July 2023 arXiv papers — page 27

Y. Y. Lau, Dion Li, David P. Chernin

We consider the limiting current from an emitting patch whose size is much smaller than the anode-cathode spacing. The limiting current is formulated in terms of an integral equation. It is solved iteratively, first to numerically recover the classical one-dimensional Child-Langmuir law, including Jaffe's extension to a constant, nonzero electron emission ve

Alexander Liu, Siqi Wu, Paul Resnick

YouTube provides features for users to indicate disinterest when presented with unwanted recommendations, such as the "Not interested" and "Don't recommend channel" buttons. These buttons purportedly allow the user to correct "mistakes" made by the recommendation system. Yet, relatively little is known about the empirical efficacy of these buttons. Neither i

Kristina Crona

We use fitness graphs, or directed cube graphs, for analyzing evolutionary reversibility. The main application is antimicrobial drug resistance. Reversible drug resistance has been observed both clinically and experimentally. If drug resistance depends on a single point mutation, then a possible scenario is that the mutation reverts back to the wild-type cod

Jianjun Yuan, Wei Lee Woon, Ludovik Coba

This paper presents an efficient algorithm to solve the sleeping bandit with multiple plays problem in the context of an online recommendation system. The problem involves bounded, adversarial loss and unknown i.i.d. distributions for arm availability. The proposed algorithm extends the sleeping bandit algorithm for single arm selection and is guaranteed to

Rinto Anugraha NQZ, Roni Muslim, Henokh Lugo Hariyanto, Fahrudin Nugroho

The order-disorder phase transition is a fascinating phenomenon in opinion dynamics models within sociophysics. This transition emerges due to noise parameters, interpreted as social behaviors such as anticonformity and independence (nonconformity) in a social context. In this study, we examine the impact of nonconformist behaviors on the macroscopic states

Yutong Wen, You Zhang, Zhiyao Duan

Individualized head-related transfer functions (HRTFs) are crucial for accurate sound positioning in virtual auditory displays. As the acoustic measurement of HRTFs is resource-intensive, predicting individualized HRTFs using machine learning models is a promising approach at scale. Training such models require a unified HRTF representation across multiple d

Robert C. Elliott, Witold A. Krzymień

In this paper, we prove that two integrals from Gradshteyn and Ryzhik (2014) [1] (namely, Eqs. 3.937 1 and 3.937 2) provide incorrect results in certain conditions. We derive those conditions herein and provide the corrections required for those two formulas. We furthermore derive improved formulas for the solutions to those integrals that are less complicat

Jesús Hernández Hernández

This work is the extension of the results by the author in [7] and [6] for low-genus surfaces. Let $S$ be an orientable, connected surface of finite topological type, with genus $g \leq 2$, empty boundary, and complexity at least $2$; as a complement of the results of [6], we prove that any graph endomorphism of the curve graph of $S$ is actually an automorp

A. Ahmine, I. C. Bandac, A. S. Barabash, V. Berest

In preparation to the CROSS $2β$ decay experiment, we installed a new detector suspension with magnetic dumping inside a pulse-tube cryostat of a dedicated low-background facility at the LSC (Spain). The suspension was tested with two scintillating bolometers based on large-volume 116CdWO4 (CWO-enr) and Li2MoO4 (LMO) crystals. The former, a reference device,

Emanuela Guglielmi, Simone Scalabrino, Gabriele Bavota, Rocco Oliveto

Context. The game industry is increasingly growing in recent years. Every day, millions of people play video games, not only as a hobby, but also for professional competitions (e.g., e-sports or speed-running) or for making business by entertaining others (e.g., streamers). The latter daily produce a large amount of gameplay videos in which they also comment

Yue Li, Shijie Shang, Jianliang Zhai

In this paper, we consider stochastic reaction-diffusion equations with super-linear drift on the real line $\mathbb{R}$ driven by space-time white noise. A Freidlin-Wentzell large deviation principle is established by a modified weak convergence method on the space $C([0,T], C_{tem}(\mathbb{R}))$. Obtaining the main result in this paper is challenging due t

Collin Cademartori

We study the identifiability of parameters and falsifiability of predictions under the process of model expansion in a Bayesian setting. Identifiability is represented by the closeness of the posterior to the prior distribution and falsifiability by the power of posterior predictive tests against alternatives. To study these two concepts formally, we develop

