Miracle-dz/UniFAR-FTUs huggingface dataset:
Real-time unmanned aerial vehicle tracking of fast moving small target on ground

Abstract. To solve the problems of occlusion and fast motion of small targets in unmanned aerial vehicle target tracking, an adaptive algorithm that fuses the improved color histogram tracking response and the correlation filter tracking response based on multichannel histogram of oriented gradient features is proposed to realize small target tracking with high accuracy. The state judgment index is used to determine whether the target is in a fast motion or an occlusion state. In the fast motion state, the search area is enlarged, and the color optimal model that suppresses the suspected area is used for rough detection. Then, redetection in the location of multiple peaks in the rough detection response is carried out using the correlation filter to accurately locate the target. In an occlusion state, the model stops updating, the search area is expanded, and the current color model is used for rough detection. Then, redetection in the place of multiple peaks in the rough detection response is carried out using the correlation filter to accurately locate the target. Experimental results show that the proposed method can track small targets accurately. The frame rate of the proposed method is 40.23 frames/s, indicating usable real-time performance.
Three-dimensional alpha shapes

Frequently, data in scientific computing is in its abstract form a finite point set in space, and it is sometimes useful or required to compute what one might call the “shape” of the set. For that purpose this paper introduces the formal notion of the family of r-x-shapes of a finite point set in IR3. Each shape is a polytope, derived from the Delaunay triangulation of the point set, with a parameter cy E R controlling the desired level of detail. An algorithm is presented that constructs the entire family of shapes for a given set of size n in worst-case time O(n’). A robust implementation of the algorithm is discussed and seveml applications in the area of scientific computing are mentioned.
Boolean kernels for interpretable kernel machines

Most of the machine learning (ML) community’s eﬀorts in the last decades have been devoted to improving the power and the prediction quality of ML models at the expense of their interpretability. However, nowadays, ML is becoming more and more ubiquitous and it is increasingly demanded the need for models that can be interpreted. To this end, in this work we propose a method for extracting explanation rules from a kernel machine. The core idea is based on using kernels with feature spaces composed by logical propositions. On top of that, a searching algorithm tries to retrieve the most relevant features/rules that can be used to explain the trained model. Experiments on several benchmarks and artiﬁcial datasets show the eﬀectiveness of the proposed approach.
Yet Another Multidimensional Model for XML Documents

The Diamond model is a multidimensional model dedicated to XML document warehouses. It considers structured and unstructured data simultaneously. Furthermore, it orders the semantics of documents via a specific semantic dimension linked to conventional dimensions, thus breaking the classical orthogonality rule of dimensions. After giving an overview of their three-phase quasi-automatic approach for the generation of the diamond model, the authors focus on the Diamond-Gen software tool that supports the proposed approach. The authors illustrate the Diamond-Gen functionalities and assess it through an experimental study using a set of 1500 XML documents issued from the PubMed collection.
Feasibility of multi-tenancy on intermittent power

Energy harvesting and battery-free sensing devices show great promise for revolutionizing computing in the home, in the wild, and on the body. The promise of cheap, dense, and ubiquitous sensing technology brings new applications for the Internet of Things. However, the future programming model is blurry and complex. With a potential for trillions of devices, and thousands of devices per person on earth, programming languages and associated operating systems must be usable, flexible, and resource efficient. Because of the thousands of applications and fine grained differences in requirements, multi-tenancy may be a part of the solution to solving this programming model crisis. This paper explores the energy and resources costs, feasibility, and motivation for multi-tenancy on these tiniest of computing devices---namely the difficulties in scheduling tasks fairly, efficiently, and simply. Because of intermittent power, resources and energy must be mostly devoted towards user tasks, we implement a rudimentary operating system with low overhead to conduct experiments and test time-sharing and scheduling protocols. We close with a discussion on challenges to implementing a multi-tenant run-time on battery-free tags, and proposals for future work.
Hardware-accelerated Data Acquisition and Authentication for High-speed Video Streams on Future Heterogeneous Automotive Processing Platforms

