October 2022 arXiv papers — page 71

Tomokage Yoneyama, Tadayasu Dotani

2S 0921$-$630 is an eclipsing low-mass X-ray binary (LMXB) with an orbital period of $\sim$ 9 days. Past X-ray observations have revealed that 2S 0921$-$630 has an extended accretion disk corona (ADC), from which most of the X-rays from the system are emitted. We report the result of our Suzaku archival data analysis of 2S 0921$-$630. The average X-ray spect

Marat Freytsis, Soubhik Kumar, Grant N. Remmen, Nicholas L. Rodd

Positivity bounds represent nontrivial limitations on effective field theories (EFTs) if those EFTs are to be completed into a Lorentz-invariant, causal, local, and unitary framework. While such positivity bounds have been applied in a wide array of physical contexts to obtain useful constraints, their application to inflationary EFTs is subtle since Lorentz

A. J. Shajib, G. Vernardos, T. E. Collett, V. Motta

Strong gravitational lensing at the galaxy scale is a valuable tool for various applications in astrophysics and cosmology. The primary uses of galaxy-scale lensing are to study elliptical galaxies' mass structure and evolution, constrain the stellar initial mass function, and measure cosmological parameters. Since the discovery of the first galaxy-scale len

Benjamin M. Tofflemire, Adam L. Kraus, Andrew W. Mann, Elisabeth R. Newton

Young eclipsing binaries (EBs) are powerful probes of early stellar evolution. Current models are unable to simultaneously reproduce the measured and derived properties that are accessible for EB systems (e.g., mass, radius, temperature, luminosity). In this study we add a benchmark EB to the pre-main-sequence population with our characterization of TOI 450

Lam Hui, Alessandro Podo, Luca Santoni, Enrico Trincherini

Quasinormal modes describe the ringdown of compact objects deformed by small perturbations. In generic theories of gravity that extend General Relativity, the linearized dynamics of these perturbations is described by a system of coupled linear differential equations of second order. We first show, under general assumptions, that such a system can be brought

Matteo Robbiati, Stavros Efthymiou, Andrea Pasquale, Stefano Carrazza

In this proceedings we present quantum machine learning optimization experiments using stochastic gradient descent with the parameter shift rule algorithm. We first describe the gradient evaluation algorithm and its optimization procedure implemented using the Qibo framework. After numerically testing the implementation using quantum simulation on classical

Daniel Bennett, Gaurav Chaudhary, Robert-Jan Slager, Eric Bousquet

Out-of-plane polar domain structures have recently been discovered in strained and twisted bilayers of inversion symmetry broken systems such as hexagonal boron nitride. Here we show that this symmetry breaking also gives rise to an in-plane component of polarization, and the form of the total polarization is determined purely from symmetry considerations. T

Naim E. Mackel, Jing Yang, Adolfo del Campo

Many-particle quantum systems with intermediate anyonic exchange statistics are supported in one spatial dimension. In this context, the anyon-anyon mapping is recast as a continuous transformation that generates shifts of the statistical parameter $\kappa$. We characterize the geometry of quantum states associated with different values of $\kappa$, i.e., di

Pengfei Li, Beiwen Tian, Yongliang Shi, Xiaoxue Chen

Current referring expression comprehension algorithms can effectively detect or segment objects indicated by nouns, but how to understand verb reference is still under-explored. As such, we study the challenging problem of task oriented detection, which aims to find objects that best afford an action indicated by verbs like sit comfortably on. Towards a fine

Vladimir Fomenko, Ismail Elezi, Deva Ramanan, Laura Leal-Taixé

We tackle the problem of novel class discovery and localization (NCDL). In this setting, we assume a source dataset with supervision for only some object classes. Instances of other classes need to be discovered, classified, and localized automatically based on visual similarity without any human supervision. To tackle NCDL, we propose a two-stage object det

Tal Reiss, Niv Cohen, Eliahu Horwitz, Ron Abutbul

Anomaly detection seeks to identify unusual phenomena, a central task in science and industry. The task is inherently unsupervised as anomalies are unexpected and unknown during training. Recent advances in self-supervised representation learning have directly driven improvements in anomaly detection. In this position paper, we first explain how self-supervi

Pallabi Chatterjee, Ranjan Modak

Space-fractional quantum mechanics (SFQM) is a generalization of the standard quantum mechanics when the Brownian trajectories in Feynman path integrals are replaced by L{\'e}vy flights. We introduce L{\'e}vy quasicrystal by discretizing the space-fractional Schr$\ddot{\text{o}}$dinger equation using the Gr$\ddot{\text{u}}$nwald-Letnikov derivatives and addi

