February 2024 arXiv papers — page 102

Vivek Ramavajjala

In recent years, a variety of ML architectures and techniques have seen success in producing skillful medium range weather forecasts. In particular, Vision Transformer (ViT)-based models (e.g. Pangu-Weather, FuXi) have shown strong performance, working nearly "out-of-the-box" by treating weather data as a multi-channel image on a rectilinear grid. While a re

Alexandre Bouchard-Côté, Trevor Campbell, Geoff Pleiss, Nikola Surjanovic

This paper is intended to appear as a chapter for the Handbook of Markov Chain Monte Carlo. The goal of this chapter is to unify various problems at the intersection of Markov chain Monte Carlo (MCMC) and machine learning$\unicode{x2014}$which includes black-box variational inference, adaptive MCMC, normalizing flow construction and transport-assisted MCMC,

Jason Bell, Chris Schulz, Jeffrey Shallit

We show that every Sturmian word has the property that the distance between consecutive ending positions of cubes occurring in the word is always bounded by $10$ and this bound is optimal, extending a result of Rampersad, who proved that the bound $9$ holds for the Fibonacci word. We then give a general result showing that for every $e \in [1,(5+\sqrt{5})/2)

Ricco Noel Hansen Flyckt, Louise Sjodsholm, Margrethe Høstgaard Bang Henriksen, Claus Lohman Brasen

Lung cancer (LC) remains the primary cause of cancer-related mortality, largely due to late-stage diagnoses. Effective strategies for early detection are therefore of paramount importance. In recent years, machine learning (ML) has demonstrated considerable potential in healthcare by facilitating the detection of various diseases. In this retrospective devel

Bas van den Heuvel

Much of the software we use in everyday life consists of distributed components (running on separate cores or even computers) that collaborate through communication (by exchanging messages). It is crucial to develop robust methods that can give reliable guarantees about the behavior of such message-passing software. With a focus on session types as communica

Timm Fabian Mörstedt, Wallace Santos Teixeira, Arto Viitanen, Heidi Kivijärvi

We experimentally demonstrate the fast generation of thermal states of a transmon using a single-junction quantum-circuit refrigerator (QCR) as an in-situ-tunable environment. Through single-shot readout, we monitor the transmon up to its third-excited state, assessing population distributions controlled by QCR drive pulses. Whereas cooling can be achieved i

Alejandro Gangui, Eduardo L. Ortiz

We analyse the scientific research carried out at the Institute of Physics of the National University of La Plata in the first half of the 20th century, and the cultural and social context in which they were immersed. We focus especially on the activities carried out by the Argentine physicist Ramon G. Loyarte, who was an emblematic personality in the scient

José Alberto Benítez-Andrades, José Emilio Labra, Enedina Quiroga, Vicente Martín

There is a great concern nowadays regarding alcohol consumption and drug abuse, especially in young people. Analyzing the social environment where these adolescents are immersed, as well as a series of measures determining the alcohol abuse risk or personal situation and perception using a number of questionnaires like AUDIT, FAS, KIDSCREEN, and others, it i

Han Huang, Pakawut Jiradilok, Elchanan Mossel

Random geometric graphs are random graph models defined on metric spaces. Such a model is defined by first sampling points from a metric space and then connecting each pair of sampled points with probability that depends on their distance, independently among pairs. In this work, we show how to efficiently reconstruct the geometry of the underlying space fro

Dimplekumar Chalishajar, K. Dhanalakshmi, K. Ramkumar, K. Ravikumar

The existence, uniqueness, and exponential stability results for mild solutions to the fractional neutral stochastic differential system are presented in this article. To demonstrate the results, the concept of bounded integral contractors is combined with the stochastic result and sequencing technique. In contrast to previous publications, we do not need to

Anton A. Zabreyko, Sanjoli Narang, Sudarsanan Rajasekaran, Manya Ghobadi

We present MLCC, a novel technique to augment today's congestion control algorithms to accelerate DNN training jobs in shared GPU clusters in a fully distributed manner. At the heart of MLCC lies a straightforward principle: DNN training flows should scale their sending rate to shift other flows' communication into their compute periods, achieving in

David Oniani, Jordan Hilsman, Chengxi Zang, Junmei Wang

A drug molecule is a substance that changes the organism's mental or physical state. Every approved drug has an indication, which refers to the therapeutic use of that drug for treating a particular medical condition. While the Large Language Model (LLM), a generative Artificial Intelligence (AI) technique, has recently demonstrated effectiveness in translat

