October 2023 arXiv papers — page 27

John W. Noonan, Kathryn Volk, David Nesvorný, William F. Bottke

We test the hypothesis that (3) Juno is a parent body of the H chondrites with dynamical modeling of an asteroid-family-forming impact and comparison to current observational data. Using a dynamical model that includes the Yarkovsky force on a simulated Juno family and a simplified cosmic ray exposure age model we examine the expected distribution of Juno fa

Charles Moore, Dakota Hester

Land use/land cover change (LULC) maps are integral resources in earth science and agricultural research. Due to the nature of such maps, the creation of LULC maps is often constrained by the time and human resources necessary to accurately annotate satellite imagery and remote sensing data. While computer vision models that perform semantic segmentation to

Raven Gassis, Matthew B. Bayliss, Keren Sharon, Guillaume Mahler

Through observational tests of strong lensing galaxy clusters, we can test simulation derived structure predictions that follow from $\Lambda$ Cold Dark Matter ($\Lambda$CDM) cosmology. The shape and centroid deviations between the total matter distribution, stellar matter distributions, and hot intracluster gas distribution serve as an observational test of

Song Yin, Xuenan Mi, Diwakar Shukla

Peptides play a pivotal role in a wide range of biological activities through participating in up to 40% protein-protein interactions in cellular processes. They also demonstrate remarkable specificity and efficacy, making them promising candidates for drug development. However, predicting peptide-protein complexes by traditional computational approaches, su

Giovanni Mongardi, Gianluca Pacienza

Chen-Gounelas-Liedtke recently introduced a powerful regeneration technique, a process opposite to specialization, to prove existence results for rational curves on projective $K3$ surfaces. We show that, for projective irreducible holomorphic symplectic manifolds, an analogous regeneration principle holds and provides a very flexible tool to prove existence

Nicholas E. Corrado, Yuxiao Qu, John U. Balis, Adam Labiosa

In offline reinforcement learning (RL), an RL agent learns to solve a task using only a fixed dataset of previously collected data. While offline RL has been successful in learning real-world robot control policies, it typically requires large amounts of expert-quality data to learn effective policies that generalize to out-of-distribution states. Unfortunat

Gian Maria Dall'Ara, Samuele Mongodi

The problem of giving a (CR-)geometric description of the best possible order of a subelliptic estimate at a boundary point in the $\bar\partial$-Neumann problem is largely open. In this paper, we introduce a novel technique based on a "$\bar\partial$-uncertainty principle" and, as an application, we determine the sharp order of subellipticity at the origin

Raghottam M. Sattigeri, Giuseppe Cuono, Ghulam Hussain, Xing Ming

Using first-principle calculations, we investigate the electronic, topological and superconducting properties of Nb$_3$X (X = Ge, Sn, Sb) and Ta$_3$Y (Y = As, Sb, Bi) A15 compounds. We demonstrate that these compounds host Dirac surface states which are related to a nontrivial Z$_2$ topological value. The spin-orbit coupling (SOC) splits the eightfold degene

Rose Hadshar

Rapid advancements in artificial intelligence (AI) have sparked growing concerns among experts, policymakers, and world leaders regarding the potential for increasingly advanced AI systems to pose existential risks. This paper reviews the evidence for existential risks from AI via misalignment, where AI systems develop goals misaligned with human values, and

Diksha Sharma, Parvinder Singh, Atul Kumar

A classical decision tree is completely based on splitting measures, which utilize the occurrence of random events in correspondence to its class labels in order to optimally segregate datasets. However, the splitting measures are based on greedy strategy, which leads to construction of an imbalanced tree and hence decreases the prediction accuracy of the cl

Philip Kitson, Tobias Haug, Antonino La Magna, Oliver Morsch

Networks of Rydberg atoms provide a powerful basis for quantum simulators and quantum technologies. Inspired by matter-wave atomtronics, here we engineer switches, diodes and universal logic gates. Our schemes control the Rydberg excitation dynamics via the anti-blockade or facilitation mechanism, allowing for much faster devices compared to cold atom system

MirHamed Jafarzadeh Asl, Mohammadhadi Shateri, Fabrice Labeau

This paper adopts Arimoto's $\alpha$-Mutual Information as a tunable privacy measure, in a privacy-preserving data release setting that aims to prevent disclosing private data to adversaries. By fine-tuning the privacy metric, we demonstrate that our approach yields superior models that effectively thwart attackers across various performance dimensions. We f

