Miracle-dz/UniFAR-FTUs huggingface dataset:
Parameter-Free Single-Pass Parallel Metropolis Light Transport with Sensor Path Visibility

We present a visibility-driven algorithm for light transport simulation, which mutates sensor paths with the visibility as the target function. The acceptance or rejection of proposed paths in our algorithm depends only on their visibility. On this basis, the exact path PDF can be obtained by dividing the original PDF by the normalization term. In addition, fully adaptive mutation step sizes are utilized for parameter-free calculations, and the image blocks can be calculated independently and parallel. We use the small adaptive step size in a single image block and take the normalization term of the image block as the probability of a large step to implement completely parameter-free algorithm. Normalization terms are non-shared between image blocks, so image blocks can be independent of each other, our algorithm can compute parallel and solve the stratified problem in image space. Experience shows that our algorithm has better robustness, which can process difficult paths and do not get stuck.
Accurate Thoracic Disease Classification via Ensemble Networks

Convolutional Neural Networks (CNNs) are being adapted to various computer-aided diagnosis applications, including recognizing thoracic diseases. To improve classification performance, recent solutions alter the structure of existing networks or require additional prior information for training. In this paper, we propose an ensemble network to accurately recognize thoracic diseases, without additional layers nor extra input data for training. To this end, we perform an exhaustive set of experiments involving various single CNN models for thoracic disease classification. We then form an ensemble from the most accurate classifier based on these experiments. Our approach achieves state-of-the-art average AUROC score of 79.32% on the ChestX-ray14 dataset, which is 2.08% higher than the previous best result. Additionally, we also attain the highest AUROC for 12 of the 14 classes. Our code and trained models are publicly available at https://github.com/arvention/ChestXRay14-Classification-PyTorch.
Data-driven Mobility Analysis and Modeling: Typical and Confined Life of a Metropolitan Population

The idea of using mobile phone data to understand the impact of the Covid-19 pandemic and that of the sanitary constraints associated with it on human mobility imposed itself as evidence in most countries. This work uses spatiotemporal aggregated mobile phone data provided by a major French telecom operator, covering a geographical region centered on Paris for early 2020, i.e., periods before and during the first French lockdown. An essential property of this data is its fine-grained spatial resolution, which, to the best of our knowledge, is unique in the COVID-related mobility literature. Contrarily to regions or country-wide resolution, it describes population mobility flows among zones ranging from 0.025~km2 to 5.40~km2, corresponding to 326 aggregated zones over the total area of 93.76~km2 of the city of Paris. We perform a data-driven mobility investigation and modeling to quantify (in space and time) the population attendance and visiting flows in different urban areas. Second, when looking at periods both before and during the lockdown, we quantify the consequences of mobility restrictions and decisions on an urban scale. For this, per zone, we define a so-called signature, which captures behaviors in terms of population attendance in the corresponding geographical region (i.e., their land use) and allows us to automatically detect activity, residential, and outlier areas. We then study three different types of graph centrality, quantifying the importance of each zone in a time-dependent weighted graph according to the habits in the mobility of the population. Combining the three centrality measures, we compute per zone of the city, its impact-factor, and employ it to quantify the global importance of zones according to the population mobility. Our results firstly reveal the population’s daily zone preferences in terms of attendance and mobility, with a high concentration on business and touristic zones. Second, results show that the lockdown mobility restrictions significantly reduced visitation and attendance patterns on zones, mainly in central Paris, and considerably changed the mobility habits of the population. As a side effect, most zones identified as mainly having activity-related population attendance in typical periods became residential-related zones during the lockdown, turning the entire city into a residential-like area. Shorter distance displacement restrictions imposed by the lockdown increased visitation to more “local” zones, i.e., close to the population’s primary residence. Decentralization was also favored by the paths preferences of the still-moving population. On the other side, “jogging activities” allowing people to be outside their residences impacted parks visitation, increasing their visitation during the lockdown. By combining the impact factor and the signatures of the zones, we notice that areas with a higher impact factor are more likely to maintain regular land use during the lockdown.
Systematic Literature Review on Industry Revolution 4.0 to Enhance Supply Chain Operation Performance

