July 2022 arXiv papers — page 104

Lanxiao Li, Michael Heizmann

Self-supervised pre-training for 3D vision has drawn increasing research interest in recent years. In order to learn informative representations, a lot of previous works exploit invariances of 3D features, e.g., perspective-invariance between views of the same scene, modality-invariance between depth and RGB images, format-invariance between point clouds and

Swayangprabha Shaw, Harsh Gupta, Shahid Mehraj Shah, Ankur Raina

The Measurement-based quantum computation provides an alternate model for quantum computation compared to the well-known gate-based model. It uses qubits prepared in a specific entangled state followed by single-qubit measurements. The stabilizers of cluster states are well defined because of their graph structure. We exploit this graph structure extensively

Rong Zhao, Nikolay Anfimov, Yu Chen, Hang Hu

In this article we present the large photo-multiplier tube (PMT) afterpulse measurement results of Jiangmen Underground Neutrino Observatory (JUNO) experiment. Totally 11 dynode-PMTs (R12860) from Hamamatsu company and 150 micro-channel plate PMTs (MCP-PMTs, GDB-6201) from NNVT company were tested, an afterpulse model is built according to the afterpulse tim

Hengrui Zhang, Wei Wayne Chen, Akshay Iyer, Daniel W. Apley

Data-driven design shows the promise of accelerating materials discovery but is challenging due to the prohibitive cost of searching the vast design space of chemistry, structure, and synthesis methods. Bayesian Optimization (BO) employs uncertainty-aware machine learning models to select promising designs to evaluate, hence reducing the cost. However, BO wi

Jon Saad-Falcon, Amanpreet Singh, Luca Soldaini, Mike D'Arcy

Real-world applications of neural language models often involve running many different models over the same corpus. The high computational cost of these runs has led to interest in techniques that can reuse the contextualized embeddings produced in previous runs to speed training and inference of future ones. We refer to this approach as embedding recycling

Benedikt Janzen

This paper studies the short-term effects of ambient temperature on mental health using data on nearly half a million helpline calls in Germany. Leveraging location-based routing of helpline calls and random day-to-day weather fluctuations, I find a negative effect of temperature extremes on mental health as revealed by an increase in the demand for telephon

Ziyang Chen, Xiangyu Wang, Song Yu, Zhengyu Li

Continuous-variable quantum key distribution (CVQKD) offers the specific advantage of sharing keys remotely by the use of standard telecom components, thereby promoting cost-effective and high-performance metropolitan applications. Nevertheless, the introduction of high-rate spectrum broadening has pushed CVQKD from a single-mode to a continuous-mode region,

Eric Gottlieb, Jelena Ilić, Matjaž Krnc

We apply the Sprague-Grundy Theorem to LCTR, a new impartial game on partitions in which players take turns removing either the Left Column or the Top Row of the corresponding Young diagram. We establish that the Sprague-Grundy value of any partition is at most $2$, and determine Sprague-Grundy values for several infinite families of partitions. Finally, we

A. N. Artemyev, A. Surzhykov, V. A. Yerokhin

We present a theoretical approach for ab initio calculations of the one-loop QED corrections to energy levels of heavy diatomic quasimolecules. This approach is based on the partial-wave expansion of the molecular wave and Green functions in the basis of monopole solutions, written in spherical coordinates. By using so generated molecular functions we employ

N. N. Hung, A. Maróti, J. Martínez

Let $G$ be a finite group and $\pi$ be a set of primes. We study finite groups with a large number of conjugacy classes of $\pi$-elements. In particular, we obtain precise lower bounds for this number in terms of the $\pi$-part of the order of $G$ to ensure the existence of a nilpotent or abelian Hall $\pi$-subgroup in $G$.

J. P. Sindel, D. Gobrecht, Ch. Helling, L. Decin

The formation of inorganic cloud particles takes place in several atmospheric environments including those of warm, hot, rocky and gaseous exoplanets, brown dwarfs, and AGB stars. The cloud particle formation needs to be triggered by the in-situ formation of condensation seeds since it can not be assumed that such condensation seeds preexist in these chemica

Julien Grange

We study the expressive power of the two-variable fragment of order-invariant first-order logic. This logic departs from first-order logic in two ways: first, formulas are only allowed to quantify over two variables. Second, formulas can use an additional binary relation, which is interpreted in the structures under scrutiny as a linear order, provided that

Alessio Di Prisa

We prove that the equivariant concordance group $\widetilde{\mathcal{C}}$ is not abelian by exhibiting an infinite family of nontrivial commutators.

