October 2023 arXiv papers — page 177

Ameneh Maghsoodi, Kaushik Bhattacharya

Liquid crystal elastomers (LCEs) containing light-sensitive molecules exhibit large reversible deformation when subjected to illumination. Here, we investigate the role of optical penetration depth on this photomechanical response. We present a model of the photomechanical behavior of photoactive LCE strips under illumination that goes beyond the common assu

Ipsita Datta, Oleg Lazarev, Chindu Mohanakumar, Angela Wu

In this paper, we give an algorithm for describing the Weinstein presentation of Weinstein subdomains obtained by carving out regular Lagrangians. Our work generalizes previous work in dimension three and requires a novel Legendrian isotopy move (the ``boat move") that changes the local index of Reeb chords in a front projection. As applications, we describe

Adrian Stein, Yebin Wang, Yusuke Sakamoto, Bingnan Wang

This work investigates an application-driven co-design problem where the motion and motors of a six degrees of freedom robotic manipulator are optimized simultaneously, and the application is characterized by a set of tasks. Unlike the state-of-the-art which selects motors from a product catalogue and performs co-design for a single task, this work designs t

Amanuel Anteneh, Olivier Pfister

We present numerical simulations of deep reinforcement learning on a measurement-based quantum processor--a time-multiplexed optical circuit sampled by photon-number-resolving detection--and find it generates squeezed cat states with an average success rate of 98%, far outperforming all other similar proposals.

Luca F. Di Cerbo, Rita Pardini

We discuss an approach towards the Hopf problem for aspherical smooth projective varieties recently proposed by Liu, Maxim, and Wang in [LMW21]. In complex dimension two, we point out that this circle of ideas suggests an intriguing conjecture regarding the geography of aspherical surfaces of general type.

Yue Huang, Jiawen Shi, Yuan Li, Chenrui Fan

Large language models (LLMs) have garnered significant attention due to their impressive natural language processing (NLP) capabilities. Recently, many studies have focused on the tool utilization ability of LLMs. They primarily investigated how LLMs effectively collaborate with given specific tools. However, in scenarios where LLMs serve as intelligent agen

Graeme E. Addison, Charles L. Bennett, Mark Halpern, Gary Hinshaw

We revisit the lensing anomaly in the Planck 2018 temperature (TT) data and examine its robustness to frequency selection and additional sky masking. Our main findings are: (1) The phenomenological lensing amplitude parameter, $A_L$, varies with ecliptic latitude, with a $2.9\sigma$ preference for $A_L>1$ near the ecliptic, and $1.0\sigma$ preference near th

Jonathan Taylor

We define a class of morphisms between \'etale groupoids and show that there is a functor from the category with these morphisms to the category of $C^*$-algebras. We show that all homomorphisms between Cartan pairs of $C^*$-algebras that preserve the Cartan structure arise from such morphisms between the underlying Weyl groupoids and twists, and attain an e

Yihan Wu, Brandon Y. Feng, Heng Huang

In this paper, we introduce an innovative method of safeguarding user privacy against the generative capabilities of Neural Radiance Fields (NeRF) models. Our novel poisoning attack method induces changes to observed views that are imperceptible to the human eye, yet potent enough to disrupt NeRF's ability to accurately reconstruct a 3D scene. To achieve thi

Georgy Gaitsgori, Richard Groenewald

We analyze a two-player, nonzero-sum Dynkin game of stopping with incomplete information. We assume that each player observes his own Brownian motion, which is not only independent of the other player's Brownian motion but also not observable by the other player. The player who stops first receives a payoff that depends on the stopping position. Under approp

Zihao Lin, Yan Sun, Yifan Shi, Xueqian Wang

With the blowout development of pre-trained models (PTMs), the efficient tuning of these models for diverse downstream applications has emerged as a pivotal research concern. Although recent investigations into prompt tuning have provided promising avenues, three salient challenges persist: (1) memory constraint: the continuous growth in the size of open-sou

Peter Eastman, Raimondas Galvelis, Raúl P. Peláez, Charlles R. A. Abreu

Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model

Salvador Bará, Raul C. Lima

Wind farm lights are a conspicuous feature in the nocturnal landscape. Their presence is a source of light pollution for residents and the environment, severely disrupting in some places the aesthetic, cultural, and scientific values of the pristine starry skies. In this work we present a simple model for quantifying the visual impact of individual wind turb

Dallas Albritton, W. Jacob Ogden

We consider a complexification of the Euler equations introduced by \v{S}ver\'ak which conserves energy. We prove that these complex Euler equations are nonlinearly ill-posed below analytic regularity and, moreover, we exhibit solutions which lose analyticity in finite time. Our examples are complex shear flows and, hence, one-dimensional. This motivates us

