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Cross-plane electrical and thermal transport in thin films of a conducting polymer (poly(3,4-ethylenedioxythiophene), PEDOT) stabilized with trifluoromethanesulfonate (OTf) is investigated in this study. We explore their electrical properties by conductive atomic force microscopy (C-AFM), which reveals the presence of highly conductive nano-domains. Thermal conductivity in cross-plane direction is measured with Null-Point scanning thermal microscopy (NP-SThM): PEDOT:OTf indeed demonstrates non-negligible electronic contribution to the thermal transport. We further investigate the correlation between electrical and thermal conductivity by applying posttreatment: chemical reduction (de-doping) for the purpose of lowering charge carrier concentration and hence, electrical conductivity and acid treatment (over-doping) to increase the latter. From our measurements, we find a vibrational thermal conductivity of 0.34 (+/- 0.04) Wm-1 K-1. From the linear dependence or the electronic contribution of thermal conductivity vs. the electronic conductivity (Widemann-Franz law), we infer a Lorenz number 6 times larger than the classical Sommerfeld value as also observed in many organic materials for in-plane thermal transport. Applying the recently proposed molecular Widemann-Franz law, we deduced a reorganization energy of 0.53 (+/- 0.06) eV.

Two new photo-switchable terphenylthiazoles molecules are synthesized and self-assembled as monolayers on Au and on ferromagnetic Co electrodes. The electron transport properties probed by conductive atomic force microscopy in ultra-high vacuum reveal a conductance of the light-induced closed (c) form larger than for the open (o) form. We report an unprecedented conductance ratio up to 380 between the closed and open forms on Co for the molecule with the anchoring group (thiol) on the side of the two N atoms of the thiazole unit. This result is rationalized by Density Functional Theory (DFT) calculations coupled to the Non-Equilibrium Green's function (NEGF) formalism. These calculations show that the high conductance in the closed form is due to a strong electronic coupling between the terphenylthiazole molecules and the Co electrode that manifests by a resonant transmission peak at the Fermi energy of the Co electrode with a large broadening. This behavior is not observed for the same molecules self-assembled on gold electrodes. These high conductance ratios make these Co-based molecular junctions attractive candidates to develop and study switchable molecular spintronic devices., Comment: Paper and supporting information

Coherent elastic neutrino-nucleus scattering (CE$\nu$NS) is a process in which neutrinos scatter on a nucleus which acts as a single particle. Though the total cross section is large by neutrino standards, CE$\nu$NS has long proven difficult to detect, since the deposited energy into the nucleus is $\sim$ keV. In 2017, the COHERENT collaboration announced the detection of CE$\nu$NS using a stopped-pion source with CsI detectors, followed up the detection of CE$\nu$NS using an Ar target. The detection of CE$\nu$NS has spawned a flurry of activities in high-energy physics, inspiring new constraints on beyond the Standard Model (BSM) physics, and new experimental methods. The CE$\nu$NS process has important implications for not only high-energy physics, but also astrophysics, nuclear physics, and beyond. This whitepaper discusses the scientific importance of CE$\nu$NS, highlighting how present experiments such as COHERENT are informing theory, and also how future experiments will provide a wealth of information across the aforementioned fields of physics., Comment: contribution to Snowmasss 2021. Contact authors: P. S. Barbeau, R. Strauss, L. E. Strigari

In recent years, considerable research has been dedicated to the application of neural models in the field of natural language generation (NLG). The primary objective is to generate text that is both linguistically natural and human-like, while also exerting control over the generation process. This paper offers a comprehensive and task-agnostic survey of the recent advancements in neural text generation. These advancements have been facilitated through a multitude of developments, which we categorize into four key areas: data construction, neural frameworks, training and inference strategies, and evaluation metrics. By examining these different aspects, we aim to provide a holistic overview of the progress made in the field. Furthermore, we explore the future directions for the advancement of neural text generation, which encompass the utilization of neural pipelines and the incorporation of background knowledge. These avenues present promising opportunities to further enhance the capabilities of NLG systems. Overall, this survey serves to consolidate the current state of the art in neural text generation and highlights potential avenues for future research and development in this dynamic field., Comment: This has been updated with some recent advances in 2023

