July 2022 arXiv papers — page 53

Philipp Kempski, Eliot Quataert, Jonathan Squire

Active Galactic Nuclei (AGN) are believed to provide the energy that prevents runaway cooling of gas in the cores of galaxy clusters. However, how this energy is transported and thermalized throughout the Intracluster Medium (ICM) remains unclear. In recent work we showed that streaming cosmic rays (CRs) destabilise sound waves in dilute ICM plasmas. Here we

Tatsuya Matsushima, Yuki Noguchi, Jumpei Arima, Toshiki Aoki

Tidying up a household environment using a mobile manipulator poses various challenges in robotics, such as adaptation to large real-world environmental variations, and safe and robust deployment in the presence of humans.The Partner Robot Challenge in World Robot Challenge (WRC) 2020, a global competition held in September 2021, benchmarked tidying tasks in

Guangjie Li, Yuval Oreg, Jukka I. Väyrynen

A Coulomb blockaded $M$-Majorana island coupled to normal metal leads realizes a novel type of Kondo effect where the effective impurity "spin" transforms under the orthogonal group $SO(M)$. The impurity spin stems from the non-local topological ground state degeneracy of the island and thus the effect is known as the topological Kondo effect. We introduce a

Zoe Ansari, Christa Gall, Roger Wesson, Oswin Krause

Context. Determining properties of dust formed in and around supernovae from observations remains challenging. This may be due to either incomplete coverage of data in wavelength or time but also due to often inconspicuous signatures of dust in the observed data. Aims. Here we address this challenge using modern machine learning methods to determine the amou

Pasquale Marra, Daisuke Inotani, Muneto Nitta

Majorana-based topological qubits are expected to exploit the nonabelian braiding statistics of Majorana modes in topological superconductors to realize fault-tolerant topological quantum computation. Scalable qubit designs require several Majorana modes localized on quantum wires networks, with braiding operations relying on the presence of the groundstate

J. W. Petley, L. K. Morabito, D. M. Alexander, A. L. Rankine

Broad Absorption Line Quasars (BALQSOs) show strong signatures of powerful outflows, with the potential to alter the cosmic history of their host galaxies. These signatures are only seen in ~10% of optically selected quasars, although the fraction significantly increases in IR and radio selected samples. A proven physical explanation for this observed fracti

J. E. Rodríguez Martín, R. M. González Delgado, G. Martínez-Solaeche, L. A. Díaz-García

The miniJPAS is a 1 deg$^2$ survey that uses the Javalambre-Physics of the Accelerating Universe Astrophysical Survey (J-PAS) filter system (54 narrow-band filters) with the Pathfinder camera. We study mJPC2470-1771, the most massive cluster detected in miniJPAS. We study the stellar population properties of the members, their star formation rates (SFR), sta

Antonio Amariti, Massimo Bianchi, Marco Fazzi, Salvo Mancani

We study various orientifold projections of 4d $\mathcal{N}=1$ toric gauge theories, associated with CY singularities known as $L^{a,b,a}/\mathbb{Z}_2$, with $a+b$ even. We obtain superconformal chiral theories that have the same central charge, anomalies and superconformal index, whereas they were different before the orientifold. Some of these projections

Kuan-Sen Lin, Giandomenico Palumbo, Zhaopeng Guo, Yoonseok Hwang

Symmetry-protected topological crystalline insulators (TCIs) have primarily been characterized by their gapless boundary states. However, in time-reversal- ($\mathcal{T}$-) invariant (helical) 3D TCI$\unicode{x2014}$termed higher-order TCIs (HOTIs)$\unicode{x2014}$the boundary signatures can manifest as a sample-dependent network of 1D hinge states. We here

Gabriel Wong, Robert Raussendorf, Bartlomiej Czech

Measurement-Based Quantum Computation (MBQC) is a model of quantum computation, which uses local measurements instead of unitary gates. Here we explain that the MBQC procedure has a fundamental basis in an underlying gauge theory. This perspective provides a theoretical foundation for global aspects of MBQC. The gauge transformations reflect the freedom of f

Chris Cade, Marten Folkertsma, Jordi Weggemans

Recently it was shown that the so-called guided local Hamiltonian problem -- estimating the smallest eigenvalue of a $k$-local Hamiltonian when provided with a description of a quantum state ('guiding state') that is guaranteed to have substantial overlap with the true groundstate -- is BQP-complete for $k \geq 6$ when the required precision is inverse polyn

Ian Harry, Johannes Noller

Theories of dark energy that affect the speed of gravitational waves $c_{\rm GW}$ on cosmological scales naturally lead to a frequency-dependent transition of that speed close to the LIGO/Virgo/KAGRA (LVK) band. While observations such as GW170817 assure us that $c_{\rm GW}$ is extremely close to the speed of light in the LVK band, a frequency-dependent tran

