November 2022 arXiv papers — page 99

Shizheng Zhou, Juntao Jiang, Xiaohan Hong, Yan Hong

Marine microalgae are widespread in the ocean and play a crucial role in the ecosystem. Automatic identification and location of marine microalgae in microscopy images would help establish marine ecological environment monitoring and water quality evaluation system. We proposed a new dataset for the detection of marine microalgae and a range of detection met

Manasse R. Mbonye

In this paper we discuss a cosmological model for a universe with self-regulating features. We set up the theoretical framework for the model and determine the time evolution of the scale-factor $a(t)$. It is shown that such a universe repeatedly goes through alternate periods of matter and dark energy domination. The resulting dynamics oscillates about the

Amir Rasouli, Randy Goebel, Matthew E. Taylor, Iuliia Kotseruba

Driving SMARTS is a regular competition designed to tackle problems caused by the distribution shift in dynamic interaction contexts that are prevalent in real-world autonomous driving (AD). The proposed competition supports methodologically diverse solutions, such as reinforcement learning (RL) and offline learning methods, trained on a combination of natur

Niklas Schmid, John Lygeros

We consider the safety evaluation of discrete time, stochastic systems over a finite horizon. Therefore, we discuss and link probabilistic invariance with reachability as well as reach-avoid problems. We show how to efficiently compute these quantities using dynamic and linear programming.

Ben Perach, Ronny Ronnen, Shahar Kvatinsky

Processing-in-memory (PIM) architectures allow software to explicitly initiate computation in the memory. This effectively makes PIM operations a new class of memory operations, alongside standard memory operations (e.g., load, store). For software correctness, it is crucial to have ordering rules for a PIM operation with other PIM operations and other memor

Ryan Abbott, Michael S. Albergo, Aleksandar Botev, Denis Boyda

Recent applications of machine-learned normalizing flows to sampling in lattice field theory suggest that such methods may be able to mitigate critical slowing down and topological freezing. However, these demonstrations have been at the scale of toy models, and it remains to be determined whether they can be applied to state-of-the-art lattice quantum chrom

Christopher C. Lovell, Will Roper, Aswin P. Vijayan, Louise Seeyave

Passive galaxies are ubiquitous in the local universe, and various physical channels have been proposed that lead to this passivity. To date, robust passive galaxy candidates have been detected up to $z \leqslant 5$, but it is still unknown if they exist at higher redshifts, what their relative abundances are, and what causes them to stop forming stars. We p

Jonas Maziero, Marcos L. W. Basso, Lucas C. Céleri

Complete complementarity relations, as e.g. $P(\rho_{A})^{2} + C(\rho_{A})^{2} + E(|\Psi\rangle_{AB})^{2}=1$, constrain the local predictability, $P$, and local coherence, $C$, and the entanglement, $E$, of bipartite pure states. For pairs of qubits prepared initially in a particular class of partially entangled pure states with null local coherence, these r

Rhea Alexander, Si Gvirtz-Chen, Nikolaos Koukoulekidis, David Jennings

Magic states are fundamental building blocks on the road to fault-tolerant quantum computing. CSS codes play a crucial role in the construction of magic distillation protocols. Previous work has cast quantum computing with magic states for odd dimension $d$ within a phase space setting in which universal quantum computing is described by the statistical mech

Maartje ter Hoeve, David Grangier, Natalie Schluter

The central bottleneck for low-resource NLP is typically regarded to be the quantity of accessible data, overlooking the contribution of data quality. This is particularly seen in the development and evaluation of low-resource systems via down sampling of high-resource language data. In this work we investigate the validity of this approach, and we specifica

Yoshiaki Kitazawa

Estimating causal effects from observational data is a central problem in many domains. A general approach is to balance covariates with weights such that the distribution of the data mimics randomization. We present generalized balancing weights, Neural Balancing Weights (NBW), to estimate the causal effects of an arbitrary mixture of discrete and continuou

Gabriel Varela-Mattatall, Paul I. Dubovan, Tales Santini, Kyle M. Gilbert

Purpose: The expanded encoding model incorporates spatially- and time-varying field perturbations for correction during reconstruction. So far, these reconstructions have used the conjugate gradient method with early stopping used as implicit regularization. However, this approach is likely suboptimal for low-SNR cases like diffusion or high-resolution MRI.

