October 2023 arXiv papers — page 164

Andreas Brandhuber, Graham R. Brown, Gang Chen, Joshua Gowdy

We compute the classical tree-level five-point amplitude for the two-to-two scattering of spinning celestial objects with the emission of a graviton. Using this five-point amplitude, we then turn to the computation of the leading-order time-domain gravitational waveform. The method we describe is suitable for arbitrary values of classical spin of Kerr black

Manami Roy, Kung-Yi Su, Stephanie Tonnesen, Drummond B. Fielding

The origin of the cold phase in the CGM is a highly debated question. We investigate the contribution of satellite galaxies to the cold gas budget in the circumgalactic medium (CGM)of a Milky Way-like host galaxy. We perform controlled experiments with three different satellite mass distributions and identify several mechanisms by which satellites can add co

A. Hoover, K. Ruisard, A. Aleksandrov, A. Zhukov

Hadron beams develop intensity-dependent transverse-longitudinal correlations within radio-frequency quadrupole (RFQ) accelerating structures. These correlations are only visible in six-dimensional phase space and are destroyed by reconstructions from low-dimensional projections. In this work, we estimate the effect of artificial decorrelation on the beam dy

Edgar A. Bering, Yulan Qing, Derrick R. Wigglesworth

An outer automorphism of a free group is geometric if it can be represented by a homeomorphism of a compact surface. Bestvina and Handel gave an algorithmic characterization of geometric irreducible outer automorphisms using relative train tracks in 1995. The general case of detecting geometric outer automorphisms remained open, with a few partial results ap

Sayan Dutta, Ayanava Mandal, Sohom Gupta, Sourin Chatterjee

We say that a chessboard filled with integer entries satisfies the neighbour-sum property if the number appearing on each cell is the sum of entries in its neighbouring cells, where neighbours are cells sharing a common edge or vertex. We show that an $n\times n$ chessboard satisfies this property if and only if $n\equiv 5\pmod 6$. Existence of solutions is

Xingzhuo Guo, Junwei Pan, Ximei Wang, Baixu Chen

Recent advances in foundation models have led to a promising trend of developing large recommendation models to leverage vast amounts of available data. Still, mainstream models remain embarrassingly small in size and na\"ive enlarging does not lead to sufficient performance gain, suggesting a deficiency in the model scalability. In this paper, we identify t

Junkun Chen, Jian Xue, Peidong Wang, Jing Pan

Simultaneous Speech-to-Text translation serves a critical role in real-time crosslingual communication. Despite the advancements in recent years, challenges remain in achieving stability in the translation process, a concern primarily manifested in the flickering of partial results. In this paper, we propose a novel revision-controllable method designed to a

Cory Johnson, Andrew Lavengood-Ryan

DNA self-assembly is an important tool that has a wide range of applications such as building nanostructures, the transport of target virotherapies, and nano-circuitry. Tools from graph theory can be used to encode the biological process of DNA self-assembly. The principle component of this process is to examine collections of branched junction molecules, ca

Phillip Diamond, Benjamin Schumacher

We examine the toy model of modal quantum theory (MQT), an analogue of actual quantum theory based on finite fields. In particular, we investigate how several essential ``no-go'' results (for cloning, deleting and hiding processes) work in MQT. Cloning and deleting are still forbidden in MQT, though the details of these results are somewhat different in the

Lars Simon, Holger Eble, Hagen-Henrik Kowalski, Manuel Radons

This article focuses on developing classical surrogates for parametrized quantum circuits using interpolation via (trigonometric) polynomials. We develop two algorithms for the construction of such surrogates and prove performance guarantees. The constructions are based on circuit evaluations which are blackbox in the sense that no structural specifics of th

Marvin Schmitt, Desi R. Ivanova, Daniel Habermann, Ullrich Köthe

We propose a method to improve the efficiency and accuracy of amortized Bayesian inference by leveraging universal symmetries in the joint probabilistic model of parameters and data. In a nutshell, we invert Bayes' theorem and estimate the marginal likelihood based on approximate representations of the joint model. Upon perfect approximation, the marginal li

A. C. Lee, S. Sarkar, K. Du, H. -H. Kung

We use polarization resolved Raman spectroscopy to demonstrate that for a 3D giant Rashba system the bulk plasmon collective mode can directly couple to the Raman response even in the long wavelength $\mathbf q \rightarrow 0$ limit. Although conventional theory predicts the plasmon spectral weight to be suppressed as the square of its quasi-momentum and thus

Aaron Anderson

Model theory, machine learning, and combinatorics each have generalizations of VC-dimension for fuzzy and real-valued versions of set systems. These different dimensions define a unique notion of a VC-class for both fuzzy sets and real-valued functions. We study these VC-classes, obtaining generalizations of certain combinatorial results from the discrete ca

Burkhard Eden, Maximilian Gottwald, Dennis le Plat, Tobias Scherdin

We consider the correlator $\langle \mathcal{L} \mathcal{K} \tilde{ \mathcal{K}} \rangle $ of the Lagrange operator of $\mathcal{N}=4$ super Yang-Mills theory and two conjugate two-excitation operators in an $su(2)$ sector. We recover the planar one-loop anomalous dimension of the renormalised operators from this hexagon computation.

