Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning in evolving video streams

With the advent of video surveillance networks, making sense of an ever-increasing amount of visual data is a growing desire and need in many applications. The automatic (re-)identification of individuals across camera views, which is known as person re-identification (ReID), has been one of the most challenging problems in the field of video analysis. Current algorithms assume that a one time training session in where labelled observations of all the classes to be observed is available. Furthermore, the visual streams are recorded during a limited time frame. These assumptions are a good approximation to some of the real-life problems, however, in many of the applications some of the previous assumptions are violated, rendering the conventional algorithms suboptimal or impractical. In this work, we look at the problem of human re-identification from a time-series mining perspective where visual streams are generated endlessly and initially we only have a vague knowledge of what concepts can be observed during the surveillance period. We formalize a learning concept suitable for multi-camera video surveillance and propose a learning methodology adapted to that new paradigm. We build a framework that resorts to evolving approaches, specifically ensembles, to track the environment online, and adapt the knowledge to accommodate the environment changes accordingly. The framework is also designed to exploit active learning strategies, in order to interact wisely with oracles, requesting assistance in more ambiguous to classify observations. First, we utilize conventional discriminative ensembles to learn from streams generated in wide-area surveillance. The framework is then extended with class-based ensembles, where an individual ensemble is trained for every single class. Despite significant improvement in performance, stability in high-dimensional visual data and finding a robust novel class detection criterion are still the main concerns. Therefore, we improve the framework resorting to the universal background models to robustly learn individual object models from small samples and to more effectively detect novel classes. However, the framework may fall into the pitfall of the growing computational complexity, when faced with never-ending streams. To avoid the effect of stability-placity dilemma without sacrificing and possibly enhancing the performance, an intelligent learning algorithm is introduced that wisely develops over time with respect to drift level in order to reflect the latest concepts. The framework emphasises on coverage and accuracy as well as efficiency. We also propose a context-sensitive solo representation for video fragments which employs a novel unsupervised criterion to give more credit to more “trustworthy” observations. In the numerous experiments that are reported, experimental evidence of the effectiveness of the learning framework and the representation scheme is provided.
Molecular investigation of tRNA genes integrity and its relation to pathogenicity islands in Shiga toxin-producing Escherichia coli (STEC) strains

tRNA genes are known target sites for the integration of pathogenicity islands (PAI) and other genetic elements, such as bacteriophages, into bacterial genome. In most STEC (Shiga toxin-producing Escherichia coli), the PAI called LEE (locus of enterocyte effacement) is related to bacterial virulence and is mostly associated to the tRNA genes selC and pheU. In this work, we first investigated the relationship of LEE with tRNA genes selC and pheU in 43 STEC strains. We found that 28 strains (65%) had a disrupted selC and/or pheU. Three of these strains (637/1, 650/5 and 654/3) were chosen to be submitted to a RAPD-PCR technique modified by the introduction of specific primers (corresponding to the 5'end of genes selC and pheU) into the reaction, which we called "anchored RAPD-PCR". The PCR fragments obtained were transferred onto membranes, and those fragments which hybridized to selC and pheU probes were isolated. One of these fragments from strain 637/1 was partially sequenced. An 85-nucleotide sequence was found to be similar to the cfxA2 gene that encodes a beta-lactamase and is part of transposon Tn4555, a pathogenicity island originally integrated into the Bacteroides genome.
jmbReview Madurahydroxylactone , an Inhibitor of Staphylococcus aureus FtsZ from Nonomuraea sp . AN 100570