Ainhize Barrainkua, Paula Gordaliza, Jose A. Lozano, Novi Quadrianto

Human lives are increasingly being affected by the outcomes of automated decision-making systems and it is essential for the latter to be, not only accurate, but also fair. The literature of algorithmic fairness has grown considerably over the last decade, where most of the approaches are evaluated under the strong assumption that the train and test samples

Burak Ulas, Ceren Ulusoy

We present the first evidence on the $\delta$~Sct type pulsations of the primary components of three eclipsing binaries IQ CMa, AW Men and W Vol in the TESS field. A comprehensive investigation of the binary properties is conducted. The light curves of the systems are analysed and the frequency analyses are performed to residual data. The systems are compare

Isobel M. Romero-Shaw, Davide Gerosa, Nicholas Loutrel

Eccentricity and spin precession are key observables in gravitational-wave astronomy, encoding precious information about the astrophysical formation of compact binaries together with fine details of the relativistic two-body problem. However, the two effects can mimic each other in the emitted signals, raising issues around their distinguishability. Since i

Marius Junge, Peixue Wu

We show the continuity property of spectral gaps and complete Logarithmic constants in terms of the jump operators of Lindblad generators in finite dimensional setting. Our method is based on the bimodule structure of the derivation space and the technique developed in [Paulsen09]. Using the same trick, we also show the continuity of the $g^2(0)$ constant us

Xun Yu

We derive a characterization of the complex projective K3 surfaces which have automorphisms of positive entropy in term of their N\'eron-Severi lattices. Along the way, we classify the projective K3 surfaces of zero entropy with infinite automorphism groups and we determine the projective K3 surfaces of Picard number at least five with almost abelian automor

Qi Song, Spencer Doyle, Grace A. Pan, Ismail El Baggari

Long viewed as passive elements, antiferromagnetic materials have emerged as promising candidates for spintronic devices due to their insensitivity to external fields and potential for high-speed switching. Recent work exploiting spin and orbital effects has identified ways to electrically control and probe the spins in metallic antiferromagnets, especially

Ayush Agrawal, Siddhartha Gadgil, Navin Goyal, Ashvni Narayanan

Mathematics formalisation is the task of writing mathematics (i.e., definitions, theorem statements, proofs) in natural language, as found in books and papers, into a formal language that can then be checked for correctness by a program. It is a thriving activity today, however formalisation remains cumbersome. In this paper, we explore the abilities of a la

Yoel Groman, Umut Varolgunes

For the base $B$ of a Maslov $0$ Lagrangian torus fibration with singularities consider the sheaf assigning to each $P\subset B$ the relative symplectic cohomology in degree $0$ of its pre-image. We compute this sheaf for nodal Lagrangian torus fibrations on four dimensional symplectic cluster manifolds. We show that it is the pushforward of the structure sh

Deepak Gupta

Question-answering (QA) that comes naturally to humans is a critical component in seamless human-computer interaction. It has emerged as one of the most convenient and natural methods to interact with the web and is especially desirable in voice-controlled environments. Despite being one of the oldest research areas, the current QA system faces the critical

Eslam Mohamed Bakr, Ahmad El Sallab, Mohsen A. Rashwan

Recently, attention mechanisms have been explored with ConvNets, both across the spatial and channel dimensions. However, from our knowledge, all the existing methods devote the attention modules to capture local interactions from a uni-scale. In this paper, we propose a Previous Knowledge Channel Attention Module(PKCAM), that captures channel-wise relations

Natalie Bolón Brun, Sofia Kypraiou, Natalia Gullón Altés, Irene Petlacalco Barrios

The way Wikipedia's contributors think can influence how they describe individuals resulting in a bias based on gender. We use a machine learning model to prove that there is a difference in how women and men are portrayed on Wikipedia. Additionally, we use the results of the model to obtain which words create bias in the overview of the biographies of the E

Varun Ojha, Bartolomeo Panto, Giuseppe Nicosia

The paper proposes a novel adaptive search space decomposition method and a novel gradient-free optimization-based formulation for the pre- and post-buckling analyses of space truss structures. Space trusses are often employed in structural engineering to build large steel constructions, such as bridges and domes, whose structural response is characterized b

Chandan Chunduru, Chun Jiang Zhu, Blake Gains, Jinbo Bi

Graph sparsification is a powerful tool to approximate an arbitrary graph and has been used in machine learning over homogeneous graphs. In heterogeneous graphs such as knowledge graphs, however, sparsification has not been systematically exploited to improve efficiency of learning tasks. In this work, we initiate the study on heterogeneous graph sparsificat

Swarnadeep Saha, Peter Hase, Nazneen Rajani, Mohit Bansal

Recent work on explainable NLP has shown that few-shot prompting can enable large pretrained language models (LLMs) to generate grammatical and factual natural language explanations for data labels. In this work, we study the connection between explainability and sample hardness by investigating the following research question - "Are LLMs and humans equally