Yan-mei Yu, B. K. Sahoo

We examine energy level-crossings of fine-structure (FS) levels in the heavier highly charged ions (HCIs) with $d^6$ and $d^8$ configurations. From the analysis, we find that some of these HCIs are tailor-made for atomic clocks with quality factors ranging from $10^{16}$ to $10^{18}$ and fractional uncertainties below $10^{-19}$ level. Many of them are also

Soju Furuta, Wataru Koshibae, Fumitaka Kagawa

Metals hosting gradually varying spatial magnetic textures are attracting attention as a new class of inductor. Under the application of an alternating current, the spin-transfer-torque effect induces oscillating dynamics of the magnetic texture, which subsequently yields the spin-motive force as a back action, resulting in an inductive voltage response. In

Davide D'Adamo, Emiliano Robert, Cristina Gena, Silvestro Roatta

Brain-computer interfaces (BCIs) are one of the few alternatives to enable locked-in syndrome (LIS) patients to communicate with the external world, while they are the only solution for complete locked-in syndrome (CLIS) patients, who lost the ability to control eye movements. However, successful usage of endogenous electroencephalogram(EEG)-based BCI applic

Junjie Shen, Yunpeng Luo, Ziwen Wan, Qi Alfred Chen

Localization in high-level Autonomous Driving (AD) systems is highly security critical. While the popular Multi-Sensor Fusion (MSF) based design can be more robust against single-source sensor spoofing attacks, it is found recently that state-of-the-art MSF algorithms is vulnerable to GPS spoofing alone due to practical factors, which can cause various road

Erfan Shayegani, Yue Dong, Nael Abu-Ghazaleh

We introduce new jailbreak attacks on vision language models (VLMs), which use aligned LLMs and are resilient to text-only jailbreak attacks. Specifically, we develop cross-modality attacks on alignment where we pair adversarial images going through the vision encoder with textual prompts to break the alignment of the language model. Our attacks employ a nov

Eric Lescano, Gabriel Menezes, Jesús A. Rodríguez

Double Field Theory (DFT) can be constructed as the double copy of a Yang-Mills theory. In this work we extend this statement by including higher-derivative terms. Starting from a four-derivative extension of Yang-Mills whose double copy is known to correspond to a conformal-gravity theory, we obtain a four-derivative theory formulated in double space, which

Sophie J. Kersting, A. Luise Kühn, Mareike Fischer

Imbalance in the 3D structure of plants can be an important indicator of insufficient light or nutrient supply, as well as excessive wind, (formerly present) physical barriers, neighbor or storm damage. It can also be a simple means to detect certain illnesses, since some diseases like the apple proliferation disease, an infection with the barley yellow dwar

Masoumeh Dashti, Duc-Lam Duong

We consider the scalar conservation law in one space dimension with a genuinely nonlinear flux. We assume that an appropriate velocity function depending on the entropy solution of the conservation law is given for the comprising particles, and study their corresponding trajectories under the flow. The differential equation that each of these trajectories sa

Huy Ha, Pete Florence, Shuran Song

We present a framework for robot skill acquisition, which 1) efficiently scale up data generation of language-labelled robot data and 2) effectively distills this data down into a robust multi-task language-conditioned visuo-motor policy. For (1), we use a large language model (LLM) to guide high-level planning, and sampling-based robot planners (e.g. motion

Alex Goodenbour

Corresponding to the Bondi-Metzner-Sachs (BMS) symmetry algebra of asymptotically-flat spacetimes are a set of BMS charges. These are formally constructed via the symplectic formalism of Wald and Zoupas, but the same charge expression may be arrived at by the simpler twistorial procedure of Dray and Streubel. Here, we formalize the connection between twistor

Yong-Jun Chen

In this article, we discover a fundamental excitation called electromagnon in the cuprate superconducting states. Doped holes render local inversion asymmetry which produces hidden Rashba spin-orbit coupling in copper-oxide plane. Rashba effect leads to Dzyaloshinskii-Moriya (DM) interaction between neighboring spins, which establishes a general spin Hamiton

Kirsten D. Morris, Tefjol Pllaha, Christine A. Kelley

Iterative decoder failures of quantum low density parity check (QLDPC) codes are attributed to substructures in the code's graph, known as trapping sets, as well as degenerate errors that can arise in quantum codes. Failure inducing sets are subsets of codeword coordinates that, when initially in error, lead to decoding failure in a trapping set. The purpose