Tharindu Lakshan Yasarathna, Lojenaa Navanesan, Simon Barque, Assanka Sayakkara

IoT (Internet of Things) refers to the network of interconnected physical devices, vehicles, home appliances, and other items embedded with sensors, software, and connectivity, enabling them to collect and exchange data. IoT Forensics is collecting and analyzing digital evidence from IoT devices to investigate cybercrimes, security breaches, and other malici

Yongyi Shi, Wenjun Xia, Ge Wang, Xuanqin Mou

Lowering radiation dose per view and utilizing sparse views per scan are two common CT scan modes, albeit often leading to distorted images characterized by noise and streak artifacts. Blind image quality assessment (BIQA) strives to evaluate perceptual quality in alignment with what radiologists perceive, which plays an important role in advancing low-dose

Avery Bailey, Zhaohuan Zhu

The hydrodynamic exchange of a protoplanet's envelope material with the background protoplanetary disk has been proposed as one mechanism to account for the diversity of observed planet envelopes which range in mass fractions of ~1% for super-Earths to ~90% for giants. Here we present and analyze 3D radiation-hydrodynamics models of protoplanet envelopes to

Avery Bailey, Jim Stone, Jeffrey Fung

In the core accretion model of planet formation, envelope cooling regulates the accretion of material and ultimately sets the timescale to form a giant planet. Given the diversity of planet-forming environments, opacity uncertainties, and the advective transport of energy by 3-dimensional recycling flows, it is unclear whether 1D models can adequately descri

W. Patrick Hooper, Pavel Javornik

We study geodesics on the Necker cube surface, $\mathbf N$, an infinite periodic Euclidean cone surface that is homeomorphic to the plane and is tiled by squares meeting three or six to a vertex. We ask: When does a geodesic on the surface close? When does a geodesic drift away periodically? We show that both questions can be answered only using knowledge ab

Ajay Jasra, Hamza Ruzayqat, Amin Wu

In this article we consider Bayesian parameter inference for a type of partially observed stochastic Volterra equation (SVE). SVEs are found in many areas such as physics and mathematical finance. In the latter field they can be used to represent long memory in unobserved volatility processes. In many cases of practical interest, SVEs must be time-discretize

Ameer Dharamshi, Monica Alexander, Celeste Winant, Magali Barbieri

Understanding patterns in mortality across subpopulations is essential for local health policy decision making. One of the key challenges of subnational mortality rate estimation is the presence of small populations and zero or near zero death counts. When studying differences between subpopulations, this challenge is compounded as the small populations are

Julia Herbinger, Susanne Dandl, Fiona K. Ewald, Sofia Loibl

Surrogate models play a crucial role in retrospectively interpreting complex and powerful black box machine learning models via model distillation. This paper focuses on using model-based trees as surrogate models which partition the feature space into interpretable regions via decision rules. Within each region, interpretable models based on additive main e

Rabia Gondur, Usama Bin Sikandar, Evan Schaffer, Mikio Christian Aoi

Characterizing the relationship between neural population activity and behavioral data is a central goal of neuroscience. While latent variable models (LVMs) are successful in describing high-dimensional time-series data, they are typically only designed for a single type of data, making it difficult to identify structure shared across different experimental

Darsha Udayanga, Ashan Serasinghe, Supun Dassanayake, Roshan Godaliyadda

Multispectral imaging coupled with Artificial Intelligence, Machine Learning and Signal Processing techniques work as a feasible alternative for laboratory testing, especially in food quality control. Most of the recent related research has been focused on reflectance multispectral imaging but a system with both reflectance, transmittance capabilities would

Joanna Sobczyk, Bijaya Acharya, Sonia Bacca, Gaute Hagen

We present calculations of the $^{40}$Ca transverse response function obtained from coupled-cluster theory used in conjunction with the Lorentz integral transform method. We employ nuclear forces derived at next-to-next-to leading order in chiral effective field theory with and without $\Delta$ degrees of freedom. We first benchmark this approach on the $^4$

Hamid Mohammadi, Ehsan Nazerfard, Tahereh Firoozi

Video violence recognition based on deep learning concerns accurate yet scalable human violence recognition. Currently, most state-of-the-art video violence recognition studies use CNN-based models to represent and categorize videos. However, recent studies suggest that pre-trained transformers are more accurate than CNN-based models on various video analysi

Yuanjian Zheng, Michael A. Klatt, Hartmut Löwen

We show that dry scalar-order active field theories (AFTs) are universally hyperuniform, i.e., density fluctuations are anomalously suppressed in the long-time limit regardless of the integrability or functional form of the active contributions up to third order in gradient terms. These AFTs include Active model B, Active model B+, and effective Cahn-Hilliar

