Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient falsification approach for autonomous vehicle validation using a parameter optimisation technique based on reinforcement learning

The widescale deployment of Autonomous Vehicles (AV) appears to be imminent despite many safety challenges that are yet to be resolved. It is well-known that there are no universally agreed Verification and Validation (VV) methodologies guarantee absolute safety, which is crucial for the acceptance of this technology. The uncertainties in the behaviour of the traffic participants and the dynamic world cause stochastic reactions in advanced autonomous systems. The addition of ML algorithms and probabilistic techniques adds significant complexity to the process for real-world testing when compared to traditional methods. Most research in this area focuses on generating challenging concrete scenarios or test cases to evaluate the system performance by looking at the frequency distribution of extracted parameters as collected from the real-world data. These approaches generally employ Monte-Carlo simulation and importance sampling to generate critical cases. This paper presents an efficient falsification method to evaluate the System Under Test. The approach is based on a parameter optimisation problem to search for challenging scenarios. The optimisation process aims at finding the challenging case that has maximum return. The method applies policy-gradient reinforcement learning algorithm to enable the learning. The riskiness of the scenario is measured by the well established RSS safety metric, euclidean distance, and instance of a collision. We demonstrate that by using the proposed method, we can more efficiently search for challenging scenarios which could cause the system to fail in order to satisfy the safety requirements.
Ambient heat and human sleep

Ambient temperatures are rising globally, with the greatest increases recorded at night. Concurrently, the prevalence of insufficient sleep is increasing in many populations, with substantial costs to human health and well-being. Even though nearly a third of the human lifespan is spent asleep, it remains unknown whether temperature and weather impact objective measures of sleep in real-world settings, globally. Here we link billions of sleep measurements from wearable devices comprising over 7 million nighttime sleep records across 68 countries to local daily meteorological data from 2015 to 2017. Rising nighttime temperatures shorten within-person sleep duration primarily through delayed onset, increasing the probability of insufficient sleep. The effect of temperature on sleep loss is substantially larger for residents from lower income countries and older adults, and females are affected more than are males. Nighttime temperature increases inflict the greatest sleep loss during summer and fall months, and we do not find evidence of short-term acclimatization. Coupling historical behavioral measurements with output from climate models, we project that climate change will further erode human sleep, producing substantial geographic inequalities. Our findings have significant implications for adaptation planning and illuminate a pathway through which rising temperatures may globally impact public health.
The generalized Cartan decomposition for classical random matrix ensembles

We present a completed classification of the classical random matrix ensembles: Hermite (Gaussian), Laguerre (Wishart), Jacobi (MANOVA) and Circular by introducing the concept of the generalized Cartan decomposition and the double coset space. Previous authors associate a symmetric space $G/K$ with a random matrix density on the double coset structure $K\backslash G/K$. However this is incomplete. Complete coverage requires the double coset structure $A = K_1\backslash G/K_2$, where $G/K_1$ and $G/K_2$ are two symmetric spaces. Furthermore, we show how the matrix factorization obtained by the generalized Cartan decomposition $G = K_1AK_2$ plays a crucial role in sampling algorithms and the derivation of the joint probability density of $A$.
Almost Tight L0-norm Certified Robustness of Top-k Predictions against Adversarial Perturbations

Top-$k$ predictions are used in many real-world applications such as machine learning as a service, recommender systems, and web searches. $\ell_0$-norm adversarial perturbation characterizes an attack that arbitrarily modifies some features of an input such that a classifier makes an incorrect prediction for the perturbed input. $\ell_0$-norm adversarial perturbation is easy to interpret and can be implemented in the physical world. Therefore, certifying robustness of top-$k$ predictions against $\ell_0$-norm adversarial perturbation is important. However, existing studies either focused on certifying $\ell_0$-norm robustness of top-$1$ predictions or $\ell_2$-norm robustness of top-$k$ predictions. In this work, we aim to bridge the gap. Our approach is based on randomized smoothing, which builds a provably robust classifier from an arbitrary classifier via randomizing an input. Our major theoretical contribution is an almost tight $\ell_0$-norm certified robustness guarantee for top-$k$ predictions. We empirically evaluate our method on CIFAR10 and ImageNet. For instance, our method can build a classifier that achieves a certified top-3 accuracy of 69.2\% on ImageNet when an attacker can arbitrarily perturb 5 pixels of a testing image.
Fact-checking via Path Embedding and Aggregation

