Miracle-dz/UniFAR-FTUs huggingface dataset:
Quality Management in the 21 st Century Enterprises Quality Management in the 21 st Century Enterprises : Research pathway towards Industry 4 . 0

Quality management is an evergreen research theme in the contemporary world. Given the dominance of technological and turbulent environment with unprecedented customer expectations, the aim of this special issue to explore the developments in quality management in the era of Industry 4.0. In particular, our focus is to unravel the advancements in quality and their implications in terms of economics, decision models, business models, human and technological perspectives. This call for papers for the special issue of IJPE ended up in attracting ten high quality studies covering the above perspectives. In addition, the special issue recommends potential unattended future research pathways such as importance of human issues in quality
Informing national immigration policy through interpretable machine learning

: We developed an interpretable machine learning method to study how the Immigration and Customs Enforcement agency (ICE) carries out major national immigration 15 policies of the United States. The method uses a combination of state-of-the-art machine learning algorithms and a natural policy experiment to discover ICE’s implicit decision-making strategies, capturing how ICE determines whom to detain and deport. We demonstrate that our algorithm, trained on individual-level administrative data, can predict ICE’s decisions with high accuracy. Our results show that despite ICE’s mandate to target immigrants convicted of serious
Maximum Entropy Reinforcement Learning with Mixture Policies

Mixture models are an expressive hypothesis class that can approximate a rich set of policies. However, using mixture policies in the Maximum Entropy (MaxEnt) framework is not straightforward. The entropy of a mixture model is not equal to the sum of its components, nor does it have a closed-form expression in most cases. Using such policies in MaxEnt algorithms, therefore, requires constructing a tractable approximation of the mixture entropy. In this paper, we derive a simple, low-variance mixture-entropy estimator. We show that it is closely related to the sum of marginal entropies. Equipped with our entropy estimator, we derive an algorithmic variant of Soft Actor-Critic (SAC) to the mixture policy case and evaluate it on a series of continuous control tasks.
Self-Supervised Adaptation for Video Super-Resolution

Recent single-image super-resolution (SISR) networks, which can adapt their network parameters to specific input images, have shown promising results by exploiting the information available within the input data as well as large external datasets. However, the extension of these self-supervised SISR approaches to video handling has yet to be studied. Thus, we present a new learning algorithm that allows conventional video super-resolution (VSR) networks to adapt their parameters to test video frames without using the ground-truth datasets. By utilizing many self-similar patches across space and time, we improve the performance of fully pre-trained VSR networks and produce temporally consistent video frames. Moreover, we present a test-time knowledge distillation technique that accelerates the adaptation speed with less hardware resources. In our experiments, we demonstrate that our novel learning algorithm can fine-tune state-of-the-art VSR networks and substantially elevate performance on numerous benchmark datasets.
AutoDRIVE Simulator: A Simulator for Scaled Autonomous Vehicle Research and Education

AutoDRIVE is envisioned to be an integrated research and education platform for scaled autonomous vehicles and related applications. This work is a stepping-stone towards achieving the greater goal of realizing such a platform. Particularly, this work introduces the AutoDRIVE Simulator, a high-fidelity simulator for scaled autonomous vehicles. The proposed simulation ecosystem is developed atop the Unity game engine, and exploits its features in order to simulate realistic system dynamics and render photorealistic graphics. It comprises of a scaled vehicle model equipped with a comprehensive sensor suite for redundant perception, a set of actuators for constrained motion control and a fully functional lighting system for illumination and signaling. It also provides a modular environment development kit, which comprises of various environment modules that aid in reconfigurable construction of the scene. Additionally, the simulator features a communication bridge in order to extend an interface to the autonomous driving software stack developed independently by the users. This work describes some of the prominent components of this simulation system along with some key features that it has to offer in order to accelerate education and research aimed at autonomous driving.
Hazelcast Jet: Low-latency Stream Processing at the 99.99th Percentile

Jet is an open-source, high-performance, distributed stream processor built at Hazelcast during the last five years. Jet was engineered with millisecond latency on the 99.99th percentile as its primary design goal. Originally Jet's purpose was to be an execution engine that performs complex business logic on top of streams generated by Hazelcast's In-memory Data Grid (IMDG): a set of high-performance, in-memory, partitioned and replicated data structures. With time, Jet evolved into a full-fledged, scale-out stream processor that can handle out-of-order streams and exactly-once processing guarantees. Jet's end-to-end latency lies in the order of milliseconds, and its throughput in the order of millions of events per CPU-core. This paper presents main design decisions we made in order to maximize the performance per CPU-core, alongside lessons learned, and an empirical performance evaluation.
A class of inexact modified Newton-type iteration methods for solving the generalized absolute value equations