Anna Sigridur Islind, Johan Lundin, Katerina Cerna, Tomas Lindroth

Designing digital artifacts is not a linear, straightforward process. This is particularly true when applying a user-centered design approach, or co-design, with users who are unable to participate in the design process. Although the reduced participation of a particular user group may harm the end result, the literature on solving this issue is sparse. In t

Yunhao Yang, Neel P. Bhatt, Tyler Ingebrand, William Ward

Although pre-trained language models encode generic knowledge beneficial for planning and control, they may fail to generate appropriate control policies for domain-specific tasks. Existing fine-tuning methods use human feedback to address this limitation, however, sourcing human feedback is labor intensive and costly. We present a fully automated approach t

Herbert Edelsbrunner, Alexey Garber, Morteza Saghafian

The local angle property of the (order-$1$) Delaunay triangulations of a generic set in $\mathbb{R}^2$ asserts that the sum of two angles opposite a common edge is less than $\pi$. This paper extends this property to higher order and uses it to generalize two classic properties from order-$1$ to order-$2$: (1) among the complete level-$2$ hypertriangulations

Jonayet Miah, Duc M Cao, Md Abu Sayed, Md. Sabbirul Haque

Artistic style transfer, a captivating application of generative artificial intelligence, involves fusing the content of one image with the artistic style of another to create unique visual compositions. This paper presents a comprehensive overview of a novel technique for style transfer using Convolutional Neural Networks (CNNs). By leveraging deep image re

Jiang-Xin Shi, Tong Wei, Yuke Xiang, Yu-Feng Li

Long-tail learning has received significant attention in recent years due to the challenge it poses with extremely imbalanced datasets. In these datasets, only a few classes (known as the head classes) have an adequate number of training samples, while the rest of the classes (known as the tail classes) are infrequent in the training data. Re-sampling is a c

Jaemin Cho, Yushi Hu, Roopal Garg, Peter Anderson

Evaluating text-to-image models is notoriously difficult. A strong recent approach for assessing text-image faithfulness is based on QG/A (question generation and answering), which uses pre-trained foundational models to automatically generate a set of questions and answers from the prompt, and output images are scored based on whether these answers extracte

Edwin Salcedo, Patricia Peñaloza

Assessing the condition and visibility of veins is a crucial step before obtaining intravenous access in the antecubital fossa, which is a common procedure to draw blood or administer intravenous therapies (IV therapies). Even though medical practitioners are highly skilled at intravenous cannulation, they usually struggle to perform the procedure in patient

Wolfgang Lang

The relevant length scales for superconductivity are of the order of nanometers. By confining the superconducting condensate to such dimensions, many physical properties change substantially, and novel phenomena emerge, which are absent in the pristine material. We discuss various methods of creating artificial nanostructures by top-down approaches in metall

Cedric Riethmüller, Erlend Storvik, Jakub Wiktor Both, Florin Adrian Radu

We investigate the well-posedness of the recently proposed Cahn-Hilliard-Biot model. The model is a three-way coupled PDE of elliptic-parabolic nature, with several nonlinearities and the fourth order term known to the Cahn-Hilliard system. We show existence of weak solutions to the variational form of the equations and uniqueness under certain conditions of

Francisco Javier Sáez-Maldonado, Juan Maroñas, Daniel Hernández-Lobato

Transformed Gaussian Processes (TGPs) are stochastic processes specified by transforming samples from the joint distribution from a prior process (typically a GP) using an invertible transformation; increasing the flexibility of the base process. Furthermore, they achieve competitive results compared with Deep Gaussian Processes (DGPs), which are another gen

Brielen Madureira, Pelin Çelikkol, David Schlangen

In NLP, incremental processors produce output in instalments, based on incoming prefixes of the linguistic input. Some tokens trigger revisions, causing edits to the output hypothesis, but little is known about why models revise when they revise. A policy that detects the time steps where revisions should happen can improve efficiency. Still, retrieving a su

Xinyang Li, Yu Zhang

The ever-growing intersection of quantum electrodynamics (QED) and molecular processes has shown remarkable and unanticipated advancements in altering molecular properties and reactivity by exploiting light-matter couplings. In recent years, multiple ab initio methods have been developed to compute the eigenstates of molecular systems strongly coupled to cav

Molly Gibbins, Arash Jafarizadeh, Adam Gammon-Smith, Bruno Bertini

We begin a systematic investigation of quench dynamics in higher-dimensional lattice systems considering the case of non-interacting fermions with conserved particle number. We prepare the system in a translational-invariant non-equilibrium initial state -- the simplest example being a classical configuration with fermions at fixed positions on the lattice -

