Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comparison of Uncertainty Estimation Approaches in Deep Learning Components for Autonomous Vehicle Applications

A key factor for ensuring safety in Autonomous Vehicles (AVs) is to avoid any abnormal behaviors under undesirable and unpredicted circumstances. As AVs increasingly rely on Deep Neural Networks (DNNs) to perform safety-critical tasks, different methods for uncertainty quantification have recently been proposed to measure the inevitable source of errors in data and models. However, uncertainty quantification in DNNs is still a challenging task. These methods require a higher computational load, a higher memory footprint, and introduce extra latency, which can be prohibitive in safety-critical applications. In this paper, we provide a brief and comparative survey of methods for uncertainty quantification in DNNs along with existing metrics to evaluate uncertainty predictions. We are particularly interested in understanding the advantages and downsides of each method for specific AV tasks and types of uncertainty sources.
Bottom-Up Human Pose Estimation by Ranking Heatmap-Guided Adaptive Keypoint Estimates

The typical bottom-up human pose estimation framework includes two stages, keypoint detection and grouping. Most existing works focus on developing grouping algorithms, e.g., associative embedding, and pixel-wise keypoint regression that we adopt in our approach. We present several schemes that are rarely or unthoroughly studied before for improving keypoint detection and grouping (keypoint regression) performance. First, we exploit the keypoint heatmaps for pixel-wise keypoint regression instead of separating them for improving keypoint regression. Second, we adopt a pixel-wise spatial transformer network to learn adaptive representations for handling the scale and orientation variance to further improve keypoint regression quality. Last, we present a joint shape and heatvalue scoring scheme to promote the estimated poses that are more likely to be true poses. Together with the tradeoff heatmap estimation loss for balancing the background and keypoint pixels and thus improving heatmap estimation quality, we get the state-of-the-art bottom-up human pose estimation result. Code is available at this https URL.
Energy-Efficient UAV Relaying Robust Resource Allocation in Uncertain Adversarial Networks

The mobile relaying technique is a critical enhancing technology in wireless communications due to a higher chance of supporting the remote user from the base station (BS) with better service quality. This paper investigates energy-efficient (EE) mobile relaying networks mounted on the unmanned aerial vehicle (UAV) while the unknown adversaries try to intercept the legitimate link. We aim to optimize robust transmit power, both UAV and BS along, with relay hovering path, speed, and acceleration. The BS sends legitimate information, which is forwarded to the user by the relay. This procedure is defined as information-causality-constraint (ICC). We jointly optimize the worst-case secrecy rate (WCSR) and UAV propulsion energy consumption (PEC) for a finite time horizon. We construct the BS-UAV, the UAV-user, and the UAV-adversary channel models. We apply the UAV PEC considering UAV speed and acceleration. We derive EE UAV relay-user maximization problem in the adversarial wireless networks at last. While the problem is non-convex, we propose an iterative and sub-optimal algorithm to optimize EE UAV relay with constraints, such as ICC, trajectory, speed, acceleration, and transmit power. First, we optimize both BS and UAV transmit power and hovering speed for known UAV path planning and acceleration. Using the optimal transmit power and speed, we obtain the optimal trajectory and acceleration. We compare our algorithm with existing algorithms and demonstrate the improved EE UAV relaying communication for our model.
Quantum engineering of superdark excited states in arrays of atoms

We suggest a regular method of achieving an extremely long lifetime of a collective singly excited state in a generic small-size ensemble of N identical atoms. The decay rate \Gamma_N of such a `superdark' state can be as small as \Gamma_N \propto \Gamma(r/\lambda)^{2(N-1)} (\Gamma is the radiative decay rate of an individual atom, r and \lambda are the system size and the wavelength of the radiation, respectively), i.e., considerably smaller than in any of the systems suggested up to now. The method is based on a special fine tuning of the atomic Hamiltonian: namely, on a proper position-dependent adjustment of atomic transition frequencies. So chosen set of the control parameters is sufficient to ensure the minimum of the spontaneous decay rate of the engineered state in a generic ensemble of atoms (`qubits').
Network broadcast mode analysis and control of turbulent flows

We present a network-based modal analysis that identifies the key dynamical paths along which perturbations amplify in an isotropic turbulent flow. This analysis is built upon the Katz centrality, which reveals the flow structures that can effectively spread perturbations over the time-evolving network of vortical elements that constitute the turbulent flow field. Motivated by the resolvent form of the Katz function, we take the singular value decomposition of the resulting communicability matrix, complementing resolvent analysis. The right-singular vector, referred to as the broadcast mode, gives insights into the sensitive regions where introduced perturbations can be effectively spread over the entire fluid-flow network as it evolves over time. The broadcast mode reveals that vortex dipoles are the important structures in spreading perturbations. By perturbing the flow with the broadcast mode, we demonstrate its capability to effectively modify the evolution of turbulent flows. The current network-inspired work presents a novel use of network analysis to guide flow control efforts, in particular for time-varying turbulent base flows.
FDA$^3$: Federated Defense Against Adversarial Attacks for Cloud-Based IIoT Applications