Ken Kikuchi

Just as spontaneous symmetry breaking can be understood in terms of energy, emergent symmetry (more precisely, its `size' and structure) can also be explained by free energy. In particular, in renormalization group flow to rational conformal field theory, we find infrared symmetry category is realized by consistent modular tensor category with minimal free e

Ibrahima Bah, Federico Bonetti, Emily Nardoni, Thomas Waddleton

We present new families of $AdS_5$ solutions in M-theory preserving 4d ${\mathcal N = 2}$ supersymmetry. We perform a systematic analysis of holographic observables for these solutions, providing evidence for an interpretation in terms of 4d superconformal field theories (SCFTs) of Argyres-Douglas type, realized in class $\mathcal S$ via a sphere with one ir

Sungwoo Hong, Gowri Kurup, Maxim Perelstein

We consider theories in which a dark sector is described by a Conformal Field Theory (CFT) over a broad range of energy scales. A coupling of the dark sector to the Standard Model breaks conformal invariance. While weak at high energies, the breaking grows in the infrared, and at a certain energy scale the theory enters a confined (hadronic) phase. One of th

F. Mernier, N. Werner, J. Bagchi, M. -L. Gendron-Marsolais

Observed in a significant fraction of clusters and groups of galaxies, diffuse radio synchrotron emission reveals the presence of relativistic electrons and magnetic fields permeating large-scale systems of galaxies. Although these non-thermal electrons are expected to upscatter cosmic microwave background photons up to hard X-ray energies, such inverse-Comp

Bin Liu, Daniel J. D'Orazio, Alejandro Vigna-Gómez, Johan Samsing

We study the dynamics of a solar-type star orbiting around a black hole binary (BHB) in a nearly coplanar system. We present a novel effect that can prompt a growth and significant oscillations of the eccentricity of the stellar orbit when the system encounters an "apsidal precession resonance", where the apsidal precession rate of the outer stellar orbit ma

Christopher Dessert, Joshua W. Foster, Yujin Park, Benjamin R. Safdi

Thermal higgsino dark matter (DM), with mass around 1 TeV, is a well-motivated, minimal DM scenario that arises in supersymmetric extensions of the Standard Model. Higgsinos may naturally be the lightest superpartners in Split-supersymmetry models that decouple the scalar superpartners while keeping higgsinos and gauginos close to the TeV scale. Higgsino DM

Nuno M. Santos, Carlos. A. R. Herdeiro, Eugen Radu

The local thermodynamic stability of a black hole (BH) in the canonical ensemble is defined by the positivity of the specific heat at constant global charges. Schwarzschild BHs in thermodynamic equilibrium with an energy reservoir are always unstable against small fluctuations of energy, whereas sufficiently near-extremal Reissner-Nordstr\"{o}m/Kerr BHs are

Juan Carlos Criado, Roman Kogler, Michael Spannowsky

The use of experimental data to constrain the values of the Wilson coefficients of an Effective Field Theory (EFT) involves minimising a $\chi^2$ function that may contain local minima. Classical optimisation algorithms can become trapped in these minima, preventing the determination of the global minimum. The quantum annealing framework has the potential to

Adam J. McRoberts, J. F. Mendez-Valderrama, Roderich Moessner, Debanjan Chowdhury

A classic route for destroying long-lived electronic quasiparticles in a weakly interacting Fermi liquid is to couple them to other low-energy degrees of freedom that effectively act as a bath. We consider here the problem of electrons scattering off the spin fluctuations of a geometrically frustrated antiferromagnet, whose non-linear Landau-Lifshitz dynamic

Lorenzo Speri, Andrea Antonelli, Laura Sberna, Stanislav Babak

Gravitational-wave observations of extreme mass ratio inspirals (EMRIs) offer the opportunity to probe the environments of active galactic nuclei (AGN) through the torques that accretion disks induce on the binary. Within a Bayesian framework, we study how well such environmental effects can be measured using gravitational wave observations from the Laser In

Zhiheng Li, Anthony Hoogs, Chenliang Xu

Deep image classifiers have been found to learn biases from datasets. To mitigate the biases, most previous methods require labels of protected attributes (e.g., age, skin tone) as full-supervision, which has two limitations: 1) it is infeasible when the labels are unavailable; 2) they are incapable of mitigating unknown biases -- biases that humans do not p

