February 2024 arXiv papers — page 130

Rostyslav Hnatyshyn, Jiayi Hong, Ross Maciejewski, Christopher Norby

The development of cancer is difficult to express on a simple and intuitive level due to its complexity. Since cancer is so widespread, raising public awareness about its mechanisms can help those affected cope with its realities, as well as inspire others to make lifestyle adjustments and screen for the disease. Unfortunately, studies have shown that cancer

Lorenzo Gavassino, Nicki Mullins, Mauricio Hippert

We construct, for the first time, a Bemfica-Disconzi-Noronha-Kovtun (BDNK) theory for linear stochastic fluctuations, which is proved to be mathematically consistent, causal, and covariantly stable. The Martin-Siggia-Rose action is shown to be bilocal in most cases, and the noise is not white. The presence of nonhydrodynamic modes induces long-range correlat

Yasmin Kafai, Luis Morales-Navarro

In this paper, we build on the 1971 memo "Twenty Things to Do With a Computer" by Seymour Papert and Cynthia Solomon and propose twenty constructionist things to do with artificial intelligence and machine learning. Several proposals build on ideas developed in the original memo while others are new and address topics in science, mathematics, and the arts. I

Austin Anderson, Mirjana Jovovic, Wayne Smith

Let $f$ and $g$ be analytic on the unit disc $\mathbb{D}$. The integral operator $T_g$ is defined by $ T_g f(z) = \int_0^z f(t)g'(t)\,dt$, $z \in \mathbb{D}$. The problem considered is characterizing those symbols $g$ for which $T_g$ acting on $H^\infty$, the space of bounded analytic functions on $\mathbb{D}$, is bounded or compact. When the symbol is univa

Santiago Prochetto, Renata Reinheimer, Georgina Stegmayer

Motivation: Unraveling the connection between genes and traits is crucial for solving many biological puzzles. Genes provide instructions for building cellular machinery, directing the processes that sustain life. RNA molecules and proteins, derived from these genetic instructions, play crucial roles in shaping cell structures, influencing reactions, and gui

Tianxiong Han, Santanu Pakhira, N. S. Sangeetha, S. X. M. Riberolles

We report neutron diffraction results for the body-centered-tetragonal series Eu(Co$_{1-x}$Ni$_x$)$_{2-y}$As$_2$, $x=0.10$, $0.20$, $0.42$, and $0.82$, $y\leq0.10$, that detail changes to the magnetic ordering with nominal hole doping. We report the antiferromagnetic (AFM) propagation vectors, magnetic transition temperatures, and the ordered magnetic moment

Zixun Lan, Binjie Hong, Jiajun Zhu, Zuo Zeng

Predicting reactants from a specified core product stands as a fundamental challenge within organic synthesis, termed retrosynthesis prediction. Recently, semi-template-based methods and graph-edits-based methods have achieved good performance in terms of both interpretability and accuracy. However, due to their mechanisms these methods cannot predict comple

Nathan M. Dunfield, Giulio Tiozzo

Motivated by an observation of Dehornoy, we study the roots of Alexander polynomials of knots and links that are closures of positive 3-strand braids. We give experimental data on random such braids and find that the roots exhibit marked patterns, which we refine into precise conjectures. We then prove several results along those lines, for example that gene

Mathieu Garrigues, Victor Onofre, Noé Bosc-Haddad

New computational strategies, such as molecular docking, are emerging to speed up the drug discovery process. This method predicts the activity of molecules at the binding site of proteins, helping to select the ones that exhibit desirable behavior and rejecting the rest. However, for large chemical libraries, it is essential to search and score configuratio

Andrey Piatnitski, Elena Zhizhina

The paper deals with the asymptotic properties of a random jump process in a high contrast periodic medium in $\mathbb R^d$, $d\geq 1$. We show that if the coordinates of the random jump process in $\mathbb R^d$ are equipped with an extra variable that characterizes the position of the process inside the period, then the limit dynamics of this two-component

Luca Chiantini, Giuseppe Alessio D'Inverno, Sara Marziali

Any kind of network can be naturally represented by a Directed Acyclic Graph (DAG); additionally, activation functions can determine the reaction of each node of the network with respect to the signal(s) incoming. We study the characterization of the signal distribution in a network under the lens of tensor algebra. More specifically, we describe every activ

Mustafa Cemil Coşkun

Precoded polar product codes are proposed, where selected component codes enable successive cancellation list decoding to generate bit-wise soft messages efficiently for iterative decoding while targeting optimized distance spectrum as opposed to eBCH or polar component codes. Rate compatibility is a byproduct of $1$-bit granularity in the component code des

