October 2023 arXiv papers — page 131

Damião J. Araújo, Marco Magliaro, Luciano Mari, Leandro F. Pessoa

In this paper, we prove some splitting results for manifolds supporting a non-constant infinity harmonic function which has at most linear growth on one side. Manifolds with non-negative Ricci or sectional curvature are considered. In dimension 2, we extend Savin's theorem on Lipschitz infinity harmonic functions in the plane to every surface with non-negati

James T. Liu, Robert J. Saskowski

At the two-derivative order, the group manifold reduction of heterotic supergravity on $S^3$ results in a half-maximal 7D gauged supergravity coupled to three vector multiplets, and a further truncation can be taken to remove the vector multiplets. We demonstrate that this truncation remains consistent at the four-derivative level; we do so both by analysis

Gus Eggert, Kevin Huo, Mike Biven, Justin Waugh

It is well-established that large, diverse datasets play a pivotal role in the performance of modern AI systems for text and image modalities. However, there are no datasets for tabular data of comparable size and diversity to those available for text and images. Thus we present "TabLib'', a compilation of 627 million tables totaling 69 TiB, along with 867B

Christos Boutsikas, Petros Drineas, Marios Mertzanidis, Alexandros Psomas

We consider the problem of a revenue-maximizing seller with a large number of items $m$ for sale to $n$ strategic bidders, whose valuations are drawn independently from high-dimensional, unknown prior distributions. It is well-known that optimal and even approximately-optimal mechanisms for this setting are notoriously difficult to characterize or compute, a

Miguel N. Walsh

We introduce a strategy to tackle some known obstructions of current approaches to the Fourier uniformity conjecture. Assuming GRH, we then show the conjecture holds for intervals of length at least $(\log X)^{\psi(X)}$, with $\psi(X) \rightarrow \infty$ an arbitrarily slowly growing function of $X$. We expect the methods should adapt to nilsequences, thus a

Simone Martini, Kimon P. Valavanis, Margareta Stefanovic, Matthew J. Rutherford

This technical note proves analytically how the exact equivalence of the Newton-Euler and Euler-Lagrange modeling formulations as applied to multirotor UAVs is achieved. This is done by deriving a revised Euler-Lagrange multirotor attitude dynamics model. A review of the published literature reveals that the commonly adopted Euler-Lagrange multirotor dynamic

Patryk Mach, Adam Cieślik, Andrzej Odrzywolek

We develop a Monte Carlo simulation method for computing stationary solutions of the general-relativistic Vlasov equation describing a gas of non-colliding particles. As specific examples, we select planar or spherically symmetric accretion models on the Schwarzschild background spacetime. In all cases the gas extends to infinity, which poses an additional d

Xiaochen Wang, Junyu Luo, Jiaqi Wang, Ziyi Yin

Pretraining has proven to be a powerful technique in natural language processing (NLP), exhibiting remarkable success in various NLP downstream tasks. However, in the medical domain, existing pretrained models on electronic health records (EHR) fail to capture the hierarchical nature of EHR data, limiting their generalization capability across diverse downst

Damien van de Berg, Nilay Shah, Ehecatl Antonio del Rio-Chanona

Planning, scheduling, and control typically constitute separate decision-making units within chemical companies. Traditionally, their integration is modelled sequentially, but recent efforts prioritize lower-level feasibility and optimality, leading to large-scale, potentially multi-level, hierarchical formulations. Data-driven techniques, like optimality su

Yanbin He, Geethu Joseph

This paper studies the problem of Kronecker-structured sparse vector recovery from an underdetermined linear system with a Kronecker-structured dictionary. Such a problem arises in many real-world applications such as the sparse channel estimation of an intelligent reflecting surface-aided multiple-input multiple-output system. The prior art only exploits th

Anirudh Krishna, Inbal Livni Navon, Mary Wootters

Quantum low-density parity-check (LDPC) codes, a class of quantum error correcting codes, are considered a blueprint for scalable quantum circuits. To use these codes, one needs efficient decoding algorithms. In the classical setting, there are multiple efficient decoding algorithms available, including Viderman's algorithm (Viderman, TOCT 2013). Viderman's

Daniele Condorelli, Massimiliano Furlan

We simulate behaviour of two independent reinforcement learning algorithms playing the Crawford and Sobel (1982) game of strategic information transmission. We adopt memoryless algorithms to capture learning in a static game where a large population interacts anonymously. We show that sender and receiver converge to Nash equilibrium play. The level of inform