Peter D. Chang

This paper introduces the DeepATLAS foundational model for localization tasks in the domain of high-dimensional biomedical data. Upon convergence of the proposed self-supervised objective, a pretrained model maps an input to an anatomically-consistent embedding from which any point or set of points (e.g., boxes or segmentations) may be identified in a one-sh

Ali Saheb Pasand, Reza Moravej, Mahdi Biparva, Ali Ghodsi

A common phenomena confining the representation quality in Self-Supervised Learning (SSL) is dimensional collapse (also known as rank degeneration), where the learned representations are mapped to a low dimensional subspace of the representation space. The State-of-the-Art SSL methods have shown to suffer from dimensional collapse and fall behind maintaining

Soham Deshmukh, Rita Singh, Bhiksha Raj

Audio-Language Models (ALM) aim to be general-purpose audio models by providing zero-shot capabilities at test time. The zero-shot performance of ALM improves by using suitable text prompts for each domain. The text prompts are usually hand-crafted through an ad-hoc process and lead to a drop in ALM generalization and out-of-distribution performance. Existin

Liang Zhang, Zhelun Chen

The potential of Machine Learning Control (MLC) in HVAC systems is hindered by its opaque nature and inference mechanisms, which is challenging for users and modelers to fully comprehend, ultimately leading to a lack of trust in MLC-based decision-making. To address this challenge, this paper investigates and explores Interpretable Machine Learning (IML), a

Yuexi Wang, Nicholas G. Polson

Bayesian hierarchical models are commonly employed for inference in count datasets, as they account for multiple levels of variation by incorporating prior distributions for parameters at different levels. Examples include Beta-Binomial, Negative-Binomial (NB), Dirichlet-Multinomial (DM) distributions. In this paper, we address two crucial challenges that ar

Pekka Mertala, Janne Fagerlund, Jukka Lehtoranta, Emilia Mattila

Objective This study investigates what kind of conceptions primary school students have about ML if they are not conceptually "primed" with the idea that in ML, humans teach computers. Method Qualitative survey responses from 197 Finnish primary schoolers were analyzed via an abductive method. Findings We identified three partly overlapping ML conception cat

Andrea Miotti, Akash Wasil

This paper provides policy recommendations to address threats from deepfakes. First, we provide background information about deepfakes and review the harms they pose. We describe how deepfakes are currently used to proliferate sexual abuse material, commit fraud, manipulate voter behavior, and pose threats to national security. Second, we review previous leg

Myeung Suk Oh, Anindya Bijoy Das, Taejoon Kim, David J. Love

A recent line of research has been investigating deep learning approaches to wireless positioning (WP). Although these WP algorithms have demonstrated high accuracy and robust performance against diverse channel conditions, they also have a major drawback: they require processing high-dimensional features, which can be prohibitive for mobile applications. In

Liang Zhang, Zhelun Chen, Vitaly Ford

The rapid progression in artificial intelligence has facilitated the emergence of large language models like ChatGPT, offering potential applications extending into specialized engineering modeling, especially physics-based building energy modeling. This paper investigates the innovative integration of large language models with building energy modeling soft

Livia Deme, Krisztian Sarneczky, Antal Igaz, Balazs Csak

We present statistical analysis of visual meteor data taken with networks of meteor cameras operating in Hungary between 2020 and 2023. We use three different camera systems: a set of traditional MetRec-based video cameras, a self-developed automated DSLR camera system and a network of newly installed AllSky7 camera stations. Similarities and differences bet

Yifan Liu, Vivek Aji

Contrary to canonical expectations we show that lattice translational symmetry breaking often accompanies uniformly ordered flux phases. We demonstrate this phenomena by studying a spinless-fermion model on a square latttice with nearest-neighbor repulsion. We find an array of flux patterns, as a function interaction strength and filling factor, that break t

Daniel Mitsutani

We define and study the foliated centralizer: the group of $C^\infty$ centralizer elements of the lift of an Anosov system on a non-compact manifold which additionally preserve the stable and unstable foliations. When the Anosov system is the geodesic flow of a closed Riemannian manifold with pinched negative sectional curvatures, we prove some rigidity prop

Wenhan Cao, Alexandre Capone, Sandra Hirche, Wei Pan

Adaptive dynamic programming (ADP) for stochastic linear quadratic regulation (LQR) demands the precise computation of stochastic integrals during policy iteration (PI). In a fully model-free problem setting, this computation can only be approximated by state samples collected at discrete time points using computational methods such as the canonical Euler-Ma