Along with the proliferation of artificial intelligence and Internet of things (IoT) techniques, various kinds of adversarial attacks are increasingly emerging to fool deep neural networks (DNNs) used by industrial IoT (IIoT) applications. Due to biased training data or vulnerable underlying models, imperceptible modifications on inputs made by adversarial attacks may result in devastating consequences. Although existing methods are promising in defending such malicious attacks, most of them can only deal with limited existing attack types, which makes the deployment of large-scale IIoT devices a great challenge. To address this problem, in this article, we present an effective federated defense approach named $\text{FDA}^3$ that can aggregate defense knowledge against adversarial examples from different sources. Inspired by federated learning, our proposed cloud-based architecture enables the sharing of defense capabilities against different attacks among IIoT devices. Comprehensive experimental results show that the generated DNNs by our approach can not only resist more malicious attacks than existing attack-specific adversarial training methods, but also prevent IIoT applications from new attacks.
High Performance Evaluation of Helmholtz Potentials using the Multi-Level Fast Multipole Algorithm

Evaluation of pair potentials is critical in a number of areas of physics. The classical N-body problem has its root in evaluating the Laplace potential, and has spawned tree-algorithms, the fast multipole method (FMM), as well as kernel independent approaches. Over the years, FMM for Laplace potential has had a profound impact on a number of disciplines as it has been possible to develop highly scalable parallel versions of these algorithms. This is in stark contrast to parallel algorithms for oscillatory potentials such as the Helmholtz potential. The principal bottlenecks to scalable parallelism are the computation and communication costs of operations necessary to traverse up, across, and down the tree. In this paper, we analyze asymptotic costs for both computation and communication in a parallel implementation, and describe techniques to overcome bottlenecks and achieve high performance evaluation of the Helmholtz potential for different distributions of particles. We demonstrate that the resulting implementation has a load balancing effect that significantly reduces the time-to-solution and enhances the scale of problems that can be treated using full wave physics.
Emergent Vegetation Coverage and Human Activities Influence Oviposition Microhabitat Selection by Invasive Bullfrogs (Lithobates catesbeianus) in Southwestern China

Abstract Oviposition site selection is critical to increase offspring fitness for aquatic-breeding anurans, which generally lack posthatching parental care. Understanding factors that influence oviposition microhabitat choice would be helpful for managing and controlling invasive amphibians, such as the American Bullfrog (Lithobates catesbeianus), implicated in the decline of anuran populations across its invasive range. Factors that influence bullfrog oviposition microhabitat selection, however, remain mostly unknown. On the basis of field surveys from 2008 to 2013, we investigated 46 oviposition sites used by bullfrogs and 46 adjacent random unused sites and evaluated eight characteristics that could potentially influence oviposition site selection by bullfrogs in Caohai wetland, southwestern China, a global biodiversity conservation hot spot. We used model averaging of generalized linear mixed-effects models, using Akaike information criterion, hierarchical partitioning, and an ordination principal components analysis to analyze site selection. Results revealed that bullfrogs preferred waters with a high proportion of emergent plant coverage, whereas the distance to the nearest boat route decreased the probability of bullfrog oviposition site presence. Our study was one of the first quantitative studies on oviposition microhabitat selection by bullfrogs within their invaded ranges that combined abiotic, biotic, and human activities. We suggest that efforts to control and manage this global invader be directed toward reducing the suitability of breeding microhabitats by targeting areas with dense emergent vegetation, far from human disturbances.
Temporal variation in the behaviour of the Australian Pelican 'Pelecanus conspicillatus' in an urban wetland environment

The behaviour of the Australian Pelican Pelecanus conspicillatus was monitored over a 3-month period (August-October 1995) to determine the daily activity budgets at a man-made wetland in Cronulla, New South Wales. Data were collected on the proportion of the time-budgets devoted to performing behaviours (preening, roosting, foraging, and other). Time of day had a significant effect on behaviour, with the majority of Pelicans allocating the morning for preening and foraging, the middle of the day for foraging, and the afternoon for preening and roosting. In addition, early courtship behaviour was noted and compared with the courtship behaviour described in previous studies from rural localities in NSW.
The effect of Er,Cr: YSGG Laser Surface Treatment on Shear Bond Strength of Resin Cement to Zirconia Ceramic

