October 2022 arXiv papers — page 129

Floor van Donkelaar, Lucio Mayer, Pedro R. Capelo, Tomas Tamfal

The formation history of globular clusters (GCs) at redshift $z > 4$ remains an unsolved problem. In this work, we use the cosmological, $N$-body hydrodynamical ``zoom-in'' simulation GigaEris to study the properties and formation of proto-GC candidates in the region surrounding the progenitor of a Milky Way-sized galaxy. The simulation employs a modern impl

Gianluca Calcagni, Lesław Rachwał

We explore quantum field theories with fractional d'Alembertian $\Box^\gamma$. Both a scalar field theory with a derivative-dependent potential and gauge theory are super-renormalizable for a fractional power $1<\gamma\leq 2$, one-loop super-renormalizable for $\gamma>2$ and finite if one introduces killer operators. Unitarity is achieved by splitting the ki

F. Yusef-Zadeh, R. G. Arendt, M. Wardle

Magnetized radio filaments are found in abundance in the inner few hundred pc of our Galaxy. Progress in understanding this population of filaments has been slow, in part due to a lack of detection elsewhere in the Galaxy or in external galaxies. Recent highly sensitive radio continuum observations of radio galaxies in galaxy clusters have revealed remarkabl

The HERA Collaboration, Zara Abdurashidova, Tyrone Adams, James E. Aguirre

We report the most sensitive upper limits to date on the 21 cm epoch of reionization power spectrum using 94 nights of observing with Phase I of the Hydrogen Epoch of Reionization Array (HERA). Using similar analysis techniques as in previously reported limits (HERA Collaboration 2022a), we find at 95% confidence that $\Delta^2(k = 0.34$ $h$ Mpc$^{-1}$) $\le

Arun Debray, Matthew Yu

We perform a bordism computation to show that the $E_{7(7)}(\mathbb{R})$ U-duality symmetry of 4d $\mathcal N = 8$ supergravity could have an anomaly invisible to perturbative methods; then we show that this anomaly is trivial. We compute the relevant bordism group using the Adams and Atiyah-Hirzebruch spectral sequences, and we show the anomaly vanishes by

D. Aguado, P. Molaro, E. Caffau, J. I. González Hernández

HE 0107$-$5240 is a hyper metal-poor star with $\rm [Fe/H]=-5.39$. We performed high-res observations with the ESPRESSO spectrograph at the VLT to constrain the kinematical properties of the binary system HE 0107$-$5240 and to probe the binarity of the sample of 8 most metal-poor stars with $\rm [Fe/H]<-4.5$. Radial velocities are obtained by using cross-cor

Sho Yaida

In this note, we first derive a one-parameter family of hyperparameter scaling strategies that interpolates between the neural-tangent scaling and mean-field/maximal-update scaling. We then calculate the scalings of dynamical observables -- network outputs, neural tangent kernels, and differentials of neural tangent kernels -- for wide and deep neural networ

Logan Gates, Alexander A. Penin

We study Majorana zero modes bound to giant vortices in topological superconductors or topological insulator/normal superconductor heterostructures. By expanding in inverse powers of a large winding number $n$, we find an analytic solution for asymptotically all $n$ zero modes required by the index theorem. Contrary to the existing estimates, the solution is

Kyle Kremer, Dongzi Li, Wenbin Lu, Anthony L. Piro

The recent detection of a repeating fast radio burst (FRB) in an old globular cluster in M81 challenges traditional FRB formation mechanisms based on magnetic activity in young neutron stars formed recently in core-collapse supernovae. Furthermore, the detection of this repeater in such a nearby galaxy implies a high local universe rate of similar events in

Mykhaylo Usatyuk

We propose a definition for the Lorentzian Jackiw-Teitelboim (JT) gravity path integral that includes Lorentzian topology changing configurations. The construction is inspired by the bosonic string genus expansion on singular Lorentzian worldsheets, with geometries known as lightcone diagrams playing a prominent role. The Lorentzian path integral is defined

Karina Calhoun, Brendan Fay, Ben Kain

We revisit the numerical evolution of Ellis-Bronnikov-Morris-Thorne wormholes, which are constructed with a massless real ghost scalar field. For our simulations, we have developed a new code based on the standard 3+1 foliation of spacetime. We confirm that, for the massless symmetric wormhole, a pulse of regular scalar field causes the wormhole throat to co

M. Sten Delos, Joseph Silk

Primordial black holes (PBHs) form from large-amplitude initial density fluctuations and may comprise some or all of the dark matter. If PBHs have a broadly extended mass spectrum, or in mixed PBH-particle dark matter scenarios, the extreme density fluctuations necessary to produce PBHs also lead to the formation of a much greater abundance of dark matter mi