Marcellus Amadeus, William Alberto Cruz Castañeda

Recent surveys on data augmentation for natural language processing have reported different techniques and advancements in the field. Several frameworks, tools, and repositories promote the implementation of text data augmentation pipelines. However, a lack of evaluation criteria and standards for method comparison due to different tasks, metrics, datasets,

Elliot Lipnowski, Doron Ravid, Denis Shishkin

A sender commits to an experiment to persuade a receiver. Accounting for the sender's experiment-choice incentives, and not presupposing a receiver tie-breaking rule when indifferent, we characterize when the sender's equilibrium payoff is unique and so coincides with her "Bayesian persuasion" value. A sufficient condition in finite models is that every acti

Stefan Dernbach, Khushbu Agarwal, Alejandro Zuniga, Michael Henry

Integrating large language models (LLMs) with knowledge graphs derived from domain-specific data represents an important advancement towards more powerful and factual reasoning. As these models grow more capable, it is crucial to enable them to perform multi-step inferences over real-world knowledge graphs while minimizing hallucination. While large language

José Ángel Martín-Baos, Ricardo García-Ródenas, Luis Rodriguez-Benitez, Michel Bierlaire

The application of kernel-based Machine Learning (ML) techniques to discrete choice modelling using large datasets often faces challenges due to memory requirements and the considerable number of parameters involved in these models. This complexity hampers the efficient training of large-scale models. This paper addresses these problems of scalability by int

Peco Myint, Ashish Tripathi, Michael J. Wojcik, Junjing Deng

Many nano and quantum devices, with their sizes often spanning from millimeters down to sub-nanometer, have intricate low-dimensional, non-uniform, or hierarchical structures on surfaces and interfaces. Since their functionalities are dependent on these structures, high-resolution surface-sensitive characterization becomes imperative to gain a comprehensive

Yiwei Ding, Alexander Lerch

Common knowledge distillation methods require the teacher model and the student model to be trained on the same task. However, the usage of embeddings as teachers has also been proposed for different source tasks and target tasks. Prior work that uses embeddings as teachers ignores the fact that the teacher embeddings are likely to contain irrelevant knowled

Athanasios Papastathopoulos-Katsaros, Thomas M. Henderson, Gustavo E. Scuseria

We present an innovative cluster-based method employing linear combinations of diverse cluster mean-field (cMF) states, and apply it to describe the ground state of strongly-correlated spin systems. In cluster mean-field theory, the ground state wavefunction is expressed as a factorized tensor product of optimized cluster states. While our prior work concent

Carlos Henrique Q. Forster, Paulo André Lima de Castro, Andrei Ramalho

In this paper, we propose a methodology for the analysis of questionnaire data along with its application on discovering insights from investor data motivated by a day trading competition. The questionnaire includes categorical questions, which are reduced to binary questions, 'yes' or 'no'. The methodology reduces dimensionality by grouping questions and pa

Martin Kittel, Wolf-Peter Schill

As variable renewable energy sources increasingly gain importance in global energy systems, there is a growing interest in understanding periods of variable renewable energy shortage ("Dunkelflauten"). Defining, quantifying, and comparing such shortage events across different renewable generation technologies and locations presents a surprisingly intricate c

Francis Brown, Oliver Schnetz

We compute the canonical integrals associated to wheel graphs, and prove that they are proportional to odd zeta values. From this we deduce that wheel classes define explicit non-zero classes in: the locally finite homology of the general linear group $\GL_n(\ZZ)$ in both odd and even ranks, the homology of the moduli spaces of tropical curves, and the modul

Jianhao Ma, Salar Fattahi

We study the problem of symmetric matrix completion, where the goal is to reconstruct a positive semidefinite matrix $\rm{X}^\star \in \mathbb{R}^{d\times d}$ of rank-$r$, parameterized by $\rm{U}\rm{U}^{\top}$, from only a subset of its observed entries. We show that the vanilla gradient descent (GD) with small initialization provably converges to the groun

Shubhchintak, G. Singh, R. Chatterjee, M. Dan

We calculate the $^{11}$B$(n,\gamma)^{12}$B reaction rate, an important constituent in nucleosynthesis networks, contributed by resonant as well as non-resonant capture. For the resonant rate, we use the narrow resonance approximation whereas the non-resonant contribution is calculated with the Coulomb dissociation method for which we use finite-range distor

David Frenklakh, Dmitri E. Kharzeev, Andrea Palermo

The chiral magnetic effect (CME) and the chiral vortical effect (CVE) induce a correlation between baryon and electric currents. We show that this correlation can be detected using a new observable: a mixed baryon-electric charge correlator. This correlator is proportional to the baryon asymmetry, suggesting a novel way to separate the chiral effects from th