Industry 4.0 is a notion in which industries automate systems and processes, innovate digitally, and share information. It aims to obtain a smart factory in an attempt to lessen required time in responding to consumer demand or unexpected circumstances and to enhance organizational productivity. The integration of Industry 4.0 and supply chain management (SCM) ensures immense development opportunities for manufacturing firms. This article provides a systematic literature review and formulation of the existing research on Industry 4.0 in SCM, resulting in some intriguing analyses that will be useful to academics and industry, particularly top managers. The content of the article is classified into three categories: exploratory vs. confirmatory, qualitative vs. quantitative, and management level vs. technology level. The findings will benefit managers in understanding the significance of Industry 4.0 and its relationship with SCM. The formation of clusters and their affiliations has resulted in the emergence of new areas requiring managerial attention. The article concludes by examining the possibilities of the present and future research.
Dopia: online parallelism management for integrated CPU/GPU architectures

Recent desktop and mobile processors often integrate CPU and GPU onto the same die. The limited memory bandwidth of these integrated architectures can negatively affect the performance of data-parallel workloads when all computational resources are active. The combination of active CPU and GPU cores achieving the maximum performance depends on a workload's characteristics, making manual tuning a time-consuming task. Dopia is a fully automated framework that improves the performance of data-parallel workloads by adjusting the Degree Of Parallelism on Integrated Architectures. Dopia transparently analyzes and rewrites OpenCL kernels before executing them with the number of CPU and GPU cores expected to yield the best performance. Evaluated on AMD and Intel integrated processors, Dopia achieves 84% of the maximum performance attainable by an oracle.
ACU-Net: Adaptive Context Network Based on U-Net for Retinal Vessel Segmentation

The accurate segmentation of retinal vessels is essential for the diagnosis of various ophthalmic diseases. However, the manual segmentation approaches are time-consuming and seriously affected by individual subjective factors. In addition, retinal vessel segmentation is confronted with challenges such as complex shape, low image contrast and uneven thickness of vessels, which is not conducive to the automatic segmentation of vessels by computer. To meet the challenges of retinal vessel segmentation, this paper proposes an adaptive context-enhanced network based on U-Net (i.e., ACU-Net). On the basis of the fully convoluted encoder-decoder infrastructure, a multi-scale Adaptive Context Module is added with dilated convolution between the encoder and decoder, which can increase the accuracy of the segmentation results by adaptively capturing the spatial information of high-level feature and multi-scale. The multi-branch dilated convolution is used to divide the features acquired by the encoder into four groups, which are subsequently input into the four branches of the Adaptive Context Module. In addition, the output of each branch is weighted during the feature fusion before fused to improve the sensitivity of different feature extraction branches. Results showed that this module could not only significantly improve the segmentation performance of retinal vessels, but also exceed other advanced deep learning-based approaches in multiple indicators when applied to the DRIVE and CHASE-DB1 fundus image data sets. For the DRIVE data set, ACU-Net achieved excellent performance in a number of test indicators, with accuracy (AC) of 0.9591, area under the curve (AUC) of 0.9839, and sensitivity (SE) of 0.8220. Besides, the Adaptive Context Module of the network can be exploited as a reference for other medical segmentation tasks.
APT-GET: profile-guided timely software prefetching

