May 2024 arXiv papers — page 83

Vladimir A. Smirnov, Fabian Wunder

We study the small-mass asymptotic behavior of so-called angular integrals, appearing in phase-space calculations in perturbative quantum field theory. For this purpose we utilize the strategy of expansion by regions, which is a universal method both for multiloop Feynman integrals and various parametric integrals. To apply the technique to angular integrals

Atrideb Chatterjee, Francisco Villaescusa-Navarro

We train a novel deep learning architecture to perform likelihood-free inference on the value of the cosmological parameters from halo catalogs of the Quijote N-body simulations. Our model takes as input a halo catalog where each halo is characterized by its position, mass, and velocity modulus. By construction, our model is E(3) invariant and is designed to

Shishir Dholakia, Larissa Palethorpe, Alexander Venner, Annelies Mortier

We report on the discovery of Gliese 12 b, the nearest transiting temperate, Earth-sized planet found to date. Gliese 12 is a bright ($V=12.6$ mag, $K=7.8$ mag) metal-poor M4V star only $12.162\pm0.005$ pc away from the Solar System with one of the lowest stellar activity levels known for an M-dwarf. A planet candidate was detected by TESS based on only 3 tr

Gia Dvali, Juan Sebastián Valbuena-Bermúdez, Michael Zantedeschi

The essence of the \textit{memory burden} effect is that a load of information carried by a system stabilizes it. This universal effect is especially prominent in systems with a high capacity of information storage, such as black holes and other objects with maximal microstate degeneracy, the entities universally referred to as \textit{saturons}. The phenome

Aoife Brennan, Luca Matrà, Sebastián Marino, David Wilner

The origin and evolution of CO gas in debris disks has been debated since its initial detection. The gas could have a primordial origin, as a remnant of the protoplanetary disk or a secondary exocometary origin. This paper investigates the origin of gas in two debris disks, HD110058 and HD131488, using HST observations of CI and CO, which play critical roles

Jack S. Baker, Pablo A. M. Casares, Modjtaba Shokrian Zini, Jaydeep Thik

There is a pressing need for more accurate computational simulations of the opto-electronic properties of defects in materials to aid in the development of quantum sensing platforms. In this work, we explore how quantum computers could be effectively utilized for this purpose. Specifically, we develop fault-tolerant quantum algorithms to simulate optically a

Subhadip Bouri, Abhishek Kumar Jha, Subhashish Banerjee

The quantum speed limits (QSLs) set fundamental lower bounds on the time required for a quantum system to evolve from a given initial state to a final state. In this work, we investigate $CP$ violation and the mass ordering problem of neutrino oscillations in matter using the QSL time as a key analytical tool. We examine the QSL time for the unitary evolutio

Mingyu Li, Haibin Zhang, Zheng Cai, Yongming Liang

Circumgalactic Lyman-alpha (Ly$\alpha$) nebulae are gaseous halos around galaxies exhibiting luminous extended Ly$\alpha$ emission. This work investigates Ly$\alpha$ nebulae from deep imaging of $\sim12~\mathrm{deg}^2$ sky, targeted by the MAMMOTH-Subaru survey. Utilizing the wide-field capability of Hyper Suprime-Cam (HSC), we present one of the largest bli

Giuseppe Calajò, Giovanni Cataldi, Marco Rigobello, Darvin Wanisch

Quantum many-body scarring (QMBS) is an intriguing mechanism of weak ergodicity breaking that has recently spurred significant attention. Particularly prominent in Abelian lattice gauge theories (LGTs), an open question is whether QMBS nontrivially arises in non-Abelian LGTs. Here, we present evidence of robust QMBS in a non-Abelian SU(2) LGT with dynamical

Jan de Boer, Diego Liska, Boris Post

We derive higher moments in the statistical distribution of OPE coefficients in holographic 2D CFTs, and show that such moments correspond to multiboundary Euclidean wormholes in pure 3D gravity. The n-th cyclic non-Gaussian contraction of heavy-heavy-light OPE coefficients follows from crossing symmetry of the thermal n-point function. We derive universal e

Francisco Holguin, Christopher C. Hayward, Xiangcheng Ma, Daniel Anglés-Alcázar

Elucidating the processes that shape the circumgalactic medium (CGM) is crucial for understanding galaxy evolution. Absorption and emission diagnostics can be interpreted using photoionization calculations to obtain information about the phase and ionization structure of the CGM. For simplicity, typically only the metagalactic background is considered in pho

Reza Ebadi, Vladimir Strokov, Erwin H. Tanin, Emanuele Berti

Galactic double white dwarf (DWD) binaries are among the guaranteed sources for the Laser Interferometer Space Antenna (LISA), an upcoming space-based gravitational wave (GW) detector. Most DWDs in the LISA band are far from merging and emit quasimonochromatic GWs. As these sources are distributed throughout the Milky Way, they experience different accelerat