Colin Guillarmou, Antti Kupiainen, Rémi Rhodes

On a given Riemann surface, we construct a path integral based on the Liouville action functional with imaginary parameters. The construction relies on the compactified Gaussian Free Field (GFF), which we perturb with a curvature term and an exponential potential. In physics this path integral is conjectured to describe the scaling limit of critical loop mod

Mohammadreza Doostmohammadian, Alireza Aghasi, Maria Vrakopoulou, Hamid R. Rabiee

This paper proposes two nonlinear dynamics to solve constrained distributed optimization problem for resource allocation over a multi-agent network. In this setup, coupling constraint refers to resource-demand balance which is preserved at all-times. The proposed solutions can address various model nonlinearities, for example, due to quantization and/or satu

Alireza Shahbazi, Mohammad Hossein Khojasteh, Behrouz Minaei-Bidgoli

Prioritized Default Logic presents an optimal solution for addressing real-world problems characterized by incomplete information and the need to establish preferences among diverse scenarios. Although it has reached great success in the theoretical aspect, its practical implementation has received less attention. In this article, we introduce Borhan, a syst

Santiago Rigamonti, Maria Troppenz, Martin Kuban, Axel Hübner

We present the Python package CELL, which provides a modular approach to the cluster expansion (CE) method. CELL can treat a wide variety of substitutional systems, including one-, two-, and three-dimensional alloys, in a general multi-component and multi-sublattice framework. It is capable of dealing with complex materials comprising several atoms in their

Ziquan Zhu, Jing Tao, Shuihua Wang, Xin Zhang

This paper proposes applying a novel deep-learning model, TBDLNet, to recognize CT images to classify multidrug-resistant and drug-sensitive tuberculosis automatically. The pre-trained ResNet50 is selected to extract features. Three randomized neural networks are used to alleviate the overfitting problem. The ensemble of three RNNs is applied to boost the ro

Stefano Di Noi, Ramona Gröber, Gudrun Heinrich, Jannis Lang

We calculate the four-top quark operator contributions to Higgs production via gluon fusion in the Standard Model Effective Field Theory. The four-top operators enter for the first time via two-loop diagrams. Due to their chiral structure they contain $\gamma_5$, so special care needs to be taken when using dimensional regularisation for the loop integrals.

Paulo Sérgio Almeida

Conflict-free Replicated Data Types (CRDTs) allow optimistic replication in a principled way. Different replicas can proceed independently, being available even under network partitions, and always converging deterministically: replicas that have received the same updates will have equivalent state, even if received in different orders. After a historical to

Prateek Mayank, Bhargav Vaidya, Wageesh Mishra, D. Chakrabarty

Coronal mass ejections (CMEs) are primary drivers of space weather and studying their evolution in the inner heliosphere is vital to prepare for a timely response. Solar wind streams, acting as background, influence their propagation in the heliosphere and associated geomagnetic storm activity. This study introduces SWASTi-CME, a newly developed MHD-based CM

Q. Chen, S. H. -Y. Huang, Q. Ma, E. M. Smith

The parallel stripe phase is remarkable both in its own right, and in relation to the other phases it co-exists with. Its inhomogeneous nature makes such states susceptible to random fields from quenched magnetic vacancies. We argue this is the case by introducing low concentrations of nonmagnetic Zn impurities (0-10%) into La$_{1.6-x}$Nd$_{0.4}$Sr$_x$CuO$_4

Simon Chu, Emma Shedden, Changjian Zhang, Rômulo Meira-Góes

The feature interaction problem occurs when two or more independently developed components interact with each other in unanticipated ways, resulting in undesirable system behaviors. Feature interaction problems remain a challenge for emerging domains in cyber-physical systems (CPS), such as the Internet of Things and autonomous drones. Existing techniques fo

Gernot Schaller, Friedemann Queisser, Seyedeh Parya Katoorani, Christian Brand

As a simplified description of the non-equilibrium dynamics of buckled dimers on the Si(001) surface, we consider the anisotropic 2D Ising model and study the freezing of spatial correlations during a cooling quench across the critical point. Depending on the cooling rate, we observe a crossover from 1D to 2D behavior. For rapid cooling, we find effectively

Ren Ozeki, Haruki Yonekura, Aidana Baimbetova, Hamada Rizk

The growing demand for ride-hailing services has led to an increasing need for accurate taxi demand prediction. Existing systems are limited to specific regions, lacking generality to unseen areas. This paper presents a novel taxi demand prediction system, harnessing the strengths of multiview graph neural networks to capture spatial-temporal dependencies an

