May 2022 arXiv papers — page 21

Chin Pang Ho, Marek Petrik, Wolfram Wiesemann

In recent years, robust Markov decision processes (MDPs) have emerged as a prominent modeling framework for dynamic decision problems affected by uncertainty. In contrast to classical MDPs, which only account for stochasticity by modeling the dynamics through a stochastic process with a known transition kernel, robust MDPs additionally account for ambiguity

Yulun Wang, Predrag S. Krstic

An algorithm and a system of quantum circuits is developed and applied to compute accurately the S matrix for the transitions between vibrational states of H2 for collisions with H. The algorithm was applied to 100 eV laboratory collision energy at a quantum circuit simulator. The effects of the discretized dissociative continuum to the transition cross sect

Liliana Arrachea

We present an overview of recent advances in the study of energy dynamics and mechanisms for energy conversion in qubit systems with special focus on realizations in superconducting quantum circuits. We briefly introduce the relevant theoretical framework to analyze heat generation, energy transport and energy conversion in these systems with and without tim

Alessandro Ursi, Marco Romani, Giovanni Piano, Francesco Verrecchia

We report the AGILE observations of GRB 220101A, which took place at the beginning of 1st January 2022 and was recognized as one of the most energetic gamma-ray bursts (GRBs) ever detected since their discovery. The AGILE satellite acquired interesting data concerning the prompt phase of this burst, providing an overall temporal and spectral description of t

Marina Knittel, Max Springer, John P. Dickerson, MohammadTaghi Hajiaghayi

Clustering is a fundamental building block of modern statistical analysis pipelines. Fair clustering has seen much attention from the machine learning community in recent years. We are some of the first to study fairness in the context of hierarchical clustering, after the results of Ahmadian et al. from NeurIPS in 2020. We evaluate our results using Dasgupt

Hang Yu, Denis Martynov, Rana X Adhikari, Yanbei Chen

The sensitivities of ground-based gravitational-wave (GW) detectors are limited by quantum shot noise at a few hundred Hertz and above. Nonetheless, one can use a quantum-correlation technique proposed by Martynov, et al. [Phys. Rev. A 95, 043831 (2017)] to remove the expectation value of the shot noise, thereby exposing underlying classical signals in the c

Nannan Wu, Ning Zhang, Wenjun Wang, Lixin Fan

Anomaly subgraph detection has been widely used in various applications, ranging from cyber attack in computer networks to malicious activities in social networks. Despite an increasing need for federated anomaly detection across multiple attributed networks, only a limited number of approaches are available for this problem. Federated anomaly detection face

Heiko H. Schütt, Wei Ji Ma

A visual system has to learn both which features to extract from images and how to group locations into (proto-)objects. Those two aspects are usually dealt with separately, although predictability is discussed as a cue for both. To incorporate features and boundaries into the same model, we model a layer of feature maps with a pairwise Markov random field m

Jack Buttcane

A formula of Kuznetsov allows one to interpret a smooth sum of Kloosterman sums as a sum over the spectrum of $GL(2)$ automorphic forms. In this paper, we construct a similar formula for the first hyper-Kloosterman sums using $GL(3)$ automorphic forms, resolving a long-standing problem of Bump, Friedberg and Goldfeld. Along the way, we develop what are appar

Tian Gu, Hyun Jung Kim, Clara Rivero-Baleine, Juejun Hu

Active optical metasurfaces are rapidly emerging as a major frontier in photonics research, development, and commercialization. They promise compact, light-weight, and energy-efficient reconfigurable optical systems with unprecedented performance and functions that can be dynamically defined on-demand. Compared to their passive counterparts, the reconfigurat

Yuping Zheng, Andrew Lamperski

Langevin algorithms are gradient descent methods augmented with additive noise, and are widely used in Markov Chain Monte Carlo (MCMC) sampling, optimization, and machine learning. In recent years, the non-asymptotic analysis of Langevin algorithms for non-convex learning has been extensively explored. For constrained problems with non-convex losses over a c

Wageesha Bangamuarachchi, Anju Chamantha, Lakmal Meegahapola, Salvador Ruiz-Correa

While the task of automatically detecting eating events has been examined in prior work using various wearable devices, the use of smartphones as standalone devices to infer eating events remains an open issue. This paper proposes a framework that infers eating vs. non-eating events from passive smartphone sensing and evaluates it on a dataset of 58 college

Elizeu França, Francesco Mercuri

We establish a criterion for the flatness of a principal circle bundle in terms of the intrinsically harmonic form problem. It states that the flatness is equivalent to the intrinsic harmonicity of a certain natural associated form.