Matthieu Schaller

In his 2021 lecture to the Canadian Association of Physicists Congress, P.J.E. Peebles pointed out that the brightest extra-galactic radio sources tend to be aligned with the plane of the de Vaucouleur Local Supercluster up to redshifts of $z=0.02$ ($d_{\rm MW}\approx 85~\rm{Mpc}$). He then asked whether such an alignment of clusters is anomalous in the stan

Guillermo Angeris, Tarun Chitra, Theo Diamandis, Kshitij Kulkarni

Miner extractable value (MEV) refers to any excess value that a transaction validator can realize by manipulating the ordering of transactions. In this work, we introduce a simple theoretical definition of the 'cost of MEV', prove some basic properties, and show that the definition is useful via a number of examples. In a variety of settings, this definition

Kin Long Kelvin Lee, Carmelo Gonzales, Matthew Spellings, Mikhail Galkin

Artificial intelligence and machine learning have shown great promise in their ability to accelerate novel materials discovery. As researchers and domain scientists seek to unify and consolidate chemical knowledge, the case for models with potential to generalize across different tasks within materials science - so-called "foundation models" - grows with amb

Goran Sandell, William Vacca

High resolution spectra with iSHELL on IRTF in the K and M band of the young, heavily accreting B 1.5e star MWC297 show numerous double-peaked CO lines. These CO lines originate in an inclined gaseous disk in Keplerian rotation. MWC297 is the only early B star known to show a Keplerian disk in CO. Analysis of the spectra show that 12CO 1 - 0 is optically thi

Yu Chen, Zihan Tan

In the Steiner point removal (SPR) problem, we are given a (weighted) graph $G$ and a subset $T$ of its vertices called terminals, and the goal is to compute a (weighted) graph $H$ on $T$ that is a minor of $G$, such that the distance between every pair of terminals is preserved to within some small multiplicative factor, that is called the stretch of $H$. I

Olena Burkovska

Phase-field models are a popular choice in computational physics to describe complex dynamics of substances with multiple phases and are widely used in various applications. We present nonlocal non-isothermal phase-field models of Cahn-Hilliard and Allen-Cahn types involving a nonsmooth double-well obstacle potential. Mathematically, in a weak form, the mode

Roldao da Rocha

AdS$_4$ generalized extremal branes are scrutinized in the context of AdS/CFT. Holographic superconductors are studied as dual objects to AdS$_4$ generalized black branes, whose coefficients of response and transport in the dual condensed matter theory are calculated and discussed. The holographic Weyl anomaly is also addressed. The holographic superconducto

Antonis Kyprianidis, A. J. Rasmusson, Philip Richerme

Trapped-ion quantum simulators have demonstrated a long history of studying the physics of interacting spin-lattice systems using globally addressed entangling operations. Here, we seek to broaden and delimit the classes of effective spin-spin interactions achievable using exclusively global driving fields. We find that new categories of interaction graphs b

Ankit Kulshrestha, Danylo Lykov, Ilya Safro, Yuri Alexeev

The current era of quantum computing has yielded several algorithms that promise high computational efficiency. While the algorithms are sound in theory and can provide potentially exponential speedup, there is little guidance on how to design proper quantum circuits to realize the appropriate unitary transformation to be applied to the input quantum state.

Yu Chen, Zihan Tan

Given a large graph $G$ with a subset $|T|=k$ of its vertices called terminals, a quality-$q$ flow sparsifier is a small graph $G'$ that contains $T$ and preserves all multicommodity flows that can be routed between terminals in $T$, to within factor $q$. The problem of constructing flow sparsifiers with good (small) quality and (small) size has been a centr

Jiho Shin, Hadi Hemmati, Moshi Wei, Song Wang

In this work, we revisit existing oracle generation studies plus ChatGPT to empirically investigate the current standing of their performance in both NLG-based and test adequacy metrics. Specifically, we train and run four state-of-the-art test oracle generation models on five NLG-based and two test adequacy metrics for our analysis. We apply two different c

Tim Lebailly, Thomas Stegmüller, Behzad Bozorgtabar, Jean-Philippe Thiran

Leveraging nearest neighbor retrieval for self-supervised representation learning has proven beneficial with object-centric images. However, this approach faces limitations when applied to scene-centric datasets, where multiple objects within an image are only implicitly captured in the global representation. Such global bootstrapping can lead to undesirable

Yu-Shun Hsiao, Siva Kumar Sastry Hari, Balakumar Sundaralingam, Jason Yik

High-dimensional motion generation requires numerical precision for smooth, collision-free solutions. Typically, double-precision or single-precision floating-point (FP) formats are utilized. Using these for big tensors imposes a strain on the memory bandwidth provided by the devices and alters the memory footprint, hence limiting their applicability to low-