Amnon Geifman, Daniel Barzilai, Ronen Basri, Meirav Galun

Wide neural networks are biased towards learning certain functions, influencing both the rate of convergence of gradient descent (GD) and the functions that are reachable with GD in finite training time. As such, there is a great need for methods that can modify this bias according to the task at hand. To that end, we introduce Modified Spectrum Kernels (MSK

Fedor Noskov, Maxim Panov

Community detection is one of the most critical problems in modern network science. Its applications can be found in various fields, from protein modeling to social network analysis. Recently, many papers appeared studying the problem of overlapping community detection, where each node of a network may belong to several communities. In this work, we consider

B. E. Miranda-Pérez, A. M. Hidalgo-Gámez

We spectroscopically analyzed a sample of 85 Sloan Digital Sky Survey compact, oxygen-dominated galaxies located at redshift $z\sim0.001-0.350$, selected because of their large equivalent width of [O III]$\lambda$5007 (larger than 200\AA). These galaxies might be considered as extreme emission-line galaxies due to their strong [O III]$\lambda$5007 emission l

Guillaume Garrigos, Robert M. Gower, Fabian Schaipp

Here we develop variants of SGD (stochastic gradient descent) with an adaptive step size that make use of the sampled loss values. In particular, we focus on solving a finite sum-of-terms problem, also known as empirical risk minimization. We first detail an idealized adaptive method called $\texttt{SPS}_+$ that makes use of the sampled loss values and assum

Thomas Manzini, Robin Murphy

This paper details the challenges in applying two computer vision systems, an EfficientDET supervised learning model and the unsupervised RX spectral classifier, to 98.9 GB of drone imagery from the Wu-Murad wilderness search and rescue (WSAR) effort in Japan and identifies 3 directions for future research. There have been at least 19 proposed approaches and

Satoshi Ohashi, Munetake Momose, Akimasa Kataoka, Aya E Higuchi

Characterizing the physical properties of dust grains in a protoplanetary disk is critical to comprehending the planet formation process. Our study presents ALMA high-resolution observations of the young protoplanetary disk around DG Tau at a 1.3 mm dust continuum. The observations, with a spatial resolution of $\approx 0.04''$, or $\approx5$ au, revealed a

B. N. Kausik

A central question in economics is whether automation will displace human labor and diminish standards of living. Whilst prior works typically frame this question as a competition between human labor and machines, we frame it as a competition between human consumers and human suppliers. Specifically, we observe that human needs favor long tail distributions,

Stephen L. Adler

I review the basic ideas of "trace dynamics", as formulated in my 2004 Cambridge University Press book "Quantum Theory as an Emergent Phenomenon", and then discuss how they have influenced much of my work of the last two decades.

Soorena Salari, Amirhossein Rasoulian, Hassan Rivaz, Yiming Xiao

Homologous anatomical landmarks between medical scans are instrumental in quantitative assessment of image registration quality in various clinical applications, such as MRI-ultrasound registration for tissue shift correction in ultrasound-guided brain tumor resection. While manually identified landmark pairs between MRI and ultrasound (US) have greatly faci

Renee D. White, Tristan Peng, Pann Sripitak, Alexander Rosenberg Johansen

A clinical trial is a study that evaluates new biomedical interventions. To design new trials, researchers draw inspiration from those current and completed. In 2022, there were on average more than 100 clinical trials submitted to ClinicalTrials.gov every day, with each trial having a mean of approximately 1500 words [1]. This makes it nearly impossible to

Ross Greer, Akshay Gopalkrishnan, Maitrayee Keskar, Mohan Trivedi

This paper explores the representation of vehicle lights in computer vision and its implications for various tasks in the field of autonomous driving. Different specifications for representing vehicle lights, including bounding boxes, center points, corner points, and segmentation masks, are discussed in terms of their strengths and weaknesses. Three importa

Daniel Gómez-Bravo, Aaron García, Guillermo Vigueras, Belén Ríos

Pattern discovery is a machine learning technique that aims to find sets of items, subsequences, or substructures that are present in a dataset with a higher frequency value than a manually set threshold. This process helps to identify recurring patterns or relationships within the data, allowing for valuable insights and knowledge extraction. In this work,

Soorena Salari, Amirhossein Rasoulian, Hassan Rivaz, Yiming Xiao

In brain tumor resection, accurate removal of cancerous tissues while preserving eloquent regions is crucial to the safety and outcomes of the treatment. However, intra-operative tissue deformation (called brain shift) can move the surgical target and render the pre-surgical plan invalid. Intra-operative ultrasound (iUS) has been adopted to provide real-time

