Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Optimization of an MPEG-2 to MPEG-4 Video Transcoder

The MPEG-2 compressed digital video content is being used in a number of products including the DVDs, Camcorders, digital TV, and HDTV. The ability to access this widely available MPEG-2 content on low-power end-user devices such as PDAs and mobile phones depends on effective techniques for transcoding the MPEG-2 content to a more appropriate, low bitrate, video format such as MPEG-4. In this paper we present the software and algorithmic optimizations performed in developing a real time MPEG-2 to MPEG-4 video transcoder. A brief overview of the transcoding architectures is also provided. The details of the transcoding architectures for MPEG-2 to MPEG-4 video transcoding can be found in. The transcoder was targeted and optimized for Windows PCs with the Intel Pentium-4 processors. The optimizations performed exploit the SIMD parallelism offered by the Intel Pentium-4 processors. The transcoder consists of two distinct components: the MPEG-2 video decoder and the MPEG-4 video transcoder. The MPEG-2 video decoder is based on the MPEG-2 Software Simulation Group’s reference implementation while MPEG-4 transcoder is developed from scratch with portions taken from the MOMUSYS implementation of the MPEG-4 video encoder. The optimizations include: 1) generic block-processing optimizations that affected both the MPEG-2 decoder and the MPEG-4 transcoder and 2) optimizations specific to the MPEG-2 video decoder and the MPEG-4 video transcoder. The optimizations resulted in significant improvements both in MPEG-2 decoding as well as the MPEG-4 transcoding. With optimizations, the total time spent by the transcoder was reduced by over 82% with MPEG-2 decoding reduced by over 56% and MPEG-4 transcoding reduced by over 86%.
Automatic road network extraction from high resolution satellite imagery using spectral classification methods

Road networks play an important role in a number of geospatial applications, such as cartographic, infrastructure planning and traffic routing software. Automatic and semiautomatic road network extraction techniques have significantly increased the extraction rate of road networks. Automated processes still yield some erroneous and incomplete results and costly human intervention is still required to evaluate results and correct errors. With the aim of improving the accuracy of road extraction systems, three objectives are defined in this thesis: Firstly, the study seeks to develop a flexible semiautomated road extraction system, capable of extracting roads from QuickBird satellite imagery. The second objective is to integrate a variety of algorithms within the road network extraction system. The benefits of using each of these algorithms within the proposed road extraction system, is illustrated. Finally, a fully automated system is proposed by incorporating a number of the algorithms investigated throughout the thesis.
Tackling Large-Scale and Combinatorial Bi-Level Problems With a Genetic Programming Hyper-Heuristic

Combinatorial bi-level optimization remains a challenging topic, especially when the lower-level is an ${\mathcal {NP}}$ -hard problem. In this paper, we tackle large-scale and combinatorial bi-level problems using GP hyper-heuristics, i.e., an approach that permits to train heuristics like a machine learning model. Our contribution aims at targeting the intensive and complex lower-level optimizations that occur when solving a large-scale and combinatorial bi-level problem. For this purpose, we consider hyper-heuristics through heuristic generation. Using a GP hyper-heuristic approach, we train greedy heuristics in order to make them more reliable when encountering unseen lower-level instances that could be generated during bi-level optimization. To validate our approach referred to as GA+AGH, we tackle instances from the bi-level cloud pricing optimization problem (BCPOP) that model the trading interactions between a cloud service provider and cloud service customers. Numerical results demonstrate the abilities of the trained heuristics to cope with the inherent nested structure that makes bi-level optimization problems so hard. Furthermore, it has been shown that training heuristics for lower-level optimization permits to outperform human-based heuristics and metaheuristics which constitute an excellent outcome for bi-level optimization.
Supplemental Material for Few-Shot Class-Incremental Learning via Class-Aware Bilateral Distillation

In this part, we provide more analyses of our key component Class-Aware Bilateral Distillation (CABD). In Fig. 1, we visualize the feature embeddings and the corresponding classification weights (i.e., prototypes) from the miniImageNet test set with (novel branch) or without (base branch) adaptation to novel classes using our proposed distillation module. For clarity, 5 base classes and 5 novel classes are randomly chosen and features of 100 per-class test samples are considered. As shown in the left part of Fig. 1 (highlighted with dark red circle), the orange novel class prototype (marked with star) is confused with the pink novel class without CABD, since the base branch is trained only on base categories which can not effectively adapt to novel concepts. By contrast, after adaptation to novel classes with our proposed CABD, the above two classes become more distinguishable in the novel branch shown in the dark green circles from the right part of Fig. 1. The above observation explains the improved performance of the novel branch in Table 2 of our main paper.
Evaporation of Soil Moisture on Burned Watersheds: An Arizona Case Study