Chen Chen, Junqing Zhang, Yingying Chen

Physical layer key generation based on reciprocal and random wireless channels has been an attractive solution for securing resource-constrained low-power wide-area networks (LPWANs). When quantizing channel measurements, namely received signal strength indicator (RSSI), into key bits, the existing works mainly adopt fixed quantization levels and guard band

Saptarshi Roy, Zehua Wang, Ambuj Tewari

We consider the problem of model selection in a high-dimensional sparse linear regression model under privacy constraints. We propose a differentially private (DP) best subset selection method with strong statistical utility properties by adopting the well-known exponential mechanism for selecting the best model. To achieve computational expediency, we propo

Rohan Hore, Rina Foygel Barber

In this work, we consider the problem of building distribution-free prediction intervals with finite-sample conditional coverage guarantees. Conformal prediction (CP) is an increasingly popular framework for building such intervals with distribution-free guarantees, but these guarantees only ensure marginal coverage: the probability of coverage is averaged o

Zhuoyan Li, Hangxiao Zhu, Zhuoran Lu, Ming Yin

The collection and curation of high-quality training data is crucial for developing text classification models with superior performance, but it is often associated with significant costs and time investment. Researchers have recently explored using large language models (LLMs) to generate synthetic datasets as an alternative approach. However, the effective

Hrishikesh Terdalkar, Arnab Bhattacharya

Sanskrit (sa\d{m}sk\d{r}ta) enjoys one of the largest and most varied literature in the whole world. Extracting the knowledge from it, however, is a challenging task due to multiple reasons including complexity of the language and paucity of standard natural language processing tools. In this paper, we target the problem of building knowledge graphs for part

Abbas Javan Jafari, Diego Elias Costa, Ahmad Abdellatif, Emad Shihab

Relying on dependency packages accelerates software development, but it also increases the exposure to security vulnerabilities that may be present in dependencies. While developers have full control over which dependency packages (and which version) they use, they have no control over the dependencies of their dependencies. Such transitive dependencies, whi

Yingjie Li, Mingju Liu, Mark Ren, Alan Mishchenko

The key methodologies of modern logic synthesis techniques are conducted on multi-level technology-independent representations such as And-Inverter-Graphs (AIGs) of the digital logic via directed-acyclic-graph (DAGs) traversal based structural rewriting, resubstitution, and refactoring. Existing state-of-the-art DAG-aware logic synthesis algorithms are all d

Tae Moon Jeong, Sergei V. Bulanov, Petr Valenta, Prokopis Hadjisolomou

The relativistic flying parabolic mirror can provide a higher laser intensity than the intensity a current laser system can reach via the optical-focusing scheme. A weakly-relativistic laser intensity (1.8$\times$10$^{17}$ W/cm$^2$, $\eta$ = 0.29) can be intensified up to a super-strong intensity of >1$\times$10$^{27}$ W/cm$^2$ ($\eta$ $\approx$ 2.2$\times$1

Jack T. Parley, Peter Sollich

Developing a unified theory describing both ductile and brittle yielding constitutes a fundamental challenge of non-equilibrium statistical physics. Recently, it has been proposed that the nature of the yielding transition is controlled by physics akin to that of the quasistatically driven Random field Ising model (RFIM), which has served as the paradigm for

Jianwei Liu, Maria Stamatopoulou, Dimitrios Kanoulas

In this work, we present DiPPeR, a novel and fast 2D path planning framework for quadrupedal locomotion, leveraging diffusion-driven techniques. Our contributions include a scalable dataset generator for map images and corresponding trajectories, an image-conditioned diffusion planner for mobile robots, and a training/inference pipeline employing CNNs. We va

Andrei O. Barvinsky, Alexander V. Kurov, Sergey M. Sibiryakov

We investigate the renormalization group flow of projectable Horava gravity in $(3+1)$ dimensions generated by marginal operators with respect to the Lifshitz scaling. The flow possesses a number of asymptotically free fixed points. We find a family of trajectories connecting one of these fixed points in the ultraviolet to the region of the parameter space w

Jacob Knaup, Jovin D'sa, Behdad Chalaki, Tyler Naes

Merging into dense highway traffic for an autonomous vehicle is a complex decision-making task, wherein the vehicle must identify a potential gap and coordinate with surrounding human drivers, each of whom may exhibit diverse driving behaviors. Many existing methods consider other drivers to be dynamic obstacles and, as a result, are incapable of capturing t