Quantum teleportation is a very helpful information-theoretic protocol that allows to transfer an unknown arbitrary quantum state from one location to another without having to transmit the quantum system through the intermediate region. Quantum states, quantum channels, and indefinite causal structures are all examples of quantum causal structures that not only enable advanced quantum information processing functions, but can also model causal structures in nonclassical spacetimes. In this letter, we develop quantum teleportation of arbitrary quantum causal structures, as formalized by the process matrix framework. Instead of teleporting all the physical degrees of freedom that implement the causal structure, the central idea is to just teleport the inputs to and outputs from the operations of agents. The communication of outcomes of Bell state measurements, which is necessary for deterministic quantum teleportation, is not possible for all causal structures that one might wish to investigate. To avoid this problem, we propose partially and fully post-selected teleportation protocols. We prove that our partially post-selected teleportation protocol is compatible with all quantum causal structures, including those that involve indefinite causal order., Comment: 5+7 pages, 28 references, 7 figures

Many low-threshold experiments observe sharply rising event rates of yet unknown origins below a few hundred eV, and larger than expected from known backgrounds. Due to the significant impact of this excess on the dark matter or neutrino sensitivity of these experiments, a collective effort has been started to share the knowledge about the individual observations. For this, the EXCESS Workshop was initiated. In its first iteration in June 2021, ten rare event search collaborations contributed to this initiative via talks and discussions. The contributing collaborations were CONNIE, CRESST, DAMIC, EDELWEISS, MINER, NEWS-G, NUCLEUS, RICOCHET, SENSEI and SuperCDMS. They presented data about their observed energy spectra and known backgrounds together with details about the respective measurements. In this paper, we summarize the presented information and give a comprehensive overview of the similarities and differences between the distinct measurements. The provided data is furthermore publicly available on the workshop's data repository together with a plotting tool for visualization., Comment: 44 pages, 20 figures; Editors: A. Fuss, M. Kaznacheeva, F. Reindl, F. Wagner; updated copyright statements and funding information

To certify UAV operations in populated areas, risk mitigation strategies -- such as Emergency Landing (EL) -- must be in place to account for potential failures. EL aims at reducing ground risk by finding safe landing areas using on-board sensors. The first contribution of this paper is to present a new EL approach, in line with safety requirements introduced in recent research. In particular, the proposed EL pipeline includes mechanisms to monitor learning based components during execution. This way, another contribution is to study the behavior of Machine Learning Runtime Monitoring (MLRM) approaches within the context of a real-world critical system. A new evaluation methodology is introduced, and applied to assess the practical safety benefits of three MLRM mechanisms. The proposed approach is compared to a default mitigation strategy (open a parachute when a failure is detected), and appears to be much safer., Comment: 7 pages, 4 figures, 1 table. To appear in the proceedings of 2022 IEEE International Conference on Robotics and Automation (ICRA)

Short-term sea level fluctuations prompted by abrupt atmospheric changes can be hazardous phenomena for coastal regions. We report on two such recent storm surges that occurred in 2020 on the shores of British Columbia, Canada. A rare concordance of ground-based and spaceborne sensors made it possible to observe these events with a variety of instruments : (1) a coastal oceanographic radar; (2) the synthetic aperture radar onboard satellite Sentinel-1B; and (3) a network of shoreside tide gauges. In the light of these case studies we show how satellite-based radar data can be used to complement the observation and interpretation of ground-based measurements in the context of ``tsunami-like'' sea level oscillations., Comment: 4 pages, 4 figures

