Miracle-dz/UniFAR-FTUs huggingface dataset:
Money Transfer Made Simple

It has recently been shown (PODC 2019) that, contrarily to a common belief, money transfer in the presence of faulty (Byzantine) processes does not require strong agreement such as consensus. This article goes one step further: namely, it shows that money transfers do not need to explicitly capture the causality relation that links individual transfers. A simple FIFO order between each pair of processes is sufficient. To this end, the article presents a generic money transfer algorithm that can be instantiated in both the crash failure model and the Byzantine failure model. The genericity dimension lies in the underlying reliable broadcast abstraction which must be suited to the appropriate failure model. Interestingly, whatever the failure model, the money transfer algorithm only requires adding a single sequence number to its messages as control information. Moreover, as a side effect of the proposed algorithm, it follows that money transfer is a weaker problem that the construction of a read/write register in the asynchronous message-passing crash-prone model.
Enzymatic halogenation of pyrazoles and pyridine derivatives.

Pyrazole, 1-methylpyrazole and 3-methylpyrazole are chlorinated by the enzyme chloroperoxidase from Caldariomyces fumago, in the presence of potassium chloride and hydrogen peroxide at pH 2.7, yielding the corresponding 4-chloro derivatives in good yields. A 4H-pyrazole is proposed as an intermediate in this reaction. 2-Aminopyridine was converted regiospecifically by the same enzyme into 2-amino-3-chloropyridine, and 8-hydroxyquinoline gave its 5,7-dibromo-derivative in very good yield when bromide ion was used as the halide substrate.
Coupled measurement of droplet size distribution and velocity distribution in a fuel spray with digital imaging analysis under elevated pressure

The aim of this work is to investigate the spray characteristics of dimethyl ether (DME) under high pressure conditions. Therefore the fuel spray with a pressure of 6 MPa and 8 MPa is injected into a atmosphere with a pressure of 2 MPa. The spray analysis is performed by a shadow technique and digital image analysis with temporal and spatial resolution. From the images spray characteristics, such as droplet velocity, droplet size and spatial distribution are measured. The correlations of droplet diameter and droplet velocity are presented as Joint Probability Density Functions (JPDF). The results show that little increase of the injection pressure has a significant effect on the spray characteristics. Finally, the ignition delay time for different injection pressures is measured with a high speed video camera. To reveal the influence of droplet evaporation on the ignition process, the effective ignition delay time of the fuel spray is compared to the delay time of the gaseous free fuel jet.
Statistical Spatial Models for Soil Parameters: A Comparative Analysis of Several Methods Applied to Dobrovăţ Basin (Eastern Romania)

This study compares the performance of several statistical methods (multiple linear regression, analysis of covariance, geographically weighted regression, regression kriging, and ordinary kriging) for deriving spatial models of soil parameters. The applications were carried out within a 186-km2 hydrographic basin situated in eastern Romania. Statistical models were computed from a sample of approximately 180 soil profiles, scattered in the eastern half of the basin. Two independent samples, each of 50 soil profiles, were used for validation inside (interpolation) and outside (extrapolation) the main sampling area. The predictors included X and Y coordinates of soil profiles, geomorphometrical parameters (altitude, slope, aspect, wetness index, terrain curvature), climate parameters (mean annual temperatures, precipitation, global radiation), the normalized difference vegetation index, the main soil types, land use, and surface lithology. For only three soil variables the geostatistical approach proved to be useful: occurrence depth of calcium carbonates, pH, and base saturation. The best spatial models were achieved using analysis of covariance, geographically weighted regression, and ordinary kriging. The most relevant continuous predictor is the mean annual precipitation, whereas the most relevant qualitative factor is the soil type.
Improving Yield and Quality of Palmarosa [Cymbopogon Martinii (Roxb.) Wats. Var. Motia Burk.] with Sulfur Fertilization