V. Arvind, Frank Fuhlbrück, Johannes Köbler, Oleg Verbitsky

We consider a hierarchy of graph invariants that naturally extends the spectral invariants defined by F\"urer (Lin. Alg. Appl. 2010) based on the angles formed by the set of standard basis vectors and their projections onto eigenspaces of the adjacency matrix. We provide a purely combinatorial characterization of this hierarchy in terms of the walk counts. T

Justin Weltz, Tanner Fiez, Alexander Volfovsky, Eric Laber

Most linear experimental design problems assume homogeneous variance although heteroskedastic noise is present in many realistic settings. Let a learner have access to a finite set of measurement vectors $\mathcal{X}\subset \mathbb{R}^d$ that can be probed to receive noisy linear responses of the form $y=x^{\top}\theta^{\ast}+\eta$. Here $\theta^{\ast}\in \m

Olfa Ben Yahia, Zineb Garroussi, Olivier Bélanger, Brunilde Sansò

High throughput satellites (HTS), with their digital payload technology, are expected to play a key role as enablers of the upcoming 6G networks. HTS are mainly designed to provide higher data rates and capacities. Fueled by technological advancements including beamforming, advanced modulation techniques, reconfigurable phased array technologies, and electro

Liam Keenan, Jonas McCandless

In this note, we establish a vanishing result for telescopically localized $\mathrm{TR}$. More precisely, we prove that $T(k)$-local $\mathrm{TR}$ vanishes on connective $L_n^{p,f}$-acyclic $\mathbf{E}_1$-rings for every $1 \leq k \leq n$ and deduce consequences for connective Morava K-theory and the Thom spectra $y(n)$. The proof relies on the relationship

Facundo Toscano, Heliana Luparello, Elizabeth Johana Gonzalez, Diego Garcia Lambas

The aim of this work is to study the anisotropic weak lensing signal associated with the mass distribution of massive clusters of galaxies using the Cosmic Microwave Background (CMB) data. For this purpose, we stack patches of the Planck Collaboration 2018 CMB lensing convergence map centered on SDSS DR8 redMaPPer clusters within the redshift range [0.4, 0.5

Adrián González Casanova, Jan Lukas Igelbrink

We construct and study branching fractional Brownian motion with Hurst parameter $H\in(1/2,1)$. The construction relies on a generalization of the discrete approximation of fractional Brownian motion (Hammond and Sheffield, Probability Theory and Related Fields, 2013) to power law P\'olya urns indexed by trees. We show that the first order of the speed of br

Yubo Jin

In this note, we study the special values for zeta functions of totally real fields using the Shintani's cone decomposition. We prove certain congruence between the special values for zeta functions under the prime degree field extension. This congruence implies the `torsion congruence' proved by Ritter-Weiss which is crucial in the proof of the noncommutati

Reiner Hähnle, Eduard Kamburjan, Marco Scaletta

The behavior of concurrent, asynchronous procedures depends in general on the call context, because of the global protocol that governs scheduling. This context cannot be specified with the state-based Hoare-style contracts common in deductive verification. Recent work generalized state-based to trace contracts, which permit to specify the internal behavior

Alexander Herbst, Timothé Estrampes, Henning Albers, Robin Corgier

The sensitivity of atom interferometers depends on their ability to realize long pulse separation times and prevent loss of contrast by limiting the expansion of the atomic ensemble within the interferometer beam through matter-wave collimation. Here we investigate the impact of atomic interactions on collimation by applying a lensing protocol to a $^{39}$K

P. Frank Winkler, Knox S. Long, William P. Blair

The nearby irregular galaxy NGC4449 has a star formation rate of about 0.4 solar masses/yr and should host of order 70 SNRs younger than 20,000 years, a typical age for SNRs expanding into to an ISM with unit density to reach the radiative phase. We have carried out an optical imaging and spectroscopic survey in an attempt to identify these SNRs. This task i

Giacomo Verardo, Magnus Boman, Samuel Bruchfeld, Marco Chiesa

Detecting anomalies in electrocardiogram data is crucial to identifying deviations from normal heartbeat patterns and providing timely intervention to at-risk patients. Various AutoEncoder models (AE) have been proposed to tackle the anomaly detection task with ML. However, these models do not consider the specific patterns of ECG leads and are unexplainable