Minsuk Kim, Filippo Radicchi

We propose a bond-percolation model intended to describe the consumption, and eventual exhaustion, of resources in transport networks. Edges forming minimum-length paths connecting demanded origin-destination nodes are removed if below a certain budget. As pairs of nodes are demanded and edges are removed, the macroscopic connected component of the graph dis

Arihant Gaur, G. Dias Pais, Pedro Miraldo

Encoding 3D points is one of the primary steps in learning-based implicit scene representation. Using features that gather information from neighbors with multi-resolution grids has proven to be the best geometric encoder for this task. However, prior techniques do not exploit some characteristics of most objects or scenes, such as surface normals and local

Bradley T. Baker, Barak A. Pearlmutter, Robyn Miller, Vince D. Calhoun

Our understanding of learning dynamics of deep neural networks (DNNs) remains incomplete. Recent research has begun to uncover the mathematical principles underlying these networks, including the phenomenon of "Neural Collapse", where linear classifiers within DNNs converge to specific geometrical structures during late-stage training. However, the role of g

Tomas Roubicek, Ulisse Stefanelli

We advance a thermodynamically consistent model of self-gravitational accretion and differentiation in planets. The system is modeled in actual variables as a compressible thermoviscoelastic fluid in a fixed, sufficiently large domain. The supply of material to the accreting and differentiating system is described as a bulk source of mass, volume, impulse, a

Daniil Nozdriukhin, Shuxin Lyu, Jerome Bonvin, Michael Reiss

Efficient drug delivery is a major challenge in modern medicine and pharmaceutical research. Micrometer-scale robots have recently been proposed as a promising venue to amplify precision of drug administration. Remotely controlled microrobots sufficiently small to navigate through microvascular networks can reach any part of the human body, yet real-time tra

Peter G. Mikhael, Itamar Chinn, Regina Barzilay

Computational screening of naturally occurring proteins has the potential to identify efficient catalysts among the hundreds of millions of sequences that remain uncharacterized. Current experimental methods remain time, cost and labor intensive, limiting the number of enzymes they can reasonably screen. In this work, we propose a computational framework for

William Gryc, Loredana Lanzani, Jue Xiong, Yuan Zhang

We study the $\bar\partial$ equation subject to various boundary value conditions on bounded simply connected Lipschitz domains $D\subset\mathbb C$: for the Dirichlet problem with datum in $L^p(bD, \sigma)$, this is simply a restatement of the fact that members of the holomorphic Hardy spaces are uniquely and completely determined by their boundary values. H

Anisha, Christoph Englert, Roman Kogler, Michael Spannowsky

The measurements of off-shell Higgs boson contributions in massive gauge boson pair production are known to probe its electroweak interactions across different energy scales. Often employed as an estimator of the Higgs boson width in restricted theories of beyond the Standard Model physics, we revisit this measurement and re-advertise its potential to constr

Santiago Lopez, Jonathan Andrade Plascencia, Gabriel N. Perdue

Quantum computers are highly susceptible to errors due to unintended interactions with their environment. It is crucial to correct these errors without gaining information about the quantum state, which would result in its destruction through back-action. Quantum Error Correction (QEC) provides information about occurred errors without compromising the quant

Cecilia De Vita, Julián Fernández Bonder, Pablo Groisman

We study the energy function of the Kuramoto model in random geometric graphs defined in the unit circle as the number of nodes diverges. We prove the existence of at least one local minimum for each winding number $q \in \mathbb{Z}$ with high probability. Hence providing a large family of graphs that support patterns that are generic. These states are in co

Julian Christou, Mark Chun, Richard Dekany, Philip Hinz

In the recent Astro2020 Decadal Report, ''Pathways to Discovery in Astronomy and Astrophysics for the 2020s'' Adaptive Optics (AO) was identified as a crucial technology for a variety of reasons. These included an emphasis on high-contrast imaging and AO systems as being part of future technology development especially with application to the two US ELT proj

Salar Sajedi, Yuemeng Feng, Hamid Sabet

In this work, we present modeling and imaging performance of a dual panel limited-angle TOF-PET system for intraoperative surgical applications using GATE monte carlo toolkit. Several detector parameters such as detector pixel dimensions, timing resolution and depth of interaction resolution along with tumor uptake ratio and phantom dimension are varied. Ult

Nikola Kovačević, Jordan Aiko Deja, Maheshya Weerasinghe, Klen Čopič Pucihar

While maps provide upfront content, this might not always be the most effective way for users to remember information. With the proliferation of interactive displays for tourists and visitors in public spaces, we can create a more playful user experience with maps than just exploring them. Adding interactions with the map could also help users retain more in