A field experiment was conducted during 2006–2007 at Hyderabad, India on sulfur-deficient alfic ustochrept soil on aromatic crop palmarosa [Cymbopogon martinii (Roxb.) Wats. var. motia Burk. ] with five rates of sulfur (S) application (0, 10, 20, 30, and 40 kg ha−1). Four harvests were performed between August 2006 and August 2007 at 90-day intervals. A fifth harvest was taken in November 2007 to study the residual effect of S. Five constituents accounting for 87.4–98.7% of the essential oil were identified by gas chromatography-flame ionization detector (GC-FID) and gas chromatography mass spectroscopy (GC-MS) analyses. The results revealed that 40 kg S ha−1 produced the highest total (total of four harvests) biomass yield (71.5 t ha−1), and total essential oil yield (382.3 kg ha−1). The highest contents of essential oil components cis-β-ocimene (2.5%), linalool (2.9%), geraniol (84.9%), geranyl acetate (9.8%), and geranyl hexanoate (2.6%) were observed in 40 kg S ha−1 treatment. Sulfur exhibited no residual effect, therefore application is recommended to each harvest.
Traffic‐Related Elements in Airborne Particulate Matter

Abstract This review covers the investigations done over the last few years to quantify key traffic‐related elements, namely, Pb, Pd, Pt, Rh, and Sb in atmospheric aerosols. From this standpoint, the role played by analytical techniques as diverse as electrothermal atomization atomic absorption spectrometry (ET‐AAS), total reflection X‐ray fluorescence (TXRF) spectrometry, and inductively coupled plasma‐mass spectrometry (ICP‐MS) is also surveyed as a substantial amount of research is presently being performed to further improve the instrumental approach. In their turn, neutron activation analysis (NAA) and particle‐induced X-ray emission (PIXE) are nuclear techniques that were and are employed in this context. The application of ICP‐MS for fractionation studies based on the separation of particles according to their aerodynamic diameter and the use of chemical extraction for metal partitioning in airborne particulate matter and their speciation analysis are also discussed. This survey contains 91 references and covers primarily the literature published over the last decade.
State Entropy Maximization with Random Encoders for Efficient Exploration

Recent exploration methods have proven to be a recipe for improving sample-efficiency in deep reinforcement learning (RL). However, efficient exploration in high-dimensional observation spaces still remains a challenge. This paper presents Random Encoders for Efficient Exploration (RE3), an exploration method that utilizes state entropy as an intrinsic reward. In order to estimate state entropy in environments with high-dimensional observations, we utilize a k-nearest neighbor entropy estimator in the low-dimensional representation space of a convolutional encoder. In particular, we find that the state entropy can be estimated in a stable and compute-efficient manner by utilizing a randomly initialized encoder, which is fixed throughout training. Our experiments show that RE3 significantly improves the sample-efficiency of both model-free and model-based RL methods on locomotion and navigation tasks from DeepMind Control Suite and MiniGrid benchmarks. We also show that RE3 allows learning diverse behaviors without extrinsic rewards, effectively improving sample-efficiency in downstream tasks. Source code and videos are available at https://sites.google. com/view/re3-rl.
Counterfactual Inference of the Mean Outcome under a Convergence of Average Logging Probability

Adaptive experiments, including efficient average treatment effect estimation and multi-armed bandit algorithms, have garnered attention in various applications, such as social experiments, clinical trials, and online advertisement optimization. This paper considers estimating the mean outcome of an action from samples obtained in adaptive experiments. In causal inference, the mean outcome of an action has a crucial role, and the estimation is an essential task, where the average treatment effect estimation and off-policy value estimation are its variants. In adaptive experiments, the probability of choosing an action (logging probability) is allowed to be sequentially updated based on past observations. Due to this logging probability depending on the past observations, the samples are often not independent and identically distributed (i.i.d.), making developing an asymptotically normal estimator difficult. A typical approach for this problem is to assume that the logging probability converges in a time-invariant function. However, this assumption is restrictive in various applications, such as when the logging probability fluctuates or becomes zero at some periods. To mitigate this limitation, we propose another assumption that the average logging probability converges to a time-invariant function and show the doubly robust (DR) estimator's asymptotic normality. Under the assumption, the logging probability itself can fluctuate or be zero for some actions. We also show the empirical properties by simulations.
Boosting for Online Convex Optimization