Guilherme F. Almeida

Using the inverse period map of the Gauss-Manin connection associated with $QH^{*}\bigl(\mathbb{CP}^2\bigr)$ and the Dubrovin construction of Landau-Ginzburg superpotential for Dubrovin-Frobenius manifolds, we construct a one-dimensional Landau-Ginzburg superpotential for the quantum cohomology of $\mathbb{CP}^2$. In the case of small quantum cohomology, the

Md Kowsher, Abdul Rafae Khan, Jia Xu

In Chaos, a minor divergence between two initial conditions exhibits exponential amplification over time, leading to far-away outcomes, known as the butterfly effect. Thus, the distant future is full of uncertainty and hard to forecast. We introduce Group Reservoir Transformer to predict long-term events more accurately and robustly by overcoming two challen

Caique Campos de Oliveira, Douglas Soares Galvao, Pedro Alves da Silva Autreto

We have combined DFT calculations with the Boltzmann semiclassical transport theory to investigate the effect of selective hydrogenation on the thermoelectric properties of tetra-penta-deca-hexagonal graphene (TPDH-gr), a recently proposed new 2D carbon allotrope. Our results show that the Seebeck coefficient is enhanced after hydrogenation. The conductivity

Michael Strickland

In this contributed chapter, I review our current understanding of the applicability of hydrodynamics to modeling the quark-gluon plasma (QGP), focusing on the question of hydrodynamization/thermalization of the QGP and the anisotropic hydrodynamics (aHydro) far-from-equilibrium hydrodynamic framework. I discuss the existence of far-from-equilibrium hydrodyn

Diego Gallego, J. Bayron Orjuela-Quintana, César A. Valenzuela-Toledo

We explore the cosmological dynamics of a minimalistic yet generic string-inspired model for multifield dark energy. Adopting a supergravity four-dimensional viewpoint, we motivate the model's structure arising from superstring compactifications involving a chiral superfield and a pure $U(1)$ gauge sector. The chiral sector gives rise to a pair of scalar fie

Andrew M. Nguyen, Jianfei Liu, Tejas Sudharshan Mathai, Peter C. Grayson

Coronary artery calcification (CAC) is a strong and independent predictor of cardiovascular disease (CVD). However, manual assessment of CAC often requires radiological expertise, time, and invasive imaging techniques. The purpose of this multicenter study is to validate an automated cardiac plaque detection model using a 3D multiclass nnU-Net for gated and

Félix Almendra-Hernández, Jesús A. De Loera, Sonja Petrović

We study a colored generalization of the famous simple-switch Markov chain for sampling the set of graphs with a fixed degree sequence. Here we consider the space of graphs with colored vertices, in which we fix the degree sequence and another statistic arising from the vertex coloring, and prove that the set can be connected with simple color-preserving swi

Xueqi Guo, Luyao Shi, Xiongchao Chen, Qiong Liu

Inter-frame motion in dynamic cardiac positron emission tomography (PET) using rubidium-82 (82-Rb) myocardial perfusion imaging impacts myocardial blood flow (MBF) quantification and the diagnosis accuracy of coronary artery diseases. However, the high cross-frame distribution variation due to rapid tracer kinetics poses a considerable challenge for inter-fr

A. Ilyin, V. Kalantarov, A. Kostianko, S. Zelik

For 2D Navier--Stokes equations in a bounded smooth domain, we construct a system of determining functionals which consists of $N$ linear continuous functionals which depend on pressure $p$ only and of one extra functional which is given by the value of vorticity at a fixed point $x_0\in\partial\Omega$.

Linfeng Cao, Haoran Deng, Yang Yang, Chunping Wang

Due to the ubiquity of graph data on the web, web graph mining has become a hot research spot. Nonetheless, the prevalence of large-scale web graphs in real applications poses significant challenges to storage, computational capacity and graph model design. Despite numerous studies to enhance the scalability of graph models, a noticeable gap remains between

Dane Brouwer, Joshua Citron, Hojung Choi, Marion Lepert

It is difficult for robots to retrieve objects in densely cluttered lateral access scenes with movable objects as jamming against adjacent objects and walls can inhibit progress. We propose the use of two action primitives -- burrowing and excavating -- that can fluidize the scene to un-jam obstacles and enable continued progress. Even when these primitives

Kshama Dwarakanath, Tucker Balch, Svitlana Vyetrenko

We present ABIDES-Economist, an agent-based simulator for economic systems that includes heterogeneous households, firms, a central bank, and a government. Agent behavior can be defined using domain-specific behavioral rules or learned through reinforcement learning by specifying their objectives. We integrate reinforcement learning capabilities for all agen