Georgia Perakis, Asterios Tsiourvas

This paper introduces scalable, sampling-based algorithms that optimize trained neural networks with ReLU activations. We first propose an iterative algorithm that takes advantage of the piecewise linear structure of ReLU neural networks and reduces the initial mixed-integer optimization problem (MIP) into multiple easy-to-solve linear optimization problems

Marco Barletta, Marcello Cinque, Luigi De Simone, Raffaele Della Corte

Time predictable edge cloud is seen as the answer for many arising needs in Industry 4.0 environments, since it is able to provide flexible, modular, and reconfigurable services with low latency and reduced costs. Orchestration systems are becoming the core component of clouds since they take decisions on the placement and lifecycle of software components. C

Karapet Mkrtchyan, Mantas Svazas

We study solutions in non-linear electrodynamics (NED) and establish several general results. We show, that the $SO(2)$ electric-magnetic duality symmetry is restrictive enough to allow for reconstruction of the NED Lagrangian from the spherically-symmetric electrostatic (Coulomb-like) solution -- although there are infinitely many different NED theories adm

Erin L. Howard, James R. A. Davenport, Kevin R. Covey

We present 370 candidate eclipsing binaries (EBs), identified from ~510,000 short cadence TESS light curves. Our statistical criteria identify 5,105 light curves with features consistent with eclipses (~1% of the initial sample). After visual confirmation of the light curves, we have a final sample of 2,288 EB candidates. Among these, we find 370 sources tha

Kaitlyn E. Moo, Joel N. Bregman, Mark T. Reynolds

The variability of blazars in the X-ray and optical regions informs both the physics of their emitting region and places demands on the observer if a program requires that the object be bright or faint. The extensive simultaneous X-ray and optical observation by Swift provides the best insight into the variable nature of these objects. This program uses \tex

Teaghan O'Briain, Carlos Uribe, Kwang Moo Yi, Jonas Teuwen

To correct for respiratory motion in PET imaging, an interpretable and unsupervised deep learning technique, FlowNet-PET, was constructed. The network was trained to predict the optical flow between two PET frames from different breathing amplitude ranges. The trained model aligns different retrospectively-gated PET images, providing a final image with simil

Ines Rehbein, Josef Ruppenhofer, Julian Bernauer

This paper investigates the use of first person plural pronouns as a rhetorical device in political speeches. We present an annotation schema for disambiguating pronoun references and use our schema to create an annotated corpus of debates from the German Bundestag. We then use our corpus to learn to automatically resolve pronoun referents in parliamentary d

Yifan Liu, Yixu Wang, Xiaoqing Guan, Tao Hu

Spherical robot is a nonlinear, nonholonomic and unstable system which increases the difficulty of the direction and trajectory tracking problem. In this study, we propose a new direction controller HTSMC, an instruction planning controller MPC, and a trajectory tracking framework MHH. The HTSMC is designed by integrating a fast terminal algorithm, a hierarc

Benjamin Wu, Hrushikesh Patil, Predrag Krstic

We study the effects of quantum noise in hybrid quantum-classical solver for sparse systems of linear equations using quantum random walks, applied to stoquastic Hamiltonian matrices. In an ideal noiseless quantum computer, sparse matrices achieve solution vectors with lower relative error than dense matrices. However, we find quantum noise reverses this eff

Fernando D. Vila, John J. Rehr, Himadri Pathak, Bo Peng

Newly developed coupled-cluster (CC) methods enable simulations of ionization potentials and spectral functions of molecular systems in a wide range of energy scales ranging from core-binding to valence. This paper discusses results obtained with the real-time equation-of-motion CC cumulant approach (RT-EOM-CC), and CC Green's function (CCGF) approaches in a

E. J. Kolmes, I. E. Ochs, M. E. Mlodik, N. J. Fisch

In a variety of different systems, high-Z ion species show a marked tendency to accumulate in regions of high plasma density. It has previously been suggested that the apparent universality of this behavior could be explained thermodynamically, in terms of the maximum-entropy state attainable when the system must obey an ambipolarity condition. However, the

Mali Halac, Murat Isik, Hasan Ayaz, Anup Das

Reconstructing perceived images from human brain activity monitored by functional magnetic resonance imaging (fMRI) is hard, especially for natural images. Existing methods often result in blurry and unintelligible reconstructions with low fidelity. In this study, we present a novel approach for enhanced image reconstruction, in which existing methods for ob

