March 2023 arXiv papers — page 43

G. A. Bobkov, I. V. Bobkova, A. M. Bobkov

Two possible physical mechanisms of superconductivity suppression at superconductor/antiferromagnet (S/AF) interfaces, which work even for interfaces with compensated antiferromagnets, were reported. One of them suggests that the Neel order of the AF induces rapidly oscillating spin-triplet correlations in the S layer. They are called Neel triplets, and they

Jeremy Worsfold, Tim Rogers

After decades of study, there are only two known mechanisms to induce global synchronization in a population of oscillators: deterministic coupling and common forcing. The inclusion of independent random forcing in these models typically serves to drive disorder, increasing the stability of the incoherent state. Here we show that the reverse is also possible

James L. McDonagh, Benjamin H. Wunsch, Stamatia Zavitsanou, Alexander Harrison

The increasing importance of carbon capture technologies for deployment in remediating CO2 emissions, and thus the necessity to improve capture materials to allow scalability and efficiency, faces the challenge of materials development, which can require substantial costs and time. Machine learning offers a promising method for reducing the time and resource

Oliver Vinzelberg, Mark David Jenkins, Gordon Morison, David McMinn

Summarisation of research results in plain language is crucial for promoting public understanding of research findings. The use of Natural Language Processing to generate lay summaries has the potential to relieve researchers' workload and bridge the gap between science and society. The aim of this narrative literature review is to describe and compare the d

Andrei Zaichenko, Aleksei Kazakov, Elizaveta Kovtun, Semen Budennyy

The study of the stock market with the attraction of machine learning approaches is a major direction for revealing hidden market regularities. This knowledge contributes to a profound understanding of financial market dynamics and getting behavioural insights, which could hardly be discovered with traditional analytical methods. Stock prices are inherently

Clément Chadebec, Stéphanie Allassonnière

This paper introduces a new latent variable generative model able to handle high dimensional longitudinal data and relying on variational inference. The time dependency between the observations of an input sequence is modelled using normalizing flows over the associated latent variables. The proposed method can be used to generate either fully synthetic long

Clemens Grabmayer

The workshop TERMGRAPH 2022 took place at Technion in Haifa, Israel, on August 1, 2022, in the Pre-FLoC workshop block (July 31-August 1) of FLoC 2022 (Federated Logic Conference 2022, July 31-August 12). As such, TERMGRAPH 2022 was a one-day satellite event of the conference FSCD 2022 (Formal Structures of Computation and Deduction 2020, August 2-5).

Yu Zheng, Jiahui Zhan, Shengfeng He, Junyu Dong

Considering the ill-posed nature, contrastive regularization has been developed for single image dehazing, introducing the information from negative images as a lower bound. However, the contrastive samples are nonconsensual, as the negatives are usually represented distantly from the clear (i.e., positive) image, leaving the solution space still under-const

Fotios Petropoulos, Gilbert Laporte, Emel Aktas, Sibel A. Alumur

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art

Arjun Vijaywargiya, Guosheng Fu

In this work, we present the construction of two distinct finite element approaches to solve the Porous Medium Equation (PME). In the first approach, we transform the PME to a log-density variable formulation and construct a continuous Galerkin method. In the second approach, we introduce additional potential and velocity variables to rewrite the PME into a

Akun Liang, Robin Turnbull, Daniel Errandonea

The goal of this work is to report a systematic and balanced review of the progress made in recent years on the high-pressure behavior of iodates, a group of materials with multiple technological applications and peculiar behaviors under external compression. This review article presents results obtained from multiple characterization techniques which includ

Garving K. Luli, Kevin O'Neill

Recent work of C. Fefferman and the first author has demonstrated that the linear system of equations \begin{equation*} \sum_{j=1}^M A_{ij}(x)F_j(x)=f_i(x)\hspace{.2in} (i=1,...,N), \end{equation*} has a $C^m$ solution $F=(F_1,...,F_M)$ if and only if $f_1,...,f_N$ satisfy a certain finite collection of partial differential equations. Here, the $A_{ij}$ are

Yixuan Zhao, Rohitha Settipalli

This research examines the propagation of rumors on social networks during public health emergencies and explores strategies to effectively manage false information in cyberspace. Using a simulation model, the study analyzes the impact of factors such as communication channel control, government intervention, and individual personalities on the spread of rum