Mason DiCicco, Vladimir Podolskii, Daniel Reichman

A nearest neighbor representation of a Boolean function $f$ is a set of vectors (anchors) labeled by $0$ or $1$ such that $f(\vec{x}) = 1$ if and only if the closest anchor to $\vec{x}$ is labeled by $1$. This model was introduced by Hajnal, Liu, and Tur\'an (2022), who studied bounds on the number of anchors required to represent Boolean functions under dif

Ahmad Al-Badawi, Sohan Kumar Jha, Anisur Rahaman

A hairy black hole (HBH) emerges due to matter surrounding the Schwarzschild metric when using the Extended Gravitational Decoupling (GD) approach. The fermionic greybody factors (GFs) and quasinormal modes (QNMs) as well as Hawking spectra and sparsity of HBH solutions are investigated. We consider massive and massless spin- 1/2 fermions, along with massles

Yifan Ding, Amrit Poudel, Qingkai Zeng, Tim Weninger

Entity Linking in natural language processing seeks to match text entities to their corresponding entries in a dictionary or knowledge base. Traditional approaches rely on contextual models, which can be complex, hard to train, and have limited transferability across different domains. Generative large language models like GPT offer a promising alternative b

Mahdi Naseri, Mahdi Biparva

Self-supervised Learning (SSL) has emerged as a powerful technique in pre-training deep learning models without relying on expensive annotated labels, instead leveraging embedded signals in unlabeled data. While SSL has shown remarkable success in computer vision tasks through intuitive data augmentation, its application to graph-structured data poses challe

Jason Arakawa, Muhammad H. Zaheer, Joshua Eby, Volodymyr Takhistov

Ultralight dark matter (ULDM) particles of mass $m_\phi \lesssim 1~{\rm eV}$ can form boson stars in DM halos. Collapse of boson stars leads to explosive bosenova emission of copious relativistic ULDM particles. In this work, we analyze sensitivity of terrestrial and space-based experiments to detect such relativistic scalar ULDM particles interacting throug

Andrew J. Larkoski

Recently, a factorization theorem was proposed for partonic flavor evolution as defined by the net flavor of the Winner-Take-All axis of a jet. We validate the factorization theorem through explicit calculation at two-loop order, and in the process extract all anomalous dimensions and renormalization factors for any ultraviolet-to-infrared flavor transition

Pragya Srivastava, Satvik Golechha, Amit Deshpande, Amit Sharma

Recent work shows that in-context learning and optimization of in-context examples (ICE) can significantly improve the accuracy of large language models (LLMs) on a wide range of tasks, leading to an apparent consensus that ICE optimization is crucial for better performance. However, most of these studies assume a fixed or no instruction provided in the prom

R. M. Green, Tianyuan Xu

We introduce a new partial order on the set of all antichains of a fixed size in any poset. When applied to minuscule posets, these partial orders give rise to distributive lattices that appear in the branching rules for minuscule representations of complex simple Lie algebras.

Matthew Willetts, Christian Harrington

Convex optimisation has provided a mechanism to determine arbitrage trades on automated market markets (AMMs) since almost their inception. Here we outline generic closed-form solutions for $N$-token geometric mean market maker pool arbitrage, that in simulation (with synthetic and historic data) provide better arbitrage opportunities than convex optimisers

MohammadHossein Bateni, Vincent Cohen-Addad, Alessandro Epasto, Silvio Lattanzi

We present a scalable algorithm for the individually fair ($p$, $k$)-clustering problem introduced by Jung et al. and Mahabadi et al. Given $n$ points $P$ in a metric space, let $\delta(x)$ for $x\in P$ be the radius of the smallest ball around $x$ containing at least $n / k$ points. A clustering is then called individually fair if it has centers within dist

Hans U. Simon

We define and study greedy matchings in vertex-ordered bipartite graphs. It is shown that each vertex-ordered bipartite graph has a unique greedy matching. The proof uses (a weak form of) Newman's lemma. The vertex ordering is called a preference relation. Given a vertex-ordered bipartite graph, the goal is to match every vertex of one vertex class but to le

Ewan Jones, Britton Smith, Romeel Davé, Desika Narayanan

We update the dust model present within the Simba galaxy simulations with a self-consistent framework for the co-evolution of dust and molecular hydrogen populations in the interstellar medium, and use this to explore $z \geq 6$ galaxy evolution. In addition to tracking the evolution of dust and molecular hydrogen abundances, our model fully integrates these