Viola Gelli, Charlotte Mason, Christopher C. Hayward

JWST is unveiling a surprising lack of evolution in the number densities of ultraviolet-selected (UV) galaxies at redshift $z\gtrsim 10$. At the same time, observations and simulations are providing evidence for highly bursty star formation in high-$z$ galaxies, resulting in significant scatter in their UV luminosities. Galaxies in low-mass dark matter halos

Marina Yoshimoto, Tomokage Yoneyama, Hirofumi Noda, Hirokazu Odaka

We present an analysis of X-ray observations of the Ultraluminous X-ray source (ULX) in IZw18 based on archival data taken with Chandra, XMM-Newton, and Suzaku. This ULX is considered to be an intermediate-mass black hole candidate simply because it is in the lowest metallicity environment among ULXs, where formation of heavy black holes is facilitated. Howe

Adrian Valadkhani, Jonas B. Profe, Andreas Kreisel, P. J. Hirschfeld

Scanning tunneling microscopy (STM) is perhaps the most promising way to detect the superconducting gap size and structure in the canonical unconventional superconductor Sr$_2$RuO$_4$ directly. However, in many cases, researchers have reported being unable to detect the gap at all in simple STM conductance measurements. Recently, an investigation of this iss

Yichul Choi, Yaman Sanghavi, Shu-Heng Shao, Yunqin Zheng

We explore exact generalized symmetries in the standard 2+1d lattice $\mathbb{Z}_2$ gauge theory coupled to the Ising model, and compare them with their continuum field theory counterparts. One model has a (non-anomalous) non-invertible symmetry, and we identify two distinct non-invertible symmetry protected topological phases. The non-invertible algebra inv

Nicholas L. Rodd, Benjamin R. Safdi, Weishuang Linda Xu

Thermal higgsino dark matter (DM), with a mass near 1.1 TeV, is one of the most well-motivated and untested DM candidates. Leveraging recent hydrodynamic cosmological simulations that give DM density profiles in Milky Way analogue galaxies we show that the line-like gamma-ray signal predicted from higgsino annihilation in the Galactic Center could be detecte

William Brandon, Mayank Mishra, Aniruddha Nrusimha, Rameswar Panda

Key-value (KV) caching plays an essential role in accelerating decoding for transformer-based autoregressive large language models (LLMs). However, the amount of memory required to store the KV cache can become prohibitive at long sequence lengths and large batch sizes. Since the invention of the transformer, two of the most effective interventions discovere

Tom Rudelius

As famously observed by Ooguri and Vafa nearly twenty years ago, scalar field moduli spaces in quantum gravity appear to exhibit various universal features. For instance, they seem to be infinite in diameter, have trivial fundamental group, and feature towers of massive particles that become light in their asymptotic limits. In this essay, we explain how the

Hanwen Jiang, Arjun Karpur, Bingyi Cao, Qixing Huang

The image matching field has been witnessing a continuous emergence of novel learnable feature matching techniques, with ever-improving performance on conventional benchmarks. However, our investigation shows that despite these gains, their potential for real-world applications is restricted by their limited generalization capabilities to novel image domains

Cusuh Ham, Matthew Fisher, James Hays, Nicholas Kolkin

We present personalized residuals and localized attention-guided sampling for efficient concept-driven generation using text-to-image diffusion models. Our method first represents concepts by freezing the weights of a pretrained text-conditioned diffusion model and learning low-rank residuals for a small subset of the model's layers. The residual-based appro

Zhen Zhang, Haoxiang Lin, Zhuo Li, Shao-Lin Xiong

Ultrarelativistic jets are believed to play an important role in producing prompt emission and afterglow of gamma-ray bursts (GRBs), but the nature of the jet is poorly known owing to the lack of decisive features observed in the prompt emission. The discovery of an emission line evolving from about 37 to 6 MeV in the brightest-of-all-time GRB 221009A provid

Luca Aceto, Daniele Gorla, Stian Lybech

In this paper we focus on TinySol, a minimal calculus for Solidity smart contracts, introduced by Bartoletti et al. We start by rephrasing its syntax (to emphasise its object-oriented flavour) and give a new big-step operational semantics. We then use it to define two security properties, namely call integrity and noninterference. These two properties have s

Anupreeta More, Raoul Canameras, Anton T. Jaelani, Yiping Shu

Efficient algorithms are being developed to search for strong gravitational lens systems owing to increasing large imaging surveys. Neural networks have been successfully used to discover galaxy-scale lens systems in imaging surveys such as the Kilo Degree Survey, Hyper-Suprime Cam (HSC) Survey and Dark Energy Survey over the last few years. Thus, it has bec