S. Brandsen, Avijit Mandal, Henry D. Pfister

Belief propagation (BP) is a classical algorithm that approximates the marginal distribution associated with a factor graph by passing messages between adjacent nodes in the graph. It gained popularity in the 1990's as a powerful decoding algorithm for LDPC codes. In 2016, Renes introduced a belief propagation with quantum messages (BPQM) and described how i

Shiri Alouf-Heffetz, Laurent Bulteau, Edith Elkind, Nimrod Talmon

We consider an agent community wishing to decide on several binary issues by means of issue-by-issue majority voting. For each issue and each agent, one of the two options is better than the other. However, some of the agents may be confused about some of the issues, in which case they may vote for the option that is objectively worse for them. A benevolent

Michele Pizzardo, Antonaldo Diaferio, Kenneth J. Rines

We show that $\varsigma$, the radial location of the minimum in the differential radial mass profile $M^\prime(r)$ of a galaxy cluster, can probe the theory of gravity. We derived $M^\prime(r)$ of the dark matter halos of galaxy clusters from N-body cosmological simulations that implement two different theories of gravity: standard gravity in the $\Lambda$CD

Pierre Colle

Since the 1980s, machine learning has been widely used for horse-racing predictions, gradually expanding to where algorithms are now playing a huge role in the betting market. Machine learning has changed the horse-racing betting market over the last ten years, but main changes are still to come. The paradigm shift of neural networks (deep learning) may not

Grégoire Schneeberger

On this paper we will present a construction of a CAT(0) cube complex (an infinite cube), on which the uncountable family of Grigorchuk groups $G_\omega$ act without bounded orbit. Moreover, if the sequence $\omega$ does not contain repetition, we prove that the action is proper and faithful. As a consequence of this result, this cube complex is a model for

Adrian Kochsiek, Fritz Niesel, Rainer Gemulla

Knowledge graph embedding (KGE) models are an effective and popular approach to represent and reason with multi-relational data. Prior studies have shown that KGE models are sensitive to hyperparameter settings, however, and that suitable choices are dataset-dependent. In this paper, we explore hyperparameter optimization (HPO) for very large knowledge graph

Ritam Majumdar, Vishal Jadhav, Anirudh Deodhar, Shirish Karande

We introduce Physics Informed Symbolic Networks (PISN) which utilize physics-informed loss to obtain a symbolic solution for a system of Partial Differential Equations (PDE). Given a context-free grammar to describe the language of symbolic expressions, we propose to use weighted sum as continuous approximation for selection of a production rule. We use this

Ting Yao, Yingwei Pan, Yehao Li, Chong-Wah Ngo

Multi-scale Vision Transformer (ViT) has emerged as a powerful backbone for computer vision tasks, while the self-attention computation in Transformer scales quadratically w.r.t. the input patch number. Thus, existing solutions commonly employ down-sampling operations (e.g., average pooling) over keys/values to dramatically reduce the computational cost. In

J. Papavassiliou

It is widely accepted nowadays that gluons, while massless at the level of the fundamental QCD Lagrangian,acquire an effective mass through the non-Abelian implementation of the classic Schwinger mechanism. The key dynamical ingredient that triggers the onset of this mechanism is the formation of composite massless poles inside the fundamental vertices of th

Ting Yao, Yehao Li, Yingwei Pan, Yu Wang

Prior works have proposed several strategies to reduce the computational cost of self-attention mechanism. Many of these works consider decomposing the self-attention procedure into regional and local feature extraction procedures that each incurs a much smaller computational complexity. However, regional information is typically only achieved at the expense

Pietro Sperotto, Sandra Pieraccini, Miguel A. Mendez

We propose a 3D meshless method to compute mean pressure fields in turbulent flows from image velocimetry. The method is an extension of the constrained Radial Basis Function (RBF) formulation by \citet{Sperotto2022} to a Reynolds Averaged Navier Stokes (RANS) framework. This is designed to handle both scattered data as in Particle Tracking Velocimetry (PTV)

Fei Ye, Adrian G. Bors

Recently, continual learning (CL) has gained significant interest because it enables deep learning models to acquire new knowledge without forgetting previously learnt information. However, most existing works require knowing the task identities and boundaries, which is not realistic in a real context. In this paper, we address a more challenging and realist