Knowledge graphs (KGs) are a useful source of background knowledge to (dis)prove facts of the form (s, p, o). Finding paths between s and o is the cornerstone of several fact-checking approaches. While paths are useful to (visually) explain why a given fact is true or false, it is not completely clear how to identify paths that are most relevant to a fact, encode them and weigh their importance. The goal of this paper is to present the Fact Checking via path Embedding and Aggregation (FEA) system. FEA starts by carefully collecting the paths between s and o that are most semantically related to the domain of p. However, instead of directly working with this subset of all paths, it learns vectorized path representations, aggregates them according to different strategies, and use them to finally (dis)prove a fact. We conducted a large set of experiments on a variety of KGs and found that our hybrid solution brings some benefits in terms of performance.
Probing Saraswati’s heart: evaluating the dynamical state of the massive galaxy cluster A2631 through a comprehensive weak-lensing and dynamical analysis

In this work, we investigate the dynamical state of the galaxy cluster Abell 2631, a massive structure located at the core of the Saraswati supercluster. To do this, we first solve a tension found in the literature regarding the weak-lensing mass determination of the cluster. We do this through a comprehensive weak-lensing analysis, exploring the power of the combination of shear and magnification data sets. We find $M_{200}^{\rm wl} = 8. 7_{-2.9}^{+2.5} \times 10^{14}$ M⊙. We also determined the mass based on the dynamics of spectroscopic members, corresponding to $M_{200}^{\rm dy} = 12.2\pm 3.0 \times 10^{14}$ M⊙, consistent within a 68 per cent CL with the weak-lensing estimate. The scenarios provided by the mass distribution and dynamics of galaxies are reconciled with those provided by X-ray observations in a scenario where A2631 is observed at a late stage of merging.
Combining the Mersenne Twister and the Xorgens Designs

We combine the design of two \emph{random number generators}, \emph{Mersenne Twister} and \emph{Xorgens}, to obtain a new class of generators with heavy-weight characteristic polynomials (exceeded only by the {\sc well} generators) and high speed (comparable with the originals). Tables with parameter combinations are included for state sizes ranging from 521 to 44497 bits and each of the word lengths 32, 64, 128. These generators passed all tests of the \emph{TestU01}-package for each 32-bit integer part and each 64-bit derived real part of the output. We determine \emph{dimension gaps} for 32-bit words, neglecting the non-linear tempering, and compare with an alternative experimental linear tempering.
Unsupervised Domain Adaptive Knowledge Distillation for Semantic Segmentation

Practical autonomous driving systems face two crucial challenges: memory constraints and domain gap issues. We present an approach to learn domain adaptive knowledge in models with limited memory, thus bestowing the model with the ability to deal with these issues in a comprehensive manner. We delve into this in the context of unsupervised domain-adaptive semantic segmentation and propose a multi-level distillation strategy to effectively distil knowledge at different levels. Further, we introduce a cross entropy loss that leverages pseudo labels from the teacher. These pseudo teacher labels play a multifaceted role towards: (i) knowledge distillation from the teacher network to the student network & (ii) serving as a proxy for the ground truth for target domain images, where the problem is completely unsupervised. We introduce four paradigms for distilling domain adaptive knowledge and carry out extensive experiments and ablation studies on real-to-real and synthetic-to-real scenarios. Our experiments demonstrate the profound success of our proposed method.
Phenomenological gravitational phase transition: Reconciliation between the late and early Universe