Slimane Thabet, Mehdi Djellabi, Igor Sokolov, Sachin Kasture

In this work, we propose novel families of positional encodings tailored to graph neural networks obtained with quantum computers. These encodings leverage the long-range correlations inherent in quantum systems that arise from mapping the topology of a graph onto interactions between qubits in a quantum computer. Our inspiration stems from the recent advanc

Juan J. Nuño-Ballesteros, Guillermo Peñafort Sanchis, Cinzia Villa

We study the analytic structure of the double and triple point spaces $M_2(f)$ and $M_3(f)$ of finite multi-germs $f\colon (X,S)\to(\mathbb{C}^{n+1},0)$, based on results of Mond and Pellikaan for the mono-germ case. We show that these spaces are Cohen-Macaulay, provided that certain dimensional conditions are satisfied, and give explicit expressions for the

Grigoriy Blekherman, Papri Dey

Lorentzian polynomials are a fascinating class of real polynomials with many applications. Their definition is specific to the nonnegative orthant. Following recent work, we examine Lorentzian polynomials on proper convex cones. For a self-dual cone $\mathcal{K}$ we find a connection between $\mathcal{K}$-Lorentzian polynomials and $\mathcal{K}$-positive lin

Thomas Winterbottom, G. Thomas Hudson, Daniel Kluvanec, Dean Slack

Next-frame prediction is a useful and powerful method for modelling and understanding the dynamics of video data. Inspired by the empirical success of causal language modelling and next-token prediction in language modelling, we explore the extent to which next-frame prediction serves as a strong foundational learning strategy (analogous to language modellin

Congqiao Li, Antonios Agapitos, Jovin Drews, Javier Duarte

The search for heavy resonances beyond the Standard Model (BSM) is a key objective at the LHC. While the recent use of advanced deep neural networks for boosted-jet tagging significantly enhances the sensitivity of dedicated searches, it is limited to specific final states, leaving vast potential BSM phase space underexplored. We introduce a novel experiment

Theodore Zhao, Yu Gu, Jianwei Yang, Naoto Usuyama

Biomedical image analysis is fundamental for biomedical discovery in cell biology, pathology, radiology, and many other biomedical domains. Holistic image analysis comprises interdependent subtasks such as segmentation, detection, and recognition of relevant objects. Here, we propose BiomedParse, a biomedical foundation model for imaging parsing that can joi

LHCb collaboration, R. Aaij, A. S. W. Abdelmotteleb, C. Abellan Beteta

A search for the lepton-flavor violating decays $B^0_s\to\phi\mu^\pm\tau^\mp$ is presented, using a sample of proton-proton collisions at center-of-mass energies of 7, 8, and 13 TeV, collected with the LHCb detector and corresponding to a total integrated luminosity of $9\,\text{fb}^{-1}$. The $\tau$ leptons are selected using decays with three charged pions

Yue Han, Junwei Zhu, Keke He, Xu Chen

Current face reenactment and swapping methods mainly rely on GAN frameworks, but recent focus has shifted to pre-trained diffusion models for their superior generation capabilities. However, training these models is resource-intensive, and the results have not yet achieved satisfactory performance levels. To address this issue, we introduce Face-Adapter, an

Yuxiang Zheng, Zhongyi Han, Yilong Yin

Noisy labels severely hinder the accuracy and generalization of machine learning models, especially when ambiguous instance features make reliable annotation difficult. Existing approaches, including transition-matrix-based label correction, struggle to capture complex relationships between instances and noisy labels, limiting their effectiveness in such set

Ronno Das, Philip Tosteson

We consider the space of holomorphic maps from a compact Riemann surface to a projective space blown up at finitely many points. We show that the homology of this mapping space equals that of the space of continuous maps that intersect the exceptional divisors positively, once the degree of the maps is sufficiently positive compared to the degree of homology

Shubhodip Mondal

In this paper, we apply stack theoretic ideas to the classification problem in Dieudonn\'e theory. First, we use crystalline cohomology of classifying stacks to directly reconstruct the classical Dieudonn\'e module of a finite, $p$-power rank, commutative group scheme $G$ over a perfect field $k$ of characteristic $p>0$. As a consequence, we give a new, much

Jingjing Liu, Alex R. Wright, Kullervo Hynynen, David E. Goertz

Thrombotic and chronic occlusions of large blood vessels are a major cause of mortality and morbidity, and so there is a need for improved treatments in many clinical circumstances. Endovascular ultrasound approaches have been shown to hold considerable potential to treat large vessel thrombotic occlusions. Here, we report the development of a novel forward-