Iason Krommydas, Ilias Cholis

Antimatter cosmic-rays are used to probe new phenomena in physics, including dark matter annihilation. We use the cosmic-ray positron fraction spectrum by the Alpha Magnetic Spectrometer, to search for such an annihilation signal in the Galaxy. We focus on dark matter with mass between 5 and 120 GeV, producing high-energy electrons and positrons. In these co

L. Y. Aaron Yung, Rachel S. Somerville, Steven L. Finkelstein, Peter Behroozi

The Nancy Grace Roman Space Telescope, NASA's next flagship observatory, will redefine deep-field galaxy survey with a field of view two orders of magnitude larger than Hubble and an angular resolution of matching quality. These future deep-wide galaxy surveys necessitate new simulations to forecast their scientific output and to optimise survey strategies.

Holmfridur S. Hannesdottir, Aditya Pathak, Matthew D. Schwartz, Iain W. Stewart

We compute the soft-drop jet-mass distribution from $pp$ collisions to NNLL accuracy while including nonperturbative corrections through a field-theory based formalism. Using these calculations, we assess the theoretical uncertainties on an $\alpha_s$ precision measurement due to higher order perturbative effects, nonperturbative corrections, and PDF uncerta

Ana-Marija Nedić, Victor L. Quito, Yuriy Sizyuk, Peter P. Orth

We propose stacked two-dimensional lattice designs of frustrated and SO(3) symmetric spin models consisting of antiferromagnetic (AFM) triangular and ferromagnetic (FM) sixfold symmetric sublattices that realize emergent Z3 Potts nematic order. Considering bilinear-biquadratic spin interactions, our models describe an SO(3)-symmetric triangular lattice AFM s

John Regan

Both the origin of, and the population demographics of, massive black holes (MBHs) remains an open question in modern day astrophysics. Here we introduce the BlackDemon suite of cosmological simulations using the Enzo code. The suite consists primarily of three, high resolution, distinct regions, each with a side length of 1 h$^{-1}$ Mpc. Two of the regions

Gabriela Wojtowicz, Archak Purkayastha, Michael Zwolak, Marek M. Rams

The simulation of open many-body quantum systems is challenging, requiring methods to both handle exponentially large Hilbert spaces and represent the influence of (infinite) particle and energy reservoirs. These two requirements are at odds with each other: Larger collections of modes can increase the fidelity of the reservoir representation but come at a s

Tengda Han, Weidi Xie, Andrew Zisserman

The objective of this paper is an efficient training method for video tasks. We make three contributions: (1) We propose Turbo training, a simple and versatile training paradigm for Transformers on multiple video tasks. (2) We illustrate the advantages of Turbo training on action classification, video-language representation learning, and long-video activity

Fangzhou Hong, Zhaoxi Chen, Yushi Lan, Liang Pan

Inverse graphics aims to recover 3D models from 2D observations. Utilizing differentiable rendering, recent 3D-aware generative models have shown impressive results of rigid object generation using 2D images. However, it remains challenging to generate articulated objects, like human bodies, due to their complexity and diversity in poses and appearances. In

Haozhi Qi, Ashish Kumar, Roberto Calandra, Yi Ma

Generalized in-hand manipulation has long been an unsolved challenge of robotics. As a small step towards this grand goal, we demonstrate how to design and learn a simple adaptive controller to achieve in-hand object rotation using only fingertips. The controller is trained entirely in simulation on only cylindrical objects, which then - without any fine-tun

Sylvestre-Alvise Rebuffi, Francesco Croce, Sven Gowal

While adversarial training is generally used as a defense mechanism, recent works show that it can also act as a regularizer. By co-training a neural network on clean and adversarial inputs, it is possible to improve classification accuracy on the clean, non-adversarial inputs. We demonstrate that, contrary to previous findings, it is not necessary to separa

Raphael Tang, Linqing Liu, Akshat Pandey, Zhiying Jiang

Large-scale diffusion neural networks represent a substantial milestone in text-to-image generation, but they remain poorly understood, lacking interpretability analyses. In this paper, we perform a text-image attribution analysis on Stable Diffusion, a recently open-sourced model. To produce pixel-level attribution maps, we upscale and aggregate cross-atten

Amy S. McKeown-Green, Justin C. Ondry, Michelle F. Crook, Jason J. Calvin

Semiconducting, core-shell nanocrystals (NCs) are promising building blocks for the construction of higher dimensional artificial nanostructures using oriented attachment. However, the assembly and epitaxial attachment steps critical to this construction introduce disorder and defects which inhibit the observation of desirable emergent electronic phenomena.