Martin Engilberge, Weizhe Liu, Pascal Fua

Multi-view approaches to people-tracking have the potential to better handle occlusions than single-view ones in crowded scenes. They often rely on the tracking-by-detection paradigm, which involves detecting people first and then connecting the detections. In this paper, we argue that an even more effective approach is to predict people motion over time and

Paul-Edouard Sarlin, Mihai Dusmanu, Johannes L. Schönberger, Pablo Speciale

Localization and mapping is the foundational technology for augmented reality (AR) that enables sharing and persistence of digital content in the real world. While significant progress has been made, researchers are still mostly driven by unrealistic benchmarks not representative of real-world AR scenarios. These benchmarks are often based on small-scale dat

Haoran Zhang, Harvineet Singh, Marzyeh Ghassemi, Shalmali Joshi

Machine learning models frequently experience performance drops under distribution shifts. The underlying cause of such shifts may be multiple simultaneous factors such as changes in data quality, differences in specific covariate distributions, or changes in the relationship between label and features. When a model does fail during deployment, attributing p

Ian Waudby-Smith, Lili Wu, Aaditya Ramdas, Nikos Karampatziakis

Contextual bandit algorithms are ubiquitous tools for active sequential experimentation in healthcare and the tech industry. They involve online learning algorithms that adaptively learn policies over time to map observed contexts $X_t$ to actions $A_t$ in an attempt to maximize stochastic rewards $R_t$. This adaptivity raises interesting but hard statistica

Eugenio Cutolo, Ananda Pascual, Simon Ruiz, Nikolaos Zarokanellos

Combining remote-sensing data with in-situ observations to achieve a comprehensive 3D reconstruction of the ocean state presents significant challenges for traditional interpolation techniques. To address this, we developed the CLuster Optimal Interpolation Neural Network (CLOINet), which combines the robust mathematical framework of the Optimal Interpolatio

James J. Hamlin

In this paper, I present an analysis of the electrical resistance graphs in Nature volume 586, pages 373-377 (2020), which reported the discovery of room temperature superconductivity in a carbonaceous sulfur hydride and was subsequently retracted on September 26th, 2022. I show that, over a single temperature interval, the electrical resistance data can be

Annie Xie, Fahim Tajwar, Archit Sharma, Chelsea Finn

A long-term goal of reinforcement learning is to design agents that can autonomously interact and learn in the world. A critical challenge to such autonomy is the presence of irreversible states which require external assistance to recover from, such as when a robot arm has pushed an object off of a table. While standard agents require constant monitoring to

Michele Pennisi

Charm and beauty quark production measurement represents a fundamental means to access the initial stage of hadronic collisions. Being produced almost exclusively in initial hard partonic scatterings due to their large masses, $m_{\text{c}}=$ 1.3 $ \text{GeV}/c^2$ and $m_{\text{b}} =$ 4.1 $ \text{GeV}/c^2$, charm and beauty quarks are ideal tools to investig

Yifan Xu, Nicklas Hansen, Zirui Wang, Yung-Chieh Chan

Reinforcement Learning (RL) algorithms can solve challenging control problems directly from image observations, but they often require millions of environment interactions to do so. Recently, model-based RL algorithms have greatly improved sample-efficiency by concurrently learning an internal model of the world, and supplementing real environment interactio

Amjad Ashoorioon, Mohammad Bagher Jahani Poshteh, Robert B. Mann

Accelerating supermassive black holes, connected to cosmic strings, could contribute to structure formation and get captured by galaxies if their velocities are small. This would mean that the acceleration of these black holes is small too. Such a slow acceleration has no significant effect on the shadow of such supermassive black holes. We also show that, f

Maret Einasto, Rain Kipper, Peeter Tenjes, Jaan Einasto

Our aim is to understand the effect of environment to galaxy quenching in various local and global environments. We focus on galaxies with very old stellar populations (VO galaxies), typically found in the centers of clusters and groups, and search for such galaxies in the lowest global density environments, watersheds between superclusters. We use the Sloan

Leo Gao, John Schulman, Jacob Hilton

In reinforcement learning from human feedback, it is common to optimize against a reward model trained to predict human preferences. Because the reward model is an imperfect proxy, optimizing its value too much can hinder ground truth performance, in accordance with Goodhart's law. This effect has been frequently observed, but not carefully measured due to t