With the increasing use of Ethernet-based communication backbones in safety-critical real-time domains, both efficient and predictable interfacing and cryptographically secure authentication of high-speed data streams are becoming very important. Although the increasing data rates of in-vehicle networks allow the integration of more demanding (e.g., camera-based) applications, processing speeds and, in particular, memory bandwidths are no longer scaling accordingly. The need for authentication, on the other hand, stems from the ongoing convergence of traditionally separated functional domains and the extended connectivity both in- (e.g., smart-phones) and outside (e.g., telemetry, cloud-based services and vehicle-to-X technologies) current vehicles. The inclusion of cryptographic measures thus requires careful interface design to meet throughput, latency, safety, security and power constraints given by the particular application domain. Over the last decades, this has forced system designers to not only optimize their software stacks accordingly, but also incrementally move interface functionalities from software to hardware. This paper discusses existing and emerging methods for dealing with high-speed data streams ranging from software-only via mixed-hardware/software approaches to fully hardware-based solutions. In particular, we introduce two approaches to acquire and authenticate GigE Vision Video Streams at full line rate of Gigabit Ethernet on Programmable SoCs suitable for future heterogeneous automotive processing platforms.
Local clustering

Graph clustering algorithms aim to divide the nodes of a graph into one or more clusters based on some measure of similarity. There are many different ways to define a cluster depending on the goal of the analysis. In graph cluster analysis the usual definition of a cluster is a group of nodes that are relatively densely interconnected but have few connections towards nodes outside of the cluster.
Evaluation of the clinical outcomes of telehealth for managing diabetes

Introduction: The objective of this study was to systematically review the literature and perform a meta-analysis comparing the clinical outcomes of telehealth and usual care in the management of diabetes. Methods: Multiple strategies, including database searches (MEDLINE, PsycINFO, PubMed, EMBASE, and CINAHL), searches of related journals and reference tracking, were employed to widely search publications from January 2005 to December 2017. The change in hemoglobin A1c (HbA1c) levels was assessed as the primary outcome, and changes in blood pressure, blood lipids, body mass index (BMI), and quality of life were examined as secondary outcomes. Results: Nineteen randomized controlled trials (n = 6294 participants) were selected. Telehealth was more effective than usual care in controlling the glycemic index in diabetes patients (weighted mean difference = −0.22%; 95% confidence intervals, −0.28 to −0.15; P < .001). This intervention showed promise in reducing systolic blood pressure levels (P < .001) and diastolic blood pressure levels (P < .001), while no benefits were observed in the control of BMI (P = .79). For total cholesterol and quality of life, telehealth was similar or superior to usual care. Conclusion: Telehealth holds promise for improving the clinical effectiveness of diabetes management. Targeting patients with higher HbA1c (≥9%) levels and delivering more frequent intervention (at least 6 times 1 year) may achieve greater improvement.
An Adaptive PR Controller for Synchronizing Grid-Connected Inverters

In this paper, an adaptive method for tuning a proportional-resonance controller for synchronization of the grid-connected inverters is presented. In the proposed approach, the grid frequency is obtained by minimizing the error signal using a frequency-locked loop mechanism that consists of a resonant adaptive filter and a perturbation-based extremum seeking algorithm. Simulations and experimental studies are presented to demonstrate performance of the proposed controller in face of the frequency variations of an emulated grid voltage waveform. The results are compared with conventional nonadaptive methods, which indicate that performance characteristics for voltage/frequency tracking and power factor can be achieved based on the IEEE 1547 standard.
3DICT: A Reliable and QoS Capable Mobile Process-In-Memory Architecture for Lookup-based CNNs in 3D XPoint ReRAMs