Andrea Caprotti, Joshua Morris, Borivoje Dakić

In quantum information theory, the accurate estimation of observables is pivotal for quantum information processing, playing a crucial role in compute and communication protocols. This work introduces a novel technique for estimating such objects, leveraging an underutilised resource in the inversion map of classical shadows that greatly refines the estimati

Lia Corrales, Eric V. Gotthelf, Efrain Gatuzz, Timothy R. Kallman

We present a high-resolution spectral study of Fe L-shell extinction by the diffuse interstellar medium (ISM) in the direction of the X-ray binaries Cygnus X-1 and GX 339-4, using the XMM-Newton reflection grating spectrometer. The majority of interstellar Fe is suspected to condense into dust grains in the diffuse ISM, but the compounds formed from this pro

Artur Avkhadiev, Phiala E. Shanahan, Michael L. Wagman, Yong Zhao

This work presents a determination of the quark Collins-Soper kernel, which relates transverse-momentum-dependent parton distributions (TMDs) at different rapidity scales, using lattice quantum chromodynamics (QCD). This is the first such determination with systematic control of quark mass, operator mixing, and discretization effects. Next-to-next-to-leading

Krinio Marouda, Daniela Cors, Hannes R. Rüter, Florian Atteneder

Critical phenomena in gravitational collapse are characterized by the emergence of surprising structure in solution space, namely the appearance of universal power-laws and periodicities near the threshold of collapse, and a universal discretely self-similar solution at the threshold itself. The seminal work of M. Choptuik spurred a comprehensive investigati

Maria Dawid, Vincenzo Cirigliano, Wouter Dekens

We perform a loop-level analysis of charged-current (CC) processes involving light leptons and quarks within the Standard Model Effective Field Theory (SMEFT). This work is motivated by the high precision reached in experiment and Standard Model calculations for CC decays of mesons, neutron, and nuclei, and by a lingering tension in the Cabibbo universality

Kareem El-Badry, Joshua D. Simon, Henrique Reggiani, Hans-Walter Rix

We report discovery and characterization of a main-sequence G star orbiting a dark object with mass $1.90\pm 0.04 M_{\odot}$. The system was discovered via Gaia astrometry and has an orbital period of 731 days. We obtained multi-epoch RV follow-up over a period of 639 days, allowing us to refine the Gaia orbital solution and precisely constrain the masses of

Sihan Chen, Swati Chaudhary, Gil Refael, Cyprian Lewandowski

Finding materials exhibiting substantial shift current holds the potential for designing shift current-based photovoltaics that outperform conventional solar cells. However, the myriad of factors governing shift current response poses significant challenges in designing devices that showcase large shift current. Here, we propose a general design principle th

Saúl Pilatowsky-Cameo, Iman Marvian, Soonwon Choi, Wen Wei Ho

Despite its long history, a canonical formulation of quantum ergodicity that applies to general classes of quantum dynamics, including driven systems, has not been fully established. Here we introduce and study a notion of quantum ergodicity for closed systems with time-dependent Hamiltonians, defined as statistical randomness exhibited in their longtime dyn

Nima Arkani-Hamed, Carolina Figueiredo, Hadleigh Frost, Giulio Salvatori

Recently a new formulation for scattering amplitudes in Tr($\Phi^3$) theory has been given based on simple combinatorial ideas in the space of kinematic data. This allows all-loop integrated amplitudes to be expressed as ''curve integrals'' defined using tropical building blocks - the ''headlight functions''. This paper shows how the formulation extends to t

Lorenzo Maria Perrone, Thomas Berlok, Christoph Pfrommer

In the hot intracluster medium (ICM) in galaxy clusters, plasma microinstabilities may play an important role in the transport of heat and momentum on the large scales. In this paper, we continue our investigation of the effect of whistler suppression of thermal conductivity on the magneto-thermal instability (MTI), which may be active in the periphery of ga

Carlo Giocoli, Lorenzo Palmucci, Giorgio F. Lesci, Lauro Moscardini

Context. Weak gravitational lensing offers a powerful method to investigate the projected matter density distribution within galaxy clusters, granting crucial insights into the broader landscape of dark matter on cluster scales. Aims. In this study, we make use of the large photometric galaxy cluster data set derived from the publicly available Third Data Re

Z. Eker, F. Soydugan, S. Bilir

Developments on various relations among stellar variables such as the main sequence empirical mass-luminosity (MLR), mass-radius (MRR) and mass-effective temperature (MTR) relations were reviewed. Conceptual changes in their understanding and usages were discussed. After its discovery, MLR was treated as one of the fundamental secrets of the cosmos. Differen

