Miracle-dz/UniFAR-FTUs huggingface dataset:
GTI: A Novel Algorithm for Identifying Outlier Gene Expression Profiles from Integrated Microarray Datasets

Background Meta-analysis of gene expression microarray datasets presents significant challenges for statistical analysis. We developed and validated a new bioinformatic method for the identification of genes upregulated in subsets of samples of a given tumour type (‘outlier genes’), a hallmark of potential oncogenes. Methodology A new statistical method (the gene tissue index, GTI) was developed by modifying and adapting algorithms originally developed for statistical problems in economics. We compared the potential of the GTI to detect outlier genes in meta-datasets with four previously defined statistical methods, COPA, the OS statistic, the t-test and ORT, using simulated data. We demonstrated that the GTI performed equally well to existing methods in a single study simulation. Next, we evaluated the performance of the GTI in the analysis of combined Affymetrix gene expression data from several published studies covering 392 normal samples of tissue from the central nervous system, 74 astrocytomas, and 353 glioblastomas. According to the results, the GTI was better able than most of the previous methods to identify known oncogenic outlier genes. In addition, the GTI identified 29 novel outlier genes in glioblastomas, including TYMS and CDKN2A. The over-expression of these genes was validated in vivo by immunohistochemical staining data from clinical glioblastoma samples. Immunohistochemical data were available for 65% (19 of 29) of these genes, and 17 of these 19 genes (90%) showed a typical outlier staining pattern. Furthermore, raltitrexed, a specific inhibitor of TYMS used in the therapy of tumour types other than glioblastoma, also effectively blocked cell proliferation in glioblastoma cell lines, thus highlighting this outlier gene candidate as a potential therapeutic target. Conclusions/Significance Taken together, these results support the GTI as a novel approach to identify potential oncogene outliers and drug targets. The algorithm is implemented in an R package (Text S1).
Recognition of partially occluded objects using perfect hashing: an efficient and robust approach

This paper presents a novel method for recognizing partially occluded objects. The proposed method uses corner points and their spatial relationship perceived through the application of triangular spatial relationship (TSR) in Guru and Nagabhushan (2001) by considering three consecutive corner points at a time. The perceived TSR among corner points are used to create a model object-base using the technique of perfect hashing. The matched sequence is preserved in a two-dimensional matrix called status matrix. Experimental results, on real images of varying complexity of a reasonably large database of objects have established the robustness of the method.
Maternal Expectations of Child Development in Two Cultural Groups in Germany

Cross-cultural research on developmental expectations has shown that the ages mothers expect children can master developmental skills vary according to their cultural background. This study examined the role of culture on developmental expectations by comparing Turkish-German (n = 107) and German (n = 127) mothers with preschool aged children in terms of their estimations regarding children’s mastery on 8 different developmental domains (e.g., cognitive, physical, social). Results have shown that Turkish-German mothers expected children to attain developmental milestones later than German mothers in nearly all domains. But the differences were not quite to the same extent in all domains. Furthermore Turkish-German mothers who grew up in Germany differed in some domains less from German mothers than mothers who grew up in Turkey. This is seen as an evident that parental ethnotheories are not adopted in a determined and probably already dysfunctional mode for the child’s adaptation to its current environment but that they are reinterpreted by integrating new information and reflecting the demands of the environment in order to allow their children an optimal adaptation to their environment.
How do Competing Interest Groups Influence Environmental Policy? The Case of Renewable Electricity in Industrialized Democracies, 1989–2007

In this article, we examine the effect of competing interest groups on environmental policy. We argue that the supporters of environmental policy should be the most influential in the absence of opposition, while the opposition's importance is maximized when the supporter coalition is strong. This highlights an important asymmetry between competing interest groups: supporters are decisive in the absence of opposition, while the opposition is only relevant if the supporters are already strong. We test the argument against data on renewable electricity generation in nineteen OECD countries, 1989–2007. Heavy industries have particularly strong incentives to oppose policies that support renewables, because heavy industries’ profitability depends on inexpensive electricity. We find that the supporter coalition has a positive effect on the growth of renewable electricity generation, but the positive effect diminishes with the strength of manufacturing. Moreover, heavy industry has a negative effect on the growth of renewable electricity generation and this effect increases with the strength of the supporter coalition.
Shakedown response of conditioned shape memory alloy wire