Davide Piras, Alicja Polanska, Alessio Spurio Mancini, Matthew A. Price

We advocate for a new paradigm of cosmological likelihood-based inference, leveraging recent developments in machine learning and its underlying technology, to accelerate Bayesian inference in high-dimensional settings. Specifically, we combine (i) emulation, where a machine learning model is trained to mimic cosmological observables, e.g. CosmoPower-JAX; (i

Bailey Miller, Rohan Sawhney, Keenan Crane, Ioannis Gkioulekas

We introduce a Monte Carlo method for computing derivatives of the solution to a partial differential equation (PDE) with respect to problem parameters (such as domain geometry or boundary conditions). Derivatives can be evaluated at arbitrary points, without performing a global solve or constructing a volumetric grid or mesh. The method is hence well suited

Ahmed Gomaa, Yixing Huang, Amr Hagag, Charlotte Schmitter

Background: This research aims to improve glioblastoma survival prediction by integrating MR images, clinical and molecular-pathologic data in a transformer-based deep learning model, addressing data heterogeneity and performance generalizability. Method: We propose and evaluate a transformer-based non-linear and non-proportional survival prediction model. T

Yuting Sun, Liang Zhang

Mathematical modeling of production systems is the foundation of all model-based approaches for production system analysis, design, improvement, and control. To construct such a model for the stochastic process of the production system more efficiently, a new modeling approach has been proposed that reversely identifies the model parameters using system perf

Shriram Chennakesavalu, Frank Hu, Sebastian Ibarraran, Grant M. Rotskoff

Searching through chemical space is an exceptionally challenging problem because the number of possible molecules grows combinatorially with the number of atoms. Large, autoregressive models trained on databases of chemical compounds have yielded powerful generators, but we still lack robust strategies for generating molecules with desired properties. This m

Francesco C. De Vecchi, Chiara Rigoni

We study the convergence of an $N$-particle Markovian controlled system to the solution of a family of stochastic McKean-Vlasov control problems, either with a finite horizon or Schr\"odinger type cost functional. Specifically, under suitable assumptions, we prove the convergence of the value functions, the fixed-time probability distributions, and the relat

Abdulaziz Y. Alkayas, Anup Teejo Mathew, Daniel Feliu-Talegon, Ping Deng

Soft robots offer remarkable adaptability and safety advantages over rigid robots, but modeling their complex, nonlinear dynamics remains challenging. Strain-based models have recently emerged as a promising candidate to describe such systems, however, they tend to be high-dimensional and time-consuming. This paper presents a novel model order reduction appr

Ilias Diakonikolas, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis

We study the task of online learning in the presence of Massart noise. Instead of assuming that the online adversary chooses an arbitrary sequence of labels, we assume that the context $\mathbf{x}$ is selected adversarially but the label $y$ presented to the learner disagrees with the ground-truth label of $\mathbf{x}$ with unknown probability at most $\eta$

Ahmad Reza Haj Saeedi Sadegh, Minh Lam Nguyen

We show the non-compactness of moduli space of solutions of the monopole equations for $3/2$-spinors on a closed 3-manifold is equivalent to the existence of `3/2-Fueter sections' that are solutions of an overdetermined non-linear elliptic differential equation. These are sections of a fiber bundle whose fiber is a special 4-dimensional submanifold of the hy

Gareth C. Jones, Andrew J. Bunker, Kseniia Telikova, Santiago Arribas

We present observations of the $z\sim5.7$ Lyman-break galaxy HZ10 with the JWST/NIRSpec IFU in high and low spectral resolution (G395H, spectral resolving power $R\sim2700$ and PRISM, $R\sim100$, respectively), as part of the GA-NIFS program. By spatially resolving the source (spatial resolution $\sim0.15''$ or $\sim0.9$kpc), we find three spatially and spec

Zheshi Zheng, Bo Yang, Peter Song

To evaluate a classification algorithm, it is common practice to plot the ROC curve using test data. However, the inherent randomness in the test data can undermine our confidence in the conclusions drawn from the ROC curve, necessitating uncertainty quantification. In this article, we propose an algorithm to construct confidence bands for the ROC curve, qua

Yujia Jin, Ishani Karmarkar, Aaron Sidford, Jiayi Wang

We provide faster randomized algorithms for computing an $\epsilon$-optimal policy in a discounted Markov decision process with $A_{\text{tot}}$-state-action pairs, bounded rewards, and discount factor $\gamma$. We provide an $\tilde{O}(A_{\text{tot}}[(1 - \gamma)^{-3}\epsilon^{-2} + (1 - \gamma)^{-2}])$-time algorithm in the sampling setting, where the prob