Elias Stengel-Eskin, Jimena Guallar-Blasco, Yi Zhou, Benjamin Van Durme

Natural language is ambiguous. Resolving ambiguous questions is key to successfully answering them. Focusing on questions about images, we create a dataset of ambiguous examples. We annotate these, grouping answers by the underlying question they address and rephrasing the question for each group to reduce ambiguity. Our analysis reveals a linguistically-ali

Anmol Agarwal, Jigar Gupta, Rahul Goel, Shyam Upadhyay

Extending semantic parsers to code-switched input has been a challenging problem, primarily due to a lack of supervised training data. In this work, we introduce CST5, a new data augmentation technique that finetunes a T5 model using a small seed set ($\approx$100 utterances) to generate code-switched utterances from English utterances. We show that CST5 gen

Burak Dagli, Bora Ketenoglu, Saleh Sultansoy

Luminosities of muon-proton and muon-nucleus collisions at a recently proposed muon-ion collider (MuIC) at Brookhaven National Laboratory (BNL) in the USA have been estimated using A Luminosity Optimizer for High Energy Physics (AloHEP) software keeping in mind beam-beam tune-shift values. It is shown that L$_{\mu p}$ = 3.6x10$^{31}$ cm$^{-2}$s$^{-1}$ and L$

Kimberly J. Daas, Derk P. Kooi, Tarik Benyahia, Michael Seidl

We study numerically the strong-interaction limit of the exchange-correlation functional for neutral atoms and for Bohr atoms as the number of electrons increases. Using a compact representation, we analyse the second-order gradient expansion, comparing it with the one for exchange (weak interaction limit). The two gradient expansions, at strong and weak int

Seung Hoon Park, Rekha Pai, Tom Melham

CHERI-C extends the C programming language by adding hardware capabilities, ensuring a certain degree of memory safety while remaining efficient. Capabilities can also be employed for higher-level security measures, such as software compartmentalization, that have to be used correctly to achieve the desired security guarantees. As the extension changes the s

Khalid Elgazzar, Haytham Khalil, Taghreed Alghamdi, Ahmed Badr

The Internet of Things (IoT) has brought the dream of ubiquitous data access from physical environments into reality. IoT embeds sensors and actuators in physical objects so that they can communicate and exchange data between themselves to improve efficiency along with enabling real-time intelligent services and offering better quality of life to people. The

Matthias Diez, Reinhard Alkofer, Christian Kohlfürst

Particle production through ultra-strong electric fields is a well-studied research field. Nevertheless, despite repeated attempts to relate the production rate within the field to the formation time of a particle, the latter is still shrouded in mystery. We provide an interpretation of a particle distribution at finite times enabling us to isolate and, ther

Pierre Auclair

We revisit the scaling properties of growing spheres randomly seeded in d=2,3 and 4 dimensions using a mean-field approach. We model the insertion probability without assuming a priori a functional form for the radius distribution. The functional form of the insertion probability shows an unprecedented agreement with numerical simulations in d=2, 3 and 4 dim

Martin Brandenburg

We present a simple proof of the fundamental theorem of Galois theory, which establishes a correspondence between the intermediate fields of a finite Galois extension and the subgroups of its Galois group. The proof is based on the combinatorial fact that a field cannot be expressed as the union of finitely many proper subfields.

Andrew Hannum, Mario A. Lopez, Saúl A. Blanco, Richard F. Betzel

The human brain is a complex network comprised of functionally and anatomically interconnected brain regions. A growing number of studies have suggested that empirical estimates of brain networks may be useful for discovery of biomarkers of disease and cognitive state. A prerequisite for realizing this aim, however, is that brain networks also serve as relia

Eoghan O'Neill

Censoring occurs when an outcome is unobserved beyond some threshold value. Methods that do not account for censoring produce biased predictions of the unobserved outcome. This paper introduces Type I Tobit Bayesian Additive Regression Tree (TOBART-1) models for censored outcomes. Simulation results and real data applications demonstrate that TOBART-1 produc

ATLAS Collaboration

A search for pair production of doubly charged Higgs bosons ($H^{\pm \pm}$), each decaying into a pair of prompt, isolated, highly energetic leptons with the same electric charge, is presented. The search uses a proton-proton collision data sample at a centre-of-mass energy of 13 TeV corresponding to an integrated luminosity of 139 fb$^{-1}$ recorded by the

