July 2022 arXiv papers — page 126

Richik Sengupta, Soumik Adhikary, Ivan Oseledets, Jacob Biamonte

A tensor network is a type of decomposition used to express and approximate large arrays of data. A given data-set, quantum state or higher dimensional multi-linear map is factored and approximated by a composition of smaller multi-linear maps. This is reminiscent to how a Boolean function might be decomposed into a gate array: this represents a special case

Osher Azulay, Inbar Ben-David, Avishai Sintov

Unlike traditional robotic hands, underactuated compliant hands are challenging to model due to inherent uncertainties. Consequently, pose estimation of a grasped object is usually performed based on visual perception. However, visual perception of the hand and object can be limited in occluded or partly-occluded environments. In this paper, we aim to explor

Hadi Salman, Saachi Jain, Andrew Ilyas, Logan Engstrom

Using transfer learning to adapt a pre-trained "source model" to a downstream "target task" can dramatically increase performance with seemingly no downside. In this work, we demonstrate that there can exist a downside after all: bias transfer, or the tendency for biases of the source model to persist even after adapting the model to the target class. Throug

Zongchen Chen, Nitya Mani, Ankur Moitra

We take an algorithmic approach to studying the solution space geometry of relatively sparse random and bounded degree $k$-CNFs for large $k$. In the course of doing so, we establish that with high probability, a random $k$-CNF $\Phi$ with $n$ variables and clause density $\alpha = m/n \lesssim 2^{k/6}$ has a giant component of solutions that are connected i

Rahul Yadav, J. de la Cruz Rodríguez, Graham S. Kerr, C. J. Díaz Baso

Solar flares release an enormous amount of energy into the corona. A substantial fraction of this energy is transported to the lower atmosphere, which results in chromospheric heating. The mechanisms that transport energy to the lower solar atmosphere during a flare are still not fully understood. We aim to estimate the temporal evolution of the radiative lo

Christopher Dörr, Martin Schlather

So far, the pseudo cross-variogram is primarily used as a tool for the structural analysis of multivariate random fields. Mainly applying recent theoretical results on the pseudo cross-variogram, we use it as a cornerstone in the construction of valid covariance models for multivariate random fields. In particular, we extend known univariate constructions to

M. Zyskin

We obtain and investigate explicit analytic solution via universal transform of the diffusion equation in a spherical particles which appears in the so-called single particle model, a popular simple model of an electric battery.

Wael Elkamhawy, Hans-Werner Hammer

We calculate the electromagnetic properties of the deformed one-neutron halo candidate $^{31}$Ne using Halo Effective Field Theory (Halo EFT). In this framework, $^{31}$Ne is bound via a resonant $P$-wave interaction between the $^{30}$Ne core and the valence neutron. We set up a spherical formalism for $^{31}$Ne in order to calculate the electromagnetic for

Jamie Harris

This report documents the history of research on AI rights and other moral consideration of artificial entities. It highlights key intellectual influences on this literature as well as research and academic discussion addressing the topic more directly. We find that researchers addressing AI rights have often seemed to be unaware of the work of colleagues wh

Fan Xu, Fang Yang

Let $\mathbb{X}_{\boldsymbol{p},\boldsymbol{\lambda}}$ be a weighted projective line. We define the quantum cluster algebra of $\mathbb{X}_{\boldsymbol{p},\boldsymbol{\lambda}}$ and realize its specialized version as the subquotient of the Hall algebra of $\mathbb{X}_{\boldsymbol{p},\boldsymbol{\lambda}}$ via the quantum cluster character map. Inspired by \c

Sebastian A. Zarrilli, Stefan Kraus, Alexander Kreplin, John D. Monnier

Context: Stellar evolution models are highly dependent on accurate mass estimates, especially for high-mass stars in the early stages of evolution. The most direct method for obtaining model-independent masses is derivation from the orbit of close binaries. Aims: To derive the first astrometric+RV orbit solution for the single-lined spectroscopic binary MWC

Dipayan Mukherjee, H. K. Jassal, Kinjalk Lochan

We use the Jordan frame-Einstein frame correspondence to explore dual universes with contrasting cosmological evolutions. We study the mapping between Einstein and Jordan frames where the Einstein frame universe describes the late-time evolution of the physical universe, which is driven by dark energy and non-relativistic matter. The Brans-Dicke theory of gr

Adam Pacheck, Hadas Kress-Gazit

A typical approach to creating complex robot behaviors is to compose atomic controllers, or skills, such that the resulting behavior satisfies a high-level task; however, when a task cannot be accomplished with a given set of skills, it is difficult to know how to modify the skills to make the task possible. We present a method for combining symbolic repair