El Moatez Billah Nagoudi, AbdelRahim Elmadany, Muhammad Abdul-Mageed

We present TURJUMAN, a neural toolkit for translating from 20 languages into Modern Standard Arabic (MSA). TURJUMAN exploits the recently-introduced text-to-text Transformer AraT5 model, endowing it with a powerful ability to decode into Arabic. The toolkit offers the possibility of employing a number of diverse decoding methods, making it suited for acquiri

Jiang Ke

An orthotropic metamaterial is composed of elements arrayed periodically in space. The element includes two cuboid structures. The first structure is the basic structure of the element, and the second structure is the transformation of the first structure of the element. The first structure of the element is a cuboid structure composed of 24 bars connected b

A. Moradpouri, Mahdi Torabian, S. A. Jafari

We formulate the Boltzmann kinetic equations for interacting tilted Dirac fermions in two space dimensions characterized by a tilt parameter $0\le\zeta<1$. Solving the linearized Boltzmann equation, we find that the broadening of the Drude pole is enhanced by $\kappa(\zeta)\times(1-\zeta^2)^{-1/2}$, where the $\kappa$ is interaction-induced enhancement facto

Abhimanyu Dubey, Alex Pentland

The cooperative bandit problem is a multi-agent decision problem involving a group of agents that interact simultaneously with a multi-armed bandit, while communicating over a network with delays. The central idea in this problem is to design algorithms that can efficiently leverage communication to obtain improvements over acting in isolation. In this paper

Lingkai Kong, Yuqing Wang, Molei Tao

The problem of optimization on Stiefel manifold, i.e., minimizing functions of (not necessarily square) matrices that satisfy orthogonality constraints, has been extensively studied. Yet, a new approach is proposed based on, for the first time, an interplay between thoughtfully designed continuous and discrete dynamics. It leads to a gradient-based optimizer

Jianwei Lyu, George Rieke

The active galactic nucleus (AGN) phenomena results from a supermassive black hole accreting its surrounding gaseous and dusty material. The infrared (IR) regime provides most of the information to characterize the dusty structures that bridge from the galaxy to the black hole, providing clues to the black hole growth and host galaxy evolution. Over the past

G. G. Guzmán-Verri, C. H. Liang, P. B. Littlewood

We consider a standard Ginzburg-Landau model of a ferroelectric whose electrical polarization is coupled to gradients of elastic strain. At the harmonic level, such flexoelectric interaction is known to hybridize acoustic and optic phonon modes and lead to phases with modulated lattice structures that precede the symmetry broken state for sufficiently large

Vania Vellucci, Edgardo Franzin, Stefano Liberati

The possible detection of echoes in late gravitational-wave signals is the most promising way to test horizonless alternatives to general relativistic black holes, and probe the physics of these hypothetical ultra-compact objects. While there is currently no evidence for the presence of such signatures, better accuracy is expected with the growing wealth of

J. -Z. Zhuang, Y. -K. Wu, L. -M. Duan

Information in a chaotic quantum system will scramble across the system, preventing any local measurement from reconstructing it. The scrambling dynamics is key to understanding a wide range of quantum many-body systems. Here we use Holevo information to quantify the scrambling dynamics, which shows a phase-transition-like behavior. When applying long random

Jordan Mirocha, Adrian Liu, Paul La Plante

The reionization of the intergalactic medium at redshifts $z\gtrsim 6$ is expected to have a lasting impact on galaxies residing in low-mass dark matter halos. Unable to accrete or retain gas photo-heated to temperatures $T \gtrsim 10^4$ K, the star formation histories of faint galaxies in the early Universe are expected to decline as they exhaust their gas

Peter L. Taylor, Katarina Markovič

To combine information from measurements of the redshift-space power spectrum from spectroscopic data with angular weak lensing, galaxy clustering and galaxy-galaxy lensing power spectra from photometric surveys (i.e. the $3 \times 2$ point statistics), we must account for the covariance between the two probes. Currently any covariance between the two types

Dietrich Bodeker, Jan Nienaber

The motion of a scalar field that interacts with a hot plasma, like the inflaton during reheating, is damped, which is a dissipative process. At high temperatures the damping can be described by a local term in the effective equation of motion. The damping coefficient is sensitive to multiple scattering. In the loop expansion its computation would require an

Daisuke Taniguchi, Kazuya Yamazaki, Shinsuke Uno

Betelgeuse, one of the most studied red supergiant stars, dimmed in the optical by ~1.2 mag between late 2019 and early 2020, reaching an historical minimum called "the Great Dimming." Thanks to enormous observational effort to date, two hypotheses remain that can explain the Dimming: a decrease in the effective temperature and an enhancement of the extincti

