January 2024 arXiv papers

C. Adambukulam, J. A. Scott, S. Q. Lim, I. Aharonovich

The germanium vacancy in diamond (GeV) is a promising candidate for color center based quantum networking. Yet, like for other group-IV vacancy defects in diamond, achieving fast, high-fidelity qubit operations using traditional magnetic resonance techniques is experimentally challenging due to a weak magnetic dipole and susceptibility to thermally induced d

Hojat Allah Salehi, Farhad Shirani, S. Sandeep Pradhan

This work considers the non-interactive source simulation problem (NISS). In the standard NISS scenario, a pair of distributed agents, Alice and Bob, observe a distributed binary memoryless source $(X^d,Y^d)$ generated based on joint distribution $P_{X,Y}$. The agents wish to produce a pair of discrete random variables $(U_d,V_d)$ with joint distribution $P_

Min-Seok Seo

We study the late time behavior of the scalar part of the volume modulus and the dilaton in stringy quintessence model, focusing on their contributions to the Hubble slow-roll parameter $ε$ which directly measures the deviation of the spacetime geometry from de Sitter space. When only one of the moduli is allowed to move, $ε$ converges to the stable fixed po

Ali Rabiee, Sima Ghafoori, Anna Cetera, Maryam Norouzi

This study aims to enhance BCI applications for individuals with motor impairments by comparing the effectiveness of tripolar EEG (tEEG) with conventional EEG. The focus is on interpreting and decoding various grasping movements, such as power grasp and precision grasp. The goal is to determine which EEG technology is more effective in processing and transla

Grover Lancaster-Cole, Georgiana Lyall, Thomas Malcolm, Qiyu Zhou

Following the work of Louisa and Michael Barnsley on results in tops of iterated function systems, we extend their work to graph-directed iterated function systems by investigating the relationship between top addresses and shift spaces. For the simplest overlapping interval IFS, we find a sufficient condition for the closure of its tops code space to be a s

Takashi Morita

This study reports an unintuitive finding that positional encoding enhances learning of recurrent neural networks (RNNs). Positional encoding is a high-dimensional representation of time indices on input data. Most famously, positional encoding complements the capabilities of Transformer neural networks, which lack an inherent mechanism for representing the

Shreya Rajagopal, Jae Ho Sohn, Hari Subramonyam, Shiwali Mohan

Generative AI systems such as ChatGPT and Claude are built upon language models that are typically evaluated for accuracy on curated benchmark datasets. Such evaluation paradigms measure predictive and reasoning capabilities of language models but do not assess if they can provide information that is useful to people. In this paper, we take some initial step

Ali Rabiee, Sima Ghafoori, Anna Cetera, Reza Abiri

This research aims to decode hand grasps from Electroencephalograms (EEGs) for dexterous neuroprosthetic development and Brain-Computer Interface (BCI) applications, especially for patients with motor disorders. Particularly, it focuses on distinguishing two complex natural power and precision grasps in addition to a neutral condition as a no-movement condit

Jonas Mayer Martins, Svetlana V. Gurevich, Julien Javaloyes

We study the dynamics of an optoelectronic circuit composed of an excitable nanoscale resonant-tunneling diode (RTD) driving a nanolaser diode (LD) coupled via time-delayed feedback. Using a combination of numerical path-continuation methods and time simulations, we demonstrate that this RTD-LD system can serve as an artificial neuron, generating pulses in t

Qijia Shen, Guangrun Wang

Generating accurate 3D models is a challenging problem that traditionally requires explicit learning from 3D datasets using supervised learning. Although recent advances have shown promise in learning 3D models from 2D images, these methods often rely on well-structured datasets with multi-view images of each instance or camera pose information. Furthermore,

Ertem Nusret Tas, David Tse, Yifei Wang

Since the creation of Bitcoin 15 years ago, there has been an explosion in the number of permissionless blockchains. Each of these blockchains provides an open ledger that anyone can read from and write to. In this multi-chain world, an important question emerges: how can we build a more secure overlay blockchain by reading from and writing to a given set of

Aaron Baughman, Stephen Hammer, Rahul Agarwal, Gozde Akay

We address the problem of scaling up the production of media content, including commentary and personalized news stories, for large-scale sports and music events worldwide. Our approach relies on generative AI models to transform a large volume of multimodal data (e.g., videos, articles, real-time scoring feeds, statistics, and fact sheets) into coherent and