Rangel Hernández-Ortiz, Kolja Knauer, Luis Pedro Montejano, Manfred Scheucher

J.-P. Roudneff conjectured in 1991 that every arrangement of $n \ge 2d+1\ge 5$ pseudohyperplanes in the real projective space $\mathbb{P}^d$ has at most $\sum_{i=0}^{d-2} \binom{n-1}{i}$ complete cells (i.e., cells bounded by each hyperplane). The conjecture is true for $d=2,3$ and for arrangements arising from Lawrence oriented matroids. The main result of

Yuan Fang, Brian C. Franczak, Sanjeena Subedi

Mixtures of shifted asymmetric Laplace distributions were introduced as a tool for model-based clustering that allowed for the direct parameterization of skewness in addition to location and scale. Following common practices, an expectation-maximization algorithm was developed to fit these mixtures. However, adaptations to account for the `infinite likelihoo

Fiona McCluskey, Andrew Wetzel, Sarah R. Loebman, Jorge Moreno

We study the kinematics of stars both at their formation and today within 14 Milky Way (MW)-mass galaxies from the FIRE-2 cosmological zoom-in simulations. We quantify the relative importance of cosmological disk settling and post-formation dynamical heating. We identify three eras: a Pre-Disk Era (typically >8 Gyr ago), when stars formed on dispersion-domin

Giulio Ballerini, Gerard Belmont, Laurence Rezeau, Francesco Califano

The Earth magnetopause, when sufficiently plane and stationary at a local scale, can be considered as a "quasi-tangential" discontinuity, since the normal component of the magnetic field Bn is typically very small but not zero. Contrary to observations, the "Classic Theory of Discontinuities" (CTD) predicts that rotational and compressional jumps should be m

Cristina Pignotti

In this paper, we analyze a semilinear damped second order evolution equation with time-dependent time delay and time-dependent delay feedback coefficient. The nonlinear term satisfies a local Lipschitz continuity assumption. Under appropriate conditions, we prove well-posedness and exponential stability of our model for small initial data. Our arguments com

Jiapeng Tang, Yinyu Nie, Lev Markhasin, Angela Dai

We present DiffuScene for indoor 3D scene synthesis based on a novel scene configuration denoising diffusion model. It generates 3D instance properties stored in an unordered object set and retrieves the most similar geometry for each object configuration, which is characterized as a concatenation of different attributes, including location, size, orientatio

Jacob L. Bean, Qiao Xue, Prune C. August, Jonathan Lunine

Atmospheric metal enrichment (i.e., elements heavier than helium, also called "metallicity") is a key diagnostic of the formation of giant planets. The giant planets of the solar system exhibit an inverse relationship between mass and both their bulk metallicities and atmospheric metallicities. Extrasolar giant planets also display an inverse relationship be

Lars Fritz, Thomas Scaffidi

The ``flow'' of electric currents and heat in standard metals is diffusive with electronic motion randomized by impurities. However, for ultraclean metals, electrons can flow like water with their flow being described by the equations of hydrodynamics. While theoretically postulated, this situation was highly elusive for decades. In the last decade, several

Giulia Pagnini, Stefania Salvadori, Martina Rossi, David Aguado

The chemical fingerprints of the first stars are retained within the photospheres of ancient unevolved metal-poor stars. A significant fraction of these stellar fossils is represented by stars known as Carbon-Enhanced Metal-Poor (CEMP), $\rm [C/Fe]>+0.7$ and $\rm [Fe/H]<-2$, which are likely imprinted by low-energy primordial supernovae. These CEMP stars are

Alek Bedroya, Yuta Hamada

We initiate the program of bottom-up derivation of string theory dualities using Swampland principles. In particular, we clarify the relation between Swampland arguments and all the string theory dualities in $d\geq9$ dimensional supersymmetric theories. Our arguments center around the sharpened distance conjecture and rely on various other Swampland princip

Maximilian Ruhdorfer, Ennio Salvioni, Andrea Wulzer

We propose to probe the Higgs boson decay to invisible particles at a muon collider by observing the forward muons that are produced in association with the Higgs in the Z-boson fusion channel. An excellent sensitivity is possible in line of principle, owing to the large number of produced Higgs bosons, provided a forward muon detector is installed. We find

Hai Tao Li, Ze Long Liu, Ivan Vitev

Centrality-dependent measurements of hadron and jet cross section attenuation in deep inelastic scattering on nuclei can shed new light on the physics of final-state interactions in the nuclear matter, including the path-length dependence of the in-medium parton shower formation and evolution. Recent simulation studies have demonstrated the feasibility of ex