In Wang et al. (J. Optim. Theory Appl., 181: 216–230, 2019), a class of effective modified Newton-tpye (MN) iteration methods are proposed for solving the generalized absolute value equations (GAVE) and it has been found that the MN iteration method involves the classical Picard iteration method as a special case. In the present paper, it will be claimed that a Douglas-Rachford splitting method for AVE is also a special case of the MN method. In addition, a class of inexact MN (IMN) iteration methods are developed to solve GAVE. Linear convergence of the IMN method is established and some specific sufficient conditions are presented for symmetric positive definite coefficient matrix. Numerical results are given to demonstrate the efficiency of the IMN iteration method. 2000 Mathematics Subject Classification. 65F10, 65H10, 90C30
The Case for High-Accuracy Classification: Think Small, Think Many!

To facilitate implementation of high-accuracy deep neural networks especially on resource-constrained devices, maintaining low computation requirements is crucial. Using very deep models for classification purposes not only decreases the neural network training speed and increases the inference time, but also need more data for higher prediction accuracy and to mitigate false positives. In this paper, we propose an efficient and lightweight deep classification ensemble structure based on a combination of simple color features, which is particularly designed for “highaccuracy” image classifications with low false positives. We designed, implemented, and evaluated our approach for explosion detection use-case applied to images and videos. Our evaluation results based on a large test test show considerable improvements on the prediction accuracy compared to the popular ResNet-50 model, while benefiting from 7.64x faster inference and lower computation cost. While we applied our approach to explosion detection, our approach is general and can be applied to other similar classification use cases as well. Given the insight gained from our experiments, we hence propose a ”think small, think many” philosophy in classification scenarios: that transforming a single, large, monolithic deep model into a verification-based step model ensemble of multiple small, simple, lightweight models with narrowed-down color spaces can possibly lead to predictions with higher accuracy.
Formation Control for Multiple Connected and Automated Vehicles on Multi-lane Roads

Coordinated decision making and control can improve traffic efficiency while guaranteeing driving safety. This paper proposes a formation control method for multiple Connected and Automated Vehicles (CAVs) on multi-lane roads. A bi-level planning framework is proposed to smoothly and effectively switch the structure of the formation in different scenarios. The relative coordinate system is established and the conflict-free relative paths are planned in the upper level. Multi-stage trajectory planning and tracking are performed in the lower level. Case study is conducted to verify the function of the proposed method and simulation in the lane-drop bottleneck scenario is carried out under different traffic volume. Numerical results indicate that the proposed method can improve traffic efficiency at high traffic volume.
SG-Net: Spatial Granularity Network for One-Stage Video Instance Segmentation

Video instance segmentation (VIS) is a new and critical task in computer vision. To date, top-performing VIS methods extend the two-stage Mask R-CNN by adding a tracking branch, leaving plenty of room for improvement. In contrast, we approach the VIS task from a new perspective and propose a one-stage spatial granularity network (SG-Net). Compared to the conventional two-stage methods, SG-Net demonstrates four advantages: 1) Our method has a one-stage compact architecture and each task head (detection, segmentation, and tracking) is crafted interdependently so they can effectively share features and enjoy the joint optimization; 2) Our mask prediction is dynamically performed on the sub-regions of each detected instance, leading to high-quality masks of fine granularity; 3) Each of our task predictions avoids using expensive proposal-based RoI features, resulting in much reduced runtime complexity per instance; 4) Our tracking head models objects’ centerness movements for tracking, which effectively enhances the tracking robustness to different object appearances. In evaluation, we present state-of-the-art comparisons on the YouTube-VIS dataset. Extensive experiments demonstrate that our compact one-stage method can achieve improved performance in both accuracy and inference speed. We hope our SG-Net could serve as a strong and flexible base-line for the VIS task. Our code will be available here 1.
A Survey on Spatio-temporal Data Analytics Systems

Due to the surge of spatio-temporal data volume, the popularity of location-based services and applications, and the importance of extracted knowledge from spatio-temporal data to solve a wide range of real-world problems, a plethora of research and development work has been done in the area of spatial and spatio-temporal data analytics in the past decade. The main goal of existing works was to develop algorithms and technologies to capture, store, manage, analyze, and visualize spatial or spatio-temporal data. The researchers have contributed either by adding spatio-temporal support with existing systems, by developing a new system from scratch, or by implementing algorithms for processing spatio-temporal data. The existing ecosystem of spatial and spatio-temporal data analytics systems can be categorized into three groups, (1) spatial databases (SQL and NoSQL), (2) big spatial data processing infrastructures, and (3) programming languages and GIS software. Since existing surveys mostly investigated infrastructures for processing big spatial data, this survey has explored the whole ecosystem of spatial and spatio-temporal analytics. This survey also portrays the importance and future of spatial and spatio-temporal data analytics.
Searching for chemical abundance variations in young star clusters in the Magellanic Clouds: NGC 411, NGC 1718, and NGC 2213