Juan Garcia-Bellido, Michael Hawkins

The recent astrometric data of hundreds of millions of stars from Gaia DR3 has allowed a precise determination of the Milky Way rotation curve up to $28$~kpc. The data suggests a rapid decline in the density of dark matter beyond $19$~kpc. We fit the whole rotation curve with four components (gas, disk, bulge and halo) and compute the microlensing optical de

Luca Donati, Christof Schutte, Marcus Weber

We have investigated how Langevin dynamics is affected by the friction coefficient using the novel algorithm ISOKANN, which combines the transfer operator approach with modern machine learning techniques. ISOKANN describes the dynamics in terms of an invariant subspace projection of the Koopman operator defined in the entire state space, avoiding approximati

Lucas Machado Moschen, María Soledad Aronna

This study presents a mathematical model for optimal vaccination strategies in interconnected metropolitan areas, considering commuting patterns. It is a compartmental model with a vaccination rate for each city, acting as a control function. The commuting patterns are incorporated through a weighted adjacency matrix and a parameter that selects day and nigh

Benjamin Merlin Bumpus, James Fairbanks, Martti Karvonen, Wilmer Leal

What is a time-varying graph, a time-varying topological space, or, more generally, a mathematical structure that evolves over time? In this work, we lay the foundations for a general theory of temporal data by introducing categories of narratives. These are sheaves on posets of time intervals that encode snapshots of a temporal object along with the relatio

Congyu Fang, Adam Dziedzic, Lin Zhang, Laura Oliva

Machine Learning (ML) has demonstrated its great potential on medical data analysis. Large datasets collected from diverse sources and settings are essential for ML models in healthcare to achieve better accuracy and generalizability. Sharing data across different healthcare institutions is challenging because of complex and varying privacy and regulatory re

Zackary Rackauckas

Infineon has identified a need for engineers, account managers, and customers to rapidly obtain product information. This problem is traditionally addressed with retrieval-augmented generation (RAG) chatbots, but in this study, I evaluated the use of the newly popularized RAG-Fusion method. RAG-Fusion combines RAG and reciprocal rank fusion (RRF) by generati

Neil Dey, Ryan Martin, Jonathan P. Williams

A common goal in statistics and machine learning is estimation of unknowns. Point estimates alone are of little value without an accompanying measure of uncertainty, but traditional uncertainty quantification methods, such as confidence sets and p-values, often require distributional or structural assumptions that may not be justified in modern applications.

Samuel Yuan, S. V. Dordevic

Effective computational search holds great potential for aiding the discovery of High-Temperature Superconductors (HTSs), especially given the lack of systematic methods for their discovery. Recent progress has been made in this area with machine learning, especially with deep generative models, which have been able to outperform traditional manual searches

Nikolay Moshchevitin, Anurag Rao, Uri Shapira

For an m by n real matrix A, we investigate the set of badly approximable targets for A as a subset of the m-torus. It is well known that this set is large in the sense that it is dense and has full Hausdorff dimension. We investigate the relationship between its measure and Diophantine properties of A. On the one hand, we give the first examples of a non-si

Junaid Iqbal Khan

The current trend in data regulation requirements and privacy-preserving machine learning has emphasized the importance of machine unlearning. The naive approach to unlearning training data by retraining over the complement of the forget samples is susceptible to computational challenges. These challenges have been effectively addressed through a collection

Aida Abiad, Afrouz Jabal Ameli, Luuk Reijnders

The exact distance $t$-power of a graph $G$, $G^{[\sharp t]}$, is a graph which has the same vertex set as $G$, with two vertices adjacent in $G^{[\sharp t]}$ if and only if they are at distance exactly $t$ in the original graph $G$. We study the clique number of this graph, also known as the $t$-equidistant number. We show that it is NP-hard to determine th

Kshitij Goel, Wennie Tabib

This paper describes continuous-space methodologies to estimate the collision probability, Euclidean distance and gradient between an ellipsoidal robot model and an environment surface modeled as a set of Gaussian distributions. Continuous-space collision probability estimation is critical for uncertainty-aware motion planning. Most collision detection and a

Atul Mohan, Surajit Mondal, Sven Wedemeyer, Natchimuthuk Gopalswamy

AD Leo is a young and active M dwarf with high flaring rates across the X-ray to radio bands. Flares accelerate particles in the outer coronal layers and often impact exospace weather. Wide-band radio dynamic spectra let us explore the evolution of particle acceleration activity across the corona. Identifying the emission features and modelling the mechanism