Mario P. Rothfelder, Otilia Boldea

We show by simulation that the test for an unknown threshold in models with endogenous regressors - proposed in Caner and Hansen (2004) - can exhibit severe size distortions both in small and in moderately large samples, pertinent to empirical applications. We propose three new tests that rectify these size distortions. The first test is based on GMM estimat

Chuhan Zhang, Ankush Gupta, Andrew Zisserman

The objective of this work is to learn an object-centric video representation, with the aim of improving transferability to novel tasks, i.e., tasks different from the pre-training task of action classification. To this end, we introduce a new object-centric video recognition model based on a transformer architecture. The model learns a set of object-centric

Swami Sankaranarayanan, Anastasios N. Angelopoulos, Stephen Bates, Yaniv Romano

Meaningful uncertainty quantification in computer vision requires reasoning about semantic information -- say, the hair color of the person in a photo or the location of a car on the street. To this end, recent breakthroughs in generative modeling allow us to represent semantic information in disentangled latent spaces, but providing uncertainties on the sem

Manuel Meierhofer, Simon Maier, Dmytro Afanasiev, Josef Freudenstein

We demonstrate an active carrier-envelope phase (CEP) stabilization scheme for optical waveforms generated by difference-frequency mixing of two spectrally detuned and phase-correlated pulses. By performing ellipsometry with spectrally overlapping parts of two co-propagating near-infrared generation pulse trains, we stabilize their relative timing to 18 as.

Rustam Sadykov, Stanislav Trunov

Introduced by Seifert and Threlfall, cylindrical neighborhoods is an essential tool in the Lusternik-Schnirelmann theory. We conjecture that every isolated critical point of a smooth function admits a cylindrical ball neighborhood. We show that the conjecture is true for cone-like critical points, Cornea reasonable critical points, and critical points that s

Venkatesh Jatla

Solar image analysis relies on the detection of coronal holes for predicting disruptions to earth's magnetic field. The coronal holes act as sources of solar wind that can reach the earth. Thus, coronal holes are used in physical models for predicting the evolution of solar wind and its potential for interfering with the earth's magnetic field. Due to inhere

Lýdia Štofanová, Aurora Simionescu, Nastasha A. Wijers, Joop Schaye

The physical properties of the faint and extremely tenuous plasma in the far outskirts of galaxy clusters, the circumgalactic media of normal galaxies, and filaments of the cosmic web, remain one of the biggest unknowns in our story of large-scale structure evolution. Modelling the spectral features due to emission and absorption from this very diffuse plasm

Sarah C. Millholland, Matthias Y. He, Jon K. Zink

Although the architectures of compact multiple-planet systems are well-characterized, there has been little examination of their "outer edges", or the locations of their outermost planets. Here we present evidence that the observed high-multiplicity Kepler systems truncate at smaller orbital periods than can be explained by geometric and detection biases alo

Vagif S. Guliyev

We give necessary and sufficient conditions for the boundedness of the maximal commutators $M_{b}$, the commutators of the maximal operator $[b, M]$ and the commutators of the sharp maximal operator $[b, M^{\sharp}]$ in Orlicz spaces $L^{\Phi}(\mathbb{G})$ on any stratified Lie group $\mathbb{G}$ when $b$ belongs to Lipschitz spaces $\dot{\Lambda}_{\beta}(\m

D. O. Oriekhov, T. T. Osterholt, T. Vakhtel, A. R. Akhmerov

We consider the propagation of electrons in a lattice with an anisotropic dispersion in the $x$--$y$ plane (lattice constant $a$), such that it supports open orbits along the $x$-axis in an out-of-plane magnetic field $B$. We show that a point source excites a "breathing mode", a state that periodically spreads out and refocuses after having propagated over

Akshay Rana, Vedanta Thapar, Hari Prasad S. V., Sandra Elsa Sanjai

In standard model, the Fermi coupling constant, $G_F$, sets the strength of electroweak decay. We attempt an approach to constrain the temporal variation of the Fermi coupling constant $G_F$. To probe it, Type Ia supernovae (SNe Ia) light curves are being used as a source of reliable primordial nucleosynthesis events across the redshifts. We utilized studies

Pablo G. Jambrina, James F. E. Croft, Junxiang Zuo, Hua Guo

Resonant scattering of optically state-prepared and aligned molecules in the cold regime allows the most detailed interrogation and control of bimolecular collisions. This technique has recently been applied to collisions of two aligned ortho-D2 molecules prepared in the j=2 rotational level of the v=2 vibrational manifold using the Stark-induced adiabatic R

Mark Mazumder, Colby Banbury, Xiaozhe Yao, Bojan Karlaš

Machine learning research has long focused on models rather than datasets, and prominent datasets are used for common ML tasks without regard to the breadth, difficulty, and faithfulness of the underlying problems. Neglecting the fundamental importance of data has given rise to inaccuracy, bias, and fragility in real-world applications, and research is hinde