Persistence, defined as the probability that a fluctuating signal has not reached a threshold up to a given observation time, plays a crucial role in the theory of random processes. It quantifies the kinetics of processes as varied as phase ordering, reaction diffusion or interface relaxation dynamics. The fact that persistence can decay algebraically with time with non trivial exponents has triggered a number of experimental and theoretical studies. However, general analytical methods to calculate persistence exponents cannot be applied to the ubiquitous case of non-Markovian systems relaxing transiently after an imposed initial perturbation. Here, we introduce a theoretical framework that enables the non perturbative determination of persistence exponents of $d$-dimensional Gaussian non-Markovian processes with general non stationary dynamics relaxing to a steady state after an initial perturbation. Two prototypical classes of situations are analyzed: either the system is subjected to a temperature quench at initial time, or its past trajectory is assumed to have been observed and thus known. Altogether, our results reveal and quantify, on the basis of Gaussian processes, the deep impact of initial perturbations on first-passage statistics of non-Markovian processes. Our theory covers the case of spatial dimension higher than one, opening the way to characterize non-trivial reaction kinetics for complex systems with non-equilibrium initial conditions.

The paradigmatic model for heterogeneous media used in diffusion studies is built from reflecting obstacles and surfaces. It is well known that the crowding effect produced by these reflecting surfaces slows the dispersion of Brownian tracers. Here, using a general adsorption desorption model with surface diffusion, we show analytically that making surfaces or obstacles attractive can accelerate dispersion. In particular, we show that this enhancement of diffusion can exist even when the surface diffusion constant is smaller than that in the bulk. Even more remarkably, this enhancement effect occurs when the effective diffusion constant, when restricted to surfaces only, is lower than the effective diffusivity with purely reflecting boundaries. We give analytical formulas for this intriguing effect in periodic arrays of spheres as well as undulating micro-channels. Our results are confirmed by numerical calculations and Monte Carlo simulations.

The RICOCHET reactor neutrino observatory is planned to be installed at the Laue Langevin Institute (ILL) starting mid-2022. Its scientific goal is to perform a low-energy and high precision measurement of the coherent elastic neutrino-nucleus scattering (CE$\nu$NS) spectrum in order to explore exotic physics scenarios. RICOCHET will host two cryogenic detector arrays: the CryoCube (Ge target) and the Q-ARRAY (Zn target), operated at 10 mK. The 1 kg Ge CryoCube will consist of 27 Ge crystals instrumented with NTD-Ge thermal sensors and charge collection electrodes for a simultaneous heat and ionization readout to reject the electromagnetic backgrounds (gamma, beta, x-rays). We present the status of its front-end electronics. The first stage of amplification is made of High Electron Mobility Transistor (HEMT) developed by CNRS/C2N laboratory, optimized to achieve ultra-low noise performance at 1K with a dissipation as low as 15 $\mu$W per channel. Our noise model predicts that 10 eV heat and 20 eVee RMS baseline resolutions are feasible with a high dynamic range for the deposited energy (up to 10 MeV) thanks to loop amplification schemes. Such resolutions are mandatory to have a high discrimination power between nuclear and electron recoils at the lowest energies., Comment: submitted to Journal of Low Temperature Physics. Special Issue for the 19th International Workshop on Low Temperature Detectors 19-29 July 2021 - Virtual event hold by NIST

We present an overview of recent progress towards the Ricochet coherent elastic neutrino nucleus scattering CE$\nu$NS experiment. The ILL research reactor in Grenoble, France has been selected as the experiment site, after in situ studies of vibration and particle backgrounds. We present background rate estimates specific to that site, along with descriptions of the planned CryoCube and Q-Array detector payloads., Comment: Proceedings for the 19th International Workshop on Low Temperature Detectors (LTD19)

We revisit the classic problem of the effective diffusion constant of a Brownian particle in a square lattice of reflecting impenetrable hard disks. This diffusion constant is also related to the effective conductivity of non-conducting and infinitely conductive disks in the same geometry. We show how a recently derived Green's function for the periodic lattice can be exploited to derive a series expansion of the diffusion constant in terms of the disk's volume fraction $\varphi$. Secondly we propose a variant of the Fick-Jacobs approximation to study the large volume fraction limit. This combination of analytical results is shown to describe the behavior of the diffusion constant for all volume fractions.