Dirk Van Eester, Ernesto Lerche

For 1-dimensional applications, Bude's method [Bude et al, Plasma Phys. Control. Fusion, 63 (2021) 035014] has been shown to be capable of accurately solving the all-FLR (Finite Larmor Radius) integro-differential wave equation as a high-order differential equation allowing to represent all physically relevant (fast, slow and Bernstein) modes upon making a p

Fabricio Toscano, Diego G. Bussandri, Gustavo M. Bosyk, Ana P. Majtey

We demonstrate that among all quantum teleportation protocols giving rise to the same average fidelity, those with aligned Bloch vectors between input and output states exhibit the minimum average trace distance. This defines optimal protocols. Furthermore, we show that optimal protocols can be interpreted as the perfect quantum teleportation protocol under

Damian Machlanski, Spyridon Samothrakis, Paul Clarke

Hyperparameters play a critical role in machine learning. Hyperparameter tuning can make the difference between state-of-the-art and poor prediction performance for any algorithm, but it is particularly challenging for structure learning due to its unsupervised nature. As a result, hyperparameter tuning is often neglected in favour of using the default value

Michael L. Nastac, Robert J. Ewart, Wrick Sengupta, Alexander A. Schekochihin

We consider a nearly collisionless plasma consisting of a species of `test particles' in 1D-1V, stirred by an externally imposed stochastic electric field. The mean effect on the particle distribution function is stochastic heating. Accompanying this heating is the generation of fine-scale structure in the distribution function, which we characterize with th

Helena Faustino Vieira, Ana Duarte-Cabral, Timothy A. Davis, Nicolas Peretto

Here we present the cloud population extracted from M51, following the application of our new high-resolution dust extinction technique to the galaxy (Faustino Vieira et al. 2023). With this technique, we are able to image the gas content of the entire disc of M51 down to 5 pc (0.14"), which allows us to perform a statistical characterisation of well-resolve

Yifei Zhang, Hao Zhu, Menglin Yang, Jiahong Liu

Learning generalizable self-supervised graph representations for downstream tasks is challenging. To this end, Contrastive Learning (CL) has emerged as a leading approach. The embeddings of CL are arranged on a hypersphere where similarity is measured by the cosine distance. However, many real-world graphs, especially of hierarchical nature, cannot be embedd

Benjamin Feuer, Yurong Liu, Chinmay Hegde, Juliana Freire

Existing deep-learning approaches to semantic column type annotation (CTA) have important shortcomings: they rely on semantic types which are fixed at training time; require a large number of training samples per type and incur large run-time inference costs; and their performance can degrade when evaluated on novel datasets, even when types remain constant.

Zishan Ahmad, Suman Saurabh, Vaishakh Sreekanth Menon, Asif Ekbal

In this paper, we propose a novel negotiation dialogue agent designed for the online marketplace. Our agent is integrative in nature i.e, it possesses the capability to negotiate on price as well as other factors, such as the addition or removal of items from a deal bundle, thereby offering a more flexible and comprehensive negotiation experience. We create

Pablo Ramón, Cristian Romero, Javier Tapia, Miguel A. Otaduy

Computer models of humans are ubiquitous throughout computer animation and computer vision. However, these models rarely represent the dynamics of human motion, as this requires adding a complex layer that solves body motion in response to external interactions and according to the laws of physics. FLSH is a library that facilitates this task for researchers

Shubham Mittal, Megha Sundriyal, Preslav Nakov

Claim span identification (CSI) is an important step in fact-checking pipelines, aiming to identify text segments that contain a checkworthy claim or assertion in a social media post. Despite its importance to journalists and human fact-checkers, it remains a severely understudied problem, and the scarce research on this topic so far has only focused on Engl

Marcell Gall, Nicola Wurz, Jens Samland, Chun Fai Chan

Fermionic atoms in optical lattices have served as a compelling model system to study and emulate the physics of strongly-correlated matter. Driven by the advances of high-resolution microscopy, the recent focus of research has been on two-dimensional systems in which several quantum phases, such as anti-ferromagnetic Mott insulators for repulsive interactio

G. Bortel, M. Tegze, M. Sikorski, R. Bean

X-ray Free Electron Lasers (XFEL) are the most advanced pulsed x-ray sources. Their extraordinary pulse parameters promise unique applications. Indeed, several new methods have been developed at XFEL-s. However, no methods are known, which would allow ab initio atomic level structure determination using only a single XFEL pulse. Here, we present experimental

António Girão, Eoin Hurley, Freddie Illingworth, Lukas Michel

We prove that a large family of pairs of graphs satisfy a polynomial dependence in asymmetric graph removal lemmas. In particular, we give an unexpected answer to a question of Gishboliner, Shapira, and Wigderson by showing that for every $t \geqslant 4$, there are $K_t$-abundant graphs of chromatic number $t$. Using similar methods, we also extend work of R