The conventional picture of coeval, chemically homogeneous, populous star clusters – known as ‘simple stellar populations’ (SSPs) – is a view of the past. Photometric and spectroscopic studies reveal that almost all ancient globular clusters in the Milky Way and our neighbouring galaxies exhibit star-to-star light-element abundance variations, typically known as ‘multiple populations’ (MPs). Here, we analyse photometric Hubble Space Telescope observations of three young (<2 Gyr-old) Large and Small Magellanic Cloud clusters, NGC 411, NGC 1718 and NGC 2213. We measure the widths of their red-giant branches (RGBs). For NGC 411, we also use a pseudo-colour–magnitude diagram (pseudo-CMD) to assess its RGB for evidence of MPs. We compare the morphologies of the clusters’ RGBs with artificially generated SSPs. We conclude that their RGBs do not show evidence of significant broadening beyond intrinsic photometric scatter, suggesting an absence of significant chemical abundance variations in our sample clusters. Specifically, for NGC 411, NGC 1718 and NGC 2213 we derive maximum helium-abundance variations of δY = 0.003 ± 0.001(Y = 0.300), 0.002 ± 0.001(Y = 0.350) and 0.004 ± 0.002(Y = 0.300), respectively. We determined an upper limit to the NGC 411 nitrogen-abundance variation of Δ[N/Fe] = 0.3 dex; the available data for our other clusters do not allow us to determine useful upper limits. It thus appears that the transition from SSPs to MPs occurs at an age of ∼2 Gyr, implying that age might play an important role in this transition. This raises the question as to whether this is indeed a fundamental minimum-age limit for the formation of MPs.
Data-Driven Wireless Communication Using Gaussian Processes

—Data-driven paradigms are well-known and salient demands of future wireless communication. Empowered by big data and machine learning, next-generation data-driven communication systems will be intelligent with the characteristics of expressiveness, scalability, interpretability, and especially uncertainty modeling, which can conﬁdently involve diversiﬁed latent demands and personalized services in the foreseeable future. In this paper, we review and present a promising family of nonparametric Bayesian machine learning methods, i.e., Gaussian processes (GPs), and their applications in wireless communication due to their interpretable learning ability with uncertainty. Speciﬁcally, we ﬁrst envision three-level motivations of data-driven wireless communication using GPs. Then, we provide the background of the GP model in terms of covariance structure and model inference. The expressiveness of the GP model is introduced by using various interpretable kernel designs, namely, stationary, non-stationary, deep, and multi-task kernels. Furthermore, we review the distributed GP with promising scalability, which is suitable for applications in wireless networks with a large number of distributed edge devices. Finally, we provide representative solutions and promising techniques that adopting GPs in wireless communication systems.
Robust and Guided Bayesian Reconstruction of Single-Photon 3D Lidar Data: Application to Multispectral and Underwater Imaging

3D Lidar imaging can be a challenging modality when using multiple wavelengths, or when imaging in high noise environments (e.g., imaging through obscurants). This paper presents a hierarchical Bayesian algorithm for the robust reconstruction of multispectral single-photon Lidar data in such environments. The algorithm exploits multi-scale information to provide robust depth and reflectivity estimates together with their uncertainties to help with decision making. The proposed weight-based strategy allows the use of available guide information that can be obtained by using state-of-the-art learning based algorithms. The proposed Bayesian model and its estimation algorithm are validated on both synthetic and real images showing competitive results regarding the quality of the inferences and the computational complexity when compared to the state-of-the-art algorithms.
Differential analysis in Transcriptomic: The strength of randomly picking 'reference' genes