Abdullah Al Ishtiaq, Sarkar Snigdha Sarathi Das, Syed Md Mukit Rashid, Ali Ranjbar

In this paper, we present Hermes, an end-to-end framework to automatically generate formal representations from natural language cellular specifications. We first develop a neural constituency parser, NEUTREX, to process transition-relevant texts and extract transition components (i.e., states, conditions, and actions). We also design a domain-specific langu

Enrico Lunghi, Beni Pazar

Motivated by new physics models which lead to final states containing a high multiplicity of bottom and top quarks, we develop a tagging strategy to suppress reducible and non-reducible multi-jet backgrounds. The idea takes advantage of the properties of light parton showers and of the gluon fragmentation into heavy quarks to reject jets that do not originat

Maciej Fidrysiak, Bartłomiej Rzeszotarski, Józef Spałek

We carry out a theoretical study of unconventional superconductivity in twisted bilayer cuprates (TBC) as a function of electron density and layer twist angle. The bilayer $t$-$J$-$U$ model is employed and analyzed within the framework of a generalized variational wave function approach in the statistically-consistent Gutzwiller formulation. The constructed

Simian Luo, Yiqin Tan, Longbo Huang, Jian Li

Latent Diffusion models (LDMs) have achieved remarkable results in synthesizing high-resolution images. However, the iterative sampling process is computationally intensive and leads to slow generation. Inspired by Consistency Models (song et al.), we propose Latent Consistency Models (LCMs), enabling swift inference with minimal steps on any pre-trained LDM

Georgios Kydonakis, Charlie Reid, Alexander Thomas

We introduce the concept of a Fock bundle, a smooth principal bundle over a surface equipped with a special kind of adjoint-valued 1-form, as a new tool for studying character varieties of surface groups. Although similar to Higgs bundles, the crucial difference is that no complex structure is fixed on the underlying surface. Fock bundles are the gauge-theor

Yuichi Sudo, Masahiro Shibata, Junya Nakamura, Yonghwan Kim

Consider that there are $k\le n$ agents in a simple, connected, and undirected graph $G=(V,E)$ with $n$ nodes and $m$ edges. The goal of the dispersion problem is to move these $k$ agents to mutually distinct nodes. Agents can communicate only when they are at the same node, and no other communication means, such as whiteboards, are available. We assume that

M. W. Denhoff, J. P. McCaffrey

Pulsed laser deposition has been used to deposit Y1Ba2Cu3O7 layer on CeO2 buffer layers on (1102) sapphire. Both layers are epitaxial with the (110) direction of the CeO2 layer aligned with the <2021> direction of the sapphire substrate. The c-axis Y1Ba2Cu3O7 layer has its <100> direction aligned with the <110> direction of the CeO2. Cross-sectional transmis

Guanyi Lu, Seyyedmaalek Momeni, Carlo Peruzzo, Fatima-Ezzahra Moukhtari

We report laboratory experiments and numerical simulations demonstrating that the anisotropic characteristics of rocks play a major role in the elongation of hydraulic fractures propagating in a plane perpendicular to bedding. Transverse anisotropy leads to larger hydraulic fracture extension in the parallel-to-bedding/divider direction compared to the perpe

Ted Moskovitz, Aaditya K. Singh, DJ Strouse, Tuomas Sandholm

Large language models are typically aligned with human preferences by optimizing $\textit{reward models}$ (RMs) fitted to human feedback. However, human preferences are multi-faceted, and it is increasingly common to derive reward from a composition of simpler reward models which each capture a different aspect of language quality. This itself presents a cha

Sophia Natalia Cisneros, Richard Ott, Meagan Crowley, Amy Roberts

One key piece of evidence for dark matter is the flat rotation curve problem: the disagreement between measured galactic rotation curves and their luminous mass. A novel solution to this problem is presented here. A model of relativistic frame effects on Doppler shifts due to the slightly curved frames of an emitting galaxy and the Milky Way is derived. This

Jiazhao Tian, Haibin Liu, Roberto Sailer, Liantuan Xiao

Decoherence-protected spins associated with nitrogen-vacancy color centers in diamond possess remarkable long coherence time, which make them one of the most promising and robust quantum registers. The current demand is to explore practical rapid control strategies for preparing and manipulating the such register. Our work provides all-microwave control stra

Vasily Semyonovich Beskin, Timur Igorevich Khalilov, Vladimir Ivanovich Pariev

Assuming that the first adiabatic invariant for radiating particles in relativistic jets is conserved, we investigate the change in brightness temperature along the jet axis. We show that in this case the observed break in the dependence of the brightness temperature on the distance to the central engine can be explained.