Ziang Chen, Jialin Liu, Xinshang Wang, Jianfeng Lu

While Mixed-integer linear programming (MILP) is NP-hard in general, practical MILP has received roughly 100--fold speedup in the past twenty years. Still, many classes of MILPs quickly become unsolvable as their sizes increase, motivating researchers to seek new acceleration techniques for MILPs. With deep learning, they have obtained strong empirical resul

Xin Liu, Xiaofei Shao, Bo Wang, Yali Li

Image guided depth completion aims to recover per-pixel dense depth maps from sparse depth measurements with the help of aligned color images, which has a wide range of applications from robotics to autonomous driving. However, the 3D nature of sparse-to-dense depth completion has not been fully explored by previous methods. In this work, we propose a Graph

Blake C. Stacey

I gamely try to disentangle ideas that have been confused with one another.

Martin Engilberge, Haixin Shi, Zhiye Wang, Pascal Fua

Data augmentation has proven its usefulness to improve model generalization and performance. While it is commonly applied in computer vision application when it comes to multi-view systems, it is rarely used. Indeed geometric data augmentation can break the alignment among views. This is problematic since multi-view data tend to be scarce and it is expensive

Pablo Blanco, Matija Bucić

The Erd\H{o}s-Hajnal conjecture is one of the most classical and well-known problems in extremal and structural combinatorics dating back to 1977. It asserts that in stark contrast to the case of a general $n$-vertex graph if one imposes even a little bit of structure on the graph, namely by forbidding a fixed graph $H$ as an induced subgraph, instead of onl

Jack T Dinsmore, Julien de Wit

Knowledge of the interior density distribution of an asteroid can reveal its composition and constrain its evolutionary history. However, most asteroid observational techniques are not sensitive to interior properties. We investigate the interior constraints accessible through monitoring variations in angular velocity during a close encounter. We derive the

Cannon M. Vogel, Kevork N. Abazajian

We examine thermal warm dark matter (WDM) models that are being probed by current constraints, and the relationship between the particle dark matter spin and commensurate thermal history. We find significant corrections to the linear matter power spectrum for given thermal WDM particle masses. Two primary classes are examined: spin-1/2 particles (e.g., therm

Yuan Lee, Wenhan Dai, Don Towsley, Dirk Englund

The absence of a common framework for benchmarking quantum networks is an obstacle to comparing the capabilities of different quantum networks. We propose a general framework for quantifying the performance of a quantum network, which is based on the value created by connecting users through quantum channels. In this framework, we define the quantum network

Syuhei Iguro, Teppei Kitahara, Ryoutaro Watanabe

Recently, the LHCb collaboration announced a preliminary result of the test of lepton flavor universality (LFU) in $B\to D^{(\ast)}$ semi-leptonic decays: $R_{D}^{\rm LHCb2022} = 0.441 \pm 0.089$ and $R_{D^{\ast}}^{\rm LHCb2022} = 0.281 \pm 0.030$ based on the LHC Run 1 data. This is the first result of $R_{D}$ for the LHCb experiment, and its precision is c

Yuxin Wen, Arpit Bansal, Hamid Kazemi, Eitan Borgnia

As industrial applications are increasingly automated by machine learning models, enforcing personal data ownership and intellectual property rights requires tracing training data back to their rightful owners. Membership inference algorithms approach this problem by using statistical techniques to discern whether a target sample was included in a model's tr

Bingbin Liu, Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy

Algorithmic reasoning requires capabilities which are most naturally understood through recurrent models of computation, like the Turing machine. However, Transformer models, while lacking recurrence, are able to perform such reasoning using far fewer layers than the number of reasoning steps. This raises the question: what solutions are learned by these sha

Liuquan Wang

Let $r\geq 1$ be a positive integer, $A$ a real positive definite symmetric $r\times r$ matrix, $B$ a vector of length $r$, and $C$ a scalar. Nahm's problem is to describe all such $A,B$ and $C$ with rational entries for which a specific $r$-fold $q$-hypergeometric series (denoted by $f_{A,B,C}(q)$) involving the parameters $A,B,C$ is modular. When the rank

William van den Bogert, James Lorenz, Xili Yi, Nima Fazeli

In this paper, we present a novel flow model and compensation strategy for high-viscosity fluid deposition that yields high quality parts in the face of large transient delays and nonlinearity. Robotic high-viscosity fluid deposition is an essential process for a broad range of manufacturing applications including additive manufacturing, adhesive and sealant