Iván Fernández-Val, Aico van Vuuren, Francis Vella

Using CPS data for 1976 to 2022 we explore how wage inequality has evolved for married couples with both spouses working full time full year, and its impact on household income inequality. We also investigate how marriage sorting patterns have changed over this period. To determine the factors driving income inequality we estimate a model explaining the join

Qingyue Zhao, Banghua Zhu

We characterize the statistical efficiency of knowledge transfer through $n$ samples from a teacher to a probabilistic student classifier with input space $\mathcal S$ over labels $\mathcal A$. We show that privileged information at three progressive levels accelerates the transfer. At the first level, only samples with hard labels are known, via which the m

Mingyang Deng, Lucas Tao, Joe Benton

Recent works have proposed that activations in language models can be modelled as sparse linear combinations of vectors corresponding to features of input text. Under this assumption, these works aimed to reconstruct feature directions using sparse coding. We develop metrics to assess the success of these sparse coding techniques and test the validity of the

Marzie Ghorbani, Zhipeng Li, Nick Birbilis

The utility of machine learning (ML) techniques in materials science has accelerated materials design and discovery. However, the accuracy of ML models - particularly deep neural networks - heavily relies on the quality and quantity of the training data. Data collection methods often have limitations arising from cost, difficulty, and resource-intensive huma

Arghya Majee, Christoph A. Weber, Frank Jülicher

We present a theory for phase-separated liquid coacervates with salt, taking into account spatial heterogeneities and interfacial profiles. We find that charged layers of alternating sign can form around the interface while the bulk phases remain approximately charge-neutral. We show that the salt concentration regulates the number of layers and the amplitud

Francesco Vissani

We retrace the first steps towards understanding neutrinos, particles predicted by Pauli in 1930 to avoid a supposed violation of time-translation symmetry. Despite the tendency to reduce the whole story to his intuition and the skill of Reines & Cowan, according to history great strides were made in thirties thanks to precious intellectual tools that combin

Lu Luo, Mahmoud RM Atalla, Simone Assali, Sebastian Koelling

Cost-effective mid-wave infrared (MWIR) optoelectronic devices are of utmost importance to a plethora of applications such as night vision, thermal sensing, autonomous vehicles, free-space communication, and spectroscopy. To this end, leveraging the ubiquitous silicon-based processing has emerged as a powerful strategy that can be accomplished through the us

Duncan Fuehne, Tobias Heibges

The Extreme Universe Space Observatory on a Super Pressure Balloon II (EUSO-SPB2) measured extensive air showers (EASs) from upward-going High Energy Cosmic Rays by flying a Cherenkov Telescope (CT) at 33 km altitude. The telescope could be tilted just above the Earth's limb, 5.8 degrees below horizontal, and 650 km away as viewed from the balloon. This conf

Aaron Defazio, Ashok Cutkosky, Harsh Mehta, Konstantin Mishchenko

Learning rate schedules used in practice bear little resemblance to those recommended by theory. We close much of this theory/practice gap, and as a consequence are able to derive new problem-adaptive learning rate schedules. Our main technical contribution is a refined analysis of learning rate schedules for a wide class of optimization algorithms (includin

Sia Gholami, Marwan Omar

Natural Language Processing (NLP) has undergone transformative changes with the advent of deep learning methodologies. One challenge persistently confronting researchers is the scarcity of high-quality, annotated datasets that drive these models. This paper explores the nuances of synthetic data generation in NLP, with a focal point on template-based questio

Yan-Fei Jiang

We review our current understanding on the outer envelope structures of massive stars based on three dimensional (3D) radiation hydrodynamic simulations. We briefly summarize the fundamental issues to construct hydrostatic one dimensional (1D) stellar evolution models when stellar luminosity approaches the Eddington value. Radiation hydrodynamic simulations

Manasi Mandal, Abhijatmedhi Chotrattanapituk, Kevin Woller, Haowei Xu

The precise controllability of the Fermi level is a critical aspect of quantum materials. For topological Weyl semimetals, there is a pressing need to fine-tune the Fermi level to the Weyl nodes and unlock exotic electronic and optoelectronic effects associated with the divergent Berry curvature. However, in contrast to 2D materials, where the Fermi level ca

Evan Fitzmaurice, Gudmundur Stefánsson, Robert D. Kavanagh, Suvrath Mahadevan

The LHS 1610 system consists of a nearby ($d=9.7$ pc) M5 dwarf hosting a candidate brown dwarf companion in a $10.6$ day, eccentric ($e \sim 0.37$) orbit. We confirm this brown dwarf designation and estimate its mass ($ 49.5_{-3.5}^{+4.3}$ $M_{\text{Jup}} $) and inclination ($ 114.5^\circ$ $_{-10.0}^{+7.4}$) by combining discovery radial velocities (RVs) fro