Riccardo Schiavone, Maria A. Zuluaga

Binary neural networks (BNNs) have demonstrated their ability to solve complex tasks with comparable accuracy as full-precision deep neural networks (DNNs), while also reducing computational power and storage requirements and increasing the processing speed. These properties make them an attractive alternative for the development and deployment of DNN-based

Thomas Biekötter, Pedro Gabriel, María Olalla Olea-Romacho, Rui Santos

We calculate the leading radiative corrections to the dark-matter-nucleon scattering in the pseudo-Nambu-Goldstone dark matter model augmented with a second Higgs doublet (S2HDM). In this model, the cross sections for the scattering of the dark-matter on nuclei vanishes at tree-level in the limit of zero momentum-transfer due to a U(1) symmetry. However, thi

Nicola Gigli, Danka Lučić, Enrico Pasqualetto

We give a general description of the dual of the pullback of a normed module. Ours is the natural generalization to the context of modules of the well-known fact that the dual of the Lebesgue-Bochner space $L^p([0,1],B)$ consists - quite roughly said - of $L^q$ maps from $[0,1]$ to the dual $B'$ of $B$ equipped with the weak$^*$ topology. In order to state o

Murad Banaji, Balázs Boros

We address the question of which small, bimolecular, mass action chemical reaction networks (CRNs) are capable of Andronov-Hopf bifurcation (from here on abbreviated to "Hopf bifurcation"). It is easily shown that any such network must have at least three species and at least four irreversible reactions, and one example of such a network with exactly three s

Jung-Wook Kim

Frame-dragging effect manifests itself as polarization direction rotation when linearly polarized electromagnetic/gravitational wave scatters from a spinning point source through gravitational interactions, an effect also known as the gravitational Faraday rotation. Treating general relativity as an effective field theory, the Faraday rotation angle and its

Lukas Klein, Mennatallah El-Assady, Paul F. Jäger

Explainable AI (XAI) is a necessity in safety-critical systems such as in clinical diagnostics due to a high risk for fatal decisions. Currently, however, XAI resembles a loose collection of methods rather than a well-defined process. In this work, we elaborate on conceptual similarities between the largest subgroup of XAI, interpretable machine learning (IM

A. S. Sukhanov, V. Ukleev, P. Vir, P. Gargiani

Multiple intriguing phenomena have recently been discovered in tetragonal Heusler compounds, where $D_{2d}$ symmetry sets a unique interplay between Dzyaloshinskii-Moriya (DM) and magnetic dipolar interactions. In the prototype $D_{2d}$ compound Mn$_{1.4}$PtSn, this has allowed the stabilization of exotic spin textures such as first-reported anti-skyrmions o

Popa Adrian, Dumitrescu Dragos, Handley Mark, Nikolaidis Georgios

Packet trimming is a primitive that has been proposed for datacenter networks: to minimize latency, switches run small queues; when the queue overflows, rather than dropping packets the switch trims off the packet payload and either forwards the header to the destination or back to the source. In this way a low latency network that is largely lossless for me

Roy A. Lacey, Niseem Magdy

Recently, F. Wang commented on our work "Scaling properties of background- and chiral-magnetically-driven charge separation in heavy ion collisions at $\sqrt{s}_{\rm NN} = 200$ GeV" and made several claims to support his conclusion that our results are fallacious. His conclusion and claims are not only incorrect; they show a fundamental disconnect with the r

Jonathan Z. Lu, Ziyan Zhu, Mattia Angeli, Daniel T. Larson

We develop a low-energy continuum model for phonons in twisted moir\'e bilayers, based on a configuration-space approach. In this approach, interatomic force constants are obtained from density functional theory (DFT) calculations of untwisted bilayers with various in-plane shifts. This allows for efficient computation of phonon properties for any small twis

Yaser Rowshan

We define a $(V_1, V_2, \ldots, V_k)$-partition for a given graph $H$ and graphical properties $P_1, P_2, \ldots, P_k$ as a partition where each $V_i$ induces a subgraph of $H$ with property $P_i$. Matamala (2007) extended this result by showing that for any graph $H$ with $\Delta(H)=p+q$, there exists a $(V_1, V_2)$-partition of $V(H)$ where $H[V_1]$ is a m

Nil Garcia, Alessio Fascista, Angelo Coluccia, Henk Wymeersch

Due to their shorter operating range and large bandwidth, automotive radars can resolve many reflections from their targets of interest, mainly vehicles. This calls for the use of extended-target models in place of simpler and more widely-adopted point-like target models. However, despite some preliminary work, the fundamental connection between the radar's

James Koch, Zhao Chen, Aaron Tuor, Jan Drgona

Networked dynamical systems are common throughout science in engineering; e.g., biological networks, reaction networks, power systems, and the like. For many such systems, nonlinearity drives populations of identical (or near-identical) units to exhibit a wide range of nontrivial behaviors, such as the emergence of coherent structures (e.g., waves and patter

Ol'ga Sipacheva

Two (strongly) zero-dimensional Lindel\"of topological groups whose product has positive covering dimension are constructed. An example of a Lindel\"of (strongly) zero-dimensional space whose free and free Abelian topological groups are not strongly zero-dimensional is given.