Andrew Sontag, Mehmet A. Noyan, James M. Kikkawa

We present a novel technique for generating beams of light carrying orbital angular momentum (OAM) that increases mode purity and decreases singularity splitting by orders of magnitude. This technique also works to control and mitigate beam divergence within propagation distances less than the Rayleigh length. Additionally, we analyze a tunable parameter of

Zhaoyang Shi, Krishnakumar Balasubramanian, Wolfgang Polonik

We derive normal approximation results for a class of stabilizing functionals of binomial or Poisson point process, that are not necessarily expressible as sums of certain score functions. Our approach is based on a flexible notion of the add-one cost operator, which helps one to deal with the second-order cost operator via suitably appropriate first-order o

Hiroyuki Tezuka, Shumpei Uno, Naoki Yamamoto

Generative modeling is an unsupervised machine learning framework, that exhibits strong performance in various machine learning tasks. Recently we find several quantum version of generative model, some of which are even proven to have quantum advantage. However, those methods are not directly applicable to construct a generative model for learning a set of q

Yoshiki Masuyama, Xuankai Chang, Samuele Cornell, Shinji Watanabe

Self-supervised learning representation (SSLR) has demonstrated its significant effectiveness in automatic speech recognition (ASR), mainly with clean speech. Recent work pointed out the strength of integrating SSLR with single-channel speech enhancement for ASR in noisy environments. This paper further advances this integration by dealing with multi-channel

Farid Manuchehrfar, Huiyu Li, Ao Ma, Jie Liang

Dynamics of reaction coordinates during barrier-crossing are key to understand activated processes in complex systems such as proteins. The default assumption from Kramers physical intuition is that of a diffusion process. However, the dynamics of barrier-crossing in natural complex molecules are largely unexplored. Here we investigate the transition dynamic

Jerome Baum, Heishiro Kanagawa, Arthur Gretton

We propose a goodness-of-fit measure for probability densities modeling observations with varying dimensionality, such as text documents of differing lengths or variable-length sequences. The proposed measure is an instance of the kernel Stein discrepancy (KSD), which has been used to construct goodness-of-fit tests for unnormalized densities. The KSD is def

Yoonseok Hwang, Yuting Qian, Junha Kang, Jehyun Lee

Topological crystalline insulators (TCIs) can host anomalous surface states which inherits the characteristics of crystalline symmetry that protects the bulk topology. Especially, the diversity of magnetic crystalline symmetries indicates the potential for novel magnetic TCIs with distinct surface characteristics. Here, we propose a topological magnetic Dira

Wentao Jiang, Felix M. Mayor, Sultan Malik, Raphaël Van Laer

A quantum network that distributes and processes entanglement would enable powerful new computers and sensors. Optical photons with a frequency of a few hundred terahertz are perhaps the only way to distribute quantum information over long distances. Superconducting qubits on the other hand, which are one of the most promising approaches for realizing large-

Stefan Antusch, Jan Hajer, Johannes Rosskopp

We study pseudo-Dirac pairs of two almost mass-degenerate sterile Majorana neutrinos which generate light neutrino masses via a low-scale seesaw mechanism. These pseudo-Dirac heavy neutral leptons can oscillate between interaction eigenstates that couple to leptons and antileptons and thus generate oscillations between lepton number conserving and lepton num

Zirui Liu, Shengyuan Chen, Kaixiong Zhou, Daochen Zha

The training of graph neural networks (GNNs) is extremely time consuming because sparse graph-based operations are hard to be accelerated by hardware. Prior art explores trading off the computational precision to reduce the time complexity via sampling-based approximation. Based on the idea, previous works successfully accelerate the dense matrix based opera

Chuanfei Dong, Liang Wang, Yi-Min Huang, Luca Comisso

Magnetohydrodynamic turbulence regulates the transfer of energy from large to small scales in many astrophysical systems, including the solar atmosphere. We perform three-dimensional magnetohydrodynamic simulations with unprecedentedly large magnetic Reynolds number to reveal how rapid reconnection of magnetic field lines changes the classical paradigm of th

Stephon Alexander

We present a model of Cosmological Electroweak Symmetry Breaking (CEWSB), where a Higgs-like field and a cosmological background of weak boson gauge fields interact with gravity to realize the epoch of cosmic inflation, which is then followed by a Higgs resonance preheating. As a result, the scale of electroweak symmetry breaking is linked with the end of in

Karim Alexander Adiprasito, Stavros Argyrios Papadakis, Vasiliki Petrotou, Johanna Kristina Steinmeyer