Nicolas Dufour, David Picard, Vicky Kalogeiton

A large body of recent work targets semantically conditioned image generation. Most such methods focus on the narrower task of pose transfer and ignore the more challenging task of subject transfer that consists in not only transferring the pose but also the appearance and background. In this work, we introduce SCAM (Semantic Cross Attention Modulation), a s

Illia Oleksiienko, Alexandros Iosifidis

Deep Ensembles, as a type of Bayesian Neural Networks, can be used to estimate uncertainty on the prediction of multiple neural networks by collecting votes from each network and computing the difference in those predictions. In this paper, we introduce a method for uncertainty estimation that considers a set of independent categorical distributions for each

Stratos Pateloudis, Georg Bergner, Masanori Hanada, Enrico Rinaldi

We test the gauge/gravity duality between the matrix model and type IIA string theory at low temperatures with unprecedented accuracy. To this end, we perform lattice Monte Carlo simulations of the Berenstein-Maldacena-Nastase (BMN) matrix model, which is the one-parameter deformation of the Banks-Fischler-Shenker-Susskind (BFSS) matrix model, taking both th

Ratip Emin Berker, Sílvia Casacuberta, Christopher Ong, Isaac Robinson

The Independence of Clones (IoC) criterion measures a voting rule's robustness to strategic nomination. Prior literature has established empirically that individuals may still submit costly, distortionary misreports even in strategy-proof (SP) settings, due to failure to recognize the SP property. The intersection of these issues motivates the search for mec

Xuan Li, Melynda Brooks, Matt Durham, Ming Liu

The future Electron-Ion Collider (EIC) will utilize a series of high-luminosity high-energy electron+proton ($e+p$) and electron+nucleus ($e+A$) collisions to explore the inner structure of nucleon and nucleus and the matter formation process. Heavy flavor hadron and jet measurements at the EIC will play an essential role in determining the nucleon/nucleus p

Francesco Cazzaro, Davide Locatelli, Ariadna Quattoni, Xavier Carreras

Prior work in semantic parsing has shown that conventional seq2seq models fail at compositional generalization tasks. This limitation led to a resurgence of methods that model alignments between sentences and their corresponding meaning representations, either implicitly through latent variables or explicitly by taking advantage of alignment annotations. We

Giuseppe Negro, Livio Nicola Carenza, Giuseppe Gonnella, Davide Marenduzzo

We study the phase behaviour of cholesteric liquid crystal shells with different geometries. We compare the cases of tangential and no anchoring at the surface, focussing on the former case, which leads to a competition between the intrinsic tendency of the cholesteric to twist and the anchoring free energy which suppresses it. We then characterise the topol

Laura Hinkle, Neda Ghaeili Ardabili, Julian Wang

Windows are one of the main contributors to building energy consumption, and emerging dynamic window technologies offer improved performance. Specifically, NIRfocused window technologies are desirable in climates that consume both heating and cooling energy. However, the whole building energy effects of changeable NIR response of building windows have not be

Simon Opie, Daniel Verscharen, Christopher H. K. Chen, Christopher J. Owen

Using high-resolution data from Solar Orbiter, we investigate the plasma conditions necessary for the proton temperature anisotropy driven mirror-mode and oblique firehose instabilities to occur in the solar wind. We find that the unstable plasma exhibits dependencies on the angle between the direction of the magnetic field and the bulk solar wind velocity w

Michael Liaofan Liu, Florian Kanitschar, Amir Arqand, Ernest Y. -Z. Tan

We derive a Lipschitz continuity bound for quantum-classical conditional entropies with respect to angular distance, with a Lipschitz constant that is independent of the dimension of the conditioning system. This bound is sharper in some situations than previous continuity bounds, which were either based on trace distance (where Lipschitz continuity is not p

Tanay Dixit, Bhargavi Paranjape, Hannaneh Hajishirzi, Luke Zettlemoyer

Counterfactual data augmentation (CDA) -- i.e., adding minimally perturbed inputs during training -- helps reduce model reliance on spurious correlations and improves generalization to out-of-distribution (OOD) data. Prior work on generating counterfactuals only considered restricted classes of perturbations, limiting their effectiveness. We present COunterf

Louis Sharrock, Jack Simons, Song Liu, Mark Beaumont

We introduce Sequential Neural Posterior Score Estimation (SNPSE), a score-based method for Bayesian inference in simulator-based models. Our method, inspired by the remarkable success of score-based methods in generative modelling, leverages conditional score-based diffusion models to generate samples from the posterior distribution of interest. The model i

Mark Niklas Müller, Franziska Eckert, Marc Fischer, Martin Vechev

To obtain, deterministic guarantees of adversarial robustness, specialized training methods are used. We propose, SABR, a novel such certified training method, based on the key insight that propagating interval bounds for a small but carefully selected subset of the adversarial input region is sufficient to approximate the worst-case loss over the whole regi