Daniel Commey, Sena Hounsinou, Garth V. Crosby

This paper addresses the challenge of enhancing cybersecurity in Blockchain-based Internet of Things (BIoTs) systems, which are increasingly vulnerable to sophisticated cyberattacks. It introduces an AI-powered system model for the dynamic deployment of honeypots, utilizing an Intrusion Detection System (IDS) integrated with smart contract functionalities on

Tommaso Cesari, Roberto Colomboni

We explore brokerage between traders in an online learning framework. At any round $t$, two traders meet to exchange an asset, provided the exchange is mutually beneficial. The broker proposes a trading price, and each trader tries to sell their asset or buy the asset from the other party, depending on whether the price is higher or lower than their private

Menglin Zhu, Joseph Lanier, Sevim Polat Genlik, Jose G. Flores

Creating new interfacial magnetic states with desired functionalities is attractive for fundamental studies and spintronics applications. The emergence of interfacial magnetic phases demands the fabrication of pristine interfaces and the characterization and understanding of atomic structure as well as electronic, magnetic, and orbital degrees of freedom at

Bruno Lévy

This article introduces a general mesh intersection algorithm that exactly computes the so-called Weiler model (also called an arrangement) and that uses it to implement boolean operations with arbitrary multi-operand expressions, CSG (constructive solid geometry) and some mesh repair operations. From an input polygon soup, the algorithm first computes the c

Velmathi G, Shangavelan M, Harish D, Krithikshun M S

This project undertakes the training and analysis of optical character recognition OCR methods applied to 10th century ancient Tamil inscriptions discovered on the walls of the Brihadeeswarar Temple.The chosen OCR methods include Tesseract,a widely used OCR engine,using modern ICR techniques to pre process the raw data and a box editing software to finetune

Rafael López

Let $\s^1$ be a circle in Euclidean plane. We consider the problem of finding the shape of a planar curve which is an extremal of the potential energy that measures the distance to $\s^1$. We describe the shape of these curves distinguishing if the curves lie in the inside or outside of $\s^1$. We extend the problem for energies that are powers to the distan

Wengxi Li, Roy Pea, Nick Haber, Hari Subramonyam

Online programming videos, including tutorials and streamcasts, are widely popular and contain a wealth of expert knowledge. However, effectively utilizing these resources to achieve targeted learning goals can be challenging. Unlike direct tutoring, video content lacks tailored guidance based on individual learning paces, personalized feedback, and interact

Rishikesh Gajjala, Jayanth Ravi

The Heilbronn triangle problem asks for the placement of $n$ points in a unit square that maximizes the smallest area of a triangle formed by any three of those points. In $1972$, Schmidt considered a natural generalization of this problem. He asked for the placement of $n$ points in a unit square that maximizes the smallest area of the convex hull formed by

Zizhao Chen, Yeqiang Qian, Xiaoxiao Yang, Chunxiang Wang

Multispectral pedestrian detection has been shown to be effective in improving performance within complex illumination scenarios. However, prevalent double-stream networks in multispectral detection employ two separate feature extraction branches for multi-modal data, leading to nearly double the inference time compared to single-stream networks utilizing on

Yi Chen, Yang Li, Cédric Lorcé, Qun Wang

We present the first systematic study of the relativistic axial-vector four-current distributions inside a nucleon. We show in particular that the slope of the axial form factor $G_A(Q^2)$ in the forward limit -- conventionally denoted as $R^2_A$ in the literature -- does not represent the three-dimensional mean-square axial radius in the Breit frame, but co

D. Bosnar, L. Abbene, C. Amsler, F. Artibani

An HPGe detector equipped with a transistor reset preamplifier and readout with a CAEN DT5781 fast pulse digitizer was employed in the measurement of X-rays from kaonic lead at the DA$\Phi$NE $e^+e^-$ collider at the Laboratori Nazionali di Frascati of INFN. A thin scintillator in front of a lead target was used to select kaons impinging on it and to form th

Lars Sandved-Smith, Lancelot Da Costa

This paper articulates metacognition using the language of statistical physics and Bayesian mechanics. Metacognitive beliefs, defined as beliefs about beliefs, find a natural description within this formalism, which allows us to define the dynamics of 'metacognitive particles', i.e., systems possessing metacognitive beliefs. We further unpack this typology o

Vladimir R. Kostic, Karim Lounici, Helene Halconruy, Timothee Devergne

We address data-driven learning of the infinitesimal generator of stochastic diffusion processes, essential for understanding numerical simulations of natural and physical systems. The unbounded nature of the generator poses significant challenges, rendering conventional analysis techniques for Hilbert-Schmidt operators ineffective. To overcome this, we intr

