najjaran

Condition monitoring of industrial systems is crucial for ensuring safety and maintenance planning, yet notable challenges arise in real-world settings due to the limited or non-existent availability of fault samples. This paper introduces an innovative solution to this problem by proposing a new method for fault detection and condition monitoring for unseen

Ricardo Heras

In a recent paper (Heras in Eur. Phys. J. Plus 138: 329, 2023), we have demonstrated that when a dyon encircles an infinitely long solenoid enclosing electric and magnetic fluxes, its wave function accumulates a quantum phase invariant under electromagnetic duality transformations. In this paper, we show that this phase, in conjunction with the Witten effect

Venkat Chandrasekhar

The dc current-voltage characteristics of small Josephson junctions reveal features that are not observed in larger junctions, in particular, a switch to the finite voltage state at current values much less than the expected critical current of the junction and a finite resistance in the nominally superconducting regime. Both phenomena are due to the increas

Rafael M. L. Silva, Ana María Cárdenas Gasca, Joshua A. Fisher, Erica Principe Cruz

This volume represents the proceedings of With or Without Permission: Site-Specific Augmented Reality for Social Justice CHI 2024 workshop.

Yafei Ou, Sadra Zargarzadeh, Paniz Sedighi, Mahdi Tavakoli

Realistic real-time surgical simulators play an increasingly important role in surgical robotics research, such as surgical robot learning and automation, and surgical skills assessment. Although there are a number of existing surgical simulators for research, they generally lack the ability to simulate the diverse types of objects and contact-rich manipulat

Letian Ai, Yihao Liu, Mehran Armand, Amir Kheradmand

Robotic-assisted medical systems (RAMS) have gained significant attention for their advantages in alleviating surgeons' fatigue and improving patients' outcomes. These systems comprise a range of human-computer interactions, including medical scene monitoring, anatomical target planning, and robot manipulation. However, despite its versatility and effectiven

Jonah Chaban, Michael I. Weinstein

Consider the Schr\"{o}dinger operator $H = -\Delta + V$, where the potential $V$ is real, $\mathbb{Z}^2$-periodic, and additionally invariant under the symmetry group of the square. We show that, under typical small linear deformations of $V$, the quadratic band degeneracy points occurring over the high-symmetry quasimomentum $\boldsymbol{M}$ (see [27, 28])

Xiaotong Guo, Jinhua Zhao

This paper addresses the pressing challenge of urban mobility in the context of growing urban populations, changing demand patterns for urban mobility, and emerging technologies like Mobility-on-Demand (MoD) platforms and Autonomous Vehicle (AV). As urban areas swell and demand pattern changes, the integration of Autonomous Mobility-on-Demand (AMoD) systems

Gabriella d'Albenzio, Ruoyan Meng, Davit Aghayan, Egidijus Pelanis

Objective: This study introduces a novel method for defining virtual resections in liver cancer surgery, aimed at enhancing the adaptability of parenchyma-sparing resection (PSR) plans. By comparing these with traditional anatomical resection (AR) plans, we explore the potential for optimization in surgical planning. Methods: Leveraging contours and spline s

Yihao Liu, Jiaming Zhang, Zhangcong She, Amir Kheradmand

Hand-eye calibration is the problem of solving the transformation from the end-effector of a robot to the sensor attached to it. Commonly employed techniques, such as AXXB or AXZB formulations, rely on regression methods that require collecting pose data from different robot configurations, which can produce low accuracy and repeatability. However, the deriv

Edgar Gasperin, Francisco Pais

This note gives a concise derivation of a twistor-initial-data characterisation of pp-wave spacetimes in vacuum. The construction is based on a similar calculation for the Minkowski spacetime in [Class. Quantum Grav. 28 075010]. The key difference is that for the Minkowski spacetime a necessary condition is that $\nabla_{A}{}^{A'}\bar{\kappa}_{A'} \neq 0$. I

Atanu Bhunia, Subrata Bera, Indranil Biswas, Indrani Chattopadhyay

An unextendible biseparable basis (UBB) is a set of orthogonal pure biseparable states which span a subspace of a given Hilbert space while the complementary subspace contains only genuinely entangled states. These biseparable bases are useful to produce genuinely entangled subspace in multipartite system. Such a subspace could be more beneficial for informa

Mohamed Elsayed Eldib

X-ray-induced acoustic computed tomography (XACT) as a novel imaging modality has shown great potential in applications ranging from biomedical imaging to nondestructive testing. Improving the signal-to-noise ratio and removing the artifacts are major challenges in XACT imaging. We introduce an efficient non-uniformity correction method for the ultrasound ri