Weiming Xu, Zhouxuan Chen, Zhili Tan, Shubo Lv

A typical neural speech enhancement (SE) approach mainly handles speech and noise mixtures, which is not optimal for singing voice enhancement scenarios. Music source separation (MSS) models treat vocals and various accompaniment components equally, which may reduce performance compared to the model that only considers vocal enhancement. In this paper, we pr

Will Crichton, Shriram Krishnamurthi

Passive documents and active programs now widely comingle. Document languages include Turing-complete programming elements, and programming languages include sophisticated document notations. However, there are no formal foundations that model these languages. This matters because the interaction between document and program can be subtle and error-prone. In

Akshit Sarpal, Qiwen Kang, Fangping Huang, Yang Song

Online marketplaces execute large volume of price updates that are initiated by individual marketplace sellers each day on the platform. This price democratization comes with increasing challenges with data quality. Lack of centralized guardrails that are available for a traditional online retailer causes a higher likelihood for inaccurate prices to get publ

Taha Shahroodi, Gagandeep Singh, Mahdi Zahedi, Haiyu Mao

Basecalling, an essential step in many genome analysis studies, relies on large Deep Neural Networks (DNNs) to achieve high accuracy. Unfortunately, these DNNs are computationally slow and inefficient, leading to considerable delays and resource constraints in the sequence analysis process. A Computation-In-Memory (CIM) architecture using memristors can sign

Meirav Amram, Praveen Kumar Roy, Uriel Sinichkin

We showcase a computation of the fundamental group of $\mathbb{CP}^2 - \mathcal{C}$ when $\mathcal{C}$ is a curve admitting a lot of symmetries. In particular, let $\mathcal{C}$ denote the Fermat line arrangement in $\mathbb{CP}^2$ defined by the vanishing locus of homogeneous polynomial $(x^n-y^n)(y^n-z^n)(z^n-x^n)$. In this article, we compute the fundamen

Zhongyuan Zhao, Gunjan Verma, Ananthram Swami, Santiago Segarra

Backpressure (BP) routing is a well-established framework for distributed routing and scheduling in wireless multi-hop networks. However, the basic BP scheme suffers from poor end-to-end delay due to the drawbacks of slow startup, random walk, and the last packet problem. Biased BP with shortest path awareness can address the first two drawbacks, and sojourn

Edward J. Hu, Moksh Jain, Eric Elmoznino, Younesse Kaddar

Autoregressive large language models (LLMs) compress knowledge from their training data through next-token conditional distributions. This limits tractable querying of this knowledge to start-to-end autoregressive sampling. However, many tasks of interest -- including sequence continuation, infilling, and other forms of constrained generation -- involve samp

Corey McInerney, Phil Sutton

Einstein's theory of relativity is largely thought of as one of the most important discoveries of the 20$^{th}$ century and continues to pass observational tests over 100 years later. Yet, it is Newtonian gravity, a 350 year old formalism proven to be less accurate than relativity, which is taught in schools. It has been shown that Einsteinian gravitational

Filip Szatkowski, Bartosz Wójcik, Mikołaj Piórczyński, Simone Scardapane

Transformer models can face practical limitations due to their high computational requirements. At the same time, such models exhibit significant activation sparsity, which can be leveraged to reduce the inference cost by converting parts of the network into equivalent Mixture-of-Experts (MoE) layers. Despite the crucial role played by activation sparsity, i

Fiche Guénolé, Sevestre Vincent, Gonzalez-Barral Camila, Leglaive Simon

Technologies play an increasingly important role in sports and become a real competitive advantage for the athletes who benefit from it. Among them, the use of motion capture is developing in various sports to optimize sporting gestures. Unfortunately, traditional motion capture systems are expensive and constraining. Recently developed computer vision-based

Cameron R. D. Bunney

We determine the conjugacy classes of the Poincar\'e group $\mathrm{ISO}^+(n,1)$ and apply this to classify the stationary trajectories of Minkowski spacetimes in terms of timelike Killing vectors. Stationary trajectories are the orbits of timelike Killing vectors and, equivalently, the solutions to Frenet-Serret equations with constant curvature coefficient

Ziyun Cui, Wen Wu, Wei-Qiang Zhang, Ji Wu

The detection of Alzheimer's disease (AD) from spontaneous speech has attracted increasing attention while the sparsity of training data remains an important issue. This paper handles the issue by knowledge transfer, specifically from both speech-generic and depression-specific knowledge. The paper first studies sequential knowledge transfer from generic fou

Pratik Patil, Daniel LeJeune

We employ random matrix theory to establish consistency of generalized cross validation (GCV) for estimating prediction risks of sketched ridge regression ensembles, enabling efficient and consistent tuning of regularization and sketching parameters. Our results hold for a broad class of asymptotically free sketches under very mild data assumptions. For squa

