Miracle-dz/UniFAR-FTUs huggingface dataset:
End-to-End Urban Driving by Imitating a Reinforcement Learning Coach

End-to-end approaches to autonomous driving commonly rely on expert demonstrations. Although humans are good drivers, they are not good coaches for end-to-end algorithms that demand dense on-policy supervision. On the contrary, automated experts that leverage privileged information can efficiently generate large scale on-policy and off-policy demonstrations. However, existing automated experts for urban driving make heavy use of hand-crafted rules and perform suboptimally even on driving simulators, where ground-truth information is available. To ad-dress these issues, we train a reinforcement learning expert that maps bird’s-eye view images to continuous low-level actions. While setting a new performance upper-bound on CARLA, our expert is also a better coach that provides in-formative supervision signals for imitation learning agents to learn from. Supervised by our reinforcement learning coach, a baseline end-to-end agent with monocular camera-input achieves expert-level performance. Our end-to-end agent achieves a 78% success rate while generalizing to a new town and new weather on the NoCrash-dense bench-mark and state-of-the-art performance on the more challenging CARLA LeaderBoard.
Multiple two‐sample testing under arbitrary covariance dependency with an application in imaging mass spectrometry

Large‐scale hypothesis testing has become a ubiquitous problem in high‐dimensional statistical inference, with broad applications in various scientific disciplines. One relevant application is constituted by imaging mass spectrometry (IMS) association studies, where a large number of tests are performed simultaneously in order to identify molecular masses that are associated with a particular phenotype, for example, a cancer subtype. Mass spectra obtained from matrix‐assisted laser desorption/ionization (MALDI) experiments are dependent, when considered as statistical quantities. False discovery proportion (FDP) estimation and control under arbitrary dependency structure among test statistics is an active topic in modern multiple testing research. In this context, we are concerned with the evaluation of associations between the binary outcome variable (describing the phenotype) and multiple predictors derived from MALDI measurements. We propose an inference procedure in which the correlation matrix of the test statistics is utilized. The approach is based on multiple marginal models. Specifically, we fit a marginal logistic regression model for each predictor individually. Asymptotic joint normality of the stacked vector of the marginal regression coefficients is established under standard regularity assumptions, and their (limiting) correlation matrix is estimated. The proposed method extracts common factors from the resulting empirical correlation matrix. Finally, we estimate the realized FDP of a thresholding procedure for the marginal p‐values. We demonstrate a practical application of the proposed workflow to MALDI IMS data in an oncological context.
Simulation and estimation of an agent-based market-model with a matching engine

An agent-based model with interacting low frequency liquidity takers inter-mediated by high-frequency liquidity providers acting collectively as market makers can be used to provide realistic simulated price impact curves. This is possible when agent-based model interactions occur asynchronously via order matching using a matching engine in event time to replace sequential calendar time market clearing. Here the matching engine infrastructure has been modified to provide a continuous feed of order confirmations and updates as message streams in order to conform more closely to live trading environments. The resulting trade and quote message data from the simulations are then aggregated, calibrated and visualised. Various stylised facts are presented along with event visualisations and price impact curves. We argue that additional realism in modelling can be achieved with a small set of agent parameters and simple interaction rules once interactions are reactive, asynchronous and in event time. We argue that the reactive nature of market agents may be a fundamental property of financial markets and when accounted for can allow for parsimonious modelling without recourse to additional sources of noise.
A theoretical model of Surtseyan bomb fragmentation