Chemical reactions generically require that particles come into contact. In practice, reaction is often imperfect and can necessitate multiple random encounters between reactants. In confined geometries, despite notable recent advances, there is to date no general analytical treatment of such imperfect transport-limited reaction kinetics. Here, we determine the kinetics of imperfect reactions in confining domains for any diffusive or anomalously diffusive Markovian transport process, and for different models of imperfect reactivity. We show that the full distribution of reaction times is obtained in the large confining volume limit from the knowledge of the mean reaction time only, which we determine explicitly. This distribution for imperfect reactions is found to be identical to that of perfect reactions upon an appropriate rescaling of parameters, which highlights the robustness of our results. Strikingly, this holds true even in the regime of low reactivity where the mean reaction time is independent of the transport process, and can lead to large fluctuations of the reaction time even in simple reaction schemes. We illustrate our results for normal diffusion in domains of generic shape, and for anomalous diffusion in complex environments, where our predictions are confirmed by numerical simulations., Comment: to appear in Communications Chemistry

Manually grading D3 data visualizations is a challenging endeavor, and is especially difficult for large classes with hundreds of students. Grading an interactive visualization requires a combination of interactive, quantitative, and qualitative evaluation that are conventionally done manually and are difficult to scale up as the visualization complexity, data size, and number of students increase. We present a first-of-its kind automatic grading method for D3 visualizations that scalably and precisely evaluates the data bindings, visual encodings, interactions, and design specifications used in a visualization. Our method has shown potential to enhance students' learning experience, enabling them to submit their code frequently and receive rapid feedback to better inform iteration and improvement to their code and visualization design. Our method promotes consistent grading and enables instructors to dedicate more focus to assist students in gaining visualization knowledge and experience. We have successfully deployed our method and auto-graded D3 submissions from more than 1000 undergraduate and graduate students in Georgia Tech's CSE6242 Data and Visual Analytics course, and received positive feedback and encouragement for expanding its adoption., Comment: Accepted to IEEE VIS'21. For a demo video, see https://youtu.be/hA2I36Gm0YM

Radar Wind Profilers (RWP) are well-established instruments for the probing of the atmospheric boundary layer, with the immense advantage of long-range and all-weather operation capability. One of their main limitations, however, is a relatively long integration time compared to other instruments such as lidars. In the context of L-band RWP we show that the use of Autoregressive (AR) modeling for the antenna signals combined with the Maximum Entropy Method (MEM) allows for a correct estimation of radial wind velocity profiles even with very short time samples. A systematical analysis of performance is made with the help of synthetic data. These numerical results are further confirmed by an experimental dataset acquired near the landing runways of Paris Charles de Gaulle (CDG) Airport, France, and validated using a colocated optical lidar at the Aerological station of Payerne, Switzerland. It is found that the AR-MEM approach can successfully derive reliable wind estimates using integration times as short as 2.5 s where the classical spectral approach can barely provide any measurement. In addition, the technique helps mitigating the impact of uncooperative flyers in the processing of the atmospheric echo, leading to a more successful application of the wind estimation algorithm., Comment: 10 pages, 9 figures, 2 tables

In this paper, we propose a method for the automatic semantic segmentation of satellite images into six classes (sparse forest, dense forest, moor, herbaceous formation, building, and road). We rely on Swin Transformer architecture and build the dataset from IGN open data. We report quantitative and qualitative segmentation results on this dataset and discuss strengths and limitations. The dataset and the trained model are made publicly available., Comment: 8 pages, 3 figures

Optically trapped particles are often subject to a non-conservative scattering force arising from radiation pressure. In this paper we present an exact solution for the steady state statistics of an overdamped Brownian particle subjected to a commonly used force field model for an optical trap. The model is the simplest of its kind that takes into account non-conservative forces. In particular, we present exact results for certain marginals of the full three dimensional steady state probability distribution as well as results for the toroidal probability currents which are present in the steady state, as well as for the circulation of theses currents. Our analytical results are confirmed by numerical solution of the steady state Fokker-Planck equation., Comment: 14 pages, 3 figures

