May 2023 arXiv papers — page 107

Yohan Brunebarbe

Let X be a normal connected complex algebraic variety equipped with a semisimple complex representation of its fundamental group. Then, under a maximality assumption, we prove that the covering space of X associated to the kernel of the representation has a proper surjective holomorphic map with connected fibres onto a normal analytic space with no positive-

Mengbo Zhu, Ehsan Roohi, Amin Ebrahimi

The gas flow characteristics in lid-driven cavities are influenced by several factors, such as cavity geometry, gas properties, and boundary conditions. In this study, the physics of heat and gas flow in cylindrical lid-driven cavities with various cross-sections, including fully or partially rounded edges, is investigated through numerical simulations using

Martin S. Feather, Alessandro Pinto

Robotic space missions have long depended on automation, defined in the 2015 NASA Technology Roadmaps as "the automatically-controlled operation of an apparatus, process, or system using a pre-planned set of instructions (e.g., a command sequence)," to react to events when a rapid response is required. Autonomy, defined there as "the capacity of a system to

Sanyam Jain, Raju gautam, Shivani Sharma, Ravi Tomar

Cybersecurity is very essential for Mobile Transactions to complete seamlessly. Mobile Commerce (Mcom.) is the very basic transaction type, which is very commonly used (2 in 5 people uses mobile as transaction medium), To secure this there are various technologies used by this research. The four factors formally known as Multi-Factor-Authentication are: two

Nidhi Simmons, David E Simmons, Michel Daoud Yacoub

We introduce a novel loss function to minimize the outage probability of an ML-based resource allocation system. A single-user multi-resource greedy allocation strategy constitutes our application scenario, for which an ML binary classification predictor assists in selecting a resource satisfying the established outage criterium. While other resource allocat

Sanyam Jain

Training machine learning models in an incremental fashion is not only important but also an efficient way to achieve artificial general intelligence. The ability that humans possess of continuous or lifelong learning helps them to not forget previously learned tasks. However, current neural network models are prone to catastrophic forgetting when it comes t

Sourav Nandy, Bhaskar Mukherjee, Arpan Bhattacharyya, Aritra Banerjee

Scar eigenstates in a many-body system refers to a small subset of non-thermal finite energy density eigenstates embedded into an otherwise thermal spectrum. This novel non-thermal behaviour has been seen in recent experiments simulating a one-dimensional PXP model with a kinetically-constrained local Hilbert space realized by a chain of Rydberg atoms. We pr

Wenrui Xu

Recent simulations find that hot gas accretion onto compact accretors are often highly turbulent and diskless, and show power-law density profiles with slope $\alpha_\rho\approx-1$. These results are consistent with observational constraints, but do not match existing self-similar solutions of radiatively inefficient accretion flows. We develop a theory for

Sanyam Jain

For a long time, detecting hand gestures and recognizing them as letters or numbers has been a challenging task. This creates communication barriers for individuals with disabilities. This paper introduces a new dataset, the Annotated Dataset for Danish Sign Language (ADDSL). Annota-tions for the dataset were made using the open-source tool LabelImg in the Y

Sudarshan Ananth, Sucheta Majumdar

In General Relativity, the allowed set of diffeomorphisms or gauge transformations at asymptotic infinity forms the BMS group, an infinite-dimensional extension of the Poincar\'e group. We focus on the structure of the BMS group in two distinct forms of Hamiltonian dynamics - the instant and front forms. Both similarities and differences in these two forms a

Jin Chen, Wei Cui, Babak Haghighat, Yi-Nan Wang

In this work we study particular TQFTs in three dimensions, known as Symmetry Topological Field Theories (or SymTFTs), to identify line defects of two-dimensional CFTs arising from the compactification of 6d $(2,0)$ SCFTs on 4-manifolds $M_4$. The mapping class group of $M_4$ and the automorphism group of the SymTFT switch between different absolute 2d theor

M. Angelinelli, S. Ettori, K. Dolag, F. Vazza

We study the redshift evolution of the baryon budget in a large set of galaxy clusters from the {\it Magneticum} suite of SPH cosmological simulations. At high redshifts, we obtain "closed box" systems independently by the mass of the systems on radii greater than $3R_{500,\mathrm c}$, whereas at lower redshifts, only the most massive halos could be consider

Morgan MacLeod, Andrea Antoni, Caroline D. Huang, Andrea Dupree

Betelgeuse, the nearest red supergiant, dimmed to an unprecedented level in early 2020. The star emerged from this Great Dimming episode with its typical, roughly 400-day pulsation cycle halved, and a new dominant period of around 200 days. The dimming event has been attributed to a surface mass ejection, in which rising material drove shocks through the ste

