Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Scalable Self-Driving Policies for Generic Traffic Scenarios

Robust and safe self-driving in complex and dynamic environments is quite challenging due to the requirement of scalable driving policies against the wide variety of traffic scenarios (e,g., road topologies, traffic rules and interaction with road agents). In this area, traditional modular frameworks scale poorly in new environments, and require tremendous and iterative hand-tuning of rules and parameters to maintain performance in all foreseeable scenarios. Recently, deep-learning based self-driving methods have shown promising results with better generalization capability but less hand engineering effort. However, most of the previous methods are trained and evaluated in limited and simple environments with scattered tasks, such as lane-following, autonomous braking and conditional driving. In this paper, we propose a graph-based deep network to achieve unified and scalable self-driving in diverse dynamic environments. The extensive evaluation results show that our model can safely navigate the vehicle in a large variety of urban, rural, and highway areas with dense traffic while obeying traffic rules. Specifically, more than 7,500 km of closed-loop driving evaluation is conducted in dynamic simulation environments, in which our method can handle complex driving situations, and achieve higher success rates (73.5%-83.2%) and driving scores than the baselines.
Comments on Efficient Singular Value Thresholding Computation.

We discuss how to evaluate the proximal operator of a convex and increasing function of a nuclear norm, which forms the key computational step in several first-order optimization algorithms such as (accelerated) proximal gradient descent and ADMM. Various special cases of the problem arise in low-rank matrix completion, dropout training in deep learning and high-order low-rank tensor recovery, although they have all been solved on a case-by-case basis. We provide an unified and efficiently computable procedure for solving this problem.
Empirical Performance Analysis of Conventional Deep Learning Models for Recognition of Objects in 2-D Images

Artificial Neural Networks, an essential part of Deep Learning, are derived from the structure and functionality of the human brain. It has a broad range of applications ranging from medical analysis to automated driving. Over the past few years, deep learning techniques have improved drastically - models can now be customized to a much greater extent by varying the network architecture, network parameters, among others. We have varied parameters like learning rate, filter size, the number of hidden layers, stride size and the activation function among others to analyze the performance of the model and thus produce a model with the highest performance. The model classifies images into 3 categories, namely, cars, faces and aeroplanes.
Fermionic Open EFT from Holography

In this work we initiate the study of open effective field theories of fermions interacting with holographic baths. As a first step in this direction, we explain how the recently identified holographic Schwinger-Keldysh saddles naturally reproduce the Fermi-Dirac statistics of boundary correlators. We study Dirac fermions propagating in the background of a doubled $AdS_{d+1}$ Schwarzchild blackbrane. We solve for both ingoing (quasi-normal) as well as outgoing (Hawking) modes of the Dirac field in a boundary gradient expansion and compare our findings against exact results in $d=2$. The holographic Schwinger-Keldysh boundary conditions automatically select a unique combination of these modes consistent with fermionic KMS relations. Evaluating the bulk on-shell action using these solutions, we obtain an explicit expression for the influence phase of the probe fermion in a gradient expansion.
Stability Certification Standards for DC Microgrid Networks with Arbitrary Load Configurations

DC microgrids are prone to small-signal instabilities due to the presence of tightly regulated loads. This paper develops a decentralized stability certificate which is capable of certifying the small-signal stability of an islanded DC network containing such loads. Utilizing a novel homotopy approach, the proposed standards ensure that no system eigenmodes are able to cross into the unstable right half plane for a continuous range of controller gain levels. The resulting "standards" can be applied to variety of grid components which meet the specified, but non-unique, criteria. These standards thus take a step towards offering plug-and-play operability of DC microgrids. The proposed theorems are explicitly illustrated and numerically validated on a test-case example of a DC microgrid network containing controlled buck converter loads.
Water ice deposition and growth in molecular clouds