Junzhe Zhang, Daxuan Ren, Zhongang Cai, Chai Kiat Yeo

Recovering a textured 3D mesh from a monocular image is highly challenging, particularly for in-the-wild objects that lack 3D ground truths. In this work, we present MeshInversion, a novel framework to improve the reconstruction by exploiting the generative prior of a 3D GAN pre-trained for 3D textured mesh synthesis. Reconstruction is achieved by searching

Karel in 't Hout, Pieter Lamotte

This paper concerns the numerical solution of the two-dimensional time-dependent partial integro-differential equation (PIDE) that holds for the values of European-style options under the two-asset Kou jump-diffusion model. A main feature of this equation is the presence of a nonlocal double integral term. For its numerical evaluation, we extend a highly eff

Brian Punsly

It has become commonplace is astronomy to describe the transverse coarse structure of jets in loosely defined terms such as "sheath" and "spine" based on discussions of parsec scale properties. But, the applicability, dimension and prominence of these features on sub-lt-yr scales has previously been unconstrained by observation. The first direct evidence of

Javier Martínez-Cifuentes, K. M. Fonseca-Romero, Nicolás Quesada

Recently, Zhong et al. performed landmark Gaussian boson sampling experiments with up to 144 modes using threshold detectors. The authors claim to have achieved quantum computational advantage with the implementation of these experiments, named Jiuzhang 1.0 and Jiuzhang 2.0. Their experimental results are validated against several classical hypotheses and ad

Friederike Ihssen, Jan M. Pawlowski

In the present work we set up a general functional renormalisation group framework for the computation of complex effective actions. For explicit computations we consider both flows of the Wilsonian effective action and the one-particle irreducible (1PI) effective action. The latter is based on an appropriate definition of a Legendre transform for complex ac

Gabriele Franciolini, Alfredo Urbano

Constraining the inflationary epoch is one of the aims of modern cosmology. In order to fully exploit current and future small-scale observations, it is necessary to devise tools to directly relate them to the early universes dynamics. We present here a novel reverse engineer approach able to connect fundamental late-time observables to consistent inflationa

Wjefferson H. S. Brandão, Acrisio L. Aguiar, Alexandre F. Fonseca, D. S. Galvão

A quasi-2D semiconductor carbon allotrope called tetrahexcarbon, also named tetragraphene, was recently proposed featuring an unusual structure combining squared and hexagonal rings. Mechanical and electronic properties of tetragraphene have been predicted based on first-principles Density Functional Theory (DFT) calculations. However, a comprehensive study

Farhang Loran, Ali Mostafazadeh

Evanescent waves are waves that decay or grow exponentially in regions of the space void of interaction. In potential scattering defined by the Schr\"odinger equation, $(-\nabla^2+v)\psi=k^2\psi$ for a local potential $v$, they arise in dimensions greater than one and are present regardless of the details of $v$. The approximation in which one ignores the co

Gyeongsik Moon, Hyeongjin Nam, Takaaki Shiratori, Kyoung Mu Lee

Although much progress has been made in 3D clothed human reconstruction, most of the existing methods fail to produce robust results from in-the-wild images, which contain diverse human poses and appearances. This is mainly due to the large domain gap between training datasets and in-the-wild datasets. The training datasets are usually synthetic ones, which

Raffaella Mulas, Jiaxi Nie

We introduce the following simpler variant of the Tur\'an problem: Given integers $n>k>r\geq 2$ and $m\geq 1$, what is the smallest integer $t$ for which there exists an $r$-uniform hypergraph with $n$ vertices, $t$ edges and $m$ connected components such that any $k$-subset of the vertex set contains at least one edge? We prove some general estimates for th

David Crosby

We consider a problem related to the Erdo\H{o}s Unit Distance Conjecture: How often can a single dot product configuration or a multiple dot product configuration occur over a Galoi Ring? We also find a bound on an inverse vector matrix multiplication problem.