In this work we investigate whether a certain phenomenological extension to the general relativity (GR), in the form of a gravitational phase transition (GPT) in the Universe, can reduce the external $Planck$ tensions with the local Hubble measurements and the distribution of matter, characterized by $\sigma_8$, as well as its internal inconsistencies in the lensing amplitude and the low--high $\ell$ parameter estimates. We introduce new degrees of freedom into the background and the two scalar perturbation equations in the Newtonian gauge, with simultaneous transitions from an early gravitational phase equivalent to GR toward a late phase. We model the transition as a "tanh" parametrized by the transition redshift $z_{\rm t}$ and width $\alpha$, with amplitudes $\Lambda(z)$ and $(\mu(z),\gamma(z))$ for the background and perturbations respectively. We verify the consistency of the datasets used in this work in the GPT framework and confirm that the individual tensions do not require conflicting transitions. We find that the joint datasets prefer a recent transition at $z_{\rm t}\approx 0.9$ in the background and perturbed Einstein equations, driven mainly by the local Hubble measurement. This transition relaxes all the tensions considered in this work.
Application of Virtualization Technologies in Novel Industrial Automation: Catalyst or Show-Stopper?

Industry 4.0 describes an adaptive and changeable production, where its factory cells have to be reconfigured at very short intervals, e.g. after each workpiece. Furthermore, this scenario cannot be realized with traditional devices, such as programmable logic controllers. Here the use of well-proven technologies of the information technology are conquering the production hall (IT-OT convergence). Therefore, both virtualization and novel communication technologies are being introduced in the field of industrial automation. In addition, these technologies are seen as the key for facilitating various emerging use cases. However, it is not yet clear whether each of the dedicated hardware and software components, which have been developed for specific control tasks and have performed well over decades, can be upgraded without major adjustments. In this paper, we examine the opportunities and challenges of hardware and operating system-level virtualization based on the stringent requirements imposed by industrial applications. For that purpose, benchmarks for different virtualization technologies are set by determining their computational and networking overhead, configuration effort, accessibility, scalability, and security.
Mixing ADAM and SGD: a Combined Optimization Method

Optimization methods (optimizers) get special attention for the efficient training of neural networks in the field of deep learning. In literature there are many papers that compare neural models trained with the use of different optimizers. Each paper demonstrates that for a particular problem an optimizer is better than the others but as the problem changes this type of result is no longer valid and we have to start from scratch. In our paper we propose to use the combination of two very different optimizers but when used simultaneously they can overcome the performances of the single optimizers in very different problems. We propose a new optimizer called MAS (Mixing ADAM and SGD) that integrates SGD and ADAM simultaneously by weighing the contributions of both through the assignment of constant weights. Rather than trying to improve SGD or ADAM we exploit both at the same time by taking the best of both. We have conducted several experiments on images and text document classification, using various CNNs, and we demonstrated by experiments that the proposed MAS optimizer produces better performance than the single SGD or ADAM optimizers. The source code and all the results of the experiments are available online at the following link this https URL\_optimizer
Proximal Left Main Coronary Artery Aneurysm Presenting as ST-Elevation Myocardial Infarction Treated by Stenting

Coronary artery aneurysm (CAA) is a rare cardiac anomaly with a reported incidence of 0.3-4.9% of patients who undergo coronary angiography. The term is used when the coronary artery diameter exceeds more than 50% or 1.5 times the reference diameter. It can be congenital or acquired. The commonest acquired cause in an adult is atherosclerosis and in a child is Kawasaki's disease. The commonest culprit vessel is the Right Coronary Artery (RCA), followed by Left Circumflex (LCx) and Left Anterior Descending (LAD). Left main coronary aneurysms are extremely rare in clinical practice. Coronary angiography is the gold standard procedure, both for diagnosis and treatment. We report a 49-year-old male who presented with anterior wall ST-Elevation Myocardial Infarction (STEMI). The initial angiography showed LAD stent thrombosis, but when the second angiography was done, there was spontaneous recanalization of the LAD. Coronary angiography was performed at our hospital, which revealed a long left main coronary artery aneurysm measuring 9.8 mm—maximum diameter. This was treated with a size 5 × 24 mm Begraft coronary stent.
A proposal for clinical trials of COVID-19 treatment using homo-harringtonine