N. Graham, H. Weigel

We consider vortices in scalar electrodynamics and compute the leading quantum correction to their energies for the BPS case of identical classical masses of the Higgs and gauge fields. In particular, we focus on the winding number $n$ dependence of these corrections, from which we can extract the binding energies of configurations with larger $n$. For both

Dimitrios Vamvourellis, Mate Attila Toth, Dhruv Desai, Dhagash Mehta

Categorization of mutual funds or Exchange-Traded-funds (ETFs) have long served the financial analysts to perform peer analysis for various purposes starting from competitor analysis, to quantifying portfolio diversification. The categorization methodology usually relies on fund composition data in the structured format extracted from the Form N-1A. Here, we

Milagros Miceli, Tianling Yang, Adriana Alvarado Garcia, Julian Posada

The opacity of machine learning data is a significant threat to ethical data work and intelligible systems. Previous research has addressed this issue by proposing standardized checklists to document datasets. This paper expands that field of inquiry by proposing a shift of perspective: from documenting datasets toward documenting data production. We draw on

Frederick Qiu, Sahil Singla

In submodular optimization we often deal with the expected value of a submodular function $f$ on a distribution $\mathcal{D}$ over sets of elements. In this work we study such submodular expectations for negatively dependent distributions. We introduce a natural notion of negative dependence, which we call Weak Negative Regression (WNR), that generalizes bot

S. Neshatpour, F. Mahmoudi

We present an overview of SuperIso v4.1 which is a public program for the calculation of flavour physics observables. We give examples of using SuperIso to constrain new physics scenarios with kaon physics and present the implications of rare B-decays.

Edgar Arribas, Vincenzo Mancuso, Vicent Cholvi

Aiding the ground cellular network with aerial base stations carried by drones has experienced an intensive raise of interest in the past years. Reconfigurable air-to-ground channels enable aerial stations to enhance users access links by means of seeking good line-of-sight connectivity while hovering in the air. In this paper, we propose an analytical frame

Sayan Bhattacharya, Thatchaphol Saranurak, Pattara Sukprasert

Designing dynamic algorithms against an adaptive adversary whose performance match the ones assuming an oblivious adversary is a major research program in the field of dynamic graph algorithms. One of the prominent examples whose oblivious-vs-adaptive gap remains maximally large is the \emph{fully dynamic spanner} problem; there exist algorithms assuming an

Ryosuke Takahashi

In this paper, we study the modified $J$-equation introduced by Li-Shi. We first show that, on compact K\"ahler manifolds, the solvability of the modified $J$-equation is equivalent to the coercivity of the modified $J$-functional. Motivated by this characterization, we formulate a Nakai-Moishezon type criterion for the existence of solutions to the modified

C. A. Downing, A. J. Toghill

The coupling between two or more objects can generally be categorized as strong or weak. In cavity quantum electrodynamics for example, when the coupling strength is larger than the loss rate the coupling is termed strong, and otherwise it is dubbed weak. Ultrastrong coupling, where the interaction energy is of the same order of magnitude as the bare energie

A. C. Fabian, G. J. Ferland, J. S. Sanders, B. R. McNamara

The radiative cooling time of the hot gas at the centres of cool cores in clusters of galaxies drops down to 10 million years and below. The observed mass cooling rate of such gas is very low, suggesting that AGN feedback is very tightly balanced or that the soft X-ray emission from cooling is somehow hidden from view. We use an intrinsic absorption model in

Lukas Herrmann, Christoph Schwab, Jakob Zech

Approximation rates are analyzed for deep surrogates of maps between infinite-dimensional function spaces, arising e.g. as data-to-solution maps of linear and nonlinear partial differential equations. Specifically, we study approximation rates for Deep Neural Operator and Generalized Polynomial Chaos (gpc) Operator surrogates for nonlinear, holomorphic maps