Weikai Lu, Ziqian Zeng, Jianwei Wang, Zhengdong Lu

Jailbreaking attacks can enable Large Language Models (LLMs) to bypass the safeguard and generate harmful content. Existing jailbreaking defense methods have failed to address the fundamental issue that harmful knowledge resides within the model, leading to potential jailbreak risks for LLMs. In this paper, we propose a novel defense method called Eraser, wh

Yu Qin, Brittany Terese Fasy, Carola Wenk, Brian Summa

Merge trees are a valuable tool in the scientific visualization of scalar fields; however, current methods for merge tree comparisons are computationally expensive, primarily due to the exhaustive matching between tree nodes. To address this challenge, we introduce the Merge Tree Neural Network (MTNN), a learned neural network model designed for merge tree c

Joshua Enwright, Fernando Figueroa, Joaquín Moraga

In this article, we study the geometry of log Calabi-Yau pairs $(X,B)$ of index one and birational complexity zero. Firstly, we propose a conjecture that characterizes such pairs $(X,B)$ in terms of their dual complex and the rationality of their log canonical places. Secondly, we show that for these pairs the open set $X\setminus B$ is divisorially covered

Micky Barthmann, Sohail Farhangi

We prove a uniform vector-valued Wiener-Wintner Theorem for a class of operators that includes compositions of ergodic Koopman operators with contractive multiplication operators. Our results are new even in the case of complex-valued functions, as they also apply to some non-positive non-contractive operators, and they give new uniform pointwise theorems fo

Shalini Saini, Nitesh Saxena

FemTech, a rising trend in mobile apps, empowers women to digitally manage their health and family planning. However, privacy and security vulnerabilities in period-tracking and fertility-monitoring apps present significant risks, such as unintended pregnancies and legal consequences. Our approach involves manual observations of privacy policies and app perm

Zifeng Wang, Chun-Liang Li, Vincent Perot, Long T. Le

Instruction tuning has emerged as the key in aligning large language models (LLMs) with specific task instructions, thereby mitigating the discrepancy between the next-token prediction objective and users' actual goals. To reduce the labor and time cost to collect or annotate data by humans, researchers start to explore the use of LLMs to generate instructio

Luis Morales-Navarro, Yasmin B. Kafai, Vedya Konda, Danaë Metaxa

As artificial intelligence/machine learning (AI/ML) applications become more pervasive in youth lives, supporting them to interact, design, and evaluate applications is crucial. This paper positions youth as auditors of their peers' ML-powered applications to better understand algorithmic systems' opaque inner workings and external impacts. In a two-week wor

Ninad Gaikwad, Shishir Lamichhane, Anamika Dubey

As the occurrence of extreme weather events is increasing so are the outages caused by them. During such unplanned outages, a house needs to be provided with an energy supply to maintain habitable conditions by maintaining thermal comfort and servicing at least critical loads. An energy system consisting of rooftop photovoltaic (PV) panels along with battery

Neelesh Gupta, Narayanan Kannan, Pengmiao Zhang, Viktor Prasanna

Convolutional Neural Networks (CNNs) have demonstrated remarkable ability throughout the field of computer vision. However, CNN inference requires a large number of arithmetic operations, making them expensive to deploy in hardware. Current approaches alleviate this issue by developing hardware-supported, algorithmic processes to simplify spatial convolution

Claus Fieker, Max Horn

OSCAR is an innovative new computer algebra system which combines and extends the power of its four cornerstone systems - GAP (group theory), Singular (algebra and algebraic geometry), Polymake (polyhedral geometry), and Antic (number theory). Assuming little familiarity with the subject, we give an introduction to computations in group theory using OSCAR, a

Aayush Jain, Philip Long, Valeria Villani, John D. Kelleher

Mass customization and shorter manufacturing cycles are becoming more important among small and medium-sized companies. However, classical industrial robots struggle to cope with product variation and dynamic environments. In this paper, we present CoBT, a collaborative programming by demonstration framework for generating reactive and modular behavior trees

Sudipta Chakraborty, Tamoghna Mukhopadhyay, Achintya Kumar Dutta

We have presented the theory, implementation, and benchmark results for the one-electronic variant of spin-free exact two-component (SFX2C1e) linear response coupled cluster (LRCCSD) theory for static and dynamic polarizabilities of atoms and molecules in the spin-adapted formulation. The resolution of identity (RI) approximation for two-electron integrals h

Ruiqi Zhang, Licong Lin, Yu Bai, Song Mei

Large Language Models (LLMs) often memorize sensitive, private, or copyrighted data during pre-training. LLM unlearning aims to eliminate the influence of undesirable data from the pre-trained model while preserving the model's utilities on other tasks. Several practical methods have recently been proposed for LLM unlearning, mostly based on gradient ascent