Catherine Carpentier-Desjardins, Masarah Paquet-Clouston, Stefan Kitzler, Bernhard Haslhofer

Decentralized finance (DeFi) has been the target of numerous profit-driven crimes, but the prevalence and cumulative impact of these crimes have not yet been assessed. This study provides a comprehensive assessment of profit-driven crimes targeting the DeFi sector. We collected data on 1141 crime events from 2017 to 2022. Of these, 1036 were related to DeFi

Harold Berjamin

The formation of shear shock waves in the brain has been proposed as one of the plausible explanations for deep intracranial injuries. In fact, such singular solutions emerge naturally in soft viscoelastic tissues under dynamic loading conditions. To improve our understanding of the mechanical processes at hand, the development of dedicated computational mod

Tom Schierenbeck, Vladimir Vutov, Thorsten Dickhaus, Michael Beetz

This study addresses the predictive limitation of probabilistic circuits and introduces transformations as a remedy to overcome it. We demonstrate this limitation in robotic scenarios. We motivate that independent component analysis is a sound tool to preserve the independence properties of probabilistic circuits. Our approach is an extension of joint probab

Amitayush Thakur, George Tsoukalas, Yeming Wen, Jimmy Xin

We present an in-context learning agent for formal theorem-proving in environments like Lean and Coq. Current state-of-the-art models for the problem are finetuned on environment-specific proof data. By contrast, our approach, called COPRA, repeatedly asks a high-capacity, general-purpose large language model (GPT-4) to propose tactic applications from withi

Camille Olivia Little, Debolina Halder Lina, Genevera I. Allen

Across various sectors such as healthcare, criminal justice, national security, finance, and technology, large-scale machine learning (ML) and artificial intelligence (AI) systems are being deployed to make critical data-driven decisions. Many have asked if we can and should trust these ML systems to be making these decisions. Two critical components are pre

Krzysztof Myśliwy, Krzysztof Jachymski

We construct the simplest density functional for the problem of a single impurity interacting with a Fermi gas via a long--ranged potential using the Thomas--Fermi approach. We find that the Fermi polaron is fully bosonized in two dimensions, as the model results in a suitable Landau--Pekar functional known from the Bose polaron problem which describes a sel

ATLAS Collaboration

A study of the polarisation and CP properties in $ZZ$ production is presented. The used data set corresponds to an integrated luminosity of 140 fb$^{-1}$ of proton-proton collisions at a centre-of-mass energy of $13$ TeV recorded by the ATLAS detector at the Large Hadron Collider. The $ZZ$ candidate events are reconstructed using two same-flavour opposite-ch

François Hélénon, Johann Huber, Faïz Ben Amar, Stéphane Doncieux

Despite recent advancements in AI for robotics, grasping remains a partially solved challenge, hindered by the lack of benchmarks and reproducibility constraints. This paper introduces a vision-based grasping framework that can easily be transferred across multiple manipulators. Leveraging Quality-Diversity (QD) algorithms, the framework generates diverse re

Duo Wang, Andrea Araldo, Mounim A. El Yacoubi

Accessibility measures how well a location is connected to surrounding opportunities. We focus on accessibility provided by Public Transit (PT). There is an evident inequality in the distribution of accessibility between city centers or close to main transportation corridors and suburbs. In the latter, poor PT service leads to a chronic car-dependency. Deman

Arghya Maity, Ahana Ghoshal

We demonstrate that the efficiency of effective negative temperature-based quantum Otto engines, already known to outperform their traditional counterparts operating with positive-temperature thermal reservoirs, can be further improved by terminating the isochoric strokes before the working substance reaches perfect equilibrium with its environment. Our inve

Fabio Castagna, Alberto Trombetta, Marco Landoni, Stefano Andreon

Computer power is a constantly increasing demand in scientific data analyses, in particular when Markov Chain Monte Carlo (MCMC) methods are involved, for example for estimating integral functions or Bayesian posterior probabilities. In this paper, we describe the benefits of a parallel computation of MCMC using a cloud-based, serverless architecture: first,

Justin Dumouchelle, Esther Julien, Jannis Kurtz, Elias B. Khalil

Robust optimization is an established framework for modeling optimization problems with uncertain parameters. While static robust optimization is often criticized for being too conservative, two-stage (or adjustable) robust optimization (2RO) provides a less conservative alternative by allowing some decisions to be made after the uncertain parameters have be

Roberto da Silva, Sandra D. Prado

This study explores the application of random matrices to track chaotic dynamics within the Chirikov standard map. Our findings highlight the potential of matrices exhibiting Wishart-like characteristics, combined with statistical insights from their eigenvalue density, as a promising avenue for chaos monitoring. Inspired by a technique originally designed f