Maaike A. M. van Kooten, Rebecca Jensen-Clem Sylvain Cetre, Sam Ragland, Charlotte Z. Bond

The behavior of an adaptive optics (AO) system for ground-based high contrast imaging (HCI) dictates the achievable contrast of the instrument. In conditions where the coherence time of the atmosphere is short compared to the speed of the AO system, the servo-lag error can become the dominant error term of the AO system. While the AO system measures the wave

Anton Vikaeus, Daniel J Whalen, Erik Zackrisson

Direct-collapse black holes (DCBHs) may be the seeds of the first quasars, over 200 of which have now been detected at $z > 6$ . The James Webb Space Telescope (JWST) could detect DCBHs in the near infrared (NIR) at $z \lesssim 20$ and probe the evolution of primordial quasars at their earliest stages, but only in narrow fields that may not capture many of t

Teo Muñoz-Darias, Gabriele Ponti

Observational signatures of accretion disc winds have been found in a significant number of low-mass X-ray binaries at either X-ray or optical wavelengths. The 2015 outburst of the black hole transient V404 Cygni provided a unique opportunity for studying both types of outflows in the same system. We used contemporaneous X-ray (Chandra Observatory) and optic

Charles L. Steinhardt, Albert Sneppen, Basel Mostafa, Hagan Hensley

The stellar initial mass function (IMF) is predicted to depend upon the temperature of gas in star-forming molecular clouds. The introduction of an additional parameter, $T_{IMF}$ , into photometric template fitting, allows galaxies to be fit with a range of IMFs. Three surprising new features appear: (1) most star-forming galaxies are best fit with a bottom

Catherine Zucker, J. E. G. Peek, Sarah R. Loebman

Recent analyses of Gaia data have resulted in the identification of new stellar structures, including a new class of extended stellar filaments called stellar "strings", first proposed by Kounkel and Covey 2019. We explore the spatial, kinematic, and chemical composition of strings to demonstrate that these newfound structures are largely inconsistent with b

Nikhil Sarin, Conor M. B. Omand, Ben Margalit, David I. Jones

A non-negligible fraction of binary neutron star mergers are expected to form long-lived neutron star remnants, dramatically altering the multi-messenger signatures of a merger. Here, we extend existing models for magnetar-driven kilonovae and explore the diversity of kilonovae and kilonova afterglows. Focusing on the role of the (uncertain) magnetic field s

Oliver Just, Miguel A. Aloy, Martin Obergaulinger, Shigehiro Nagataki

Collapsar disks have been proposed to be rich factories of heavy elements, but the major question of whether their outflows are neutron-rich, and could therefore represent significant sites of the rapid neutron-capture (r-) process, or dominated by iron-group elements remains unresolved. We present the first global models of collapsars that start from a stel

Wilfred Salmon, Sergii Strelchuk, David Arvidsson-Shukur

Measurements of quantum states form a key component in quantum-information processing. It is therefore an important task to compare measurements and furthermore decide if a measurement strategy is optimal. Entropic quantities, such as the quantum Fisher information, capture asymptotic optimality but not optimality with finite resources. We introduce a framew

Yixuan Wei, Han Hu, Zhenda Xie, Zheng Zhang

Masked image modeling (MIM) learns representations with remarkably good fine-tuning performances, overshadowing previous prevalent pre-training approaches such as image classification, instance contrastive learning, and image-text alignment. In this paper, we show that the inferior fine-tuning performance of these pre-training approaches can be significantly

Eldar David Abraham, Karel D'Oosterlinck, Amir Feder, Yair Ori Gat

The increasing size and complexity of modern ML systems has improved their predictive capabilities but made their behavior harder to explain. Many techniques for model explanation have been developed in response, but we lack clear criteria for assessing these techniques. In this paper, we cast model explanation as the causal inference problem of estimating c

Kaushik Bhattacharya, Burigede Liu, Andrew M. Stuart, Margaret Trautner

Fully resolving dynamics of materials with rapidly-varying features involves expensive fine-scale computations which need to be conducted on macroscopic scales. The theory of homogenization provides an approach to derive effective macroscopic equations which eliminates the small scales by exploiting scale separation. An accurate homogenized model avoids the