Rakesh K. Dora, Ajit C. Balram

We study the competition between the electron liquid and solid phases, such as Wigner crystal and bubbles, in partially filled Landau levels (LLs) of multilayer graphene. Graphene systems offer a versatile platform for controlling band dispersion by varying the number of its stacked layers. The band dispersion determines the LL wave functions, and consequent

Carter Hinsley, James Scully, Andrey L. Shilnikov

We study homoclinic bifurcations in an interval map associated with a saddle-focus of (2, 1)-type in $\mathbb{Z}_2$-symmetric systems. Our study of this map reveals the homoclinic structure of the saddle-focus, with a bifurcation unfolding guided by the codimension-two Belyakov bifurcation. We consider three parameters of the map, corresponding to the saddle

Meike Nauta, Christin Seifert

Interpretable part-prototype models are computer vision models that are explainable by design. The models learn prototypical parts and recognise these components in an image, thereby combining classification and explanation. Despite the recent attention for intrinsically interpretable models, there is no comprehensive overview on evaluating the explanation q

E. Longo, A. Markou, C. Felser, M. Belli

Half-metallic half-Heusler compounds with strong spin-orbit-coupling and broken inversion symmetry in their crystal structure are promising materials for generating and absorbing spin-currents, thus enabling the electric manipulation of magnetization in energy-efficient spintronic devices. In this work, we report the spin-to-charge conversion in sputtered ul

Armin Hatefi, Ehsan Hatefi, Roberto J. López-Sastre

In this paper, we study the black hole solution of self-similar gravitational collapse in the Einstein-axion-dilaton system for the elliptic class in four dimensions. The solution is invariant under space-time dilation, which is combined with internal SL(2,R) transformations. Due to the complex and highly nonlinear pattern of the equations of motion in the p

Yu Hsuan Liang, Hong-Zhou Ye, Timothy C. Berkelbach

Achieving kJ/mol accuracy in the cohesive energy of molecular crystals, as necessary for crystal structure prediction and the resolution of polymorphism, is an ongoing challenge in computational materials science. Here, we evaluate the performance of second-order M{\o}ller-Plesset perturbation theory (MP2), including its spin-component scaled models, by calc

Majid Rahro Zargar, Ghader Ghasemi

Let $I$ be an ideal of a commutative Noetherian complete local ring $R$. In the present paper, we establish the equality $\dim R/(I+\Ann_R M)=\dim M$ for all $I$-cofinite $R$-modules $M$.

Mohammad Mehdi Morovati, Amin Nikanjam, Florian Tambon, Foutse Khomh

Rapid growth of applying Machine Learning (ML) in different domains, especially in safety-critical areas, increases the need for reliable ML components, i.e., a software component operating based on ML. Understanding the bugs characteristics and maintenance challenges in ML-based systems can help developers of these systems to identify where to focus mainten

Nimrod Dvir, Elaine Friedman, Suraj Commuri, Fan Yang

This research draws upon cognitive psychology and information systems studies to anticipate user engagement and decision-making on digital platforms. By employing natural language processing (NLP) techniques and insights from cognitive bias research, we delve into user interactions with synonyms within digital content. Our methodology synthesizes four cognit

Yijiong Lin, Mauro Comi, Alex Church, Dandan Zhang

High-resolution tactile sensing can provide accurate information about local contact in contact-rich robotic tasks. However, the deployment of such tasks in unstructured environments remains under-investigated. To improve the robustness of tactile robot control in unstructured environments, we propose and study a new concept: \textit{tactile saliency} for ro

Guang Yang, Casey Papovich, Micaela Bagley, Henry Ferguson

The Cosmic Evolution Early Release Science Survey (CEERS), targeting the Extended Groth Strip extragalactic field, is one of the JWST Director's Discretionary Early Release Science programs. To date, all observations have been executed and include NIRCam/MIRI imaging and NIRSpec/NIRCam spectroscopic exposures. Here, we discuss the MIRI imaging, which include

Jason Saroni, Henry Lamm, Peter P. Orth, Thomas Iadecola

Simulating the nonequilibrium dynamics of thermal states is a fundamental problem across scales from high energy to condensed matter physics. Quantum computers may provide a way to solve this problem efficiently. Preparing a thermal state on a quantum computer is challenging, but there exist methods to circumvent this by computing a weighted sum of time-depe