Adam Tropper, Tianli Wang

The BFSS matrix model relates flat space M-theory to a large N limit of matrix quantum mechanics describing N D0-branes. M-theory, being a theory of gravity in flat space, has a rich infrared structure that includes various soft theorems and an infinite set of conserved charges associated to asymptotic symmetries. In this work, we ask: to what extent is this

Matteo Inglese, Dario Martelli, Antonio Pittelli

We present a new supersymmetric index for three-dimensional ${\cal N}=2$ gauge theories defined on $\Sigma \times S^1$, where $\Sigma$ is a spindle, with twist or anti-twist for the $R$-symmetry background gauge field. We start examining general supersymmetric backgrounds of Euclidean new minimal supergravity admitting two Killing spinors of opposite $R$-cha

Xiaoyang Wu, Xin Wen, Xihui Liu, Hengshuang Zhao

As a pioneering work, PointContrast conducts unsupervised 3D representation learning via leveraging contrastive learning over raw RGB-D frames and proves its effectiveness on various downstream tasks. However, the trend of large-scale unsupervised learning in 3D has yet to emerge due to two stumbling blocks: the inefficiency of matching RGB-D frames as contr

Ziang Cheng, Junxuan Li, Hongdong Li

This paper proposes a practical photometric solution for the challenging problem of in-the-wild inverse rendering under unknown ambient lighting. Our system recovers scene geometry and reflectance using only multi-view images captured by a smartphone. The key idea is to exploit smartphone's built-in flashlight as a minimally controlled light source, and deco

Pavan Kumar Anasosalu Vasu, James Gabriel, Jeff Zhu, Oncel Tuzel

The recent amalgamation of transformer and convolutional designs has led to steady improvements in accuracy and efficiency of the models. In this work, we introduce FastViT, a hybrid vision transformer architecture that obtains the state-of-the-art latency-accuracy trade-off. To this end, we introduce a novel token mixing operator, RepMixer, a building block

Jianyong Sun, Jens Kober, Michael Gienger, Jihong Zhu

Learning from Demonstration (LfD) enables robots to acquire versatile skills by learning motion policies from human demonstrations. It endows users with an intuitive interface to transfer new skills to robots without the need for time-consuming robot programming and inefficient solution exploration. During task executions, the robot motion is usually influen

Xiaobiao Huang, Xi Yang

We improved a previously proposed method of using closed-orbit modulation for linear optics correction. Instead of fitting individual closed orbits, the improved method decomposes the orbit oscillation data into two orthogonal modes and fits the amplitudes of the modes at all BPMs. While the original method is limited to process around tens to a hundred orbi

Sung Min Park, Kristian Georgiev, Andrew Ilyas, Guillaume Leclerc

The goal of data attribution is to trace model predictions back to training data. Despite a long line of work towards this goal, existing approaches to data attribution tend to force users to choose between computational tractability and efficacy. That is, computationally tractable methods can struggle with accurately attributing model predictions in non-con

FASER Collaboration, Henso Abreu, John Anders, Claire Antel

We report the first direct observation of neutrino interactions at a particle collider experiment. Neutrino candidate events are identified in a 13.6 TeV center-of-mass energy $pp$ collision data set of 35.4 fb${}^{-1}$ using the active electronic components of the FASER detector at the Large Hadron Collider. The candidates are required to have a track propa

Junshu Tang, Tengfei Wang, Bo Zhang, Ting Zhang

In this work, we investigate the problem of creating high-fidelity 3D content from only a single image. This is inherently challenging: it essentially involves estimating the underlying 3D geometry while simultaneously hallucinating unseen textures. To address this challenge, we leverage prior knowledge from a well-trained 2D diffusion model to act as 3D-awa

Karl Otness, Laure Zanna, Joan Bruna

We propose a multiscale approach for predicting quantities in dynamical systems which is explicitly structured to extract information in both fine-to-coarse and coarse-to-fine directions. We envision this method being generally applicable to problems with significant self-similarity or in which the prediction task is challenging and where stability of a lear

Changqing Zhu, Patrick Pilch, Anneke Reinold, Dennis Kudlacik

We report on time-resolved optical and terahertz ultrafast spectroscopy of charge-carrier dynamics in the room-temperature antiferromagnetic semiconductor $\alpha $-MnTe. By optically pumping the system with 1.55 eV photons at room temperature, we excite charge carriers in the conduction band through the indirect band gap and investigate the dynamical respon