The reduced transition probabilities $B(E2; 0^+_{g.s.}\rightarrow2_1^+,2^+_2)$ in $^{70}$Zn and the full $B(E2; 0^+_{g.s.}\rightarrow2^+)$ strength up to S$_n$=7.79 MeV in $^{68}$Ni have been determined at the LISE/GANIL facility using the Coulomb-excitation technique at intermediate beam energy on a $^{208}$Pb target. The $\gamma$ rays emitted in-flight were detected with an array of 46 BaF$_2$ crystals. The angles of the deflected nuclei were determined in order to disentangle and extract the Coulomb and nuclear contributions to the excitation of the 2$^+$ states. The measured $B(E2; 0^+_{g.s.}\rightarrow2_1^+)$ of 1432(124) e$^2$fm$^4$ for $^{70}$Zn falls in the lower part of the published values which clustered either around 1600 or above 2000 e$^2$fm$^4$, while the $B(E2; 0^+_{g.s.}\rightarrow2^+_2)$ of 53(7) e$^2$fm$^4$ agrees very well with the two published values. The relatively low $B(E2; 0^+_{g.s.}\rightarrow2_1^+)$ of 301(38) e$^2$fm$^4$ for $^{68}$Ni agrees with previous studies and confirms a local magicity at $Z=28, N=40$. Combining the results of the low-energy spectra of $^{68}$Ni and $^{70}$Zn and their shell-model interpretations, it is interesting to notice that four different shapes (spherical, oblate, prolate and triaxial) are present. Finally, a summed $E2$ strength of only about 150 e$^2$fm$^4$ has been found experimentally at high excitation energy, likely due to proton excitations across the $Z=28$ gap. The experimental distribution of this high-energy $E2$ excitation agrees with SM calculations, but its strength is about two times weaker.

In this note we study the induced $p$-norm of circulant matrices $A(n,\pm a, b)$, acting as operators on the Euclidean space $\mathbb{R}^n$. For circulant matrices whose entries are nonnegative real numbers, in particular for $A(n,a,b)$, we provide an explicit formula for the $p$-norm, $1 \leq p \leq \infty$. The calculation for $A(n,-a,b)$ is more complex. The 2-norm is precisely determined. As for the other values of $p$, two different categories of upper and lower bounds are obtained. These bounds are optimal at the end points (i.e. $p=1$ and $p = \infty$) as well as at $p=2$., Comment: 14 pages, to be published in Linear and Multilinear Algebra

This article is a survey on deep learning methods for single and multiple sound source localization. We are particularly interested in sound source localization in indoor/domestic environment, where reverberation and diffuse noise are present. We provide an exhaustive topography of the neural-based localization literature in this context, organized according to several aspects: the neural network architecture, the type of input features, the output strategy (classification or regression), the types of data used for model training and evaluation, and the model training strategy. This way, an interested reader can easily comprehend the vast panorama of the deep learning-based sound source localization methods. Tables summarizing the literature survey are provided at the end of the paper for a quick search of methods with a given set of target characteristics., Comment: Accepted for publication in The Journal of the Acoustical Society of America

In this work, we propose a novel self-attention based neural network for robust multi-speaker localization from Ambisonics recordings. Starting from a state-of-the-art convolutional recurrent neural network, we investigate the benefit of replacing the recurrent layers by self-attention encoders, inherited from the Transformer architecture. We evaluate these models on synthetic and real-world data, with up to 3 simultaneous speakers. The obtained results indicate that the majority of the proposed architectures either perform on par, or outperform the CRNN baseline, especially in the multisource scenario. Moreover, by avoiding the recurrent layers, the proposed models lend themselves to parallel computing, which is shown to produce considerable savings in execution time., Comment: Accepted to Workshop on Applications of Signal Processing to Audio and Acoustics

Video activity recognition by deep neural networks is impressive for many classes. However, it falls short of human performance, especially for challenging to discriminate activities. Humans differentiate these complex activities by recognising critical spatio-temporal relations among explicitly recognised objects and parts, for example, an object entering the aperture of a container. Deep neural networks can struggle to learn such critical relationships effectively. Therefore we propose a more human-like approach to activity recognition, which interprets a video in sequential temporal phases and extracts specific relationships among objects and hands in those phases. Random forest classifiers are learnt from these extracted relationships. We apply the method to a challenging subset of the something-something dataset and achieve a more robust performance against neural network baselines on challenging activities.