Emma Deist, Yue-Hui Lu, Jacquelyn Ho, Mary Kate Pasha

Subsystem readout during a quantum process, or mid-circuit measurement, is crucial for error correction in quantum computation, simulation, and metrology. Ideal mid-circuit measurement should be faster than the decoherence of the system, high-fidelity, and nondestructive to the unmeasured qubits. Here, we use a strongly coupled optical cavity to read out the

Christian Berger

Today's software engineering already needs to deal with challenges originating from the multidisciplinarity that is required to realize IoT products: Many variants consist of sensor/actuator-powered systems that already today use AI/ML systems to better cope with the unstructuredness of their intended operational design domain (ODD), while, at the same time,

Kevin Smith, Hai Lin, Praveen Tiwari, Marjorie Sayer

Property Specification Language (PSL) is a form of temporal logic that has been mainly used in discrete domains (e.g. formal hardware verification). In this paper, we show that by merging machine learning techniques with PSL monitors, we can extend PSL to work on continuous domains. We apply this technique in machine learning-based anomaly detection to analy

N. Zen

By drilling periodic thru-holes in a suspended film, the phonon system can be modified. Being motivated by the BCS theory, the technique, so-called phonon engineering, was applied to a niobium sheet. The newly emergent high-$T_{c}$ superconductivity, however, cannot be accounted for by the BCS theory. Rather, its exposed configuration, namely a square-lattic

Tri Dao, Daniel Y. Fu, Stefano Ermon, Atri Rudra

Transformers are slow and memory-hungry on long sequences, since the time and memory complexity of self-attention are quadratic in sequence length. Approximate attention methods have attempted to address this problem by trading off model quality to reduce the compute complexity, but often do not achieve wall-clock speedup. We argue that a missing principle i

Ippocratis D. Saltas, Jørgen Christensen-Dalsgaard

General extensions of General Relativity (GR) based on bona fide degrees of freedom predict a fifth force which operates within massive objects, opening up an exciting opportunity to perform precision tests of gravity at stellar scales. Here, focusing on general scalar-tensor theories for dark energy, we utilize the Sun as our laboratory and search for impri

Chris Elliott, Fabian Hahner, Ingmar Saberi

We construct a derived generalization of the pure spinor superfield formalism and prove that it exhibits an equivalence of dg-categories between multiplets for a supertranslation algebra and equivariant modules over its Chevalley-Eilenberg cochains. This equivalence is closely linked to Koszul duality for the supertranslation algebra. After introducing and d

Milan Korda, Rodolfo Rios-Zertuche

Recent works have proposed linear programming relaxations of variational optimization problems subject to nonlinear PDE constraints based on the occupation measure formalism. The main appeal of these methods is the fact that they rely on convex optimization, typically semidefinite programming. In this work we close an open question related to this approach.

Tobias Kreutz

We compute the image of the $p$-adic period map for polarized K3 surfaces with supersingular reduction. This gives rise to a Rapoport-Zink type uniformization of their moduli space by an explicit open rigid analytic subvariety of a local Shimura variety of orthogonal type. In contrast to the case of Rapoport-Zink uniformization of Shimura varieties and in an

Georgy A. Kazakov, Swadheen Dubey, Anna Bychek, Uwe Sterr

Active optical frequency standards provide interesting alternatives to their passive counterparts. Particularly, such a clock alone continuously generates highly-stable narrow-line laser radiation. Thus a local oscillator is not required to keep the optical phase during a dead time between interrogations as in passive clocks, but only to boost the active clo

Kanu Sinha, Saeed A. Khan, Elif Cüce, Hakan E. Türeci

We study the radiative properties -- the Lamb shift, Purcell decay rate and the spontaneous emission dynamics -- of an artificial atom coupled to a long, multimode cavity formed by an array of Josephson junctions. Introducing a tunable coupling element between the atom and the array, we demonstrate that such a system can exhibit a crossover from a perturbati

Maria-Florina Balcan, Keegan Harris, Mikhail Khodak, Zhiwei Steven Wu

We study online learning with bandit feedback across multiple tasks, with the goal of improving average performance across tasks if they are similar according to some natural task-similarity measure. As the first to target the adversarial setting, we design a unified meta-algorithm that yields setting-specific guarantees for two important cases: multi-armed

Long Chen, Ruchi Guo, Jun Zou

Maxwell interface problems are of great importance in many electromagnetic applications. Unfitted mesh methods are especially attractive in 3D computation as they can circumvent generating complex 3D interface-fitted meshes. However, many unfitted mesh methods rely on non-conforming approximation spaces, which may cause a loss of accuracy for solving Maxwell