Peyman Nejat, Areej Alsaafin, Ghazal Alabtah, Nneka Comfere

Patching gigapixel whole slide images (WSIs) is an important task in computational pathology. Some methods have been proposed to select a subset of patches as WSI representation for downstream tasks. While most of the computational pathology tasks are designed to classify or detect the presence of pathological lesions in each WSI, the confounding role and re

Yuan Deng, Mohammad Mahdian, Jieming Mao, Vahab Mirrokni

We study the price of anarchy of the generalized second-price auction where bidders are value maximizers (i.e., autobidders). We show that in general the price of anarchy can be as bad as $0$. For comparison, the price of anarchy of running VCG is $1/2$ in the autobidding world. We further show a fined-grained price of anarchy with respect to the discount fa

Weiwei Kong, Andrés Muñoz Medina, Mónica Ribero

Unsupervised pre-training is a common step in developing computer vision models and large language models. In this setting, the absence of labels requires the use of similarity-based loss functions, such as contrastive loss, that favor minimizing the distance between similar inputs and maximizing the distance between distinct inputs. As privacy concerns moun

Guoyizhe Wei, Feng Wang, Anshul Shah, Rama Chellappa

Prompt learning has recently become a very efficient transfer learning paradigm for Contrastive Language Image Pretraining (CLIP) models. Compared with fine-tuning the entire encoder, prompt learning can obtain highly competitive results by optimizing only a small number of parameters, which presents considerably exciting benefits for federated learning appl

T. Vakhtel, P. D. Kurilovich, M. Pita-Vidal, A. Bargerbos

Fluxons in a superconducting loop can be coherently coupled by quantum phase slips occurring at a weak link such as a Josephson junction. If Cooper pair tunneling at the junction occurs through a resonant level, $2\pi$ quantum phase slips are suppressed, and fluxons are predominantly coupled by $4\pi$ quantum phase slips. We analyze this scenario by computin

Judith Thu Ølberg, Patrik Bohlinger, Øyvind Breivik, Kai H. Christensen

This study reviews the design and signal processing of ship borne ultrasonic altimeter wave measurements. The system combines a downward facing ultrasonic altimeter to capture the sea surface elevation as a time series, and an inertial measurement unit to compensate for the ship's motion. The methodology is cost-effective, open source, and adaptable to vario

Rodrigo Andrade e Silva

We develop the non-perturbative reduced phase space quantization of causal diamonds in (2+1)-dimensional gravity with a nonpositive cosmological constant. In Part I we described the classical reduction process and the reduced phase space, $\widetilde{\mathcal P} = T^*(\text{Diff}^+\!(S^1)/\text{PSL}(2, \mathbb R))$, while in Part II we discuss the quantizati

Jonathan Barmak, Marian Mrozek, Thomas Wanner

We develop Conley's theory for multivalued maps on finite topological spaces. More precisely, for discrete-time dynamical systems generated by the iteration of a multivalued map which satisfies appropriate regularity conditions, we establish the notions of isolated invariant sets and index pairs, and use them to introduce a well-defined Conley index. In addi

Zichen Zhu, Xiao Hu, Manos Athanassoulis

Storage-based joins are still commonly used today because the memory budget does not always scale with the data size. One of the many join algorithms developed that has been widely deployed and proven to be efficient is the Hybrid Hash Join (HHJ), which is designed to exploit any available memory to maximize the data that is joined directly in memory. Howeve

Thomas J. Haworth, Gavin A. L. Coleman, Lin Qiao, Andrew D. Sellek

We present a new FRIED grid of mass loss rates for externally far-ultraviolet (FUV) irradiated protoplanetary discs. As a precursor to the new grid, we also explore the microphysics of external photoevaporation, determining the impact of polycyclic aromatic hydrocarbon (PAH) abundance, metallicity, coolant depletion (via freeze out and radial drift) and grai

Ramón Alain Miranda-Quintana, Taewon D. Kim, Rugwed A. Lokhande, M. Richer

We propose a new Perturbation Theory framework that can be used to help with the projective solution of the Schr\"odinger equation for arbitrary wavefunctions. This Flexible Ansatz for N-body Perturbation Theory (FANPT) is based on our previously proposed Flexible Ansatz for N-body Configuration Interaction (FANCI). We derive recursive FANPT expressions incl

Hossein B. Jond

In this paper, I study optimizing the opinion formation of a social network of a population of individuals on a graph whose opinion evolves according to the Hegselmann-Krause model for opinion dynamics. I propose an optimization problem based on a differential game for a population of individuals who are not stubborn. The objective of each individual is to s