We consider the decision-making framework of online convex optimization with a very large number of experts. This setting is ubiquitous in contextual and reinforcement learning problems, where the size of the policy class renders enumeration and search within the policy class infeasible. Instead, we consider generalizing the methodology of online boosting. We define a weak learning algorithm as a mechanism that guarantees multiplicatively approximate regret against a base class of experts. In this access model, we give an efficient boosting algorithm that guarantees near-optimal regret against the convex hull of the base class. We consider both full and partial (a.k.a. bandit) information feedback models. We also give an analogous efficient boosting algorithm for the i.i. d. statistical setting. Our results simultaneously generalize online boosting and gradient boosting guarantees to contextual learning model, online convex optimization and bandit linear optimization settings.
Improving Hierarchical Adversarial Robustness of Deep Neural Networks

Do all adversarial examples have the same consequences? An autonomous driving system misclassifying a pedestrian as a car may induce a far more dangerous -- and even potentially lethal -- behavior than, for instance, a car as a bus. In order to better tackle this important problematic, we introduce the concept of hierarchical adversarial robustness. Given a dataset whose classes can be grouped into coarse-level labels, we define hierarchical adversarial examples as the ones leading to a misclassification at the coarse level. To improve the resistance of neural networks to hierarchical attacks, we introduce a hierarchical adversarially robust (HAR) network design that decomposes a single classification task into one coarse and multiple fine classification tasks, before being specifically trained by adversarial defense techniques. As an alternative to an end-to-end learning approach, we show that HAR significantly improves the robustness of the network against $\ell_2$ and $\ell_{\infty}$ bounded hierarchical attacks on the CIFAR-10 and CIFAR-100 dataset.
Truncation-Free Matching System for Display Advertising at Alibaba

Matching module plays a critical role in display advertising systems. Without query from user, it is challenging for system to match user traffic and ads suitably. System packs up a group of users with common properties such as the same gender or similar shopping interests into a crowd. Here term crowd can be viewed as a tag over users. Then advertisers bid for different crowds and deliver their ads to those targeted users. Matching module in most industrial display advertising systems follows a two-stage paradigm. When receiving a user request, matching system (i) finds the crowds that the user belongs to; (ii) retrieves all ads that have targeted those crowds. However, in applications such as display advertising at Alibaba, with very large volumes of crowds and ads, both stages of matching have to truncate the long-tailed parts for online serving, under limited latency. That's to say, not all ads have the chance to participate in online matching. This results in sub-optimal result for both advertising performance and platform revenue. In this paper, we study the truncation problem and propose a Truncation Free Matching System (TFMS). The basic idea is to decouple the matching computation from the online pipeline. Instead of executing the two-stage matching when user visits, TFMS utilizes a near-line truncation-free matching to pre-calculate and store those top valuable ads for each user. Then the online pipeline just needs to fetch the pre-stored ads as matching results. In this way, we can jump out of online system's latency and computation cost limitations, and leverage flexible computation resource to finish the user-ad matching. TFMS has been deployed in our productive system since 2019, bringing (i) more than 50% improvement of impressions for advertisers who encountered truncation before, (ii) 9.4% Revenue Per Mile gain, which is significant enough for the business.
Alternative Chirp Spread Spectrum Techniques for LPWANs