Vinicius Bernardes, Andrei Mikhailov, Eggon Viana

We develop a framework for systematic study of symmetry transformations of sigma-model currents in a special situation, when symmetries have a well-defined projection onto the target space. We then apply this formalism to pure spinor sigma-models, and describe the resulting geometric structures in the target space (which in our approach includes the pure spi

Shuangyou Zhang, Toby Bi, Pascal Del'Haye

Advances in high-resolution laser spectroscopy have enabled many scientific breakthroughs in physics, chemistry, biology and astronomy. Optical frequency combs have pushed measurement limits with ultrahigh-frequency accuracy and fast-measurement speed while tunable diode laser spectroscopy is used in scenarios that require high power and continuous spectral

Jenny Blessing, Ross Anderson

European lawmakers have ruled that users on different platforms should be able to exchange messages with each other. Yet messaging interoperability opens up a Pandora's box of security and privacy challenges. While championed not just as an anti-trust measure but as a means of providing a better experience for the end user, interoperability runs the risk of

Suchin Gururangan, Margaret Li, Mike Lewis, Weijia Shi

Large language models are typically trained densely: all parameters are updated with respect to all inputs. This requires synchronization of billions of parameters across thousands of GPUs. We introduce a simple but effective method to asynchronously train large, sparse language models on arbitrary text corpora. Our method clusters a corpus into sets of rela

Asude Aydin, Mathias Gehrig, Daniel Gehrig, Davide Scaramuzza

Spiking Neural Networks (SNN) are a class of bio-inspired neural networks that promise to bring low-power and low-latency inference to edge devices through asynchronous and sparse processing. However, being temporal models, SNNs depend heavily on expressive states to generate predictions on par with classical artificial neural networks (ANNs). These states c

Ye Zhu, Jie Yang, Si-Qi Liu, Ruimao Zhang

Semi-supervised medical image segmentation has attracted much attention in recent years because of the high cost of medical image annotations. In this paper, we propose a novel Inherent Consistent Learning (ICL) method, aims to learn robust semantic category representations through the semantic consistency guidance of labeled and unlabeled data to help segme

Camilo Sanchez, Felix A. Epp

As technological innovation continues to shape our world at an accelerating pace, policy makers struggle to keep up with the unintended consequences of these new technologies. To address this policy-novelty gap, Responsible Research Innovation (RRI) has been proposed as a way to drive science and technology innovation towards socially desirable goals. This w

Charles Godfrey, Henry Kvinge, Elise Bishoff, Myles Mckay

Past work exploring adversarial vulnerability have focused on situations where an adversary can perturb all dimensions of model input. On the other hand, a range of recent works consider the case where either (i) an adversary can perturb a limited number of input parameters or (ii) a subset of modalities in a multimodal problem. In both of these cases, adver

S. Lesage, P. Veres, M. S. Briggs, A. Goldstein

We report the discovery of GRB 221009A, the highest flux gamma-ray burst ever observed by the Fermi Gamma-ray Burst Monitor (GBM). This GRB has continuous prompt emission lasting more than 600 seconds which smoothly transitions to afterglow visible in the GBM energy range (8 keV--40 MeV), and total energetics higher than any other burst in the GBM sample. By

Anton Savin, Elmar Schrohe

We consider an algebra $\mathscr A$ of Fourier integral operators on $\mathbb R^n$. It consists of all operators $D: \mathscr S(\mathbb R^n)\to \mathscr S(\mathbb R^n)$ on the Schwartz space $\mathscr S(\mathbb R^n)$ that can be written as finite sums $$ D= \sum R_gT_w A, $$ with Shubin type pseudodifferential operators $A$, Heisenberg-Weyl operators $T_w$,

Lexin Ding, Gesa Dünnweber, Christian Schilling

In [arXiv:2207.03377] the first closed formula of a faithful entanglement measure applicable to realistic electron systems has been derived. In the present work, we build on this key achievement with the ultimate goal of guiding the development of quantum technologies. For this, we first elucidate the process of entanglement swapping in electron systems such

Huandong Chen, Nan Wang, Hefei Liu, Han Wang

Phase change materials, which show different electrical characteristics across the phase transitions, have attracted considerable research attention for their potential electronic device applications. Materials with metal-to-insulator or charge density wave (CDW) transitions such as VO2 and 1T-TaS2 have demonstrated voltage oscillations due to their robust b

Joseph F. Wild, Heng Chen, Keyue Liang, Jiayu Liu

A general method of separating isotopes by centrifuging dissolved chemical compounds in a liquid solution is introduced. This technique can be applied to almost all elements and leads to large separation factors. The method has been demonstrated in several isotopic systems including Ca, Mo, O, and Li with single-stage selectivities of 1.046-1.067 per unit ma