In interstellar clouds, the deposition of water ice on to grains only occurs at visual extinctions above some threshold value (Ath). At extinctions greater than Ath, there is a (near-linear) correlation between the inferred column density of the water ice and AV. For individual cloud complexes such as Taurus, Serpens, and ρ-Ophiuchi, Ath and the gradients of the correlation are very similar along all lines of sight. We have investigated the origin of this phenomenon, with careful consideration of the various possible mechanisms that may be involved and have applied a full chemical model to analyse the behaviours and sensitivities in quiescent molecular clouds. Our key results are as follows: (i) the ubiquity of the phenomenon points to a common cause, so that the lines-of-sight probe regions with similar, advanced, chemical, and dynamical evolution; (ii) for Taurus and Serpens Ath and the slope of the correlation can be explained as resulting from the balance of freeze-out of oxygen atoms and photodesorption of H2O molecules. No other mechanism can satisfactorily explain the phenomenon; (iii) Ath depends on the local density, suggesting that there is a correlation between local volume density and column density; (iv) the different values of Ath for Taurus and Serpens are probably due to variations in the local mean radiation field strength; (v) most ice is accreted on to grains that are initially very small (<0.01$\,\mu$m); and (vi) the very high value of Ath observed in ρ-Ophiuchi cannot be explained in the same way, unless there is complex microstructure and/or a modification to the extinction characteristics.
SHAD3S: A model to Sketch, Shade and Shadow

Hatching is a common method used by artists to accentuate the third dimension of a sketch, and to illuminate the scene. Our system SHAD3S 1 attempts to compete with a human at hatching generic three-dimensional (3D) shapes, and also tries to assist her in a form exploration exercise. The novelty of our approach lies in the fact that we make no assumptions about the input other than that it represents a 3D shape, and yet, given a contextual information of illumination and texture, we synthesise an accurate hatch pattern over the sketch, without access to 3D or pseudo 3D. In the process, we contribute towards a) a cheap yet effective method to synthesise a sufficiently large high fidelity dataset, pertinent to task; b) creating a pipeline with conditional generative adversarial network (CGAN); and c) creating an interactive utility with GIMP, that is a tool for artists to engage with automated hatching or a form-exploration exercise. User evaluation of the tool suggests that the model performance does generalise satisfactorily over diverse input, both in terms of style as well as shape. A simple comparison of inception scores suggest that the generated distribution is as diverse as the ground truth.
Nuclear Fusion by Inertial Confinement: A Comprehensive Treatise

An Introduction to Nuclear Fusion by Inertial Confinement (J.M. Martinez-Val, G. Velarde, and Y. Ronen). The Physics of Directly Driven Targets (S. Eliezer and H. Hora). Laser-Target Interaction: Laser Energy Absorption and Nonlinear Plasma Phenomena (V. Yu. Bychenkov and V.T. Tikhonchuk). Energy Transfer to the Plasma in Laser Targets (Yu.V. Afanasiev and S.Yu.Gus'kov). Ion Beam-Target Interaction (Z. Zinamon). Atomic Physics in Dense Plasma (R.M. More). Electron Heat Conduction in ICF Targets (J.R. Sanmartin). Radiation Transport in ICF Targets (E. Minguez). Charged Particle Transport (J.J. Honrubia). ICF Neutronics (M. Piera and J.M. Martinez-Val). The Compression Phase in ICF Targets (T. Yabe). Ignition and Burn Propagation in ICF Targets (S. Gus'kov and V. Rozanov). Hydrodynamic Instability of Target Implosion in ICF (E.G. Gamaly). Solid State Lasers (J.M. Soures). Excimer Lasers (L. Rosocha, S.J. Czuchlewski, B.J. Krohn, and J. McLeod). Free Electron Lasers for ICF (W.B. Colson, J. Blau, D. Craun, D. Gillingham, D. Kiel, J.H. Park, and R. Souza). Heavy Ion Accelerators for ICF (R.W. Muller). Light-Ion Accelerators for ICF (J.P. VanDevender and H.-J. Bluhm). Magnetically Imploded Plasma as a Driver for ICF (J.G. Linhart). Diagnostics of Laser-Imploded Plasma (C. Yamanaka). Target Fabrication (Y. Izawa and T. Norimatsu). Laser-Driven ICF Experiments (R.L. McCrory, Jr.). Particle Beam-Driven ICF Experiments (H.U. Karow and J.P. VanDevender). Irradiation Effects and Activation in Structural Materials (J.M. Perlado and J. Sanz). ICF Reactors-Conceptual Design Studies (G. Kessler, G.L. Kulcinski, and R.R. Peterson). Appendix: Units and Physical Constants. Index.
Asteroseismology of the DAV star R808