Surtseyan eruptions are an important class of mostly basaltic volcanic eruptions first identified in the 1960s, where erupting magma at an air–water interface interacts with large quantities of slurry, a mixture of previously ejected tephra that re-enters the crater together with water. During a Surtseyan eruption, hot magma bombs are ejected that initially contain pockets of slurry. Despite the formation of steam and anticipated subsequent high pressures inside these bombs, many survive to land without exploding. We seek to explain this by building and solving a simplified spherical mathematical model that describes the coupled evolution of pressure and temperature due to the flashing of liquid to vapour within a Surtseyan bomb while it is in flight. Analysis of the model provides a criterion for fragmentation of the bomb due to steam pressure build-up, and predicts that if diffusive steam flow through the porous bomb is sufficiently rapid the bomb will survive the flight intact. This criterion explicitly relates fragmentation to bomb properties, and describes how a Surtseyan bomb can survive in flight despite containing flashing liquid water, contributing to an ongoing discussion in volcanology about the origins of the inclusions found inside bombs.
Affective Decoding for Empathetic Response Generation

Understanding speaker’s feelings and producing appropriate responses with emotion connection is a key communicative skill for empathetic dialogue systems. In this paper, we propose a simple technique called Affective Decoding for empathetic response generation. Our method can effectively incorporate emotion signals during each decoding step, and can additionally be augmented with an auxiliary dual emotion encoder, which learns separate embeddings for the speaker and listener given the emotion base of the dialogue. Extensive empirical studies show that our models are perceived to be more empathetic by human evaluations, in comparison to several strong mainstream methods for empathetic responding.
A comparative study of universal quantum computing models: towards a physical unification

Quantum computing has been a fascinating research field in quantum physics. Recent progresses motivate us to study in depth the universal quantum computing models (UQCM), which lie at the foundation of quantum computing and have tight connections with fundamental physics. Although being developed decades ago, a physically concise principle or picture to formalize and understand UQCM is still lacking. This is challenging given the diversity of still-emerging models, but important to understand the difference between classical and quantum computing. In this work, we carried out a primary attempt to unify UQCM by classifying a few of them as two categories, hence making a table of models. With such a table, some known models or schemes appear as hybridization or combination of models, and more importantly, it leads to new schemes that have not been explored yet. Our study of UQCM also leads to some insights into quantum algorithms. This work reveals the importance and feasibility of systematic study of computing models.
Global solutions of a doubly tactic resource consumption model with logistic source

which is proposed in [13] accounting for social interaction between different species. Here, u = u(x, t) and v = v(x, t) denote the population densities of foragers and scroungers, respectively, and w = w(x, t) represents the nutrient concentration with external resupply r. The problem (1.1) includes two taxis mechanisms, the taxis for u says that the movement of foragers is directed by the higher concentrations of nutrient, while the taxis for v says that intelligent scroungers orient their movement towards higher concentrations of the forager to find the nutrient indirectly. If we use ‘ → ‘ to denote the moving favor of the species, the taxis mechanisms in (1.1) are v → u→ w.
Mining for Gluon Saturation at Colliders

Quantum chromodynamics (QCD) is the theory of strong interactions of quarks and gluons collectively called partons, the basic constituents of all nuclear matter. Its non-abelian character manifests in nature in the form of two remarkable properties: color confinement and asymptotic freedom. At high energies, perturbation theory can result in the growth and dominance of very gluon densities at small-x. If left uncontrolled, this growth can result in gluons eternally growing violating a number of mathematical bounds. The resolution to this problem lies by balancing gluon emissions by recombinating gluons at high energies: phenomena of gluon saturation. High energy nuclear and particle physics experiments have spent the past decades quantifying the structure of protons and nuclei in terms of their fundamental constituents confirming predicted extraordinary behavior of matter at extreme density and pressure conditions. In the process they have also measured seemingly unexpected phenomena. We will give a state of the art review of the underlying theoretical and experimental tools and measurements pertinent to gluon saturation physics. We will argue for the need of high energy electron-proton/ion colliders such as the proposed EIC (USA) and LHeC (Europe) to consolidate our knowledge of QCD knowledge in the small x kinematic domains.
GGP: A Graph-based Grouping Planner for Explicit Control of Long Text Generation