We study semigroup algebras arising from lattice polytopes, compute their volume polynomials (particularizing work of Hochster), and establish strong Lefschetz properties (generalizing work of the first three authors). This resolves several conjectures concerning unimodality properties of the $h^\ast$-polynomial of lattice polytopes arising within Ehrhart th

Gideon Lee, Connor T. Hann, Shruti Puri, S. M. Girvin

Efficient suppression of errors without full error correction is crucial for applications with NISQ devices. Error mitigation allows us to suppress errors in extracting expectation values without the need for any error correction code, but its applications are limited to estimating expectation values, and cannot provide us with high-fidelity quantum operatio

Junshi Xia, Naoto Yokoya, Bruno Adriano, Clifford Broni-Bediako

We introduce OpenEarthMap, a benchmark dataset, for global high-resolution land cover mapping. OpenEarthMap consists of 2.2 million segments of 5000 aerial and satellite images covering 97 regions from 44 countries across 6 continents, with manually annotated 8-class land cover labels at a 0.25--0.5m ground sampling distance. Semantic segmentation models tra

Rostislav Akhmechet, Melissa Zhang

We study Khovanov homology over the Frobenius algebra $\mathbb{F}[U,V,X]/((X-U)(X-V))$, or $U(1) \times U(1)$-equivariant Khovanov homology, and extract two families of concordance invariants using the algebraic $U$-power and $V$-power filtrations on the chain complex. We also further develop the reduced version of the theory and study its behavior under mir

Levent Alpöge, Manjul Bhargava, Ari Shnidman

We prove that a positive proportion of integers are expressible as the sum of two rational cubes, and a positive proportion are not so expressible, thus proving a conjecture of Davenport. More generally, we prove that a positive proportion (in fact, at least one sixth) of elliptic curves in any cubic twist family have rank 0, and a positive proportion (in fa

Michael Aizenman, Giorgio Cipolloni

The familiar second derivative test for convexity, combined with resolvent calculus, is shown to yield a useful tool for the study of convex matrix-valued functions. We demonstrate the applicability of this approach on a number of theorems in this field. These include convexity principles which play an essential role in the Lieb-Ruskai proof of the strong su

Mirbek Turduev, Emre Bor, Onur Alparslan, Y. Sinan Hanay

In this paper, we introduce the design approach of integrated photonic devices by employing reinforcement learning known as attractor selection. Here, we combined three-dimensional finite-difference time-domain method with attractor selection algorithm, which is based on artificial neural networks, to achieve ultra-compact and highly efficient photonic devic

John M. Campbell, R. Keith Ellis, Tobias Neumann, Satyajit Seth

Diboson processes are one of the most accessible and stringent probes of the Standard Model's electroweak gauge structure at the LHC. They will be probed at the percent level at the high-luminosity LHC, challenging current theory predictions. We present transverse momentum resummed calculations at N3LL+NNLO for the processes $ZZ$, $WZ$, $WH$ and $ZH$, compar

Stefan Hegselmann, Alejandro Buendia, Hunter Lang, Monica Agrawal

We study the application of large language models to zero-shot and few-shot classification of tabular data. We prompt the large language model with a serialization of the tabular data to a natural-language string, together with a short description of the classification problem. In the few-shot setting, we fine-tune the large language model using some labeled

Yutao Mou, Pei Wang, Keqing He, Yanan Wu

Detecting out-of-domain (OOD) intents from user queries is essential for avoiding wrong operations in task-oriented dialogue systems. The key challenge is how to distinguish in-domain (IND) and OOD intents. Previous methods ignore the alignment between representation learning and scoring function, limiting the OOD detection performance. In this paper, we pro

Promita Roy, Purba Bhattacharya, Vishal Kumar, Supratik Mukhopadhyay

THick Gas Electron Multipliers (THGEMs) are robust and high gain Micro Pattern Gaseous Detectors which are economically manufactured by standard drilling and etching of thin printed circuit boards. In this paper, we present our recent simulation as well as experimental studies on THGEMs which have been fabricated in India using local expertise. Two types of

Muratcan Ayik, Hamza Kurt, Oleg V. Minin, Igor V. Minin

In this manuscript, we demonstrate the design and experimental proof of an optical cloaking structure which multi-directionally conceals a perfectly electric conductor (PEC) object from an incident plane wave. The dielectric modulation around the highly reflective scattering PEC object is determined by an optimization process for multi-directional cloaking p

Víctor Elvira, Emilie Chouzenoux, Ömer Deniz Akyildiz, Luca Martino

Importance sampling (IS) is a powerful Monte Carlo methodology for the approximation of intractable integrals, very often involving a target probability density function. The performance of IS heavily depends on the appropriate selection of the proposal distributions where the samples are simulated from. In this paper, we propose an adaptive importance sampl