Yu Qin, Alex Sheremet

In the brain, cross-frequency coupling has been hypothesized to result from the activity of specialized microcircuits. For example, theta-gamma coupling is assumed to be generated by specialized cell pairs (PING and ING mechanisms), or special cells (e.g., fast bursting neurons). However, this implies that the generating mechanisms is uniquely specific to th

Xin Chen, Alex Reibman, Sanjay Arora

Timeliness and contextual accuracy of recommendations are increasingly important when delivering contemporary digital marketing experiences. Conventional recommender systems (RS) suggest relevant but time-invariant items to users by accounting for their past purchases. These recommendations only map to customers' general preferences rather than a customer's

Grzegorz Marcjasz, Michał Narajewski, Rafał Weron, Florian Ziel

We present a novel approach to probabilistic electricity price forecasting which utilizes distributional neural networks. The model structure is based on a deep neural network that contains a so-called probability layer. The network's output is a parametric distribution with 2 (normal) or 4 (Johnson's SU) parameters. In a forecasting study involving day-ahea

Jeremias Aguilera Damia, Riccardo Argurio, Eduardo Garcia-Valdecasas

We show that very simple theories of abelian gauge fields with a cubic Chern-Simons term in 5d have an infinite number of non-invertible co-dimension two defects. They arise by dressing the symmetry operators of the broken electric 1-form symmetry with a suitable topological field theory, for any rational angle. We further discuss the same theories in the pr

Abhishek Kumar, Yantao Li, Babak Seradjeh

We formulate a theory of bulk optical current for a periodically driven system, which accounts for the mixing of external drive and laser field frequencies and, therefore, the broadening of the harmonic spectrum compared to the undriven system. We express the current in terms of Floquet-Bloch bands and their non-adiabatic Berry connection and curvature. Usin

Davoud Ataee Tarzanagh, Parvin Nazari, Bojian Hou, Li Shen

This paper introduces \textit{online bilevel optimization} in which a sequence of time-varying bilevel problems is revealed one after the other. We extend the known regret bounds for online single-level algorithms to the bilevel setting. Specifically, we provide new notions of \textit{bilevel regret}, develop an online alternating time-averaged gradient meth

Uri Bader, Alessandro Sisto

We characterise acylindrical hyperbolicity of a group in terms of properties of an action of the group on a set (without any extra structure). In particular, this applies to the action of the group on itself by left multiplication, as well as the action on a (full measure subset of the) Furstenberg-Poisson boundary.

William Beatrez, Arjun Pillai, Otto Janes, Dieter Suter

We report on experiments that quantify the role of a central electronic spin as a relaxation source for nuclear spins in its nanoscale environment. Our strategy exploits hyperpolarization injection from the electron as a means to controllably probe an increasing number of nuclear spins in the bath, and subsequently interrogate them with high fidelity. Our ex

F. R. Klinkhamer

A new way is proposed to cancel the cosmological constant. The proposal involves the metric determinant acting as a type of self-adjusting $q$-field without need of a fine-tuned chemical potential. Since the determinant of the metric now plays a role in the physics, the allowed coordinate transformations are restricted to those with unit Jacobian. This appro

Md. Raju, Abhi Mukherjee, Jyoti Prasad Saha

The anomalous magnetic moment of muons has been a long-standing problem in SM. The current deviation of experimental value of the $(g-2)_{\mu}$ from the standard model prediction is exactly $4.2\sigma$. Two Higgs Doublet Models can accommodate this discrepancy but such type of model naturally generate flavor changing neutral current(FCNC). To prevent this it

Marko Čuljak, Andreas Spitz, Robert West, Akhil Arora

Named entity linking (NEL) in news is a challenging endeavour due to the frequency of unseen and emerging entities, which necessitates the use of unsupervised or zero-shot methods. However, such methods tend to come with caveats, such as no integration of suitable knowledge bases (like Wikidata) for emerging entities, a lack of scalability, and poor interpre

G. C. Grime, M. Roberto, R. L. Viana, Y. Elskens

Some internal transport barriers in tokamaks have been related to the vicinity of extrema of the plasma equilibrium profiles. This effect is numerically investigated by considering the guiding-center trajectories of plasma particles undergoing ExB drift motion, considering that the electric field has a stationary nonmonotonic radial profile and an electrosta

Ira Fesefeldt, Joost-Pieter Katoen, Thomas Noll

In this paper, we develop a novel verification technique to reason about programs featuring concurrency, pointers and randomization. While the integration of concurrency and pointers is well studied, little is known about the combination of all three paradigms. To close this gap, we combine two kinds of separation logic -- Quantitative Separation Logic and C