Existing data-driven methods can well handle short text generation. However, when applied to the long-text generation scenarios such as story generation or advertising text generation in the commercial scenario, these methods may generate illogical and uncontrollable texts. To address these aforementioned issues, we propose a graph-based grouping planner~(GGP) following the idea of first-plan-then-generate. Specifically, given a collection of key phrases, GGP firstly encodes these phrases into a instance-level sequential representation and a corpus-level graph-based representation separately. With these two synergic representations, we then regroup these phrases into a fine-grained plan, based on which we generate the final long text. We conduct our experiments on three long text generation datasets and the experimental results reveal that GGP significantly outperforms baselines, which proves that GGP can control the long text generation with knowing how to say and in what order.
Purity and ascent for Gorenstein Flat Cotorsion modules

The extension of scalars functor along a finite ring homomorphism is a classic example of a functor which preserves purity and pure injectivity. We consider how this functor behaves when restricted to the class of Gorenstein flat modules over a right coherent rings, and give particular attention to the Frobenius category of Gorenstein flat and cotorsion modules by showing there is an induced triangulated functor on the stable categories. This enables a comparison between pure injectivity for Gorenstein flat modules and pure injectivity in the triangulated categories as well as an investigation in how purity for Gorenstein flat modules is transferred along the homomorphism. Throughout motivating examples from commutative algebra are considered, including over hypersurfaces where a pure injective analogue of Knörrer periodicity for Gorenstein flat modules is developed.
Deep Hybrid Self-Prior for Full 3D Mesh Generation

We present a deep learning pipeline that leverages network self-prior to recover a full 3D model consisting of both a triangular mesh and a texture map from the colored 3D point cloud. Different from previous methods either exploiting 2D self-prior for image editing or 3D self-prior for pure surface reconstruction, we propose to exploit a novel hybrid 2D-3D self-prior in deep neural networks to significantly improve the geometry quality and produce a high-resolution texture map, which is typically missing from the output of commodity-level 3D scanners. In particular, we first generate an initial mesh using a 3D convolutional neural network with 3D self-prior, and then encode both 3D information and color information in the 2D UV atlas, which is further refined by 2D convolutional neural networks with the self-prior. In this way, both 2D and 3D self-priors are utilized for the mesh and texture recovery. Experiments show that, without the need of any additional training data, our method recovers the 3D textured mesh model of high quality from sparse input, and outperforms the state-of-the-art methods in terms of both geometry and texture quality.
Signaling Pathways of Cardiac Remodeling Related to Angiotensin II

Additional information is available at the end of the chapter Abstract Heart failure affects more than 23 million people worldwide, and its prognosis remains poor. Hypertension is one of the most prominent human health problem and places individuals at a higher risk for heart failure. Several factors interplay the development of hypertension contributing for decompensated heart hypertrophy. The renin-angiotensin system (RAS) has been shown to be the foremost regulator of blood pressure. Many evidences have pointed out the importance of RAS and its key mediator, angiotensin II (Ang II), on signaling pathways involved in cardiac remodeling. The Ang II-induced hypertrophic effects seem to be related to increased reactive oxygen species (ROS). Under oxidative stress conditions, as those observed in hypertension and heart failure, the matrix metalloproteinases (MMP) is activated. Ang II is connected with TNF-α and TGF-β by ROS-NF-κB-MMP mechanisms, which are involved in heart failure. The rationale of the present chapter is structured on the progression of heart failure related to Ang II, TNF-α and TGF-β by common signaling pathways. Pharmacothera-peutics approaches to the heart failure abound, but the mortality rates remain high. This chapter will also describe molecular mechanisms involved in heart failure highlighting that TGF-β and/or TNF-α inhibitors could contribute to treatment to this serious clinical condition.
Higher Order Derivative-Based Receiver Preprocessing for Molecular Communications