Isaac H. Kim, Ting-Chun Lin, Daniel Ranard, Bowen Shi

We show that in two spatial dimensions, when a quantum state has entanglement entropy obeying a strict area law, meaning $S(A)=\alpha |\partial A| - \gamma$ for constants $\alpha, \gamma$ independent of lattice region $A$, then it admits a commuting parent Hamiltonian. More generally, we prove that the entanglement bootstrap axioms in 2D imply the existence

Aida Mostafazadeh Davani, Sagar Gubbi, Sunipa Dev, Shachi Dave

Generative language models are transforming our digital ecosystem, but they often inherit societal biases, for instance stereotypes associating certain attributes with specific identity groups. While whether and how these biases are mitigated may depend on the specific use cases, being able to effectively detect instances of stereotype perpetuation is a cruc

Qi Dai, Ryan Davis, Houlin Hong, Ying Gu

Objectives: To assess the effectiveness of digital scanning techniques for self-assessment and of preparations and restorations in preclinical dental education when compared to traditional faculty grading. Methods: Forty-four separate Class I (#30-O), Class II (#30-MO) preparations, and class II amalgam restorations (#31-MO) were generated respectively under

Omar Alrabiah, Venkatesan Guruswami

We prove that a binary linear code of block length $n$ that is locally correctable with $3$ queries against a fraction $\delta > 0$ of adversarial errors must have dimension at most $O_{\delta}(\log^2 n \cdot \log \log n)$. This is almost tight in view of quadratic Reed-Muller codes being a $3$-query locally correctable code (LCC) with dimension $\Theta(\log

Rodrigo Aldana-Lopez, Richard Seeber, Hernan Haimovich, David Gomez-Gutierrez

The signal differentiation problem involves the development of algorithms that allow to recover a signal's derivatives from noisy measurements. This paper develops a first-order differentiator with the following combination of properties: robustness to measurement noise, exactness in the absence of noise, optimal worst-case differentiation error, and Lipschi

Ying-Lin Chen, Jacob Deforce, Vic De Ridder, Bappaditya Dey

Due to potential pitch reduction, the semiconductor industry is adopting High-NA EUVL technology. However, its low depth of focus presents challenges for High Volume Manufacturing. To address this, suppliers are exploring thinner photoresists and new underlayers/hardmasks. These may suffer from poor SNR, complicating defect detection. Vision-based ML algorit

Alexander J. Gates, Jianjian Gao, Indraneel Mane

Global science is often portrayed as a unified system of shared knowledge and open exchange. Yet this vision contrasts with emerging evidence that scientific recognition is uneven and increasingly fragmented along regional and cultural lines. Traditional models emphasize Western dominance in knowledge production but overlook regional dynamics, reinforcing a

Samen Hossein, Shannon Starr

Given $\pi \in S_n$, let $Z_{n,k}(\pi)=\sum_{1\leq i_1<\dots<i_k\leq n} \mathbf{1}(\{ \pi_{i_1}<\dots<\pi_{i_k}\}$ denote the number of increasing subsequences of length $k$. Consider the "generalized Ulam problem," studying the distribution of $Z_{n,k}$ for general $k$ and $n$. For the 2nd moment, Ross Pinsky initiated a combinatorial study by considering a

Ahmed Faisal Abdelrahman, Matias Valdenegro-Toro, Maren Bennewitz, Paul G. Plöger

Neuromorphic computing mimics computational principles of the brain in $\textit{silico}$ and motivates research into event-based vision and spiking neural networks (SNNs). Event cameras (ECs) exclusively capture local intensity changes and offer superior power consumption, response latencies, and dynamic ranges. SNNs replicate biological neuronal dynamics an

Roy Rabaglia, Ryan Barnett, Ari M. Turner

By examining rotating ferromagnetic spinor condensates through the perspective of large spin, we identify a novel type of topological point defects in the magnetization texture. These defects are not predicted by conventional homotopy analysis but rather by the Riemann-Hurwitz formula. The magnetization texture in the system is described by an equal-area map

Louis DeBiasio

The $r$-color size-Ramsey number of a graph $H$, denoted by $\widehat{R}_r(H)$, is the minimum number of edges in a graph $G$ having the property that every $r$-coloring of the edges of $G$ contains a monochromatic copy of $H$. Krivelevich proved that $\widehat{R}_r(P_{m+1})=\Omega(r^2m)$ where $P_{m+1}$ is the path on $m$ edges. He explains that his proof a

Alessio Marta

We prove a local well-posedness result for an evolution problem consisting of a semilinear wave equation with subcritical nonlinearities posed on a time-dependent compact Riemannian manifold and supplied with a nonlinear dynamical boundary condition of Wentzell type.