It is extremely challenging to deploy computing-intensive convolutional neural networks (CNNs) with rich parameters in mobile devices because of their limited computing resources and low power budgets. Although prior works build fast and energy-efficient CNN accelerators by greatly sacrificing test accuracy, mobile devices have to guarantee high CNN test accuracy for critical applications, e.g., unlocking phones by face recognitions. In this paper, we propose a 3D XPoint ReRAM-based process-in-memory architecture, 3DICT, to provide various test accuracies to applications with different priorities by lookup-based CNN tests that dynamically exploit the trade-off between test accuracy and latency. Compared to the state-of-the-art accelerators, on average, 3DICT improves the CNN test performance per Watt by 13% ∼ 61× and guarantees 9-year endurance under various CNN test accuracy requirements.
Ripser.py: A Lean Persistent Homology Library for Python

Topological data analysis (TDA) (Edelsbrunner & Harer, 2010), (Carlsson, 2009) is a field focused on understanding the shape and structure of data by computing topological descriptors that summarize features as connected components, loops, and voids. TDA has found wide applications across nonlinear time series analysis (Perea & Harer, 2015), computer vision (Perea & Carlsson, 2014), computational neuroscience (Giusti, Pastalkova, Curto, & Itskov, 2015), (Bendich, Marron, Miller, Pieloch, & Skwerer, 2016), computational biology (Iyer-Pascuzzi et al., 2010), (Wu et al., 2017), and materials science (Kramar, Goullet, Kondic, & Mischaikow, 2013), to name a few of the many areas of impact in recent years.
Direct Capture Cross Section and the E_{p}=71 and 105 keV Resonances in the ^{22}Ne(p,γ)^{23}Na Reaction.

The ^{22}Ne(p,γ)^{23}Na reaction, part of the neon-sodium cycle of hydrogen burning, may explain the observed anticorrelation between sodium and oxygen abundances in globular cluster stars. Its rate is controlled by a number of low-energy resonances and a slowly varying nonresonant component. Three new resonances at E_{p}=156.2, 189.5, and 259.7 keV have recently been observed and confirmed. However, significant uncertainty on the reaction rate remains due to the nonresonant process and to two suggested resonances at E_{p}=71 and 105 keV. Here, new ^{22}Ne(p,γ)^{23}Na data with high statistics and low background are reported. Stringent upper limits of 6×10^{-11} and 7×10^{-11} eV (90% confidence level), respectively, are placed on the two suggested resonances. In addition, the off-resonant S factor has been measured at unprecedented low energy, constraining the contributions from a subthreshold resonance and the direct capture process. As a result, at a temperature of 0.1 GK the error bar of the ^{22}Ne(p,γ)^{23}Na rate is now reduced by 3 orders of magnitude.
Comorbidities, age and period of diagnosis influence treatment and outcomes in early breast cancer

Survival for breast cancer (BC) is lower in eastern than northern/central Europe, and in older than younger women. We analysed how comorbidities at diagnosis affected whether selected standard treatments (STs) were given, across Europe and over time, also assessing consequences for survival/relapse. We analysed 7581 stage I/IIA cases diagnosed in 9 European countries in 2009–2013, and 4 STs: surgery; breast‐conserving surgery plus radiotherapy (BCS + RT); reconstruction after mastectomy; and prompt treatment (≤6 weeks after diagnosis). Covariate‐adjusted models estimated odds of receiving STs and risks of death/relapse, according to comorbidities. Pearson's R assessed correlations between odds and risks. The z‐test assessed the significance of time‐trends. Most women received surgery: 72% BCS; 24% mastectomy. Mastectomied patients were older with more comorbidities than BCS patients (p < 0.001). Women given breast reconstruction (25% of mastectomies) were younger with fewer comorbidities than those without reconstruction (p < 0.001). Women treated promptly (45%) were younger than those treated later (p = 0.001), and more often without comorbidities (p < 0.001). Receiving surgery/BCS + RT correlated strongly (R = −0.9), but prompt treatment weakly (R = −0.01/−0.02), with reduced death/relapse risks. The proportion receiving BCS + RT increased significantly (p < 0.001) with time in most countries. This appears to be the first analysis of the influence of comorbidities on receiving STs, and of consequences for outcomes. Increase in BCS + RT with time is encouraging. Although women without comorbidities usually received STs, elderly patients often received non‐standard less prompt treatments, irrespective of comorbidities, with increased risk of mortality/relapse. All women, particularly the elderly, should receive ST wherever possible to maximise the benefits of modern evidence‐based treatments.
Shape Based Pakistan Sign Language Categorization Using Statistical Features and Support Vector Machines