Haoran Xu, Xianyuan Zhan, Honglei Yin, Huiling Qin

We study the problem of offline Imitation Learning (IL) where an agent aims to learn an optimal expert behavior policy without additional online environment interactions. Instead, the agent is provided with a supplementary offline dataset from suboptimal behaviors. Prior works that address this problem either require that expert data occupies the majority pr

Boris Knyazev

Training large neural networks is possible by training a smaller hypernetwork that predicts parameters for the large ones. A recently released Graph HyperNetwork (GHN) trained this way on one million smaller ImageNet architectures is able to predict parameters for large unseen networks such as ResNet-50. While networks with predicted parameters lose performa

R. D. Mota, D. Ojeda-Guillén

We introduce a generalization of the Dunkl-derivative with two parameters to study the Schr\"odinger equation in Cartesian and polar coordinates in two dimensions. The eigenfunctions and the energy spectrum for the harmonic oscillator and the Coulomb problem are derived in an analytical way and it is shown that our results are properly reduced to those previ

Yingyan Li, Yuntao Chen, Jiawei He, Zhaoxiang Zhang

Estimating accurate 3D locations of objects from monocular images is a challenging problem because of lacking depth. Previous work shows that utilizing the object's keypoint projection constraints to estimate multiple depth candidates boosts the detection performance. However, the existing methods can only utilize vertical edges as projection constraints for

Adarsh M. Subramaniam, Akshayaa Magesh, Venugopal V. Veeravalli

Compressed Stochastic Gradient Descent (SGD) algorithms have been recently proposed to address the communication bottleneck in distributed and decentralized optimization problems, such as those that arise in federated machine learning. Existing compressed SGD algorithms assume the use of non-adaptive step-sizes(constant or diminishing) to provide theoretical

Po-Wei Lo, Chao-Ming Jian, Michael J Lawler

Supersymmetry has been studied at a linear level between normal modes of metamaterials described by rigidity matrices and non-interacting quantum Hamiltonians. The connection between classical and quantum was made through the matrices involved in each problem. Recently, insight into the behavior of nonlinear mechanical systems was found by defining topologic

Fuqiang Wang

The experimental status is reviewed on the search for the chiral magnetic effect (CME) in relativistic heavy-ion collisions. Emphasis is put on background contributions to the CME-sensitive charge correlation measurements and their effects on data interpretation.

M. A. J. Herrera, D. Bercioux

Non-symmorphic symmetries protect Dirac nodal lines and cones in lattice systems. Here, we investigate the spectral properties of a two-dimensional lattice belonging to a non-symmorphic group. Specifically, we look at the herringbone lattice, characterised by two sets of glide symmetries applied in two orthogonal directions. We describe the system using a ne

Aria Mansouri Tehrani, Jian-Rui Soh, Jana Pásztorová, Maximilian E. Merkel

We examine the role of charge, structural, and spin degrees of freedom in the previously poorly understood phase transition in the 5$d^1$ transition-metal double perovskite Cs$_2$TaCl$_6$, using a combination of computational and experimental techniques. Our heat capacity measurements of single-crystalline Cs$_2$TaCl$_6$, reveal a clear anomaly at the transi

Marco Abbadini, Luca Reggio

It has long been known that a key ingredient for a sheaf representation of a universal algebra A consists in a distributive lattice of commuting congruences on A. The sheaf representations of universal algebras (over stably compact spaces) that arise in this manner have been recently characterised by Gehrke and van Gool (J. Pure Appl. Algebra, 2018), who ide

Zhiyuan Mao, Ajay Jaiswal, Zhangyang Wang, Stanley H. Chan

Image restoration algorithms for atmospheric turbulence are known to be much more challenging to design than traditional ones such as blur or noise because the distortion caused by the turbulence is an entanglement of spatially varying blur, geometric distortion, and sensor noise. Existing CNN-based restoration methods built upon convolutional kernels with s

Steffen Aksteiner, Bernardo Araneda

We obtain a closed formula for the Kaehler potential of a broad class of four-dimensional Lorentzian or Euclidean conformal "Kaehler" geometries, including the Plebanski-Demianski class and various gravitational instantons such as Fubini-Study and Chen-Teo. We show that the Kaehler potentials of Schwarzschild and Kerr are related by a Newman-Janis shift. Our

Maria Osório, Luís Sa-Couto, Andreas Wichert

It is generally assumed that the brain uses something akin to sparse distributed representations. These representations, however, are high-dimensional and consequently they affect classification performance of traditional Machine Learning models due to "the curse of dimensionality". In tasks for which there is a vast amount of labeled data, Deep Networks see

Fabio Maltoni, Giovanni Ridolfi, Maria Ubiali, Marco Zaro

We compute the perturbative component of the fragmentation function of the $b$ quark to the best of the present theoretical knowledge. The fixed-order calculation to order $\alpha_s^2$ of the fragmentation function at the initial scale is matched with soft-emission logarithm resummation to next-to-next-to-leading logarithmic accuracy, so that order-$\alpha_s

Józef Dodziuk

We give a characterization of Whitney forms on an $n$-simplex $\sigma$ and prove that for every real valued simplicial $k$-cochain $c$ on $\sigma$, the form $Wc$ is the unique differential $k$-form $\varphi$ on $\sigma$ with affine coefficients that pulls back to a constant form of degree $k$ on every $k$-face $\tau$ of $\sigma$ and satisfies $\int_{\tau} \v