Taniya Parikh, Roberto Saglia, Jens Thomas, Kianusch Mehrgan

Stellar population studies of massive early-type galaxies (ETGs) suggest that the stellar initial mass function may not be universal. In particular, the centres of ETGs seem to contain an excess of low-mass dwarf stars compared to our own Galaxy. Through high resolution MUSE IFU data, we carry out a detailed study of the stellar populations of eight massive

Alexander Pan, Erik Jones, Meena Jagadeesan, Jacob Steinhardt

Language models influence the external world: they query APIs that read and write to web pages, generate content that shapes human behavior, and run system commands as autonomous agents. These interactions form feedback loops: LLM outputs affect the world, which in turn affect subsequent LLM outputs. In this work, we show that feedback loops can cause in-con

Nathaniel Sauerberg, Caspar Oesterheld

Prior work has studied the computational complexity of computing optimal strategies to commit to in Stackelberg or leadership games, where a leader commits to a strategy which is observed by one or more followers. We extend this setting to one where the leader can additionally commit to outcome-conditional utility transfers. We characterize the computational

Satyapriya Krishna, Chirag Agarwal, Himabindu Lakkaraju

The development of Large Language Models (LLMs) has notably transformed numerous sectors, offering impressive text generation capabilities. Yet, the reliability and truthfulness of these models remain pressing concerns. To this end, we investigate iterative prompting, a strategy hypothesized to refine LLM responses, assessing its impact on LLM truthfulness,

Ana Jovanovic, Allison Sullivan

Writing declarative models has numerous benefits, ranging from automated reasoning and correction of design-level properties before systems are built, to automated testing and debugging of their implementations after they are built. Alloy is a declarative modeling language that is well-suited for verifying system designs. A key strength of Alloy is its scena

Macarena Arenas, Max Neumann-Coto

We study the geometric and combinatorial effect of smoothing an intersection point in a collection of arcs or curves on a surface. We prove that all taut arcs with fixed endpoints and all taut 1-manifolds with at least two non-disjoint components on an orientable surface with negative Euler characteristic admit a taut smoothing, and also that all taut arcs w

Antonio J. Tallón-Ballesteros, César Hervás-Martínez

This paper presents a procedure to add broader diversity at the beginning of the evolutionary process. It consists of creating two initial populations with different parameter settings, evolving them for a small number of generations, selecting the best individuals from each population in the same proportion and combining them to constitute a new initial pop

Giorgio Di Russo, Francesco Fucito, Jose Francisco Morales

We study the gravitational tidal response of D1D5, Top Star and (1,0,n) strata horizonless geometries. We find that the tidal interactions in fuzzball geometries, unlike in the case of black holes, exhibits a sequence of resonant peaks associated to the existence of metastable bound states. The spectrum of resonant frequencies is computed by semi-analytical

Alexander Strack, Christopher Taylor, Patrick Diehl, Dirk Pflüger

Parallel algorithms relying on synchronous parallelization libraries often experience adverse performance due to global synchronization barriers. Asynchronous many-task runtimes offer task futurization capabilities that minimize or remove the need for global synchronization barriers. This paper conducts a case study of the multidimensional Fast Fourier Trans

Andrea Di Lorenzo, Roberto Pirisi

We compute the $\ell$-primary torsion of the Brauer group of the moduli stack of smooth curves of genus three over any field of characteristic different from two and the Brauer group of the moduli stacks of smooth plane curves of degree $d$ over any algebraically closed field of characteristic different from two, three and coprime to $d$. We achieve this res

Shivalika Singh, Freddie Vargus, Daniel Dsouza, Börje F. Karlsson

Datasets are foundational to many breakthroughs in modern artificial intelligence. Many recent achievements in the space of natural language processing (NLP) can be attributed to the finetuning of pre-trained models on a diverse set of tasks that enables a large language model (LLM) to respond to instructions. Instruction fine-tuning (IFT) requires specifica

Roland Miyamoto

The iterates $h_0,h_1,h_2,\dotsc$ constructed in [8,5] and converging to the only solution $g=h\colon[0,1]\to[0,1]$ of the iterative differential equation $-\gamma g'= g^{-1}$, $\gamma>0$, are parametrised by polynomials over $\Bbb Q$, and the corresponding constant $\gamma=\kappa\approx0.278877$ is estimated by rational numbers.