A series of experiments is presented examining the thermo-electro-mechanical response of commercially-available, conditioned, shape memory alloy (SMA) wires (Flexinol, from Dynalloy, Corp.) during cyclic thermomechanical loading. A specialized experimental setup enables temperature control via a thermoelectric/heatsink in thermal contact with the wire specimen during various modes of testing. It allows simultaneous measurement of elongation, load, strain and resistivity in a selected gage length. It also allows full-field optical and infrared imaging to be performed during testing. A moderately high transition temperature NiTi-based shape memory wire (90C Flexinol) is characterized first by differential scanning calorimetry and a series of isothermal experiments over a range of temperatures. Subsequent experiments examine the shakedown behavior over a range of dead loading temperature cycles. Results show a significant two-way shape memory effect, suggesting that both residual stresses and locked-in oriented Martensite are considerable in this commercial alloy. Repeatable behavior (little shakedown) is confirmed at relatively low stress levels, but significant evolution in the response (shakedown behavior) exists at higher stress levels during the first several temperature cycles.
Towards Integrating Hierarchical Goal Networks and Motion Planners to Support Planning for Human Robot Collaboration in Assembly Cells

Low-level motion planning techniques must be combined with high-level task planning formalisms to generate realistic plans that can be carried out by humans and robots. A representative example is planning for fenceless assembly cells where robots can collaborate seamlessly with humans to perform assembly tasks. Key constituent components include assembly sequence generation (Morato, Kaipa, and Gupta 2013), task decomposition between human and robot (Kaipa et al. 2014), system state monitoring (tracking human, robot, and assembly parts) (Morato et al. 2014a), instruction generation for humans (Kaipa et al. 2012), safety (Morato et al. 2014b), and error recovery (Morato et al. 2014a). In order to enable a coherent integration among these components, a high-level planner, interleaved with motion planners, is needed at several levels of the system hierarchy. For example, given a CAD model of a product to be assembled, motion planning methods can generate improved assembly precedence constraints (Morato, Kaipa, and Gupta 2013), which can be compiled into a high-level planning problem. Humans and robots share complimentary strengths. The planning framework can incorporate this knowledge to decompose the tasks effectively. Further, an integral planner must be able to perform plan-repair in order to handle contingencies: (1) low-level deviations in the geometric state without affecting the corresponding symbolic state (e.g., human places part in a wrong posture), which can be corrected at the motion planning level, or (2) deviations in the symbolic state (e.g., human picks incorrect part; improved alternative sequence may or may not exist), which needs to be corrected at both levels of planning. Task planning formalisms typically used to achieve this integration are Classical Planning (Cambon, Alami, and Gravot 2009; Erdem et al. 2011; Dornhege et al. 2009; Burbridge and Dearden 2013) and Hierarchical Task Network (HTN) Planning (Kaelbling and Lozano-Pérez 2011; Hadfield-Menell, Kaelbling, and Lozano-Perez 2013; Wolfe, Marthi, and Russell 2010). Whereas Classical planning is not scalable, HTNs impose stringent completeness requirements on domain models, which are difficult to guarantee in open, dynamic environments. Recently, we
Memantine for the treatment of frontotemporal dementia: a meta-analysis

Background There is no conclusive evidence supporting the efficacy of memantine in frontotemporal dementia (FTD). We conducted a comprehensive meta-analysis of memantine concerning the efficacy and tolerability of memantine in FTD. Methods Studies were identified through searches of PubMed, databases of the Cochrane Library, and PsycINFO citations up to April 10, 2015. Outcomes were Clinical Global Impression (primary), Mini-Mental State Examination, Neuropsychiatric Inventory, and Zarit Burden Interview scores as well as all-cause discontinuation. Standardized mean difference and risk ratio with 95% confidence interval were calculated. Results Two randomized controlled trials (RCTs) (total n=130) met the inclusion criteria. Memantine was marginally superior to placebo as assessed by the Clinical Global Impression scores (standardized mean difference =−0.34, 95% confidence interval =−0.68–0.01, P=0.06). However, there were no significant differences in Mini-Mental State Examination, Neuropsychiatric Inventory, and Zarit Burden Interview scores as well as all-cause discontinuation between memantine and placebo. Conclusion Our results suggest that memantine may benefit FTD patients. However, because only two randomized controlled trials have addressed this issue, further studies using larger samples are needed.
The Emergent semantic web: a consensus approach for deriving semantic knowledge on the web

The recent and growing interest in the Semantic Web has given rise to a flurry of activity in standardization bodies (such as the W3C) to specify semantics using formal languages and inference mechanisms. The real challenge, however, is to link formal semantics with deeper meaning as reflected by consensus discovered among users on the Semantic Web. We believe the process of deriving and formally describing ontologies for the Web (expressed using standardized languages) is necessarily a social-cultural one; hence, new consensus-based tools are required to derive shared semantic systems for different communities of interest. This paper introduces Consensus Analysis as a means for deriving semantic knowledge from the information provided by subject matter experts and describes the Schemer System prototype for acquiring and processing this information. The results of a trial application of this approach and prototype on technologists asked to identify current mass market consumer trends in the domain of Internet privacy and security are reported. These findings implicate Consensus Analysis as a powerful tool capable of enabling the semantic Web by yielding core knowledge such as controlled vocabularies and domain ontologies.
A NEW METHOD FOR FPGA IMPLEMENTATION OF ARTIFICIAL NEURAL NETWORK USED IN SMART DEVICES

