Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic clustering of gene ontology by genetic algorithm

Abstract—Nowadays, Gene Ontology has been used widely by many researchers for biological data mining and information retrieval, integration of biological databases, finding genes, and incorporating knowledge in the Gene Ontology for gene clustering. However, the increase in size of the Gene Ontology has caused problems in maintaining and processing them. One way to obtain their accessibility is by clustering them into fragmented groups. Clustering the Gene Ontology is a difficult combinatorial problem and can be modeled as a graph partitioning problem. Additionally, deciding the number k of clusters to use is not easily perceived and is a hard algorithmic problem. Therefore, an approach for solving the automatic clustering of the Gene Ontology is proposed by incorporating cohesion-and-coupling metric into a hybrid algorithm consisting of a genetic algorithm and a split-and-merge algorithm. Experimental results and an example of modularized Gene Ontology in RDF/XML format are given to illustrate the effectiveness of the algorithm
Aphasia and its therapy

1. A historical overview 2. Classification of the aphasias 3. Aphasia therapy from World War I to the 1970's 4. Efficacy of aphasia therapy 5. Cognitive neuropsychology 6. The lexicon 7. Cognitive rehabilitation 8. In search of a theory of aphasia therapy 9. Rehabilitation of lexical and sentence disorders 10. Severe aphasia and pragmatics 11. Final remarks Appendix 1 Appendix 2
Glial Reactivity in Ciliary Neurotrophic Factor-Deficient Mice after Optic Nerve Lesion

There is evidence that ciliary neurotrophic factor (CNTF), in addition to its neurotrophic activity, positively regulates astrogliosis after CNS injury. CNTF and its receptor, CNTFRα, are strongly upregulated in activated astrocytes. Application of CNTF upregulates GFAP expression in cultured astrocytes and induces various aspects of gliosis in the intact brain. Here we examined whether inactivation of the CNTF gene results in the expected changes in glial reactivity by analyzing gliosis in the superior colliculus (SC) after optic nerve crush. Basal expression levels of GFAP and vimentin in unlesioned CNTF-deficient mice were reduced by 66 and 37%, respectively. Absolute numbers of astrocytes were found not to be different. Surprisingly, however, lesion induced robust activation of astrocytes in CNTF-deficient mice; the time course of activation was even accelerated as compared with wild-type animals. At later time points, activation reached the same level. With respect to microglial cells, basal expression of microglial markers was unaltered in CNTF–knock-out animals. Lesion-induced upregulation of Iba-1, ICAM-1, and F4/80 in microglial cells was unaffected in CNTF-deficient animals. Differences were observed with respect to the time course of microglial activation, different markers being affected differentially. We further demonstrate that lesion induces upregulation of CNTF-related cytokines (LIF, NNT-1) and, interestingly, a more pronounced upregulation of cytokine receptor components (LIF receptor β, gp130) and TGFβ in CNTF-deficient animals. Our results thus indicate that CNTF is required for the development and maintenance of the mature astrocyte phenotype and provide evidence that CNTF is part of the complex regulatory network modulating lesional glial reactivity after lesion.
Investigations on Line-Edge Roughness (LER) and Line-Width Roughness (LWR) in Nanoscale CMOS Technology: Part I–Modeling and Simulation Method

In this paper, the correlation between line-edge roughness (LER) and line-width roughness (LWR) is investigated. Based on the characterization methodology of auto-correlation functions (ACF), a new theoretical model of LWR is proposed, which indicates that the LWR ACF is composed of two parts: one involves LER information; the other involves the cross-correlation of the two edges. Additional characteristic parameters for LER/LWR are proposed to represent the missing cross-correlation information in conventional approaches of LER/LWR description, other than LER/LWR amplitude and auto-correlation length. An improved simulation method for correlated LERs is also proposed, which can provide helpful guidelines for the characterization, modeling, and the optimization of LER/LWR in nanoscale CMOS technology. The experimental results and device simulation results are discussed in detail in the part II of this paper.
Clinical involvement and protein expression in individuals with the FMR1 premutation.