Mostafa Masumi, Seyed Soroush Majd, Mehrnoush Shamsfard, Hamid Beigy

We introduce FaBERT, a Persian BERT-base model pre-trained on the HmBlogs corpus, encompassing both informal and formal Persian texts. FaBERT is designed to excel in traditional Natural Language Understanding (NLU) tasks, addressing the intricacies of diverse sentence structures and linguistic styles prevalent in the Persian language. In our comprehensive ev

Madhumita Mondal, Areejit Samal, Florentin Münch, Jürgen Jost

The characterization of complex networks with tools originating in geometry, for instance through the statistics of so-called Ricci curvatures, is a well established tool of network science. There exist various types of such Ricci curvatures, capturing different aspects of network geometry. In the present work, we investigate Bakry-\'Emery-Ricci curvature, a

Marie Frédérique Dumas, Benjamin Groleau-Paré, Alexander McDonald, Manuel H. Muñoz-Arias

Despite the high measurement fidelity that can now be reached, the dispersive qubit readout of circuit quantum electrodynamics is plagued by a loss of its quantum nondemolition character and a decrease in fidelity with increased measurement strength. In this work, we elucidate the nature of this dynamical process, which we refer to as transmon ionization. We

Vinod Raman, Unique Subedi, Ambuj Tewari

We study the problem of learning to predict the next state of a dynamical system when the underlying evolution function is unknown. Unlike previous work, we place no parametric assumptions on the dynamical system, and study the problem from a learning theory perspective. We define new combinatorial measures and dimensions and show that they quantify the opti

Adeela Afzal, Anish Ghoshal

We study the possibility that primordial black holes (PBHs) can be formed from large curvature perturbations generated during the waterfall phase transition due to the effects of one-loop radiative corrections of Yukawa couplings between the inflaton and a dark fermion in a non-supersymmetric hybrid inflationary model. We obtain a spectral index $n_s$, and a

Dobromir Dotov, Dante Camarena, Zack Harris, Joanna Spyra

Music is an inherently social activity that allows people to share experiences and feel connected with one another. There has been little progress in designing artificial partners exhibiting a similar social experience as playing with another person. Neural network architectures that implement generative models, such as large language models, are suited for

Jason P. Bell, Be'eri Greenfeld

We study point modules of monomial algebras associated with symbolic dynamical systems, parametrized by proalgebraic varieties which 'linearize' the underlying dynamical systems. Faithful point modules correspond to transitive sub-systems, equivalently, to monomial algebras associated with infinite words. In particular, we prove that the space of point modul

Max Meyer, Amadeus Langer, Max Mehltretter, Dries Beyer

Increasing the degree of digitisation and automation in the concrete production process can play a crucial role in reducing the CO$_2$ emissions that are associated with the production of concrete. In this paper, a method is presented that makes it possible to predict the properties of fresh concrete during the mixing process based on stereoscopic image sequ

Hoon Hong, Irina A. Kogan

We develop a theory and an algorithm for constructing minimal-degree polynomial moving frames for polynomial curves in an affine space. The algorithm is equivariant under volume-preserving affine transformations of the ambient space and the parameter shifts. We show that any matrix-completion algorithm can be turned into an equivariant moving frame algorithm

Rui-Jie Yew, Lucy Qin, Suresh Venkatasubramanian

Data forms the backbone of artificial intelligence (AI). Privacy and data protection laws thus have strong bearing on AI systems. Shielded by the rhetoric of compliance with data protection and privacy regulations, privacy-preserving techniques have enabled the extraction of more and new forms of data. We illustrate how the application of privacy-preserving

Sudhir Agarwal, Anu Sreepathy

We study the problem of generating plans for given natural language planning task requests. On one hand, LLMs excel at natural language processing but do not perform well on planning. On the other hand, classical planning tools excel at planning tasks but require input in a structured language such as the Planning Domain Definition Language (PDDL). We levera

Joshua Lin, Di Luo, Xiaojun Yao, Phiala E. Shanahan

Ab-initio simulations of multiple heavy quarks propagating in a Quark-Gluon Plasma are computationally difficult to perform due to the large dimension of the space of density matrices. This work develops machine learning algorithms to overcome this difficulty by approximating exact quantum states with neural network parametrisations, specifically Neural Dens

Ce Feng, Parv Venkitasubramaniam

The rise of IoT devices has prompted the demand for deploying machine learning at-the-edge with real-time, efficient, and secure data processing. In this context, implementing machine learning (ML) models with real-valued weight parameters can prove to be impractical particularly for large models, and there is a need to train models with quantized discrete w

Mario Raeth, Klaus Hallatschek, Katharina Kormann

With the increase in computational capabilities over the last years it becomes possible to simulate more and more complex and accurate physical models. Gyrokinetic theory has been introduced in the 1960s and 1970s in the need of describing a plasma with more accurate models than fluid equations, but eliminating the complexity of the fast gyration about the m