Aranka Hrušková

The recently developed notion of action convergence by Backhausz and Szegedy unifies and generalises the dense (graphon) and local-global (graphing) convergences of graph sequences. This is done through viewing graphs as operators and examining their dynamical properties. Suppose $(A_n)_n^\infty$ is a sequence of operators representing graphs, Cauchy with re

Charlotte Van Petegem, Rien Maertens, Niko Strijbol, Jorg Van Renterghem

Dodona (dodona.ugent.be) is an intelligent tutoring system for computer programming. It bridges the gap between assessment and learning by providing real-time data and feedback to help students learn better, teachers teach better and educational technology become more effective. We demonstrate how Dodona can be used as a virtual co-teacher to stimulate activ

Haitao Mao, Lixin Zou, Yujia Zheng, Jiliang Tang

The page presentation biases in the information retrieval system, especially on the click behavior, is a well-known challenge that hinders improving ranking models' performance with implicit user feedback. Unbiased Learning to Rank~(ULTR) algorithms are then proposed to learn an unbiased ranking model with biased click data. However, most existing algorithms

Alexandria J. Moore, Andrew M. Weiner

Entanglement -- the coherent correlations between parties in a joint quantum system -- is well-understood and quantifiable in the two-dimensional, two-party case. Higher (>2)-dimensional entangled systems hold promise in extending the capabilities of various quantum information applications. Despite the utility of such systems, methods for quantifying high-d

Philippe Weinzaepfel, Vincent Leroy, Thomas Lucas, Romain Brégier

Masked Image Modeling (MIM) has recently been established as a potent pre-training paradigm. A pretext task is constructed by masking patches in an input image, and this masked content is then predicted by a neural network using visible patches as sole input. This pre-training leads to state-of-the-art performance when finetuned for high-level semantic tasks

Henry Li, Yuval Kluger

We introduce a simple modification to the standard maximum likelihood estimation (MLE) framework. Rather than maximizing a single unconditional likelihood of the data under the model, we maximize a family of \textit{noise conditional} likelihoods consisting of the data perturbed by a continuum of noise levels. We find that models trained this way are more ro

AÏcha Balhag, Zakaria Mazgouri, Michel Théra

In this paper, we introduce an inertial proximal method for solving a bilevel problem involving two monotone equilibrium bifunctions in Hilbert spaces. Under suitable conditions and without any restrictive assumption on the trajectories, the weak and strong convergence of the sequence generated by the iterative method are established. Two particular cases il

Cheolhee Han, Yigal Meir, Eran Sela

It is desirable to relate entanglement of many-body systems to measurable observables. In systems with a conserved charge, it was recently shown that the number entanglement entropy (NEE) - i.e. the entropy change due to an unselective subsystem charge measurement - is an entanglement monotone. Here we derive finite-temperature equilibrium relations between

Pierrick Bousseau, Pierre Descombes, Bruno Le Floch, Boris Pioline

The spectrum of BPS states in type IIA string theory compactified on a Calabi-Yau threefold famously jumps across codimension-one walls in complexified K\"ahler moduli space, leading to an intricate chamber structure. The Split Attractor Flow Conjecture posits that the BPS index $\Omega_z(\gamma)$ for given charge $\gamma$ and moduli $z$ can be reconstructed

Steve M. Young, Aaron M. Katzenmeyer, Evan M. Anderson, Ting S. Luk

The classical Drude model provides an accurate description of the plasma resonance of three-dimensional materials, but only partially explains two-dimensional systems where quantum mechanical effects dominate such as P:$\delta$-layers - atomically thin sheets of phosphorus dopants in silicon that induce novel electronic properties beyond traditional doping.

Piotr Borodulin-Nadzieja, Sebastian Jachimek, Anna Pelczar-Barwacz

We present quasi-Banach spaces which are closely related to the duals of combinatorial Banach spaces. More precisely, for a compact family $\mathcal{F}$ of finite subsets of $\omega$ we define a quasi-norm $\lVert \cdot \rVert^\mathcal{F}$ whose Banach envelope is the dual norm for the combinatorial space generated by $\mathcal{F}$. Such quasi-norms seem to

Yunzhi Yao, Shengyu Mao, Ningyu Zhang, Xiang Chen

With the development of pre-trained language models, many prompt-based approaches to data-efficient knowledge graph construction have been proposed and achieved impressive performance. However, existing prompt-based learning methods for knowledge graph construction are still susceptible to several potential limitations: (i) semantic gap between natural langu

Ratan Lal, Vipul Kakkar

In this paper, we have computed the automorphism groups of all groups of order $p^{2}q^{2}$, where $p$ and $q$ are distinct primes.