Martin Schlather

We define a general notion of entropy in elementary, algebraic terms. Based on that, weak forms of a scalar product and a distance measure are derived. We give basic properties of these quantities, generalize the Cauchy-Schwarz inequality, and relate our approach to the theory of scoring rules. Many supporting examples illustrate our approach and give new pe

Gabriel San Martín Silva, Enrique López Droguett

Fault Trees represent an essential tool in the reliability and risk assessment of engineering systems. By decomposing the structure of the system into Boolean function, Fault Trees allow the quantitative and qualitative analysis of the system. One of the main important tasks in Fault Tree analysis is the identification of Minimal Cut Sets, defined as groups

Duco van Straten

We describe a system of plane algebraic curves defined over \Z, attached naturally to the exponential function. On of these is a remarkable curve of degree 6 that has genus equal to 1. As the sectic curve has rational points, it is an elliptic curveand can be tranformed over \Q into the curve 1584.j1 of the LMFDB. One is left to wonder what the number 11, ap

Mihael Hategan-Marandiuc

Entanglement entropy, taken here to be geometric, requires a geometrically separable Hilbert space. In lattice gauge theories, it is not immediately clear if the physical Hilbert space is geometrically separable. In a previous paper we have shown that the physical Hilbert space in pure gauge abelian lattice theories exhibits some form of geometric scaling wi

Tianhao Ren, Yao Shen, Marton Lajer, Sophia F. R. TenHuisen

Although entanglement is both a central ingredient in our understanding of quantum many-body systems and an essential resource for quantum technologies, we only have a limited ability to quantify entanglement in real quantum materials. Thus far, entanglement metrology in quantum materials has been limited to measurements involving Hermitian operators, such a

Halil Ismail Helvaci, Sen-ching Samson Cheung, Chen-Nee Chuah, Sally Ozonoff

Autism Spectrum Disorder (ASD) presents significant challenges in early diagnosis and intervention, impacting children and their families. With prevalence rates rising, there is a critical need for accessible and efficient screening tools. Leveraging machine learning (ML) techniques, in particular Temporal Action Localization (TAL), holds promise for automat

Ivan Dimitrijevic, Branko Dragovich, Zoran Rakic, Jelena Stankovic

It is already known that a simple nonlocal de Sitter gravity model, which we denote as $\sqrt{dS}$ gravity, contains an exact vacuum cosmological solution which mimics dark energy and dark matter and is in very good agreement with the standard model of cosmology. This success of $\sqrt{dS}$ gravity motivated us to investigate how it works at lower than cosmi

Finlay McGee

Globally, artificial intelligence (AI) implementation is growing, holding the capability to fundamentally alter organisational processes and decision making. Simultaneously, this brings a multitude of emergent risks to organisations, exposing vulnerabilities in their extant risk management frameworks. This necessitates a greater understanding of how organisa

Ionut Predoaia, James Harbin, Simos Gerasimou, Christina Vasiliou

Tree-based model editors and hybrid graphical-textual model editors have advantages and limitations when editing domain models. Data is displayed hierarchically in tree-based model editors, whereas hybrid graphical-textual model editors capture high-level domain concepts graphically and low-level domain details textually. We conducted an empirical user study

Muhammad Sedik, Shijun Sun, Arthur P. Ramirez, Sergey Syzranov

Vacancy defects in disordered magnetic materials are known to act as effective spins, ``quasispins'', in response to an external magnetic field. In the dilute limit, the contributions of such ``quasispins'' to the magnetic susceptibility $\chi_\text{vac}(T)\propto N_\text{vac}/T$ are singular in the limit of low temperatures $T$ and match those of free spins

Ole Sönnerborn

Nonadiabatic holonomic quantum computation has been proposed as a method to implement quantum logic gates with robustness comparable to that of adiabatic holonomic gates but with shorter execution times. In this paper, we establish an isoholonomic inequality for quantum gates, which provides a lower bound on the lengths of cyclic transformations of the compu

Franz A. Heinsen

We propose a simple modification to the conventional attention mechanism applied by Transformers: Instead of quantifying pairwise query-key similarity with scaled dot-products, we quantify it with the logarithms of scaled dot-products of exponentials. Our modification linearizes attention with exponential kernel feature maps, whose corresponding feature func

Thomas Pasquale, Cristina Gena, Fabiana Vernero

This paper presents an empirical evaluation of mid-air gestures in a web setting. Fifty-six (56) subjects, all of them HCI students, were divided into 16 groups and involved as designers. Each group worked separately with the same requirements. Firstly, designers identified the main actions required for a web-based interaction with a university classroom sea