Lei Li, Xiang Chen, Shuofei Qiao, Feiyu Xiong

Multimodal relation extraction is an essential task for knowledge graph construction. In this paper, we take an in-depth empirical analysis that indicates the inaccurate information in the visual scene graph leads to poor modal alignment weights, further degrading performance. Moreover, the visual shuffle experiments illustrate that the current approaches ma

Prayaag Venkat

We initiate the study of the algorithmic problem of certifying lower bounds on the discrepancy of random matrices: given an input matrix $A \in \mathbb{R}^{m \times n}$, output a value that is a lower bound on $\mathsf{disc}(A) = \min_{x \in \{\pm 1\}^n} ||Ax||_\infty$ for every $A$, but is close to the typical value of $\mathsf{disc}(A)$ with high probabili

Raphaël Kou, James G. Bartlett

Galaxies, diffuse gas, and dark matter make up the cosmic web that defines the large-scale structure of the Universe. We constrained the joint distribution of these constituents by cross-correlating galaxy samples binned by stellar mass from the Sloan Digital Sky Survey CMASS catalog with maps of lensing convergence and the thermal Sunyaev-Zeldovich (tSZ) ef

Steve Huntsman

The theory of magnitude provides a mathematical framework for quantifying and maximizing diversity. We apply this framework to formulate quality-diversity algorithms in generic dissimilarity spaces. In particular, we instantiate and demonstrate a very general version of Go-Explore with promising performance.

Yong-Lu Li, Hongwei Fan, Zuoyu Qiu, Yiming Dou

Spatio-temporal Human-Object Interaction (ST-HOI) detection aims at detecting HOIs from videos, which is crucial for activity understanding. In daily HOIs, humans often interact with a variety of objects, e.g., holding and touching dozens of household items in cleaning. However, existing whole body-object interaction video benchmarks usually provide limited

S. S. Afonin

One of the simplest extensions of the Standard Model (SM) consists in adding a scalar singlet. This second Higgs boson is able to solve several fundamental problems of SM. Additional scalar particles arise naturally in composite Higgs scenarios in which some confining "strong sector" beyond the SM drives the electroweak symmetry breaking. The underlying stro

Aman Priyanshu, Supriti Vijay

Keyword extraction has been an important topic for modern natural language processing. With its applications ranging from ontology generation, fact verification in summarized text, and recommendation systems. While it has had significant data-intensive applications, it is often hampered when the data set is small. Downstream training for keyword extractors i

Gaoping Long, Chun-Yen Lin

The weak coupling loop quantum theory with Abelian gauge group provides us a new perspective to study the weak coupling properties of LQG. In this paper, the weak coupling theory of all dimensional loop quantum gravity is established based on a symplectic-morphism between the $SO(D+1)$ holonomy-flux phase space and the $U(1)^{\frac{D(D+1)}{2}}$ holonomy-flux

R. Chan, M. F. A. da Silva, V. H. Satheeshkumar

We attempt to answer whether Birkhoff's theorem (BT) is valid in the Einstein-Aether (EA) theory. The BT states that any spherically symmetric solution of the vacuum field equations must be static, unique, and asymptotically flat. For a general spherically symmetric metric with metric functions $A(r,t)$ \& $B(r,t)$, and aether components $a(r,t)$ \& $b(r,t)$

Kotaro Hijikawa, Tomoya Kinugawa, Ataru Tanikawa, Takashi Yoshida

The current gravitational wave (GW) detectors have successfully observed many binary compact objects, and the third generation ground-based GW detectors such as Einstein telescope and space-borne detectors such as LISA will start their GW observation in a decade. Ahead of the arrival of this new era, we perform a binary population synthesis calculation for v

Jack-William Barotta, Stuart J. Thomson, Luke F. L. Alventosa, Maya Lewis

When a solid body floats at the interface of a vibrating liquid bath, the relative motion between the object and interface generates outwardly propagating surface waves. It has recently been demonstrated that millimetric objects with fore-aft mass asymmetry generate an associated asymmetric wavefield and consequently self-propel in unidirectional motion. Har

Ling Lin, Yongguan Ke, Chaohong Lee

Beyond the well-known topological band theory for single-particle systems, it is a great challenge to characterize the topological nature of interacting multi-particle quantum systems. Here, we uncover the relation between topological invariants defined through the twist boundary condition (TBC) and the center-of-mass (c.m.) momentum state in multi-particle

Anastasia Kuznetsova, Aswin Sivaraman, Minje Kim

With the advances in deep learning, speech enhancement systems benefited from large neural network architectures and achieved state-of-the-art quality. However, speaker-agnostic methods are not always desirable, both in terms of quality and their complexity, when they are to be used in a resource-constrained environment. One promising way is personalized spe