While molecular communication via diffusion experiences significant inter-symbol interference (ISI), recent work suggests that ISI can be mitigated via time differentiation pre-processing which achieves pulse narrowing. Herein, the approach is generalized to higher order differentiation. The fundamental trade-off between ISI mitigation and noise amplification is characterized, showing the existence of an optimal derivative order that minimizes the bit error rate (BER). Theoretical analyses of the BER and a signal-to-interference-plus-noise ratio are provided, the derivative order optimization problem is posed and solved for threshold-based detectors. For more complex detectors which exploit a window memory, it is shown that derivative pre-processing can strongly reduce the size of the needed window. Extensive numerical results confirm the accuracy of theoretical derivations, the gains in performance via derivative pre-processing over other methods and the impact of the optimal derivative order. Derivative pre-processing offers a low complexity/high-performance method for reducing ISI at the expense of increased transmission power to reduce noise amplification.
Combined experimental and numerical study of ethanol laminar flame extinction

ABSTRACT Extinction strain rates of ethanol at atmospheric pressure were investigated in laminar counter-flow diffusion flames as a function of nitrogen dilution through both experiment and two-dimensional axisymmetric full domain numerical analysis. An opposed-jet burner configuration with straight-tube fuel and air nozzles and having two different design nozzle exit flow profiles was used in the experiments. An advanced numerical solution algorithm, which makes use of a block-based parallel implicit finite-volume scheme, was used to obtain the corresponding simulation results. A comparison of the numerical results for two different chemical kinetic mechanisms helped to identify key factors affecting the flame extinction. While the predicted values over estimated the experimentally observed global strain rate extinction limits, the general trends and nozzle flow profiles were well captured. The local strain rates near extinction were also examined numerically and shown to be independent of the nozzle velocity flow profile and a fundamental property of the reactants. The experimental data from the present study were also compared to previous measurements and good agreement was demonstrated. The predicted nozzle exit and axial velocity flow profiles arising from the two-dimensional analysis were examined and limitations of conventional one-dimensional counter-flow analyses were highlighted.
Dependence of the hybrid solar cell efficiency on the thickness of ZnO nanoparticle optical spacer interlayer

ABSTRACT Optical spacer interlayers are commonly used in solar cells to improve the power conversion efficiency (PCE) of the device. This paper reports a study on the effect of zinc oxide (ZnO) optical spacer interlayer on PCPDTBT:ZnO hybrid solar cell. Optical spacers help to concentrate the spatial distribution of electric field due to light onto the active layer, and thus, improve the light absorption in the active layer. Finite-Difference Time-Domain (FDTD) calculations was carried out on stacked solar cell structure using a simulation software, Lumerical FDTD solutions. Maxwell's equations were solved and the light absorbed by the active layer was computed. The design was optimized with regard to active layer thickness and ZnO optical spacer thickness.
Spectrophotometric Microdetermination of Tretinoin, Isotretinoinusing Iodine and Tazarotene Microdetermination Via Reaction with Rose-Bengal Reagent

THE reactions of Iodine or Rose-Bengal (Rbng) reagents with three of retinoid drugs Tretinoin (Tret), Isotretinoin (Itret) and Tazarotene (Taz) had been studied for the development of simple, rapid, sensitive spectrophotometric methods for micro-determining of these drugs in pure and in their pharmaceutical formulation. These methods are based on the formation of reaction product between the drugs and iodine or rose -bengal reagent. The spectra of the formed reaction products were measured at selected proper conditions of time, temperature, pH and selected wavelength. The analytical parameters such as standard deviation (SD), relative standard deviation (RSD), Sandell’s sensitivity (S), LOQ and LOD were calculated in order to check accuracy, sensitivity and precision of the given procedures. The values of SD = 0.1312 - 1.100, RSD = 0.5556 - 1.946 %, S = 0.0229 – 0.0508 μg cm-2, LOQ = 13.17 – 17.45 μg mL-1, LOD = 4.347 – 5.757 μg mL-1 obtained of these parameters refer to the accuracy and sensitivity of the suggested procedures and can be applied for analyses of these drugs in their pharmaceutical formulations. Beer's law was valid in the range 9.041 – 29.71, 35.75 – 119.6 and 10.48 – 71.11 μg mL-1 with recovery of (97.84 - 102.8 %), (98.67 - 101.8 %), (98.32 - 102.0 %) for Tretinoin, Isotretinoin and Tazarotene respectively, The importance of this research stems from applications of these retinoid derivatives in skin improvements. They are always used as creams and found to be effective for photo-damage and for protection from skin irritation. Therefore, the proposed methods had been applied successfully for the analysis of the studied drugs in pure forms and pharmaceutical formulations. The results obtained were found to be in good agreement with those obtained by official methods. This evaluation had been done by F- and t- tests
Processing-dependent thermal stability of a prototypical amorphous metal oxide