Multidrug-resistant bacteria such as methicillin-resistant Staphylococcus aureus (MRSA) have become a significant global health concern [1-3]. One approach to combat antibiotic resistance is to identify new drugs that function through novel mechanisms of action [4]. FtsZ, a bacterial cell-division protein, is an attractive target for antibacterial drug discovery, since the target is essential for the viability of bacteria and well conserved throughout bacterial species [5-7]. During bacterial cell division, FtsZ undergoes GTPdependent polymerization to form the Z ring at the midcell. So far, few compounds have been reported to inhibit FtsZ and subsequently inhibit bacterial cell division [8-11]. The compounds berberine [12], sanguinarine [13], and viriditoxin [10, 14] were reported as FtsZ inhibitors of natural origin. As a synthetic inhibitor, PC190723 was reported to be a potent inhibitor of S. aureus FtsZ in both in vitro and in vivo mice infection models, which demonstrated FtsZ as an antibacterial target [8, 15]. Microorganisms have been a rich source for new antibiotics. Until now, only viriditoxin is reported as a FtsZ inhibitor of microbial origin [10]. Thus, more unique FtsZ inhibitors are needed from microorganisms. In the course of screening for an inhibitor of S. aureus FtsZ from microbial sources, a benzo[a]naphthacenequinonetype metabolite, madurahydroxylactone (1), and four related derivatives 2-5 were isolated from the fermentation broth of an actinomycetal strain, AN100570 (Fig. 1). Madurahydroxylactone was previously isolated as a potent antibacterial from Nonomuria rubra (formerly Actinomadura rubra) [16]. Its antibacterial mechanism, however, has not been reported yet. In this paper, we report that 1 showed its potent antibacterial activity by inhibiting FtsZ. Additionally, the NMR assignments of 1-5 were reported in this study for the first time. The actinomycetal strain AN100570 was isolated from soil collected near Gongju city, Chungcheongnam-do, Korea. The strain was identified as Nonomuraea sp. based on the 16S rRNA sequence. Culture was carried out in a liquid Received: August 18, 2017 Revised: September 1, 2017 Accepted: September 6, 2017
Netnography as a marketing research tool in the fashion industry in Southeast Europe

In the past decades, marketing has been revolutionized by digital sources, which provide marketers with rich information on potential consumers. Consequently, this article explores the evolving opportunities that online communities present to marketers in collecting consumer insights. It advances Southeast Europe’s marketing researchers’ understanding of netnography by introducing them to its concept, procedures, and implications. This study triangulates the data through utilizing seven one-to-one in-depth interviews with fashion designers, employing two focus groups with fashion consumers who actively congregate in online communities, and through conducting netnography on a fashion-related online community. This article demonstrates netnography practices, and experiences with the goal of having fashion designers and marketers understand its potential as an efficient method for providing effective qualitative market intelligence. It shows that netnography is a relatively easy, cost-effective and time-efficient approach, and it supports brand development through achieving a better understanding of consumer perceptions. Overall, netnography has great potential as a marketing research tool. Online fashion community members’ views support it as most of them prefer to participate in netnographic research. Nevertheless, the majority of fashion designers in Southeast Europe are not fully aware of the method and its exact procedures and, hence, avoid using it.
Near-Infrared Emitting AgInS2/ZnS Nanocrystals

Near-infrared emitting AgInS2/ZnS nanocrystals were synthesized by carefully controlling the growth conditions in a Ag/In/Zn/S solution with less zinc relative to the other precursors. The role of having a smaller amount of zinc (8 atom %) was systematically investigated in an effort to understand the mechanism of the largely red-shifted emission. The AgInS2/ZnS nanocrystals can be transferred to aqueous solutions while retaining the emission intensity. The near-infrared emission and solubility in aqueous solutions make AgInS2/ZnS nanocrystals excellent candidates for bioimaging and photocatalytic applications.
Grand Minimum of the Total Solar Irradiance Leads to the Little Ice Age