Juan A. Rodriguez, David Vazquez, Issam Laradji, Marco Pedersoli

Synthetic image generation has recently experienced significant improvements in domains such as natural image or art generation. However, the problem of figure and diagram generation remains unexplored. A challenging aspect of generating figures and diagrams is effectively rendering readable texts within the images. To alleviate this problem, we present OCR-

N. Pulatova, A. Tugay, L. Zadorozhna, R. Seeburger

We cross-matched the 4XMM-DR10 catalog with the HyperLEDA database and obtained the new sample of galaxies that contain X-ray sources. Excluding duplicate observations and false matches, we present a total of 7759 galaxies with X-ray sources. In the current work, we present general properties of the sample: namely the distribution in equatorial coordinates,

Søren Toxvaerd

The dynamics of galaxies in an expanding universe is often determined for gravitational and dark matter in an Einstein-de Sitter universe, or alternatively by modifying the gravitational long-range attractions in the Newtonian dynamics (MOND). Here the time evolution of galaxies is determined by simulations of systems with pure gravitational forces by classi

Sergey K. Tolpygo, Evan B. Golden, Terence J. Weir, Vladimir Bolkhovsky

We present measurements of the self- and mutual inductance of NbN and bilayer NbN/Nb inductors with Nb ground plane(s) fabricated in an advanced process for superconductor electronics developed at MIT Lincoln Laboratory. In this process, the signal traces of logic cell inductors are made either of a 200-nm NbN layer with $T_c$=15 K or of an in-situ deposited

Mahmoud Benkhalifa

Let $X$ be a \text{\rm{2}}-connected and \text{\rm{6}}-dimensional CW-complex $X$ such that $H_{3}(X)\otimes\Z_2=0$. This paper aims to describe the group $\E(X)$ of the self-homotopy equivalences of $X$ modulo its normal subgroup $\E_{*}(X)$ of the elements that induce the identity on the homology groups. Making use of the Whitehead exact sequence of $X$, d

Andrea Pugnana, Salvatore Ruggieri

Selective classification (or classification with a reject option) pairs a classifier with a selection function to determine whether or not a prediction should be accepted. This framework trades off coverage (probability of accepting a prediction) with predictive performance, typically measured by distributive loss functions. In many application scenarios, su

Cyril Furtlehner

Regression models usually tend to recover a noisy signal in the form of a combination of regressors, also called features in machine learning, themselves being the result of a learning process.The alignment of the prior covariance feature matrix with the signal is known to play a key role in the generalization properties of the model, i.e. its ability to mak

David Parmenter, Mark Pollicott

We shall describe a new construction of equilibrium states for a class of partially hyperbolic systems. This generalises our construction for Gibbs measures in the uniformly hyperbolic setting. This more general setting introduces new issues that we need to address carefully, in particular requiring additional assumptions on the transformation. We treat two

Carlos Manuel de Armas-Morejón, Ask Hjorth Larsen, Luis A. Montero-Cabrera, Angel Rubio

This paper proposes a machine learning (ML) method to predict stable molecular geometries from their chemical composition. The method is useful for generating molecular conformations which may serve as initial geometries for saving time during expensive structure optimizations by quantum mechanical calculations of large molecules. Conformations are found by

Li Zhou, Bo Ma, Yonghao Wang, Yinan Zhu

We are using archived data from HST of transiting exoplanet L~98-59~b to place constraints on its potentially hot atmosphere. We analyze the data from five transit visits and extract the final combined transmission spectrum using Iraclis. Then we use the inverse atmospheric retrieval code TauREx to analyze the combined transmission spectrum. There is a weak

Laixin Xie, Ziming Wu, Peng Xu, Wei Li

Massively multiplayer online role-playing games create virtual communities that support heterogeneous "social roles" determined by gameplay interaction behaviors under a specific social context. For all social roles, formal roles are pre-defined, obvious, and explicitly ascribed to the people holding the roles, whereas informal roles are not well-defined and

Nicolò Drago, Christiaan J. F. van de Ven

We define a strict deformation quantization which is compatible with any Hamiltonian with local spin interaction (e.g. the Heisenberg Hamiltonian) for a spin chain. This is a generalization of previous results known for mean-field theories. The main idea is to study the asymptotic properties of a suitably defined algebra of sequences invariant under the grou