Marta Bilkova, Sabine Frittella, Daniil Kozhemiachenko

We introduce a paraconsistent expansion of the G\"{o}del logic with a De Morgan negation $\neg$ and modalities $\blacksquare$ and $\blacklozenge$. We equip it with Kripke semantics on frames with two (possibly fuzzy) relations: $R^+$ and $R^-$ (interpreted as the degree of trust in affirmations and denials by a given source) and valuations $v_1$ and $v_2$ (p

Yuanbo Yang, Yifei Yang, Hanlei Guo, Rong Xiong

Generating photorealistic images with controllable camera pose and scene contents is essential for many applications including AR/VR and simulation. Despite the fact that rapid progress has been made in 3D-aware generative models, most existing methods focus on object-centric images and are not applicable to generating urban scenes for free camera viewpoint

Isobel Kolbé

High-$p_T$ jets are an important tool for characterizing the quark-gluon plasma (QGP) created in heavy-ion collisions. However, a precise understanding of the jet-medium interaction is still lacking, and the development of more sophisticated observables is needed. This work presents a tool that allows for the exploration of alternative high-$p_T$ observables

Gabriel R. Jaffe, Gregory R. Holdman, Min Seok Jang, Demeng Feng

Laser sails propelled by gigawatt-scale ground-based laser arrays have the potential to reach relativistic speeds, traversing the solar system in hours and reaching nearby stars in years. Here, we describe the danger interplanetary dust poses to the survival of a laser sail during its acceleration phase. We show through multi-physics simulations how localize

Marta Bilkova, Sabine Frittella, Daniil Kozhemiachenko

We further develop the paraconsistent G\"{o}del modal logic. In this paper, we consider its version endowed with Kripke semantics on $[0,1]$-valued frames with two fuzzy relations $R^+$ and $R^-$ (degrees of trust in assertions and denials) and two valuations $v_1$ and $v_2$ (support of truth and support of falsity) linked with a De Morgan negation $\neg$. W

Isaac Labrie-Boulay, Thomas Brian Winkler, Daniel Franzen, Alena Romanova

One of the most important magnetic spin structure is the topologically stabilised skyrmion quasi-particle. Its interesting physical properties make them candidates for memory and efficient neuromorphic computation schemes. For the device operation, detection of the position, shape, and size of skyrmions is required and magnetic imaging is typically employed.

Kastan Day, Daniel Christl, Rohan Salvi, Pranav Sriram

We present Video Pre-trained Transformer. VPT uses four SOTA encoder models from prior work to convert a video into a sequence of compact embeddings. Our backbone, based on a reference Flan-T5-11B architecture, learns a universal representation of the video that is a non-linear sum of the encoder models. It learns using an autoregressive causal language mode

Marcelo R. Ubriaco

With use of the Second Inverse Maximum Entropy Principle we find entropy functions for systems with fractal distribution functions with order parameter $q$. We compare these entropy functions with those given by the Bose-Einstein and Fermi-Dirac cases.

Mehrdad Morsali, Mahmoud Nazzal, Abdallah Khreishah, Shaahin Angizi

In this paper, we propose IMA-GNN as an In-Memory Accelerator for centralized and decentralized Graph Neural Network inference, explore its potential in both settings and provide a guideline for the community targeting flexible and efficient edge computation. Leveraging IMA-GNN, we first model the computation and communication latencies of edge devices. We t

Vitaliy Kurlin

A finite set of unlabelled points in Euclidean space is the simplest representation of many real objects from mineral rocks to sculptures. Since most solid objects are rigid, their natural equivalence is rigid motion or isometry maintaining all inter-point distances. More generally, any finite metric space is an example of a metric-measure space that has a p

David Prinz

We discuss how the incorporation of a cosmological constant affects the perturbative quantization of (effective) Quantum General Relativity. To this end, we derive the gravitational Slavnov--Taylor identities and appropriate renormalization conditions for the cosmological constant. Additionally, we calculate the corresponding Feynman rules for any vertex val

Xin Wang

A classic problem in metabolism is that fast-proliferating cells use seemingly wasteful fermentation for energy biogenesis in the presence of sufficient oxygen. This counterintuitive phenomenon, known as overflow metabolism or the Warburg effect, is universal across various organisms. Despite extensive research, its origin and function remain unclear. Here,