Murong Yue, Jie Zhao, Min Zhang, Liang Du

Large language models (LLMs) such as GPT-4 have exhibited remarkable performance in a variety of tasks, but this strong performance often comes with the high expense of using paid API services. In this paper, we are motivated to study building an LLM cascade to save the cost of using LLMs, particularly for performing reasoning (e.g., mathematical, causal) ta

H. M. Fausey, A. J. van der Horst, N. E. White, M. Seiffert

Future detection of high-redshift gamma-ray bursts (GRBs) will be an important tool for studying the early Universe. Fast and accurate redshift estimation for detected GRBs is key for encouraging rapid follow-up observations by ground- and space-based telescopes. Low-redshift dusty interlopers pose the biggest challenge for GRB redshift estimation using broa

Ido Ben-Dayan, Utkarsh Kumar

We revisit the theoretical priors used for inferring Dark Energy (DE) parameters. Any DE model must have some form of a tracker mechanism such that it behaved as matter or radiation in the past. Otherwise, the model is fine-tuned. We construct a model-independent parametrization that takes this prior into account and allows for a relatively sudden transition

Daile Osorio-Roig, Lazaro J. Gonzalez-Soler, Christian Rathgeb, Christoph Busch

The development of large-scale identification systems that ensure the privacy protection of enrolled subjects represents a major challenge. Biometric deployments that provide interoperability and usability by including efficient multi-biometric solutions are a recent requirement. In the context of privacy protection, several template protection schemes have

Keitarou Matsumoto, Satoshi Masuda, Takafumi Kaneko

Many particles are accelerated during solar flares. To understand the acceleration and propagation processes of electrons, we require the pitch-angle distributions of the particles. The pitch angle of accelerated electrons has been estimated from the propagation velocity of a nonthermal microwave source archived in Nobeyama Radioheliograph data. We analyzed

Timo Heister, Maxim A. Olshanskii, Vladimir Yushutin

The paper introduces an adaptive version of the stabilized Trace Finite Element Method (TraceFEM) designed to solve low-regularity elliptic problems on level-set surfaces using a shape-regular bulk mesh in the embedding space. Two stabilization variants, gradient-jump face and normal-gradient volume, are considered for continuous trace spaces of the first an

Solon Falas, Markos Asprou, Charalambos Konstantinou, Maria K. Michael

State estimation is the cornerstone of the power system control center since it provides the operating condition of the system in consecutive time intervals. This work investigates the application of physics-informed neural networks (PINNs) for accelerating power systems state estimation in monitoring the operation of power systems. Traditional state estimat

D. Gazda, A. Pérez-Obiol, A. Gal, E. Friedman

Over the last decade, conflicting values of the hypertriton ${}_{\Lambda}^3\mathrm{H}$ lifetime $\tau({}_{\Lambda}^3\mathrm{H})$ were extracted from relativistic heavy-ion (RHI) collision experiments, ranging from values compatible with the free-$\Lambda$ lifetime $\tau_\Lambda$-as expected naively for a very weakly bound $\Lambda$ in ${}_{\Lambda}^3\mathrm{

Hugo Lévy, Joël Bergé, Jean-Philippe Uzan

Scalar-tensor theories with screening mechanisms come with non-linearities that make it difficult to study setups of complex geometry without resorting to numerical simulations. In this article, we use the $\textit{femtoscope}$ code that we introduced in a previous work in order to compute the fifth force arising in the chameleon model in the Earth orbit. We

Deniz Bayazit, Negar Foroutan, Zeming Chen, Gail Weiss

Pretrained language models (LMs) encode implicit representations of knowledge in their parameters. However, localizing these representations and disentangling them from each other remains an open problem. In this work, we investigate whether pretrained language models contain various knowledge-critical subnetworks: particular sparse computational subgraphs t

Shalini Kurinchi-Vendhan, Marion Farcy, Michaela Hirschmann, Francesco Valentino

Using the cosmological simulations IllustrisTNG, we perform a comprehensive analysis of quiescent, massive galaxies at $z \gtrsim 3$. The goal is to understand what suppresses their star formation so early in cosmic time, and how other similar mass galaxies remain highly star-forming. As a first-order result, the simulations are able to produce massive, quie

Kristian Mæland, Asle Sudbø

Hybrid systems of superconductors and magnets display several intriguing properties, both from a fundamental physics point of view and with practical applications. Promising applications in superconducting spintronics motivate a search for systems where superconductivity can survive larger in-plane critical magnetic fields than the conventional limit. The Ch

Jordan C. J. D'Silva, Simon P. Driver, Claudia D. P. Lagos, Aaron S. G. Robotham

We consider the effect of including an active galactic nuclei (AGN) component when fitting spectral energy distributions of 109 spectroscopically confirmed $z\approx 3.5-12.5$ galaxies with JWST. Remarkably, we find that the resulting cosmic star formation history is $\approx 0.4$ dex lower at $z\gtrsim 9.5$ when an AGN component is included in the fitting.