Yann Alexanian, Julien Robert, Virginie Simonet, Benjamin Langérôme

While the spin liquid state in the frustrated pyrochlore Tb$_{2+x}$Ti$_{2-x}$O$_{7+y}$ has been studied both experimentally and theoretically for more than two decades, no definite description of this unconventional state has been achieved. Using synchrotron based THz spectroscopy in combination with quantum numerical simulations, we highlight a significant

Lue Fan, Feng Wang, Naiyan Wang, Zhaoxiang Zhang

As the perception range of LiDAR increases, LiDAR-based 3D object detection becomes a dominant task in the long-range perception task of autonomous driving. The mainstream 3D object detectors usually build dense feature maps in the network backbone and prediction head. However, the computational and spatial costs on the dense feature map are quadratic to the

D. Schaerer, R. Marques-Chaves, L. Barrufet, P. Oesch

Deep images and near-IR spectra of galaxies in the field of the lensing cluster SMACS J0723.3-7327 were recently taken in the Early Release Observations program of JWST. Among these, two NIRSpec spectra of galaxies at $z=7.7$ and one at $z=8.5$ were obtained, revealing for the first time rest-frame optical emission line spectra of galaxies in the epoch of re

Abhay Iyer, Mohammad Alali, Hemanth Bodala, Sunit Vaidya

Action quality assessment (AQA) is an active research problem in video-based applications that is a challenging task due to the score variance per frame. Existing methods address this problem via convolutional-based approaches but suffer from its limitation of effectively capturing long-range dependencies. With the recent advancements in Transformers, we sho

José Alberto Nava Aquino, Rogério de Sousa

Impurity spins randomly distributed at the surfaces and interfaces of superconducting wires are known to cause flux noise in Superconducting Quantum Interference Devices, providing a mechanism for decoherence in superconducting qubits. While flux noise is well characterised experimentally, the microscopic model underlying spin dynamics remains unknown. First

Malte Pedersen, Joakim Bruslund Haurum, Patrick Dendorfer, Thomas B. Moeslund

There exists no comprehensive metric for describing the complexity of Multi-Object Tracking (MOT) sequences. This lack of metrics decreases explainability, complicates comparison of datasets, and reduces the conversation on tracker performance to a matter of leader board position. As a remedy, we present the novel MOT dataset complexity metric (MOTCOM), whic

Mahmoud Kalash, Maria V. Chekhova

Wigner function tomography is indispensable for characterizing quantum states, but its commonly used version, balanced homodyne detection, suffers from several weaknesses. First, it requires efficient detection, which is critical for measuring fragile non-Gaussian states, especially bright ones. Second, it needs a local oscillator, tailored to match the spat

Nahid Ahmadi, Mahdiyar Noorbala, Niloufar Feyzabadi, Fatemeh Eghbalpoor

Transitions between different inflationary slow-roll scenarios are known to provide short non-slow-roll periods with non-trivial consequences. We consider the effect of quantum diffusion on the inflationary dynamics in a transition process. Using the stochastic {\delta}N formalism, we follow the detailed evolution of noises through a sharp transition modeled

Hongzhi Huang, Xian-Tao Huang

In this paper, we introduce a notion, called generalized Reifenberg condition, under which we prove a smooth fibration theorem for collapsed manifolds with Ricci curvature bounded below, which gives a unified proof of smooth fibration theorems in many previous works (including the ones proved by Fukaya and Yamaguchi respectively). A key tool in the proof of

Ping Lu, Long Yang, Jishi Zhang, Danyang Wang

Secondary eyewalls occur in 70% of major tropical cyclones (TCs), and are associated with rapid changes in storm intensity and rapid broadening of strong winds. While mechanisms of secondary eyewall formation have been investigated from various perspectives, the explicit conditions on which secondary eyewalls occur in TCs remain veiled, leaving substantial u

Stephen Jun Villejo, Janine B Illian, Ben Swallow

This paper proposes a two-stage estimation approach for a spatial misalignment scenario that is motivated by the epidemiological problem of linking pollutant exposures and health outcomes. We use the integrated nested Laplace approximation method to estimate the parameters of a two-stage spatio-temporal model; the first stage models the exposures while the s

Kehan Li, Runyi Yu, Zhennan Wang, Li Yuan

While the Vision Transformer (VT) architecture is becoming trendy in computer vision, pure VT models perform poorly on tiny datasets. To address this issue, this paper proposes the locality guidance for improving the performance of VTs on tiny datasets. We first analyze that the local information, which is of great importance for understanding images, is har