Miao Peng, Ben Liu, Qianqian Xie, Wenjie Xu

Link prediction is the task of inferring missing links between entities in knowledge graphs. Embedding-based methods have shown effectiveness in addressing this problem by modeling relational patterns in triples. However, the link prediction task often requires contextual information in entity neighborhoods, while most existing embedding-based methods fail t

Alise Danielle Midtfjord, Riccardo De Bin, Arne Bang Huseby

A characteristic feature of time-to-event data analysis is possible censoring of the event time. Most of the statistical learning methods for handling censored data are limited by the assumption of independent censoring, even if this can lead to biased predictions when the assumption does not hold. This paper introduces Clayton-boost, a boosting approach bui

Krish Kabra, Alexander Xiong, Wenbin Li, Minxuan Luo

Monitoring of colonial waterbird nesting islands is essential to tracking waterbird population trends, which are used for evaluating ecosystem health and informing conservation management decisions. Recently, unmanned aerial vehicles, or drones, have emerged as a viable technology to precisely monitor waterbird colonies. However, manually counting waterbirds

Bruce A. Corliss, Yaotian Wang, Heman Shakeri, Philip E. Bourne

At every phase of scientific research, scientists must decide how to allocate limited resources to pursue the research inquiries with the greatest potential. This prioritization dictates which controlled interventions are studied, awarded funding, published, reproduced with repeated experiments, investigated in related contexts, and translated for societal u

Nicolas Bähler, Majed El Helou, Étienne Objois, Kaan Okumuş

Image noise can often be accurately fitted to a Poisson-Gaussian distribution. However, estimating the distribution parameters from a noisy image only is a challenging task. Here, we study the case when paired noisy and noise-free samples are accessible. No method is currently available to exploit the noise-free information, which may help to achieve more ac

Sebastián Basterrech, Michal Woźniak

Recently, continual learning has received a lot of attention. One of the significant problems is the occurrence of \emph{concept drift}, which consists of changing probabilistic characteristics of the incoming data. In the case of the classification task, this phenomenon destabilizes the model's performance and negatively affects the achieved prediction qual

Meera Hahn, James M. Rehg

We address the challenging task of Localization via Embodied Dialog (LED). Given a dialog from two agents, an Observer navigating through an unknown environment and a Locator who is attempting to identify the Observer's location, the goal is to predict the Observer's final location in a map. We develop a novel LED-Bert architecture and present an effective p

R. de la Fuente Marcos, C. de la Fuente Marcos

Context. Most flybys in the Galactic disk are distant, beyond 10,000 AU, and have characteristic velocities of about 70 km/s. However, deep and fast encounters also take place, albeit with lower probability, particularly if one of the objects involved is a stellar remnant ejected during a supernova. WD 0810-353 might be a high velocity white dwarf, and it wa

Tobias Eckert, Matthias Schmidt, Daniel de las Heras

Both polydispersity and the presence of a gravitational field are inherent to essentially any colloidal experiment. While several theoretical works have focused on the effect of polydispersity on the bulk phase behavior of a colloidal system, little is known about the effect of a gravitational field on a polydisperse colloidal suspension. We extend here sedi

Celsina N. Azevedo, Victor P. Goncalves, Bruno D. Moreira

In this paper we analyze the associated $\phi$ and $J/\Psi$ photoproduction in ultraperipheral $PbPb$ collisions through the double scattering mechanism for the energies of the Large Hadron Collider (LHC) and Future Circular Collider (FCC). Our results complement a previous analysis for the $\rho\rho$, $J/\Psi J/\Psi$ and $\rho J/\Psi$ production. We present

Atish Agarwala, Fabian Pedregosa, Jeffrey Pennington

Recent studies of gradient descent with large step sizes have shown that there is often a regime with an initial increase in the largest eigenvalue of the loss Hessian (progressive sharpening), followed by a stabilization of the eigenvalue near the maximum value which allows convergence (edge of stability). These phenomena are intrinsically non-linear and do

Kai Dong, Silvia Mura, Marouan Mizmizi, Dario Tagliaferri

Smart Repeaters (SR) can potentially enhance the coverage in Millimeter-wave (mmWave) wireless communications. However, the angular coverage of the existing two-panel SR is too limited to make the SR a truly cost-effective mmWave range extender. This paper proposes the usage of a tri-sectoral Advanced SR (ASR) to extend the angular coverage with respect to c

Ching-Peng Huang

We prove eigenvalue processes from dynamical random matrix theory including Dyson Brownian motion, Wishart process, and Dynkin's Brownian motion of ellipsoids are results of projecting Brownian motion through Riemannian submersions induced by isometric action of compact Lie groups, whose orbits have nonzero mean curvature, which contributes to drift terms an