All over the world, deaf use sign language to communicate each other. The signs are built and communicated through movements and the shapes of the hands. Pakistan sign language (PSL) is used by the deaf community of Pakistan. Automatic recognition of PSL alphabets into the predefined categories is presented here. The seven categories are defined using the visibility, shape, and the orientations of both the fingers and the hand. The histograms of the uniform local binary (lbp) for the neighboring distance of one, two, and three are computed and concatenated to form a single vector. At a later step, six statistical features of the combined histogram are computed, i.e., standard deviation, variance, skewness, kurtosis, entropy, and energy. The classification is achieved using support vector machines (SVMs). The modality of one-verses-one is used for the adoption of binary SVM into the multi-class SVM. The proposed technique is validated over the data set of 3414 PSL signs, taken through the help of seven native signers. The performance of the proposed technique is evaluated by precision, recall, accuracy, and f-measure, while it is elaborated through the classification matrix, tabular, and graph representations.
A Photocrosslinking‐Based RNA Chemical Proteomics Approach to Profile m6A‐Regulated Protein‐RNA Interactions

Post‐transcriptional modifications play an important role in RNA biology. In particular, the addition of small chemical groups to the nucleobases of mRNA can affect how modified transcripts are processed in the cell, thereby impacting gene expression programs. In order to study the molecular mechanisms underlying these modifications, it is necessary to characterize their ‘readers’, that is, proteins that directly bind to these modifications to mediate their functional consequences; this is a major challenge because we lack approaches to precisely manipulate RNA chemistry in the cell and because protein–modified RNA interactions can be low affinity. In this unit, we describe in detail a photocrosslinking‐based RNA chemical proteomics approach to profile the protein–modified RNA interactome modulated by N6‐methyladenosine (m6A), the most abundant internal modification in eukaryotic mRNA. First, we present protocols for the synthesis and characterization of short, diazirine‐containing synthetic RNA probes, followed by a description of their use in mass spectrometry–based proteomics with HeLa cell lysate and a short commentary on data analysis and result interpretation. © 2018 by John Wiley & Sons, Inc.
Decomposing responses to mobile notifications

Notifications from mobile devices frequently prompt us with information, either to merely inform us or to elicit a reaction. This has led to increasing research interest in considering an individual’s interruptibility prior to issuing notifications, in order for them to be positively received. To achieve this, predictive models need to be built from previous response behaviour where the individual’s interruptibility is known. However, there are several degrees of freedom in achieving this, from different definitions in what it means to be interruptible and a notification to be successful, to various methods for collecting data, and building predictive models. The primary focus of this thesis is to improve upon the typical convention used for labelling interruptibility, an area which has had limited direct attention. This includes the proposal of a flexible framework, called the decision-on-information-gain model, which passively observes response behaviour in order to support various interruptibility definitions. In contrast, previous studies have largely surrounded the investigation of influential contextual factors on predicting interruptibility, using a broad labelling convention that relies on notifications being responded to fully and potentially a survey needing to be completed. The approach is supported through two in-the-wild studies of Android notifications, one with 11,000 notifications across 90 users, and another with 32,000,000 across 3000 users. Analysis of these datasets shows that: a) responses to notifications is a decisionmaking process, whereby individuals can be reachable but not receptive to their content, supporting the premise of the approach; b) the approach is implementable on typical Android devices and capable of adapting to different notification designs and user preferences; and c) the different labels produced by the model are predictable using data sources that do not require invasive permissions or persistent background monitoring; however there are notable performance differences between different machine learning strategies for training and evaluation.
Robust hand pose recognition from stereoscopic capture