Leandro G. Althaus, Alejandro H. Córsico

Asteroseismology is a powerful tool to infer the evolutionary status and chemical stratification of white dwarf (WD) stars, and to explore the physical processes that lead to their formation. This is particularly true for the variable H-rich atmosphere (DA) WDs, known as DAV or ZZ Ceti stars. We present a new grid of DA WD models that take into account the l

Vincent A. Cicirello

Evolutionary algorithms solve problems by simulating the evolution of a population of candidate solutions. We focus on evolving permutations for ordering problems like the traveling salesperson problem (TSP), as well as assignment problems like the quadratic assignment problem (QAP) and largest common subgraph (LCS). We propose cycle mutation, a new mutation

Asen Pashov, Pawel Kowalczyk, Jacek Szczepkowski, Wlodzimierz Jastrzebski

We report a systematic study of the C(2)$^1\Pi_u$ electronic state in rubidium dimer, observed in polarization labelling spectroscopy experiment through the C $\leftarrow$ X$^{1}\Sigma^{+}_{g}$ transitions recorded under rotational resolution in two isotopologues $^{85}$Rb$_2$ and $^{85}$Rb$^{87}$Rb. Regularity of the vibrational progressions was distorted b

Nicole F. Bell, Matthew J. Dolan, Sandra Robles

The Hyper-Kamiokande (HyperK) experiment is expected to precisely measure the Diffuse Supernova Neutrino Background (DSNB). This requires that the backgrounds in the relevant energy range are well understood. One possible background that has not been considered thus far is the annihilation of low-mass dark matter (DM) to neutrinos. We conduct simulations of

Filip Radenovic, Abhimanyu Dubey, Dhruv Mahajan

Due to the widespread use of complex machine learning models in real-world applications, it is becoming critical to explain model predictions. However, these models are typically black-box deep neural networks, explained post-hoc via methods with known faithfulness limitations. Generalized Additive Models (GAMs) are an inherently interpretable class of model

Jeffrey K. Greason, Dmytro Yakymenko, Mathias N. Larrouturou, Andrew J. Higgins

A propulsion concept in which a spacecraft interacts with high-velocity pellets and the interstellar medium is proposed. The pellets are slower than the spacecraft and are accelerated backwards as they are overtaken, imparting a forward acceleration on the spacecraft. This maneuver is possible due to the interaction with a fixed medium (interstellar medium,

Alexandre Forel, Axel Parmentier, Thibaut Vidal

Counterfactual explanations describe how to modify a feature vector in order to flip the outcome of a trained classifier. Obtaining robust counterfactual explanations is essential to provide valid algorithmic recourse and meaningful explanations. We study the robustness of explanations of randomized ensembles, which are always subject to algorithmic uncertai

M. A. Bezuglov, A. V. Kotikov, A. I. Onishchenko

We consider new ways of obtaining series and integral representations for master integrals arising in the process of matching of QCD to NRQCD. The latter results are exact in space-time dimension $d$. In addition, we discuss series expansion of the obtained results at fixed values of $d$.

Karine Beauchard, Frédéric Marbach

We present a unified approach for determining and proving obstructions to small-time local controllability of scalar-input control systems. Our approach views obstructions to controllability as resulting from interpolation inequalities between the functionals associated with the formal Lie brackets of the system. Using this approach, we give compact unified

Connor E. Bray, Larry J. Hiller, Kyle G. Leach, Stephan Friedrich

Superconducting Tunnel Junctions (STJs) are used as high-resolution quantum sensors to search for evidence of sterile neutrinos in the electron capture decay of $^7$Be. We are developing spatially-resolved Monte-Carlo simulations of the energy relaxation in superconductors to understand electron escape after the $^7$Be decay and distinguish details in the ST

Connor Malone, Sourav Garg, Ming Xu, Thierry Peynot

Road segmentation in challenging domains, such as night, snow or rain, is a difficult task. Most current approaches boost performance using fine-tuning, domain adaptation, style transfer, or by referencing previously acquired imagery. These approaches share one or more of three significant limitations: a reliance on large amounts of annotated training data t

Feng Dai, Andriy Prymak

We show that optimal polynomial meshes exist for every convex body in $\mathbb{R}^d$, confirming a conjecture by A. Kroo.