Jae-Yeop Jeong, Yeong-Gi Hong, JiYeon Oh, Sumin Hong

Facial expression in-the-wild is essential for various interactive computing domains. Especially, "Learning from Synthetic Data" (LSD) is an important topic in the facial expression recognition task. In this paper, we propose a multi-task learning-based facial expression recognition approach which consists of emotion and appearance learning branches that can

WonJun Moon, Junho Park, Hyun Seok Seong, Cheol-Ho Cho

Open set recognition (OSR) assumes unknown instances appear out of the blue at the inference time. The main challenge of OSR is that the response of models for unknowns is totally unpredictable. Furthermore, the diversity of open set makes it harder since instances have different difficulty levels. Therefore, we present a novel framework, DIfficulty-Aware Si

WonJun Moon, Ji-Hwan Kim, Jae-Pil Heo

Recently, it is shown that deploying a proper self-supervision is a prospective way to enhance the performance of supervised learning. Yet, the benefits of self-supervision are not fully exploited as previous pretext tasks are specialized for unsupervised representation learning. To this end, we begin by presenting three desirable properties for such auxilia

Shintaro Shiba, Yoshimitsu Aoki, Guillermo Gallego

Event cameras respond to scene dynamics and offer advantages to estimate motion. Following recent image-based deep-learning achievements, optical flow estimation methods for event cameras have rushed to combine those image-based methods with event data. However, it requires several adaptations (data conversion, loss function, etc.) as they have very differen

Kathleen Cachel, Elke Rundensteiner, Lane Harrison

Combining the preferences of many rankers into one single consensus ranking is critical for consequential applications from hiring and admissions to lending. While group fairness has been extensively studied for classification, group fairness in rankings and in particular rank aggregation remains in its infancy. Recent work introduced the concept of fair ran

Francesco Bonsante, Andrea Seppi, Peter Smillie

We prove four results towards a description, in terms of the null support function, of the set of isometric embeddings of the hyperbolic plane into Minkowski 3-space. We show that for sufficiently tame null support function, the corresponding entire surface of constant curvature -1 is complete, and for sufficiently sharp null support function, it is incomple

Guanchu Wang, Mengnan Du, Ninghao Liu, Na Zou

Existing work on fairness modeling commonly assumes that sensitive attributes for all instances are fully available, which may not be true in many real-world applications due to the high cost of acquiring sensitive information. When sensitive attributes are not disclosed or available, it is needed to manually annotate a small part of the training data to mit

Timo Rohrer, Anahita Farhang Ghahfarokhi, Mohamed Behery, Gerhard Lakemeyer

The automation and digitalization of business processes has resulted in large amounts of data captured in information systems, which can aid businesses in understanding their processes better, improve workflows, or provide operational support. By making predictions about ongoing processes, bottlenecks can be identified and resources reallocated, as well as i

Taylor J. Smith

A typewriter automaton is a special variant of a two-dimensional automaton that receives two-dimensional words as input and is only capable of moving its input head through its input word in three directions: downward, leftward, and rightward. In addition, downward and leftward moves may only be made via a special "reset" move that simulates the action of a

Qianyu Zhou, Ke-Yue Zhang, Taiping Yao, Ran Yi

Face anti-spoofing (FAS) approaches based on unsupervised domain adaption (UDA) have drawn growing attention due to promising performances for target scenarios. Most existing UDA FAS methods typically fit the trained models to the target domain via aligning the distribution of semantic high-level features. However, insufficient supervision of unlabeled targe

Alejandro Bravo-Doddoli

The space of $2$-jets of a real function of two real variables, denoted by $J^2(\mathbb{R}^2,\mathbb{R})$, admits the structure of a metabelian Carnot group, so $J^2(\mathbb{R}^2,\mathbb{R})$ has a normal abelian sub-group $\mathbb{A}$. As any sub-Riemannian manifold, $J^2(\mathbb{R}^2,\mathbb{R})$ has an associated Hamiltonian geodesic flow. The Hamiltonian

Emily Tallman, Mike West

Entropic tilting (ET) is a Bayesian decision-analytic method for constraining distributions to satisfy defined targets or bounds for sets of expectations. This report recapitulates the foundations and basic theory of ET for conditioning predictive distributions on such constraints, recognising the increasing interest in ET in several application areas. Contr

Patricio A. Gallardo, Bradford Benson, John Carlstrom, Simon R. Dicker

CMB-S4 -- the next-generation ground-based cosmic microwave background (CMB) experiment - will significantly advance the sensitivity of CMB measurements and improve our understanding of the origin and evolution of the universe. CMB-S4 will deploy large-aperture telescopes fielding hundreds of thousands of detectors at millimeter wavelengths. We present the b