Bowen Wen, Jonathan Tremblay, Valts Blukis, Stephen Tyree

We present a near real-time method for 6-DoF tracking of an unknown object from a monocular RGBD video sequence, while simultaneously performing neural 3D reconstruction of the object. Our method works for arbitrary rigid objects, even when visual texture is largely absent. The object is assumed to be segmented in the first frame only. No additional informat

Thuan Hoang Nguyen, Thanh Van Le, Anh Tran

Any-scale image synthesis offers an efficient and scalable solution to synthesize photo-realistic images at any scale, even going beyond 2K resolution. However, existing GAN-based solutions depend excessively on convolutions and a hierarchical architecture, which introduce inconsistency and the $``$texture sticking$"$ issue when scaling the output resolution

Oksana Iarygina, M. C. David Marsh, Gustavo Salinas

We show that theories of inflation with multiple, rapidly turning fields can generate large amounts of non-Gaussianity. We consider a general theory with two fields, an arbitrary field-space metric, and a potential that supports sustained, rapidly turning field trajectories. Our analysis accounts for non-zero field cross-correlation and does not fix the powe

Emilien Flayac, Karim Dahia, Bruno Hérissé, Frédéric Jean

This paper presents a joint optimisation framework for optimal estimation and stochastic optimal control with imperfect information. It provides a estimation and control scheme that can be decomposed into a classical optimal estimation step and an optimal control step where a new term coming from optimal estimation is added to the cost. It is shown that a sp

Hiroyuki Fuji, Masahide Manabe

Via Andersen-Borot-Orantin's geometric recursion, a twist of the topological recursion was proposed, and a recursion for the Masur-Veech polynomials was uncovered. The purpose of this article is to explore generalizations of Mirzakhani's recursion based on physical two-dimensional gravity models related to the Jackiw-Teitelboim gravity and to provide an intr

S. A. Rizvi, R. Tang, X. Jiang, X. Ma

The proliferation of Deep Learning (DL)-based methods for radiographic image analysis has created a great demand for expert-labeled radiology data. Recent self-supervised frameworks have alleviated the need for expert labeling by obtaining supervision from associated radiology reports. These frameworks, however, struggle to distinguish the subtle differences

Nikolas Lamb, Cameron Palmer, Benjamin Molloy, Sean Banerjee

Automated shape repair approaches currently lack access to datasets that describe real-world damaged geometry. We present Fantastic Breaks (and Where to Find Them: https://terascale-all-sensing-research-studio.github.io/FantasticBreaks), a dataset containing scanned, waterproofed, and cleaned 3D meshes for 150 broken objects, paired and geometrically aligned

Rylan Schaeffer, Mikail Khona, Zachary Robertson, Akhilan Boopathy

Double descent is a surprising phenomenon in machine learning, in which as the number of model parameters grows relative to the number of data, test error drops as models grow ever larger into the highly overparameterized (data undersampled) regime. This drop in test error flies against classical learning theory on overfitting and has arguably underpinned th

P. Schmelcher

Symmetries are known to dictate important physical properties and can be used as a design principle in particular in wave physics, including wave structures and the resulting propagation dynamics. Local symmetries, in the sense of a symmetry that holds only in a finite domain of space, can be either the result of a self-organization process or a structural i

Thiago Carvalho Corso

We derive a two-term asymptotic expansion for the exchange energy of the free electron gas on strictly tessellating polytopes and fundamental domains of lattices in the thermodynamic limit. This expansion comprises a bulk (volume-dependent) term, the celebrated Dirac exchange, and a novel surface correction stemming from a boundary layer and finite-size effe

Ioannis D. Gialamas, Alexandros Karam, Thomas D. Pappas, Eemeli Tomberg

We present an introduction to cosmic inflation in the framework of Palatini gravity, which provides an intriguing alternative to the conventional metric formulation of gravity. In the latter, only the metric specifies the spacetime geometry, whereas in the former, the metric and the spacetime connection are independent variables-an option that can result in

J. M. Carrasco, N. Cardiel, E. Masana, J. Zamorano

Recent works have made strong efforts to produce standardised photometry in RGB bands. For this purpose, we carefully defined the transmissivity curves of RGB bands and defined a set of standard sources using the photometric information present in Gaia EDR3. This work aims not only to significantly increase the number and accuracy of RGB standards but also t

T. Schweyer, J. Sollerman, A. Jerkstrand, M. Ergon

We present and analyze observations of the Type Ib supernova (SN) 2019odp (a.k.a ZTF19abqwtfu) covering epochs within days of the explosion to the nebular phase at 360 d post-explosion. We discuss them in the context of recombination cooling emission for the early excess emission and consider progenitor models based on the nebular phase spectra. Our observat