Saúl Sánchez-González, Amador García-Fuente, Jaime Ferrer

Noble metals break preferably exposing (111)-oriented surfaces, that host Shockley type surface states (SSs). Nevertheless, the relevance of SSs on the electrical properties of gold-based molecular junctions has not been explored in detail yet. Here, we present ab initio simulations that show how the gold (111) SS, that lies approximately 0.5 eV below the Fe

Yigit Baran Can, Alexander Liniger, Danda Pani Paudel, Luc Van Gool

We study the problem of estimating 3D shape and pose of an object in terms of keypoints, from a single 2D image. The shape and pose are learned directly from images collected by categories and their partial 2D keypoint annotations.. In this work, we first propose an end-to-end training framework for intermediate 2D keypoints extraction and final 3D shape and

Stefan Schoder

This paper presents a novel derivation of the perturbed convective wave equation by utilizing the instantaneous velocity field as the foundation for the convective operator. This approach holds particular significance in the context of modeling fluid-structure-acoustic interaction (FSAI) problems, such as those encountered in human phonation or systems invol

L. E. Suelves, W. J. Pearson, A. Pollo

Aims. We present the application of a fully connected neural network (NN) for galaxy merger identification using exclusively photometric information. Our purpose is not only to test the method's efficiency, but also to understand what merger properties the NN can learn and what their physical interpretation is. Methods. We created a class-balanced training d

Ben Black, Trivikram Dokka, Christopher Kirkbride

This paper considers robust Markov decision processes under parametric transition distributions. We assume that the true transition distribution is uniquely specified by some parametric distribution, and explicitly enforce that the worst-case distribution from the model is uniquely specified by a distribution in the same parametric family. After formulating

Rafaël Bocquet

Voevodsky's univalence axiom is often motivated as a realization of the equivalence principle; the idea that equivalent mathematical structures satisfy the same properties. Indeed, in Homotopy Type Theory, properties and structures can be transported over type equivalences. However, we may wish to explain the equivalence principle without relying on the univ

James S. D. Blake, Leigh N. Fletcher, Glenn S. Orton, Arrate Antuñano

A multi-decade record of ground-based mid-infrared (7-25 $\mu$m) images of Saturn is used to explore seasonal and non-seasonal variability in thermal emission over more than a Saturnian year (1984-2022). Thermal emission measured by 3-m and 8-m-class observatories compares favourably with synthetic images based on both Cassini-derived temperature records and

Aleksandrs Slivkins, Xingyu Zhou, Karthik Abinav Sankararaman, Dylan J. Foster

We consider contextual bandits with linear constraints (CBwLC), a variant of contextual bandits in which the algorithm consumes multiple resources subject to linear constraints on total consumption. This problem generalizes contextual bandits with knapsacks (CBwK), allowing for packing and covering constraints, as well as positive and negative resource consu

Nguyen Anh Vu Doan, Arda Yüksel, Chih-Hong Cheng

This work aims to explore and identify tiny and seemingly unrelated perturbations of images in object detection that will lead to performance degradation. While tininess can naturally be defined using $L_p$ norms, we characterize the degree of "unrelatedness" of an object by the pixel distance between the occurred perturbation and the object. Triggering erro

Zimu Li, Zihan Pengmei, Han Zheng, Erik Thiede

Many learning tasks, including learning potential energy surfaces from ab initio calculations, involve global spatial symmetries and permutational symmetry between atoms or general particles. Equivariant graph neural networks are a standard approach to such problems, with one of the most successful methods employing tensor products between various tensors th

Hitesh Garg, R Rajesh, Satyavani Vemparala

Extensive coarse grained molecular dynamics simulations are performed to investigate the conformational phase diagram of a neutral polymer in the presence of attractive crowders. We show that, for low crowded densities, the polymer predominantly shows three phases as a function of both intra polymer and polymer-crowder interactions: (1) weak intra polymer an

Gregory M. Campbell, Jessica Yin, Yuyang Song, Umesh Gandhi

Soft robotic actuators are safe and adaptable devices with inherent compliance, which makes them attractive for manipulating delicate and complex objects. Researchers have integrated stiff materials into soft actuators to increase their force capacity and direct their deformation. However, these embedded materials have largely been pre-prescribed and static,

Yurun Tian, Osman Yagan

It's been controversial whether re-opening school will facilitate viral spread among household communities with mitigation strategies such as mask-wearing in place. In this work, we propose an epidemiological model that explores the viral transmission over the multi-layer contact network composed of the school layer and community layer with population hetero