The effects of a fire on soil moisture are linked to the loss of vegetation during the combustion process which (in turn) lowers evapotranspiration losses, leaving more water in the soil than would be present in the absence of burning. Overland flow and ultimately streamflow are generally more responsive to post -fire precipitation events as a consequence of this increased soil moisture (DeBano et al. 1998; Neary et al. 2005). However, this situation does not hold true for all cases. Campbell et al. (1977) observed a reduction in soil moisture in the upper 12 inches of the limestone soil profile on a 20 -acre watershed near Flagstaff, Arizona, after twothirds of the pre -fire ponderosa pine forest overstory and much of the herbaceous understory were destroyed by a wildfire in comparison to an unburned (control) watershed. Greater overland flow on the burned watershed was the assumed factor underlying this difference. To determine whether this situation is the exception, evaporation of soil moisture on two watersheds in the ponderosa pine forests burned by the Rodeo -Chediski wildfire are compared to each other and to field capacity through three -plus years of post -fire sampling.
Place of technology management as a key process area within construction process improvement: A critical analysis

Process improvement has been identified as an important strategy to address the current unpredictability and under-achievements of the UK construction industry. Within the technological view of process improvement, information technology (IT) has been identified as a key enabler. Various studies about the information technology in construction have revealed that construction show a slow IT adoptability and IT has failed to convince the stakeholders of the construction industry. Within some of these researches, it has been argued that immature processes within the construction industry are responsible for this unsatisfactory level of performance of IT. On the other hand, it has also been argued that, Information Technology is a primary stimulant of process improvement, thus process maturity can be driven by the IT push. Leading to a dilemma, this indicates that new information technological innovations could use by immature organisations within their process improvement strategies. This dilemma triggered the necessity to evaluate the place of the Technology Change Management within the construction context. Thus, a literature survey was conducted to identify the construction process improvement initiatives and its relationship with the IT usage in construction with a special emphasis on the SPICE approach, which has provided the basis for this critical analysis. The SPICE is a five levelled framework based on the popular Software Capability Maturity Model (CMM), and presents an assessment tool for the maturity of construction process. However, within the CMM the Technology Change Management has been considered as a key process area within the fifth maturity level, indicating that this is a concern of matured organisations. However, the IT usage within construction shows that the immature construction organisations have successfully adopted IT in an ad-hoc manner with the aim of achieving short term benefits. On the other hand construction literature have criticised this ad-hoc IT usage and linked that to the relative slow IT adoptability in construction. This in effect challenges the allocation of a fixed place for Technology Change Management as a Key Process Area with the construction process improvement, from the organisation maturity point of view.
Training self-supervised class-conditional GAN by adjusting categorical latent distribution

Class-conditional GAN is a conditional GAN that can generate class-conditional distribution. Among class-conditional GANs, InfoGAN with categorical latent distribution can generate class-conditional data through a self-supervised (unsupervised) method without labeled data. Instead, InfoGAN requires optimal categorical latent distribution to train the model. In this paper, we propose a novel GAN that allows the model to perform self-supervised class-conditional data generation and clustering. The proposed method uses Bayesian inference to estimate optimal categorical latent distribution from the classifier output distribution. In the proposed method, based on the classifier output distribution of the fake data and the current categorical latent distribution, the categorical latent distribution is updated to fit the classifier output distribution of the real data. As training progresses, the entropy of the categorical latent distribution gradually decreases and converges to the appropriate value. The approximated categorical latent distribution becomes appropriate to represent the discrete part of the data distribution. The proposed method does not require labeled data, optimal categorical latent distribution, and a good metric to calculate the distance between data. Also, a classifier used in training can be used for clustering.
The antiaging protein Klotho enhances oligodendrocyte maturation and myelination of the CNS