Daniel A. Rehn, Towfiq Ahmed, Jinkyoung Yoo, Rohit Prasankumar

High harmonic generation (HHG) is a powerful probe of electron dynamics on attosecond to femtosecond timescales and has been successfully used to detect electronic and structural changes in solid-state quantum materials, including transition metal dichalcogenides (TMDs). Among TMDs, bulk NbSe2 exhibits charge density wave (CDW) order below 33 K and becomes s

Qing Chen, Shuang-Yong Zhou

We consider a model where the interaction between dark matter and the Standard Model particles are mediated by a ghost-free bi-gravity portal. The bi-gravity model invokes a massive spin-2 particle coupled to the usual massless graviton as well as generic bi-metric matter couplings. The cross-sections for dark matter direct detection are computed and confron

Abhishek Kumar, Kemal Aziz, Ahana Chakraborty, Andreas W. W. Ludwig

Measurement-induced phase transitions (MIPTs) are known to be described by non-unitary conformal field theories (CFTs) whose precise nature remains unknown. Most physical quantities of interest, such as the entanglement features of quantum trajectories, are described by boundary observables in this CFT. We introduce a transfer matrix approach to study the bo

Matthew R. Gomer, Dominique Sluse, Lyne Van de Vyvere, Simon Birrer

Galaxy-scale gravitational lenses are often modeled with two-component mass profiles where one component represents the stellar mass and the second is an NFW profile representing the dark matter. Outside of the spherical case, the NFW profile is costly to implement, and so it is approximated via two different methods; ellipticity can be introduced via the le

Gary T. Horowitz, Henry Leung, Leonel Queimada, Ying Zhao

Matter falling into a Schwarzschild-AdS black hole from the left causes increased focussing of ingoing geodesics from the right, and, as a consequence, they reach the singularity sooner. In a standard Penrose diagram, the singularity "bends down". We show how to detect this feature of the singularity holographically, using a boundary two-point function. We m

Anya Dovgal, Kim A. Venn, Federico Sestito, Christian R. Hayes

Pristine_183.6849+04.8619 (P1836849) is an extremely metal-poor ([Fe/H]$=-3.3\pm0.1$) star on a prograde orbit confined to the Galactic disk. Such stars are rare and may have their origins in protogalactic fragments that formed the early Milky Way, in low mass satellites accreted later, or forming in situ in the Galactic plane. Here we present a chemo-dynami

Stefano Rinaldi, Walter Del Pozzo, Michela Mapelli, Ana Lorenzo-Medina

We investigate the joint primary mass, mass ratio, and redshift observed distribution of astrophysical black holes using the gravitational wave events detected by the LIGO-Virgo-KAGRA collaboration and included in the third gravitational wave transient catalogue. We reconstruct this distribution using Bayesian non-parametric methods, which are data-driven mo

Kim HyeongHan, M. James Jee, Sangjun Cha, Hyejeon Cho

Our concordance cosmological model predicts that galaxy clusters grow at the intersection of filaments structuring the cosmic web stretching tens of Mega parsecs. Although this hypothesis has been supported by the baryonic components, no observational study has detected the dark matter component of the intracluster filaments (ICFs), the terminal segment of t

Sheng Qu, Vishal K. Sharma, Jaco Geuchies, Maksim Grechko

Electron-phonon coupling (EPC) is foundational in condensed matter physics, determining intriguing phenomena and properties in both conventional and quantum materials. In this manuscript, we propose and demonstrate a novel two dimensional (2D) EPC spectroscopy which allows for direct extraction of EPC matrix elements for specific phonon modes and different e

Andrew Zhao, Akimasa Miyake

Estimating expectation values is a key subroutine in quantum algorithms. Near-term implementations face two major challenges: a limited number of samples required to learn a large collection of observables, and the accumulation of errors in devices without quantum error correction. To address these challenges simultaneously, we develop a quantum error-mitiga

Carlos Heredia, Josep Llosa

This article focuses on three main contributions. Firstly, we provide an in-depth overview of the nonlocal Lagrangian formalism. Secondly, we introduce an extended version of the second Noether's theorem tailored for nonlocal Lagrangians. Finally, we apply both the formalism and the extended theorem to the context of non-commutative U(1) gauge theory, includ