David Belius, Marius A. Schmidt

We solve the Thouless-Anderson-Palmer (TAP) variational principle associated to the spherical pure $p$-spin mean field spin glass Hamiltonian and present a detailed phase diagram. In the high temperature phase the maximum of variational principle is the annealed free energy of the model. In the low temperature phase the maximum, for which we give a formula,

Aritra Ghosh, Chandrasekhar Bhamidipati, Sudipta Mukherji

We compute logarithmic corrections to the black hole entropy $S_{\rm bh}$ in a holographic set up where the cosmological constant $\Lambda$ and Newton's constant $G_D$ are taken to be thermodynamic parameters, related to variations in bulk pressure \(P\) and central charge \(c\). In the bulk, the logarithmic corrections are of the form: $\mathcal{S} = S_{\rm

Pouria Sarhadi, Wasif Naeem, Karen Fraser, David Wilson

Due to the nature of most postgraduate theses in control engineering and their similarities to industrial and software engineering projects, invoking novel project control techniques could be effective. In recent decades, agile techniques have attracted popularity thanks to their attributes in delivering successful projects. Hence exploiting those methods in

Ilmun Kim

In this short note, we identify and address an error in the proof of Theorem 1.3 in Canonne et al. (2018), a recent breakthrough in conditional independence testing. After correcting the error, we show that the general sample complexity result established in Canonne et al. (2018) remains the same.

Markus Fröb, Albert Much, Kyriakos Papadopoulos

Trying to connect a fundamentally non-commutative spacetime with the conservative perturbative approach to quantum gravity, we are led to the natural question: are non-commutative geometrical effects already present in the regime where perturbative quantum gravity provides a predictive framework? Moreover, is it necessary to introduce non-commutativity by ha

Soham Chakraborty, Sourav Patel, Murti V Salapaka

Grid-following and grid-forming inverters are integral components of microgrids and for integration of renewable energy sources with the grid. For grid following inverters, which need to emulate controllable current sources, a significant challenge is to address the large uncertainty of the grid impedance. For grid forming inverters, which need to emulate a

Raghavendra Addanki, Andrew McGregor, Cameron Musco

We study the problem of estimating the number of edges in an $n$-vertex graph, accessed via the Bipartite Independent Set query model introduced by Beame et al. (ITCS '18). In this model, each query returns a Boolean, indicating the existence of at least one edge between two specified sets of nodes. We present a non-adaptive algorithm that returns a $(1\pm \

Mikhail Karpukhin, Jean Lagacé

Recently, D. Bucur and M. Nahon used boundary homogenisation to show the remarkable flexibility of Steklov eigenvalues of planar domains. In the present paper we extend their result to higher dimensions and to arbitrary manifolds with boundary, even though in those cases the boundary does not generally exhibit any periodic structure. Our arguments use framew

Yuqi Tian, Chun Li, Shengxin Tu, Nathan T. James

Detection limits (DLs), where a variable is unable to be measured outside of a certain range, are common in research. Most approaches to handle DLs in the response variable implicitly make parametric assumptions on the distribution of data outside DLs. We propose a new approach to deal with DLs based on a widely used ordinal regression model, the cumulative

Yu-Kun Yan, Shanquan Lan, Yu Tian, Peng Yang

Although holographic duality has been regarded as a complementary tool in helping understand the non-equilibrium dynamics of strongly coupled many-body systems, it still remains a remarkable challenge how to confront its predictions quantitatively with the real experimental scenarios. By matching the holographic vortex dynamics with the phenomenological diss

Lilian Witthauer, Juan Pedro Cascales, Emmanuel Roussakis, Xiaolei Li

Measurement of intramuscular oxygen could play a key role in the early diagnosis of acute compartment syndrome, a common condition occurring after severe trauma leading to ischemia and long-term consequences including rhabdomyolysis, limb loss, and death. However, to date, there is no existing oxygen sensor approved for such a purpose. To address the need to

Hong-Tao An, Zhan-Wei Liu, Fu-Sheng Yu, Xiang Liu

Inspired by the very recently discovered tetraquark states $T_{c\bar s 0}^a(2900)^{0,++}$ from the LHCb Collaboration, we predict the existence of a new charmed-strange pentaquark system, $c\bar s nnn$, which is closely connected to $c\bar s n\bar n$ by exchanging $\bar n$ into $nn$ with $n=u,d$. Especially, it is suggested to experimentally search for the p