Shuxiang Cao, Deep Lall, Mustafa Bakr, Giulio Campanaro

Gate-set tomography (GST) characterizes the process matrix of quantum logic gates, along with measurement and state preparation errors in quantum processors. GST typically requires extensive data collection and significant computational resources for model estimation. We propose a more efficient GST approach for qudits, utilizing the qudit Hadamard and virtu

Yangshuai Wang, James R. Kermode, Christoph Ortner, Lei Zhang

Hybrid quantum/molecular mechanics (QM/MM) models play a pivotal role in molecular simulations. These models provide a balance between accuracy, surpassing pure MM models, and computational efficiency, offering advantages over pure QM models. Adaptive approaches have been developed to further improve this balance by allowing on-the-fly selection of the QM an

Etera R. Livine, Qiaoyin Pan

Three-dimensional gravity is a topological field theory, which can be quantized as the Ponzano-Regge state-sum model built from the $\{3nj\}$-symbols of the recoupling of the $\SU(2)$ representations, in which spins are interpreted as quantized edge lengths in Planck units. It describes the flat spacetime as gluing of three-dimensional cells with a fixed bou

Nicolas Chenavier, Ahmad Darwiche

Let $(S_n)_{n \geq 0}$ be a transient random walk in the domain of attraction of a stable law and let $(\xi(s))_{s \in \mathbb{Z}}$ be a stationary sequence of random variables. In a previous work, under conditions of type $D(u_n)$ and $D'(u_n)$, we established a limit theorem for the maximum of the first $n$ terms of the sequence $(\xi(S_n))_{n\geq 0}$ as $

T. Schenkel, A. M. Snijders, K. Nakamura, P. A. Seidl

Nanostructured surfaces enhance ion yields in matrix assisted laser desorption-ionization mass spectrometry (MALDI-MS). The spike protein complex, S1, is one fingerprint signature of Sars-CoV-2 with a mass of 75 kDa. Here, we show that MALDI-MS yields of Sars-CoV-2 spike protein ions in the 100 kDa range are enhanced 50-fold when the matrix-analyte solution

Zifan Xu, Anirudh Nair, Xuesu Xiao, Peter Stone

Machine learning approaches have recently enabled autonomous navigation for mobile robots in a data-driven manner. Since most existing learning-based navigation systems are trained with data generated in artificially created training environments, during real-world deployment at scale, it is inevitable that robots will encounter unseen scenarios, which are o

Vincent Astier, Thomas Unger

We prove Pfister's local-global principle for hermitian forms over Azumaya algebras with involution over semilocal rings, and show in particular that the Witt group of nonsingular hermitian forms is $2$-primary torsion. Our proof relies on a hermitian version of Sylvester's law of inertia, which is obtained from an investigation of the connections between a

Andreas Eberl, Bernhard Klar

The concept of kurtosis is used to describe and compare theoretical and empirical distributions in a multitude of applications. In this connection, it is commonly applied to asymmetric distributions. However, there is no rigorous mathematical foundation establishing what is meant by kurtosis of an asymmetric distribution and what is required to measure it pr

Patricio Clark Di Leoni, Karuna Agarwal, Tamer Zaki, Charles Meneveau

Volume-resolving imaging techniques are rapidly advancing progress in experimental fluid mechanics. However, reconstructing the full and structured Eulerian velocity and pressure fields from sparse and noisy particle tracks obtained experimentally remains a significant challenge. We introduce a new method for this reconstruction, based on Physics-Informed Ne

Stefano Torniamenti

Most stars form in clumpy and sub-structured clusters. These properties also emerge in hydro-dynamical simulations of star-forming clouds, which provide a way to generate realistic initial conditions for $N-$body runs of young stellar clusters. However, producing large sets of initial conditions by hydro-dynamical simulations is prohibitively expensive in te

Ruilong Li, Matthew Tancik, Angjoo Kanazawa

We propose NerfAcc, a toolbox for efficient volumetric rendering of radiance fields. We build on the techniques proposed in Instant-NGP, and extend these techniques to not only support bounded static scenes, but also for dynamic scenes and unbounded scenes. NerfAcc comes with a user-friendly Python API, and is ready for plug-and-play acceleration of most NeR

Rasoul Kheiri, Alexey A Tsukanov

Molecular dynamic (MD) simulations are applied to investigate the dependency of the kinetic friction coefficient on the temperature at the nano-scale. The system is comprised of an aluminum spherical particle consisting of 32000 atoms in an FCC lattice sliding on a stack of several layers of graphene, and the simulations have done using LAMMPS. The interacti