Most individuals with the fragile X premutation are clinically unaffected; however, some show clinical manifestations, including learning difficulties, emotional problems, or even mental retardation. The basis of clinical involvement in these individuals is unknown. Premutation alleles are reportedly associated with normal levels of mRNA and protein (FMRP). To examine this issue in more detail, we studied six individuals with a premutation. We are reporting these cases to demonstrate a spectrum of phenotypic involvement which can be seen clinically. These cases include one individual with the premutation who has no evidence of FMR1 gene dysfunction but has mental retardation from other causes. Other cases presented here show varying degrees of FMR1 gene dysfunction as assessed by FMRP and FMR1 mRNA levels and various clinical features of fragile X. In two cases we observed a significant reduction in FMRP expression and an elevated FMR1 mRNA expression level associated with moderate cognitive deficit. Thus, the utilization of FMRP measures can be helpful in understanding for which premutation patients clinical involvement is caused by dysfunction of the FMR1 gene.
Source Address Filtering for Large Scale Network: A Cooperative Software Mechanism Design

Source address filtering is used as an important mechanism to prevent malicious traffic. Currently, most networks store filters in hardware such as TCAM, which has limited capacity, high power consumption and high cost. Although software can accommodate large number of filters, it needs multiple accesses to memory on the border router, which bears much more additional burden than other routers. In this paper, we propose a software-based mechanism for source address filtering. In our mechanism, we only need to check a few bits in source addresses on each router, rather than checking all bits on the ingress router. Through cooperation among routers, our mechanism ensures that malicious traffic will be filtered in the network. We formulate this problem as finding a cooperative scheme such that the loads on all routers are optimally balanced. We show that the problem can be optimally solved by dynamic programming. We evaluate our algorithms using comprehensive simulations with BRITE generated topologies and real world topologies. We conduct a case study on China Education and Research Network 2 (CERNET2) configurations, a large IPv6 network. Compared to checking 128-bit IP addresses on ingress routers, our algorithm checks at most 40 bits on each router.
A distributional attack scenario monitoring system based on dynamic peer-to-peer overlay hierarchy

Many attacks and suspicious actions cause a huge number of alerts raised by Intrusion Detection Systems (IDSs) on Internet. It is very hot to reveal attacks from these alerts. These alerts are high-quantity but low-quality because of many false alerts raised by IDS and non-relevant alerts caused by different attacks or suspicious actions. We find that various attacks usually adopt similar strategies on internet. So, in this paper, we construct a predefined attack scenario to illustrate the behaviors of attacks and detect attacks adopting known strategies on network. To distinguish different attacks dispersed on cyberspace, we implement a prototype on a novel P2P architecture, which can improve the efficiency of detecting attacks significantly. What is more, our prototype can monitor network in unlimited scale online and perform efficiently and accurately.
A diagnostic head and neck fine needle aspiration service can be provided using liquid‐based cytology only

Liquid‐based cytology (LBC) has been used for non‐gynaecological specimens since its introduction into routine use in cervical cytology in the mid‐1990s. There are still relatively few large studies comparing performance in reporting the head and neck fine‐needle aspirations (H&N FNA) processed by LBC only to conventional direct smears (CDS).
Graph Pricing Problem on Bounded Treewidth, Bounded Genus and k-Partite Graphs