Hengjie Yang, Richard D. Wesel

In this paper, we present new achievability bounds on the maximal achievable rate of variable-length stop-feedback (VLSF) codes operating over a binary erasure channel (BEC) at a fixed message size $M = 2^k$. We provide new bounds for VLSF codes with zero error, infinite decoding times and with nonzero error, finite decoding times. Both new achievability bou

Y. V. Pershin

Memcomputing logic gates generalize the traditional Boolean logic gates for operation in the reverse direction. According to the literature, this functionality enables the efficient solution of computationally-intensive problems including factorization and NP-complete problems. To approach the deployment of memcomputing gates in hardware, this paper introduc

Raihan Islam Arnob, Gregory J. Stein

We improve reliable, long-horizon, goal-directed navigation in partially-mapped environments by using non-locally available information to predict the goodness of temporally-extended actions that enter unseen space. Making predictions about where to navigate in general requires non-local information: any observations the robot has seen so far may provide inf

Nimrod Dvir, Elaine Friedman, Suraj Commuri, Fan yang

This study introduces and empirically tests a novel predictive model for digital information engagement (IE) - the READ model, an acronym for the four pivotal attributes of engaging information: Representativeness, Ease-of-use, Affect, and Distribution. Conceptualized within the theoretical framework of Cumulative Prospect Theory, the model integrates key co

Lingwei Kong

The Hansen-Jagannathan (HJ) distance statistic is one of the most dominant measures of model misspecification. However, the conventional HJ specification test procedure has poor finite sample performance, and we show that it can be size distorted even in large samples when (proxy) factors exhibit small correlations with asset returns. In other words, applied

Hani Mavalizadeh, Luis A. Duffaut Espinosa, Mads R. Almassalkhi

With the increasing use of renewable generation in power systems, responsive resources will be necessary to support primary frequency control in future low-inertia/under-damped power systems. Flexible loads can provide fast-frequency response services if coordinated effectively. However, practical implementations of such synthetic damping services require bo

David M. Wirth, Chi Chung Li, Jonathan K. Pokorski, Hayden K. Taylor

The field of additive manufacturing (AM) has advanced considerably over recent decades through the development of novel methods, materials, and systems. However, as the field approaches maturity, it is relevant to investigate the scaling frontiers and fundamental limits of AM in a generalized sense. Here we propose a simplified universal mathematical model t

Alan Lew

The independence complex of a graph $G=(V,E)$ is the simplicial complex $I(G)$ on vertex set $V$ whose simplices are the independent sets in $G$. We present new lower bounds on the eigenvalues of the $k$-dimensional Laplacian $L_k(I(G))$ in terms of the eigenvalues of the graph Laplacian $L(G)$. As a consequence, we show that for all $k\geq 0$, the dimension

Stephen Robson

It is widely recognised that over-reliance on GNSS (e.g GPS) for time synchronisation represents an acute threat to modern society, and a diversity of alternatives are required to mitigate the threat of an outage. This paper proposes a GNSS alternative using time dissemination over national scale transmission or distribution networks. The method utilises the

Hanwen Zhang, Vladimir Rokhlin

We present a scheme for finding all roots of an analytic function in a square domain in the complex plane. The scheme can be viewed as a generalization of the classical approach to finding roots of a function on the real line, by first approximating it by a polynomial in the Chebyshev basis, followed by diagonalizing the so-called ''colleague matrices''. Our

Robert F. Bailey, Abigail K. Rowsell

This paper studies induced paths in strongly regular graphs. We give an elementary proof that a strongly regular graph contains a path $P_4$ as an induced subgraph if and only if it is primitive, i.e. it is neither a complete multipartite graph nor its complement. Also, we investigate when a strongly regular graph has an induced subgraph isomorphic to $P_5$

Shaull Almagor, Guy Avni, Henry Sinclair-Banks, Asaf Yeshurun

A $k$-Counter Net ($k$-CN) is a finite-state automaton equipped with $k$ integer counters that are not allowed to become negative, but do not have explicit zero tests. This language-recognition model can be thought of as labelled vector addition systems with states, some of which are accepting. Certain decision problems for $k$-CNs become easier, or indeed d