Adrian Bulat, Ricardo Guerrero, Brais Martinez, Georgios Tzimiropoulos

This paper is on Few-Shot Object Detection (FSOD), where given a few templates (examples) depicting a novel class (not seen during training), the goal is to detect all of its occurrences within a set of images. From a practical perspective, an FSOD system must fulfil the following desiderata: (a) it must be used as is, without requiring any fine-tuning at te

Monika Dörfler, Franz Luef, Henry McNulty, Eirik Skrettingland

We introduce an operator valued Short-Time Fourier Transform for certain classes of operators with operator windows, and show that the transform acts in an analogous way to the Short-Time Fourier Transform for functions, in particular giving rise to a family of vector-valued reproducing kernel Banach spaces, the so called coorbit spaces, as spaces of operato

Matthew T. Jackson, Shreshth A. Malik, Michael T. Matthews, Yousuf Mohamed-Ahmed

When experience is scarce, models may have insufficient information to adapt to a new task. In this case, auxiliary information - such as a textual description of the task - can enable improved task inference and adaptation. In this work, we propose an extension to the Model-Agnostic Meta-Learning algorithm (MAML), which allows the model to adapt using auxil

Lingfei Zhao, Zubair Iftikhar, Trevyn F. Q. Larson, Ethan G. Arnault

We perform a systematic study of Andreev conversion at the interface between a superconductor and graphene in the quantum Hall (QH) regime. We find that the probability of Andreev conversion from electrons to holes follows an unexpected but clear trend: the dependencies on temperature and magnetic field are nearly decoupled. We discuss these trends and the r

Jia Wei, Yi-Heng Wang, Chun-Sheng An, Cheng-Rong Deng

Inspired by the states $T_{cs0}(2900)^0$, $T_{cs1}(2900)^0$, $T^a_{c\bar{s}0}(2900)^{0}$ and $T^a_{c\bar{s}0}(2900)^{++}$ reported by the LHCb Collaboration, we carry out a systematical investigation on the properties of the ground and $P$-wave states $[cs][\bar{u}\bar{d}]$ and $[cu][\bar{s}\bar{d}]$ with various spin, isospin or $U$-spin, and color combinat

Saiteja Utpala, Andi Han, Pratik Jawanpuria, Bamdev Mishra

We present Rieoptax, an open source Python library for Riemannian optimization in JAX. We show that many differential geometric primitives, such as Riemannian exponential and logarithm maps, are usually faster in Rieoptax than existing frameworks in Python, both on CPU and GPU. We support various range of basic and advanced stochastic optimization solvers li

Zifan Xu, Bo Liu, Xuesu Xiao, Anirudh Nair

Deep reinforcement learning (RL) has brought many successes for autonomous robot navigation. However, there still exists important limitations that prevent real-world use of RL-based navigation systems. For example, most learning approaches lack safety guarantees; and learned navigation systems may not generalize well to unseen environments. Despite a variet

Joel David Hamkins

Every mathematical structure has an elementary extension to a pseudo-countable structure, one that is seen as countable inside a suitable class model of set theory, even though it may actually be uncountable. This observation, proved easily with the Boolean ultrapower theorem, enables a sweeping generalization of results concerning countable models to a rich

Gregory W. Horndeski

In this paper I shall consider field theories in a space of four-dimensions which have field variables consisting of the components of a metric tensor and scalar field. The field equations of these scalar-tensor field theories will be derivable from a variational principle using a Lagrange scalar density which is a concomitant of the field variables and thei

R. McOwen, P. Topalov

We prove that the Navier-Stokes equation for a viscous incompressible fluid in $\mathbb{R}^d$ is locally well-posed in spaces of functions allowing spatial asymptotic expansions with log terms as $|x|\to\infty$ of any a priori given order. The solution depends analytically on the initial data and time so that for any $0<\vartheta<\pi/2$ it can be holomorphic

Jorge Jimenez Urroz, Jacek Pomykala

In the present paper we provide a probabilistic polynomial time algorithm that reduces the complete factorization of any squarefree integer $n$ to counting points on elliptic curves modulo $n$, succeeding with probability $1-\varepsilon$, for any $\varepsilon<0$ .