Pablo Peso Parada, Agnieszka Dobrowolska, Karthikeyan Saravanan, Mete Ozay

We propose a novel Patched Multi-Condition Training (pMCT) method for robust Automatic Speech Recognition (ASR). pMCT employs Multi-condition Audio Modification and Patching (MAMP) via mixing {\it patches} of the same utterance extracted from clean and distorted speech. Training using patch-modified signals improves robustness of models in noisy reverberant

Min Cai, Yunfan Liang, Zeyu Jiang, Mao-Peng Miao

Polarons are entities of excess electrons dressed with local response of lattices, whose atomic-scale characterization is essential for understanding the many body physics arising from the electron-lattice entanglement, but yet difficult to achieve. Here, using scanning tunneling microscopy and spectroscopy (STM/STS), we show the visualization and manipulati

Ramya Tekumalla, Juan M. Banda

Social media is often utilized as a lifeline for communication during natural disasters. Traditionally, natural disaster tweets are filtered from the Twitter stream using the name of the natural disaster and the filtered tweets are sent for human annotation. The process of human annotation to create labeled sets for machine learning models is laborious, time

R. Abbasi, M. Ackermann, J. Adams, J. A. Aguilar

The majority of astrophysical neutrinos have undetermined origins. The IceCube Neutrino Observatory has observed astrophysical neutrinos but has not yet identified their sources. Blazars are promising source candidates, but previous searches for neutrino emission from populations of blazars detected in $\gtrsim$ GeV gamma-rays have not observed any significa

Jie Qin, Shuaihang Yuan, Jiaxin Chen, Boulbaba Ben Amor

Sketch-based 3D shape retrieval (SBSR) is an important yet challenging task, which has drawn more and more attention in recent years. Existing approaches address the problem in a restricted setting, without appropriately simulating real application scenarios. To mimic the realistic setting, in this track, we adopt large-scale sketches drawn by amateurs of di

Yuan Zhang

In this paper, we prove weighted $L^p$ estimates for the canonical solutions on product domains. As an application, we show that if $p\in [4, \infty)$, the $\bar\partial$ equation on the Hartogs triangle with $L^p$ data admits $L^p$ solutions with the desired estimates. For any $\epsilon>0$, by constructing an example with $L^p$ data but having no $L^{p+\eps

Anna Stuhlmacher, Johanna L. Mathieu

Recent work has demonstrated that water supply pumps in the drinking water distribution network can be leveraged to provide flexibility to the power network, but existing approaches are computationally demanding and/or overly conservative. In this paper, we develop a computationally tractable probabilistic approach to schedule and control water pumping to pr

Manli Zhu, Edmond S. L. Ho, Hubert P. H. Shum

Detecting human-object interactions is essential for comprehensive understanding of visual scenes. In particular, spatial connections between humans and objects are important cues for reasoning interactions. To this end, we propose a skeleton-aware graph convolutional network for human-object interaction detection, named SGCN4HOI. Our network exploits the sp

E. Maccioni, N. Beverini, G. Carelli, G. Di Somma

This work demonstrates that a middle size ring laser gyroscope (RLG) can be a very sensitive and robust instrument for rotational seismology, even if it operates in a quite noisy environment. The RLG has a square cavity, $1.60\times 1.60$ m$^2$, and it lies in a plane orthogonal to the Earth rotational axis. The Fabry-Perot optical cavities along the diagona

Tanguy Marchand, Misaki Ozawa, Giulio Biroli, Stéphane Mallat

We develop a multiscale approach to estimate high-dimensional probability distributions from a dataset of physical fields or configurations observed in experiments or simulations. In this way we can estimate energy functions (or Hamiltonians) and efficiently generate new samples of many-body systems in various domains, from statistical physics to cosmology.

J. A. Gracey

We apply the Local Composite Operator method to construct the three loop effective potential for the dimension two operator $\frac{1}{2} { A_\mu^a }^2$ in the Landau gauge in Quantum Chromodynamics. For $SU(3)$ we show that the three loop value of the effective mass of the gluon is similar to the two loop estimates when the number of massless quarks is stric

Zoltán Guba, István Finta, Ákos Budai, Lóránt Farkas

In distributed computing, a Byzantine fault is a condition where a component behaves inconsistently, showing different symptoms to different components of the system. Consensus among the correct components can be reached by appropriately crafted communication protocols even in the presence of byzantine faults. Quantum-aided protocols built upon distributed e