Tao Luo, Qixuan Zhou

As a popular and easy-to-implement machine learning method for solving differential equations, the physics-informed neural network (PINN) sometimes may fail and find poor solutions which bias against the exact ones. In this paper, we establish a framework of modified equation to explain the failure phenomenon and characterize the implicit bias of a general r

Federica Galluzzi, Bert van Geemen

A specialization of a K3 surface with Picard rank one to a K3 with rank two defines a vanishing class of order two in the Brauer group of the general K3 surface. We give the B-field invariants of this class. We apply this to the K3 double plane defined by a cubic fourfold with a plane. The specialization of such a cubic fourfold whose group of codimension tw

Wiebke Middelberg, Henri Gode, Simon Doclo

In many multi-microphone algorithms for noise reduction, an estimate of the relative transfer function (RTF) vector of the target speaker is required. The state-of-the-art covariance whitening (CW) method estimates the RTF vector as the principal eigenvector of the whitened noisy covariance matrix, where whitening is performed using an estimate of the noise

Mintong Kang, Dawn Song, Bo Li

Diffusion-based purification defenses leverage diffusion models to remove crafted perturbations of adversarial examples and achieve state-of-the-art robustness. Recent studies show that even advanced attacks cannot break such defenses effectively, since the purification process induces an extremely deep computational graph which poses the potential problem o

Magnus Petersen

Cellular Automata (CA) have long been foundational in simulating dynamical systems computationally. With recent innovations, this model class has been brought into the realm of deep learning by parameterizing the CA's update rule using an artificial neural network, termed Neural Cellular Automata (NCA). This allows NCAs to be trained via gradient descent, en

Karim Elsayed, Wasiur R. KhudaBukhsh, Amr Rizk

Quantum entanglement is the key to quantum communications over considerable distances. The first step for entanglement distribution among quantum communication nodes is to generate link-level Einstein-Podolsky-Rosen (EPR) pairs between adjacent communication nodes. EPR pairs may be continuously generated and stored in a few quantum memories to be ready for u

Martin Hutzenthaler, Katharina Pohl

The classical Feynman-Kac identity represents solutions of linear partial differential equations in terms of stochastic differential euqations. This representation has been generalized to nonlinear partial differential equations on the one hand via backward stochastic differential equations and on the other hand via stochastic fixed-point equations. In this

Indrani Pal, Anju A., H. Sreehari, Gitika Rameshan

We carried out a uniform and systematic analysis of a sample of 112 nearby bright Seyfert 1 type AGN, the observations of which were carried out by the Nuclear Spectroscopic Telescope Array (NuSTAR) between August 2013 and May 2022. The main goal of this analysis is to investigate the nature of the X-ray corona in Seyfert 1 galaxies. By fitting a physical mo

Jiyu Chen, Francesco Petocchi, Viktor Christiansson, Philipp Werner

The metal-insulator transition of VO$_2$, which in equilibrium is associated with a structural phase transition, has been intensively studied for decades. In particular, it is challenging to disentangle the role of Mott physics from dimerization effects in the insulating phase. Femtosecond time-resolved experiments showed that optical excitations can induce

Cónal Murphy, Eoin P. O'Reilly, Christopher A. Broderick

We present a theoretical analysis of mid-infrared radiative recombination in InAs/GaSb superlattices (SLs). We employ a semi-analytical plane wave expansion method in conjunction with an 8-band $\mathbf{k} \cdot \mathbf{p}$ Hamiltonian to compute the SL electronic structure, paying careful attention to the identification and mitigation of spurious solutions.

Martin Ho, Henry CW Price, Tim S Evans, Eoin O'Sullivan

Translation as a concept coordinates participation in innovation but remains a qualitative construct. We provide multivariate accounting of linkages between market entries of vaccines, clinical trials, patents, publications, funders, and grants to quantify biomedical translation. We found that the most prevalent types of biomedical translation are those betw

Saba Heidari Gheshlaghi, Milan Aryal, Nasim Yahyasoltani, Masoud Ganji

Whole slide images~(WSIs) are digitized images of tissues placed in glass slides using advanced scanners. The digital processing of WSIs is challenging as they are gigapixel images and stored in multi-resolution format. A common challenge with WSIs is that perturbations/artifacts are inevitable during storing the glass slides and digitizing them. These pertu