Jane Pan, Tianyu Gao, Howard Chen, Danqi Chen

Large language models (LLMs) exploit in-context learning (ICL) to solve tasks with only a few demonstrations, but its mechanisms are not yet well-understood. Some works suggest that LLMs only recall already learned concepts from pre-training, while others hint that ICL performs implicit learning over demonstrations. We characterize two ways through which ICL

Daiki Suenaga, Makoto Oka

Masses of positive-parity and negative-parity diquarks are investigated at finite temperature with a quark chemical potential. We employ the three-flavor Nambu-Jona-Lasinio model, in order to delineate chiral properties of the diquarks, in particular, the mass degeneracy of chiral partners under extreme conditions. We focus on the effects of $U(1)_A$ axial a

Xinyu Fu, Irwin King

Heterogeneous graph neural networks (HGNNs) can learn from typed and relational graph data more effectively than conventional GNNs. With larger parameter spaces, HGNNs may require more training data, which is often scarce in real-world applications due to privacy regulations (e.g., GDPR). Federated graph learning (FGL) enables multiple clients to train a GNN

Dmitry S. Ageev, Askar A. Iliasov

In this paper, we investigate topological modes of different physical systems defined on arbitrary two-dimensional curved surfaces. We consider the shallow water equations, inhomogeneous Maxwell's equations, Jackiw-Rebbi model and show how the topological protection mechanism responses to the presence of curvature in different situations. We show the existen

Matthew Brooks, Charles Tahan

Joint measurements of two-Pauli observables are a powerful tool for both the control and protection of quantum information. By following a simple recipe for measurement choices, single- and two- qubit rotations using two-Pauli parity and single qubit measurements are guaranteed to be unitary whilst requiring only a single ancilla qubit. This language for mea

George Eskandar, Diandian Guo, Karim Guirguis, Bin Yang

The need for large amounts of training and validation data is a huge concern in scaling AI algorithms for autonomous driving. Semantic Image Synthesis (SIS), or label-to-image translation, promises to address this issue by translating semantic layouts to images, providing a controllable generation of photorealistic data. However, they require a large amount

Abhishek Hegade K R, Justin L. Ripley, Nicolás Yunes

Out-of-equilibrium effects may play an important role in the dynamics of neutron star mergers and in heavy-ion collisions. Bemfica, Disconzi, Noronha and Kovtun (BDNK) recently derived a causal, locally well-posed, and modally stable relativistic fluid model that incorporates the effects of viscosity and heat diffusion. We study the non-relativistic limit of

Christopher Whittall, Leor Barack

We develop a frequency-domain method for calculating the self-force acting on a scalar charge on a fixed scattering geodesic in Schwarzschild spacetime. Existing frequency-domain methods, which are tailored for bound orbits, are inadequate here for several reasons. One must account for the continuous spectrum in the scattering problem, deal with slowly-conve

Arvind K. Dattatrey, R. K. S. Yadav, Gourav Kumawat, Sharmila Rani

We report the discovery of four blue lurkers with low and extremely low-mass white dwarf (ELM WDs) companions in the Galactic globular cluster NGC 362 using AstroSat Ultra Violet Imaging Telescope (UVIT). We analyzed the multi-wavelength spectral energy distribution (SED) of FUV-bright MS stars using data from the UVIT, UVOT, GAIA EDR3, and 2.2m ESO/MPI tele

Nathaniel Craig, Yu-Tse Lee, Xiaochuan Lu, Dave Sutherland

We present a formulation of scalar effective field theories in terms of the geometry of Lagrange spaces. The horizontal geometry of the Lagrange space generalizes the Riemannian geometry on the scalar field manifold, inducing a broad class of affine connections that can be used to covariantly express and simplify tree-level scattering amplitudes. Meanwhile,

Joseph S. W. Lewis, Annalisa Pillepich, Dylan Nelson, Ralf S. Klessen

Observations of the 21cm line of neutral hydrogen are poised to revolutionize our knowledge of cosmic reionization and the high-redshift population of galaxies. However, harnessing such information requires robust and comprehensive theoretical modeling. We study the non-linear effects of hydrodynamics and astrophysical feedback processes, including stellar a

Hamsa Padmanabhan, Roy Maartens, Obinna Umeh, Stefano Camera

The first direct measurements of the HI intensity mapping power spectrum were recently made using the MeerKAT telescope. These measurements are on nonlinear scales, at redshifts 0.32 and 0.44. We develop a formalism for modelling small-scale power in redshift space, within the context of the mass-weighted HI halo model framework. This model is consistent wit