Matthew J Doyle, Andrei I Golov, Paul M Walmsley, Andrew W Baggaley

We present a numerical study, using the vortex filament model, of vortex tangles in a flow of pure superfluid $^4$He in the $T = 0$ limit through a channel of width $D = 1$ mm for various applied velocities $V$. The flat channel walls are assumed to be microscopically rough such that vortices terminating at the walls are permanently pinned; vortices are libe

Weiying Zhao, Paul Riot, Charles-Alban Deledalle, Henri Maître

In coherent imaging systems, speckle is a signal-dependent noise that visually strongly degrades images' appearance. A huge amount of SAR data has been acquired from different sensors with different wavelengths, resolutions, incidences and polarizations. We extend the nonlocal filtering strategy to the temporal domain and propose a patch-based adaptive tempo

Mohammadreza M. Kalan, Samory Kpotufe

We consider the problem of Neyman-Pearson classification which models unbalanced classification settings where error w.r.t. a distribution $\mu_1$ is to be minimized subject to low error w.r.t. a different distribution $\mu_0$. Given a fixed VC class $\mathcal{H}$ of classifiers to be minimized over, we provide a full characterization of possible distributio

Dian Weerakonda, Benjamin Bryant, Anthony Dennis, Tobia Nava

Bulk high-temperature superconductors (HTS) are capable of generating very strong magnetic fields while maintaining a relatively compact form factor. Solenoids constructed using stacks of ring-shaped bulk HTS have been demonstrated to be capable of nuclear magnetic resonance (NMR) spectroscopy and magnetic resonance imaging (MRI). However, these stacks were

Ziyang Song, Qincheng Lu, He Zhu, David Buckeridge

Learning time-series representations for discriminative tasks, such as classification and regression, has been a long-standing challenge in the healthcare domain. Current pre-training methods are limited in either unidirectional next-token prediction or randomly masked token prediction. We propose a novel architecture called Bidirectional Timely Generative P

Xueting Guan, Christoph Treude

Deep learning techniques applied to program analysis tasks such as code classification, summarization, and bug detection have seen widespread interest. Traditional approaches, however, treat programming source code as natural language text, which may neglect significant structural or semantic details. Additionally, most current methods of representing source

Mika Sutela, Nino Lindström

We expand on earlier research on the topic by discussing an infinitely repeated game model with a subgame perfect equilibrium strategy profile (SPE) as a solution concept that diminishes incentives to violate speed limits in a carrot and stick fashion. In attempts to construct an SPE strategy profile, the initial state is chosen such that the drivers are pla

Eriks Klotins, Michael Unterkalmsteiner, Panagiota Chatzipetrou, Tony Gorschek

Context Software start-ups have shown their ability to develop and launch innovative software products and services. Small, motivated teams and uncertain project scope makes start-ups good candidates for adopting Agile practices. Objective We explore how start-ups use Agile practices and what effects can be associated with the use of those practices. Method

Gert Brodin, Haidar Al-Naseri

We use a recently developed kinetic model derived from the Dirac equation, in order to study electromagnetic wave propagation in superstrong magnetic fields, such as in magnetars, where relativistic Landau quantization is prominent. The leading contribution to the conductivity tensor in such a plasma is calculated. It is found that the electron Hall current

Dilli Prasad Sharma, Nasim Beigi-Mohammadi, Hongxiang Geng, Dawn Dixon

Emergency events in a city cause considerable economic loss to individuals, their families, and the community. Accurate and timely prediction of events can help the emergency fire and rescue services in preparing for and mitigating the consequences of emergency events. In this paper, we present a systematic development of predictive models for various types

Narun Raman, Taylor Lundy, Samuel Amouyal, Yoav Levine

There is increasing interest in using LLMs as decision-making "agents." Doing so includes many degrees of freedom: which model should be used; how should it be prompted; should it be asked to introspect, conduct chain-of-thought reasoning, etc? Settling these questions -- and more broadly, determining whether an LLM agent is reliable enough to be trusted --

Beyza Dabak, Venkatesh Khammammetti, Saif Khan Mohammed, Robert Calderbank

Orthogonal Time Frequency Space (OTFS) is a framework for communications and active sensing that processes signals in the delay-Doppler (DD) domain. It is informed by 6G propagation environments, where Doppler spreads measured in kHz make it more and more difficult to estimate channels, and the standard model-dependent approach to wireless communication is s

Qiang Wang, Yixin Deng, Francisco Roldan Sanchez, Keru Wang

Offline policy learning aims to discover decision-making policies from previously-collected datasets without additional online interactions with the environment. As the training dataset is fixed, its quality becomes a crucial determining factor in the performance of the learned policy. This paper studies a dataset characteristic that we refer to as multi-beh