Fady Rezk, Antreas Antoniou, Henry Gouk, Timothy Hospedales

We analyze VeLO (versatile learned optimizer), the largest scale attempt to train a general purpose "foundational" optimizer to date. VeLO was trained on thousands of machine learning tasks using over 4000 TPU months with the goal of producing an optimizer capable of generalizing to new problems while being hyperparameter free, and outperforming industry sta

Yanzhao Guo, John P. Hadden, Rachel N. Clark, Samuel G. Bishop

Aluminum nitride is a technologically important wide bandgap semiconductor which has been shown to host bright quantum emitters. In this paper, we probe the photodynamics of quantum emitters in aluminum nitride using photon emission correlations and time-resolved spectroscopy. We identify that each emitter contains as many as 6 internal energy levels with di

Bernhard Vos-Ginés, Santiago Avila, Violeta Gonzalez-Perez, Gustavo Yepes

Halo Occupation Distribution (HOD) models help us to connect observations and theory, by assigning galaxies to dark matter haloes. In this work we study one of the components of HOD models: the probability distribution function (PDF), which is used to assign a discrete number of galaxies to a halo, given a mean number of galaxies. For satellite galaxies, the

Zhenkun Yuan, Diana Dahliah, Muhammad Rubaiat Hasan, Gideon Kassa

Thin-film photovoltaics offers a path to significantly decarbonize our energy production. Unfortunately, current materials commercialized or under development as thin-film solar cell absorbers are far from optimal as they show either low power conversion efficiency or issues with earth-abundance and stability. Entirely new and disruptive materials platforms

Rico Friedrich, Stefano Curtarolo

Accurate thermodynamic stability predictions enable data-driven computational materials design. Standard density functional theory (DFT) approximations have limited accuracy with average errors of a few hundred meV/atom for ionic materials such as oxides and nitrides. Thus, insightful correction schemes as given by the coordination corrected enthalpies (CCE)

Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines

In this paper, we introduce Randomized Q-learning (RandQL), a novel randomized model-free algorithm for regret minimization in episodic Markov Decision Processes (MDPs). To the best of our knowledge, RandQL is the first tractable model-free posterior sampling-based algorithm. We analyze the performance of RandQL in both tabular and non-tabular metric space s

M. Fix, J. H. Atkinson, F. Müller, E. del Barco

The spin-reversal in dilute Li$_2$(Li$_{1-x}$Fe$_{x}$)N with $x < 1$ % is dominated by resonant quantum tunneling of spatially well-separated states. We report on the effect of finite couplings between those states that give rise to cooperative, simultaneous quantum tunneling of two spins. This phenomenon, known as spin-spin cross relaxation, effectively elu

Cuong V. Nguyen, Hieu Minh Duong, Cuong D. Do

In practical electrocardiography (ECG) interpretation, the scarcity of well-annotated data is a common challenge. Transfer learning techniques are valuable in such situations, yet the assessment of transferability has received limited attention. To tackle this issue, we introduce MELEP, which stands for Muti-label Expected Log of Empirical Predictions, a mea

Samuel D. Tomlinson, Frédéric Gibou, Paolo Luzzatto-Fegiz, Fernando Temprano-Coleto

Recognising that surfactants can impede the drag reduction resulting from superhydrophobic surfaces (SHSs), we investigate the impact of spatio-temporal fluctuations in surfactant concentration on the drag-reduction properties of SHSs. We model the unsteady transport of soluble surfactant in a channel flow bounded by two SHSs. The flow is laminar, pressure-d

A. M. Morgen, S. S. Balling, K. Knakkergaard Nielsen, T. Pohl

The physics of impurities in a bosonic quantum environment is a paradigmatic and challenging many-body problem that remains to be understood in its full complexity. Here, this problem is investigated for impurities with strong repulsive interactions based on Ramsey interferometry in a quantum degenerate gas of 39K atoms. We observe an oscillatory signal that

Roberto Araujo

We establish a structure result for the isometry group of non-compact, homogeneous manifolds admitting an immortal homogeneous Ricci flow solution.