Yingchen Yu, Fangneng Zhan, Rongliang Wu, Jiahui Zhang

Leveraging StyleGAN's expressivity and its disentangled latent codes, existing methods can achieve realistic editing of different visual attributes such as age and gender of facial images. An intriguing yet challenging problem arises: Can generative models achieve counterfactual editing against their learnt priors? Due to the lack of counterfactual samples i

Ivan Shugurov, Ivan Pavlov, Sergey Zakharov, Slobodan Ilic

This paper introduces a novel multi-view 6 DoF object pose refinement approach focusing on improving methods trained on synthetic data. It is based on the DPOD detector, which produces dense 2D-3D correspondences between the model vertices and the image pixels in each frame. We have opted for the use of multiple frames with known relative camera transformati

Boyang Xu, Philippe Guyenne

Direct phase-resolved simulations are performed to investigate the propagation and scattering of nonlinear ocean waves in fragmented sea ice. The numerical model solves the full time-dependent equations for nonlinear potential flow coupled with a nonlinear thin-plate representation of the ice cover, and neglects dissipative processes. The two-dimensional set

Nick Pant, Woncheol Lee, Nocona Sanders, Emmanouil Kioupakis

Alloy scattering in random AlGaN alloys drastically reduces the electron mobility and therefore the power-electronics figure of merit. As a result, Al compositions greater than 75% are required to obtain even a two-fold increase of the Baliga figure of merit compared to GaN. However, beyond approximately 80% Al composition, donors in AlGaN undergo the DX tra

Martim Sousa, Ana Maria Tomé, José Moreira

Conformalized quantile regression is a procedure that inherits the advantages of conformal prediction and quantile regression. That is, we use quantile regression to estimate the true conditional quantile and then apply a conformal step on a calibration set to ensure marginal coverage. In this way, we get adaptive prediction intervals that account for hetero

Emanuele Bagnaschi, Lukas Fritz, Stefan Liebler, Margarete Mühlleitner

One of the most important mechanisms at the Large Hadron Collider (LHC) for the production of the pseudoscalar Higgs boson of the Minimal Supersymmetric Standard Model (MSSM) is the loop-induced gluon fusion process $gg\to A$. The higher-order QCD corrections have been obtained a long time ago and turned out to be large. However, the genuine supersymmetric (

Yifan Gao, Ammon Fischer, Lennart Klebl, Martin Claassen

Moir\'e heterostructures hold the promise to provide platforms to tailor strongly correlated and topological states of matter. Here, we theoretically propose the emergence of an effective, rectangular moir\'e lattice in twisted bilayers of SnS with nonsymmorphic symmetry. Based on first-principles calculations, we demonstrate that strong intrinsic spin-orbit

Ivan Shugurov, Sergey Zakharov, Slobodan Ilic

We propose a three-stage 6 DoF object detection method called DPODv2 (Dense Pose Object Detector) that relies on dense correspondences. We combine a 2D object detector with a dense correspondence estimation network and a multi-view pose refinement method to estimate a full 6 DoF pose. Unlike other deep learning methods that are typically restricted to monocu

Alexander Tsymbaliuk

We present a shuffle realization of the GKLO-type homomorphisms for shifted quantum affine, toroidal, and quiver algebras, thus generalizing its rational version of arXiv:2104.14518 and the type A construction of arXiv:1811.12137. As an application, this allows us to construct large families of commuting and q-commuting difference operators, in particular, p

Jiazhi Guan, Hang Zhou, Zhibin Hong, Errui Ding

Recent advances in face forgery techniques produce nearly visually untraceable deepfake videos, which could be leveraged with malicious intentions. As a result, researchers have been devoted to deepfake detection. Previous studies have identified the importance of local low-level cues and temporal information in pursuit to generalize well across deepfake met

Qianglong Chen, Xiangji Zeng, Jiangang Zhu, Yin Zhang

Gazetteer is widely used in Chinese named entity recognition (NER) to enhance span boundary detection and type classification. However, to further understand the generalizability and effectiveness of gazetteers, the NLP community still lacks a systematic analysis of the gazetteer-enhanced NER model. In this paper, we first re-examine the effectiveness severa

Attila Gergely, Zoltán Néda

A 2D numerical hydrodynamics approach is considered for modelling recent experimental results on the oscillation and collective behavior of convective flows. Our simulations consider the rising dynamics of heated fluid columns in a gravitational field. Simulations are done on two entirely different length-scales, showing also the generality of the investigat