Zhenqiao Song, Yunlong Zhao, Wenxian Shi, Yang Yang

Proteins are macromolecules responsible for essential functions in almost all living organisms. Designing reasonable proteins with desired functions is crucial. A protein's sequence and structure are strongly correlated and they together determine its function. In this paper, we propose NAEPro, a model to jointly design Protein sequence and structure based o

Zhiyi Yao, Bowen Ding, Qianlan Bai, Yuedong Xu

Data silos create barriers in accessing and utilizing data dispersed over networks. Directly sharing data easily suffers from the long downloading time, the single point failure and the untraceable data usage. In this paper, we present Minerva, a peer-to-peer cross-cluster data query system based on InterPlanetary File System (IPFS). Minerva makes use of the

Andrei Teleman

Let $X$ be a connected, compact complex manifold and $S\subset X$ a separating real hypersurface, so that $X$ decomposes as a union of compact complex manifolds with boundary $\bar X^\pm$. Let $\mathcal{M}$ be the moduli space of $S$-framed holomorphic bundles, i.e. of pairs $(E,\theta)$ of fixed topological type consisting of a holomorphic bundle $E$ on $X$

Immanuel Bomze, Bo Peng, Yuzhou Qiu, E. Alper Yıldırım

Standard quadratic optimization problems (StQPs) provide a versatile modelling tool in various applications. In this paper, we consider StQPs with a hard sparsity constraint, referred to as sparse StQPs. We focus on various tractable convex relaxations of sparse StQPs arising from a mixed-binary quadratic formulation, namely, the linear optimization relaxati

Sobhit Singh, A. C. Garcia-Castro

Kagome metals are reported to exhibit remarkable properties, including superconductivity, charge density wave order, and a large anomalous Hall conductivity, which facilitate the implementation of spintronic devices. In this work, we study a novel kagome metal based on Mn magnetic sites in a KMn$_3$Sb$_5$ stoichiometry. By means of first-principles density f

Ferenc Bencs, Khallil Berrekkal, Guus Regts

We show that the anti-ferromagnetic Potts model on trees exhibits strong spatial mixing for a near-optimal range of parameters. Our work complements recent results of Chen, Liu, Mani, and Moitra [arXiv.2304.01954] who showed this to be true in the infinite temperature setting, corresponding to uniform proper colorings. We furthermore prove weak spatial mixin

Wenxuan He, Abhishek Karmakar, Grant Rowlands, Samuel Schirmacher

The unacceptably high stroke rate of HeartMate III VAD without signs of adherent pump thrombosis is hypothesized to be the result of the thrombi originating on the inflow cannula, ingesting and ejecting emboli from the VAD. Therefore, inflow cannula thrombosis has been an emerging focus. The inflow cannula of contemporary VADs, which incorporate both polishe

Zoran Stoiljkovic

This thesis provides an overview of the recent advances in reinforcement learning in pricing and hedging financial instruments, with a primary focus on a detailed explanation of the Q-Learning Black Scholes approach, introduced by Halperin (2017). This reinforcement learning approach bridges the traditional Black and Scholes (1973) model with novel artificia

Poonam Chandra, S. Suresh Kumar, Sanjay Kudale, Devojyoti Kansabanik

Our work aims to investigate the polarisation convention of the Giant Metrewave Radio Telescope (GMRT) radio telescope and understand whether the telescope follows the standard IAU/IEEE convention. The GMRT antennas are prime focus antennas, i.e. the radiation falling on the antenna feed reverses its circular polarisation. If this reflection is not taken int

Guangji Bai, Qilong Zhao, Xiaoyang Jiang, Yifei Zhang

Continual Learning is a burgeoning domain in next-generation AI, focusing on training neural networks over a sequence of tasks akin to human learning. While CL provides an edge over traditional supervised learning, its central challenge remains to counteract catastrophic forgetting and ensure the retention of prior tasks during subsequent learning. Amongst v

Zachary Wiethorn, Kye Hunter, Tim Zuehlsdorff, Andrés Montoya-Castillo

While dark transitions made bright by molecular motions determine the optoelectronic properties of many materials, simulating such non-Condon effects in condensed-phase spectroscopy remains a fundamental challenge. We derive a Gaussian theory to predict and analyze condensed phase optical spectra beyond the Condon limit. Our theory introduces novel quantitie

Bart M. P. Jansen, Shivesh K. Roy

We study a new graph separation problem called Multiway Near-Separator. Given an undirected graph $G$, integer $k$, and terminal set $T \subseteq V(G)$, it asks whether there is a vertex set $S \subseteq V(G) \setminus T$ of size at most $k$ such that in graph $G-S$, no pair of distinct terminals can be connected by two pairwise internally vertex-disjoint pa