Quasibicentennial variation of the energy solar radiation absorbed by the Earth remains uncompensated by the energy emission to space over the interval of time that is determined by the thermal inertia. That is why the debit and credit parts of the average annual energy budget of the terrestrial globe with its air and water envelope are always in an unbalanced state, which is the basic state of the climatic system. The average annual balance of the thermal budget of the system Earth-atmosphere during long time periods will reliably determine the course and value of both the energy excess accumulated by the Earth or the energy deficit in the thermal budget, which, with account for the data of the forecasted variations of the Total Solar Irradiance (TSI) in the future, can define and predict well in advance the direction and amplitude of the forthcoming climate changes with high accuracy. Since the early 90 has been observed a decrease in both the TSI and the portion of energy absorbed by the Earth. The Earth as a planet will also have a negative balance in the energy budget in the future, because the Sun has entered the decline phase of the quasibicentennial cycle of the TSI variations. This will lead to a drop in the temperature and to the beginning of the epoch of the Little Ice Age approximately since the year 2014. The increase in the Bond (global) albedo and the decrease in the greenhouse gases concentration in the atmosphere will lead to an additional reduction of the absorbed solar energy and reduce the greenhouse effect. The influence of the consecutive chain of feedback effects will lead to additional drop of temperature, which can surpass the influence of the effect of the TSI decrease. The start of Grand Maunder-type Minimum of the TSI of the quasibicentennial cycle is to be anticipated around the year 2043 ± 11 and the beginning of the phase of deep cooling of the 19th Little Ice Age in the past 7,500 years in the year 2060 ± 11. Longterm cyclic variations of the TSI are the main fundamental cause of the corresponding climate variations.
Face recognition based on image sets

In this thesis, we study the problem of face recognition based on image sets. The main objective of our work is to develop set-based distance metrics that are able to measure the similarity between image sets, rather than conventional distance metrics that can only measure the distance between samples. The face images obtained from real-life impose great challenges to the conventional face recognition systems. Large variations in appearances and various imperfections such as occlusions and misalignments in the face images severely degrade the recognition performance. One possible solution is to utilize more face images of each person for testing, e.g., a collection of photos from personal galleries or frames extracted from a video clip. Under such circumstances, the face recognition task becomes the process of modeling and matching image sets. Our investigation then focuses on developing appropriate models and set-based distance metrics for representing and matching different image sets. In general, the methods for solving the problem of face recognition based on image sets can be divided into two predominant categories, namely the single model-based methods and the multi-model based methods. Firstly, we investigate the single modelbased methods to exploit its nice properties such as computational efficiency. Through a close look into the existing methods, we propose a generalized subspace distance (GSD) framework to illustrate the underlying relationships among the existing methods, which can be considered as special cases of the proposed framework. In view of the unsupervised
Deriving Novel Posterior Feature Spaces For Conditional Random Field - Based Phone Recognition

Conditional Random Fields (CRFs) are undirected graphical models that can be used to define the joint probability distribution over a label sequences given a set of observation sequences to be labeled. A key advantage of CRFs is their great flexibility to include a wide variety of non-independent features of the input. Faced with this freedom, an important question remains: what features should be used? This thesis describes two techniques for deriving novel features for use in Conditional Random Fields-based phone recognition, extending previous techniques that incorporated multiclass posteriors of phone classes or phonological features estimated by Multi-Layer Perceptrons. The first technique investigates the integration of suprasegmental knowledge into the MLP classification system that is part of the CRF recognizer. CRFs are used to integrate MLP posterior estimates, particularly of phonological features or phonetic classes, which stand in as representations of the acoustics; this thesis shows that incorporating suprasegmental information as part of the MLP classification system augments the acoustic space in a beneficial way for phonological feature based CRF models. TIMIT phone recognition experiments show a small but statistically significant improvement due to both techniques. The second experiment combines phonological feature scores from two different systems that gives a statistically significant improvement in Conditional Random
Can Financialization Save Nature? The Case of Endangered Species†