Eshwar Ram Arunachaleswaran, Natalie Collina, Jon Schneider

We study the problem of characterizing optimal learning algorithms for playing repeated games against an adversary with unknown payoffs. In this problem, the first player (called the learner) commits to a learning algorithm against a second player (called the optimizer), and the optimizer best-responds by choosing the optimal dynamic strategy for their (unkn

Andrew Cinar, Forrest Laine

Two-vehicle racing is natural example of a competitive dynamic game. As with most dynamic games, there are many ways in which the underlying solution concept can be structured, resulting in different equilibrium concepts. The assumed solution concept influences the behaviors of two interacting players in racing. For example, blocking behavior emerges natural

Olivia Di Matteo

The ability to incorporate quantum phenomena in computing unlocks a host of new ways to make mistakes. This work surveys existing studies and approaches to debugging quantum programs. It then presents a set of examples that stem from first-hand experience, intended to motivate future research on the subject and the development of novel tools and techniques.

Congcong Wen, Jiazhao Liang, Shuaihang Yuan, Hao Huang

In the field of robotics and automation, navigation systems based on Large Language Models (LLMs) have recently demonstrated impressive performance. However, the security aspects of these systems have received relatively less attention. This paper pioneers the exploration of vulnerabilities in LLM-based navigation models in urban outdoor environments, a crit

Muayad J. Aljafar, Rasika Joshi, John M. Acken

Security is a growing problem that needs hardware support. Memristors provide an alternative technology for hardware-supported security implementation. This paper presents a specific technique that utilizes the benefits of hybrid CMOS-memristors technology demonstrated with SHA3 over implementations that use only memristor technology. In the proposed techniq

Curtis Struck

It is widely believed that resonant orbits play an important role in formation and evolution of bars and large-scale spirals in galaxy discs. These resonant orbits have been studied in a number of specific potentials, often with an imposed bar component. In this paper I show that families of resonant (e.g., two-dimensional $x_1$) orbits of differing eccentri

Hanbing Wang, Xiaorui Liu, Wenqi Fan, Xiangyu Zhao

Recently, sequential recommendation has been adapted to the LLM paradigm to enjoy the power of LLMs. LLM-based methods usually formulate recommendation information into natural language and the model is trained to predict the next item in an auto-regressive manner. Despite their notable success, the substantial computational overhead of inference poses a sig

Marianna Pensky

The paper introduces a Signed Generalized Random Dot Product Graph (SGRDPG) model, which is a variant of the Generalized Random Dot Product Graph (GRDPG), where, in addition, edges can be positive or negative. The setting is extended to a multiplex version, where all layers have the same collection of nodes and follow the SGRDPG. The only common feature of t

Jason Yoo, Yunpeng Liu, Frank Wood, Geoff Pleiss

In online continual learning, a neural network incrementally learns from a non-i.i.d. data stream. Nearly all online continual learning methods employ experience replay to simultaneously prevent catastrophic forgetting and underfitting on past data. Our work demonstrates a limitation of this approach: neural networks trained with experience replay tend to ha

Huynh Khanh Vi Tran, Michael Unterkalmsteiner, Jürgen Börstler, Nauman bin Ali

Context: Modern software development increasingly relies on software testing for an ever more frequent delivery of high quality software. This puts high demands on the quality of the central artifacts in software testing, test suites and test cases. Objective: We aim to develop a comprehensive model for capturing the dimensions of test case/suite quality, wh

Andrew Lowy, Zhuohang Li, Jing Liu, Toshiaki Koike-Akino

For small privacy parameter $\epsilon$, $\epsilon$-differential privacy (DP) provides a strong worst-case guarantee that no membership inference attack (MIA) can succeed at determining whether a person's data was used to train a machine learning model. The guarantee of DP is worst-case because: a) it holds even if the attacker already knows the records of al

Abhisek Sahu

We analyse spherically symmetric spacetimes obtained by gluing a cosmological region to a Schwarzschild black hole across a singular co-dimension one hypersurface. Assuming an arbitrary homogeneous and isotropic cosmology, and working in spacetime dimensions greater than three with general cosmological constant, we derive the stress-energy tensor required on

Jonathan Sillito, Matt Pope

Due to various sources of uncertainty, emergent behavior, and ongoing changes, the reliability of many socio-technical systems depends on an iterative and collaborative process in which organizations (1) analyze and learn from system failures, and then (2) co-evolve both the technical and human parts of their systems based on what they learn. Many organizati