Charith Peris, Lizhen Tan, Thomas Gueudre, Turan Gojayev

Teacher-student knowledge distillation is a popular technique for compressing today's prevailing large language models into manageable sizes that fit low-latency downstream applications. Both the teacher and the choice of transfer set used for distillation are crucial ingredients in creating a high quality student. Yet, the generic corpora used to pretrain t

Wubing Chen, Wenbin Li, Xiao Liu, Shangdong Yang

Cooperative multi-agent policy gradient (MAPG) algorithms have recently attracted wide attention and are regarded as a general scheme for the multi-agent system. Credit assignment plays an important role in MAPG and can induce cooperation among multiple agents. However, most MAPG algorithms cannot achieve good credit assignment because of the game-theoretic

Ricardo Javier Peña Román, Yves Auad, Lucas Grasso, Lazaro A Padilha

We present the design, implementation, and illustrative results of a light collection/injection strategy based on an off-axis parabolic mirror collector for a low-temperature Scanning Tunneling Microscope (STM). This device allows us to perform STM induced Light Emission (STM-LE) and Cathodoluminescence (STM-CL) experiments and in situ Photoluminescence (PL)

G. Di Sciascio, INFN - Roma Tor Vergata

A shower array exploiting the full coverage approach with a high segmentation of the readout allow to image the front of atmospheric showers with unprecedented resolution and detail. The grid distance determines the energy threshold (small energy showers are lost in the gap between detectors) and the quality of the shower sampling. Therefore, this experiment

Yunhe Gao, Xingjian Shi, Yi Zhu, Hao Wang

Models should be able to adapt to unseen data during test-time to avoid performance drops caused by inevitable distribution shifts in real-world deployment scenarios. In this work, we tackle the practical yet challenging test-time adaptation (TTA) problem, where a model adapts to the target domain without accessing the source data. We propose a simple recipe

Sarah Jenkins, Levente Rozsa, Unai Atxitia, Richard F. L. Evans

The Mermin-Wagner theorem states that long-range magnetic order does not exist in one- or two-dimensional (2D) isotropic magnets with short-ranged interactions. The theorem has been a milestone in magnetism and has been driving the research of recently discovered 2D van der Waals (vdW) magnetic materials from fundamentals up to potential applications. In suc

Pinelopi Papalampidi, Mirella Lapata

In this paper, we focus on video-to-text summarization and investigate how to best utilize multimodal information for summarizing long inputs (e.g., an hour-long TV show) into long outputs (e.g., a multi-sentence summary). We extend SummScreen (Chen et al., 2021), a dialogue summarization dataset consisting of transcripts of TV episodes with reference summar

Guanyi Chen, Fahime Same, Kees van Deemter

Previous work on Neural Referring Expression Generation (REG) all uses WebNLG, an English dataset that has been shown to reflect a very limited range of referring expression (RE) use. To tackle this issue, we build a dataset based on the OntoNotes corpus that contains a broader range of RE use in both English and Chinese (a language that uses zero pronouns).

Matthew Selwood, Giorgio Calderone, Sotiria Fotopoulou, Malcolm Bremer

Driven by the imminent need to rapidly process and classify millions of AGN spectra drawn from next generation astronomical facilities, we present a spectral fitting routine for Type 2 AGN spectra optimised for high volume processing, using the Quasar Spectral Fitting library (QSFit). We analyse an optically selected sample of 813 luminous Type 2 AGN spectra

Guilherme Espada, Leon Ingelse, Paulo Canelas, Pedro Barbosa

Genetic Programming (GP) is an heuristic method that can be applied to many Machine Learning, Optimization and Engineering problems. In particular, it has been widely used in Software Engineering for Test-case generation, Program Synthesis and Improvement of Software (GI). Grammar-Guided Genetic Programming (GGGP) approaches allow the user to refine the doma

She-Sheng Xue

The ultraviolet completion is the Standard Model (SM) gauge-symmetric four-fermion couplings at the high-energy cutoff. Composite particles appear in the gauge symmetric phase in contrast with SM particles in the spontaneous symmetry-breaking phase. The critical point between the two phases is a weak first-order transition. It relates to an ultraviolet fixed

Thomas Wocial, Konstantin D. Stefanov, William E. Martin, John R. Barnes

We present a readout scheme for CMOS image sensors that can be used to achieve arbitrarily high dynamic range (HDR) in principle. The linear full well capacity (LFWC) in high signal regions was extended 50 times from 20 ke$^{-}$ to 984 ke$^{-}$ via an interlaced row-wise readout order, whilst the noise floor remained unchanged in low signal regions, resultin

Emilio Franco

We describe here a degeneration of the symplectic desingularization of the moduli spaces of topologically trivial $GL(2,\mathbb{C})$ and $SL(2,\mathbb{C})$-Higgs bundles over a hyperelliptic curve, into O'Grady's ten and six dimensional exceptional examples of irreducible holomorphic symplectic manifolds. Most of this note is a survey of work on these degene

Ziyue Zou, Eric Beyerle, Sun-Ting Tsai, Pratyush Tiwary

Crystal nucleation is relevant across the domains of fundamental and applied sciences. However, in many cases its mechanism remains unclear due to a lack of temporal or spatial resolution. To gain insights to the molecular details of nucleation, some form of molecular dynamics simulations is typically performed; these simulations, in turn, are limited by the