V. G. Gnevyshev, S. I. Badulin

The asymptotic solutions for linear waves generated by oscillating source of elliptic shape in the motionless media is constructed with the recently developed Reference Solution Approach (RSA). Pronounced anisotropy of the solutions is found for elongated sources both for amplitudes and phases of the resulting wave pattern. The classic Kelvin angles of the s

Arthur Casa Nova Nonnig, Alexandre da Cas Viegas, Fabiano Mesquita da Rosa, Paulo Pureur

The Spin-Orbit Proximity Effect is the raise of Spin-Orbit Coupling at a layer near to the interface with a strong spin-orbit material. It has been seen in several system such as graphene and ferromagnetic layers. The control of the Spin-Orbit Coupling can be a pathway to discover novel and exotic phases in superconductor and semimetallic systems. Here, we s

Sam Nariman

Haefliger-Thurston's conjecture predicts that Haefliger's classifying space for $C^r$-foliations of codimension $n$ whose normal bundles are trivial is $2n$-connected. In this paper, we confirm this conjecture for PL foliations of codimension $2$. As a consequence, we use a version of Mather-Thurston's theorem for PL homeomorphisms due to the author to deriv

Karthik Jain, Barilang Mawlong

The observation of anomalies in the charged current $b\rightarrow c\bar{\ell}\nu_{\ell}$ transitions hints the possibility of the existence of new physics beyond the standard model. Inspired by the work done in the beauty quark sector, we explore new physics in the charm quark sector with $c \rightarrow (s,d)\bar{\ell}\nu_{\ell}$ charged current transitions.

Sebastian Müller, Stefania Petra, Matthias Zisler

We present a geometric multilevel optimization approach that smoothly incorporates box constraints. Given a box constrained optimization problem, we consider a hierarchy of models with varying discretization levels. Finer models are accurate but expensive to compute, while coarser models are less accurate but cheaper to compute. When working at the fine leve

Deepayan Banerjee, Aaron V. Diebold, David R. Smith, Michael Boyarsky

We present the design of a phased, modulated, power distribution network for metasurface array antennas. The specific metasurface array design comprises a series of waveguides, each feeding a sub-array of dynamically tunable metamaterial elements that radiate at microwave (X-band) frequencies. To remain a one-port device, the composite array requires a power

Xia Xiong, Yong-Cong Chen, Chunxiao Shi, Ping Ao

Stochastic gradient descent (SGD), a widely used algorithm in deep-learning neural networks has attracted continuing studies for the theoretical principles behind its success. A recent work reports an anomaly (inverse) relation between the variance of neural weights and the landscape flatness of the loss function driven under SGD [Feng & Tu, PNAS 118, 0027 (

Antoine Lhomme, Olivier Romane, Nicolas Catusse, Nadia Brauner

Computing lower and upper bounds on the competitive ratio of online algorithms is a challenging question: For a minimization combinatorial problem, proving a competitive ratio for a given algorithm leads to an upper bound. However computing lower bounds requires a proof on all algorithms. This can be modeled as a 2-player game where a strategy for one of the

Fabien Le Floc'h

The measures of roughness of the volatility in the litterature are based on the realized volatility of high frequency data. Some authors show that this leads to a biased estimate, and does not necessarily indicate roughness of the underlying volatility process. Here, we attempt to measure the roughness of the implied volatility of short term options, as well

Qianjun Lyu, Wing Suen

An uninformed sender publicly commits to an informative experiment about an uncertain state, privately observes its outcome, and sends a cheap-talk message to a receiver. We provide an algorithm valid for arbitrary state-dependent preferences that will determine the sender's optimal experiment and his equilibrium payoff under binary state space. We give suff

Kanhaiya Gupta

The latest LHC collaborations results on $\sigma_{H^{\pm}}BR(H^{\pm} \rightarrow \tau^{\pm}\nu)$ and $\sigma_{H^{\pm}}BR(H^{+} \rightarrow t\bar{b})$ are used to impose constraints on the charged Higgs $H^{\pm}$ parameters within the Two Higgs Doublet Model (2HDM). But it leaves $1.5 \leq tan \beta \leq 3$ window unexplored where the $BR(H^{\pm} \rightarrow

Piotr Bozek

The rapidity dependent directed flow of particles produced in a relativistic heavy ion collision can be generated in the hydrodynamic expansion of a tilted source. The asymmetry of the pressure leads to a build up of a directed flow of matter with respect to the collision axis. The experimentally observed ordering of the directed flow of baryons, pions and a