Connor R. Forsythe, Cristian Arteaga, John P. Helveston

This paper introduces the Mixed Aggregate Preference Logit (MAPL, pronounced "maple'') model, a novel class of discrete choice models that leverages machine learning to model unobserved heterogeneity in discrete choice analysis. The traditional mixed logit model (also known as "random parameters logit'') parameterizes preference heter

Philipp Otto, Alessandro Fassò, Paolo Maranzano

This review article focuses on regularised estimation procedures applicable to geostatistical and spatial econometric models. These methods are particularly relevant in the case of big geospatial data for dimensionality reduction or model selection. To structure the review, we initially consider the most general case of multivariate spatiotemporal processes

S. Autti, A. Casey, N. Eng, N. Darvishi

We report the results of radioactivity assays and heat leak calculations for a range of common cryogenic materials, considered for use in the QUEST-DMC superfluid 3He dark matter detector. The bolometer, instrumented with nanomechanical resonators, will be sensitive to energy deposits from dark matter interactions. Events from radioactive decays and cosmic r

Tony Feng, Jonathan Wang

We study conjectures of Ben-Zvi--Sakellaridis--Venkatesh that categorify the relationship between automorphic periods and $L$-functions in the context of the Geometric Langlands equivalence. We provide evidence for these conjectures in some low-rank examples, by using derived Fourier analysis and the theory of chiral algebras to categorify the Rankin-Selberg

Atiquer Rahman Sarkar, Yao-Shun Chuang, Noman Mohammed, Xiaoqian Jiang

For sharing privacy-sensitive data, de-identification is commonly regarded as adequate for safeguarding privacy. Synthetic data is also being considered as a privacy-preserving alternative. Recent successes with numerical and tabular data generative models and the breakthroughs in large generative language models raise the question of whether synthetically g

Petros Georgiou, Sharu Theresa Jose, Osvaldo Simeone

In a manner analogous to their classical counterparts, quantum classifiers are vulnerable to adversarial attacks that perturb their inputs. A promising countermeasure is to train the quantum classifier by adopting an attack-aware, or adversarial, loss function. This paper studies the generalization properties of quantum classifiers that are adversarially tra

Gaston Giribet, Olivera Mišković, Nahuel Yazbek, Jorge Zanelli

AdS supergravity admits supersymmetric solutions that describe BPS defects. Here, we investigate such solutions in AdS$_3$ supergravity, which is formulated as a Chern-Simons theory on $\mathrm{OSp}(2|1)\,\times\, \mathrm{OSp}(2|1)$. We compute the Killing spinor equation on the BTZ geometry in different ways, looking for BPS solutions on the entire space of

Francesca Bianchi, Enis Kaya, J. Steffen Müller

In this paper, we develop an algorithm for computing Coleman--Gross (and hence Nekovář) $p$-adic heights on hyperelliptic curves over number fields with arbitrary reduction type above $p$. This height is defined as a sum of local heights at each finite place and we use algorithms for Vologodsky integrals, developed by Katz and the second-named author, to com

Zhenghao Zeng, David Arbour, Avi Feller, Raghavendra Addanki

In many real-world causal inference applications, the primary outcomes (labels) are often partially missing, especially if they are expensive or difficult to collect. If the missingness depends on covariates (i.e., missingness is not completely at random), analyses based on fully observed samples alone may be biased. Incorporating surrogates, which are fully

Kartikey Sharma, Deborah Hendrych, Mathieu Besançon, Sebastian Pokutta

We tackle the network design problem for centralized traffic assignment, which can be cast as a mixed-integer convex optimization (MICO) problem. For this task, we propose different formulations and solution methods in both a deterministic and a stochastic setting in which the demand is unknown in the design phase. We leverage the recently proposed Boscia fr

Adrien Bolland, Gaspard Lambrechts, Damien Ernst

In order to compute near-optimal policies with policy-gradient algorithms, it is common in practice to include intrinsic exploration terms in the learning objective. Although the effectiveness of these terms is usually justified by an intrinsic need to explore environments, we propose a novel analysis with the lens of numerical optimization. Two criteria are

Ivan Y. Tyukin, Tatiana Tyukina, Daniel van Helden, Zedong Zheng

We present a new methodology for handling AI errors by introducing weakly supervised AI error correctors with a priori performance guarantees. These AI correctors are auxiliary maps whose role is to moderate the decisions of some previously constructed underlying classifier by either approving or rejecting its decisions. The rejection of a decision can be us

Simon A. Lee, Sujay Jain, Alex Chen, Kyoka Ono

In this work, we introduce the Multiple Embedding Model for EHR (MEME), an approach that serializes multimodal EHR tabular data into text using pseudo-notes, mimicking clinical text generation. This conversion not only preserves better representations of categorical data and learns contexts but also enables the effective employment of pretrained foundation m