Chirp spread spectrum (CSS) is the modulation technique currently employed by Long-Range (LoRa), which is one of the most prominent Internet of Things wireless communications standards. The LoRa physical layer (PHY) employs CSS on top of a variant of frequency shift keying, and non-coherent detection is employed at the receiver. While it offers a good trade-off among coverage, data rate and device simplicity, its maximum achievable data rate is still a limiting factor for some applications. Moreover, the current LoRa standard does not fully exploit the CSS generic case, i.e., when data to be transmitted is encoded in different waveform parameters. Therefore, the goal of this paper is to investigate the performance of CSS while exploring different parameter settings aiming to increase the maximum achievable throughput, and hence increase spectral efficiency. Moreover, coherent and non-coherent reception algorithm design is presented under the framework of maximum likelihood estimation. For the practical receiver design, the formulation of a channel estimation technique is also presented. The performance evaluation of the different variants of CSS is carried out by inspection of the symbol error ratio as a function of the signal-to-noise ratio together with the maximum achievable throughput each scheme can achieve.
StablePose: Learning 6D Object Poses from Geometrically Stable Patches

We introduce the concept of geometric stability to the problem of 6D object pose estimation and propose to learn pose inference based on geometrically stable patches extracted from observed 3D point clouds. According to the theory of geometric stability analysis, a minimal set of three planar/cylindrical patches are geometrically stable and determine the full 6DoFs of the object pose. We train a deep neural network to regress 6D object pose based on geometrically stable patch groups via learning both intra-patch geometric features and inter-patch contextual features. A subnetwork is jointly trained to predict per-patch poses. This auxiliary task is a relaxation of the group pose prediction: A single patch cannot determine the full 6DoFs but is able to improve pose accuracy in its corresponding DoFs. Working with patch groups makes our method generalize well for random occlusion and unseen instances. The method is easily amenable to resolve symmetry ambiguities. Our method achieves the state-of-the-art results on public benchmarks compared not only to depth-only but also to RGBD methods. It also performs well in category-level pose estimation.
Coordinated Receding-Horizon Control of Battery Electric Vehicle Speed and Gearshift Using Relaxed Mixed-Integer Nonlinear Programming

In this article, we investigate coordinated receding-horizon control of vehicle speed and transmission gearshift for energy-efficient operation of automated battery electric vehicles (BEVs). The introduction of multispeed transmissions in BEVs enables manipulation of electric motor operating point under given vehicle speed and acceleration command, thus creating the opportunity to further improve BEV energy efficiency. However, co-optimizing vehicle speed and transmission gearshift leads to a mixed-integer nonlinear programming (MINLP) problem, and it is well known that solving MINLP problems is computationally very challenging. To address this challenge, we propose a novel continuous relaxation technique that enables the computation of solutions to the speed and gearshift co-optimization problem using off-the-shelf nonlinear programming solvers. After analyzing theoretical properties of the proposed relaxation technique, we demonstrate its effectiveness through simulation-based case studies, where we show that co-optimizing vehicle speed and transmission gearshift can lead to considerably greater energy efficiency than optimizing them separately or sequentially and the proposed relaxation technique can reduce the computational cost of the co-optimization problem to a level that is comparable to the time budget available for onboard implementation.
Optimizing Large-Scale Hyperparameters via Automated Learning Algorithm

Modern machine learning algorithms usually involve tuning multiple (from one to thousands) hyperparameters which play a pivotal role in terms of model generalizability. Black-box optimization and gradient-based algorithms are two dominant approaches to hyperparameter optimization while they have totally distinct advantages. How to design a new hyperparameter optimization technique inheriting all benefits from both approaches is still an open problem. To address this challenging problem, in this paper, we propose a new hyperparameter optimization method with zeroth-order hyper-gradients (HOZOG). Specifically, we first exactly formulate hyperparameter optimization as an A-based constrained optimization problem, where A is a black-box optimization algorithm (such as deep neural network). Then, we use the average zeroth-order hyper-gradients to update hyperparameters. We provide the feasibility analysis of using HOZOG to achieve hyperparameter optimization. Finally, the experimental results on three representative hyperparameter (the size is from 1 to 1250) optimization tasks demonstrate the benefits of HOZOG in terms of simplicity, scalability, flexibility, effectiveness and efficiency compared with the state-of-the-art hyperparameter optimization methods.
Self-Organized Criticality in the Brain