Joerg Bruedern, Trevor D. Wooley

We survey the potential for progress in additive number theory arising from recent advances concerning major arc bounds associated with mean value estimates for smooth Weyl sums. We focus attention on the problem of representing large positive integers as sums of a square and a number of $k$-th powers. We show that such representations exist when the number

Alejandro Bris, Vicent Mateu

Processes involving only massless or massive quarks at tree-level get corrections from massive (lighter, heavier, or equal-mass) secondary quarks starting at two-loop order, generated by a virtual gluon splitting into a massive quark anti-quark pair. One convenient approach to compute such two-loop corrections is starting with the one-loop diagram considerin

Zoltan Ligeti

This writeup follows the presentation at the Symposium, with emphasis on topics and ideas discussed there. It is purposefully informal, not a review of the field, and neither does it include a complete list of references. However, I hope that readers might find some comments useful or amusing, and may appreciate the challenges and reasons for excitement abou

Marcin Kolakowski, Vitomir Djaja-Josko

Ultra-wideband positioning systems intended for indoor applications often work in non-line of sight conditions, which result in insufficient precision and accuracy of derived localizations. One of the possible solutions is the implementation of cooperative positioning techniques. The following paper describes a cooperative ultra-wideband positioning system w

Julie Inglis, Nicole L. Wallack, Jerry W. Xuan, Heather A. Knutson

Previous attempts have been made to characterize the atmospheres of directly imaged planets at low-resolution (R$\sim$10s-100s), but the presence of clouds has often led to degeneracies in the retrieved atmospheric abundances with cloud opacity and temperature structure that bias retrieved compositions. In this study, we perform retrievals on the ultra-young

Shuxiang Cao, Zhen Shao, Jian-Qing Zheng, Mohammed Alghadeer

Quantum non-demolition measurement plays an essential role in quantum technology, crucial for quantum error correction, metrology, and sensing. Conventionally, the qubit state is classified from the raw or integrated time-domain measurement record. Here, we demonstrate a method to enhance the assignment fidelity of the readout by considering the "path signat

Helmut Harbrecht, Michael Multerer, Jacopo Quizi

The present article is concerned scattered data approximation for higher dimensional data sets which exhibit an anisotropic behavior in the different dimensions. Tailoring sparse polynomial interpolation to this specific situation, we derive very efficient degenerate kernel approximations which we then use in a dimension weighted fast multipole method. This

Edgar Heinert, Matthias Rottmann, Kira Maag, Karsten Kahl

Convolutional neural networks (CNNs) for image processing tend to focus on localized texture patterns, commonly referred to as texture bias. While most of the previous works in the literature focus on the task of image classification, we go beyond this and study the texture bias of CNNs in semantic segmentation. In this work, we propose to train CNNs on pre-

Benjamin Holmgren, Eli Quist, Jordan Schupbach, Brittany Terese Fasy

We introduce the manifold density function, which is an intrinsic method to validate manifold learning techniques. Our approach adapts and extends Ripley's $K$-function, and categorizes in an unsupervised setting the extent to which an output of a manifold learning algorithm captures the structure of a latent manifold. Our manifold density function generaliz

Michael Unterkalmsteiner, Deepika Badampudi, Ricardo Britto, Nauman bin Ali

Background: Modern Code Review (MCR) is a key component for delivering high-quality software and sharing knowledge among developers. Effective reviews require an in-depth understanding of the code and demand from the reviewers to contextualize the change from different perspectives. Aim: While there is a plethora of research on solutions that support develop

Muhammad Haseeb, Jinkun Geng, Daniel Duclos-Cavalcanti, Ulysses Butler

Financial exchanges are migrating to the cloud, but the best-effort nature of the public cloud is at odds with the stringent latency requirements of exchanges. We present Jasper, a system for meeting the networking requirements of financial exchanges on the public cloud. Jasper uses an overlay tree to scalably multicast market data from an exchange to ~1000

Zhengyangguang Gong, Anik Halder, Annabelle Bohrdt, Stella Seitz

Modern cosmological research in large scale structure has witnessed an increasing number of applications of machine learning methods. Among them, Convolutional Neural Networks (CNNs) have received substantial attention due to their outstanding performance in image classification, cosmological parameter inference and various other tasks. However, many models

Dhananjay Saikumar

Scanning Tunneling Microscopy (STM) is a powerful technique that utilizes quantum tunneling to visualize atomic surfaces with high precision. This study presents detailed topographic maps and evaluates the local density of states (LDOS) for three distinct materials: Highly Oriented Pyrolytic Graphite (HOPG), gold, and silicon. By meticulously measuring the t