Luca Soldaini, Rodney Kinney, Akshita Bhagia, Dustin Schwenk

Information about pretraining corpora used to train the current best-performing language models is seldom discussed: commercial models rarely detail their data, and even open models are often released without accompanying training data or recipes to reproduce them. As a result, it is challenging to conduct and advance scientific research on language modeling

Lien Cartaya, Stephen Griffeth

We propose a generalization of Haiman's conjecture on the diagonal coinvariant rings of real reflection groups to the context of irreducible quaternionic reflection groups (also known as symplectic reflection groups). For a reflection group $W$ acting on a quaternionic vector space $V$, by regarding $V$ as a complex vector space we consider the scheme-th

Janice Ahn, Rishu Verma, Renze Lou, Di Liu

Mathematical reasoning serves as a cornerstone for assessing the fundamental cognitive capabilities of human intelligence. In recent times, there has been a notable surge in the development of Large Language Models (LLMs) geared towards the automated resolution of mathematical problems. However, the landscape of mathematical problem types is vast and varied,

Hyeok Kim, Yea-Seul Kim, Jessica Hullman

Data sonification-mapping data variables to auditory variables, such as pitch or volume-is used for data accessibility, scientific exploration, and data-driven art (e.g., museum exhibitions) among others. While a substantial amount of research has been made on effective and intuitive sonification design, software support is not commensurate, limiting researc

Ajmery Jaman, Guanbo Wang, Ashkan Ertefaie, Michèle Bally

Effect modification occurs when the impact of the treatment on an outcome varies based on the levels of other covariates known as effect modifiers. Modeling these effect differences is important for etiological goals and for purposes of optimizing treatment. Structural nested mean models (SNMMs) are useful causal models for estimating the potentially heterog

Sherif Abdulkader Tawfik, Tri Minh Nguyen, Salvy P. Russo, Truyen Tran

At the heart of the flourishing field of machine learning potentials are graph neural networks, where deep learning is interwoven with physics-informed machine learning (PIML) architectures. Various PIML models, upon training with density functional theory (DFT) material structure-property datasets, have achieved unprecedented prediction accuracy for a range

Sandeep Puri, Andrew Gillespie, Ian Jones, Cuikun Lin

Emerging studies are geared toward exploring new methods of nuclear rocket propulsion to provide more efficient space transit beyond Earth's orbit. One method is to employ a Fission Fragment Rocket Engine utilizing fissionable layers embedded in a low-density aerogel. A quantitative understanding of particle attenuation is essential for developing a func

Aaron Brunk, Dennis Schumann

In this work we propose and analyse a structure-preserving approximation of the non-isothermal Cahn-Hilliard-Navier-Stokes system using conforming finite elements in space and implicit time discretisation with convex-concave splitting. The system is first reformulated into a variational form which reveal the structure of the equations, which is then used in

Jiaxing Yuan, Tine Curk

We investigate the collapse and expansion dynamics of a linear polyelectrolyte (PE) with hydrodynamic interactions. Using dissipative particle dynamics with a bead-spring PE model, long-range electrostatics and explicit ions we examine how the timescales of collapse $t_\text{col}$ and expansion $t_\text{exp}$ depend on the chain length $N$, and obtain scalin

Miguel A. Cardona

We have revised the softness property introduced by Jörg Brendle and Haim Judah (perfect sets of random reals. Israel J. Math., 83(1-2):153-176, 1993), to present a new definition of a class of posets called $σ$-soft-linked. Our work demonstrates that these posets work well to preserve the evasion number as well as the bounding number small in generic extens

J. E. Herrera, Y. Bonder

Unimodular gravity is a compelling modified theory of gravity that offers a natural solution to the cosmological constant problem. However, for unimodular gravity to be considered a viable theory of gravity, one has to show that it has a well-posed initial value formulation. Working in vacuum, we apply Dirac's algorithm to find all the constraints of the

Eduardo Guendelman

We expect the final theory of gravity to have more symmetries than we suspect and our research points in this direction. To start with, standard general coordinate invariance can be extended to complex holomorphic general coordinate transformations. This is possible by introducing a non Riemannian Measure of integration (NRMI) and where we avoid the non holo

Akbar Rafiey

Submodular functions, as well as the sub-class of decomposable submodular functions, and their optimization appear in a wide range of applications in machine learning, recommendation systems, and welfare maximization. However, optimization of decomposable submodular functions with millions of component functions is computationally prohibitive. Furthermore, t