Purpose: The purpose of the study is to contribute to our understanding of how biodiversity accounting is being shaped, and more particularly how conservation performance measures are being built and used. Notably, the case study analyzes the mediating role of the new accounting towards the different stakeholders. This research also furthers our understanding of the role of analogy with accounting and finance in building sustainability accounting innovations. Finally, it emphasizes the power of visuals in mediating conservation performance. Design/methodology/approach: This study uses a qualitative approach. Data was collected through 18 semi-directed interviews within the organization, and one with a peer organization’s CEO. Secondary data was collected via internal documents related to the tool being presented in board meetings and workshops, public data related to the organization and media output around the launch of the index. Findings: I analyze how the index for conservation performance was territorialized via two means: first the embedding of the tool within a wide array of trusted scientific elements of conservation measures, and secondly through constant reference to financial terms used to incarnate a sense of urgent necessity for its deployment. Secondly, I analyze the mediating power of the visuals developed to communicate conservation performance. Finally, I examine how the new tool allows for both adjudicating and subjectivizing of people and species. Originality/value: Through this empirical setting, I was able to analyze the first performance measurement tool designed by a conservation NGO. The theoretical framework of Power and Miller (2013) is mobilized to frame the development of how conservation is being territorialized, how it becomes a mediating tool and allows adjudication of conservation.
Progress in String Theory - TASI 2003 Lecture Notes

# D-Branes on Calabi-Yau Manifolds (P S Aspinwall) # Lectures on AdS/CFT (J M Maldacena) # Tachyon Dynamics in Open String Theory (A Sen) # TASI/PITP/ISS Lectures on Moduli and Microphysics (E Silverstein) # The Duality Cascade (M J Strassler) # Perturbative Computations in String Field Theory (W Taylor)
Performance of resource access protocols:time measurement of multicore real time resource access protocols

A clear trend [1] in computers and electronics is the rapid increase in the number of cores per chip. These extra cores share access to resources such as I/O and memory. To keep a favourable scalability of code executed vs number of cores, it is important to have efficient access to shared resources. Cores that share resources might get blocked by the resource access of another core. Widely used resource access protocols either wait for the resource to become available or already start processing some other task in the meanwhile. Both of these resource access protocols have their own advantages. Non-preemptive waiting on the release of the resource allows a task to directly start executing the moment the resource becomes available. Instead of waiting, allowing other tasks to process in the meanwhile means processing time is spend on tasks instead of waiting. Using a resource access protocol that sometimes waits on the release of a resource and sometimes allows other tasks to execute can benefit from the advantages of both protocols. The project consists of the implementation of new resource locking techniques proposed by Afshar et al in [2] and [3]. Measurements of introduced time and memory overhead of flexible spin-lock protocols are provided.
A Film Recommendation Method Based on Collaborative Filtering

Users looking for a favorite video in vast amounts of network resources often need frequent operating, and personalized recommendation service can be an effective solution to this problem. Against the current lower recommendation accuracy, this paper presents an improved recommendation method based on collaborative filtering. It determines a movies subset that is recommended according to the past records of similar users namely neighbors set. Then it mines the preferences of current user, establishes the interest model of current user, and matches with the movies to recommend. Recommendation is in accordance with the level of matching degree. Afterwards, it classifies the film sets that are recommended to adapt to multi-user viewing in families. Additionally, it recommends similar films in the system early running to solve the cold-start problem in a certain degree. Experimental results show that the improved recommended method has distinct higher recommendation accuracy than the existing collaborative filtering algorithm.
A User-centered Approach for Optimizing Information Visualizations

The optimization of information visualizations is time consuming and expensive. To reduce this we propose an improvement of existing optimization approaches based on user-centered design, focusing on readability, comprehensibility, and user satisfaction as optimization goals. The changes comprise (1) a separate optimization of user interface and representation, (2) a fully automated evaluation of the representation, and (3) qualitative user studies for simultaneously creating and evaluating interface variants. On the basis of these results we are able to find a local optimum of an information visualization in an efficient way.
Klasterisasi Judul Buku dengan Menggunakan Metode K-Means