The DAV star R808 was observed by 13 different telescopes for more than 170 hours in April 2008 on the WET run XCOV26. 25 independent pulsation frequencies were identified by this data set. We assumed 19 $m$ = 0 modes and performed an asteroseismological study on those 19 modes. We evolve grids of DAV star models by \texttt{WDEC} adopting the element diffusion scheme with pure and screened Coulomb potential effect. The core compositions are from white dwarf models evolved by \texttt{MESA}, which are thermal nuclear burning results. Our best fitting model is from the screened Coulomb potential scenario, which has parameters of log($M_{\rm He}/M_{\rm *}$) = -2.4, log($M_{\rm H}/M_{\rm *}$) = -5.2, $T_{\rm eff}$ = 11100\,K, $M_{\rm *}$ = 0.710\,$M_{\odot}$, log$g$ = 8.194, and $\sigma_{RMS}$ = 2.86\,s. The value of $\sigma_{RMS}$ is the smallest among the four existing asteroseismological work. The average period spacing is 46.299\,s for $l$ = 1 modes and 25.647\,s for $l$ = 2 modes. The other 6 observed modes can be fitted by $m$ $\neq$ 0 components of some modes for our best fitting model. Fitting the 25 observed modes, we obtain a $\sigma_{RMS}$ value of 2.59\,s. Considering the period spacings, we also assume, that at least in one case, we detect an $l$ = 2 trapped mode.
Conjugated Polymers : Theory, Synthesis, Properties, and Characterization

THEORY OF CONJUGATED POLYMERS On the Transport, Optical, and Self-Assembly Properties of p-Conjugated Materials: A Combined Theoretical/Experimental Insight David Beljonne, Jerome Cornil, Veaceslav Coropceanu, Demetrio A. da Silva Filho, Victor Geskin, Roberto Lazzaroni, Philippe Leclere, and Jean-Luc Bredas Theoretical Studies of Electron-Lattice Dynamics in Organic System Sven Stafstrom SYNTHESIS AND CLASSES OF CONJUGATED POLYMERS Helical Polyacetylene Synthesized in Chiral Nematic Liquid Crystal Kazuo Akagi Synthesis and Properties of Poly(arylene vinylene)s Andrew C. Grimsdale and Andrew B. Holmes Blue-Emitting Poly(para-Phenylene)-Type Polymers Emil Joachim Wolfgang List and Ullrich Scherf Poly(paraPhenyleneethynylene)s and Poly(aryleneethynylene)s: Materials with a Bright Future Uwe H.F. Bunz Polyaniline Nanofibers: Synthesis, Properties, and Applications Jiaxing Huang and Richard B. Kaner Recent Advances in Polypyrrole Seung Hyun Cho, Ki Tae Song, and Jun Young Lee Regioregular Polythiophenes Malika Jeffries-El and Richard D. McCullough Poly(3,4-Ethylenedioxythiophene)-Scientific Importance, Remarkable Properties, and Applications Stephan Kirchmeyer, Knud Reuter, and Jill C. Simpson Thienothiophenes: From Monomers to Polymers Gregory A. Sotzing, Venkataramanan Seshadri, and Francis J. Waller Low Bandgap Conducting Polymers Seth C. Rasmussen and Martin Pomerantz Advanced Functional Polythiophenes Based on Tailored Precursors Philippe Blanchard, Philippe Leriche, Pierre Frere, and Jean Roncali Structure-Property Relationships and Applications of Conjugated Polyelectrolytes Kirk S. Schanze and Xiaoyong Zhao PROPERTIES AND CHARACTERIZATION OF CONJUGATED POLYMERS Insulator-Metal Transition and Metallic State in Conducting Polymers Arthur J. Epstein One-Dimensional Charge Transport in Conducting Polymer Nanofibers A.N. Aleshin and Y.W. Park Structure Studies of p- and s-Conjugated Polymers Michael J. Winokur Electrochemistry of Conducting Polymers P. Audebert and Fabien Miomandre Internal Fields and Electrode Interfaces in Organic Semiconductor Devices: Noninvasive Investigations via Electroabsorption Thomas M. Brown and Franco Cacialli Electrochromism of Conjugated Conducting Polymers Aubrey L. Dyer and John R. Reynolds Photoelectron Spectroscopy of Conjugated Polymers M.P. de Jong, G. Greczynski, W. Osikowicz, R. Friedlein, X. Crispin, M. Fahlman, and W.R. Salaneck Ultrafast Electron Dynamics and Laser Action in p-Conjugated Semiconductors Z. Valy Vardeny and O. Korovyanko
Constraining velocity-dependent Lorentz and 
CPT
 violations using lunar laser ranging