Ding Jia

It is often said that in relativistic quantum physics, a fundamental particle ontology is not viable. However, previous works have shown that certain relativistic quantum field theories can be reformulated as path integrals over particle configurations. Drawing on these works, I argue that a fundamental particle ontology is actually viable in relativistic qu

Jean-Philippe Montillet, Wolfgang Finsterle, Gael Kermarrec, Rok Sikonja

Since the late 1970s, successive satellite missions have been monitoring the sun's activity and recording the total solar irradiance (TSI). Some of these measurements have lasted for more than a decade. In order to obtain a seamless record whose duration exceeds that of the individual instruments, the time series have to be merged. Climate models can be bett

Jack A. Thorne

Let $\rho$ be the $p$-adic Galois representation attached to a cuspidal, regular algebraic, polarizable automorphic representation of $GL_n$. Assuming only that $\rho$ satisfies an irreducibility condition, we prove the vanishing of the adjoint Bloch--Kato Selmer group attached to $\rho$. This generalizes previous work of the author and James Newton.

Norma Sidik Risdianto

We investigated inflation in the Higgs-$R^2$ model and assumed the trajectory to follow a single-field approximation called minimal two-field mode. Using this approximation, we tried to constrain the Higgs' non-minimal coupling $\xi$. During inflation, we investigated the effect of $\xi$ on the non-gaussianity. We found that $\xi$ could not provide the large

Dimitrios M. Thilikos, Sebastian Wiederrecht

The Graph Minors Structure Theorem of Robertson and Seymour asserts that, for every graph $H,$ every $H$-minor-free graph can be obtained by clique-sums of ``almost embeddable'' graphs. Here a graph is ``almost embeddable'' if it can be obtained from a graph of bounded Euler-genus by pasting graphs of bounded pathwidth in an ``orderly fashion'' into a bounde

Lei Li, Yuliang Wang

The diffusion approximation of stochastic gradient descent (SGD) in current literature is only valid on a finite time interval. In this paper, we establish the uniform-in-time diffusion approximation of SGD, by only assuming that the expected loss is strongly convex and some other mild conditions, without assuming the convexity of each random loss function.

Ahmad Bazzi, Marwa Chafii

This article studies and derives beamforming design in a dual-functional radar-communication (DFRC) multiple-input-multiple-output system. We focus on a scenario, where the DFRC base station communicates with downlink communication users, with imperfect channel state information knowledge, and performs target detection, all via the same transmit signal. Thro

Jean-Sebastien Gagnon

The Intergovernmental Panel on Climate Change reports indicate that the global mean temperature is about one-degree Celsius higher than pre-industrial levels, that this increase is anthropogenic, and that there is a causal relationship between this higher temperature and an increase in frequency and magnitude of extreme weather events. This causal relationsh

M. Albertsson

This dissertation deals with theoretical descriptions of nuclear fission and synthesis of superheavy elements via fusion. The associated shape evolutions are treated using a random-walk approach where both the potential energy and the nuclear level density influence the dynamics. The work in this thesis extends the random-walk model by, in addition to the pr

Eugenia Ellis, Rafael Parra

We establish connections between the concepts of Noetherian, regular coherent, and regular n-coherent categories for Z-linear categories with finitely many objects and the corresponding notions for unital rings. These connections enable us to obtain a negative K-theory vanishing result, a fundamental theorem, and a homotopy invariance result for the K-theory

Christos Panagiotou, Iossif Papadakis, Erin Kara, Elias Kammoun

The UV/optical variability of AGN has long been thought to be driven by the X-ray illumination of the accretion disk. However, recent multi-wavelength campaigns of nearby Seyfert galaxies seem to challenge this paradigm, with an apparent discrepancy between observations and the underlying theory. In order to further probe the connection between the UV/optica

R. J Ellis

Brittin and Gamow have used quantum theory to predict that sunlight lowers the entropy level at the earth's surface, apparently contrary to the second law of thermodynamics. We have found that this requires new physics to explain it. Another little known property of light, is that it makes it possible to detect magnetic monopoles. We present evidence, from t

Mrdjan Jankovic, Mario Santillo, Yan Wang

Assuring system stability is typically a major control design objective. In this paper, we present a system where instability provides a crucial benefit. We consider multi-agent collision avoidance using Control Barrier Functions (CBF) and study trade-offs between safety and liveness -- the ability to reach a destination without large detours or gridlock. We