Nadav Drechsler, Omri Lesser, Yuval Oreg

Detecting Majorana zero modes (MZMs) in topological superconductors remains challenging, as localized non-topological states can mimic MZM signatures. Here, we propose electron interferometry by non-local transport measurements as a definitive probe to distinguish MZMs from non-topological states. We develop an analytical minimal model showing that interfere

Vasilis Belis, Patrick Odagiu, Michele Grossi, Florentin Reiter

Quantum machine learning provides a fundamentally different approach to analyzing data. However, many interesting datasets are too complex for currently available quantum computers. Present quantum machine learning applications usually diminish this complexity by reducing the dimensionality of the data, e.g., via auto-encoders, before passing it through the

Anubhav Kumar Srivastava, Guillem Müller-Rigat, Maciej Lewenstein, Grzegorz Rajchel-Mieldzioć

The quantum mechanics formalism introduced new revolutionary concepts challenging our everyday perceptions. Arguably, quantum entanglement, which explains correlations that cannot be reproduced classically, is the most notable of them. Besides its fundamental aspect, entanglement is also a resource, fueling emergent technologies such as quantum simulators an

Pawel Caputa, Hyun-Sik Jeong, Sinong Liu, Juan F. Pedraza

Quantifying complexity in quantum systems has witnessed a surge of interest in recent years, with Krylov-based measures such as Krylov complexity ($C_K$) and Spread complexity ($C_S$) gaining prominence. In this study, we investigate their interplay by considering the complexity of states represented by density matrix operators. After setting up the problem,

Jun-Yi Shan, Jonathan B. Curtis, Mingyao Guo, Chang Jae Roh

Uncovering pathways to optically drive magnetic order-disorder transitions on ultrashort timescales can lead to the realization of novel out-of-equilibrium quantum phenomena. A long-sought pathway is to directly excite a highly non-thermal energy-momentum distribution of magnons, bypassing both charge and lattice degrees of freedom. However, this remains elu

Aswin Parayil Mana, Yabo Li, Hiroki Sukeno, Tzu-Chieh Wei

We give an explicit operator representation (via a sequential circuit and projection to symmetry subspaces) of Kramers-Wannier duality transformation in higher-dimensional subsystem symmetric models generalizing the construction in the 1D transverse-field Ising model. Using the Kramers-Wannier duality operator, we also construct the Kennedy-Tasaki transforma

Maneesha Sushama Pradeep, Noriyuki Sogabe, Mikhail Stephanov, Ho-Ung Yee

We investigate the effect of the quantum chromodynamics (QCD) critical point on the isentropic trajectories in the QCD phase diagram. We point out that the universality of the critical equation of state and the third law of thermodynamics require the specific entropy (per baryon) along the coexistence (first-order transition) line to be nonmonotonic at least

Natalia Lahén, Thorsten Naab, Dorottya Szécsi

Stellar winds of massive ($\gtrsim9\,\mathrm{M_\odot}$) and very massive ($\gtrsim100\,\mathrm{M_\odot}$) stars may play an important role in the metal-enrichment during the formation of star clusters. With novel high-resolution hydrodynamical \textsc{griffin}-project simulations, we investigate the rapid recycling of stellar wind-material during the formati

Maxim Dzero

I consider a nonlinear response of conventional superconductors contaminated with potential impurities or imperfections to a circular polarized light. I focus on dc contributions to the induced current density which describe the emergence of the static magnetization in a superconductor. This effect is known as inverse Faraday effect. By employing quasiclassi

Zhuan Li, Roger S. K. Mong

Topological phases of matter offer a promising platform for quantum computation and quantum error correction. Nevertheless, unlike its counterpart in pure states, descriptions of topological order in mixed states remain relatively under-explored. Our work give two definitions for replica topological order in mixed states, which involve $n$ copies of density

Joel Louwsma

We give an algorithm that produces all solutions of the equation $\sum_{i=1}^n 1/x_i = 1$ in integers of the form $2^a k^b$, where $k$ is a fixed positive integer that is not a power of $2$, $a$ is an element of $\{0,1,2\}$ that can vary from term to term, and $b$ is a nonnegative integer that can vary from term to term. We also completely characterize the p

Mario Cadelano, Emanuele Dalessandro, Enrico Vesperini

All multiple population (MP) formation models in globular clusters (GCs) predict that second population (SP) stars form more centrally concentrated than the first population (FP). As dynamical evolution proceeds, differences are progressively erased, and only dynamically young clusters are expected to still retain a partial memory of the initial structural d