Yi-Song Lu, You-Kai Wang, Xiang-Yuan You

Being one of the golden channels for precise measurement of Higgs properties, the process $gg \to H\to ZZ \rightarrow 4l$ provides an opportunity to detect the anomalous $HZZ$ couplings in searching of new physics beyond the Standard Model. In this paper, we adopt the method of spinor helicity amplitudes to calculate the amplitudes of process $gg \to H \to Z

Z. H. Zhang, L. T. Yang, Q. Yue, K. J. Kang

We present novel constraints on boosted light dark matter particles (denoted as ``$\chi$'') from evaporating primordial black holes (PBHs) using 205.4 kg$\cdot$day data from the China Jinping Underground Laboratory's CDEX-10 p-type point contact germanium detector with a 160 eVee analysis threshold. $\chi$ from PBHs with masses ranging from 1$\times$10$^{15}

Troy McMahon, Shlomo Havlin, Lazaros K. Gallos

The COVID-19 pandemic has evolved over time through multiple spatial and temporal dynamics. The varying extent of interactions among different geographical areas can result to a complex pattern of spreading so that influences between these areas can be hard to discern. Here, we use cross-correlation analysis to detect synchronous evolution and potential inte

Vijay Jain, Vladislav D. Kurilovich, Yanni D. Dahmani, Chan U Lei

Acoustic spontaneous emission into bulk dielectrics can be a strong source of decoherence in quantum devices, especially when a qubit is in the presence of piezoelectric materials. We study the dynamics of a qubit coupled to an acoustic resonator by a piezoelectric film. By varying the surface topography of the resonator from rough to polished to shaped, we

Luhong Su, Hui Jiang, Zhan Wang, Shu Chen

Non-Hermitian skin effect (NHSE) is a novel phenomenon appearing in non-Hermitian systems. Here, we report the experimental observation of NHSE. Different from the previous non-reciprocal circuit implementation scheme using logic components, we construct our one-dimensional (1D) circuits using linear components only. Besides, we achieve the non-reciprocity b

Martin Palmer, Xiaolei Wu

We prove that the mapping class group of the one-holed Cantor tree surface is acyclic. This in turn determines the homology of the mapping class group of the once-punctured Cantor tree surface (i.e. the plane minus a Cantor set), in particular answering a recent question of Calegari and Chen. We in fact prove these results for a general class of infinite-typ

Shea Cardozo, Gabriel Islas Montero, Dmitry Kazhdan, Botty Dimanov

Recent work has suggested post-hoc explainers might be ineffective for detecting spurious correlations in Deep Neural Networks (DNNs). However, we show there are serious weaknesses with the existing evaluation frameworks for this setting. Previously proposed metrics are extremely difficult to interpret and are not directly comparable between explainer method

Alejandro Cárdenas-Avendaño, Alexandru Lupsasca, Hengrui Zhu

Recent interferometric observations by the Event Horizon Telescope have resolved the horizon-scale emission from sources in the vicinity of nearby supermassive black holes. Future space-based interferometers promise to measure the "photon ring"--a narrow, ring-shaped, lensed feature predicted by general relativity, but not yet observed--and thereby open a ne

Ed Gallagher, Roger Moser

On the two-sphere $\Sigma$, we consider the problem of minimising among suitable immersions $f \,\colon \Sigma \rightarrow \mathbb{R}^3$ the weighted $L^\infty$ norm of the mean curvature $H$, with weighting given by a prescribed ambient function $\xi$, subject to a fixed surface area constraint. We show that, under a low-energy assumption which prevents top

Leonard Bauersfeld, Angel Romero, Manasi Muglikar, Davide Scaramuzza

Double-blind peer review is considered a pillar of academic research because it is perceived to ensure a fair, unbiased, and fact-centered scientific discussion. Yet, experienced researchers can often correctly guess from which research group an anonymous submission originates, biasing the peer-review process. In this work, we present a transformer-based, ne

Nien Fang Cheng, Turgay Pamuklu, Melike Erol-Kantarci

Network slicing envisions the 5th generation (5G) mobile network resource allocation to be based on different requirements for different services, such as Ultra-Reliable Low Latency Communication (URLLC) and Enhanced Mobile Broadband (eMBB). Open Radio Access Network (O-RAN), proposes an open and disaggregated concept of RAN by modulizing the functionalities