A scheme for treating COVID-19 was published in NSR in April [1] based on blocking the translation of very large viral proteins. Since then, additional supporting data have greatly strengthened the proposal. The drug, HHT (homoharringtonine, or omacetaxine), is readily available and inexpensive as it has been approved since 2012 for treating leukemia. The in vitro and in vivo data clearly justify the planning of clinical trials. Given the specificmechanism, we believe a trial of modest scale would be sufficient to prove, or disprove, the efficacy of the treatment scheme. Such trials, however, are only feasible in regions with many new infections. This letter, commenting on Wu and Wen [1], has an expanded authorship that includes physicians with first-hand experience in treating COVID-19 patients and virologists with recent publications on SARS-CoV-2. We wish to bring this proposal to the attentionof the global community as there are few new cases in China for such trials.
Improving efficiency of inference in clinical trials with external control data

Suppose we are interested in the effect of a treatment in a clinical trial. The efficiency of inference may be limited due to small sample size. However, external control data are often available from historical studies. Motivated by an application to Helicobacter pylori infection, we show how to borrow strength from such data to improve efficiency of inference in the clinical trial. Under an exchangeability assumption about the potential outcome mean, we show that the semiparametric efficiency bound for estimating the average treatment effect can be reduced by incorporating both the clinical trial data and external controls. We then derive a doubly robust and locally efficient estimator. The improvement in efficiency is prominent especially when the external control data set has a large sample size and small variability. Our method allows for a relaxed overlap assumption, and we illustrate with the case where the clinical trial only contains a treated group. We also develop doubly robust and locally efficient approaches that extrapolate the causal effect in the clinical trial to the external population and the overall population. Our results also offer a meaningful implication for trial design and data collection. We evaluate the finite‐sample performance of the proposed estimators via simulation. In the Helicobacter pylori infection application, our approach shows that the combination treatment has potential efficacy advantages over the triple therapy.
On the Transferability of VAE Embeddings using Relational Knowledge with Semi-Supervision

We propose a new model for relational VAE semi-supervision capable of balancing disentanglement and low complexity modelling of relations with different symbolic properties. We compare the relative benefits of relation-decoder complexity and latent space structure on both inductive and transductive transfer learning. Our results depict a complex picture where enforcing structure on semi-supervised representations can greatly improve zero-shot transductive transfer, but may be less favourable or even impact negatively the capacity for inductive transfer.
Emerging Role of Janus Kinase Inhibitors for the Treatment of Atopic Dermatitis

Background Atopic dermatitis (AD) is a common chronic, inflammatory skin condition. The pathogenesis of AD involves many cytokines that utilize the Janus kinase/signal transducer and activator of transcription (JAK-STAT) signaling cascade; therefore, JAK inhibitors may be used in the treatment of AD. This review aims to evaluate the pathophysiology, efficacy, and safety of JAK inhibitors and their emerging role as a therapeutic option for patients with AD. Methods A PubMed search of Phase I, II, and III clinical trials was conducted for relevant literature published between January 2015 and June 2020 utilizing the key terms: JAK inhibitors, atopic dermatitis, efficacy, safety, and treatment. The search was subsequently expanded to include additional terms. Results In multiple Phase II and III clinical trials, JAK inhibitors were more efficacious than placebo or vehicle controls and slightly more efficacious in direct comparisons to corticosteroids. Overall, JAK inhibitors have a moderate safety profile for use in AD. Some of the more severe theoretical adverse events included thrombosis and reactivation of viral infections. While data remain limited for the long-term efficacy and safety of JAK inhibitor use in patients with AD, many ongoing clinical trials have promising preliminary results. Discussion Short-term data suggest that both topical and oral JAK inhibitors are efficacious and safe for use in patients with AD, although cases of thrombosis and viral disease have been reported. While the current standard treatments for AD are likely preferred, failed therapy with these agents or corticosteroid phobia may be indications for the use of JAK inhibitors in patients with AD.
Ego2Hands: A Dataset for Egocentric Two-hand Segmentation and Detection