Jan-Tino Brethouwer, Bart van Ginkel, Roy Lindelauf

We introduce General Lotto games with Scouts: a General Lotto game with asymmetric information. There are two players, Red and Blue, who both allocate resources to a field. However, scouting capabilities afford Blue to gain information, with some probability, on the number of Red's resources before allocating his own. We derive optimal strategies for this ga

Andre R Kuroswiski, Annie S Wu, Angelo Passaro

In this paper, we introduce an alternative approach to enhancing Multi-Agent Reinforcement Learning (MARL) through the integration of domain knowledge and attention-based policy mechanisms. Our methodology focuses on the incorporation of domain-specific expertise into the learning process, which simplifies the development of collaborative behaviors. This app

Milan Straka, Jana Straková, Federica Gamba

We present LatinPipe, the winning submission to the EvaLatin 2024 Dependency Parsing shared task. Our system consists of a fine-tuned concatenation of base and large pre-trained LMs, with a dot-product attention head for parsing and softmax classification heads for morphology to jointly learn both dependency parsing and morphological analysis. It is trained

Pablo Arrighi, Marin Costes, Gilles Dowek, Luidnel Maignan

We study non-terminating graph rewriting models, whose local rules are applied non-deterministically -- and yet enjoy a strong form of determinism, namely space-time determinism. Of course in the case of terminating computation it is well-known that the mess introduced by asynchronous rule applications may not matter to the end result, as confluence conspire

Yu Jin, Mariami Rusishvili, Marco Govoni, Giulia Galli

We present a theoretical study of the formation of self-trapped excitons (STEs) and the associated broadband emission in metal-halide perovskites Cs$_4$SnBr$_6$ and Cs$_2$AgInCl$_6$, using time-dependent density functional theory (TDDFT) with the dielectric-dependent hybrid (DDH) functional. Our approach allows for an accurate description of the excitonic ef

Petr Prucha, Peter Madzik, Lukas Falat

Robotic Process Automation (RPA) has rapidly evolved into a widely recognized and influential software technology. Its growing relevance has sparked diverse research efforts across various disciplines. This study aims to map the scientific landscape of RPA by identifying key thematic areas, tracking their development over time, and assessing their academic i

Henrik Hose, Alexander Gräfe, Sebastian Trimpe

Model Predictive Control (MPC) is a method to control nonlinear systems with guaranteed stability and constraint satisfaction but suffers from high computation times. Approximate MPC (AMPC) with neural networks (NNs) has emerged to address this limitation, enabling deployment on resource-constrained embedded systems. However, when tuning AMPCs for real-world

Tengfei Luo, Zhijie Li, Zelong Yuan, Wenhui Peng

The Fourier neural operator (FNO) framework is applied to the large eddy simulation (LES) of three-dimensional compressible Rayleigh-Taylor (RT) turbulence with miscible fluids at Atwood number $A_t=0.5$, stratification parameter $Sr=1.0$, and Reynolds numbers $Re=10000$ and 30000. The FNO model is first used for predicting three-dimensional compressible tur

Xinzhi Zhong, Yang Zhou, Varshini Kamaraj, Zhenhao Zhou

This paper develops a novel car-following control method to reduce voluntary driver interventions and improve traffic stability in Automated Vehicles (AVs). Through a combination of experimental and empirical analysis, we show how voluntary driver interventions can instigate substantial traffic disturbances that are amplified along the traffic upstream. Moti

Negin Moharrami Allafi, Michael H. Kolodrubetz, Marin Bukov, Vadim Oganesyan

In the realm of open quantum systems, steady states and high-harmonic generation (HHG) existing far from equilibrium have become core pillars of ultrafast science. Most solid-state research explores charge HHG with limited investigations into spin degrees of freedom. In this study, we theoretically address spin HHG in the steady state resulting from the tera

Zoltan Csaki, Bo Li, Jonathan Li, Qiantong Xu

Despite the widespread availability of LLMs, there remains a substantial gap in their capabilities and availability across diverse languages. One approach to address these issues has been to take an existing pre-trained LLM and continue to train it on new languages. While prior works have experimented with language adaptation, many questions around best prac

Fabian Perez, Jhon Lopez, Henry Arguello

In the era of cloud computing and data-driven applications, it is crucial to protect sensitive information to maintain data privacy, ensuring truly reliable systems. As a result, preserving privacy in deep learning systems has become a critical concern. Existing methods for privacy preservation rely on image encryption or perceptual transformation approaches