Smart devices development with leaming capabilities and adaptive behavior is a need of these days. The implementation of such devices is possible using artificial neural networks (ANN). The present work shows a new, efficient and rapid method to design, train and implement in FPGA neural networks. lSystem Generator tool for Simulink is u~ed for ANN design using neural networks specific blocks, created by author. The Matlab is used to perform the off-chip leaming task. System Generator also allows the easy generation of hardware Description Language (HDL) code from the system represented in Simulink. The VHDL design can then be synthesized for implementation in the XiliIL'<. family ofFPGA devices.
Evolutionary feature design for object recognition with hierarchical networks

A major problem in designing neural vision models is the large dimensionality of the search space for defining the needed networks. By using hierarchical vision models inspired by biology we narrow the space of possible architectures. We perform evolutionary optimization of remaining critical network parts e.g. the combination features, which are up to now mostly subject to manually determination. We show that the evolutionary approach leads to an optimized recognition system with respect to speed and performance, which is highly competitive with other state of the art systems.
An architecture for automatic opinion classification in Western online news

Drawing on studies in media discourse analysis and advances in natural language processing, data mining and information access, a framework for automated sentiment analysis of news report from Western mainstream media is proposed and a preliminary experiment is reported in this paper. The automated sentiment analysis system architecture comprises (1) the identification of Chinese-English equivalent query expressions; (2) the construction of domain and linguistic knowledge bases; (3) the recovery of news events; and (4) the sentiment analysis over news texts. The workflow consists of two phases, i.e. an offline construction of resources, such as comparable or parallel corpora and domain knowledge bases, which are utilized in real-time processing, and an online analysis of news texts by means of machine learning and data mining techniques. The proposed methodology leads to a quick response to different information gathered from overseas media and an efficient decision-making procedure for egovernment.
On the Complete Join of Permutative Combinatorial Rees–Sushkevich Varieties

A semigroup variety is a Rees-Sushkevich variety if it is contained in a periodic variety generated by 0-simple semigroups. The collection of all permutative combinatorial Rees-Sushkevich varieties constitutes an incomplete lattice that does not contain the complete join J of all its varieties. The objective of this article is to investigate the subvarieties of J. It is shown that J is locally finite, non-finitely generated, and contains only finitely based subvarieties. The subvarieties of J are precisely the combinatorial Rees-Sushkevich varieties that do not contain a certain semigroup of order four. Mathematics Subject Classification: 20M07, 08B15.
Improving ICT for ODL in the UNISA Department of Public Administration

The University of South Africa (UNISA) is a comprehensive open and distance learning(ODL) institution that aims to be the premier online teaching and eLearning institution ofchoice. This article presents a case study of the Department Public Administration inUNISA with regard for the expansion of ODL practices to improve online teaching andeLearning. The purpose of this article is firstly to recommend that the teaching and learningmethods presently used by the Department of Public Administration (UNISA) should becombined with more interactive technology-enriched teaching and learning methodologiesso that the department can “be ahead of the game” when it comes to online teaching andeLearning. The significance of this study is to improve Public Administration teaching andlearning by the Department of Public Administration (UNISA) through creating a virtualstudy environment and in ensuring the department becomes a fully fourth and fifthgeneration ODL provider. This includes an explanation of the blended use of Informationand Communication Technology (ICT) tools such as interactive satellite-based platforms,web-based management system and social networks, as mechanisms for improving onlineteaching and eLearning methodologies in Public Administration.
Medical Problem Classification Using Layered Genetic Programming with Feature Generation

This paper proposes a novel architecture of genetic programming ( GP ) called layered genetic programming ( LAYERGP ) to generate new features of a given dataset. LAYERGP is a new architecture of multipopulation GP ( MGP ) that contains a number of layers each of which comprises a set of independent populations. New features are created by evaluating the best functions generated by a layer for all populations and combined with original features to be a new training set. The new training set is used for the successive layer. In this work, we use LAYERGP to be a classifier on a number of real world 2-class medical problems. Many experiments with different parameter settings are conducted to illustrate classification performance of LAYERGP. Experimental results prove that accuracy of our method is comparable with single population GP ( SGP ) .
WLAN User Location Estimation Based on Receiving Signal Strength Indicator