Prefetching which predicts future memory accesses and preloads them from main memory, is a widely-adopted technique to overcome the processor-memory performance gap. Unfortunately, hardware prefetchers implemented in today's processors cannot identify complex and irregular memory access patterns exhibited by modern data-driven applications and hence developers need to rely on software prefetching techniques. We investigate the challenges of enabling effective, automated software data prefetching. Our investigation reveals that the state-of-the-art compiler-based prefetching mechanism falls short in achieving high performance due to its static nature. Based on this insight, we design APT-GET, a novel profile-guided technique that ensures prefetch timeliness by leveraging dynamic execution time information. APT-GET leverages efficient hardware support such as Intel's Last Branch Record (LBR), for collecting application execution profiles with negligible overhead to characterize the execution time of loads. APT-GET then introduces a novel analytical model to find the optimal prefetch-distance and prefetch injection site based on the collected profile to enable timely prefetches. We study APT-GET in the context of 10 real-world applications and demonstrate that it achieves a speedup of up to 1.98× and of 1.30× on average. By ensuring prefetch timeliness, APT-GET improves the performance by 1.25× over the state-of-the-art software data prefetching mechanism.
Kite: lightweight critical service domains

Converged multi-level secure (MLS) systems, such as Qubes OS or SecureView, heavily rely on virtualization and service virtual machines (VMs). Traditionally, driver domains - isolated VMs that run device drivers - and daemon VMs use full-blown general-purpose OSs. It seems that specialized lightweight OSs, known as unikernels, would be a better fit for those. Surprisingly, to this day, driver domains can only be built from Linux. We discuss how unikernels can be beneficial in this context - they improve security and isolation, reduce memory overheads, and simplify software configuration and deployment. We specifically propose to use unikernels that borrow device drivers from existing general-purpose OSs. We present Kite which implements network and storage unikernel-based VMs and serve two essential classes of devices. We compare our approach against Linux using a number of typical micro- and macrobenchmarks used for networking and storage. Our approach achieves performance similar to that of Linux. However, we demonstrate that the number of system calls and ROP gadgets can be greatly reduced with our approach compared to Linux. We also demonstrate that our approach has resilience to an array of CVEs (e.g., CVE-2021-35039, CVE-2016-4963, and CVE-2013-2072), smaller image size, and improved startup time. Finally, unikernelizing is doable for the remaining (non-driver) service VMs as evidenced by our unikernelized DHCP server.
Micro-expression Detection based on Action Units and Multi-region Feature Fusion

Recently, micro-expression detection has attracted much attention, since micro-expression has potential lie detection applications. In this paper, a micro-expression detection algorithm based on action units (AUs) and multi-region feature fusion is proposed. First, the regions of interest (ROIs) are selected based on AUs. Second, the optical flow features in spatio-temporal domain and frequency domain features are fused as a final feature descriptions of micro-expressions. Finally, a decision criterion based on the characteristic of the optical flow angle, magnitude and frequency magnitude is applied to spot onset, apex, and offset frames from micro-expression video sequences. The experiments are performed on SDU_spotting and CASME II datasets. The results show that the proposed method obtains higher performance than the state-of-the-art methods in terms of accuracy.
Glioblastoma states are defined by cohabitating cellular populations with progression-, imaging- and sex-distinct patterns

Glioblastomas (GBMs) are biologically heterogeneous within and between patients. Many previous attempts to characterize this heterogeneity have classified tumors according to their omics similarities. These discrete classifications have predominantly focused on characterizing malignant cells, neglecting the immune and other cell populations that are known to be present. We leverage a manifold learning algorithm to define a low-dimensional transcriptional continuum along which heterogeneous GBM samples organize. This reveals three polarized states: invasive, immune/inflammatory, and proliferative. The location of each sample along this continuum correlates with the abundance of eighteen malignant, immune, and other cell populations. We connect these cell abundances with magnetic resonance imaging and find that the relationship between contrast enhancement and tumor composition varies with patient sex and treatment status. These findings suggest that GBM transcriptional biology is a predictably constrained continuum that contains a limited spectrum of viable cell cohabitation ecologies. Since the relationships between this ecological continuum and imaging vary with patient sex and tumor treatment status, studies that integrate imaging features with tumor biology should incorporate these variables in their design.
Telomerase-independent maintenance of telomere length in a vertebrate