The possibility for Lorentz/CPT-breaking, which is motivated by unification theories, can be systematically tested within the standard-model extension framework. In the pure gravity sector, the mass dimension 5 operators produce new Lorentz and CPT-breaking terms in the 2-body equations of motion that depend on the relative velocity of the bodies. In this Letter, we report new constraints on 15 independent SME coefficients for Lorentz/CPT-violations with mass dimension 5 using lunar laser ranging. We perform a global analysis of lunar ranging data within the SME framework using more than 26,000 normal points between 1969 and 2018. We also perform a jackknife analysis in order to provide realistic estimates of the systematic uncertainties. No deviation from Lorentz/CPT symmetries is reported. In addition, when fitting simultaneously for the 15 canonical SME coefficients for Lorentz/CPT-violations, we improve up to three orders of magnitude previous post-fit constraints from radio pulsars.
The SLT 2021 Children Speech Recognition Challenge: Open Datasets, Rules and Baselines

Automatic speech recognition (ASR) has been significantly advanced with the use of deep learning and big data. How-ever improving robustness, including achieving equally good performance on diverse speakers and accents, is still a challenging problem. In particular, the performance of children speech recognition (CSR) still lags behind due to 1) the speech and language characteristics of children’s voice are substantially different from those of adults and 2) sizable open dataset for children speech is still not available in the research community. To address these problems, we launch the Children Speech Recognition Challenge (CSRC), as a flagship satellite event of IEEE SLT 2021 workshop. The challenge will release about 400 hours of Mandarin speech data for registered teams and set up two challenge tracks and provide a common testbed to benchmark the CSR performance. In this paper, we introduce the datasets, rules, evaluation method as well as baselines.
Household Bargaining and Spending on Children: Experimental Evidence from Tanzania

It is frequently assumed that money in the hands of women leads to better out-comes for their children than money in the hands of men. However, empirical and theoretical evidence are mixed. We conduct a novel between-subject lab-in-the-field experiment to study whether increasing the wife's control over resources causes a couple to allocate more to their child. The paper provides two main insights. First, increasing the wife's bargaining power does not increase the share allocated to the child, but leads to more gender-equal allocations to children. Second, time preferences are important in explaining household decision-making; it is better for the child that the most patient spouse has more relative bargaining power. Our results highlight the importance of taking a broader set of preferences into account when studying household decision-making, and suggest that policy aimed to increase spending on children should target the spouse with preferences most aligned with such spending.
Kernel Density Estimation through Density Constrained Near Neighbor Search

In this paper we revisit the kernel density estimation problem: given a kernel $K(x, y)$ and a dataset of $n$ points in high dimensional Euclidean space, prepare a data structure that can quickly output, given a query $q$, a ($1+ \epsilon$)-approximation to $\mu:=\frac{1}{\vert P\vert}\sum\nolimits_{p\in P}K(p, q)$. First, we give a single data structure based on classical near neighbor search techniques that improves upon or essentially matches the query time and space complexity for all radial kernels considered in the literature so far. We then show how to improve both the query complexity and runtime by using recent advances in data-dependent near neighbor search. We achieve our results by giving an new implementation of the natural importance sampling scheme. Unlike previous approaches, our algorithm first samples the dataset uniformly (considering a geometric sequence of sampling rates), and then uses existing approximate near neighbor search techniques on the resulting smaller dataset to retrieve the sampled points that lie at an appropriate distance from the query. We show that the resulting sampled dataset has strong geometric structure, making approximate near neighbor search return the required samples much more efficiently than for worst case datasets of the same size. As an example application, we show that this approach yields a data structure that achieves query time and space complexity for the Gaussian kernel. Our data dependent approach achieves query time and space for the Gaussian kernel. The data dependent analysis relies on new techniques for tracking the geometric structure of the input datasets in a recursive hashing process that we hope will be of interest in other applications in near neighbor search.
iHorology: Lowering the Barrier to Microsecond-Level Internet Time