Josh Vogwell, Laura Rego, Olga Smirnova, David Ayuso

We introduce an ultrafast all-optical approach for efficient chiral recognition which relies on the interference between two low-order nonlinear processes which are ubiquitous in nonlinear optics: sum-frequency generation and third-harmonic generation. In contrast to traditional sum-frequency generation, our approach encodes the medium's handedness in the in

Vsevolod A. Afanasev, Andrey Mamontov

We study $6$-transposition groups, i.e. groups generated by a normal set of involutions $D$, such that the order of the product of any two elements from $D$ does not exceed $6$. We classify most of the groups generated by $3$ elements from $D$, two of which commute, and prove they are finite.

Evan King, Haoxiang Yu, Sangsu Lee, Christine Julien

The right response to someone who says "get ready for a party" is deeply influenced by meaning and context. For a smart home assistant (e.g., Google Home), the ideal response might be to survey the available devices in the home and change their state to create a festive atmosphere. Current practical systems cannot service such requests since they require the

Jonay I. González Hernández, David S. Aguado, Carlos Allende-Prieto, Adam Burgasser

We have been exploring large spectroscopic databases such as SDSS to search for unique stars with extremely low iron content with the goal of extracting detailed information from the early phases of the Galaxy. We recently identified two extremely iron-poor dwarf stars J0815+4729 (Aguado et al. 2018a) and J0023+0307 (Aguado et al. 2018b) from SDSS/BOSS datab

Vincenzo Fazio, Nicola Maria Pugno, Giuseppe Puglisi

When humidified at different moisture conditions, restrained spider silk fibers can exhibit a very high supercontraction phenomenon. The hydration water molecules induce a Hydrogen-bonds disruption process that, due to entropic effects, decreases the natural -- zero force -- end-to-end chains length. By considering a bundle of macromolecules, we describe sup

Stefano Iubini, Antonio Politi, Paolo Politi

We study nonequilibrium steady states of a one-dimensional stochastic model, originally introduced as an approximation of the Discrete Nonlinear Schr\"odinger equation. This model is characterized by two conserved quantities, namely mass and energy; it displays a ``normal", homogeneous phase, separated by a condensed (negative-temperature) phase, where a mac

Yizhuo Lu, Changde Du, Dianpeng Wang, Huiguang He

Reconstructing visual stimuli from measured functional magnetic resonance imaging (fMRI) has been a meaningful and challenging task. Previous studies have successfully achieved reconstructions with structures similar to the original images, such as the outlines and size of some natural images. However, these reconstructions lack explicit semantic information

Tobias Golling, Takuya Nobe, Dimitrios Proios, John Andrew Raine

In the hunt for new and unobserved phenomena in particle physics, attention has turned in recent years to using advanced machine learning techniques for model independent searches. In this paper we highlight the main challenge of applying anomaly detection to jet physics, where preserving an unbiased estimator of the jet mass remains a critical piece of any

Junhao Dong, Junxi Chen, Xiaohua Xie, Jianhuang Lai

Deep learning techniques have achieved superior performance in computer-aided medical image analysis, yet they are still vulnerable to imperceptible adversarial attacks, resulting in potential misdiagnosis in clinical practice. Oppositely, recent years have also witnessed remarkable progress in defense against these tailored adversarial examples in deep medi

Wentao Ye, Jiaju Zhang

We investigate the Shannon entropy of the total system and its subsystems, as well as the subsystem Shannon mutual information, in quasiparticle excited states of free bosonic and fermionic chains and the ferromagnetic phase of the spin-1/2 XXX chain. For single-particle and double-particle states, we derive various analytical formulas for free bosonic and f

Georgina Curto, Svetlana Kiritchenko, Isar Nejadgholi, Kathleen C. Fraser

The criminalization of poverty has been widely denounced as a collective bias against the most vulnerable. NGOs and international organizations claim that the poor are blamed for their situation, are more often associated with criminal offenses than the wealthy strata of society and even incur criminal offenses simply as a result of being poor. While no evid

Jordan J. Bird, Ahmad Lotfi

Recent technological advances in synthetic data have enabled the generation of images with such high quality that human beings cannot tell the difference between real-life photographs and Artificial Intelligence (AI) generated images. Given the critical necessity of data reliability and authentication, this article proposes to enhance our ability to recognis