Self-organized criticality (SOC) refers to the ability of complex systems to evolve toward a second-order phase transition at which interactions between system components lead to scale-invariant events that are beneficial for system performance. For the last two decades, considerable experimental evidence has accumulated that the mammalian cortex with its diversity in cell types, interconnectivity, and plasticity might exhibit SOC. Here, we review the experimental findings of isolated, layered cortex preparations to self-organize toward four dynamical motifs presently identified in the intact cortex in vivo: up-states, oscillations, neuronal avalanches, and coherence potentials. During up-states, the synchronization observed for nested theta/gamma oscillations embeds scale-invariant neuronal avalanches, which can be identified by robust power law scaling in avalanche sizes with a slope of −3/2 and a critical branching parameter of 1. This precise dynamical coordination, tracked in the negative transients of the local field potential (nLFP) and spiking activity of pyramidal neurons using two-photon imaging, emerges autonomously in superficial layers of organotypic cortex cultures and acute cortex slices, is homeostatically regulated, exhibits separation of time scales, and reveals unique size vs. quiet time dependencies. A subclass of avalanches, the coherence potentials, exhibits precise maintenance of the time course in propagated local synchrony. Avalanches emerge in superficial layers of the cortex under conditions of strong external drive. The balance of excitation and inhibition (E/I), as well as neuromodulators such as dopamine, establishes powerful control parameters for avalanche dynamics. This rich dynamical repertoire is not observed in dissociated cortex cultures, which lack the differentiation into cortical layers and exhibit a dynamical phenotype expected for a first-order phase transition. The precise interactions between up-states, nested oscillations, and avalanches in superficial layers of the cortex provide compelling evidence for SOC in the brain.
Gifsplanation via Latent Shift: A Simple Autoencoder Approach to Progressive Exaggeration on Chest X-rays

Motivation: Traditional image attribution methods struggle to satisfactorily explain predictions of neural networks. Prediction explanation is important, especially in the medical imaging, for avoiding the unintended consequences of deploying AI systems when false positive predictions can impact patient care. Thus, there is a pressing need to develop improved models for model explainability and introspection. Specific Problem: A new approach is to transform input images to increase or decrease features which cause the prediction. However, current approaches are difficult to implement as they are monolithic or rely on GANs. These hurdles prevent wide adoption. Our approach: Given an arbitrary classifier, we propose a simple autoencoder and gradient update (Latent Shift) that can transform the latent representation of an input image to exaggerate or curtail the features used for prediction. We use this method to study chest X-ray classifiers and evaluate their performance. We conduct a reader study with two radiologists assessing 240 chest X-ray predictions to identify which ones are false positives (half are) using traditional attribution maps or our proposed method. Results: We found low overlap with ground truth pathology masks for models with reasonably high accuracy. However, the results from our reader study indicate that these models are generally looking at the correct features. We also found that the Latent Shift explanation allows a user to have more confidence in true positive predictions compared to traditional approaches (0.15$\pm$0.95 in a 5 point scale with p=0.01) with only a small increase in false positive predictions (0.04$\pm$1.06 with p=0.57). Accompanying webpage: https://mlmed.org/gifsplanation Source code: https://github.com/mlmed/gifsplanation
Automated Detection of Equine Facial Action Units

The recently developed Equine Facial Action Coding System (EquiFACS) provides a precise and exhaustive, but laborious, manual labelling method of facial action units of the horse. To automate parts of this process, we propose a Deep Learning-based method to detect EquiFACS units automatically from images. We use a cascade framework; we firstly train several object detectors to detect the predefined Region-of-Interest (ROI), and secondly apply binary classifiers for each action unit in related regions. We experiment with both regular CNNs and a more tailored model transferred from human facial action unit recognition. Promising initial results are presented for nine action units in the eye and lower face regions. Code for the project is publicly available.