Hrishikesh Terdalkar, Arnab Bhattacharya

One of the primary obstacles in the advancement of Natural Language Processing (NLP) technologies for low-resource languages is the lack of annotated datasets for training and testing machine learning models. In this paper, we present Antarlekhaka, a tool for manual annotation of a comprehensive set of tasks relevant to NLP. The tool is Unicode-compatible, l

Riddhi S. Gupta, Ewout van den Berg, Maika Takita, Diego Riste

Probabilistic error cancellation (PEC) is a technique that generates error-mitigated estimates of expectation values from ensembles of quantum circuits. In this work we extend the application of PEC from unitary-only circuits to dynamic circuits with measurement-based operations, such as mid-circuit measurements and classically-controlled (feedforward) Cliff

Beyza Zeynep Ucpinar, Mustafa Altay Karamuftuoglu, Sasan Razmkhah, Massoud Pedram

We present an on-chip trainable neuron circuit. Our proposed circuit suits bio-inspired spike-based time-dependent data computation for training spiking neural networks (SNN). The thresholds of neurons can be increased or decreased depending on the desired application-specific spike generation rate. This mechanism provides us with a flexible design and scala

Andrés Anabalón, Horatiu Nastase

We show that the d'Alembertian operator with a possible mass term in the AdS soliton and more general confining gravity dual backrounds admits infinitely many different spectra. These can be interpreted as different theories in the infrared and correspond to multitrace deformations of either the Dirichlet or the Neumann theory. We prove that all these fluctu

Zhefeng Huang, Anthony L. Gunderman, Samuel E. Wilcox, Saikat Sengupta

MR-guided microwave ablation (MWA) has proven effective in treating hepatocellular carcinoma (HCC) with small-sized tumors, but the state-of-the-art technique suffers from sub-optimal workflow due to speed and accuracy of needle placement. This paper presents a compact body-mounted MR-conditional robot that can operate in closed-bore MR scanners for accurate

Yu Zhang, Yue Zhang, Leyang Cui, Guohong Fu

Recent work has witnessed a paradigm shift from Seq2Seq to Seq2Edit in the field of text editing, with the aim of addressing the slow autoregressive inference problem posed by the former. Despite promising results, Seq2Edit approaches still face several challenges such as inflexibility in generation and difficulty in generalizing to other languages. In this

Nate Gruver, Marc Finzi, Shikai Qiu, Andrew Gordon Wilson

By encoding time series as a string of numerical digits, we can frame time series forecasting as next-token prediction in text. Developing this approach, we find that large language models (LLMs) such as GPT-3 and LLaMA-2 can surprisingly zero-shot extrapolate time series at a level comparable to or exceeding the performance of purpose-built time series mode

Andreas Madsen, Siva Reddy, Sarath Chandar

A common approach to explaining NLP models is to use importance measures that express which tokens are important for a prediction. Unfortunately, such explanations are often wrong despite being persuasive. Therefore, it is essential to measure their faithfulness. One such metric is if tokens are truly important, then masking them should result in worse model

Thilini Wijesiriwardene, Ruwan Wickramarachchi, Aishwarya Naresh Reganti, Vinija Jain

The ability of Large Language Models (LLMs) to encode syntactic and semantic structures of language is well examined in NLP. Additionally, analogy identification, in the form of word analogies are extensively studied in the last decade of language modeling literature. In this work we specifically look at how LLMs' abilities to capture sentence analogies (sen

Satarupa Bhattacharjee, Bing Li, Lingzhou Xue

Random objects are complex non-Euclidean data taking value in general metric space, possibly devoid of any underlying vector space structure. Such data are getting increasingly abundant with the rapid advancement in technology. Examples include probability distributions, positive semi-definite matrices, and data on Riemannian manifolds. However, except for r

Isaiah A. Moses, Wesley F. Reinhart

Isolating the features associated with different materials growth conditions is important to facilitate the tuning of these conditions for effective materials growth and characterization. This study presents machine learning models for classifying atomic force microscopy (AFM) images of thin film MoS$_2$ based on their growth temperatures. By employing nine

Bowen Jin, Hansi Zeng, Guoyin Wang, Xiusi Chen

Semantic identifier (ID) is an important concept in information retrieval that aims to preserve the semantics of objects such as documents and items inside their IDs. Previous studies typically adopt a two-stage pipeline to learn semantic IDs by first procuring embeddings using off-the-shelf text encoders and then deriving IDs based on the embeddings. Howeve