Telomere shortening places a key limitation on cell proliferation1. In all vertebrates explored to date, this limitation is overcome by telomerase-dependent telomere extension. Failure to maintain telomere length results in premature ageing and functional impairments in highly replicative cell populations as telomeres erode2. Alternative lengthening of telomeres (ALT), a telomerase-independent mechanism, compensates for telomere loss in a subset of human cancer cell lines 2. Here, we demonstrate that the highly regenerative newt Pleurodeles waltl lacks telomerase activity, contains telomeres distinct from all known vertebrates in both sequence and structure, and deploys ALT for physiological telomere maintenance. This constitutes the first report of telomerase-independent resolution of the end-replication problem at the whole-organism level within Chordata. One-Sentence Summary P. waltl telomere biology is distinct amongst vertebrates and uses ALT at the whole-organism level.
RNA-targeting CRISPR-Cas13 Provides Broad-spectrum Phage Immunity

CRISPR-Cas13 proteins are RNA-guided RNA nucleases that defend against invasive phages through general, non-specific RNA degradation upon complementary target transcript binding. Despite being RNA nucleases, Cas13 effectors are capable of inhibiting the infection of dsDNA phages but have only been investigated across a relatively small sampling of phage diversity. Here, we employ a systematic, phage-centric approach to determine the anti-phage capacity of Cas13 and find LbuCas13a to be a remarkably potent phage inhibitor. LbuCas13a confers robust, consistent antiviral activity regardless of gene essentiality, gene expression timing or target sequence location. Furthermore, after challenging LbuCas13a with eight diverse E. coli phages distributed across E. coli phage phylogenetic groups, we find no apparent phage-encoded limits to its potent antiviral activity. In contrast to other Class 2 CRISPR-Cas proteins, these results suggest that DNA phages are generally vulnerable to Cas13a targeting. Leveraging this effective anti-phage activity, LbuCas13a can be used seamlessly as a counter-selection agent for broad-spectrum phage editing. Using a two-step phage editing and enrichment approach, we show that LbuCas13a enables markerless genome edits in phages with exceptionally high efficiency and precision, including edits as small as a single codon. By taking advantage of the broad vulnerability of RNA during viral infection, Cas13a enables a generalizable strategy for editing the most abundant and diverse biological entities on Earth.
Robust optimisation-based microgrid scheduling with islanding constraints

This study proposes a robust optimisation-based optimal scheduling model for microgrid operation considering constraints of islanding capability. The objective is to minimise the total operation cost, including generation cost and spinning reserve cost of local resources as well as purchasing cost of energy from the main grid. To ensure the resiliency of a microgrid and improve the reliability of the local electricity supply, the microgrid is required to maintain enough spinning reserve (both up and down) to meet local demand and accommodate local renewable generation when the supply of power from the main grid is interrupted suddenly, i.e. microgrid transitions from grid-connected into islanded mode. Prevailing operational uncertainties in renewable energy resources and load are considered and captured using a robust optimisation method. With proper robust level, the solution of the proposed scheduling model ensures successful islanding of the microgrid with minimum load curtailment and guarantees robustness against all possible realisations of the modelled operational uncertainties. Numerical simulations on a microgrid consisting of a wind turbine, a PV panel, a fuel cell, a micro-turbine, a diesel generator and a battery demonstrate the effectiveness of the proposed scheduling model.
RELATION OF TEMPERATURE IN GERMINATION ATTRIBUTES OF SOME BREAD WHEAT CULTIVARS