K. Azizi, Y. Sarac, H. Sundu

The present study provides spectroscopic investigations of spin-$\frac{3}{2}$ $Λ$ baryons with both positive and negative parities. The analysis mainly focuses on three states, namely $1P$, $2P$, and $2S$, and corresponding masses are calculated using the QCD sum rule method. To implement the method, we apply two types of interpolating currents with octet an

Nathaniel A. Collins, Joshua A. Grochow, Michael Levet, Armin Weiß

We investigate the constant-depth circuit complexity of the Isomorphism Problem, Minimum Generating Set Problem (MGS), and Sub(quasi)group Membership Problem (Membership) for groups and quasigroups (=Latin squares), given as input in terms of their multiplication (Cayley) tables. Despite decades of research on these problems, lower bounds for these problems

Daniele Cattaneo, Abhinav Valada

LiDARs are widely used for mapping and localization in dynamic environments. However, their high cost limits their widespread adoption. On the other hand, monocular localization in LiDAR maps using inexpensive cameras is a cost-effective alternative for large-scale deployment. Nevertheless, most existing approaches struggle to generalize to new sensor setups

Abdul Hannan Khan, Syed Tahseen Raza Rizvi, Andreas Dengel

With recent advances in computer vision, it appears that autonomous driving will be part of modern society sooner rather than later. However, there are still a significant number of concerns to address. Although modern computer vision techniques demonstrate superior performance, they tend to prioritize accuracy over efficiency, which is a crucial aspect of r

Wen-Tao Xu, Frank Pollmann, Michael Knap

A nonlocal string order parameter detecting topological order and deconfinement has been proposed by Fredenhagen and Marcu (FM). However, due to the lack of exact internal symmetries for lattice models and the nonlinear dependence of the FM string order parameter on ground states, it is a priori not guaranteed that it is a genuine order parameter for topolog

Yue Zhang, Ben Colman, Xiao Guo, Ali Shahriyari

State-of-the-art deepfake detection approaches rely on image-based features extracted via neural networks. While these approaches trained in a supervised manner extract likely fake features, they may fall short in representing unnatural `non-physical' semantic facial attributes -- blurry hairlines, double eyebrows, rigid eye pupils, or unnatural skin sha

Tim Adamo, Riccardo Gonzo, Anton Ilderton

With the aim of computing bound waveforms from scattering amplitudes, we explore gravitational two-body dynamics using the Schwinger-Dyson equations and Bethe-Salpeter recursion. We show that the tree-level scattering waveform admits a natural analytic continuation, in rapidity, to the bound waveform, which we confirm from an independent calculation, in the

Sagi Shaier, Kevin Bennett, Lawrence E Hunter, Katharina von der Wense

The differences between cloze-task language model (LM) probing with 1) expert-made templates and 2) naturally-occurring text have often been overlooked. Here, we evaluate 16 different LMs on 10 probing English datasets -- 4 template-based and 6 template-free -- in general and biomedical domains to answer the following research questions: (RQ1) Do model ranki

Daniel Aloni, Eduardo García-Valdecasas, Matthew Reece, Motoo Suzuki

Spontaneous breaking of symmetries leads to universal phenomena. We extend this notion to $(-1)$-form U(1) symmetries. The spontaneous breaking is diagnosed by a dependence of the vacuum energy on a constant background field $θ$, which can be probed by the topological susceptibility. This leads to a reinterpretation of the Strong CP problem as arising from a

Pablo Martínez-Miravé, Irene Tamborra, Mariam Tórtola

The large distances travelled by neutrinos emitted from the Sun and core-collapse supernovae together with the characteristic energy of such neutrinos provide ideal conditions to probe their lifetime, when the decay products evade detection. We investigate the prospects of probing invisible neutrino decay capitalising on the detection of solar and supernova

Salvador Centelles Chuliá, O. G. Miranda, Jose W. F. Valle

Precision measurements of neutrino-electron scattering may provide a viable way to test the non-minimal form of the charged and neutral current weak interactions within a hypothetical near-detector setup for the Deep Underground Neutrino Experiment (DUNE). Although low-statistics, these processes are clean and provide information complementing the results de

Alfred Li, Herschel A. Rabitz, Benjamin Lienhard

Efficacious quantum information processing relies on extended coherence and precise control. Investigating the limitations surrounding quantum processors is vital for their advancement. In their operation, one challenge is inadvertent wave function collapse. Decoherence-free subspaces, theoretically capable of mitigating specific non unitary dynamics, presen