Bahareh Khabbazan, Marc Riera, Antonio González

The constant growth of DNNs makes them challenging to implement and run efficiently on traditional compute-centric architectures. Some accelerators have attempted to add more compute units and on-chip buffers to solve the memory wall problem without much success, and sometimes even worsening the issue since more compute units also require higher memory bandw

Shicong Liu, Xianghao Yu, Zhen Gao, Derrick Wing Kwan Ng

Future sixth-generation (6G) systems are expected to leverage extremely large-scale multiple-input multiple-output (XL-MIMO) technology, which significantly expands the range of the near-field region. While accurate channel estimation is essential for beamforming and data detection, the unique characteristics of near-field channels pose additional challenges

J. Karpie, R. M. Whitehill, W. Melnitchouk, C. Monahan

We perform a new global analysis of spin-dependent parton distribution functions with the inclusion of Ioffe time pseudo-distributions computed in lattice QCD (LQCD), which are directly sensitive to the gluon helicity distribution, $\Delta g$. These lattice data have an analogous relationship to parton distributions as do experimental cross sections, and can

Ying Zang, Chaotao Ding, Tianrun Chen, Papa Mao

The rise of AR/VR has led to an increased demand for 3D content. However, the traditional method of creating 3D content using Computer-Aided Design (CAD) is a labor-intensive and skill-demanding process, making it difficult to use for novice users. Sketch-based 3D modeling provides a promising solution by leveraging the intuitive nature of human-computer int

Chad Schafer, Sukhdeep Singh, Yesukhei Jagvaral

This work explores the relationships between galaxy sizes and related observable galaxy properties in a large volume cosmological hydrodynamical simulation. The objectives of this work are to both develop a better understanding of the correlations between galaxy properties and the influence of environment on galaxy physics in order to build an improved model

Zachary M. Geballe, Suzy M. Vitale, Jing Yang, Francesca Miozzi

When diamond anvil cell (DAC) sample chambers are outfitted with both thermal insulation and electrodes, two cutting-edge experimental methods are enabled: Joule heating with spectroradiometric temperature measurement, and electrical resistance measurements of samples heated to thousands of kelvin. The accuracy of temperature and resistance measurements, how

Reeta Devi, Nimisha Dutta, Arindam Boruah, Saumen Acharjee

In this work, we have studied the spin dynamics of a synthethic Antiferromagnet (SAFM)$|$Heavy Metal (HM)$|$Ferromagnet (FM) double barrier magnetic tunnel junction (MTJ) in presence of Ruderman-Kittel-Kasuya-Yoside interaction (RKKYI), interfacial Dzyaloshinskii-Moriya interaction (iDMI), N\'eel field and Spin-Orbit Coupling (SOC) with different Spin Transf

Marcello Lanfranchi

Affine schemes can be understood as objects of the opposite of the category of commutative and unital algebras. Similarly, $\mathscr{P}$-affine schemes can be defined as objects of the opposite of the category of algebras over an operad $\mathscr{P}$. An example is the opposite of the category of associative algebras. The category of operadic schemes of an o

Zhuo Li, Lin Wang, Xiu Liu, Jiayu Li

In nature, nano/micro-structured materials are adopted by various species to generate colors or achieve camouflage. Here, inspired by leafhopper-generated brochosomes, we design binary metastructures composed of pixel twins to achieve pixelated thermal signature control at the microscale. In the infrared range, the pixel twins exhibit distinct emissivities,

Anton A. Smirnov, Anisa T. Bajkova, Vadim V. Bobylev

Studies of the dynamics of globular clusters assume different values of bar parameters (mass, velocity, size) and analyse the results of orbit classifications over the range of the chosen values. It is also a usual thing that a spherical bulge component is converted into the bar to obtain a non-axisymmetric potential from an axisymmetric one. The choice of b

Hamzah Khan, David Fridovich-Keil

Effectively predicting intent and behavior requires inferring leadership in multi-agent interactions. Dynamic games provide an expressive theoretical framework for modeling these interactions. Employing this framework, we propose a novel method to infer the leader in a two-agent game by observing the agents' behavior in complex, long-horizon interactions. We

Wei Zhou

Chat GPT belongs to the category of Generative Pre-trained Transformer (GPT) language models, which have received specialized training to produce text based on natural language inputs. Its purpose is to imitate human-like conversation and can be implemented in multiple applications, such as chatbots, virtual assistants, and language translation systems, star

Yinbang Lin, Sz-Sheng Wang

Given a (projective) conifold transition of smooth projective threefolds from $X$ to $Y$, we show that if the Gromov--Witten/Pandharipande--Thomas descendent correspondence holds for the resolution $Y$, then it also holds for the smoothing $X$ with stationary descendent insertions. As applications, we show the correspondence in new cases.