The problem of user location estimation using the receiving signal strength in a radio-frequency wireless local area network (WLAN) is discussed in this paper. Instead of taking the physical properties of the signal propagation int o account directly, an method based on a machine learning framework is presented. This method employs a multivariable Gauss distribution model and consists of an offline training phase and a real time locating phase. In the offline phase the received signal strength of many training locations are recorded and the parameters of the Gauss distribution are calculated, and in the real time phase a user location is determined by matching the received signal strength patterns against the training patterns. Experiments demonstrate that the proposed method has lower locating errors and is feasible to locate user in a WLAN.
High lipid order of Arabidopsis cell-plate membranes mediated by sterol and DYNAMIN-RELATED PROTEIN1A function

Membranes of eukaryotic cells contain high lipid-order sterol-rich domains that are thought to mediate temporal and spatial organization of cellular processes. Sterols are crucial for execution of cytokinesis, the last stage of cell division, in diverse eukaryotes. The cell plate of higher-plant cells is the membrane structure that separates daughter cells during somatic cytokinesis. Cell-plate formation in Arabidopsis relies on sterol- and DYNAMIN-RELATED PROTEIN1A (DRP1A)-dependent endocytosis. However, functional relationships between lipid membrane order or lipid packing and endocytic machinery components during eukaryotic cytokinesis have not been elucidated. Using ratiometric live imaging of lipid order-sensitive fluorescent probes, we show that the cell plate of Arabidopsis thaliana represents a dynamic, high lipid-order membrane domain. The cell-plate lipid order was found to be sensitive to pharmacological and genetic alterations of sterol composition. Sterols co-localize with DRP1A at the cell plate, and DRP1A accumulates in detergent-resistant membrane fractions. Modifications of sterol concentration or composition reduce cell-plate membrane order and affect DRP1A localization. Strikingly, DRP1A function itself is essential for high lipid order at the cell plate. Our findings provide evidence that the cell plate represents a high lipid-order domain, and pave the way to explore potential feedback between lipid order and function of dynamin-related proteins during cytokinesis.
Static replication of exotic options

In the Black-Scholes model, stocks and bonds can be continuously traded to replicate the payoff of any derivative security. In practice, frequent trading is both costly and impractical. Static replication attempts to address this problem by creating replicating strategies that only trade rarely. In this thesis, we will study the static replication of exotic options by plain vanilla options. In particular, we will examine barrier options, variants of barrier options, and lookback options. Under the Black-Scholes assumptions, we will prove the existence of static replication strategies for all of these options. In addition, we will examine static replication when the drift and/or volatility is time-dependent. Finally, we conclude with a computational study to test the practical plausibility of static replication. Thesis Supervisor: Michael F. Sipser Title: Professor of Mathematics
Both Sides of the Digital Battle for a High Rank from a Search Engine

Because of the financial gain in achieving a high search engine rank, modifying a web page to unfairly alter its ranking has become a common practice. Techniques to achieve this are generally known as web spam because of the adverse affect on the relevance of the results returned by the search engine. This paper contains an overview of the techniques used to create web spam and the defenses available to search engines. In addition, we comment on why further research into combating web spam is required. This paper provides insights into the state-of-the-art of both sides of the battle to achieve an unfairly high search engine placement for a page.
Scanpath Trend Analysis on Web Pages

Eye tracking studies have widely been used in improving the design and usability of web pages and in the research of understanding how users navigate them. However, there is limited research in clustering users’ eye movement sequences (i.e., scanpaths) on web pages to identify a general direction they follow. Existing research tends to be reductionist, which means that the resulting path is so short that it is not useful. Moreover, there is little work on correlating users’ scanpaths with visual elements of web pages and the underlying source code, which means the result cannot be used for further processing. In order to address these limitations, we introduce a new concept in clustering scanpaths called Scanpath Trend Analysis (STA) that not only considers the visual elements visited by all users, but also considers the visual elements visited by the majority in any order. We present an algorithm which automatically does this trend analysis to identify a trending scanpath for multiple web users in terms of visual elements of a web page. In contrast to existing research, the STA algorithm first analyzes the most visited visual elements in given scanpaths, clusters the scanpaths by arranging these visual elements based on their overall positions in the individual scanpaths, and then constructs a trending scanpath in terms of these visual elements. This algorithm was experimentally evaluated by an eye tracking study on six web pages for two different kinds of tasks (12 cases in total). Our experimental results show that the STA algorithm generates a trending scanpath that addresses the reductionist problem of existing work by preventing the loss of commonly visited visual elements for all cases. Based on the statistical tests, the STA algorithm also generates a trending scanpath that is significantly more similar to the inputted scanpaths compared to other existing work in 10 out of 12 cases. In the remaining cases, the STA algorithm still performs significantly better than some other existing work. This algorithm contributes to behavior analysis research on the web that can be used for different purposes: for example, re-engineering web pages guided by the trending scanpath to improve users’ experience or guiding designers to improve their design.