Cai Yu, Peng Chen, Jiahe Tian, Jin Liu

As AI-generated content (AIGC) thrives, deepfakes have expanded from single-modality falsification to cross-modal fake content creation, where either audio or visual components can be manipulated. While using two unimodal detectors can detect audio-visual deepfakes, cross-modal forgery clues could be overlooked. Existing multimodal deepfake detection methods

Brandon Mayer, Anton Tsitsulin, Hendrik Fichtenberger, Jonathan Halcrow

Graphs are a representation of structured data that captures the relationships between sets of objects. With the ubiquity of available network data, there is increasing industrial and academic need to quickly analyze graphs with billions of nodes and trillions of edges. A common first step for network understanding is Graph Embedding, the process of creating

Canyu Zhang, Qing Guo, Xiaoguang Li, Renjie Wan

In this work, we introduce a challenging image restoration task, referred to as SuperInpaint, which aims to reconstruct missing regions in low-resolution images and generate completed images with arbitrarily higher resolutions. We have found that this task cannot be effectively addressed by stacking state-of-the-art super-resolution and image inpainting meth

Jongwoo Choi, Kevin J. McGown

We give an asymptotic formula for the number of monic Eisenstein polynomials of odd prime degree satisfying an additional condition that arises in the study of the genus number of an algebraic number field.

Jin Wang, Yu Hu, Lirong Xiang, Gota Morota

Computer vision (CV), a non-intrusive and cost-effective technology, has furthered the development of precision livestock farming by enabling optimized decision-making through timely and individualized animal care. The availability of affordable two- and three-dimensional camera sensors, combined with various machine learning and deep learning algorithms, ha

Yu-Wei Fan, Kuan-Wen Lai

We give counting formulas for the number of Fourier-Mukai partners of the K3 category of a very general special cubic fourfold.

Martín Sambarino, José Vieitez

We give sufficient conditions for an expansive partially hyperbolic diffeomorphism with one-dimensional center to be (topologically) Anosov.

Giulio Caviglia, Alessandro De Stefani

The purpose of this note is to show that a finitely generated graded module $M$ over $S=k[x_1,\ldots,x_n]$, $k$ a field, is sequentially Cohen-Macaulay if and only if its arithmetic degree ${\rm adeg}(M)$ agrees with ${\rm adeg}(F/{\rm gin}_{revlex}(U))$, where $F$ is a graded free $S$-module and $M \cong F/U$. This answers positively a conjecture of Lu and

Timothy L. Kline, Sumana Ramanathan, Harrison C. Gottlich, Panagiotis Korfiatis

Purpose: This study evaluated the out-of-domain performance and generalization capabilities of automated medical image segmentation models, with a particular focus on adaptation to new image acquisitions and disease type. Materials: Datasets from both non-contrast and contrast-enhanced abdominal CT scans of healthy patients and those with polycystic kidney d

Liudong Chen, Bolun Xu

Time-varying pricing tariffs incentivize consumers to shift their electricity demand and reduce costs, but may increase the energy burden for consumers with limited response capability. The utility must thus balance affordability and response incentives when designing these tariffs by considering consumers' response expectations. This paper proposes a joint

Pedro Chamorro-Posada

In this work, the properties of the radiation emitted by a corner reflector with an electric dipole feeder are analyzed in the optical domain where the distance between the dipole and the corner apex can be large in terms of the optical wavelength. A comprehensive study of the fractal properties of the radiated intensity patterns is presented. The use of thi

Chen Chen, Vasudev Gohil, Rahul Kande, Ahmad-Reza Sadeghi

Hardware security vulnerabilities in computing systems compromise the security defenses of not only the hardware but also the software running on it. Recent research has shown that hardware fuzzing is a promising technique to efficiently detect such vulnerabilities in large-scale designs such as modern processors. However, the current fuzzing techniques do n

Alexandre Mussa, Romain Berthelard, Frédéric Caupin, Bruno Issenmann

We report measurements of the shear viscosity $\eta$ in water up to $150\,\mathrm{MPa}$ and down to $229.5\,\mathrm{K}$. This corresponds to more than $30\,\mathrm{K}$ supercooling below the melting line. The temperature dependence is non-Arrhenius at all pressures, but its functional form at $0.1\,\mathrm{MPa}$ is qualitatively different from that at all pr

Darren C. Pagan, Lukas A. Kissell, Matthew L. Whitaker

Elevated pressures are encountered in many metal forming processes that can alter microstructural evolution rates. Here we measure rate changes with pressure in recrystallization and grain growth in Fe through adaptation of synchrotron-compatible multi-anvil presses, originally designed for study of the mantle. Recrystallization and grain growth are monitore