Davide Mascitti, Marco Conti, Andrea Passarella, Laura Ricci

Opportunistic computing is a paradigm for completely self-organised pervasive networks. Instead of relying only on fixed infrastructures as the cloud, users' devices act as service providers for each other. They use pairwise contacts to collect information about services provided and amount of time to provide them by the encountered nodes. At each node, upon

Yancheng Wang, Yingzhen Yang

Graph Neural Networks (GNNs) have been widely used to learn node representations and with outstanding performance on various tasks such as node classification. However, noise, which inevitably exists in real-world graph data, would considerably degrade the performance of GNNs as the noise is easily propagated via the graph structure. In this work, we propose

Abhimanyu Dubey, Filip Radenovic, Dhruv Mahajan

Generalized Additive Models (GAMs) have quickly become the leading choice for inherently-interpretable machine learning. However, unlike uninterpretable methods such as DNNs, they lack expressive power and easy scalability, and are hence not a feasible alternative for real-world tasks. We present a new class of GAMs that use tensor rank decompositions of pol

Kai Sheng Tai, Taipeng Tian, Ser-Nam Lim

We present Spartan, a method for training sparse neural network models with a predetermined level of sparsity. Spartan is based on a combination of two techniques: (1) soft top-k masking of low-magnitude parameters via a regularized optimal transportation problem and (2) dual averaging-based parameter updates with hard sparsification in the forward pass. Thi

Umair Sadiq, Mohan Kumar, Andrea Passarella, Marco Conti

Pervasive networks formed by users' mobile devices have the potential to exploit a rich set of distributed service components that can be composed to provide each user with a multitude of application level services. However, in many challenging scenarios, opportunistic networking techniques are required to enable communication as devices suffer from intermit

Thomas D. Barrett, Christopher W. F. Parsonson, Alexandre Laterre

From logistics to the natural sciences, combinatorial optimisation on graphs underpins numerous real-world applications. Reinforcement learning (RL) has shown particular promise in this setting as it can adapt to specific problem structures and does not require pre-solved instances for these, often NP-hard, problems. However, state-of-the-art (SOTA) approach

Xing Han, Tongzheng Ren, Jing Hu, Joydeep Ghosh

We propose a novel approach to the problem of clustering hierarchically aggregated time-series data, which has remained an understudied problem though it has several commercial applications. We first group time series at each aggregated level, while simultaneously leveraging local and global information. The proposed method can cluster hierarchical time seri

Robert W. Bickley, Sara L. Ellison, David R. Patton, Connor Bottrell

The importance of the post-merger epoch in galaxy evolution has been well-documented, but post-mergers are notoriously difficult to identify. While the features induced by mergers can sometimes be distinctive, they are frequently missed by visual inspection. In addition, visual classification efforts are highly inefficient because of the inherent rarity of p

Richard Csaky, Mats Van Es, Oiwi Parker Jones, Mark Woolrich

Decoding brain imaging data are gaining popularity, with applications in brain-computer interfaces and the study of neural representations. Decoding is typicallysubject-specific and does not generalise well over subjects, due to high amounts ofbetween subject variability. Techniques that overcome this will not only providericher neuroscientific insights but

MohammadTaghi Hajiaghayi, Marina Knittel, Jan Olkowski, Hamed Saleh

We study the Weighted Min Cut problem in the Adaptive Massively Parallel Computation (AMPC) model. In 2019, Behnezhad et al. [3] introduced the AMPC model as an extension of the Massively Parallel Computation (MPC) model. In the past decade, research on highly scalable algorithms has had significant impact on many massive systems. The MPC model, introduced i

Stefan Dix, Jonas Gutsche, Erik Waller, Georg von Freymann

We present a robust, fiber based endoscope with a silver direct-laser-written (DLW) structure for radio frequency (RF) emission next to the optical fiber facet. Thereby, we are able to excite and probe a sample, such as nitrogen vacancy (NV) centers in diamond, with RF and optical signals simultaneously and specifically measure the fluorescence of the sample

Marco Conti, Andrea Passarella, Sajal K. Das

Cyber-Physical convergence, the fast expansion of the Internet at its edge, and tighter interactions between human users and their personal mobile devices push towards an Internet where the human user becomes more central than ever, and where their personal devices become their proxies in the cyber world, in addition to acting as a fundamental tool to sense

Jianfeng Wang, Zhengyuan Yang, Xiaowei Hu, Linjie Li

In this paper, we design and train a Generative Image-to-text Transformer, GIT, to unify vision-language tasks such as image/video captioning and question answering. While generative models provide a consistent network architecture between pre-training and fine-tuning, existing work typically contains complex structures (uni/multi-modal encoder/decoder) and