Donato Bini, Thibault Damour, Stefano De Angelis, Andrea Geralico

We revisit the quantum-amplitude-based derivation of the gravitational waveform emitted by the scattering of two spinless massive bodies at the third order in Newton's constant, $h \sim G+G^2+G^3$ (one-loop level), and correspondingly update its comparison with its classically-derived multipolar-post-Minkowskian counterpart. A spurious-pole-free reorganizati

Nemanja Draganić, Richard Montgomery, David Munhá Correia, Alexey Pokrovskiy

An $n$-vertex graph $G$ is a $C$-expander if $|N(X)|\geq C|X|$ for every $X\subseteq V(G)$ with $|X|< n/2C$ and there is an edge between every two disjoint sets of at least $n/2C$ vertices. We show that there is some constant $C>0$ for which every $C$-expander is Hamiltonian. In particular, this implies the well known conjecture of Krivelevich and Sudakov fr

Garrett B. King, Saori Pastore

Nuclei will play a prominent role in searches for physics beyond the Standard Model as the active material in experiments. In order to reliably interpret new physics signals, one needs an accurate model of the underlying nuclear dynamics. In this review, we discuss recent progress made with quantum Monte Carlo approaches for calculating the electroweak struc

Enrique Fernández-Cara, Arnaud Münch, Diego A. Souza

The aim of this work is to present some strategies to solve numerically controllability problems for the two-dimensional heat equation, the Stokes equations and the Navier-Stokes equations with Dirichlet boundary conditions. The main idea is to adapt the Fursikov-Imanuvilov formulation, see~[A.V. Fursikov, O.Yu. Imanuvilov: {\it Controllability of Evolutions

Ahmee Christensen

We prove the completeness of a first-order analogue of the Fischer Servi logic $\mathsf{FS}$ with respect to its expected birelational semantics. To this end we introduce the notion of the $\textit{trace model}$ and, much like in a canonical model argument, prove a truth lemma. We conclude by examining a number of other first-order Fischer Servi logics, incl

Xingxuan Zhang, Jiansheng Li, Wenjing Chu, Junjia Hai

We investigate the generalization boundaries of current Multimodal Large Language Models (MLLMs) via comprehensive evaluation under out-of-distribution scenarios and domain-specific tasks. We evaluate their zero-shot generalization across synthetic images, real-world distributional shifts, and specialized datasets like medical and molecular imagery. Empirica

Dávid Hidvégi, Khashayar Etemadi, Sofia Bobadilla, Martin Monperrus

Large language models (LLM) have proven to be effective at automated program repair (APR). However, using LLMs can be costly, with companies invoicing users by the number of tokens. In this paper, we propose CigaR, the first LLM-based APR tool that focuses on minimizing the repair cost. CigaR works in two major steps: generating a first plausible patch and m

Giulia Piccitto, Davide Rossini, Angelo Russomanno

We consider a free-fermion chain undergoing dephasing, described by two different random-measurement protocols (unravelings): a quantum-state-diffusion and a quantum-jump one. Both protocols keep the state in a Slater-determinant form, allowing to address quite large system sizes. We find a bifurcation in the distribution of the measurement operators along t

Mingzhe Xing, Rongkai Zhang, Hui Xue, Qi Chen

Large language models (LLMs) have empowered intelligent agents to execute intricate tasks within domain-specific software such as browsers and games. However, when applied to general-purpose software systems like operating systems, LLM agents face three primary challenges. Firstly, the action space is vast and dynamic, posing difficulties for LLM agents to m

Jose Luis Hernandez, Cristina Manuel, Laura Tolos

We study the damping of density oscillations in the quark matter phase that might occur in compact stars. To this end we compute the bulk viscosity and the associated damping time in three-flavor quark matter, considering both nonleptonic and semileptonic electroweak processes. We use two different equations of state of quark matter, more precisely, the MIT

Ayush Thakur, Ashwani Kumar Dubey

The Emergence of Artificial Intelligence (AI) has significantly impacted our engagement with violence, sparking ethical deliberations regarding the algorithmic creation of violent imagery. This paper scrutinizes the "Gore Diffusion LoRA Model," an innovative AI model proficient in generating hyper-realistic visuals portraying intense violence and bloodshed.

Stuart Baumann, Carl Singleton

Boxing has a long-standing problem with biased judging, impacting both professional and Olympic bouts. "Robberies", where boxers are widely seen as being denied rightful victories, threaten to drive fans and athletes away from the sport. To tackle this problem, we propose a minimalist adjustment in how boxing is scored: the winner would be decided by the maj