Zhangyue Yin, Qiushi Sun, Qipeng Guo, Zhiyuan Zeng

Recent advancements in Chain-of-Thought prompting have facilitated significant breakthroughs for Large Language Models (LLMs) in complex reasoning tasks. Current research enhances the reasoning performance of LLMs by sampling multiple reasoning chains and ensembling based on the answer frequency. However, this approach fails in scenarios where the correct an

Kristina Kehrer, Martin Weiser, Tim Conrad

This paper introduces a novel hybrid model combining Partial Differential Equations (PDEs) and Ordinary Differential Equations (ODEs) to simulate infectious disease dynamics across geographic regions. By leveraging the spatial detail of PDEs and the computational efficiency of ODEs, the model enables rapid evaluation of public health interventions. Applied t

Andrea Baiocchi, Asmad Razzaque

Limitation of the cost of coordination and contention among a large number of nodes calls for grant-free approaches, exploiting physical layer techniques to solve collisions. Successive Interference Cancellation (SIC) is becoming a key building block of multiple access channel receiver, in an effort to support massive Internet of Things (IoT). In this paper,

Song-Bo Zhao, Lu Sun, Cai-Xin Yuan, Ying-Chen Mao

The model inputs play a key role in the performance of the Bayesian optimization approach. In this paper, we investigate the influence of the inputs on the improved predictions of phenomenological nuclear charge radius formulas using an approach combining those original formulas and the Bayesian neural network (BNN). We find that there is no improvement in p

Federico Borletto, Luca Giacomelli, Cristiano Ciuti

We investigate the occurrence of direct and dual Shapiro steps for a Josephson junction coupled to a finite-size transmission line resonator. We treat both problems through a circuit QED approach with a large, but finite number of photon modes. For the dual case, we do not assume the (approximate) charge-phase duality, but include the full multi-band dynamic

Peter J. Bentley, Rajat Mathur, Soo Ling Lim, Sid Narang

Consumers wish to choose sustainable accommodation for their travels, and in the case of corporations, may be required to do so. Yet accommodation marketplaces provide no meaningful capability for sustainable choice: typically CO2 estimates are provided that are identical for all accommodation of the same type across an entire country. We propose a decision

Bilgehan Sel, Priya Shanmugasundaram, Mohammad Kachuee, Kun Zhou

Large Language Models (LLMs) have shown remarkable capabilities in tasks such as summarization, arithmetic reasoning, and question answering. However, they encounter significant challenges in the domain of moral reasoning and ethical decision-making, especially in complex scenarios with multiple stakeholders. This paper introduces the Skin-in-the-Game (SKIG)

Patrick Diehl, Noujoud Nader, Steve Brandt, Hartmut Kaiser

This study evaluates the capabilities of ChatGPT versions 3.5 and 4 in generating code across a diverse range of programming languages. Our objective is to assess the effectiveness of these AI models for generating scientific programs. To this end, we asked ChatGPT to generate three distinct codes: a simple numerical integration, a conjugate gradient solver,

Roger K. Romani

A previously unexplained background called the Low Energy Excess (LEE) has negatively impacted the reach of a variety of low threshold calorimeters including light dark matter direct detection and coherent elastic neutrino-nucleus scattering experiments. The relaxation of stressed aluminium films as mediated by the motion of dislocations may account for thes

A. K. Nasirov, A. Yusupov, B. M. Kayumov

The smallness of the cross section of evaporation residues formed in the hot fusion reaction $^{48}$Ca+$^{232}$Th is analyzed by the dinuclear system model (DNS). The capture probability has been calculated by solving the dynamical equations of motion for the relative distance between the centers-of-mass of the DNS nuclei. Fusion of nuclei is considered as e

Vuthea Chheang, Saurabh Narain, Garrett Hooten, Robert Cerda

Digital twins (DTs) are an emerging capability in additive manufacturing (AM), set to revolutionize design optimization, inspection, in situ monitoring, and root cause analysis. AM DTs typically incorporate multimodal data streams, ranging from machine toolpaths and in-process imaging to X-ray CT scans and performance metrics. Despite the evolution of DT pla

Andres Hernandez, Zhongqi Miao, Luisa Vargas, Sara Beery

The alarming decline in global biodiversity, driven by various factors, underscores the urgent need for large-scale wildlife monitoring. In response, scientists have turned to automated deep learning methods for data processing in wildlife monitoring. However, applying these advanced methods in real-world scenarios is challenging due to their complexity and

Anjali Yadav, Tanya Garg, Matej Klemen, Matej Ulcar

Code-mixed discourse combines multiple languages in a single text. It is commonly used in informal discourse in countries with several official languages, but also in many other countries in combination with English or neighboring languages. As recently large language models have dominated most natural language processing tasks, we investigated their perform