Manoochehr Shirzaei

Sentinel-1, the Synthetic Aperture Radar (SAR) mission operated by the European Space Agency (ESA) as part of the Copernicus program, provides Free and Open Data with global coverage, transforming our ability to monitor subtle changes in land surface elevation due to natural and anthropogenic process. Here, we use SAR datasets over New York spanning 2015/03/

Ben Perach, Shahar Kvatinsky

The emerging Spin Transfer Torque Magnetic Tunnel Junction (STT-MTJ) technology exhibits interesting stochastic behavior combined with small area and low operation energy. It is, therefore, a promising technology for security applications, specifically the generation of random numbers. In this paper, STT-MTJ is used to construct an asynchronous true random n

Ali Muhammad Ali Rushdi, Fares Ahmad Muhammad Ghaleb

This paper deals with the Boolean-based analysis of a prominent class of non-repairable coherent multistate systems with independent nonidentical multistate components. This class of systems is represented by a multistate coherent truly threshold system of several states, which is not necessarily binary-imaged. The paper represents such a system via Boolean

Fabian Kieser, Peter Wulff, Jochen Kuhn, Stefan Küchemann

Generative AI technologies such as large language models show novel potentials to enhance educational research. For example, generative large language models were shown to be capable to solve quantitative reasoning tasks in physics and concept tests such as the Force Concept Inventory. Given the importance of such concept inventories for physics education re

Anthony M. Polloreno

Reservoir computation is a recurrent framework for learning and predicting time series data, that benefits from extremely simple training and interpretability, often as the the dynamics of a physical system. In this paper, we will study the impact of noise on the learning capabilities of analog reservoir computers. Recent work on reservoir computation has sh

Ann Merchant Boesgaard, Constantine P. Deliyannis

The light element lithium is formed by nucleosynthesis during the Big Bang. Its abundance can help to define the parameters of the early universe. To find this primordial value, it is necessary to determine Li abundances in the oldest stars because it is readily destroyed by nuclear reactions in stellar interiors. We have made high-resolution (~45,000) spect

Piotr Bialas, Zdzislaw Burda, Desmond A. Johnston

We calculate analytically the Renyi entropy for the zeta-urn model with a Gibbs measure definition of the micro-state probabilities. This allows us to obtain the singularities in the R\'enyi entropy from those of the thermodynamic potential, which is directly related to the free energy density of the model. We enumerate the various possible behaviours of the

Ismail Kuru, Colin S. Gordon

Operating system kernels employ virtual memory subsystems, which use a CPU's memory management units (MMUs) to virtualize the addresses of memory regions Operating systems manipulate these virtualized memory mappings to isolate untrusted processes, restrict which memory is accessible to different processes, hide memory limits from user programs, ensure proce

Gabriel Monteiro da Silva, Jennifer Y. Cui, David C. Dalgarno, George P. Lisi

This paper presents a novel approach for predicting the relative populations of protein conformations using AlphaFold 2, an AI-powered method that has revolutionized biology by enabling the accurate prediction of protein structures. While AlphaFold 2 has shown exceptional accuracy and speed, it is designed to predict proteins' single ground state conformatio

Emily Escamilla, Lamia Salsabil, Martin Klein, Jian Wu

Paper publications are no longer the only form of research product. Due to recent initiatives by publication venues and funding institutions, open access datasets and software products are increasingly considered research products and URIs to these products are growing more prevalent in scholarly publications. However, as with all URIs, resources found on th

Nadav Meir, Aris Papadopoulos

We consider finitary approximations of the (embedding) Ramsey property. Using a class of homogeneous reducts of random ordered hypergraphs, we prove that these properties form a strict hierarchy. We also show that every class of finite structures in which every structure of size at most 2 is a "Ramsey object" essentially consists of ordered structures, gener

F. H. Haydarov

There are many research works devoted to Gibbs measure for models on Cayley trees. Among these works, there are some works in which the general results are identical, but the considered models are various. In this article, we present the construction of Gibbs measures in the language of measure theory and reply to the question ``When can we construct Gibbs s

Piotr Bialas, Zdzislaw Burda, Desmond A. Johnston

We discuss the phase transition and critical exponents in the random allocation model (urn model) for different statistical ensembles. We provide a unified presentation of the statistical properties of the model in the thermodynamic limit, uncover new relationships between the thermodynamic potentials and fill some lacunae in previous results on the singular