The antiaging protein Klotho enhances oligodendrocyte maturation and myelination of the CNS" (2013). Cell and Developmental Biology Publications and Presentations. Paper 114. Comments Copyright of all material published in The Journal of Neuroscience remains with the authors. The authors grant the Society for Neuroscience an exclusive license to publish their work for the first 6 months. After 6 months the work becomes available to the public to copy, distribute, or display under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported license. At the time of publication, Eustathia Lela Giannaris was not yet affiliated with the University of Massachusetts Medical School. We have previously shown that myelin abnormalities characterize the normal aging process of the brain and that an age-associated reduction in Klotho is conserved across species. Predominantly generated in brain and kidney, Klotho overexpression extends life span, whereas loss of Klotho accelerates the development of aging-like phenotypes. Although the function of Klotho in brain is unknown, loss of Klotho expression leads to cognitive deficits. We found significant effects of Klotho on oligodendrocyte functions, including induced maturation of rat primary oligodendrocytic progenitor cells (OPCs) in vitro and myelination. Phosphoprotein analysis indicated that Klotho's downstream effects involve Akt and ERK signal pathways. Klotho increased OPC maturation, and inhibition of Akt or ERK function blocked this effect on OPCs. In vivo studies of Klotho knockout mice and control littermates revealed that knockout mice have a significant reduction in major myelin protein and gene expression. By immunohistochemistry, the number of total and mature oligodendrocytes was significantly lower in Klotho knockout mice. Strikingly, at the ultrastructural level, Klotho knockout mice exhibited significantly impaired myelination of the optic nerve and corpus callosum. These mice also displayed severe abnormalities at the nodes of Ranvier. To decipher the mechanisms by which Klotho affects oligodendrocytes, we used luciferase pathway reporters to identify the transcription factors involved. Together, these studies provide novel evidence for Klotho as a key player in myelin biology, which may thus be a useful therapeutic target in efforts to protect brain myelin against age-dependent changes and promote repair in multiple sclerosis.
Optimistic Thompson Sampling-based algorithms for episodic reinforcement learning

We propose two Thompson Sampling-like, model-based learning algorithms for episodic Markov decision processes (MDPs) with a ﬁnite time horizon. Our proposed algorithms are inspired by Op-timistic Thompson Sampling (O-TS), empirically studied in Chapelle and Li [2011], May et al. [2012] for stochastic multi-armed bandits. The key idea for the original O-TS is to clip the posterior distribution in an optimistic way to ensure that the sampled models are always better than the empirical models. Both of our proposed algorithms are easy to implement and only need one posterior sample to construct an episode-dependent model. Our ﬁrst algorithm, Optimistic Thompson Sampling for MDPs (O-TS-MDP), achieves a (cid:101) O (cid:16) √ AS 2 H 4 T (cid:17) regret bound, where S is the size of the state space, A is the size of the action space, H is the number of time-steps per episode and T is the number of episodes. Our second al-gorithm, Optimistic Thompson Sampling plus for MDPs (O-TS-MDP + ), achieves the (near)-optimal (cid:101) O (cid:16) √ ASH 3 T (cid:17) regret bound by taking a more aggressive clipping strategy. Since O-TS was only empirically studied previously, we derive regret bounds of O-TS for stochastic bandits. In addition, we propose, O-TS-Bandit + , a randomized version of UCB1 [Auer et al., 2002], for stochastic bandits. Both O-TS and O-TS-Bandit + achieve the optimal
QUALITATIVE RESEARCH IN PHARMACY EDUCATION A Review of Strategies for Enhancing Clarity and Reader Accessibility of Qualitative Research Results

Objective. To characterize elements of the results section of qualitative research reports that make ﬁndings more accessible to readers. Methods. Two analytical methods were used for this review. First, published reviews and textbooks written by experts outlining how to evaluate qualitative research were retrieved and reviewed to identify common elements that enhance clarity of the results section. In the second analysis, the authors analyzed the results sections of a subset of qualitative studies to identify, from a reader’s point of view, aspects that enhanced and detracted from communication of the results. Findings. Four elements improve accessibility of the results section for readers of qualitative research reports. Content, the ﬁrst element, describes what information the reader should look for in the results section. Style of results, the second element, identiﬁes wording choices that improve reader accessibility and understanding. Narrative ﬂow, the third element, describes a results section that ﬂows smoothly and logically. Structural cohesiveness, the ﬁnal element, outlines effective organization of the results section. Results. While authors take several approaches to the presentation of results in qualitative research reports, some strategies appear to be more common and effective than others. The efﬁcient presentation of results can impact a reader’s assessment of the quality and credibility of a study. Identiﬁed content and stylistic elements should be considered by authors hoping to make the results of their qualitative research more accessible and comprehensible to readers.
Multi-view Contrastive Learning Hypergraph Neural Network for Drug-Microbe-Disease Association Prediction

Identifying the potential associations among drugs, microbes and diseases is of great significance in exploring the pathogenesis and improving precision medicine. There are plenty of computational methods for pair-wise association prediction, such as drug-microbe and microbe-disease associations, but few methods focus on the higher-order triple-wise drug-microbe-disease (DMD) associations. Driven by the advancement of hypergraph neural networks (HGNNs), we expect them to fully capture high-order interaction patterns behind the hypergraph formulated by DMD associations and realize sound prediction performance. However, the confirmed DMD associations are insufficient due to the high cost of in vitro screening, which forms a sparse DMD hypergraph and thus brings in suboptimal generalization ability. To mitigate the limitation, we propose a Multi-view Contrastive Learning Hypergraph Neural Network, named MCHNN, for DMD association prediction. We design a novel multi-view contrastive learning on the DMD hypergraph as an auxiliary task, which guides the HGNN to learn more discriminative representations and enhances the generalization ability. Extensive computational experiments show that MCHNN achieves satisfactory performance in DMD association prediction and, more importantly, demonstrate the effectiveness of our devised multi-view contrastive learning on the sparse DMD hypergraph.
Laboratory Information Management System for NGS Genomics Labs