Clemens Isert, Kenneth Atz, Gisbert Schneider

Structure-based drug design uses three-dimensional geometric information of macromolecules, such as proteins or nucleic acids, to identify suitable ligands. Geometric deep learning, an emerging concept of neural-network-based machine learning, has been applied to macromolecular structures. This review provides an overview of the recent applications of geomet

Matthew Houtput, Jacques Tempere

In a polar solid, electrons or other charge carriers can interact with the phonons of the ionic lattice, leading to the formation of polaron quasiparticles. The optical conductivity and optical absorption spectrum of a material are affected by this electron-phonon coupling, most notably leading to an absorption peak in the mid-infrared region. Recently, a mo

Tim Baumgärtner, Leonardo F. R. Ribeiro, Nils Reimers, Iryna Gurevych

Pairing a lexical retriever with a neural re-ranking model has set state-of-the-art performance on large-scale information retrieval datasets. This pipeline covers scenarios like question answering or navigational queries, however, for information-seeking scenarios, users often provide information on whether a document is relevant to their query in form of c

Wojciech Jamroga, Peter Y. A. Ryan, Yan Kim

Voting procedures are designed and implemented by people, for people, and with significant human involvement. Thus, one should take into account the human factors in order to comprehensively analyze properties of an election and detect threats. In particular, it is essential to assess how actions and strategies of the involved agents (voters, municipal offic

Mingzhe Du

Scene graphs provide structured semantic understanding beyond images. For downstream tasks, such as image retrieval, visual question answering, visual relationship detection, and even autonomous vehicle technology, scene graphs can not only distil complex image information but also correct the bias of visual models using semantic-level relations, which has b

Hongxin Zhang, Yanzhe Zhang, Ruiyi Zhang, Diyi Yang

Demonstration-based learning has shown great potential in stimulating pretrained language models' ability under limited data scenario. Simply augmenting the input with some demonstrations can significantly improve performance on few-shot NER. However, why such demonstrations are beneficial for the learning process remains unclear since there is no explicit a

Idris Abdulmumin, Michael Beukman, Jesujoba O. Alabi, Chris Emezue

We participated in the WMT 2022 Large-Scale Machine Translation Evaluation for the African Languages Shared Task. This work describes our approach, which is based on filtering the given noisy data using a sentence-pair classifier that was built by fine-tuning a pre-trained language model. To train the classifier, we obtain positive samples (i.e. high-quality

Niklas Kochdumper, Hanna Krasowski, Xiao Wang, Stanley Bak

While reinforcement learning produces very promising results for many applications, its main disadvantage is the lack of safety guarantees, which prevents its use in safety-critical systems. In this work, we address this issue by a safety shield for nonlinear continuous systems that solve reach-avoid tasks. Our safety shield prevents applying potentially uns

De-Jun Wu

We investigate relativistic star solutions in Mass-Varying Massive Gravity (MVMG) with a diagonal metric. Contrary to the intuition that there is no fundamental difference between diagonal metric and non-diagonal metric solutions regarding relativistic stars, we find that with a diagonal metric, well-behaved relativistic star solutions may not exist except f

Isar Nejadgholi, Esma Balkır, Kathleen C. Fraser, Svetlana Kiritchenko

Previous works on the fairness of toxic language classifiers compare the output of models with different identity terms as input features but do not consider the impact of other important concepts present in the context. Here, besides identity terms, we take into account high-level latent features learned by the classifier and investigate the interaction bet

Paul Shah, Pablo Lemos, Ofer Lahav

When Type Ia supernovae are used to infer cosmological parameters, their luminosities are compared to those from a homogeneous cosmology. In this note we propose a test to examine to what degree SN Ia have been observed on lines of sight where the average matter density is \textit{not} representative of the homogeneous background. We apply our test to the Pa

Agustin Zaballos, Adria Mallorqui, Joan Navarro

The broad adoption of the Internet of Things during the last decade has widened the application horizons of distributed sensor networks, ranging from smart home appliances to automation, including remote sensing. Typically, these distributed systems are composed of several nodes attached to sensing devices linked by a heterogeneous communication network. The

Kiyokazu Nagatomo, Yuichi Sakai, Don Zagier

The aim in this paper is to give expressions for modular linear differential operators of any order. In particular, we show that they can all be described in terms of Rankin-Cohen brackets and a modified Rankin-Cohen bracket found by Kaneko and Koike. We also give more uniform descriptions of MLDOs in terms of canonically defined higher Serre derivatives and