Martin Balko, Adam Sheffer, Ruiwen Tang

We study a lower bound for the constant of the Szemer\'edi-Trotter theorem. In particular, we show that a recent infinite family of point-line configurations satisfies $I({\mathcal P},{\mathcal L})\ge (c+o(1)) |{\mathcal P}|^{2/3}|{\mathcal L}|^{2/3}$, with $c\approx 1.27$. Our technique is based on studying a variety of properties of Euler's totient functio

Junmin Zhong, Ruofan Wu, Jennie Si

High variances in reinforcement learning have shown impeding successful convergence and hurting task performance. As reward signal plays an important role in learning behavior, multi-step methods have been considered to mitigate the problem, and are believed to be more effective than single step methods. However, there is a lack of comprehensive and systemat

Shikha Surana, Bryan Lim, Antoine Cully

Data-driven learning based methods have recently been particularly successful at learning robust locomotion controllers for a variety of unstructured terrains. Prior work has shown that incorporating good locomotion priors in the form of trajectory generators (TGs) is effective at efficiently learning complex locomotion skills. However, defining a good, sing

Martin F. Schiffner

Ultrafast imaging modes, such as coherent plane-wave compounding (CPWC), capture a large field of view in a single pulse-echo measurement using parallel receive focusing. The number of foci or, equivalently, the number of volume elements (voxels) in the image determines the computational costs and the memory consumption of the image formation. Herein, 120{\d

Amartya Sanyal, Giorgia Ramponi

Online learning, in the mistake bound model, is one of the most fundamental concepts in learning theory. Differential privacy, instead, is the most widely used statistical concept of privacy in the machine learning community. It is thus clear that defining learning problems that are online differentially privately learnable is of great interest. In this pape

Mohammed R. Al-Sinan, Aseel F. Haneef, Hamzah Luqman

Wearing a face mask is one of the adjustments we had to follow to reduce the spread of the coronavirus. Having our faces covered by masks constantly has driven the need to understand and investigate how this behavior affects the recognition capability of face recognition systems. Current face recognition systems have extremely high accuracy when dealing with

Michael Deistler, Pedro J Goncalves, Jakob H Macke

Simulation-based inference (SBI) solves statistical inverse problems by repeatedly running a stochastic simulator and inferring posterior distributions from model-simulations. To improve simulation efficiency, several inference methods take a sequential approach and iteratively adapt the proposal distributions from which model simulations are generated. Howe

Zhubing Jia, Shilin Huang, Mingyu Kang, Ke Sun

In trapped-ion quantum computers, two-qubit entangling gates are generated by applying spin-dependent force which uses phonons to mediate interaction between the internal states of the ions. To maintain high-fidelity two-qubit gates under fluctuating experimental parameters, robust pulse-design methods are applied to remove the residual spin-motion entanglem

Roland B. Ilyes, Qi Heng Ho, Morteza Lahijanian

In this work, we present a novel robustness measure for continuous-time stochastic trajectories with respect to Signal Temporal Logic (STL) specifications. We show the soundness of the measure and develop a monitor for reasoning about partial trajectories. Using this monitor, we introduce an STL sampling-based motion planning algorithm for robots under uncer

M. Biagetti, G. Franciolini, A. Riotto

Several bright and massive galaxy candidates at high redshifts have been recently observed by the James Webb Space Telescope. Such early massive galaxies seem difficult to reconcile with standard $\Lambda$ Cold Dark Matter model predictions. We discuss under which circumstances such observed massive galaxy candidates can be explained by introducing primordia

Xiaoyu Chen, Xiaoning Kang, Ran Jin, Xinwei Deng

Fog manufacturing can greatly enhance traditional manufacturing systems through distributed Fog computation units, which are governed by predictive computational workload offloading methods under different Industrial Internet architectures. It is known that the predictive offloading methods highly depend on accurate prediction and uncertainty quantification

Bin Hu, Kaiqing Zhang, Na Li, Mehran Mesbahi

Gradient-based methods have been widely used for system design and optimization in diverse application domains. Recently, there has been a renewed interest in studying theoretical properties of these methods in the context of control and reinforcement learning. This article surveys some of the recent developments on policy optimization, a gradient-based iter

Domenico Monaco, Thaddeus Roussigné

Motivated by recent developments in quantum simulation of synthetic dimensions, e.g. in optical lattices of ultracold atoms, we discuss here $d$-dimensional periodic, gapped quantum systems for $d \le 4$, with focus on the topology of the occupied energy states. We perform this analysis by asking whether the spectral subspace below the gap can be spanned by