The goal of genome sequencing is to unravel the ordered sequence of nucleic acids that form the DNA or RNA of a given sample. Genome sequencing lab requires the ability to select and track a large amount of samples through many experimental steps. Therefore, laboratory information management system (LIMS) is needed to provide a way of automating the laboratory experimental procedures and track the samples. LIMSs have been proposed and developed for many years, but still remain difficult for labs to implement successfully. In this paper, we demonstrate our genomic next generation sequencing (NGS) LIMS solution. We developed a web-based LIMS with flexible configuration and customization for NGS laboratories, and can help laboratories track samples and optimize experimental procedures and business workflows. We also describe our solution of integrating LIMS with the existing enterprise business information systems. Finally, we share our experience for the implementation of a successful LIMS.
Unsupervised network anomaly detection. (Détection non supervisée d'anomalies dans les réseaux de communication)

Anomaly detection has become a vital component of any network in today's Internet. Ranging from non-malicious unexpected events such as flash-crowds and failures, to network attacks such as denials-of-service and network scans, network traffic anomalies can have serious detrimental effects on the performance and integrity of the network. The continuous arising of new anomalies and attacks create a continuous challenge to cope with events that put the network integrity at risk. Moreover, the inner polymorphic nature of traffic caused, among other things, by a highly changing protocol landscape, complicates anomaly detection system's task. In fact, most network anomaly detection systems proposed so far employ knowledge-dependent techniques, using either misuse detection signature-based detection methods or anomaly detection relying on supervisedlearning techniques. However, both approaches present major limitations: the former fails to detect and characterize unknown anomalies (letting the network unprotected for long periods) and the latter requires training over labeled normal traffic, which is a difficult and expensive stage that need to be updated on a regular basis to follow network traffic evolution. Such limitations impose a serious bottleneck to the previously presented problem. We introduce an unsupervised approach to detect and characterize network anomalies, without relying on signatures, statistical training, or labeled traffic, which represents a significant step towards the autonomy of networks. Unsupervised detection is accomplished by means of robust data-clustering techniques, combining Sub-Space clustering with Evidence Accumulation or Inter-Clustering Results Association, to blindly identify anomalies in traffic flows. Correlating the results of several unsupervised detections is also performed to improve detection robustness. The correlation results are further used along other anomaly characteristics to build an anomaly hierarchy in terms of dange rousness. Characterization is then achieved by building efficient filtering rules to describe a detected anomaly. The detection and characterization performances and sensitivities to parameters are evaluated over a substantial subset of the MAWI repository which contains real network traffic traces. Our work shows that unsupervised learning techniques allow anomaly detection systems to isolate anomalous traffic without any previous knowledge. We think that this contribution constitutes a great step towards autonomous network anomaly detection. This PhD thesis has been funded through the ECODE project by the European Commission under the Framework Programme 7. The goal of this project is to develop, implement, and validate experimentally a cognitive routing system that meet the challenges experienced by the Internet in terms of manageability and security, availability and accountability, as well as routing system scalability and quality. The concerned use case inside the ECODE project is network anomaly detection.
Differential gene expression detection and sample classification using penalized linear regression models

Differential gene expression detection and sample classification using microarray data have received much research interest recently. Owing to the large number of genes p and small number of samples n (p >> n), microarray data analysis poses big challenges for statistical analysis. An obvious problem owing to the 'large p small n' is over-fitting. Just by chance, we are likely to find some non-differentially expressed genes that can classify the samples very well. The idea of shrinkage is to regularize the model parameters to reduce the effects of noise and produce reliable inferences. Shrinkage has been successfully applied in the microarray data analysis. The SAM statistics proposed by Tusher et al. and the 'nearest shrunken centroid' proposed by Tibshirani et al. are ad hoc shrinkage methods. Both methods are simple, intuitive and prove to be useful in empirical studies. Recently Wu proposed the penalized t/F-statistics with shrinkage by formally using the (1) penalized linear regression models for two-class microarray data, showing good performance. In this paper we systematically discussed the use of penalized regression models for analyzing microarray data. We generalize the two-class penalized t/F-statistics proposed by Wu to multi-class microarray data. We formally derive the ad hoc shrunken centroid used by Tibshirani et al. using the (1) penalized regression models. And we show that the penalized linear regression models provide a rigorous and unified statistical framework for sample classification and differential gene expression detection.