A laboratory experiment was conducted during 2011 at the laboratories of the Faculty of Science at the University of Plymouth in UK, to determine the germination capacity of bread wheat seeds (Triticum aestivum L.) for five cultivars (Fateh, IPA 99, Sham 6, Clare, and Caxton) under the effect of three temperatures (10, 15 and 20 oC). A completed randomized design with four replications was used. The results showed that the cultivars IPA 99 and Fateh were superior in the percentage of germination in the first count (FG) 92.5, 93.2 % and final count (FIG) 97.0, 98.8 %, coefficient of velocity of germination (CVG) 37.8, 35.5 %. d -1 , mean germination time (MGT) 2.8, 3.0 d and germination rate index (GRI) 38.5, 36.9 %. d -1 . The temperature 20 oC was superior in FG 83.1 %, CVG 38.7 %. d -1 , MGT 2.7 d, GRI 37.9 %. d -1 , RL 10.5 cm, PL 8.3 cm and SDW 0.0121 mg. IPA 99 and Sham 6 were superior under the three temperatures 20, 15 and 10 oC in FG. The interactions (Sham 6 × 20 oC) and (IPA 99 × 20 oC) were superior in MGT, GRI and PL. A significant positive correlation was found between FIG and between each of FG 0.652, CVG 0.324 and GRI 0.533, while a negative correlation was found between MGT and between each of FG -0.869, FIG -0.343, CVG -0.950 and GRI -0.925. It can be concluded that the cultivars IPA 99 and Sham 6 are suitable for sowing in areas with low temperatures during the germination stage and should be investigate the potential performance and ability of seeds of IPA99 and Sham 6 under field condition.
Study on the Effect of Variation of Flow in Sequencing Batch Reactor Using PCA and ANOVA

Wastewater treatment using Sequencing Batch Reactor (SBR) technology is one of the state-of-the art wastewater management systems. In this technology equalization, biological treatment and secondary clarification are performed in a single reactor in a time control sequence. SBR system is idler for the areas where the available land is limited, since it operates in less space and very cost effective even on small scales. The control of the operational parameters during the process of biological wastewater treatment is often complicated due to the dynamic change in the composition and characteristics of the raw wastewater, flow rates and other parameters influencing the complex nature of the treatment process and the process in SBR has a unique cyclic batch operation. The performance of the SBR was studied using pilot and real plant at Puducherry. The parameters considered in this study are flow, Mixed Liquor Suspended Solids (MLSS), pH, temperature, influent and effluent of Total Suspended Solids (TSS), Biochemical Oxygen Demand (BOD) and Chemical Oxygen Demand (COD). As a part of the study, the effect of constant flow and varying flow on the organic loading of the influent TSS, BOD and COD and their influence on the organic load of the effluent parameters were examined to identify the level of significance of the parameters in relation to the flow. The impact of flow on other parameters was also examined. The experimental data obtained from pilot and real plants were analyzed using multi variate statistical analyses like correlation analysis, Principal Component Analysis (PCA) and Analysis of variance (ANOVA). The statistical analysis revealed that constant flow had no significant role and the influent parameters alone had the critical role, whereas varying flow as well as the influent parameters had the significant role on the performance of SBR.
Internet of Things Strategic Research Roadmap

" What most people need to learn in life is how to love people and use things instead of using people and loving things. " " It is not because things are difficult that we do not dare, it is because we do not dare that things are difficult. " Seneca " All things appear and disappear because of the concurrence of causes and conditions. Nothing ever exists entirely alone; everything is in relation to everything else. " Hindu Prince Gautama Siddharta
To assess the magnitude of autism spectrum disorder in Jharkhand by M-CHAT-R as a screening tool

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder which is a clinically heterogenous condition with a wide range of etiological factors and causing significant public health burden. If diagnosed at an earlier age, early interventions can be started this leads to functional outcome of children with ASD with respect to social, behavior and occupational sphere. Therefore, early detection and intervention are widely recommended in these children. So screening of toddlers who were identified to be “at risk” can be diagnosed using screening questionnaires by interviewing parents. Overall with this study we can conclude that, toddlers identified to be “at risk” and those diagnosed with ASD were not uncommon and M-CHAT-R is a useful screening test for the identification of “at risk” toddlers for Autism Spectrum Disorder in Jharkhand.