Background: The study purposed to evaluate the effect of Er,Cr: YSGG Lasersurface treatment method on the shear bond strength (SBS) of resin cement to zirconia ceramic. Materials and Methods: Fifty composites and zirconia samples (4mm diameter and 4mm height) were prepared for each material. According to surface treatments, sintered zirconia samples were divided into five groups (n=10) as no treatment (NT; control), sandblasting (SB), laser treatment (LT), silica coating (CT), and 9.5% hydrofluoric acid-etching (HF). Composite cylinders resin samples bonded zirconia samples were with resin cement (Panavia F2.0; Kurayay, Okuyama, Tokyo, Japan). The shear bond strength test was performed with a universal test machine for each sample.Data were analyzed with one-way ANOVA and post hoc test (Tukey's). A statistically significant difference was found between the groups. Results:The highest value was in the laser abrasion group (32.43 ± 3.72), this group was not statistically significant from the silica coating group (28.29 ± 3.16). The sandblasting group (20.27 ± 2.33) and the acid etching group (11.58 ± 1.57) were also statistically different from other groups. Conclusion:Er,Cr : YSGG laser and silica coating treatments can be an alternative method to enhance the bond of resin cement to dental zirconia ceramic. Key Word: Shear Bond Strength, Zirconia, Silica Coating, Sandblasting, Er,Cr : YSGG Laser, Hydrofluoric Acid-etching -------------------------------------------------------------------------------------------------------------------------------------Date of Submission: 26-05-2020 Date of Acceptance: 13-06-2020 -------------------------------------------------------------------------------------------------------------------------------------
Two dimensional soliton in tumor induced angiogenesis

Ensemble averages of a stochastic model show that, after a formation stage, the tips of active blood vessels in an angiogenic network form a moving two dimensional stable diffusive soliton, which advances toward sources of growth factor. Here we use methods of multiple scales to find the diffusive soliton as a solution of a deterministic equation for the mean density of active endothelial cells tips. We characterize the diffusive soliton shape in a general geometry, and find that its vector velocity and the trajectory of its center of mass along curvilinear coordinates solve appropriate collective coordinate equations. The vessel tip density predicted by the soliton compares well with that obtained by ensemble averages of simulations of the stochastic model.
PCLNet: A Practical Way for Unsupervised Deep PolSAR Representations and Few-Shot Classification

Deep learning and convolutional neural networks (CNNs) have made progress in polarimetric synthetic aperture radar (PolSAR) image classification over the past few years. However, a crucial issue has not been addressed, i.e., the requirement of CNNs for abundant labeled samples versus the insufficient human annotations of PolSAR images. It is well-known that following the supervised learning paradigm may lead to the overfitting of training data, and the lack of supervision information of PolSAR images undoubtedly aggravates this problem, which greatly affects the generalization performance of CNN-based classifiers in large-scale applications. To handle this problem, in this paper, learning transferrable representations from unlabeled PolSAR data through convolutional architectures is explored for the first time. Specifically, a PolSAR-tailored contrastive learning network (PCLNet) is proposed for unsupervised deep PolSAR representation learning and few-shot classification. Different from the utilization of optical processing methods, a diversity stimulation mechanism is constructed to narrow the application gap between optics and PolSAR. Beyond the conventional supervised methods, PCLNet develops an auxiliary pre-training phase based on the proxy objective of contrastive instance discrimination to learn useful representations from unlabeled PolSAR data. The acquired representations are transferred to the downstream task, i.e., few-shot PolSAR classification. Experiments on two widely used PolSAR benchmark datasets confirm the validity of PCLNet. Besides, this work may enlighten how to efficiently utilize the massive unlabeled PolSAR data to alleviate the greedy demands of CNN-based methods for human annotations.
Modeling Generalization in Machine Learning: A Methodological and Computational Study

As machine learning becomes more and more available to the general public, theoretical questions are turning into pressing practical issues. Possibly, one of the most relevant concerns is the assessment of our confidence in trusting machine learning predictions. In many real-world cases, it is of utmost importance to estimate the capabilities of a machine learning algorithm to generalize, i.e., to provide accurate predictions on unseen data, depending on the characteristics of the target problem. In this work, we perform a meta-analysis of 109 publicly-available classification data sets, modeling machine learning generalization as a function of a variety of data set characteristics, ranging from number of samples to intrinsic dimensionality, from class-wise feature skewness to $F1$ evaluated on test samples falling outside the convex hull of the training set. Experimental results demonstrate the relevance of using the concept of the convex hull of the training data in assessing machine learning generalization, by emphasizing the difference between interpolated and extrapolated predictions. Besides several predictable correlations, we observe unexpectedly weak associations between the generalization ability of machine learning models and all metrics related to dimensionality, thus challenging the common assumption that the \textit{curse of dimensionality} might impair generalization in machine learning.
High order chiral Lagrangians with vector mesons in different approaches