Svetlana Boyarchenko, Sergei Levendorskiĭ

We prove simple general formulas for expectations of functions of a random walk and its running extremum. Under additional conditions, we derive analytical formulas using the inverse $Z$-transform, the Fourier/Laplace inversion and Wiener-Hopf factorization, and discuss efficient numerical methods for realization of these formulas. As applications, the cumul

Siddarth Kannan, Stefano Serpente, Claudia He Yun

Let $\bar{\mathcal{M}}_{g, m|n}$ denote Hassett's moduli space of weighted pointed stable curves of genus $g$ for the heavy/light weight data $\left(1^{(m)}, 1/n^{(n)}\right)$, and let $\mathcal{M}_{g, m|n} \subset \bar{\mathcal{M}}_{g, m|n}$ be the locus parameterizing smooth, not necessarily distinctly marked curves. We give a change-of-variables formula w

Mathias Lindholm, Ronald Richman, Andreas Tsanakas, Mario V. Wüthrich

In applications of predictive modeling, such as insurance pricing, indirect or proxy discrimination is an issue of major concern. Namely, there exists the possibility that protected policyholder characteristics are implicitly inferred from non-protected ones by predictive models, and are thus having an undesirable (or illegal) impact on prices. A technical s

Brecht G. Simon, Samer Kurdi, Joris J. Carmiggelt, Michael Borst

Nitrogen-vacancy (NV) magnetometry is a new technique for imaging spin waves in magnetic materials. It detects spin waves by their microwave magnetic stray fields, which decay evanescently on the scale of the spin-wavelength. Here, we use nanoscale control of a single-NV sensor as a wavelength filter to characterize frequency-degenerate spin waves excited by

Nicholas Konz, Hanxue Gu, Haoyu Dong, Maciej A. Mazurowski

The manifold hypothesis is a core mechanism behind the success of deep learning, so understanding the intrinsic manifold structure of image data is central to studying how neural networks learn from the data. Intrinsic dataset manifolds and their relationship to learning difficulty have recently begun to be studied for the common domain of natural images, bu

Wenjie Li, Juncheng Li, Guangwei Gao, Jiantao Zhou

Recently, Transformer-based methods have shown impressive performance in single image super-resolution (SISR) tasks due to the ability of global feature extraction. However, the capabilities of Transformers that need to incorporate contextual information to extract features dynamically are neglected. To address this issue, we propose a lightweight Cross-rece

Esther Conrad

Product throttling answers the question of minimizing the product of the resources needed to accomplish a task, and the time in which it takes to accomplish the task. In product throttling for positive semidefinite zero forcing, task that we wish to accomplish is positive semidefinite zero forcing. Positive semidefinite zero forcing is a game played on a gra

Oren Mangoubi, Yikai Wu, Satyen Kale, Abhradeep Guha Thakurta

Consider the following optimization problem: Given $n \times n$ matrices $A$ and $\Lambda$, maximize $\langle A, U\Lambda U^*\rangle$ where $U$ varies over the unitary group $\mathrm{U}(n)$. This problem seeks to approximate $A$ by a matrix whose spectrum is the same as $\Lambda$ and, by setting $\Lambda$ to be appropriate diagonal matrices, one can recover

Svetlana Boyarchenko, Sergei Levendorskiĭ

We prove simple general formulas for expectations of functions of a L\'evy process and its running extremum. Under additional conditions, we derive analytical formulas using the Fourier/Laplace inversion and Wiener-Hopf factorization, and discuss efficient numerical methods for realization of these formulas. As applications, the cumulative probability distri

Mallesham Dasari, Ramanujan K Sheshadri, Karthikeyan Sundaresan, Samir R. Das

The plethora of sensors in our commodity devices provides a rich substrate for sensor-fused tracking. Yet, today's solutions are unable to deliver robust and high tracking accuracies across multiple agents in practical, everyday environments - a feature central to the future of immersive and collaborative applications. This can be attributed to the limited s

Wei-chen Guo, Zuo Wang, Pei-fang Wu, Li-jun Lang

We investigate the steady-state and dynamical properties of a reciprocal many-body system consisting of self-propelled active particles with local alignment interactions that exists within a fan-shaped neighborhood of each particle. We find that the nonreciprocity can emerge in this reciprocal system once the spontaneous symmetry breaking is present, and the

D. V. Forero, C. Giunti, C. A. Ternes, O. Tyagi

The existence of Large Extra Dimensions can be probed in various neutrino experiments. We analyze several neutrino data sets in a model with a dominant large extra dimension. We show that the Gallium anomaly can be explained with neutrino oscillations induced by the large extra dimension, but the region of parameter space which is preferred by the Gallium an