Consider the following problem. A seller has infinite copies of $n$ products represented by nodes in a graph. There are $m$ consumers, each has a budget and wants to buy two products. Consumers are represented by weighted edges. Given the prices of products, each consumer will buy both products she wants, at the given price, if she can afford to. Our objective is to help the seller price the products to maximize her profit. This problem is called {\em graph vertex pricing} ({\sf GVP}) problem and has resisted several recent attempts despite its current simple solution. This motivates the study of this problem on special classes of graphs. In this paper, we study this problem on a large class of graphs such as graphs with bounded treewidth, bounded genus and $k$-partite graphs. We show that there exists an {\sf FPTAS} for {\sf GVP} on graphs with bounded treewidth. This result is also extended to an {\sf FPTAS} for the more general {\em single-minded pricing} problem. On bounded genus graphs we present a {\sf PTAS} and show that {\sf GVP} is {\sf NP}-hard even on planar graphs. We study the Sherali-Adams hierarchy applied to a natural Integer Program formulation that $(1+\epsilon)$-approximates the optimal solution of {\sf GVP}. Sherali-Adams hierarchy has gained much interest recently as a possible approach to develop new approximation algorithms. We show that, when the input graph has bounded treewidth or bounded genus, applying a constant number of rounds of Sherali-Adams hierarchy makes the integrality gap of this natural {\sf LP} arbitrarily small, thus giving a $(1+\epsilon)$-approximate solution to the original {\sf GVP} instance. On $k$-partite graphs, we present a constant-factor approximation algorithm. We further improve the approximation factors for paths, cycles and graphs with degree at most three.
Perturbation of central transportation polytopes of order kn n

We describe a perturbation method that can be used to compute the multivariate generating function (MGF) of a non-simple polyhedron, and then construct a perturbation that works for any transportation polytope. Applying this perturbation to the family of central transportation polytopes of order kn n; we obtain formulas for the MGF of the polytope. The formulas we obtain are enumerated by combinatorial objects. A special case of the formulas recovers the results on Birkhoff polytopes given by the author and De Loera and Yoshida. We also recover the formula for the number of maximum vertices of transportation polytopes of order kn n: R´ esum´
Does Marijuana Use Impair Human Capital Formation?

In this paper we examine the relationship between marijuana use and human capital formation by examining performance on standardized tests among a nationally representative sample of youths from the National Education Longitudinal Survey. We find that much of the negative association between cross-sectional measures of marijuana use and cognitive ability appears to be attenuated by individual differences in school attachment and general deviance. However, difference-in-difference estimates examining changes in test scores across 10th and 12th grade reveal that marijuana use remains statistically associated with a 15% reduction in performance on standardized math tests.
Segmentation of the foveal microvasculature using deep learning networks

Abstract. Accurate segmentation of the retinal microvasculature is a critical step in the quantitative analysis of the retinal circulation, which can be an important marker in evaluating the severity of retinal diseases. As manual segmentation remains the gold standard for segmentation of optical coherence tomography angiography (OCT-A) images, we present a method for automating the segmentation of OCT-A images using deep neural networks (DNNs). Eighty OCT-A images of the foveal region in 12 eyes from 6 healthy volunteers were acquired using a prototype OCT-A system and subsequently manually segmented. The automated segmentation of the blood vessels in the OCT-A images was then performed by classifying each pixel into vessel or nonvessel class using deep convolutional neural networks. When the automated results were compared against the manual segmentation results, a maximum mean accuracy of 0.83 was obtained. When the automated results were compared with inter and intrarater accuracies, the automated results were shown to be comparable to the human raters suggesting that segmentation using DNNs is comparable to a second manual rater. As manually segmenting the retinal microvasculature is a tedious task, having a reliable automated output such as automated segmentation by DNNs, is an important step in creating an automated output.
Shameless! Reconceiving the Problem of Plagiarism.