Aqeel Ahmed, Zackaria Chacko, Niral Desai, Sanket Doshi

We study a class of models in which the particle that constitutes dark matter arises as a composite state of a strongly coupled hidden sector. The hidden sector interacts with the Standard Model through the neutrino portal, allowing the relic abundance of dark matter to be set by annihilation into final states containing neutrinos. The coupling to the hidden

Belal Nazzal, Anton Nedelin, Shlomo S. Razamat

We derive ground state eigenfunctions and eigenvalues of various relativistic elliptic integrable models. The models we discuss appear in computations of superconformal indices of four-dimensional theories obtained by compactifying six-dimensional models on Riemann surfaces. These include, among others, the Ruijsenaars-Schneider model and the van Diejen mode

G. Miniutti, M. Giustini, R. Arcodia, R. D. Saxton

X-ray quasi-periodic eruptions (QPEs) are intense repeating soft X-ray bursts from the nuclei of nearby galaxies. Their physical origin is still largely unconstrained, and several theoretical models have been proposed to date. We present here results from a recent XMM-Newton observation of GSN 069, the galactic nucleus where QPEs were first discovered. After

Max Jaarsma, Rudi Rahn, Wouter J. Waalewijn

In double parton scattering (DPS), two partonic collisions take place between one pair of colliding hadrons. The effect of DPS can be significant for precision measurements due to the additional radiation from secondary partonic collisions, and especially for specific processes such as same-sign WW production. Its effect is usually included through Monte Car

Anya Phillips, C. S. Kochanek, Tharindu Jayasinghe, Lyra Cao

We examine the properties of $\sim50,000$ rotational variables from the ASAS-SN survey using distances, stellar properties, and probes of binarity from $\textit{Gaia}$ DR3 and the SDSS APOGEE survey. They have high amplitudes and span a broader period range than previously studied $\textit{Kepler}$ rotators. We find they divide into three groups of main sequ

Joris Witstok, Gareth C. Jones, Roberto Maiolino, Renske Smit

We present an empirical analysis of the properties of dust-continuum emission in a sample of 17 galaxies in the early Universe ($4 < z < 8$) with well-sampled far-infrared (FIR) spectral energy distributions (SEDs) compiled from the literature. We place our results into context by self-consistently comparing to samples of nearby star-forming galaxies, lumino

Yichul Choi, Brandon C. Rayhaun, Yaman Sanghavi, Shu-Heng Shao

What does it mean for a boundary condition to be symmetric with respect to a non-invertible global symmetry? We discuss two possible definitions in 1+1d. On the one hand, we call a boundary weakly symmetric if the symmetry defects can terminate topologically on it, leading to conserved operators for the Hamiltonian on an interval (in the open string channel)

Manuel A. Buen-Abad, Jae Hyeok Chang, Anson Hook

We initiate a study of the gravitational-wave signatures of a phase transition that occurs as the Universe's temperature increases during reheating. The gravitational-wave signatures of such a heating phase transition are different from those of a cooling phase transition, and their detection could allow us to probe reheating. In the lucky case that the grav

SHANHE Collaboration, Zhenxing Tang, Bo Wang, Yifan Chen

Dark photons have emerged as promising candidates for dark matter, and their search is a top priority in particle physics, astrophysics, and cosmology. We report the first use of a tunable niobium superconducting radio-frequency cavity for a scan search of dark photon dark matter with innovative data analysis techniques. We mechanically adjusted the resonant

Jaime Salcido, Ian G. McCarthy, Juliana Kwan, Amol Upadhye

Upcoming large-scale structure surveys will measure the matter power spectrum to approximately percent level accuracy with the aim of searching for evidence for new physics beyond the standard model of cosmology. In order to avoid biasing our conclusions, the theoretical predictions need to be at least as accurate as the measurements for a given choice of co

Vasily Kokorev, Shuowen Jin, Carlos Gómez-Guijarro, Georgios E. Magdis

We present NOEMA CO (2-1) line and ALMA 870 $\mu$m continuum observations of a main-sequence galaxy at $z=1.38$. The galaxy was initially selected as a "gas-giant", based on the gas mass derived from sub-mm continuum (log$(M_{\rm gas}/M_{\odot})=11.20\pm0.20$), however the gas mass derived from CO (2-1) luminosity brings down the gas mass to a value consiste

Chongyu Qu, Tiezheng Zhang, Hualin Qiao, Jie Liu

Annotating medical images, particularly for organ segmentation, is laborious and time-consuming. For example, annotating an abdominal organ requires an estimated rate of 30-60 minutes per CT volume based on the expertise of an annotator and the size, visibility, and complexity of the organ. Therefore, publicly available datasets for multi-organ segmentation