Hand segmentation and detection in truly unconstrained RGB-based settings is important for many applications. However, existing datasets are far from sufficient both in terms of size and variety due to the infeasibility of manual annotation of large amounts of segmentation and detection data. As a result, current methods are limited by many underlying assumptions such as constrained environment, consistent skin color and lighting. In this work, we present a large-scale RGB-based egocentric hand segmentation/detection dataset Ego2Hands that is automatically annotated and a color-invariant compositing-based data generation technique capable of creating unlimited training data with variety. For quantitative analysis, we manually annotated an evaluation set that significantly exceeds existing benchmarks in quantity, diversity and annotation accuracy. We show that our dataset and training technique can produce models that generalize to unseen environments without domain adaptation. We introduce Convolutional Segmentation Machine (CSM) as an architecture that better balances accuracy, size and speed and provide thorough analysis on the performance of state-of-the-art models on the Ego2Hands dataset.
Few-shot Object Grounding and Mapping for Natural Language Robot Instruction Following

We study the problem of learning a robot policy to follow natural language instructions that can be easily extended to reason about new objects. We introduce a few-shot language-conditioned object grounding method trained from augmented reality data that uses exemplars to identify objects and align them to their mentions in instructions. We present a learned map representation that encodes object locations and their instructed use, and construct it from our few-shot grounding output. We integrate this mapping approach into an instruction-following policy, thereby allowing it to reason about previously unseen objects at test-time by simply adding exemplars. We evaluate on the task of learning to map raw observations and instructions to continuous control of a physical quadcopter. Our approach significantly outperforms the prior state of the art in the presence of new objects, even when the prior approach observes all objects during training.
Model-Driven Synthesis for Programming Tutors

When giving automated feedback to a student working on a beginner's exercise, many programming tutors run into a completeness problem. On the one hand, we want a student to experiment freely. On the other hand, we want a student to write her program in such a way that we can provide constructive feedback. We propose to investigate how we can overcome this problem by using program synthesis, which we use to generate correct solutions that closely match a student program, and give feedback based on the results.
LUCIDGames: Online Unscented Inverse Dynamic Games for Adaptive Trajectory Prediction and Planning

Existing game-theoretic planning methods assume that the robot knows the objective functions of the other agents a priori while, in practical scenarios, this is rarely the case. This letter introduces LUCIDGames, an inverse optimal control algorithm that is able to estimate the other agents’ objective functions in real time, and incorporate those estimates online into a receding-horizon game-theoretic planner. LUCIDGames solves the inverse optimal control problem by recasting it in a recursive parameter-estimation framework. LUCIDGames uses an unscented Kalman filter (UKF) to iteratively update a Bayesian estimate of the other agents’ cost function parameters, improving that estimate online as more data is gathered from the other agents’ observed trajectories. The planner then takes account of the uncertainty in the Bayesian parameter estimates of other agents by planning a trajectory for the robot subject to uncertainty ellipse constraints. The algorithm assumes no explicit communication or coordination between the robot and the other agents in the environment. An MPC implementation of LUCIDGames demonstrates real-time performance on complex autonomous driving scenarios with an update frequency of 40 Hz. Empirical results demonstrate that LUCIDGames improves the robot's performance over existing game-theoretic and traditional MPC planning approaches. Our implementation of LUCIDGames is available at https://github.com/RoboticExplorationLab/LUCIDGames.jl.