Martin Rudorfer, Markus Suchi, Mohan Sridharan, Markus Vincze

This paper presents BURG-Toolkit, a set of open-source tools for Benchmarking and Understanding Robotic Grasping. Our tools allow researchers to: (1) create virtual scenes for generating training data and performing grasping in simulation; (2) recreate the scene by arranging the corresponding objects accurately in the physical world for real robot experiment

Johannes Müller, Guido Montúfar

Reward optimization in fully observable Markov decision processes is equivalent to a linear program over the polytope of state-action frequencies. Taking a similar perspective in the case of partially observable Markov decision processes with memoryless stochastic policies, the problem was recently formulated as the optimization of a linear objective subject

Oliver Knill

An abstract group (G,*) is natural if there exists a metric structure (G,d) on G such that (G,*) is up to abstract group isomorphisms the only group structure on G for which all right translations are isometries of (G,d). Every connected Lie group G is natural. Disconnected Lie groups can be non-natural, like R \times C_2 or Pin^-(2)=Dic(S^1,-1). There are a

Rohit Chhiber

Observations from Parker Solar Probe's first five orbits are used to investigate the helioradial evolution of probability density functions (PDFs) of fluctuations of magnetic field components, between \(\sim 28\) - 200 \(\rs\). Transformation of the magnetic field vector to a local mean-field coordinate system permits examination of anisotropy relative to th

Julie Pagès

Although the $(g-2)_\mu$ anomaly can be explained by New Physics (NP) involving only muons, a more general flavor structure is usually expected for NP operators in the Standard Model (SM) Effective Field Theory (SMEFT). In particular, if one wants to provide a combined explanation of several beyond the SM effects, like Lepton Flavor Universality (LFU) Violat

Melanie Bernhardt, Fabio De Sousa Ribeiro, Ben Glocker

Failure detection in automated image classification is a critical safeguard for clinical deployment. Detected failure cases can be referred to human assessment, ensuring patient safety in computer-aided clinical decision making. Despite its paramount importance, there is insufficient evidence about the ability of state-of-the-art confidence scoring methods t

Wojciech Kamiński, Maciej Kolanowski, Jerzy Lewandowski

We start a systematic investigation of possible isometries of the asymptotically de Sitter solutions to Einstein equations. We reformulate the Killing equation as conformal equations for the initial data at $\mathcal{I}^+$. This allows for partial classification of possible symmetry algebras. In particular, if they are not maximal, they may be at most $4$-di

Csaba Toth, Darrick Lee, Celia Hacker, Harald Oberhauser

Convolutional layers within graph neural networks operate by aggregating information about local neighbourhood structures; one common way to encode such substructures is through random walks. The distribution of these random walks evolves according to a diffusion equation defined using the graph Laplacian. We extend this approach by leveraging classic mathem

Matthew Russell Barnes, Vincenzo Nicosia, Richard G. Clegg

The centrality of a node within a network, however it is measured, is a vital proxy for the importance or influence of that node, and the differences in node centrality generate hierarchies and inequalities. If the network is evolving in time, the influence of each node changes in time as well, and the corresponding hierarchies are modified accordingly. Howe

Michael H. Goerz, Sebastián C. Carrasco, Vladimir S. Malinovsky

We develop a framework of "semi-automatic differentiation" that combines existing gradient-based methods of quantum optimal control with automatic differentiation. The approach allows to optimize practically any computable functional and is implemented in two open source Julia packages, GRAPE.jl and Krotov.jl, part of the QuantumControl.jl framework. Our met

V. Martin-Mayor, J. J. Ruiz-Lorenzo, B. Seoane, A. P. Young

Use of dedicated computers in spin glass simulations allows one to equilibrate very large samples (of size as large as $L=32$) and to carry out "computer experiments" that can be compared to (and analyzed in combination with) laboratory experiments on spin-glass samples. In the absence of a magnetic field, the most economic conclusion of the combined analysi

Patrick S. Noerr, Jose E. Zamora Alvarado, Farnaz Golnaraghi, Kara E. McCloskey

Cells self-organize into functional, ordered structures during tissue morphogenesis, a process that is evocative of colloidal self-assembly into engineered soft materials. Understanding how inter-cellular mechanical interactions may drive the formation of ordered and functional multicellular structures is important in developmental biology and tissue enginee

Guohang Yan, Liu Zhuochun, Chengjie Wang, Chunlei Shi

Accurate sensor calibration is a prerequisite for multi-sensor perception and localization systems for autonomous vehicles. The intrinsic parameter calibration of the sensor is to obtain the mapping relationship inside the sensor, and the extrinsic parameter calibration is to transform two or more sensors into a unified spatial coordinate system. Most sensor