Miguel Crispim Romão, Djuna Croon

This paper presents a machine learning-based method for the detection of the unique gravitational microlensing signatures of extended dark objects, such as boson stars, axion miniclusters and subhalos. We adapt MicroLIA, a machine learning-based package tailored to handle the challenges posed by low-cadence data in microlensing surveys. Using realistic obser

Di Wu, Shuang-Yong Gu, Xiao-Dan Zhu, Qing-Quan Jiang

In this paper, we investigate, in the framework of the topological approach to black hole thermodynamics, using the generalized off-shell Helmholtz free energy, the topological numbers of the static multi-charge AdS black holes in four- and five-dimensional gauged supergravities. We find that the topological number of the static-charged AdS black holes in fo

Kartik Prabhu, Gautam Satishchandran

Any non-trivial scattering with any massless fields in four spacetime dimensions will generically produce an "out" state with memory which gives rise to infrared divergences in the standard $S$-matrix. To obtain an infrared-finite scattering theory, one must suitably include states with memory. However, except in the case of QED with massive charged

Joshua Eby, Volodymyr Takhistov

Relativistic axions can be readily produced in a broad variety of transient sources, such as axion star bosenova explosions, supernovae or even evaporating primordial black holes. We develop a general framework describing the resulting persistent diffuse axion background (D$a$B) due to accumulated axions from historic transient events. We derive strong const

Thomas Steingasser, David I. Kaiser

We revisit the path integral description of quantum tunneling and lay the groundwork for its generalization to excites states through real-time path integral techniques. For clarity, we focus on the simple toy model of a point particle in a double-well potential, for which we perform all steps explicitly. Instead of performing the familiar Wick rotation from

Mykhaylo Usatyuk, Zi-Yue Wang, Ying Zhao

We study closed universes in simple models of two dimensional gravity, such as Jackiw-Teiteilboim (JT) gravity coupled to matter, and a toy topological model that captures the key features of the former. We find there is a stark contrast, as well as some connections, between the perturbative and non-perturbative aspects of the theory. We find rich semi-class

Marcus DuPont, Andrei Gruzinov, Andrew MacFadyen

We show that strong bow shocks are turbulent and non-universal near the head, but asymptote to a universal steady, self-similar, and analytically solvable flow in the downstream. The turbulence is essentially 3D, and has been confirmed by a 3D simulation. The asymptotic behavior is confirmed with high resolution 2D and 3D simulations of a cold uniform wind e

Coleman Hooper, Sehoon Kim, Hiva Mohammadzadeh, Michael W. Mahoney

LLMs are seeing growing use for applications which require large context windows, and with these large context windows KV cache activations surface as the dominant contributor to memory consumption during inference. Quantization is a promising approach for compressing KV cache activations; however, existing solutions fail to represent activations accurately

Alexandre S. Göttel, Aldo Ejlli, Kanioar Karan, Sander M. Vermeulen

We report on a direct search for scalar field dark matter using data from LIGO's third observing run. We analyse the coupling of size oscillations of the interferometer's beamsplitter and arm test masses that may be caused by scalar field dark matter. Using new efficient search methods to maximise sensitivity for signatures of such oscillations, we s

Jiezhi Yang, Khushi Desai, Charles Packer, Harshil Bhatia

We propose CARFF, a method for predicting future 3D scenes given past observations. Our method maps 2D ego-centric images to a distribution over plausible 3D latent scene configurations and predicts the evolution of hypothesized scenes through time. Our latents condition a global Neural Radiance Field (NeRF) to represent a 3D scene model, enabling explainabl

Laurin E. Fischer, Timothée Dao, Ivano Tavernelli, Francesco Tacchino

Randomized measurement protocols such as classical shadows represent powerful resources for quantum technologies, with applications ranging from quantum state characterization and process tomography to machine learning and error mitigation. Recently, the notion of measurement dual frames, in which classical shadows are generalized to dual operators of POVM e

Andreas Opedal, Alessandro Stolfo, Haruki Shirakami, Ying Jiao

There is increasing interest in employing large language models (LLMs) as cognitive models. For such purposes, it is central to understand which properties of human cognition are well-modeled by LLMs, and which are not. In this work, we study the biases of LLMs in relation to those known in children when solving arithmetic word problems. Surveying the learni