The chiral Lagrangians with vector mesons are constructed in different approaches, including the next-to-leading order Lagrangian in the vector-field approach, the next-to-next-to-leading order Lagrangians in the tensor-field and the hidden local symmetry approaches. Some redundant terms are found at the next-to-leading order in the tensor-field and the hidden local symmetry approaches. The corresponding relationships between the next-to-next-to-leading order pseudoscalar mesonic low-energy constants and the ones in the hidden local symmetry approach are obtained at the tree level. The equivalence between the tensor-field approach and the hidden local symmetry approach is also discussed.
Title-loss of βcatenin is an independent prognostic factor in ovarian carcinomas: A multivariate analysis

Summary In ovarian carcinomas, numerous studies have shown consistent prognostic significance of FIGO stage and residual tumour as independent prognostic factors. However, these prognostic factors alone cannot accurately predict disease outcome since a considerable degree of heterogeneity remains within the various subgroups limiting the predictive value of these factors. Therefore, the identification of new molecular markers that may possibly distinguish patients at a higher risk is of great importance. In two previous studies, the individual loss of E-cadherin and the individual loss of β-catenin were important prognostic factors of poorer overall survival in patients with ovarian carcinomas. Purpose of the present study was to re-analyse the immunohistochemical expression of Ecadherin and β-catenin in 104 patients with ovarian carcinomas, and evaluate whether these two proteins continue
Community detection and percolation of information in a geometric setting

We make the first steps towards generalising the theory of stochastic block models, in the sparse regime, towards a model where the discrete community structure is replaced by an underlying geometry. We consider a geometric random graph over a homogeneous metric space where the probability of two vertices to be connected is an arbitrary function of the distance. We give sufficient conditions under which the locations can be recovered (up to an isomorphism of the space) in the sparse regime. Moreover, we define a geometric counterpart of the model of flow of information on trees, due to Mossel and Peres, in which one considers a branching random walk on a sphere and the goal is to recover the location of the root based on the locations of leaves. We give some sufficient conditions for percolation and for non-percolation of information in this model.
Molecular Cloning and Biological Activity of α-, β-, and γ-Megaspermin, Three Elicitins Secreted by Phytophthora megasperma H20

We report on the molecular cloning of thePhytophthora megasperma H20 (PmH20) glycoprotein shown previously as an inducer of the hypersensitive response, of localized acquired resistance and of systemic acquired resistance in tobacco (Nicotiana tabacum), and of the PmH20 α- and β-megaspermin, two elicitins of class I-A and I-B, respectively. The structure of the glycoprotein shows a signal peptide of 20 amino acids followed by the typical elicitin 98-amino acid-long domain and a 77-amino acid-long C-terminal domain carrying anO-glycosylated moiety. The molecular mass deduced from the translated cDNA sequence is 14,920 and 18,676 D as determined by mass spectrometry. This structure together with multiple sequence alignments and phylogenetic analyses indicate that the glycoprotein belongs to class III elicitins. It is the first class III elicitin protein characterized, which we named γ-megaspermin. We compared the biological activity of the three PmH20 elicitins when applied to tobacco cv Samsun NN plants. Although α- and γ-megaspermin were similarly active, β-megaspermin was the most active in inducing the hypersensitive response and localized acquired resistance, which was assessed by measuring the levels of acidic and basic pathogenesis-related proteins and of the antioxidant phytoalexin scopoletin. The three elicitins induced similar levels of systemic acquired resistance measured as the expression of acidic PR proteins and is increased resistance to challenge tobacco mosaic virus infection.
Differentially Expressed Proteins of Soybean (Glycine max) Pulvinus in Light and Dark Conditions

Abstract: Some plant species both track and avoid the sun through turgor changes of the pulvinus tissue at the base of their leaves, maximizing light reception in dim conditions and minimizing cellular damage due to excessive light. Pulvinar response is known to be affected by both diurnally varying environmental factors and circadian patterns. Differential expression of the proteins between light and darkness are not well-known. In this study we used two-dimensional gel electrophoresis and mass spectrometry to separate and identify proteins in the soybean leaf pulvinus that were differentially expressed in the light compared to a dark control. Out of 165 protein spots previously identified (data not shown) 11 were found to have decreased expression in the light and 7 had increased light expression. The proteins that were more highly expressed in the light were mostly stress response proteins, while the under-expressed proteins were categorized as energy proteins. While the higher levels of expression of stress response proteins in the light align with other studies, the under-expressed light proteins require further examination to rule out artefactual results. These findings can provide a better understanding of the circadian pattern of protein expression in the legume pulvinus proteome.