Oliver Atkinson, Matthew Black, Christoph Englert, Alexander Lenz

Two Higgs doublet models are attractive scenarios for physics beyond the Standard Model. In particular, lepton-specific manifestations remain contenders to explain the observed discrepancy between the anomalous magnetic moment of the muon $a_\mu$ predicted within the Standard Model and recent observations at Fermilab and BNL. Dominant uncertainties that affe

Zechang Sun, Yuan-Sen Ting, Zheng Cai

Modeling quasar spectra is a fundamental task in astrophysics as quasars are the tell-tale sign of cosmic evolution. We introduce a novel unsupervised learning algorithm, Quasar Factor Analysis (QFA), for recovering the intrinsic quasar continua from noisy quasar spectra. QFA assumes that the Ly$\alpha$ forest can be approximated as a Gaussian process, and t

Jiashu Pan, Yuan-Sen Ting, Jie Yu

We introduce Astroconformer, a Transformer-based model to analyze stellar light curves from the Kepler mission. We demonstrate that Astrconformer can robustly infer the stellar surface gravity as a supervised task. Importantly, as Transformer captures long-range information in the time series, it outperforms the state-of-the-art data-driven method in the fie

Kwok Sun Tang, Yuan-Sen Ting

A key yet unresolved question in modern-day astronomy is how galaxies formed and evolved under the paradigm of the $\Lambda$CDM model. A critical limiting factor lies in the lack of robust tools to describe the merger history through a statistical model. In this work, we employ a generative graph network, E(n) Equivariant Graph Normalizing Flows Model. We de

Ioana Ciuca, Yuan-Sen Ting

Stellar spectra encode detailed information about the stars. However, most machine learning approaches in stellar spectroscopy focus on supervised learning. We introduce Mendis, an unsupervised learning method, which adopts normalizing flows consisting of Neural Spline Flows and GLOW to describe the complex distribution of spectral space. A key advantage of

Julien Gargani

Relative sea level records climatic change as well as vertical land movement. In Barbados, uplift variation is necessary to interpret one of the most complete coral reef records. Here we show that an abrupt mass unloading of 30 km3 caused an uplift variation of ~0.45 mm/yr using a modelling approach. Simulations have been conducted for different volumes and

Marek Kaluba, Piotr Mizerka, Piotr W. Nowak

We show that the cohomological Laplacian in degree 1 in the group cohomology of $\operatorname{SL}_3(\mathbb{Z})$ is a sum of hermitian squares in the algebra $\mathbb{M}_n(\mathbb{R}G)$. We provide an estimate of the spectral gap for this Laplacian for every unitary representation.

Ignacio Ema, Guillermo Ramírez, Rafael López, José Manuel García de la Vega

A new family of Gaussian-type basis sets named sigma basis sets is presented and preliminarily tested. Sigma basis sets for H, C, N, O and P are reported and their performance is tested in some atomic and molecular calculations.

Linfeng Yu, Jinyuan Xu, Bo Peng, Guangzhao Qin

Nanoclusters like fullerenes as the unit to build intriguing two-dimensional topological structures is of great challenge. Here we propose three bridged fullerene monolayers and comprehensively investigate the novel fullerene monolayer as synthesized experimentally Zheng et al.,[Nature 606, 507-510 (2022)] by state of the art first principles calculations. O

Nuno Moedas, Morgan Deal, Diego Bossini, Bernardo Campilho

Chemical composition is an important factor that affects stellar evolution. The element abundance on the stellar surface evolves along the lifetime of the star because of transport processes, including atomic diffusion. However, models of stars with masses higher than about 1.2Msun predict unrealistic variations at the stellar surface. This indicates the nee

Daniel W Polak, Mariana T do Casal, Josene M Toldo, Xiantao Hu

Diketopyrrolopyrroles are a popular class of electron-withdrawing unit in optoelectronic materials. When combined with electron donating side-chain functional groups such as thiophenes, they form a very broad class of donor-acceptor molecules: thiophene-diketopyrrolopyrroles (TDPPs). Despite their widescale use in biosensors and photovoltaic materials, studi

Mario Morvan, Nikolaos Nikolaou, Kai Hou Yip, Ingo Waldmann

Astrophysical light curves are particularly challenging data objects due to the intensity and variety of noise contaminating them. Yet, despite the astronomical volumes of light curves available, the majority of algorithms used to process them are still operating on a per-sample basis. To remedy this, we propose a simple Transformer model -- called Denoising