Neeraj Kumar, Ankur Narang, Brejesh Lall

In this paper, we present a Diffusion GAN based approach (Prosodic Diff-TTS) to generate the corresponding high-fidelity speech based on the style description and content text as an input to generate speech samples within only 4 denoising steps. It leverages the novel conditional prosodic layer normalization to incorporate the style embeddings into the multi

Nitish Joshi, Javier Rando, Abulhair Saparov, Najoung Kim

Large language models (LLMs) are trained on vast amounts of text from the internet, which contains both factual and misleading information about the world. While unintuitive from a classic view of LMs, recent work has shown that the truth value of a statement can be elicited from the model's representations. This paper presents an explanation for why LMs app

Guoxin Chen, Yiming Qian, Bowen Wang, Liangzhi Li

The large language models have achieved superior performance on various natural language tasks. One major drawback of such approaches is they are resource-intensive in fine-tuning new datasets. Soft-prompt tuning presents a resource-efficient solution to fine-tune the pre-trained language models (PLMs) while keeping their weight frozen. Existing soft prompt

Danielle Marshall, Dominic Orchard

Ownership and borrowing systems, designed to enforce safe memory management without the need for garbage collection, have been brought to the fore by the Rust programming language. Rust also aims to bring some guarantees offered by functional programming into the realm of performant systems code, but the type system is largely separate from the ownership mod

Baskoro Adi Pratomo, Toby Jackson, Pete Burnap, Andrew Hood

Analysing malware is important to understand how malicious software works and to develop appropriate detection and prevention methods. Dynamic analysis can overcome evasion techniques commonly used to bypass static analysis and provide insights into malware runtime activities. Much research on dynamic analysis focused on investigating machine-level informati

Félix Locas, Jean-François Renaud

We consider de Finetti's optimal dividends problem with absolutely continuous strategies in a spectrally negative L\'evy model with Parisian ruin as the termination time. The problem considered is essentially a generalization of both the control problems considered by Kyprianou, Loeffen & P\'erez (2012) and by Renaud (2019). Using the language of scale funct

Rahil Baber, Natalie Behague, Asier Calbet, David Ellis

One of the great pleasures of working with Imre Leader is to experience his infectious delight on encountering a compelling combinatorial problem. This collection of open problems in combinatorics has been put together by a subset of his former PhD students and students-of-students for the occasion of his 60th birthday. All of the contributors have been infl

Srijoni Majumdar, Soumen Paul, Debjyoti Paul, Ayan Bandyopadhyay

The Information Retrieval in Software Engineering (IRSE) track aims to develop solutions for automated evaluation of code comments in a machine learning framework based on human and large language model generated labels. In this track, there is a binary classification task to classify comments as useful and not useful. The dataset consists of 9048 code comme

Leon Kellerhals, Jannik Peters

We study the proportional clustering problem of Chen et al. [ICML'19] and relate it to the area of multiwinner voting in computational social choice. We show that any clustering satisfying a weak proportionality notion of Brill and Peters [EC'23] simultaneously obtains the best known approximations to the proportional fairness notion of Chen et al. [ICML'19]

Zichuan Zhou, Amany Kassem, James Seddon, Eric Sillekens

We generate and transmit 75-GHz-bandwidth OFDM signals over the air using three mutually frequency-locked lasers, achieving minimal frequency gap between the wireless W and D bands using optical-assisted approaches, resulting in 173.5 Gb/s detected capacity.

Shahab O. Dabironezare, Sven van Berkel, Pierre M. Echternach, Peter K. Day

Future generation of astronomical imaging spectrometers are targeting the far infrared wavelengths to close the THz astronomy gap. Similar to lens antenna coupled Microwave Kinetic Inductance Detectors (MKIDs), lens absorber coupled MKIDs are a candidate for highly sensitive large format detector arrays. However, the latter is more robust to misalignment and

Shion Yamashika, Filiberto Ares, Pasquale Calabrese

We study the time evolution of the R\'enyi entanglement entropies following a quantum quench in a two-dimensional (2D) free-fermion system. By employing dimensional reduction, we effectively transform the 2D problem into decoupled chains, a technique applicable when the system exhibits translational invariance in one direction. Various initial configurations

Leandro C. de Almeida, Washington Rodrigo Dias da Silva, Thiago C. Tavares, Rafael Pasquini

Active Queue Management (AQM) is a mechanism employed to alleviate transient congestion in network device buffers, such as routers and switches. Traditional AQM algorithms use fixed thresholds, like target delay or queue occupancy, to compute random packet drop probabilities. A very small target delay can increase packet losses and reduce link utilization, w

Stephen Fairhurst, Cameron Mills, Monica Colpi, Raffaella Schneider

We investigate the detectability of single-event coalescing black hole binaries with total mass of $100-600 M_{\odot}$ at cosmological distances ($5 \lesssim z \lesssim 20$) with the next generation of terrestrial gravitational wave observatories, specifically Einstein Telescope and Cosmic Explorer. Our ability to observe these binaries is limited by the low