Arman Maesumi, Paul Guerrero, Vladimir G. Kim, Matthew Fisher

Exploring variations of 3D shapes is a time-consuming process in traditional 3D modeling tools. Deep generative models of 3D shapes often feature continuous latent spaces that can, in principle, be used to explore potential variations starting from a set of input shapes. In practice, doing so can be problematic: latent spaces are high dimensional and hard to

Monica Conte, Vinicius Zampronio, Malte Röntgen, Cristiane Morais Smith

Here, we investigate the fractal-lattice Hubbard model using various numerical methods: exact diagonalization, the self-consistent diagonalization of a (mean-field) Hartree-Fock Hamiltonian and state-of-the-art Auxiliary-Field Quantum Monte Carlo. We focus on the Sierpinski triangle with Hausdorff dimension $1.58$ and consider several generations. In the tig

Gellért Weisz, András György, Csaba Szepesvári

We consider online reinforcement learning (RL) in episodic Markov decision processes (MDPs) under the linear $q^\pi$-realizability assumption, where it is assumed that the action-values of all policies can be expressed as linear functions of state-action features. This class is known to be more general than linear MDPs, where the transition kernel and the re

Freiman Triana-Arango, Roberto Ramírez-Alarcón, Gabriel Ramos-Ortiz

Recently different experimental schemes have been proposed to study the elusive phenomenon of entangled two-photon absorption (ETPA) in nonlinear materials. The attempts to detect ETPA using transmission-based schemes have led to results whose validity is currently under debate since the ETPA signal can be corrupted or emulated by artifacts associated with l

Anuran Makur, Marios Mertzanidis, Alexandros Psomas, Athina Terzoglou

We study the problem of designing mechanisms when agents' valuation functions are drawn from unknown and correlated prior distributions. In particular, we are given a prior distribution $\D$, and we are interested in designing a (truthful) mechanism that has good performance for all ``true distributions'' that are close to $\D$ in Total Variation (TV) distan

Akanksha Singh, S. Saha Ray

This paper presents an efficient numerical technique for solving multi-dimensional fractional optimal control problems using fractional-order generalized Bernoulli wavelets. The numerical results obtained by this method have been compared with the results obtained by the method using orthonormal Bernoulli wavelets. Using fractional-order generalized Bernoull

Ensiye Kiyamousavi, Boris Kraychev, Ivan Koychev

Federated learning (FL) is a decentralized machine learning approach where independent learners process data privately. Its goal is to create a robust and accurate model by aggregating and retraining local models over multiple rounds. However, FL faces challenges regarding data heterogeneity and model aggregation effectiveness. In order to simulate real-worl

Hassan Hessari, Fatemeh Daneshmandi, Tahmineh Nategh

Purpose: Technology plays a pivotal role in shaping the fate of organizations, both positively and negatively. One of its detrimental consequences is the emergence of "Technostress," a form of destructive stress. This paper investigates the impact of technostress on Perceived Organizational Commitment (POC) through the lens of individual innovation. The obje

Tianrong Chen, Jiatao Gu, Laurent Dinh, Evangelos A. Theodorou

Diffusion models (DMs) represent state-of-the-art generative models for continuous inputs. DMs work by constructing a Stochastic Differential Equation (SDE) in the input space (ie, position space), and using a neural network to reverse it. In this work, we introduce a novel generative modeling framework grounded in \textbf{phase space dynamics}, where a phas

Javier Martínez

This article proposes a cognitive mechanism of humour of general applicability, not restricted to verbal communication. It is indebted to Raskin's concept of script overlap, and conforms to the incongruity-resolution theoretical framework, but it is built on the notion of constraint, an abstract correspondence between sets of data. Under this view, script ov

Lindsay Sanneman, Mycal Tucker, Julie Shah

Recent advances in artificial intelligence (AI) have underscored the need for explainable AI (XAI) to support human understanding of AI systems. Consideration of human factors that impact explanation efficacy, such as mental workload and human understanding, is central to effective XAI design. Existing work in XAI has demonstrated a tradeoff between understa

Bahareh Nikpour, Narges Armanfard

Attention mechanisms have demonstrated significant potential in enhancing learning models by identifying key portions of input data, particularly in scenarios with limited training samples. Inspired by human perception, we propose that focusing on essential data segments, rather than the entire dataset, can improve the accuracy and reliability of the learnin

Zhongji Zhang, Yuhang Wang, Yinghao Zhu, Xinyu Ma

Emerging diseases present challenges in symptom recognition and timely clinical intervention due to limited available information. An effective prognostic model could assist physicians in making accurate diagnoses and designing personalized treatment plans to prevent adverse outcomes. However, in the early stages of disease emergence, several factors hamper