Mirjam Cvetič, Jonathan J. Heckman, Max Hübner, Ethan Torres

The stringy realization of generalized symmetry operators involves wrapping "branes at infinity". We argue that in the case of continuous (as opposed to discrete) symmetries, the appropriate objects are fluxbranes. We use this perspective to revisit the phase structure of Verlinde's monopole, a proposed particle which is BPS when gravity is decoupled, but is

Shengyi Qian, David F. Fouhey

Humans can easily understand a single image as depicting multiple potential objects permitting interaction. We use this skill to plan our interactions with the world and accelerate understanding new objects without engaging in interaction. In this paper, we would like to endow machines with the similar ability, so that intelligent agents can better explore t

Antonio M. García-García, Lucas Sá, Jacobus J. M. Verbaarschot, Can Yin

In the infrared limit, a nearly anti-de Sitter spacetime in two dimensions (AdS$_2$) perturbed by a weak double trace deformation and a two-site $(q>2)$-body Sachdev-Ye-Kitaev (SYK) model with $N$ Majoranas and a weak $2r$-body intersite coupling share the same near-conformal dynamics described by a traversable wormhole. We exploit this relation to propose a

Samaneh Azadi, Akbar Shah, Thomas Hayes, Devi Parikh

Text-guided human motion generation has drawn significant interest because of its impactful applications spanning animation and robotics. Recently, application of diffusion models for motion generation has enabled improvements in the quality of generated motions. However, existing approaches are limited by their reliance on relatively small-scale motion capt

Samuel Talkington, Santiago Grijalva

Phase retrieval is a prevalent problem in digital signal processing and experimental physics that consists of estimating a complex signal from magnitude measurements. This paper expands the classical phase retrieval framework to electric power systems with unknown network models and limited access to observations of voltage magnitudes, active power injection

Raisa Fairooz Meem, Khandaker Tabin Hasan

The field of clinical image analysis has been applying transfer learning models increasingly due to their less computational complexity, better accuracy etc. These are pre-trained models that don't require to be trained from scratch which eliminates the necessity of large datasets. Transfer learning models are mostly used for the analysis of brain, breast, o

Jose Blanchet, Miao Lu, Tong Zhang, Han Zhong

In this paper, we study distributionally robust offline reinforcement learning (robust offline RL), which seeks to find an optimal policy purely from an offline dataset that can perform well in perturbed environments. In specific, we propose a generic algorithm framework called Doubly Pessimistic Model-based Policy Optimization ($P^2MPO$), which features a n

Vamsi K Vytla, Larry Doolittle

Large scale scientific instrumentation-and-control FPGA gateware designs have numerous run-time settable parameters. These can be used either for user-level control or by automated processes (e.g., calibration). The number of such parameters in a single design can reach on the order of 1000, and keeps evolving as the gateware and its functionality evolves. O

Xi Ye, Qiaochu Chen, Isil Dillig, Greg Durrett

Prior work has combined chain-of-thought prompting in large language models (LLMs) with programmatic representations to perform effective and transparent reasoning. While such an approach works well for tasks that only require forward reasoning (e.g., straightforward arithmetic), it is less effective for constraint solving problems that require more sophisti

Sanyam Jain

This research paper presents an experimental approach to using the Reptile algorithm for reinforcement learning to train a neural network to play Super Mario Bros. We implement the Reptile algorithm using the Super Mario Bros Gym library and TensorFlow in Python, creating a neural network model with a single convolutional layer, a flatten layer, and a dense

Maria E. Steinrueck, Tommi Koskinen, Panayotis Lavvas, Vivien Parmentier

Photochemical hazes are expected to form in hot Jupiter atmospheres and may explain the strong scattering slopes and muted spectral features observed in the transmission spectra of many hot Jupiters. Absorption and scattering by photochemical hazes have the potential to drastically alter temperature structure and atmospheric circulation of these planets but

Scott E. Smart, Davis M. Welakuh, Prineha Narang

Calculating ground and excited states is an exciting prospect for near-term quantum computing applications, and accurate and efficient algorithms are needed to assess viable directions. We develop an excited state approach based on the contracted quantum eigensolver (ES-CQE), which iteratively attempts to find a solution to a contraction of the Schr{\"o}ding

Mutian He, Philip N. Garner

End-to-end spoken language understanding (SLU) remains elusive even with current large pretrained language models on text and speech, especially in multilingual cases. Machine translation has been established as a powerful pretraining objective on text as it enables the model to capture high-level semantics of the input utterance and associations between dif