Abdullah Al Maruf, Alexander Bakhtin, Tomas Cerny, Davide Taibi

Microservices bring various benefits to software systems. They also bring decentralization and lose coupling across self-contained system parts. Since these systems likely evolve in a decentralized manner, they need to be monitored to identify when possibly poorly designed extensions deteriorate the overall system quality. For monolith systems, such tasks ha

Andrea Mannocci, Ornella Irrera, Paolo Manghi

Authorship of scientific articles has profoundly changed from early science until now. While once upon a time a paper was authored by a handful of authors, scientific collaborations are much more prominent on average nowadays. As authorship (and citation) is essentially the primary reward mechanism according to the traditional research evaluation frameworks,

H. Pfeffer, M. Davidson, N. Curfman, T. Omark

The Long Baseline Neutrino Facility (LBNF) will produce the worlds most intense neutrino beam. Three series connected magnetic horns will require 5 kV, 300 kA, 800 $\mu$s pulses at a rate of 1.4 Hz to focus the beam. Connecting a single power supply to these focusing horns will require a low impedance connection measuring over 60 m in length. To meet the cha

Audrey Cui, Ali Jahanian, Agata Lapedriza, Antonio Torralba

We introduce the task of local relighting, which changes a photograph of a scene by switching on and off the light sources that are visible within the image. This new task differs from the traditional image relighting problem, as it introduces the challenge of detecting light sources and inferring the pattern of light that emanates from them. We propose an a

Dugang Liu, Pengxiang Cheng, Hong Zhu, Xing Tang

Tabular data is one of the most common data storage formats behind many real-world web applications such as retail, banking, and e-commerce. The success of these web applications largely depends on the ability of the employed machine learning model to accurately distinguish influential features from all the predetermined features in tabular data. Intuitively

Rajko Nenadov

Consider the following two-player game on the edges of $K_n$, the complete graph with $n$ vertices: Starting with an empty graph $G$ on the vertex set of $K_n$, in each round the first player chooses $b \in \mathbb{N}$ edges from $K_n$ which have not previously been chosen, and the second player immediately and irrevocably picks one of these edges and adds i

Francesco Iacovelli, Michele Mancarella, Stefano Foffa, Michele Maggiore

We introduce $\texttt{GWFAST}$, a Fisher information matrix $\texttt{Python}$ code that allows easy and efficient estimation of signal-to-noise ratios and parameter measurement errors for large catalogs of resolved sources observed by networks of gravitational-wave detectors. In particular, $\texttt{GWFAST}$ includes the effects of the Earth's motion during

Francesco Iacovelli, Michele Mancarella, Stefano Foffa, Michele Maggiore

We introduce $\texttt{GWFAST}$, a novel Fisher-matrix code for gravitational-wave studies, tuned toward third-generation gravitational-wave detectors such as Einstein Telescope (ET) and Cosmic Explorer (CE). We use it to perform a comprehensive study of the capabilities of ET alone, and of a network made by ET and two CE detectors, as well as to provide fore

Herbert F Fotso

A quantum emitter in a dynamic environment may have its energy levels drift uncontrollably in time with the fluctuating bath. This can result in an emission/absorption spectrum that is spread over a broad range of frequencies and presents a challenging hurdle for various applications. We consider a quantum emitter in an environment that alters the energy lev

Jack Binysh, Indrajit Chakraborty, Mykyta V. Chubynsky, Vicente Luis Diaz Melian

The elastic Leidenfrost effect occurs when a vaporizable soft solid is lowered onto a hot surface. Evaporative flow couples to elastic deformation, giving spontaneous bouncing or steady-state floating. The effect embodies an unexplored interplay between thermodynamics, elasticity, and lubrication: despite being observed, its basic theoretical description rem

Diego M. Fieguth, James R. Anglin

Useful dynamical processes often begin through barrier-crossing dynamical transitions; engineering system dynamics in order to make such transitions reliably is therefore an important task for biological or artificial microscopic machinery. Here we first show by example that adding even a small amount of back-reaction to a control parameter, so that it respo

Pouria Sarhadi, Wasif Naeem, Nikolaos Athanasopoulos

Machine Learning (ML) techniques have gained significant traction as a means of improving the autonomy of marine vehicles over the last few years. This article surveys the recent ML approaches utilised for ship collision avoidance (COLAV) and mission planning. Following an overview of the ever-expanding ML exploitation for maritime vehicles, key topics in th

C. C. Jensen, T. Omark, H. Pfeffer, K. Roon

The Long Baseline Neutrino Facility (LBNF) will produce the worlds most intense neutrino beam. Three series connected magnetic horns will require 5kV, 300kA, 800$\mu$s pulses at a rate of 0.7Hz to focus the beam. Fermilab has designed and built pulsed high current supplies for horns in the past. Pulsed currents of 205 kA for Neutrinos at Main Injector (NuMI