Tejasvi Chebrolu, Rohan Chowdary, N Harsha Vardhan, Ponnurangam Kumaraguru

In covering elections, journalists often use conflict frames which depict events and issues as adversarial, often highlighting confrontations between opposing parties. Although conflict frames result in more citizen engagement, they may distract from substantive policy discussion. In this work, we analyze the use of conflict frames in online English-language

Bianca Viggiano, Thomas Basset, Mickaël Bourgoin, Raúl Bayoán Cal

Turbulence is prevalent in nature and industry, from large-scale wave dynamics to small-scale combustion nozzle sprays. In addition to the multi-scale nonlinear complexity and both randomness and coherent structures in its dynamics, practical turbulence is often non-homogeneous and anisotropic, leading to great modeling challenges. In this letter, an efficie

Leijie Wang, Nicolas Vincent, Julija Rukanskaitė, Amy X. Zhang

Internet users have formed a wide array of online communities with nuanced and diverse community goals and norms. However, most online platforms only offer a limited set of governance models in their software infrastructure and leave little room for customization. Consequently, technical proficiency becomes a prerequisite for online communities to build gove

Archibald Fraikin, Adrien Bennetot, Stéphanie Allassonnière

Multivariate time series present challenges to standard machine learning techniques, as they are often unlabeled, high dimensional, noisy, and contain missing data. To address this, we propose T-Rep, a self-supervised method to learn time series representations at a timestep granularity. T-Rep learns vector embeddings of time alongside its feature extractor,

Noah Schutte, Krzysztof Postek, Neil Yorke-Smith

Optimization models used to make discrete decisions often contain uncertain parameters that are context-dependent and estimated through prediction. To account for the quality of the decision made based on the prediction, decision-focused learning (end-to-end predict-then-optimize) aims at training the predictive model to minimize regret, i.e., the loss incur

Saqib Ameen, Levi H. S. Lelis

Cost-guided bottom-up search (BUS) algorithms use a cost function to guide the search to solve program synthesis tasks. In this paper, we show that current state-of-the-art cost-guided BUS algorithms suffer from a common problem: they can lose useful information given by the model and fail to perform the search in a best-first order according to a cost funct

Anupama K Xavier, Jonathan Demaeyer, Stéphane Vannitsem

This study delves into the predictability of atmospheric blocking, zonal, and transition patterns utilizing a simplified coupled model. Initially, we comprehensively scrutinize the model's responses to environmental parameters like solar radiation, surface friction, and atmosphere-ground heat exchange. Subsequently, employing Gaussian mixture clustering, we

M. Thumin, G. Bouzerar

Within ten years, flat band (FB) superconductivity has gained a huge interest for its remarkable features and connection to quantum geometry. We investigate the superconductivity in a FB system whose orbitals are inequivalent and in which the gap and the quantum metric are tunable. The key feature of the present theoretical study is to show a unique and simp

Raul Carrasco, Angel Rincon, Joel Saavedra, Nelson Videla

In the present work, we perform a comparative study of different interacting dark energy (DE) models using the Statefinder diagnostics. In particular, 17 different forms of the energy transfer rate $Q$ between DE and dark matter (DM) were focused on, belonging to the following categories: i) linear models in energy densities of DE and DM, ii) non-linear mode

Yuxin Pan, Yize Chen, Fangzhen Lin

Designing effective policies for the online 3D bin packing problem (3D-BPP) has been a long-standing challenge, primarily due to the unpredictable nature of incoming box sequences and stringent physical constraints. While current deep reinforcement learning (DRL) methods for online 3D-BPP have shown promising results in optimizing average performance over an

Nicolas Ruffray, Ueli M. Angst, Thilo Schmid, Zhidong Zhang

While it is widely accepted that the steel-concrete interface (SCI) plays an important role in governing the long-term durability of reinforced concrete structures, understanding about the primary features of the SCI that influence corrosion degradation mechanisms has remained elusive. This lack of knowledge can be attributed, on the one hand, to the complex

Andrea Gloppen Johnsen, Lesia Mitridati, Donato Zarrilli, Jalal Kazempour

Although primarily designed for hydrogen production, electrolyzers can support power systems by providing various ancillary services, opening new revenue streams that enhance their economic viability. This paper investigates the participation of an electrolyzer in frequency-supporting reserve markets, analyzing how bid structures and activation intensities a

Riccardo Morbidini, Robert M. Edkins, Mark Devonport, Gøran Nilsen

Binary mixtures of water with lower alcohols display non-linear phase behaviour upon mixing which are attributed to potential cluster formation at molecular level. Unravelling such elusive structures requires the investigation of hydrogen-bonding sub-nanosecond dynamics. We employ high-resolution neutron time-of-flight spectroscopy with polarization analysis