Thu Le, Dinh-Liem Nguyen, Vu Nguyen, Trung Truong

We consider the inverse problem of determining the geometry of penetrable objects from scattering data generated by one incident wave at a fixed frequency. We first study an orthogonality sampling type method which is fast, simple to implement, and robust against noise in the data. This sampling method has a new imaging functional that is applicable to data

Gershom Bazerman

Functors with an instance of the Traversable type class can be thought of as data structures which permit a traversal of their elements. This has been made precise by the correspondence between traversable functors and finitary containers (also known as polynomial functors) -- established in the context of total, necessarily terminating, functions. However,

Cody D. Schimming, Jorge Viñals

We study two dimensional tactoids in nematic liquid crystals by using a $\mathbf{Q}$-tensor representation. A bulk free energy of the Maier-Saupe form with eigenvalue constraints on $\mathbf{Q}$, plus elastic terms up to cubic order in $\mathbf{Q}$ are used to understand the effects of anisotropic anchoring and Frank-Oseen elasticity on the morphology of nem

Yanyan Li, Federico Tombari

Minimal solutions for relative rotation and translation estimation tasks have been explored in different scenarios, typically relying on the so-called co-visibility graph. However, how to build direct rotation relationships between two frames without overlap is still an open topic, which, if solved, could greatly improve the accuracy of visual odometry. In t

Nishchay Suri, Joseph Barreto, Stuart Hadfield, Nathan Wiebe

Simulating general quantum processes that describe realistic interactions of quantum systems following a non-unitary evolution is challenging for conventional quantum computers that directly implement unitary gates. We analyze complexities for promising methods such as the Sz.-Nagy dilation and linear combination of unitaries that can simulate open systems b

Amirhossein Hajavi, Ali Etemad

Attention mechanisms have emerged as important tools that boost the performance of deep models by allowing them to focus on key parts of learned embeddings. However, current attention mechanisms used in speaker recognition tasks fail to consider fine-grained information items such as frequency bins in input spectral representations used by the deep networks.

Hector Pasten

A negative solution to Hilbert's tenth problem for the ring of integers $O_F$ of a number field $F$ would follow if $\mathbb{Z}$ were Diophantine in $O_F$. Denef and Lipshitz conjectured that the latter occurs for every number field $F$. In this note we show that the conjecture of Denef and Lipshitz is a consequence of a well-known conjecture on elliptic sur

Boris N. Narozhny

The last few years have seen an explosion of interest in hydrodynamic effects in interacting electron systems in ultra-pure materials. One such material, graphene, is not only an excellent platform for the experimental realization of the hydrodynamic flow of electrons, but also allows for a controlled derivation of the hydrodynamic equations on the basis of

Hyungjun Lee, Hwangyu Lim, Sejoon Lim

With the improved performance of deep learning, the number of studies trying to apply deep learning to human emotion analysis is increasing rapidly. But even with this trend going on, it is still difficult to obtain high-quality images and annotations. For this reason, the Learning from Synthetic Data (LSD) Challenge, which learns from synthetic images and i

Piyapat Saranrittichai, Chaithanya Kumar Mummadi, Claudia Blaiotta, Mauricio Munoz

Shortcut learning occurs when a deep neural network overly relies on spurious correlations in the training dataset in order to solve downstream tasks. Prior works have shown how this impairs the compositional generalization capability of deep learning models. To address this problem, we propose a novel approach to mitigate shortcut learning in uncontrolled t

Ankur Ghosh, C. S. Vaishnava, L. Resmi, Kuntal Misra

Short gamma-ray bursts (GRBs) are the aftermath of compact binary mergers involving neutron stars. If the merger remnant is a millisecond magnetar instead of a black hole, a significant proportion of the rotational energy deposited to the emerging ejecta can produce a late-time radio brightening from its interaction with the ambient medium. Detection of this

Elbert Du, Cynthia Dwork

Differential privacy is known to protect against threats to validity incurred due to adaptive, or exploratory, data analysis -- even when the analyst adversarially searches for a statistical estimate that diverges from the true value of the quantity of interest on the underlying population. The cost of this protection is the accuracy loss incurred by differe

Shaohua Yuan, Nizar Naitlho, Roman Samulyak, Bernard Pégourié

Numerical studies of the ablation of pellets and shattered pellet injection (SPI) fragments into a runaway electron beam in ITER have been performed using a time-dependent pellet ablation code [R. Samulyak at el., Nucl Fusion, 61 (4), 046007 (2021)]. The code resolves detailed ablation physics near pellet fragments and large-scale expansion of ablated clouds