There’s been an awful lot of hysteria lately over the increasing incidence of plagiarism in student assignments. No doubt much of the anguish over the issue derives from recent public reports on ‘soft marking’ of fee-paying students and on students being awarded degrees despite having been caught submitting – shamelessly – somebody else’s work as their own. These reports have been followed by firm statements from university administrations that plagiarists will not be tolerated and that responses to plagiarism will be swift and unswerving. In this respect, Justin Zobel and Margaret Hamilton’s piece in the November issue of Australian Universities Review (2002, pp.23-30) is just one example of the call to take the issue of plagiarism seriously and to punish its perpetrators appropriately. It seems to me, however, that the rush to condemn acts of plagiarism risks riding roughshod over a problem that may turn out to be a far more complex – behaviourally, ethically, conceptually, and even linguistically – than has been previously granted. For instance, it strikes me as significant that there aren’t really any ready substitutes for the word ‘plagiarism’. In The Angus & Robertson Dictionary and Thesaurus in One Volume sitting on my desk, the entry for ‘plagiarise’ lists the terms ‘appropriate’, ‘borrow’, ‘crib (Inf.)’, ‘infringe’, ‘lift (Inf.)’, ‘pirate’, ‘steal’, and ‘thieve’ as its synonyms, while the set thesaurus for Microsoft Word 97 offers to replace ‘plagiarism’ with ‘copying’ (which the thesaurus also lists as the meaning of ‘plagiarism’), ‘lifting’, ‘stealing’, ‘illegal use’, ‘breach of copyright’, or ‘bootlegging’. In both cases the alternatives can be separated roughly into two groups, depending on the terms’ moral charge. Thus ‘copying’ and ‘borrowing’, as morally neutral terms, describe the essential act constituting plagiarism, but without sufficiently indicating the inappropriateness of that act. What is inappropriate about plagiarism, of course, is that it constitutes not simply the ‘copying’ of someone’s work or ideas but rather the unacknowledged copying of such work and the subsequent submission of that work as one’s own. Hence the use of terms like ‘stealing’ and ‘cheating’ in order to underscore the seriousness of the ‘crime’– stressing the immorality of the act in order to encourage all right-minded students not to acquiesce to what might appear to be the easier way. Accordingly, Zobel and Hamilton begin their discussion of strategies for managing student plagiarism by depicting plagiarism (by definition) as a species of cheating: ‘Cheating, and particularly plagiarism – in this context, the inclusion of someone else’s work in an assignment without acknowledgment – is hardly a new phenomenon, but in modern universities it is exceedingly common’ (2002, p. 23).
Using NAS Parallel Benchmarks to evaluate HPC performance in clouds

Cloud computing is a reality nowadays, however there are few studies trying to understand what happens in the actual cloud infrastructures for HPC applications. The focus of this study is the evaluation of NAS Parallel Benchmarks on cloud computing environments. We analyze the execution of applications from NAS Parallel Benchmarks (LU and SP), comparing the execution behavior in different infrastructures: a public cloud, a private cloud and a NUMA multiprocessor system. Our broad goal is to estimate the performance of actual HPC applications on cloud, based on its communication characteristics. We conclude that HPC users should be careful with Virtual Machines with higher virtual CPU count, thanks to Google usage of Hyper-Threading technology and Virtual Machine instances scheduling.
OSCAR: A Collaborative Bandwidth Aggregation System

The exponential increase in mobile data demand, coupled with growing user expectation to be connected in all places at all times, have introduced novel challenges for researchers to address. Fortunately, the wide spread deployment of various network technologies and the increased adoption of multi-interface enabled devices have enabled researchers to develop solutions for those challenges. Such solutions aim to exploit available interfaces on such devices in both solitary and collaborative forms. These solutions, however, have faced a steep deployment barrier. In this paper, we present OSCAR, a multi-objective, incentive-based, collaborative, and deployable bandwidth aggregation system. We present the OSCAR architecture that does not introduce any intermediate hardware nor require changes to current applications or legacy servers. The OSCAR architecture is designed to automatically estimate the system's context, dynamically schedule various connections and/or packets to different interfaces, be backwards compatible with the current Internet architecture, and provide the user with incentives for collaboration. We also formulate the OSCAR scheduler as a multi-objective, multi-modal scheduler that maximizes system throughput while minimizing energy consumption or financial cost. We evaluate OSCAR via implementation on Linux, as well as via simulation, and compare our results to the current optimal achievable throughput, cost, and energy consumption. Our evaluation shows that, in the throughput maximization mode, we provide up to 150% enhancement in throughput compared to current operating systems, without any changes to legacy servers. Moreover, this performance gain further increases with the availability of connection resume-supporting, or OSCAR-enabled servers, reaching the maximum achievable upper-bound throughput.
Social Class in Modern Britain