Yuxin Ren, Zihan Zhong, Xingjian Shi, Yi Zhu

It has been commonly observed that a teacher model with superior performance does not necessarily result in a stronger student, highlighting a discrepancy between current teacher training practices and effective knowledge transfer. In order to enhance the guidance of the teacher training process, we introduce the concept of distillation influence to determin

Thorsten Lang, Susanne Schander

This paper represents the second in a series of works aimed at reinvigorating the quantum geometrodynamics program. Our approach introduces a lattice regularization of the hypersurface deformation algebra, such that each lattice site carries a set of canonical variables given by the components of the spatial metric and the corresponding conjugate momenta. In

N. Ampilogov, S. Cometti, J. Agarwala, V. Chmill

A prototype of a dual-readout calorimeter using brass capillary tubes surrounding scintillating and clear plastic optical fibres was tested using beams of particles with energies between 10 and 100 GeV produced by the CERN SPS. The scope of the test was to characterise the performance of the tube-based detector response to positrons in terms of linearity, en

Shengchao Hu, Li Shen, Ya Zhang, Dacheng Tao

Prompt-tuning has emerged as a promising method for adapting pre-trained models to downstream tasks or aligning with human preferences. Prompt learning is widely used in NLP but has limited applicability to RL due to the complex physical meaning and environment-specific information contained within RL prompts. These factors require supervised learning to imi

George Eskandar, Mohamed Abdelsamad, Karim Armanious, Shuai Zhang

Semantic Image Synthesis (SIS) is a subclass of image-to-image translation where a semantic layout is used to generate a photorealistic image. State-of-the-art conditional Generative Adversarial Networks (GANs) need a huge amount of paired data to accomplish this task while generic unpaired image-to-image translation frameworks underperform in comparison, be

Joanna Komorniczak, Pawel Ksieniewicz

The article presents the torchosr package - a Python package compatible with PyTorch library - offering tools and methods dedicated to Open Set Recognition in Deep Neural Networks. The package offers two state-of-the-art methods in the field, a set of functions for handling base sets and generation of derived sets for the Open Set Recognition task (where som

Jinhao Jiang, Kun Zhou, Zican Dong, Keming Ye

In this paper, we study how to improve the zero-shot reasoning ability of large language models~(LLMs) over structured data in a unified way. Inspired by the study on tool augmentation for LLMs, we develop an \emph{Iterative Reading-then-Reasoning~(IRR)} approach for solving question answering tasks based on structured data, called \textbf{StructGPT}. In our

Jack Collins, Mark Robson, Jun Yamada, Mohan Sridharan

We introduce RAMP, an open-source robotics benchmark inspired by real-world industrial assembly tasks. RAMP consists of beams that a robot must assemble into specified goal configurations using pegs as fasteners. As such, it assesses planning and execution capabilities, and poses challenges in perception, reasoning, manipulation, diagnostics, fault recovery,

Aaromal Venugopal, Vanshika Seth, Shreya Subhash Naik, Sreya Valappil

Herein, we report a minireview to give a brief introduction of applications of nanomaterials in the field of forensic science. The materials that have their size in nanoscale (1 - 100 nm) comes under the category of nanomaterials. Nanomaterials possess various applications in different fields like cosmetic production, medical, photoconductivity etc. because

Léo Régnier, Maxim Dolgushev, Olivier Bénichou

How long is needed for an observable to exceed its previous highest value and establish a new record? This time, known as the age of a record plays a crucial role in quantifying record statistics. Until now, general methods for determining record age statistics have been limited to observations of either independent random variables or successive positions o

Alexandros Lattas, Stylianos Moschoglou, Stylianos Ploumpis, Baris Gecer

In this paper, we introduce FitMe, a facial reflectance model and a differentiable rendering optimization pipeline, that can be used to acquire high-fidelity renderable human avatars from single or multiple images. The model consists of a multi-modal style-based generator, that captures facial appearance in terms of diffuse and specular reflectance, and a PC

Alejandra Duque-Torres, Dietmar Pfahl, Claus Klammer, Stefan Fischer

Metamorphic Testing (MT) is a testing technique that can effectively alleviate the oracle problem. MT uses Metamorphic Relations (MRs) to determine if a test case passes or fails. MRs specify how the outputs should vary in response to specific input changes when executing the System Under Test (SUT). If a particular MR is violated for at least one test input