Arjun Ashoka, Satyawan Nagane, Nives Strkalj, Bart Roose

Photoinduced spin-charge interconversion in semiconductors with spin-orbit coupling could provide a route to optically addressable spintronics without the use of external magnetic fields. A central question is whether the resulting spin-associated charge currents are robust to structural disorder, which is inherent to polycrystalline semiconductors that are

Cory Merkel

Adversarial attacks on state-of-the-art machine learning models pose a significant threat to the safety and security of mission-critical autonomous systems. This paper considers the additional vulnerability of machine learning models when attackers can measure the power consumption of their underlying hardware platform. In particular, we explore the utility

Xin Cao

In this paper, a new gradient-based optimization approach by automatically adjusting the learning rate is proposed. This approach can be applied to design non-adaptive learning rate and adaptive learning rate. Firstly, I will introduce the non-adaptive learning rate optimization method: Binary Forward Exploration (BFE), and then the corresponding adaptive pe

Sara Cooper, Francesco Ferro

This paper describes the final prototype of an assistive robot used for increasing engagement of older adults in the context of SHAPES project. It then highlights lessons learned from hands-on training during the first phases of the pilots at Clinica Humana and Can Granada residence in Mallorca (Spain).

Siarhei Finski

We study the asymptotics of the $L^2$-optimal holomorphic extensions of holomorphic jets associated with high tensor powers of a positive line bundle along submanifolds. More precisely, for a fixed complex submanifold in a complex manifold, we consider the operator which for a given holomorphic jet along the submanifold of a positive line bundle associates t

Maya Bechler-Speicher, Amir Globerson, Ran Gilad-Bachrach

When dealing with tabular data, models based on decision trees are a popular choice due to their high accuracy on these data types, their ease of application, and explainability properties. However, when it comes to graph-structured data, it is not clear how to apply them effectively, in a way that incorporates the topological information with the tabular da

M. W. AlMasri, M. R. B. Wahiddin

Motivated by the fact that twice the Fourier transform plays the role of parity operator. We systematically study integral transforms in the case of $\mathcal{PT}$-symmetric Hamiltonian. First, we obtain a closed analytical formula for the exponential Fourier transform of a general $\mathcal{PT}$-symmetric Hamiltonian. Using the Segal-Bargmann transform, we

Golan Bel, Eli Tziperman

Nonlinear interaction and breaking of internal ocean waves are responsible for much of the interior ocean mixing, affecting ocean carbon storage and the global overturning circulation. These interactions may affect the observed Garrett-Munk wave energy spectrum, in addition to the recently explored interaction of waves with ocean eddies. According to the res

H. Garcilazo, A. Valcarce

We study hidden-flavor pentaquarks, $Q\bar Q qqq$, based on a constituent quark-model with a standard quark-quark interaction that reproduces the low-energy meson and baryon spectra. We make use of dynamical correlations between the heavy quarks arising from the Coulomb-like nature of the short-range interaction. A detailed comparison is made with other resu

Zihang Lin, Chaolei Tan, Jian-Fang Hu, Zhi Jin

In this technical report, we introduce our solution to human-centric spatio-temporal video grounding task. We propose a concise and effective framework named STVGFormer, which models spatiotemporal visual-linguistic dependencies with a static branch and a dynamic branch. The static branch performs cross-modal understanding in a single frame and learns to loc

Arindam Ghosh, Mark Fuhs, Deblin Bagchi, Bahman Farahani

As virtual assistants have become more diverse and specialized, so has the demand for application or brand-specific wake words. However, the wake-word-specific datasets typically used to train wake-word detectors are costly to create. In this paper, we explore two techniques to leverage acoustic modeling data for large-vocabulary speech recognition to improv

M. W. AlMasri, M. R. B. Wahiddin

We introduce a quantum decomposition algorithm (QDA) that decomposes the problem $\frac{\partial \rho}{\partial t}=\mathcal{L}\rho=\lambda \rho$ into a summation of eigenvalues times phase-space variables. One interesting feature of QDA stems from its ability to simulate damped spin systems by means of pure quantum harmonic oscillators adjusted with the eige

Yifan Wang, Pengzhan Jin, Hehu Xie

In this paper, we introduce a type of tensor neural network. For the first time, we propose its numerical integration scheme and prove the computational complexity to be the polynomial scale of the dimension. Based on the tensor product structure, we develop an efficient numerical integration method by using fixed quadrature points for the functions of the t