Transcriptomic analysis are characterized by being not directly quantitative and only providing relative measurements of expression levels up to an unknown individual scaling factor. This difficulty is enhanced for differential expression analysis. Several methods have been proposed to circumvent this lack of knowledge by estimating the unknown individual scaling factors however, even the most used one, are suffering from being built on hardly justifiable biological hypotheses or from having weak statistical background. Only two methods withstand this analysis: one based on largest connected graph component hardly usable for large amount of expressions like in NGS, the second based on $\log$-linear fits which unfortunately require a first step which uses one of the methods described before. We introduce a new procedure for differential analysis in the context of transcriptomic data. It is the result of pooling together several differential analyses each based on randomly picked genes used as reference genes. It provides a differential analysis free from the estimation of the individual scaling factors or any other knowledge. Theoretical properties are investigated both in term of FWER and power. Moreover in the context of Poisson or negative binomial modelization of the transcriptomic expressions, we derived a test with non asymptotic control of its bounds. We complete our study by some empirical simulations and apply our procedure to a real data set of hepatic miRNA expressions from a mouse model of non-alcoholic steatohepatitis (NASH), the CDAHFD model. This study on real data provides new hits with good biological explanations.
Which to Match? Selecting Consistent GT-Proposal Assignment for Pedestrian Detection

Accurate pedestrian classification and localization have received considerable attention due to their wide applications such as security monitoring, autonomous driving, etc. Although pedestrian detectors have made great progress in recent years, the fixed Intersection over Union (IoU) based assignment-regression manner still limits their performance. Two main factors are responsible for this: 1) the IoU threshold faces a dilemma that a lower one will result in more false positives, while a higher one will filter out the matched positives; 2) the IoU-based GT-Proposal assignment suffers from the inconsistent supervision problem that spatially adjacent proposals with similar features are assigned to different ground-truth boxes, which means some very similar proposals may be forced to regress towards different targets, and thus confuses the bounding-box regression when predicting the location results. In this paper, we first put forward the question that \textbf{Regression Direction} would affect the performance for pedestrian detection. Consequently, we address the weakness of IoU by introducing one geometric sensitive search algorithm as a new assignment and regression metric. Different from the previous IoU-based \textbf{one-to-one} assignment manner of one proposal to one ground-truth box, the proposed method attempts to seek a reasonable matching between the sets of proposals and ground-truth boxes. Specifically, we boost the MR-FPPI under R$_{75}$ by 8.8\% on Citypersons dataset. Furthermore, by incorporating this method as a metric into the state-of-the-art pedestrian detectors, we show a consistent improvement.
Metal free visible light driven oxidation of alcohols to carbonyl derivatives using 3,6-di(pyridin-2-yl)-1,2,4,5-tetrazine (pytz) as catalyst

3,6-Di(pyridin-2-yl)-1,2,4,5-tetrazine (pytz) catalyzed oxidation of alcohols to the corresponding carbonyl compounds under visible light irradiation is described. This reaction occurs smoothly at room temperature and shows good tolerance of functional groups. It provides an alternative approach for the synthesis of alkyl and aryl aldehydes and ketones.
Optimal transport framework for efﬁcient prototype selection

Summarizing data via representative examples is an important problem in several machine learning applications where human understanding of the learning models and underlying data distribution is essential for decision making. In this work, we develop an optimal transport (OT) based framework to select informative prototypical examples that best represent a given target dataset. We model the prototype selection problem as learning a sparse (empirical) probability distribution having minimum OT distance from the target distribution. The learned probability measure supported on the chosen prototypes directly corresponds to their importance in representing and summarizing the target data. We show that our objective function enjoys a key property of submodularity and propose a parallelizable greedy method that is both computationally fast and possess deterministic approximation guarantees. Empirical results on several real world benchmarks illustrate the efﬁcacy of our approach.
Analyzing Uplink Grant-Free Sparse Code Multiple Access System in Massive IoT Networks

Grant-free sparse code multiple access (GF-SCMA) is considered to be a promising multiple access candidate for future wireless networks. In this article, we focus on characterizing the performance of uplink GF-SCMA schemes in a network with ubiquitous connections, such as the Internet-of-Things (IoT) networks. To provide a tractable approach to evaluate the performance of GF-SCMA, we first develop a theoretical model taking into account the property of multiuser detection (MUD) in the SCMA system. Then, the error rate performance of GF-SCMA in the case of codebook collision is analyzed to investigate the reliability of GF-SCMA when reusing codebook in massive IoT networks. For performance evaluation, accurate approximations for both success probability and average symbol error probability (ASEP) are derived. To elaborate further, the analytical results are utilized to discuss the impact of codeword sparse degree in GF-SCMA. After that, we conduct a comparative study between SCMA and its variant, dense code multiple access (DCMA), with GF transmission to offer insights into the effectiveness of these two schemes. This facilitates the GF-SCMA system design in practical implementation. Simulation results show that denser codebooks can help to support more user equipments (UEs) and increase the reliability of data transmission in a GF-SCMA network. Moreover, a higher success probability can be achieved by GF-SCMA with denser UE deployment at low detection thresholds since SCMA can achieve overloading gain.