J. Daniel Christensen, Jarl G. Taxerås Flaten

Ext groups are fundamental homological invariants which have important applications in homotopy theory and algebra. In particular, they appear in the classical universal coefficient theorem, a key computational tool in homotopy theory. Motivated by the goal of extending such tools to synethetic homotopy theory, we develop the theory of Yoneda Ext groups [Yon

William J. Huggins, Jarrod R. McClean

Real-world applications of computing can be extremely time-sensitive. It would be valuable if we could accelerate such tasks by performing some of the work ahead of time. Motivated by this, we propose a cost model for quantum algorithms that allows quantum precomputation, i.e., for a polynomial amount of "free" computation before the input to an algorithm is

Sergio Contreras, Jonás Chaves-Montero, Raul E. Angulo

Several analyses have shown that LCDM-based models cannot jointly describe the clustering (GC) and galaxy-galaxy lensing (GGL) of galaxies in the SDSS-III BOSS survey, which is commonly known as the 'lensing-is-low problem'. In this work, we show that an extension of Subhalo Abundance Matching, dubbed SHAMe, successfully solves this problem. First, we show t

Zalán Borsos, Matt Sharifi, Damien Vincent, Eugene Kharitonov

We present SoundStorm, a model for efficient, non-autoregressive audio generation. SoundStorm receives as input the semantic tokens of AudioLM, and relies on bidirectional attention and confidence-based parallel decoding to generate the tokens of a neural audio codec. Compared to the autoregressive generation approach of AudioLM, our model produces audio of

Guus Avis, Robert Knegjens, Anders S. Sørensen, Stephanie Wehner

Restrictions imposed by existing infrastructure can make it hard to ensure an even spacing between the nodes of future fiber-based quantum networks. We here investigate the negative effects of asymmetric node placement by considering separately the placement of midpoint stations required for heralded entanglement generation, as well as of processing-node qua

Damien Busatto-Gaston, Debraj Chakraborty, Anirban Majumdar, Sayan Mukherjee

We consider lexicographic bi-objective problems on Markov Decision Processes (MDPs), where we optimize one objective while guaranteeing optimality of another. We propose a two-stage technique for solving such problems when the objectives are related (in a way that we formalize). We instantiate our technique for two natural pairs of objectives: minimizing the

Franziska Hagelstein, Vadim Lensky, Vladimir Pascalutsa

The ongoing experimental efforts to measure the hyperfine transition in muonic hydrogen prompt an accurate evaluation of the proton-structure effects. At the leading order in $\alpha$, which is $O(\alpha^5)$ in the hyperfine splitting (hfs), these effects are usually evaluated in a data-driven fashion, using the empirical information on the proton electromag

Daniel Halpern-Leistner, Andres Fernandez Herrero

For a reductive group $G$, Harder-Narasimhan theory gives a structure theorem for principal $G$ bundles on a smooth projective curve $C$. A bundle is either semistable, or it admits a canonical parabolic reduction whose associated Levi bundle is semistable. We extend this structure theorem by constructing a $\Theta$-stratification of the moduli stack of gaug

Cédric Deffayet, Aaron Held, Shinji Mukohyama, Alexander Vikman

Negative kinetic energies correspond to ghost degrees of freedom, which are potentially of relevance for cosmology, quantum gravity, and high energy physics. We present a novel wide class of stable mechanical systems where a positive energy degree of freedom interacts with a ghost. These theories have Hamiltonians unbounded from above and from below, are int

Alexandros Karam, Niko Koivunen, Eemeli Tomberg, Antonio Racioppi

We investigate the formation of large peaks in the inflationary curvature power spectrum from double-well potentials. In such scenarios, the initial CMB spectrum is created at large field values. Subsequently, the inflaton will cross one of the minima and will decelerate rapidly as it reaches the local maximum at the origin, either falling back or crossing i

Qingyang Li, Weiguang Cui, Xiaohu Yang, Romeel Dave

The distribution of baryons provides a significant way to understand the formation of galaxy clusters by revealing the details of its internal structure and changes over time. In this paper, we present theoretical studies on the scaled profiles of physical properties associated with the baryonic components, including gas density, temperature, metallicity, pr

Jed Mills, Jia Hu, Geyong Min

In Federated Learning (FL) client devices connected over the internet collaboratively train a machine learning model without sharing their private data with a central server or with other clients. The seminal Federated Averaging (FedAvg) algorithm trains a single global model by performing rounds of local training on clients followed by model averaging. FedA

Sabber Ahamed, Md Mesbah Uddin

In this paper, we present a machine learning-based data generator framework tailored to aid researchers who utilize simulations to examine various physical systems or processes. High computational costs and the resulting limited data often pose significant challenges to gaining insights into these systems or processes. Our approach involves a two-step proces