P. -M. Zhang, P. A. Horvathy

Particles initially at rest hit by a passing sandwich gravitational wave exhibit, in general, thevelocity memory effect (VM): they fly apart with constant velocity. For specific values of the wave parameters their motion can however become pure displacement (DM) as suggested by Zel'dovich and Polnarev. For such a ``miraculous'' value, the particle trajectory

Serap A. Savari

Image registration is a classical problem in machine vision which seeks methods to align discrete images of the same scene to subpixel accuracy in general situations. As with all estimation problems, the underlying difficulty is the partial information available about the ground truth. We consider a basic and idealized one-dimensional image registration prob

Manh Khoi Duong, Stefan Conrad

In this paper, we deal with bias mitigation techniques that remove specific data points from the training set to aim for a fair representation of the population in that set. Machine learning models are trained on these pre-processed datasets, and their predictions are expected to be fair. However, such approaches may exclude relevant data, making the attaine

Di Fang, Diyi Liu, Rahul Sarkar

Hamiltonian simulation becomes more challenging as the underlying unitary becomes more oscillatory. In such cases, an algorithm with commutator scaling and a weak dependence, such as logarithmic, on the derivatives of the Hamiltonian is desired. We introduce a new time-dependent Hamiltonian simulation algorithm based on the Magnus series expansion that exhib

Ana M. Bianco, Graciela Boente, Wenceslao González--Manteiga, Francisco Gude Sampedro

Statistical analysis on compositional data has gained a lot of attention due to their great potential of applications. A feature of these data is that they are multivariate vectors that lie in the simplex, that is, the components of each vector are positive and sum up a constant value. This fact poses a challenge to the analyst due to the internal dependency

Chirag Shah, Ryen W. White

The emergence of generative artificial intelligence (GenAI) is transforming information interaction. For decades, search engines such as Google and Bing have been the primary means of locating relevant information for the general population. They have provided search results in the same standard format (the so-called "10 blue links"). The recent ability to c

Bohdan Khromets, Jonathan Baugh

Low-frequency $1/f^\alpha$ charge noise significantly hinders the performance of voltage-controlled spin qubits in quantum dots. Here, we utilize fractional calculus to design voltage control pulses yielding the highest average fidelities for noisy quantum gate operations. We focus specifically on the exponential voltage control of the exchange interaction g

Doron Shafrir

We show that the rational subset membership problem in $G$ can be reduced to the submonoid membership problem in $G{\times}H$ where $H$ is virtually Abelian. We use this to show that there is no algorithm reducing submonoid membership to a finite index subgroup uniformly for all virtually nilpotent groups. We also provide evidence towards the existence of a

Tim Menzies, Andre Lustosa

This paper proposes a new challenge problem for software analytics. In the process we shall call "software review", a panel of SMEs (subject matter experts) review examples of software behavior to recommend how to improve that's software's operation. SME time is usually extremely limited so, ideally, this panel can complete this optimization task after looki

Bachir Bekka

Let $G$ be a semisimple Lie group. We describe the irreducible representations of $G$ by linear isometries on $L_p$-spaces for $p\in (1,+\infty)$ with $p\neq 2.$ More precisely, we show that, for every such representation $\pi,$ there exists a parabolic subgroup $Q$ of $G$ such that $\pi$ is equivalent to the natural representation of $G$ on $L_p(G/Q)$ twist

Álvaro Tovar-Pardo, Pedro De La Torre Luque, Miguel Ángel Sánchez-Conde

Recent $\gamma$-ray and neutrino observations seem to favor the consideration of non-uniform diffusion of cosmic rays (CRs) throughout the Galaxy. In this study, we investigate the consequences of spatially-dependent inhomogeneous propagation of CRs on the fluxes of secondary CRs and antiprotons detected at Earth. A comparison is made among different scenari

Zev Shirazi

Several monads of probability measures have been shown to have presentations as codensity monads over small categories of stochastic maps. This paper studies how three key properties of these probability monads, relevant to categorical approaches to probability, can arise from their codensity presentations. We first derive the existence of a Kleisli law into

Antônio C. Lourenço, Denis R. Candido, Eduardo I. Duzzioni

Here, we calculate and study correlations of the Dicke model in the presence of qubit-qubit interaction. Whereas the analysis of correlations among its subsystems is essential for the understanding of corresponding critical phenomena and for performing quantum information tasks, the majority of correlation measures are restricted to bipartitions due to the i

Xingyuan Pan, Luyang Huang, Liyan Kang, Zhicheng Liu

Large Language Models (LLMs) have demonstrated remarkable abilities in general scenarios. Instruction finetuning empowers them to align with humans in various tasks. Nevertheless, the Diversity and Quality of the instruction data remain two main challenges for instruction finetuning. With regard to this, in this paper, we propose a novel gradient-based metho