Y. Henry Wen, David Heim, Matthew Zimmermann, Roman A. Shugayev

Numerous applications in quantum and classical optics require scalable, high-speed modulators that cover visible-NIR wavelengths with low footprint, drive voltage (V) and power dissipation. A critical figure of merit for electro-optic (EO) modulators is the transmission change per voltage, dT/dV. Conventional approaches in wave-guided modulators seek to maxi

Mingrui Jing, Geng Liu, Hongbin Ren, Xin Wang

Learning probability distribution is an essential framework in classical learning theory. As a counterpart, quantum state learning has spurred the exploration of quantum machine learning theory. However, as dimensionality increases, learning a high-dimensional unknown quantum state via conventional quantum neural network approaches remains challenging due to

Fernando Alvarado, Di An, Luis Alvarez-Ruso, Stefan Leupold

The nucleon isovector electromagnetic form factors are calculated up to next-to-next-to-leading order by combining relativistic chiral perturbation theory (ChPT) of pion, nucleon, and $\Delta$(1232) with dispersion theory. We specifically address the light-quark mass dependence of the form factors, achieving a good description of recent Lattice QCD results o

Siru Ouyang, Jiaxin Huang, Pranav Pillai, Yunyi Zhang

Fine-grained entity typing (FET) is the task of identifying specific entity types at a fine-grained level for entity mentions based on their contextual information. Conventional methods for FET require extensive human annotation, which is time-consuming and costly. Recent studies have been developing weakly supervised or zero-shot approaches. We study the se

Changhe Chen, Mozhgan Pourkeshavarz, Amir Rasouli

Benchmarking is a common method for evaluating trajectory prediction models for autonomous driving. Existing benchmarks rely on datasets, which are biased towards more common scenarios, such as cruising, and distance-based metrics that are computed by averaging over all scenarios. Following such a regiment provides a little insight into the properties of the

Ruotong Liao, Xu Jia, Yangzhe Li, Yunpu Ma

The rapid advancements in large language models (LLMs) have ignited interest in the temporal knowledge graph (tKG) domain, where conventional embedding-based and rule-based methods dominate. The question remains open of whether pre-trained LLMs can understand structured temporal relational data and replace them as the foundation model for temporal relational

Lei Chu, Abdullah Alghafis, Andreas F. Molisch

Localization in GPS-denied outdoor locations, such as street canyons in an urban or metropolitan environment, has many applications. Machine Learning (ML) is widely used to tackle this critical problem. One challenge lies in the mixture of line-of-sight (LOS), obstructed LOS (OLOS), and non-LOS (NLOS) conditions. In this paper, we consider a semantic localiz

Joshua W. Foster, Christina Gao, William Halperin, Yonatan Kahn

The homogeneous precession domain (HPD) of superfluid $^{3}$He has recently been identified as a detection medium which might provide sensitivity to the axion-nucleon coupling $g_{aNN}$ competitive with, or surpassing, existing experimental proposals. In this work, we make a detailed study of the statistical and dynamical properties of the HPD system in orde

Martin Feldkircher, Karin Klieber

This paper examines the degree of integration at euro area financial markets. To that end, we estimate overall and country-specific integration indices based on a panel vector-autoregression with factor stochastic volatility. Our results indicate a more heterogeneous bond market compared to the market for lending rates. At both markets, the global financial

Oluwasola E. Omoju, Emily E. Ikhide, Iyabo A. Olanrele, Lucy E. Abeng

Youth unemployment is a major socioeconomic problem in Nigeria, and several youth-employment programs have been initiated and implemented to address the challenge. While detailed analyses of the impacts of some of these programs have been conducted, empirical analysis of implementation challenges and of the influence of limited political inclusivity on distr

Sumit Mahajan, Arbaz Khan

In this paper, the numerical approximation of the generalized Burgers'-Huxley equation (GBHE) with weakly singular kernels using non-conforming methods will be presented. Specifically, we discuss two new formulations. The first formulation is based on the non-conforming finite element method (NCFEM). The other formulation is based on discontinuous Galerkin f

Philip Wong, Phue Thant, Pratiksha Yadav, Ruta Antaliya

This paper discusses predictive performance and processes undertaken on flight pricing data utilizing r2(r-square) and RMSE that leverages a large dataset, originally from Expedia.com, consisting of approximately 20 million records or 4.68 gigabytes. The project aims to determine the best models usable in the real world to predict airline ticket fares for no