Firoozeh Shomal Zadeh, Sevda Molani, Maysam Orouskhani, Marziyeh Rezaei

In medical imaging, image synthesis is the estimation process of one image (sequence, modality) from another image (sequence, modality). Since images with different modalities provide diverse biomarkers and capture various features, multi-modality imaging is crucial in medicine. While multi-screening is expensive, costly, and time-consuming to report by radi

Yufan Li, Jialiang Mao, Iavor Bojinov

Phased releases are a common strategy in the technology industry for gradually releasing new products or updates through a sequence of A/B tests in which the number of treated units gradually grows until full deployment or deprecation. Performing phased releases in a principled way requires selecting the proportion of units assigned to the new release in a w

Xuehan Zhang, Lijian Jiang

In this article, we present a data-driven method for parametric models with noisy observation data. Gaussian process regression based reduced order modeling (GPR-based ROM) can realize fast online predictions without using equations in the offline stage. However, GPR-based ROM does not perform well for complex systems since POD projection are naturally linea

Vincent Vanlaer, Conny Aerts, Earl P. Bellinger, Jørgen Christensen-Dalsgaard

Gravity-mode asteroseismology has significantly improved our understanding of mixing in intermediate mass stars. However, theoretical pulsation periods of stellar models remain in tension with observations, and it is often unclear how the models of these stars should be further improved. Inversions provide a path forward by directly probing the internal stru

Hui Cao, Tomáš Čižmár, Sergey Turtaev, Tomáš Tyc

Light transport in a highly multimode fiber exhibits complex behavior in space, time, frequency and polarization, especially in the presence of mode coupling. The newly developed techniques of spatial wavefront shaping turn out to be highly suitable to harness such enormous complexity: a spatial light modulator enables precise characterization of field propa

Luca Battaglia, Sergio Cruz-Blázquez, Angela Pistoia

In this paper we address two boundary cases of the classical Kazdan-Warner problem. More precisely, we consider the problem of prescribing the Gaussian and boundary geodesic curvature on a disk of R^2, and the scalar and mean curvature on a ball in higher dimensions, via a conformal change of the metric. We deal with the case of negative interior curvature a

Dennis M. J. van de Sande, Julian P. Merkofer, Sina Amirrajab, Mitko Veta

This literature review presents a comprehensive overview of machine learning (ML) applications in proton magnetic resonance spectroscopy (MRS). As the use of ML techniques in MRS continues to grow, this review aims to provide the MRS community with a structured overview of the state-of-the-art methods. Specifically, we examine and summarize studies published

Daniel Pfrommer, Max Simchowitz, Tyler Westenbroek, Nikolai Matni

A common pipeline in learning-based control is to iteratively estimate a model of system dynamics, and apply a trajectory optimization algorithm - e.g.~$\mathtt{iLQR}$ - on the learned model to minimize a target cost. This paper conducts a rigorous analysis of a simplified variant of this strategy for general nonlinear systems. We analyze an algorithm which

Timo Reis, Manuel Schaller

We present Oseen equations on Lipschitz domains in a port-Hamiltonian context. Such equations arise, for instance, by linearization of the Navier-Stokes equations. In our setup, the external port consists of the boundary traces of velocity and the normal component of the stress tensor, and boundary control is imposed by velocity and normal stress tensor pres

Karan Singhal, Tao Tu, Juraj Gottweis, Rory Sayres

Recent artificial intelligence (AI) systems have reached milestones in "grand challenges" ranging from Go to protein-folding. The capability to retrieve medical knowledge, reason over it, and answer medical questions comparably to physicians has long been viewed as one such grand challenge. Large language models (LLMs) have catalyzed significant progress in

Ivan Dutta, Kush Saha

We report the presence of exactly and nearly flat bands with non-trivial topology in three-dimensional (3D) lattice models. We first show that an exactly flat band can be realized in a 3D lattice model characterized by a 3D topological invariant, namely Hopf invariant. In contrast, we find another distinct 3D model, exhibiting both 2D Chern and 3D Hopf invar

David Krumm, Diego Marques, Carlos Gustavo Moreira, Pavel Trojovský

We prove the existence of transcendental entire functions $f$ having a property studied by Mahler, namely that $f(\overline{\mathbb{Q}})\subseteq \overline{\mathbb{Q}}$ and $f^{-1}(\overline{\mathbb{Q}})\subseteq \overline{\mathbb{Q}}$, and in addition having a prescribed number of $k$-periodic algebraic orbits, for all $k\geq 1$. Under a suitable topology,

Yohan Brunebarbe

We propose a generalization of the Green-Griffiths-Lang conjecture to the relative setting and prove that a strong form of it holds for families of varieties of maximal Albanese dimension. A key step of the proof consists in a truncated second main theorem type estimate in Nevanlinna theory for families of abelian varieties.