Yu-Peng Wang, Chen Fang, Jie Ren

Investigating the behavior of noninteracting fermions subjected to local dephasing, we reveal that quasi-particle dephasing can induce superdiffusive transport. This superdiffusion arises from nodal points within the momentum distribution of local dephasing quasi-particles, leading to asymptotic long-lived modes. By studying the dynamics of the Wigner functi

Peizhi Du, Daniel Egaña-Ugrinovic, Rouven Essig, Mukul Sholapurkar

The reach of sub-GeV dark-matter detectors is at present severely affected by low-energy events from various origins. We present the theoretical methods to compute the single- and few-electron events that arise from secondary radiation emitted by high-energy particles passing through detector materials and perform simulations to quantify them at (Skipper) CC

Michele Perna, Santiago Arribas, Isabella Lamperti, Chiara Circosta

Merger events can trigger gas accretion onto supermassive black holes (SMBHs) located at the centre of galaxies, and form close pairs of AGN. The fraction of AGN in pairs offers critical insights into the dynamics of galaxy interactions, SMBH growth, and their co-evolution with host galaxies. However, the identification of dual AGN is difficult, as it requir

Haojie Xu, Hekun Li, Jun Zhang, Xiaohu Yang

We present a tentative constraint on cosmological parameters $\Omega_m$ and $\sigma_8$ from a joint analysis of galaxy clustering and galaxy-galaxy lensing from DESI Legacy Imaging Surveys Data Release 9 (DR9), covering approximately 10000 square degrees and spanning the redshift range of 0.1 to 0.9. To study the dependence of cosmological parameters on lens

Viola Gelli, Stefania Salvadori, Andrea Ferrara, Andrea Pallottini

JWST is providing the unique opportunity to directly study feedback processes regulating star formation (SF) in early galaxies. The two $z>5$ quiescent systems (JADES-GS-z7-01-QU and MACS0417-z5BBG) detected so far show a recent starburst after which SF is suppressed. To clarify whether such quenching is due to supernova (SN) feedback, we have developed a mi

Daattavya Aggarwal, Yang-Hui He, Elli Heyes, Edward Hirst

We propose a machine learning approach to study topological quantities related to the Sasakian and $G_2$-geometries of contact Calabi-Yau $7$-manifolds. Specifically, we compute datasets for certain Sasakian Hodge numbers and for the Crowley-N\"ordstrom invariant of the natural $G_2$-structure of the $7$-dimensional link of a weighted projective Calabi-Yau $

Guillaume Desprez, Nicholas S. Martis, Yoshihisa Asada, Marcin Sawicki

Early JWST observations that targeted so-called double-break sources (attributed to Lyman and Balmer breaks at $z>7$), reported a previously unknown population of very massive, evolved high-redshift galaxies. This surprising discovery led to a flurry of attempts to explain these objects' unexpected existence including invoking alternatives to the standard $\

Stanimir Letchev, Jonathan Crass, Justin R. Crepp

The nonlinear curvature wavefront sensor (nlCWFS) offers improved sensitivity for adaptive optics (AO) systems compared to existing wavefront sensors, such as the Shack-Hartmann. The nominal nlCWFS design uses a series of imaging planes offset from the pupil along the optical propagation axis as inputs to a numerically-iterative reconstruction algorithm. Res

Shane P. Kelly, Jamir Marino

We present an entanglement transition in an array of qubits, induced by the transfer of quantum information from a system to a quantum computer. This quantum-data collection is an essential protocol in quantum machine learning algorithms that promise exponential advantage over their classical counterparts. In this and an accompanying work [Phys. Rev. A 111,

Hao Sha, Yao Mu, Yuxuan Jiang, Li Chen

Existing learning-based autonomous driving (AD) systems face challenges in comprehending high-level information, generalizing to rare events, and providing interpretability. To address these problems, this work employs Large Language Models (LLMs) as a decision-making component for complex AD scenarios that require human commonsense understanding. We devise

Peng Xu, Wei Ping, Xianchao Wu, Lawrence McAfee

Extending the context window of large language models (LLMs) is getting popular recently, while the solution of augmenting LLMs with retrieval has existed for years. The natural questions are: i) Retrieval-augmentation versus long context window, which one is better for downstream tasks? ii) Can both methods be combined to get the best of both worlds? In thi

Liam Parker, Francois Lanusse, Siavash Golkar, Leopoldo Sarra

We present AstroCLIP, a single, versatile model that can embed both galaxy images and spectra into a shared, physically meaningful latent space. These embeddings can then be used - without any model fine-tuning - for a variety of downstream tasks including (1) accurate in-modality and cross-modality semantic similarity search, (2) photometric redshift estima