Xiang-Qian Li, Hao-Peng Yan, Xiao-Jun Yue, Shi-Wei Zhou

In this study, we focus on a black hole immersed in a cosmological Chaplygin-like dark fluid (CDF), characterized by the equation of state $p=-B/ρ$ and an additional parameter $q$ influencing the energy density of the fluid. We investigate the geodesic structure, shadow, and optical appearance of such a black hole. Through analysis on the effective potential

Colin Benjamin, Rajdeep Tah

Analytically, finding the origins of cooperative behavior in infinite-player games is an exciting topic of current interest. In this paper, we compare three analytical methods, i.e., Nash equilibrium mapping (NEM), Darwinian selection (DS) and Aggregate selection (AS), with a numerical Agent based method (ABM) via the game susceptibility, correlation, and pa

Zhiyuan Yin, Liya Bi, Yueqing Shi, Shaowei Li

We present the design of a helium liquefaction system tailored to efficiently recover helium vapor from either an individual or a small cluster of vibration-sensitive cryogenic instruments. This design prioritizes a compact footprint, mitigating potential contamination sources such as gas bags and oil-lubricated compressors while maximizing the recovery rate

Alessio Zaccone

The dielectric properties of thin films are of paramount important in a variety of technological applications, and of fundamental importance for solid state research. In spite of this, there is currently no theoretical understanding of the dependence of the dielectric permittivity on the thickness of thin films. We develop a confinement model within the Lore

Qianying Ren, Dongsheng Luo, Dongjin Song

Recently, various contrastive learning techniques have been developed to categorize time series data and exhibit promising performance. A general paradigm is to utilize appropriate augmentations and construct feasible positive samples such that the encoder can yield robust and discriminative representations by mapping similar data points closer together in t

Carmelo Mordini, Alfredo Ricci Vasquez, Yuto Motohashi, Mose Müller

Multiplexed operations and extended coherent control over multiple trapping sites are fundamental requirements for a trapped-ion processor in a large scale architecture. Here we demonstrate these building blocks using a surface-electrode trap with integrated photonic components which are scalable to larger numbers of zones. We implement a Ramsey sequence usi

Gabriel Ryan, Siddhartha Jain, Mingyue Shang, Shiqi Wang

Testing plays a pivotal role in ensuring software quality, yet conventional Search Based Software Testing (SBST) methods often struggle with complex software units, achieving suboptimal test coverage. Recent works using large language models (LLMs) for test generation have focused on improving generation quality through optimizing the test generation context

Ashwani Pandey, Mary Loli Martínez-Aldama, Bożena Czerny, Swayamtrupta Panda

We present a set of new theoretical Fe II templates for bright quasars covering a wavelength range of 1000-10000 Å\, based on the recent atomic database available in the C23.00 version of the photoionization code CLOUDY. We compute a grid of models for a range of incident photon flux, gas density, and multiple microturbulence velocities. We examine the equiv

Vitaly L. Galinsky, Lawrence R. Frank

We demonstrate that our recently developed theory of electric field wave propagation in anisotropic and inhomogeneous brain tissues, which has been shown to explain a broad range of observed coherent synchronous brain electrical processes, also explains the spiking behavior of single neurons, thus bridging the gap between the fundamental element of brain ele

Shaoyuan Ou, Kaiwen Xue, Lian Zhou, Chun-ho Lee

The escalating data volume and complexity resulting from the rapid expansion of artificial intelligence (AI), internet of things (IoT) and 5G/6G mobile networks is creating an urgent need for energy-efficient, scalable computing hardware. Here we demonstrate a hypermultiplexed integratedphotonics-based tensor optical processor (HITOP) that can perform trilli

Quanling Deng, Nan Chen, Samuel N. Stechmann, Jiuhua Hu

Lagrangian trajectories are widely used as observations for recovering the underlying flow field via Lagrangian data assimilation (DA). However, the strong nonlinearity in the observational process and the high dimensionality of the problems often cause challenges in applying standard Lagrangian DA. In this paper, a Lagrangian-Eulerian multiscale DA (LEMDA)

Berta Franzluebbers, Donald Dunagan, Miloš Stanojević, Jan Buys

Humans understand sentences word-by-word, in the order that they hear them. This incrementality entails resolving temporary ambiguities about syntactic relationships. We investigate how humans process these syntactic ambiguities by correlating predictions from incremental generative dependency parsers with timecourse data from people undergoing functional ne

A. Veillon, C. Piquard, P. Glidic, Y. Sato

Unconventional quasiparticles emerging in the fractional quantum Hall regime present the challenge of observing their exotic properties unambiguously. Although the fractional charge of quasiparticles has been demonstrated since nearly three decades, the first convincing evidence of their anyonic quantum statistics has only recently been obtained and, so far,