Noah Ziems, Wenhao Yu, Zhihan Zhang, Meng Jiang

Document retrieval is a key stage of standard Web search engines. Existing dual-encoder dense retrievers obtain representations for questions and documents independently, allowing for only shallow interactions between them. To overcome this limitation, recent autoregressive search engines replace the dual-encoder architecture by directly generating identifie

Julián C. Cano, Carlos A. Di Prisco

Topological Ramsey theory studies a class of combinatorial topological spaces, known as topological Ramsey spaces, unifying the essential features of those combinatorial frames where the Ramsey property is equivalent to the Baire property. In this article, we present a general overview of the combinatorial structure of topological Ramsey spaces and their mai

Denis Gudovskiy, Tomoyuki Okuno, Yohei Nakata

Recent semantic segmentation models accurately classify test-time examples that are similar to a training dataset distribution. However, their discriminative closed-set approach is not robust in practical data setups with distributional shifts and out-of-distribution (OOD) classes. As a result, the predicted probabilities can be very imprecise when used as c

Boštjan Gabrovšek, Giovanni Molica Bisci, Dušan D. Repovš

In this paper, a class of nonlocal fractional Dirichlet problems is studied. By using a variational principle due to Ricceri (whose original version was given in J. Comput. Appl. Math. 113 (2000), 401-410), the existence of infinitely many weak solutions for these problems is established by requiring that the nonlinear term $f$ has a suitable oscillating beh

Garima Malik, Mucahit Cevik, Ayşe Başar

This paper explores the use of text data augmentation techniques to enhance conflict and duplicate detection in software engineering tasks through sentence pair classification. The study adapts generic augmentation techniques such as shuffling, back translation, and paraphrasing and proposes new data augmentation techniques such as Noun-Verb Substitution, ta

H. E. S. S. Collaboration, :, F. Aharonian, F. Ait Benkhali

The origin of the gamma-ray emission from M87 is currently a matter of debate. This work aims to localize the VHE (100 GeV-100 TeV) gamma-ray emission from M87 and probe a potential extended hadronic emission component in the inner Virgo Cluster. The search for a steady and extended gamma-ray signal around M87 can constrain the cosmic-ray energy density and

Joshua Hoegerman, Dylan P. Losey

Robots can learn to imitate humans by inferring what the human is optimizing for. One common framework for this is Bayesian reward learning, where the robot treats the human's demonstrations and corrections as observations of their underlying reward function. Unfortunately, this inference is doubly-intractable: the robot must reason over all the trajectories

Francisco Vargas, Teodora Reu, Anna Kerekes, Michael M Bronstein

Denoising diffusion models are a class of generative models which have recently achieved state-of-the-art results across many domains. Gradual noise is added to the data using a diffusion process, which transforms the data distribution into a Gaussian. Samples from the generative model are then obtained by simulating an approximation of the time reversal of

Nilberto Bezerra, Van Sérgio Alves, Leandro O. Nascimento, Luis Fernandez

We describe both the Fermi velocity and the mass renormalization due to the two-dimensional Coulomb interaction in the presence of a thermal bath. To achieve this, we consider an anisotropic version of pseudo quantum electrodynamics (PQED), within a perturbative approach in the fine-structure constant $\alpha$. Thereafter, we use the so-called imaginary-time

Tal Kadosh, Nadav Schneider, Niranjan Hasabnis, Timothy Mattson

There is an ever-present need for shared memory parallelization schemes to exploit the full potential of multi-core architectures. The most common parallelization API addressing this need today is OpenMP. Nevertheless, writing parallel code manually is complex and effort-intensive. Thus, many deterministic source-to-source (S2S) compilers have emerged, inten

Chunhai Lyu, Christoph H. Keitel, Zoltán Harman

Highly charged ions (HCIs) are insensitive to external perturbations and are attractive for the development of ultrastable clocks. However, only a few HCI candidates are known to provide optical clock transitions. In this Letter, we show that, as a result of strong relativistic effects, there are more than 100 suitable optical HCI clock candidates in more th

George Eskandar, Youssef Farag, Tarun Yenamandra, Daniel Cremers

A promise of Generative Adversarial Networks (GANs) is to provide cheap photorealistic data for training and validating AI models in autonomous driving. Despite their huge success, their performance on complex images featuring multiple objects is understudied. While some frameworks produce high-quality street scenes with little to no control over the image c