Zheqing Zhu, Yueyang Liu, Xu Kuang, Benjamin Van Roy

Real-world applications of contextual bandits often exhibit non-stationarity due to seasonality, serendipity, and evolving social trends. While a number of non-stationary contextual bandit learning algorithms have been proposed in the literature, they excessively explore due to a lack of prioritization for information of enduring value, or are designed in wa

Simon Henry, Christopher Townsend

We construct a localic groupoid $\mathbb{G}_{KH}$ such that for any locale $X$ the category of compact Hausdorff locales in the topos of sheaves over $X$ is equivalent to a category whose objects are principal $\mathbb{G}_{KH}$-bundles over $X$ and whose morphisms are $\mathbb{S}$-homotopies (where $\mathbb{S}$ is the Sierpi\'{n}ski locale). This result can

Ping Chen, Avishay Gal-Yam, Jesper Sollerman, Steve Schulze

Neutron stars and stellar-mass black holes are the remnants of massive star explosions. Most massive stars reside in close binary systems, and the interplay between the companion star and the newly formed compact object has been theoretically explored, but signatures for binarity or evidence for the formation of a compact object during a supernova explosion

Allen Jian Yang, Liang Wu, Yanran Liu, Xinyu Zhang

Correlated oxides and related heterostructures are intriguing for developing future multifunctional devices by exploiting their exotic properties, but their integration with other materials, especially on Si-based platforms, is challenging. Here, van der Waals heterostructures of La$_{0.7}$Sr$_{0.3}$MnO$_3$ (LSMO), a correlated manganite perovskite, and MoS$

Caleb Tung, Nicholas Eliopoulos, Purvish Jajal, Gowri Ramshankar

Computer vision often uses highly accurate Convolutional Neural Networks (CNNs), but these deep learning models are associated with ever-increasing energy and computation requirements. Producing more energy-efficient CNNs often requires model training which can be cost-prohibitive. We propose a novel, automated method to make a pretrained CNN more energy-eff

Jieneng Chen, Jieru Mei, Xianhang Li, Yongyi Lu

Medical image segmentation plays a crucial role in advancing healthcare systems for disease diagnosis and treatment planning. The u-shaped architecture, popularly known as U-Net, has proven highly successful for various medical image segmentation tasks. However, U-Net's convolution-based operations inherently limit its ability to model long-range dependencie

Linbo Liu, Trong Nghia Hoang, Lam M. Nguyen, Tsui-Wei Weng

Randomized smoothing has recently attracted attentions in the field of adversarial robustness to provide provable robustness guarantees on smoothed neural network classifiers. However, existing works show that vanilla randomized smoothing usually does not provide good robustness performance and often requires (re)training techniques on the base classifier in

Julie Jiang, Luca Luceri, Joseph B. Walther, Emilio Ferrara

Online hate messaging is a pervasive issue plaguing the well-being of social media users. This research empirically investigates a novel theory positing that online hate may be driven primarily by the pursuit of social approval rather than a direct desire to harm the targets. Results show that toxicity is homophilous in users' social networks and that a user

Oren Ben-Bassat, Sourav Das, Tony Pantev

In this article, we construct a flat degeneration of the derived moduli stack of Higgs bundles on smooth curves using the stack of expanded degenerations of Jun Li. We show that there is an intrinsic relative log-symplectic form on the degeneration and we compare it with the one constructed by the second author. We show that the Hitchin map of the degenerati

Dawid Brzeminski, Anson Hook

The near equality of the dark matter and baryon energy densities is a remarkable coincidence, especially when one realizes that the baryon mass is exponentially sensitive to UV parameters in the form of dimensional transmutation. We explore a new dynamical mechanism, where in the presence of an arbitrary number density of baryons and dark matter, a scalar ad

Kimmo Kainulainen, Harri Parkkinen

We develop a formalism to model neutrino evolution encompassing both flavor and particle-antiparticle mixings and decohering collisions. Our results include a quantum kinetic equation (a set of coupled scalar equations) for the generalized neutrino density matrix, valid for arbitrary neutrino masses and kinematics, and a comprehensive set of Feynman rules to

Myeongjae Lee

Generalized strata of meromorphic differentials are loci in the usual strata of differentials, where certain sets of residues sum up to zero. They appear naturally in the boundary of the multi-scale compactification of the usual strata. Enumerating the connected components of generalized strata is necessary to understand the boundary complex of the multi-sca

Oscar Watts, Yuta Kikuchi, Luuk Coopmans

Semidefinite programs (SDPs) are a particular class of convex optimization problems with applications in combinatorial optimization, operational research, and quantum information science. Seminal work by Brand\~{a}o and Svore shows that a ``quantization'' of the matrix multiplicative-weight algorithm can provide approximate solutions to SDPs quadratically fa