High accuracy, synchronized clocks are essential to a growing number of Internet applications. Standard protocols and their associated server infrastructure typically enable client clocks to synchronize to the order of tens of milliseconds. We address one of the key challenges to high precision Internet timekeeping – the intrinsic contribution to clock error of underlying path asymmetry between client and time server, a fundamental barrier to microsecond level accuracy. We first exploit results of a unique measurement study to reliably quantify asymmetry by taking routing changes into account for the first time, and then to infer the impacts on timing. We then describe three approaches to addressing the path asymmetry problem: LBBE, SBBE and K-SBBE, each based on timestamp exchange with multiple servers, with the goal of tightening bounds on asymmetry for each client. We explore their capabilities and limitations through simulation and model-based argument. We show that substantial improvements are possible, and discuss whether, and how, the goal of microsecond accuracy might be attained.
Dynamical symmetries of periodically-driven quantum systems and their spectroscopic signatures

Spatial symmetries of quantum systems leads to important effects in spectroscopy, such as selection rules and dark states. Motivated by the increasing strength of light-matter interaction achieved in recent experiments, we investigate a set of dynamically-generalized symmetries for quantum systems, which are subject to a strong periodic driving. Based on Floquet response theory, we study rotational, particle-hole, chiral and time-reversal symmetries and their signatures in spectroscopy, including symmetry-protected dark states (spDS), a Floquet band selection rule (FBSR), and symmetry-induced transparency (siT). Specifically, a dynamical rotational symmetry establishes dark state conditions, as well as selection rules for inelastic light scattering processes; a particle-hole symmetry introduces dark states for symmetry related Floquet states and also a transparency effect at quasienergy crossings; chiral symmetry and time-reversal symmetry alone do not imply dark state conditions, but can be combined to the particle-hole symmetry. Our predictions reveal new physical phenomena when a quantum system reaches the strong light-matter coupling regime, important for superconducting qubits, atoms and molecules in optical or plasmonic field cavities, and optomechanical systems.
Relative Drone-Ground Vehicle Localization using LiDAR and Fisheye Cameras through Direct and Indirect Observations

Estimating the pose of an unmanned aerial vehicle (UAV) or drone is a challenging task. It is useful for many applications such as navigation, surveillance, tracking objects on the ground, and 3D reconstruction. In this work, we present a LiDAR-camera-based relative pose estimation method between a drone and a ground vehicle, using a LiDAR sensor and a fisheye camera on the vehicle's roof and another fisheye camera mounted under the drone. The LiDAR sensor directly observes the drone and measures its position, and the two cameras estimate the relative orientation using indirect observation of the surrounding objects. We propose a dynamically adaptive kernel-based method for drone detection and tracking using the LiDAR. We detect vanishing points in both cameras and find their correspondences to estimate the relative orientation. Additionally, we propose a rotation correction technique by relying on the observed motion of the drone through the LiDAR. In our experiments, we were able to achieve very fast initial detection and real-time tracking of the drone. Our method is fully automatic.
The TW Hya Rosetta Stone Project. I. Radial and Vertical Distributions of DCN and DCO+

Molecular D/H ratios are frequently used to probe the chemical past of solar system volatiles. Yet it is unclear which parts of the solar nebula hosted an active deuterium fractionation chemistry. To address this question, we present 0. ″2–0. ″4 Atacama Large Millimeter/submillimeter Array (ALMA) observations of DCO+ and DCN 2–1, 3–2, and 4–3 toward the nearby protoplanetary disk around TW Hya, taken as part of the TW Hya Rosetta Stone project, augmented with archival data. DCO+ is characterized by an excitation temperature of ∼40 K across the 70 au radius pebble disk, indicative of emission from a warm, elevated molecular layer. Tentatively, DCN is present at even higher temperatures. Both DCO+ and DCN present substantial emission cavities in the inner disk, while in the outer disk the DCO+ and DCN morphologies diverge: most DCN emission originates from a narrow ring peaking around 30 au, with some additional diffuse DCN emission present at larger radii, while DCO+ is present in a broad structured ring that extends past the pebble disk. Based on a set of simple parametric disk abundance models, these emission patterns can be explained by a near-constant DCN abundance exterior to the cavity, and an increasing DCO+ abundance with radius. In conclusion, the ALMA observations reveal an active deuterium fractionation chemistry in multiple disk regions around TW Hya, but not in the cold planetesimal-forming midplane and in the inner disk. More observations are needed to explore whether deuterium fractionation is actually absent in these latter regions, and if its absence is a common feature or something peculiar to the old TW Hya disk.