Mingxiao Huo, Mingyu Ding, Chenfeng Xu, Thomas Tian

Humans inherently possess generalizable visual representations that empower them to efficiently explore and interact with the environments in manipulation tasks. We advocate that such a representation automatically arises from simultaneously learning about multiple simple perceptual skills that are critical for everyday scenarios (e.g., hand detection, state

Jeonghye Kim, Suyoung Lee, Woojun Kim, Youngchul Sung

The recent success of Transformer in natural language processing has sparked its use in various domains. In offline reinforcement learning (RL), Decision Transformer (DT) is emerging as a promising model based on Transformer. However, we discovered that the attention module of DT is not appropriate to capture the inherent local dependence pattern in trajecto

Sarah Libanore, Jordan Flitter, Ely D. Kovetz, Zhaozhou Li

Different star-formation models at Cosmic Dawn produce detectable signatures in the observables of upcoming 21-cm experiments. In this work, we consider the physical scenario of feedback-free starbursts (FFB), according to which the star-formation efficiency (SFE) is enhanced in sufficiently massive halos at early enough times, thus explaining the indication

Jianglong Ye, Peng Wang, Kejie Li, Yichun Shi

Zero-shot novel view synthesis (NVS) from a single image is an essential problem in 3D object understanding. While recent approaches that leverage pre-trained generative models can synthesize high-quality novel views from in-the-wild inputs, they still struggle to maintain 3D consistency across different views. In this paper, we present Consistent-1-to-3, wh

Luis C. Fernandez, Nathan J. Secrest, Megan C. Johnson, Travis C. Fischer

Using the Very Long Baseline Array, we observed the active galactic nucleus (AGN) in NGC 3079 over a span of six months to test for variability in the two main parsec-scale radio components, $A$ and $B$, which lie on either side of the AGN. We found evidence for positional differences in the positions of $A$ and $B$ over the six months consistent with the ap

Kuan-Po Huang, Chih-Kai Yang, Yu-Kuan Fu, Ewan Dunbar

We introduce a new zero resource code-switched speech benchmark designed to directly assess the code-switching capabilities of self-supervised speech encoders. We showcase a baseline system of language modeling on discrete units to demonstrate how the code-switching abilities of speech encoders can be assessed in a zero-resource manner. Our experiments encom

Yuxuan Sun, Kai Zhang, Yu Su

Multimodal information extraction (MIE) aims to extract structured information from unstructured multimedia content. Due to the diversity of tasks and settings, most current MIE models are task-specific and data-intensive, which limits their generalization to real-world scenarios with diverse task requirements and limited labeled data. To address these issue

Satwik Bhattamishra, Arkil Patel, Phil Blunsom, Varun Kanade

In order to understand the in-context learning phenomenon, recent works have adopted a stylized experimental framework and demonstrated that Transformers can learn gradient-based learning algorithms for various classes of real-valued functions. However, the limitations of Transformers in implementing learning algorithms, and their ability to learn other form

Yifan Jiang, Hao Tang, Jen-Hao Rick Chang, Liangchen Song

The task of novel view synthesis aims to generate unseen perspectives of an object or scene from a limited set of input images. Nevertheless, synthesizing novel views from a single image still remains a significant challenge in the realm of computer vision. Previous approaches tackle this problem by adopting mesh prediction, multi-plain image construction, o

Jorge Manero

Some physicists believe that superselection rules should be implemented to get rid of inconsistencies when a theory is framed in terms of a new mathematical formulation, whilst others think that this new formulation should be modified instead of implementing those rules, at the expense of introducing additional mathematical structure. The outcome, however, i

Siyuan Li, Weiyang Jin, Zedong Wang, Fang Wu

Semi-supervised learning (SSL) has witnessed great progress with various improvements in the self-training framework with pseudo labeling. The main challenge is how to distinguish high-quality pseudo labels against the confirmation bias. However, existing pseudo-label selection strategies are limited to pre-defined schemes or complex hand-crafted policies sp

Valentina Scotti, Antonio Anastasio, Alfonso Boiano, Francesco Cafagna

EUSO-SPB2 (Extreme Universe Space Observatory on a Super Pressure Balloon II) is a precursor mission for a future space observatory for multi-messenger astrophysics, planned to be launched in Spring 2023 with a flight duration target of 100 days. The Fluorescence Telescope (FT) hosted on board is designed to detect Ultra High Energy Cosmic Rays via the UV fl

Alexander M. Dalzell, Sam McArdle, Mario Berta, Przemyslaw Bienias

The anticipated applications of quantum computers span across science and industry, ranging from quantum chemistry and many-body physics to optimization, finance, and machine learning. Proposed quantum solutions in these areas typically combine multiple quantum algorithmic primitives into an overall quantum algorithm, which must then incorporate the methods