Nicholas DePorzio, Lisa Randall, Zhong-Zhi Xianyu

Stellar mass binary black holes of unknown formation mechanism have been observed, motivating new methods for distinguishing distinct black hole populations. This work explores how the orbital eccentricity of stellar mass binary black holes is a viable conduit for making such distinctions. Four different production mechanisms, and their corresponding eccentr

Roberto Soria, Siying Cheng, Manfred W. Pakull, Christian Motch

We studied the multiband properties of two ultraluminous X-ray sources (2CXO J225728.9-410211 = X-1 and 2CXO J225724.7-410343 = X-2) and their surroundings, in the spiral galaxy NGC 7424. Both sources have approached X-ray luminosities L_{X} ~ 10^{40} erg/s at some epochs. Thanks to a more accurate astrometric solution (based on Australia Telescope Compact A

Zhenyu Cai, Adrian Chapman, Hamza Jnane, Bálint Koczor

Extracting classical information from quantum systems is of fundamental importance, and classical shadows allow us to extract a large amount of information using relatively few measurements. Conventional shadow estimators are unbiased and thus approach the true mean in the infinite-sample limit. In this work, we consider a biased scheme, intentionally introd

Pradip Kattel, Abay Zhakenov, Parameshwar R. Pasnoori, Patrick Azaria

Non-Hermitian Hamiltonians capture several aspects of open quantum systems, such as dissipation of energy and non-unitary evolution. An example is an optical lattice where the inelastic scattering between the two orbital mobile atoms in their ground state and the atom in a metastable excited state trapped at a particular site and acting as an impurity, resul

Andrew King, Jean-Pierre Lasota

Observations show that the 12.4 d binary system descending from the recent supernova SN 2022jli closely fits hypotheses of how low-mass X-ray binaries form, but requires an apparently super-Eddington accretion luminosity from the accreting component. We show that this agrees very well with the type of accretion-induced beaming found in ultraluminous X-ray so

Liwei Lin, Gus Xia, Yixiao Zhang, Junyan Jiang

Controllable music generation plays a vital role in human-AI music co-creation. While Large Language Models (LLMs) have shown promise in generating high-quality music, their focus on autoregressive generation limits their utility in music editing tasks. To address this gap, we propose a novel approach leveraging a parameter-efficient heterogeneous adapter co

Javier Fuentes-Martín, Javier M. Lizana

Flavor deconstruction refers to ultraviolet completions of the Standard Model where the gauge group is split into multiple factors under which fermions transform non-universally. We propose a mechanism for charging same-family fermions into different factors of a deconstructed gauge theory in a way that gauge anomalies are avoided. The mechanism relies in th

Xuepeng Wang, J. F. Mendez-Valderrama, Johannes S. Hofmann, Debanjan Chowdhury

Multi-orbital electronic models hosting a non-trivial band-topology in the regime of strong electronic interactions are an ideal playground for exploring a host of complex phenomenology. We consider here a sign-problem-free and time-reversal symmetric model with isolated topological (chern) bands involving both spin and valley degrees of freedom in the prese

Bin B. Ren, Kevin Fogarty, John H. Debes, Eileen T. Meyer

The close-in regions of bright quasars' host galaxies have been difficult to image due to the overwhelming light from the quasars. With coronagraphic observations in visible light using the Space Telescope Imaging Spectrograph (STIS) on the Hubble Space Telescope, we removed 3C 273 quasar light using color-matching reference stars. The observations revealed

Lev Krayzman, Chan U Lei, Suhas Ganjam, James Teoh

A promising way to store quantum information is by encoding it in the bosonic excitations of microwave resonators. This provides for long coherence times, low dephasing rates, as well as a hardware-efficient approach to quantum error correction. There are two main methods used to make superconducting microwave resonators: traditionally machined out of bulk m

Alfredo Glioti, Riccardo Rattazzi, Lorenzo Ricci, Luca Vecchi

We explore flavor dynamics in the broad scenario of a strongly interacting light Higgs (SILH). Our study focuses on the mechanism of partial fermion compositeness, but is otherwise as systematic as possible. Concretely, we classify the options for the underlying flavor (and CP) symmetries, which are necessary in order to bring this scenario safely within the

Maine Christos, Henry Shackleton, Subir Sachdev, Zhu-Xi Luo

We consider a SU(2) lattice gauge theory on the square lattice, with a single fundamental complex fermion and a single fundamental complex boson on each lattice site. Projective symmetries of the gauge-charged fermions are chosen so that they match with those of the spinons of the $\pi$-flux spin liquid. Global symmetries of all gauge-invariant observables a