Christopher D. Carone, Noah L. Donald

We propose an ansatz for encoding the physics of nonlocal spacetime defects in the Green's functions for a scalar field theory defined on a causal set. This allows us to numerically study the effects of nonlocal spacetime defects on the discrete Feynman propagator of the theory defined on the causal set in 1+1 dimensions, and to compare to the defect-free li

Anaelia Ovalle, Davi Liang, Alicia Boyd

Smartphones are integral to our daily lives and activities, providing us with basic functions like texting and phone calls to more complex motion-based functionalities like navigation, mobile gaming, and fitness-tracking. To facilitate these functionalities, smartphones rely on integrated sensors like accelerometers and gyroscopes. These sensors provide pers

Julian Koellermeier, Philipp Krah, Julius Reiss, Zachary Schellin

Kinetic equations are crucial for modeling non-equilibrium phenomena, but their computational complexity is a challenge. This paper presents a data-driven approach using reduced order models (ROM) to efficiently model non-equilibrium flows in kinetic equations by comparing two ROM approaches: Proper Orthogonal Decomposition (POD) and autoencoder neural netwo

Andrea Pierré, Tuan Pham, Jonah Pearl, Sandeep Robert Datta

Neuroscience research has evolved to generate increasingly large and complex experimental data sets, and advanced data science tools are taking on central roles in neuroscience research. Neurodata Without Borders (NWB), a standard language for neurophysiology data, has recently emerged as a powerful solution for data management, analysis, and sharing. We her

Haoyun Deng, Luis Mendoza, N. E. Bonesteel

We study interlayer pairing of composite fermions in the total $\nu=1/2+1/2$ quantum Hall bilayer as a possible framework for understanding the experimentally observed transition from a compressible state at large layer spacing to a bilayer quantum Hall state at small layer spacing. We consider a model in which the effective interlayer composite fermion pair

Hyeok Kim, Arjun Srinivasan, Matthew Brehmer

In enterprise organizations, data-driven decision making processes include the use of business intelligence dashboards and collaborative deliberation on communication platforms such as Slack. However, apart from those in data analyst roles, there is shallow engagement with dashboard content due to insufficient context, poor representation choices, or a lack

Jianglin Lu, Yi Xu, Huan Wang, Yue Bai

Latent graph inference (LGI) aims to jointly learn the underlying graph structure and node representations from data features. However, existing LGI methods commonly suffer from the issue of supervision starvation, where massive edge weights are learned without semantic supervision and do not contribute to the training loss. Consequently, these supervision-s

Dasol Choi, Jooyoung Song, Eunsun Lee, Jinwoo Seo

With the growth of online services, the need for advanced text classification algorithms, such as sentiment analysis and biased text detection, has become increasingly evident. The anonymous nature of online services often leads to the presence of biased and harmful language, posing challenges to maintaining the health of online communities. This phenomenon

Roger Koppl, Kira Pronin, Nick Cowen, Marta Podemska-Mikluch

Scholars have not asked why so many governments created ad hoc scientific advisory bodies (ahSABs) to address the Covid-19 pandemic instead of relying on existing public health infrastructure. We address this neglected question with an exploratory study of the US, UK, Sweden, Italy, Poland, and Uganda. Drawing on our case studies and the blame-avoidance lite

Selim F. Yilmaz, Ezgi Ozyilkan, Deniz Gunduz, Elza Erkip

We consider low-latency image transmission over a noisy wireless channel when correlated side information is present only at the receiver side (the Wyner-Ziv scenario). In particular, we are interested in developing practical schemes using a data-driven joint source-channel coding (JSCC) approach, which has been previously shown to outperform conventional se

F. Bagarello, F. Gargano, M. Gorgone, F. Oliveri

This paper concerns the modeling of the spread of information through a complex, multi-layered network, where the information is transferred from an initial transmitter to a final receiver. The mathematical model is deduced within the framework of operatorial methods, according to the formal mathematical apparatus typical of quantum mechanics. Two different

J. I. Royo Prieto, M. Saralegi Aranguren, R. Wolak

Starting with a manifold $M$ and a semi-free action of $S^3$ on it, we have the Smith-Gysin sequence: $$ \cdots \to H^{*}( M) \to H^{*-3}(M/S^3, M^{S^3}) \oplus H^{*} (M^{S^3}) \to H^{*+1}(M/S^3, M^{S^3}) \to H^{*+1}(M) \to \cdots $$ In this paper, we construct a Smith-Gysin sequence that does not require the semi-free condition. This sequence includes a new