Gerard Ben Arous, Reza Gheissari, Jiaoyang Huang, Aukosh Jagannath

We rigorously study the relation between the training dynamics via stochastic gradient descent (SGD) and the spectra of empirical Hessian and gradient matrices. We prove that in two canonical classification tasks for multi-class high-dimensional mixtures and either 1 or 2-layer neural networks, both the SGD trajectory and emergent outlier eigenspaces of the

Himanshu Lohani, Paul Foulquier, Patrick Le Fevre, Francois Bertran

Co$_3$Sn$_2$S$_2$ has been established as a prototype of magnetic Weyl semimetal, exhibiting a ''giant'' anomalous Hall effect in its ferromagnetic phase. An attractive feature of this material is that Weyl points lie close to Fermi level, so that one can expect a high reactivity of the topological properties to hole or electron doping. We present here a dir

Stefano Scali, Simon Horsley, Janet Anders, Federico Cerisola

SpiDy.jl solves the non-Markovian stochastic dynamics of interacting classical spin vectors and harmonic oscillator networks in contact with a dissipative environment. The methods implemented allow the user to include arbitrary memory effects and colored quantum noise spectra. In this way, SpiDy.jl provides key tools for the simulation of classical and quant

Hao Chen, Qi Zhang, Zenan Huang, Haobo Wang

Distributional shift between domains poses great challenges to modern machine learning algorithms. The domain generalization (DG) signifies a popular line targeting this issue, where these methods intend to uncover universal patterns across disparate distributions. Noted, the crucial challenge behind DG is the existence of irrelevant domain features, and mos

Zhizheng Liu, Mattia Segu, Fisher Yu

Continual learning allows a model to learn multiple tasks sequentially while retaining the old knowledge without the training data of the preceding tasks. This paper extends the scope of continual learning research to class-incremental learning for multiple object tracking (MOT), which is desirable to accommodate the continuously evolving needs of autonomous

Daile Osorio-Roig, Paul A. Gerlitz, Christian Rathgeb, Christoph Busch

Generally, privacy-enhancing face recognition systems are designed to offer permanent protection of face embeddings. Recently, so-called soft-biometric privacy-enhancement approaches have been introduced with the aim of canceling soft-biometric attributes. These methods limit the amount of soft-biometric information (gender or skin-colour) that can be inferr

Tanmay Gautam, Reid Pryzant, Ziyi Yang, Chenguang Zhu

Vector Quantization (VQ) is a well-known technique in deep learning for extracting informative discrete latent representations. VQ-embedded models have shown impressive results in a range of applications including image and speech generation. VQ operates as a parametric K-means algorithm that quantizes inputs using a single codebook vector in the forward pas

Siddharth Samsi, Dan Zhao, Joseph McDonald, Baolin Li

Large language models (LLMs) have exploded in popularity due to their new generative capabilities that go far beyond prior state-of-the-art. These technologies are increasingly being leveraged in various domains such as law, finance, and medicine. However, these models carry significant computational challenges, especially the compute and energy costs requir

Samira Briongos, Ghassan Karame, Claudio Soriente, Annika Wilde

Forking attacks against TEEs like Intel SGX can be carried out either by rolling back the application to a previous state, or by cloning the application and by partitioning its inputs across the cloned instances. Current solutions to forking attacks require Trusted Third Parties (TTP) that are hard to find in real-world deployments. In the absence of a TTP,

Felipe de Castro Teixeira Carvalho, Kamaljyoti Nath, Alberto Luiz Serpa, George Em Karniadakis

Electrical submersible pumps (ESPs) are prevalently utilized as artificial lift systems in the oil and gas industry. These pumps frequently encounter multiphase flows comprising a complex mixture of hydrocarbons, water, and sediments. Such mixtures lead to the formation of emulsions, characterized by an effective viscosity distinct from that of the individua

Juan Carlos Gómez-Izquierdo, Asahel Enrique Pozas Ramírez

Cobimaximal mixing predicts $\pi/4$ and $3\pi/2$ for the atmospheric angle and the Dirac CP-violating phase, respectively. These values are in tension with the neutrino globals fits. If this pattern was behind the lepton mixings, then it would have to be broken. In that case, in this paper, we explore the $\mathbf{S}_{3}$ flavor symmetry within the $B-L$ gau

Miguel Gonçalves

We study the scaling of the entanglement entropy in different classes of one-dimensional fermionic quasiperiodic systems with and without pairing, focusing on multifractal critical points/phases. We find that the entanglement entropy scales logarithmically with the subsystem size $N_{A}$ with a proportionality coefficient $\mathcal{C}$, as in homogeneous cri