List of tables Preface Social class and social inequality When is a social class? Constructing the Wright classes Class formation and social mobility The structure of class processes The moral order of a capitalist society Making and unmaking class consciousness Goodbye to social class? Class politics Conclusion Bibliography Appendix - technical details of the British survey Coda - constructing the Goldthorpe classes Index
The challenges and opportunities of teaching sport and exercise psychology at a distance

Distance learning continues to increase in popularity as a teaching method in higher education, but its implementation requires careful planning and consideration. Whilst delivering effective distance learning can be challenging, if planned effectively it can provide great opportunities to both the student and academic that are not always available in face-to-face settings. This article examines the challenges and opportunities of teaching sport and exercise psychology at a distance, using the example of a successful sport and exercise psychology module at the Open University, a UK university specialising in distance education. The flexibility offered by distance learning and the consequent diversity of students are discussed along with the important role of the tutor in ensuring the success of sport and exercise psychology distance learning. Strategies to encourage an applied understanding of the topic area and minimise the perceived distance between students are also considered.
Effective Fault Localization Using Dynamic Slicing and an SMT Solver

Dynamic slicing and spectrum-based fault localization (SFL) are widely used as fault localization methods. While these methods are effective for localizing a faulty statement in a program, they have some practical drawbacks. One of the drawbacks with dynamic slicing is that if a program is large, the sliced program will also remain large in general. One of the drawbacks with SFL is that the suspiciousness of faulty statements may become low, even if only one fault exists in a program. To overcome these drawbacks, we propose an effective fault localization method that iteratively generates test cases that cause an error using a satisfiability modulo theories (SMT) solver such that the result of dynamic slicing for each generated failed test case is distinct. Using test cases generated by our method, the suspiciousness of a faulty statement is always maximum when a target has only one fault. Furthermore, the number of statements that have maximum suspiciousness, that is, the number of fault candidates, is smaller than that obtained by conventional dynamic slicing. In this paper, we explain our proposed method by applying it to MATLAB/Simulink models.
Trauma and the self: A theoretical and clinical perspective.

Constructivist self development theory (CSDT) provides a theoretical framework for understanding the impact of trauma on the self. The theory integrates psychoanalytic theory with theories of social cognition. This article describes the impact of childhood abuse and neglect in a CSDT framework, focusing on the self capacities, or inner abilities that maintain a cohesive, consistent sense of self. The article outlines the psychological and behavioral sequelae of undeveloped self capacities resulting from emotional trauma. It then provides a detailed approach to the psychotherapeutic development of self capacities.
Weakly Supervised Automatic Annotation of Pedestrian Bounding Boxes

Among the components of a pedestrian detector, its trained pedestrian classifier is crucial for achieving the desired performance. The initial task of the training process consists in collecting samples of pedestrians and background, which involves tiresome manual annotation of pedestrian bounding boxes (BBs). Thus, recent works have assessed the use of automatically collected samples from photo-realistic virtual worlds. However, learning from virtual-world samples and testing in real-world images may suffer the dataset shift problem. Accordingly, in this paper we assess an strategy to collect samples from the real world and retrain with them, thus avoiding the dataset shift, but in such a way that no BBs of real-world pedestrians have to be provided. In particular, we train a pedestrian classifier based on virtual-world samples (no human annotation required). Then, using such a classifier we collect pedestrian samples from real-world images by detection. After, a human oracle rejects the false detections efficiently (weak annotation). Finally, a new classifier is trained with the accepted detections. We show that this classifier is competitive with respect to the counterpart trained with samples collected by manually annotating hundreds of pedestrian BBs.