Francisco Casacuberta, Alexandru Ceausu, Khalid Choukri, Miltos Deligiannis

This work presents the results of the machine translation (MT) task from the Covid-19 MLIA @ Eval initiative, a community effort to improve the generation of MT systems focused on the current Covid-19 crisis. Nine teams took part in this event, which was divided in two rounds and involved seven different language pairs. Two different scenarios were considere

Yanwen Luo, Xu Xu, Siqi Zhang

In 2004, Bowers-Stephenson [2] introduced the inversive distance circle packings as a natural generalization of Thurston's circle packings. They further conjectured the rigidity of infinite inversive distance circle packings in the plane. Motivated by the recent work of Luo-Sun-Wu [22] on Luo's vertex scaling, we prove Bower-Stephenson's conjecture for inver

Ray Garner, J. Christopher Mihos, Paul Harding, Aaron E. Watkins

We present deep, narrowband imaging of the nearby spiral galaxy M101 and its satellites to analyze the oxygen abundances of their HII regions. Using CWRU's Burrell Schmidt telescope, we add to the narrowband dataset of the M101 Group, consisting of H$\alpha$, H$\beta$, and [OIII] emission lines, the blue [OII]$\lambda$3727 emission line for the first time. T

Salah A Faroughi, Nikhil Pawar, Celio Fernandes, Maziar Raissi

Recent breakthroughs in computing power have made it feasible to use machine learning and deep learning to advance scientific computing in many fields, including fluid mechanics, solid mechanics, materials science, etc. Neural networks, in particular, play a central role in this hybridization. Due to their intrinsic architecture, conventional neural networks

Hamed Masaood

We construct a scattering theory for the linearised Einstein equations on a Schwarzschild background in a double null gauge. We build on the results of Part I \cite{Mas20}, where we used the energy conservation enjoyed by the Regge--Wheeler equation associated with the stationarity of the Schwarzschild background to construct a scattering theory for the Teuk

Apurva Tiwari, Avijit Chatterjee

The Discontinuous Galerkin time-domain method is well suited for adaptive algorithms to solve the time-domain Maxwell's equations and depends on robust and economically computable drivers. Adaptive algorithms utilize local indicators to dynamically identify regions and assign spatial operators of varying accuracy in the computational domain. This work identi

Nanne van Noord, Melvin Wevers, Tobias Blanke, Julia Noordegraaf

There is a bidirectional relationship between culture and AI; AI models are increasingly used to analyse culture, thereby shaping our understanding of culture. On the other hand, the models are trained on collections of cultural artifacts thereby implicitly, and not always correctly, encoding expressions of culture. This creates a tension that both limits th

Yuxin Huang, Andong Yang, Zirui Wu, Yuantao Chen

It has been shown that learning radiance fields with depth rendering and depth supervision can effectively promote the quality and convergence of view synthesis. However, this paradigm requires input RGB-D sequences to be synchronized, hindering its usage in the UAV city modeling scenario. As there exists asynchrony between RGB images and depth images due to

Lanchao Wang, Yaojun Chen

Let $X$ and $Y$ be any two graphs of order $n$. The friends-and-strangers graph $\mathsf{FS}(X,Y)$ of $X$ and $Y$ is a graph with vertex set consisting of all bijections $\sigma :V(X) \mapsto V(Y)$, in which two bijections $\sigma$, $\sigma'$ are adjacent if and only if they differ precisely on two adjacent vertices of $X$, and the corresponding mappings are

Roman Pöschl

The next generation of collider detectors will make full use of Particle Flow Algorithms, requiring high-precision tracking and full imaging calorimeters. The latter, thanks to granularity improvements by two to three orders of magnitude compared to existing devices, have been developed during the past 15 years by the CALICE collaboration and are now reachin

Guillem Belda-Ferrín, Eloi Ruiz-Gironés, Abel Gargallo-Peiró, Xevi Roca

We present an $n$-dimensional marked bisection method for unstructured conformal meshes. We devise the method for local refinement in adaptive $n$-dimensional applications. To this end, we propose a mesh marking pre-process and three marked bisection stages. The pre-process marks the initial mesh conformingly. Then, in the first $n-1$ bisections, the method

Parinya Karndumri

We find a large class of new supersymmetric $AdS_5$ black strings from five-dimensional $N=4$ gauged supergravity coupled to five vector multiplets with $SO(2)_D\times SO(3)\times SO(3)$ gauge group. These solutions have near horizon geometries of the form $AdS_3\times \Sigma^2$ for $\Sigma^2$ being a two-sphere ($S^2$) or a hyperbolic space ($H^2$). There a