Andrea Guiggiani

The dynamics of bubbles nucleated during a first-order phase transition is controlled by the non-equilibrium fluctuations generated by the traveling domain wall. An accurate modelling of the out-of-equilibrium properties of the plasma is necessary for the characterization of the phase transition relics, such as the signal of gravitational waves. In this thes

Navin Kamuni, Hardik Shah, Sathishkumar Chintala, Naveen Kunchakuri

End-to-end multi-task dialogue systems are usually designed with separate modules for the dialogue pipeline. Among these, the policy module is essential for deciding what to do in response to user input. This policy is trained by reinforcement learning algorithms by taking advantage of an environment in which an agent receives feedback in the form of a rewar

Fang Qin, Rui Chen, Ching Hua Lee

It is well known that a nontrivial Chern number results in quantized Hall conductance. What is less known is that, generically, the Hall response can be dramatically different from its quantized value in materials with broken inversion symmetry. This stems from the leading Hall contribution beyond the linear order, known as the Berry curvature dipole (BCD).

Mitodru Niyogi, Eric Gaussier, Arnab Bhattacharya

Multilingual large language models (LLMs) are expensive to pretrain and often suffer from imbalances across languages and datasets, English-centric bias, tokenizer oversegmentation for morphologically rich low-resource languages, and the curse of multilinguality. We introduce PARAMANU, the first family of Indian-only autoregressive language models trained fr

Víctor Hernández-Santamaría, Luis Fernando López Ríos, Alberto Saldaña

We study the optimal boundary regularity of solutions to Dirichlet problems involving the logarithmic Laplacian. Our proofs are based on the construction of suitable barriers via the Kelvin transform and direct computations. As applications of our results, we show a Hopf-type Lemma for nonnegative weak solutions and the uniqueness of solutions to some nonlin

Shiwei Liu, Guanchen Tao, Yifei Zou, Derek Chow

The self-attention mechanism distinguishes transformer-based large language models (LLMs) apart from convolutional and recurrent neural networks. Despite the performance improvement, achieving real-time LLM inference on silicon remains challenging due to the extensive use of Softmax in self-attention. In addition to the non-linearity, the low arithmetic inte

Navneet Agrawal, Renato L. G. Cavalcante, Sławomir Stańczak

We consider problems where agents in a network seek a common quantity, measured independently and periodically by each agent through a local time-varying process. Numerous solvers addressing such problems have been developed in the past, featuring various adaptations of the local processing and the consensus step. However, existing solvers still lack support

Mowafak Allaham, Nicholas Diakopoulos

Gaining insight into the potential negative impacts of emerging Artificial Intelligence (AI) technologies in society is a challenge for implementing anticipatory governance approaches. One approach to produce such insight is to use Large Language Models (LLMs) to support and guide experts in the process of ideating and exploring the range of undesirable cons

Vladimir S. Gerdjikov, Georgi G. Grahovski

The paper deals with affine 2-dimensional Toda field theories related to simple Lie algebras of the classical series ${\bf D}_r$. We demonstrate that the complexification procedure followed by a restriction to a specified real Hamiltonian form commutes with the external automorphisms of $\mathfrak{g}$. This is illustrated on the examples ${\bf D}_{r+1}^{(1)}

D. Dell'Aquila, I. Lombardo, L. Redigolo, M. Vigilante

A detailed knowledge of the decay properties of the so called Hoyle state in the $^{12}$C nucleus ($E_x=7.654$ MeV, $0^+$) is required to calculate the rate at which carbon is forged in typical red-giant stars. This paper reports on a new almost background-free measurement of the radiative decay branching ratio of the Hoyle state using advanced charged parti

Oussama Bensaid, Anthony Genevois, Romain Tessera

In this article, we introduce and study a natural notion of coarse separation for metric spaces, with an emphasis on coarse separation by subspaces of polynomial or subexponential growth. For instance, we show that symmetric spaces of non-compact type different from $\mathbb{H}_\mathbb{R}^2$ and thick Euclidean buildings of rank $\geq 2$ cannot be coarsely s

Aadyaa Maddi, Swadhin Routray, Alexander Goldberg, Giulia Fanti

Differential privacy (DP) is increasingly used to protect the release of hierarchical, tabular population data, such as census data. A common approach for implementing DP in this setting is to release noisy responses to a predefined set of queries. For example, this is the approach of the TopDown algorithm used by the US Census Bureau. Such methods have an i