Samantha Syeda Khairunnesa, Shibbir Ahmed, Sayem Mohammad Imtiaz, Hridesh Rajan

Recent work has shown that Machine Learning (ML) programs are error-prone and called for contracts for ML code. Contracts, as in the design by contract methodology, help document APIs and aid API users in writing correct code. The question is: what kinds of contracts would provide the most help to API users? We are especially interested in what kinds of cont

Abir Riahi, Éric Plourde

Speech enhancement (SE) is crucial for reliable communication devices or robust speech recognition systems. Although conventional artificial neural networks (ANN) have demonstrated remarkable performance in SE, they require significant computational power, along with high energy costs. In this paper, we propose a novel approach to SE using a spiking neural n

Christis Katsouris

We establish the asymptotic validity of the bootstrap-based IVX estimator proposed by Phillips and Magdalinos (2009) for the predictive regression model parameter based on a local-to-unity specification of the autoregressive coefficient which covers both nearly nonstationary and nearly stationary processes. A mixed Gaussian limit distribution is obtained for

Diego Ferraz

In this paper we study the existence of bound states of the following class of quasilinear problems, \begin{equation*} \left\{ \begin{aligned} &-\varepsilon ^p\Delta_pu+V(x)u^{p-1}=f(u)+u^{p^\ast -1},\ u>0,\ \text{in}\ \mathbb{R}^{N}, &\lim _{|x|\rightarrow \infty }u(x) = 0 , \end{aligned} \right. \end{equation*} where $\varepsilon>0$ is small, $1<p<N,$ $f$

Caterina Puca, Amar Hadzihasanovic, Fabrizio Genovese, Bob Coecke

Compositionality is at the heart of computer science and several other areas of applied category theory such as computational linguistics, categorical quantum mechanics, interpretable AI, dynamical systems, compositional game theory, and Petri nets. However, the meaning of the term seems to vary across the many different applications. This work contributes t

Rhys G. Povey, Ming-Han Chou, Gustav Andersson, Christopher R. Conner

In the field of quantum computation and communication there is a compelling need for quantum-coherent frequency conversion between microwave electronics and infra-red optics. A promising platform for this is an optomechanical crystal resonator that uses simultaneous photonic and phononic crystals to create a co-localized cavity coupling an electromagnetic mo

Reiner Birkl, Diana Wofk, Matthias Müller

We release MiDaS v3.1 for monocular depth estimation, offering a variety of new models based on different encoder backbones. This release is motivated by the success of transformers in computer vision, with a large variety of pretrained vision transformers now available. We explore how using the most promising vision transformers as image encoders impacts de

Joshua Viszlai, Teague Tomesh, Pranav Gokhale, Eric Anschuetz

Recent work has proposed and explored using coreset techniques for quantum algorithms that operate on classical data sets to accelerate the applicability of these algorithms on near-term quantum devices. We apply these ideas to Quantum Boltzmann Machines (QBM) where gradient-based steps which require Gibbs state sampling are the main computational bottleneck

Qi-Yuan Mao, Liang Ma, H. Lu

We consider Einstein gravity extended with Riemann-squared term and construct the leading-order perturbative solution to the rotating black hole with all equal angular momenta in $D=7$. We find that in the extremal limit, the linear perturbation involves irrational powers in the near-horizon expansion. We argue that, despite that all curvature tensor invaria

Ryan Lingo

The realm of data science, once reserved for specialists, is undergoing a revolution with the rapid emergence of generative AI, particularly through tools like ChatGPT. This paper posits ChatGPT as a pivotal bridge, drastically lowering the steep learning curve traditionally associated with complex data analysis. By generating intuitive data narratives and o

Evelina Leivada, Gary Marcus, Fritz Günther, Elliot Murphy

Modern Artificial Intelligence applications show great potential for language-related tasks that rely on next-word prediction. The current generation of Large Language Models (LLMs) have been linked to claims about human-like linguistic performance and their applications are hailed both as a step towards artificial general intelligence and as a major advance

Marc Mars, Raül Vera

Our aim in this paper is two-fold. We establish a novel geometric characterization of the Roberson-Walker (RW) spacetime and, along the process, we find a canonical form of the RW metric associated to an arbitrary timelike curve and an arbitrary space frame. A known characterization establishes that a spacetime foliated by constant curvature leaves whose ort