Hand pose is emerging as an important interface for human-computer interaction. The problem of hand pose estimation from passive stereo inputs has received less attention in the literature compared to active depth sensors. This thesis seeks to address this gap by presenting a data-driven method to estimate a hand pose from a stereoscopic camera input, with experimental results comparable to more expensive active depth sensors. The frameworks presented in this thesis are based on a two camera stereo rig capture as it yields a simpler and cheaper set-up and calibration. Three frameworks are presented, describing the sequential steps taken to solve the problem of depth and pose estimation of hands. The first is a data-driven method to estimate a high quality depth map of a hand from a stereoscopic camera input by introducing a novel regression framework. The method first computes disparity using a robust stereo matching technique. Then, it applies a machine learning technique based on Random Forest to learn the mapping between the estimated disparity and depth given ground truth data. We introduce Eigen Leaf Node Features (ELNFs) that perform feature selection at the leaf nodes in each tree to identify features that are most discriminative for depth regression. The system provides a robust method for generating a depth image with an inexpensive stereo camera. The second framework improves on the task of hand depth estimation from stereo capture by introducing a novel superpixel-based regression framework that takes advantage of the smoothness of the depth surface of the hand. To this end, it introduces Conditional Regressive Random Forest (CRRF), a method that combines a Conditional Random Field (CRF) and a Regressive Random Forest (RRF) to model the mapping from a stereo RGB image pair to a depth image. The RRF provides a unary term that adaptively selects different stereo-matching measures as it implicitly determines matching pixels in a coarse-to-fine manner. While the RRF makes depth prediction for each super-pixel independently, the CRF unifies the prediction of depth by modeling pair-wise interactions between adjacent superpixels. The final framework introduces a stochastic approach to propose potential depth solutions to the observed stereo capture and evaluate these proposals using two convolutional neural networks (CNNs). The first CNN, configured in a Siamese network architecture, evaluates how consistent the proposed depth solution is to the observed stereo capture. The second CNN estimates a hand pose given the proposed depth. Unlike sequential approaches that reconstruct pose from a known depth, this method jointly optimizes the hand pose and depth estimation through Markov-chain Monte Carlo (MCMC) sampling. This way, pose estimation can correct for errors in depth estimation, and vice versa. Experimental results using an inexpensive stereo camera show that the proposed system measures pose more accurately than competing methods. More importantly, it presents the possibility of pose recovery from stereo capture that is on par with depth based pose recovery.
A sharper bound on the Rademacher complexity of margin multi-category classifiers

One of the main open problems in the theory of margin multi-category pattern classiﬁcation is the dependency of a guaranteed risk on the number C of categories, the sample size m and the margin parameter γ . This paper derives a new bound on the probability of error of margin multi-category classiﬁers under minimal learnability assumptions. It improves the dependency on C over the state of the art. This is achieved through the introduction of a new Sauer-Shelah lemma.
Evaluating usefulness of reviews based on evaluation standpoints of consumers

Browsing product reviews is an important information resource for taking actions such as choosing and purchasing a product. However, there are a large number of reviews on electronic commerce sites; hence, it is necessary to efficiently judge the usefulness of reviews to obtain pertinent information. Conventional judging methods make the assumption that all reviews contain useful information. However, it is apparent that some reviews do not contain useful evaluation standpoints of consumers. That is, the conventional methods have the potential to not evaluate the usefulness of the reviews accurately. In this paper, we propose a method for judging the usefulness of reviews from the evaluation standpoints of consumers. To grasp the evaluation standpoints of consumers, we utilize both comprehensiveness and specificity (also known as true positive rate and positive predictive value, respectively), which can be estimated from contributory factors influencing the usefulness of reviews.