Secara umum perpustakaan merupakan tempat yang menyediakan berbagai bahan pustaka yang digunakan untuk memenuhi kebutuhan semua orang. Koleksi buku yang semakin banyak di Perpustakaan akan memudahkan banyak orang untuk mencari studi pustaka yang diinginkan. Namun banyaknya koleksi buku juga akan menyulitkan dalam pengelolaan letak buku di perpustakaan sehingga ada beberapa buku yang tidak terbaca dan tidak terpinjam. Kemudian untuk penyelesaian masalah tersebut maka akan diterapkan teknik clustering dengan metode K - Means pada pengelolaan buku di perpustakaan. Teknik clustering merupakan sebuah teknik pengelompokan sejumlah data/obyek ke dalam cluster (group) sehingga dalam setiap cluster aka n berisi data yang semirip mungki.n Pada penelitian ini, teknik clustering tersebut akan diterapkan pada Perpustakaan Pusat Universitas Islam Indonesia. Teknik clustering akan mengelompokan judul buku sesuai dengan kategorinya. Buku-buku yang memiliki cluster yang sama akan digunakan sebagai bahan untuk analisis dalam pengambilan keputusan yang bertujuan untuk mempermudah pustakawan(petugas perpustakaan) dalam pengelolaan peletakan buku yang diminati dan merancang strategi dalam meningkatkan minat baca maha siswa
Investigating the Mechanism of Programmed Nuclear Destruction during Yeast Sporulation

In the presence of a non-fermentable carbon source, nitrogen-starved diploid cells of the yeast Saccharomyces cerevisiae undergo a meiotic program called sporulation to form gametes called spores. While four spores are produced under standard laboratory sporulation conditions, spore number is known to be regulated by carbon availability: under carbon-depleted conditions, yeast cells package a portion of the four haploid meiotic nuclei into spores. Our lab has demonstrated that these unpackaged meiotic products undergo programmed nuclear destruction (PND) that is associated with apoptotic-like DNA fragmentation. Nevertheless, the mechanism that mediates PND remained to be elucidated. Here, I describe the execution of PND through an unusual form of autophagy that has not been documented previously in yeast. This form of autophagy is most similar to megaautophagy in plants and lysosomal membrane permeabilization in mammals. My results demonstrate further diversity in cell death programs in unicellular microbes that is potentially conserved across eukaryotes.
Neuroinflammation and protein aggregation co-localize across the frontotemporal dementia spectrum

Bevan-Jones, Cope et al. report that neuroinflammation co-localizes with protein aggregation in all major types of frontotemporal dementia, both in vivo with positron emission tomography, and at post mortem. In vivo neuroinflammation patterns are disease-specific and can accurately classify patients into groups.
Cost of Maintaining CRP in Presence of Biofuels

We analyze the effect of an emergence of biofuel industry on the Conservation Reserve Program. The government expenditure on Conservation Reserve Program needs to increase dramatically to keep the current scale of CRP program when the biofuel industry is considered. We propose that the development of bioenergy crops on expiring CRP land is a potential way to reconcile the conflict between a sharp increase in government CRP budget and its environmental protection goal. CRP program can also be combined with the Biomass Crop Assistance Program (BCAP) to achieve the goal of environmental protection and low carbon society at the same time.
APPLICATION OF FUZZY NEURAL COMPUTING IN CIRCUIT PARTITIONING

Fuzzy set theory is adopted in a new neural computing algorithm-fuzzy neural' computing. It has been proved that the new algorithm gets rid of two fatal defects facing most of the existing neural computing algorithms, known as the slow training speed and network parameter sensibility. It is well suited to general pattern classification tasks. Application to the partitioning of electrical circuits show that Fuzzy neural computing is superior to the Kohonen's self-organizing neural computing both in the sense of training speed and final results.
Impact of attribute selection on the accuracy of Multilayer Perceptron

Data classification is an essential operation in any data analysis process. The purpose of classification techniques is to accurately classify the data. It has been found that whenever attribute selection techniques are applied before classification, accuracy is improved significantly. Here we present the performance analysis of Multi- Layer Perceptron classification technique when applied before and after six mostly used attribute selection techniques. The experiment showed significant improvements. The experiments are conducted using WEKA 3.6. 10 tool.