Marco Tranzatto, Mihir Dharmadhikari, Lukas Bernreiter, Marco Camurri

This article presents the CERBERUS robotic system-of-systems, which won the DARPA Subterranean Challenge Final Event in 2021. The Subterranean Challenge was organized by DARPA with the vision to facilitate the novel technologies necessary to reliably explore diverse underground environments despite the grueling challenges they present for robotic autonomy. D

Jingge Wang, Liyan Xie, Yao Xie, Shao-Lun Huang

Domain generalization aims at learning a universal model that performs well on unseen target domains, incorporating knowledge from multiple source domains. In this research, we consider the scenario where different domain shifts occur among conditional distributions of different classes across domains. When labeled samples in the source domains are limited,

Yaniv Nemcovsky, Matan Jacoby, Alex M. Bronstein, Chaim Baskin

Deep neural networks are known to be susceptible to adversarial perturbations -- small perturbations that alter the output of the network and exist under strict norm limitations. While such perturbations are usually discussed as tailored to a specific input, a universal perturbation can be constructed to alter the model's output on a set of inputs. Universal

Leora Schmerler, Noam Hazon, Sarit Kraus

In the celebrated stable-matching problem, there are two sets of agents M and W, and the members of M only have preferences over the members of W and vice versa. It is usually assumed that each member of M and W is a single entity. However, there are many cases in which each member of M or W represents a team that consists of several individuals with common

Evangelos Kolyvas, Spyros Voulgaris

Despite their development for over a decade, a key problem blockchains are still facing is scalability in terms of throughput, typically limited to a few transactions per second. A fundamental factor limiting this metric is the propagation latency of blocks through the underlying peer-to-peer network, which is typically constructed by means of random connect

Michal Hnatič, Georgii Kalagov

We reveal the critical properties of the phase transition towards superfluid order that has been proposed to occur in large spin fermionic systems. For this purpose, we consider the bosonic field theory for fluctuations of the complex skew-symmetric rank-2 tensor order parameter close to the transition. We then nonperturbatively determine the scale dependenc

Yingying Han, Minchen Qiao, Xiao-Qing Luo, Tie-Fu Li

Quantum interference effects in the unmodulated quantum systems with light-matter interaction have been widely studied, such as electromagnetically induced transparency (EIT) and Autler-Townes splitting (ATS). However, the similar quantum interference effects in the Floquet systems (i.e., periodically modulated systems), which might cover rich new physics, w

Aldi Piroli, Vinzenz Dallabetta, Marc Walessa, Daniel Meissner

LiDAR sensors used in autonomous driving applications are negatively affected by adverse weather conditions. One common, but understudied effect, is the condensation of vehicle gas exhaust in cold weather. This everyday phenomenon can severely impact the quality of LiDAR measurements, resulting in a less accurate environment perception by creating artifacts

Jiaqi Jiang, Guanqun Cao, Thanh-Toan Do, Shan Luo

Transparent objects are widely used in our daily lives and therefore robots need to be able to handle them. However, transparent objects suffer from light reflection and refraction, which makes it challenging to obtain the accurate depth maps required to perform handling tasks. In this paper, we propose a novel affordance-based framework for depth reconstruc

Jian Yang, Yuwei Yin, Shuming Ma, Dongdong Zhang

Multilingual neural machine translation (MNMT) trained in multiple language pairs has attracted considerable attention due to fewer model parameters and lower training costs by sharing knowledge among multiple languages. Nonetheless, multilingual training is plagued by language interference degeneration in shared parameters because of the negative interferen

Frederic Lechenault, Iyad Ramdane, Sebastien Moulinet, Martin Roman-Faure

Cutting mozzarella with a dull blade results in poorly shaped slices: the process occurs in a configuration so deformed as to yield unexpectedly curved surfaces. We study the rich morphogenetics arising from such process through the example of coring: when a thin cylindrical hollow punch is pushed into a soft elastomer, the extracted core is "clarinet-shaped

Shaolin Su, Hanhe Lin, Vlad Hosu, Oliver Wiedemann

An accurate computational model for image quality assessment (IQA) benefits many vision applications, such as image filtering, image processing, and image generation. Although the study of face images is an important subfield in computer vision research, the lack of face IQA data and models limits the precision of current IQA metrics on face image processing

Roger de Belsunce, Steven Gratton, George Efstathiou

We present constraints on primordial B modes from large angular scale cosmic microwave background polarisation anisotropies measured with the Planck satellite. To remove Galactic polarised foregrounds, we use a Bayesian parametric component separation method, modelling synchrotron radiation as a power law and thermal dust emission as a modified blackbody. Th