Bo He, Xitong Yang, Hanyu Wang, Zuxuan Wu

Implicit neural representations (INR) have gained increasing attention in representing 3D scenes and images, and have been recently applied to encode videos (e.g., NeRV, E-NeRV). While achieving promising results, existing INR-based methods are limited to encoding a handful of short videos (e.g., seven 5-second videos in the UVG dataset) with redundant visua

Wentao Chen, Chenyang Si, Zhang Zhang, Liang Wang

Few-shot learning is a challenging problem since only a few examples are provided to recognize a new class. Several recent studies exploit additional semantic information, e.g. text embeddings of class names, to address the issue of rare samples through combining semantic prototypes with visual prototypes. However, these methods still suffer from the spuriou

Zu Hawn Sung, Paulina Kulyavtsev, Martina Martinello, Dan Gonnella

A series of experiments were carried out in an effort to develop a simple method for predicting magnetic flux expulsion behavior of high purity niobium used to fabricate superconducting radio frequency (SRF) cavities. Using conventional metallographic characterizations in conjunction with high spatial resolution electron backscattered diffraction-orientation

Guang-Yu Sun, Ru-Hui Lian, Shu Zhang, Xiong Yang

Surface flashover across insulator in vacuum is a destructive plasma discharge which undermines the behaviors of a range of applications in electrical engineering, particle physics, space engineering, etc. This phenomenon is widely modeled by the particle-in-cell (PIC) simulation, here the continuum and kinetic simulation method is first proposed and impleme

Mark Steudtner, Sam Morley-Short, William Pol, Sukin Sim

Over the past three decades significant reductions have been made to the cost of estimating ground-state energies of molecular Hamiltonians with quantum computers. However, comparatively little attention has been paid to estimating the expectation values of other observables with respect to said ground states, which is important for many industrial applicati

Guan-Sen Wang, Zhan-Fang Chen, Lei Zu, Hao Gong

Omega Centauri, the largest known globular cluster in the Milky Way, is believed to be the remains of a dwarf galaxy's core. Giving its potential abundance of dark matter (DM), it is an attractive target for investigating the nature of this elusive substance in our local environment. Our study demonstrates that by observing Omega Centauri with the SKA for 10

Shunsuke Kitada

With the dramatic advances in deep learning technology, machine learning research is focusing on improving the interpretability of model predictions as well as prediction performance in both basic and applied research. While deep learning models have much higher prediction performance than traditional machine learning models, the specific prediction process

Tobias Kalb, Jürgen Beyerer

Deep neural networks for scene perception in automated vehicles achieve excellent results for the domains they were trained on. However, in real-world conditions, the domain of operation and its underlying data distribution are subject to change. Adverse weather conditions, in particular, can significantly decrease model performance when such data are not av

Shay Snyder, Hunter Thompson, Md Abdullah-Al Kaiser, Gregory Schwartz

Neuromorphic (event-based) image sensors draw inspiration from the human-retina to create an electronic device that can process visual stimuli in a way that closely resembles its biological counterpart. These sensors process information significantly different than the traditional RGB sensors. Specifically, the sensory information generated by event-based im

Mina Montazeri, Pegah Rokhforoz, Hamed Kebriaei, Olga Fink

We propose an incentive mechanism for the sponsored content provider market in which the communication of users can be represented by a graph and the private information of the users is assumed to have a continuous distribution function. The content provider stipulates incentive rewards to encourage users to reveal their private information truthfully and in

Marco-Tulio F. Rodrigues, Sathish Rajendran, Stephen E. Trask, Alison R. Dunlop

It is generally believed that silicon-based anodes for Li-ion batteries would benefit from stronger binders, as cyclic volume changes would not disrupt the cohesion of the composite electrode. Here, we put this belief to the proof by testing electrodes containing SiOx particles and an aromatic polyimide binder. We observe that the electrodes can stretch late

Simon Lutz, Daniil Kaminskyi, Florian Wittbold, Simon Dierl

Automata learning is a successful tool for many application domains such as robotics and automatic verification. Typically, automata learning techniques operate in a supervised learning setting (active or passive) where they learn a finite state machine in contexts where additional information, such as labeled system executions, is available. However, other

Zsolt Benedek, Rohit Babar, Ádám Ganyecz, Tibor Szilvási

Point defect quantum bits in semiconductors have the potential to revolutionize sensing at atomic scales. Currently, vacancy related defects, such as the NV center in diamond and the VB$^-$ in hexagonal boron nitride (hBN), are at the forefront of high spatial resolution and low dimensional sensing. On the other hand, vacancies' reactive nature and instabili