Amorphous metal oxides (AMOs) are important candidate materials for fabricating next-generation thin-film transistors. While much attention has been directed toward the synthesis and electrical properties of AMOs, less is known about growth conditions that allow AMOs to retain their desirable amorphous state when subjected to high operating temperatures. Using in situ x-ray scattering and level-set simulations, we explore the time evolution of the crystallization process for a set of amorphous $\mathrm{I}{\mathrm{n}}_{2}{\mathrm{O}}_{3}$ thin films synthesized by pulsed-laser deposition at deposition temperatures (${T}_{d}$) of \ensuremath{-}50, \ensuremath{-}25, and 0 \ifmmode^\circ\else\textdegree\fi{}C. The films were annealed isothermally and the degree of crystallinity was determined by a quantitative analysis of the time-evolved x-ray scattering patterns. As expected, for films grown at the same ${T}_{d}$, an increase in the annealing temperature ${T}_{A}$ led to a shorter delay prior to the onset of crystallization, and a faster crystallization rate. Moreover, when lowering the deposition temperature by 25 \ifmmode^\circ\else\textdegree\fi{}C, a 40 \ifmmode^\circ\else\textdegree\fi{}C increase in annealing temperature is needed to achieve the same time interval for the crystals to grow from 10 to 90% volume fraction of the sample. Films grown at ${T}_{d}=0{\phantom{\rule{0.16em}{0ex}}}^{\ensuremath{\circ}}\mathrm{C}$ exhibited strong cubic texture after crystallization. A level-set method was employed to quantitatively model the texture that develops in the microstructures and to determine key parameters, such as the interface growth velocity, the nucleation density, and the activation energy. The differences observed in the crystallization processes are attributed to the changes in the atomic structure of the oxide and possible nanocrystalline inclusions that formed during the deposition of the amorphous phase.
The Rsm (Csr) post-transcriptional regulatory pathway coordinately controls multiple CRISPR–Cas immune systems

Abstract CRISPR–Cas systems provide bacteria with adaptive immunity against phages and plasmids; however, pathways regulating their activity are not well defined. We recently developed a high-throughput genome-wide method (SorTn-seq) and used this to uncover CRISPR–Cas regulators. Here, we demonstrate that the widespread Rsm/Csr pathway regulates the expression of multiple CRISPR–Cas systems in Serratia (type I-E, I-F and III-A). The main pathway component, RsmA (CsrA), is an RNA-binding post-transcriptional regulator of carbon utilisation, virulence and motility. RsmA binds cas mRNAs and suppresses type I and III CRISPR–Cas interference in addition to adaptation by type I systems. Coregulation of CRISPR–Cas and flagella by the Rsm pathway allows modulation of adaptive immunity when changes in receptor availability would alter susceptibility to flagella-tropic phages. Furthermore, we show that Rsm controls CRISPR–Cas in other genera, suggesting conservation of this regulatory strategy. Finally, we identify genes encoding RsmA homologues in phages, which have the potential to manipulate the physiology of host bacteria and might provide an anti-CRISPR activity.