Zhiyu Tan, Mengping Yang, Luozheng Qin, Hao Yang

One critical prerequisite for faithful text-to-image generation is the accurate understanding of text inputs. Existing methods leverage the text encoder of the CLIP model to represent input prompts. However, the pre-trained CLIP model can merely encode English with a maximum token length of 77. Moreover, the model capacity of the text encoder from CLIP is re

Alan Junzhe Zhou, Yin Li, Scott Dodelson, Rachel Mandelbaum

The analyses of the next generation cosmological surveys demand an accurate, efficient, and differentiable method for simulating the universe and its observables across cosmological volumes. We present Hamiltonian ray tracing (HRT) -- the first post-Born (accounting for lens-lens coupling and without relying on the Born approximation), three-dimensional (wit

Madeleine Pollack, Lauren N. Steimle

Markov Decision Processes (MDPs) are mathematical models of sequential decision-making under uncertainty that have found applications in healthcare, manufacturing, logistics, and others. In these models, a decision-maker observes the state of a stochastic process and determines which action to take with the goal of maximizing the expected total discounted re

Antonio Martínez, A. L. Martínez-Triviño, J. P. dos Santos

We describe a general correspondence between weighted minimal surfaces in $\mathbb{R}^3$ and weighted maximal surfaces with some admissible singularities in $\mathbb{L}^3$, for a class of functions $\varphi$ which provides the corresponding weight. For these families of surfaces, we provide a Weierstrass representation when $\dot{\varphi}\neq 0$ and analyze

Holli Sargeant, Ahmed Izzidien, Felix Steffek

This paper addresses a critical gap in legal analytics by developing and applying a novel taxonomy for topic classification of summary judgment cases in the United Kingdom. Using a curated dataset of summary judgment cases, we use the Large Language Model Claude 3 Opus to explore functional topics and trends. We find that Claude 3 Opus correctly classified t

Jon Aycock

The Jacobian is an algebraic invariant of a graph which is often seen in analogy to the class group of a number field. In particular, there have been multiple investigations into the Iwasawa theory of graphs with the Jacobian playing the role of the class group. In this paper, we construct an Iwasawa module related to the Jacobian of a $\mathbb{Z}_p$-tower o

Patrick McNamee, Miroslav Krstić, Zahra Nili Ahmadabadi

For a map that is strictly but not strongly convex, model-based gradient extremum seeking has an eigenvalue of zero at the extremum, i.e., it fails at exponential convergence. Interestingly, perturbation-based model-free extremum seeking has a negative Jacobian, in the average, meaning that its (practical) convergence is exponential, even though the map's He

Pieter van Dokkum, Dayi David Li, Roberto Abraham, Shany Danieli

CDG-1 is a tight grouping of four likely globular clusters in the Perseus cluster, and a candidate dark galaxy with little or no diffuse light. Here we provide new constraints on the luminosity of any underlying stellar emission, using HST/UVIS F200LP imaging. No diffuse emission is detected, with a 2$\sigma$ upper limit of F200LP>28.1 mag/arcsec$^2$ on the

Artem Kaznatcheev, Melle van Marle

We examine the complexity of maximising fitness via local search on valued constraint satisfaction problems (VCSPs). We consider two kinds of local ascents: (1) steepest ascents, where each step changes the domain that produces a maximal increase in fitness; and (2) $\prec$-ordered ascents, where -- of the domains with available fitness increasing changes --

Wanyue Lin, Yuchen Wu, Christopher Broyles, Tai Kong

Single crystals of U$_2$Mn$_3$Ge and and U$_2$Fe$_3$Ge with a Kagome lattice structure were synthesized using a high-temperature self-flux crystal growth method. The physical properties of these crystals were characterized through measurements of resistivity, magnetism, and specific heat. U$_2$Fe$_3$Ge exhibits ferromagnetic ground state and Anomalous Hall E

Arthur V. Straube, Felix Höfling

The transport of individual particles in inhomogeneous environments is complex and exhibits non-Markovian responses. The latter may be quantified by a memory function within the framework of the linear generalised Langevin equation (GLE). Here, we exemplify the implications of steady driving on the memory function of a colloidal model system for Brownian mot

Martin Blaha, Arno Rauschenbeutel, Riccardo Pennetta

The introduction of low-loss optical fibers probably represents the single most important advance in the growth of our telecommunication system. To meet our needs for secure communications, it is likely that our classical network will soon be operating alongside what is known as a quantum network. The latter is very sensitive to loss and thus poses new const