Jialiang Xu, Mengyu Zhou, Xinyi He, Shi Han

Numerical Question Answering is the task of answering questions that require numerical capabilities. Previous works introduce general adversarial attacks to Numerical Question Answering, while not systematically exploring numerical capabilities specific to the topic. In this paper, we propose to conduct numerical capability diagnosis on a series of Numerical

Mengyang Zhao, Xinhua Zeng, Yang Liu, Jing Liu

Video anomaly detection (VAD) has been intensively studied for years because of its potential applications in intelligent video systems. Existing unsupervised VAD methods tend to learn normality from training sets consisting of only normal videos and regard instances deviating from such normality as anomalies. However, they often consider only local or globa

Kai Cieliebak, Oleg Lazarev, Thomas Massoni, Agustin Moreno

A smooth Anosov flow on a closed oriented three manifold $M$ gives rise to a Liouville structure on the four manifold $[-1,1]\times M$ which is not Weinstein, by a construction of Mitsumatsu and Hozoori. We call it the associated Anosov Liouville domain. It is well defined up to homotopy and only depends on the homotopy class of the original Anosov flow; its

Barry T. Chiang, Jeremiah P. Ostriker, Hsi-Yu Schive

Ultralight axion-like particles $m_a \sim 10^{-22}$ eV, or Fuzzy Dark Matter (FDM), behave comparably to cold dark matter (CDM) on cosmological scales and exhibit a kpc-size de Broglie wavelength capable of alleviating established (sub-)galactic-scale problems of CDM. Substructures inside an FDM halo incur gravitational potential perturbations, resulting in

V. Gioia, M. Fasiolo, J. Browell, R. Bellio

Forecasts of regional electricity net-demand, consumption minus embedded generation, are an essential input for reliable and economic power system operation, and energy trading. While such forecasts are typically performed region by region, operations such as managing power flows require spatially coherent joint forecasts, which account for cross-regional de

Jorge Caravantes, Sonia Pérez-Díaz, J. Rafael Sendra

Given a unirational parameterization of a surface, we present a general algorithm to determine a birational parameterization without using parameterization algorithms. Additionally, if the surface is assumed to have a birational parametrization with empty base locus, and the input parametrization is transversal, the degree of the solution is determined in ad

Seyed Saman Saboksayr, Gonzalo Mateos

We investigate online network topology identification from smooth nodal observations acquired in a streaming fashion. Different from non-adaptive batch solutions, our distinctive goal is to track the (possibly) dynamic adjacency matrix with affordable memory and computational costs by processing signal snapshots online. To this end, we leverage and truncate

Alessandro Ciattoni

Relativistic electrons experience very slight wave packet distortion and negligible momentum recoil when interacting with nanometer-sized samples, as a consequence of the ultra-short interaction time. Accordingly, modeling fast electrons as classical point-charges provides extremely accurate theoretical predictions of energy-loss spectra. Here we investigate

Adam Khakhar, Jacob Buckman

In this work, we demonstrate that a major limitation of regression using a mean-squared error loss is its sensitivity to the scale of its targets. This makes learning settings consisting of target's whose values take on varying scales challenging. A recently-proposed alternative loss function, known as histogram loss, avoids this issue. However, its computat

Louis Vaslin, Samuel Calvet, Vincent Barra, Julien Donini

In high Energy Physics, it is common to look for a localized deviation in data with respect to a given reference. For this task, the well known BumpHunter algorithm allows for a model-independent deviation search with the advantage of estimating a global p-value to account for the Look Elsewhere Effect. However, this method relies on the generation and scan

Davoud Shariat Panah, Andrew Hines, Susan McKeever

The development of data-driven heart sound classification models has been an active area of research in recent years. To develop such data-driven models in the first place, heart sound signals need to be captured using a signal acquisition device. However, it is almost impossible to capture noise-free heart sound signals due to the presence of internal and e

Alessandro Santini, Andrea Solfanelli, Stefano Gherardini, Guido Giachetti

On a quantum superconducting processor we observe partial and infinite-temperature thermalization induced by a sequence of repeated quantum projective measurements, interspersed by a unitary (Hamiltonian) evolution. Specifically, on a qubit and two-qubit systems, we test the state convergence of a monitored quantum system in the limit of a large number of qu

Elias Stengel-Eskin, Benjamin Van Durme

Sequence generation models are increasingly being used to translate natural language into programs, i.e. to perform executable semantic parsing. The fact that semantic parsing aims to predict programs that can lead to executed actions in the real world motivates developing safe systems. This in turn makes measuring calibration -- a central component to safet