nils-herrmann/clean_scirepeval huggingface dataset:
Simulation and the design building block approach in the design of ships and other complex systems

This paper is in many respects a continuation of the earlier paper by the author published  1998 entitled ‘A comprehensive methodology for the design of ships (and other complex systems)’. The earlier paper described the approach to the initial design of ships developedby the author during some 35years of design practice, including two previous secondments to teach ship design atUCL.Thepresent paper not only takes thatdevelopment forward, it also explains how the research tool demonstrating the author’s approach to initial ship design has now been incorporated in an industry based design system to provide a working graphically and numerically integrated design system. This achievement is exemplified by a series of practical design investigations, undertaken by the UCL Design Research Centre led by the author, which were mainly undertaken for industry clients in order to investigate real problems towhich the approachhasbrought significant insights.The other new strand in the present paper is the emphasis on the human factors or large scale ergonomics dimension, vital to complex and large scale design products but rarely hitherto beengiven sufficientprominence in the crucial formative stagesof large scale designbecauseof the inherent difficulties in doing so. The UCL Design Building Block approach has now been incorporated in the established PARAMARINE ship design system through a module entitled SURFCON. Work is now underway on an Engineering and Physical Sciences Research Council joint project with the University of Greenwich to interface the latter’s escape simulation toolmaritimeEXODUSwithSURFCONtoprovide initial design guidance to ship designers on personnelmovement. The paper’s concluding section considers the wider applicability of the integration of simulation during initial design with the graphically driven synthesis to other complex and large scale design tasks. The paper concludes by suggesting how such an approach to complex design can contribute to the teaching of designers and, moreover, how this designapproach can enable a creative qualitative approach to engineering design to be sustained despite the risk that advances in computer based methods might encourage emphasis being accorded to solely to quantitative analysis.
Combatting bacterial persister cells.

Most bacterial cells lead lives of quiet desperation in biofilms, combatting stress; yet, their prevalence attests to their ability to alter gene regulation to cope with myriad insults. Since biofilm bacteria are faced with starvation and other environmental stress (e.g., antibiotics from competitors, oxidative stress from host immune systems), it behooves them to be able to ramp down their metabolism in a highly regulated manner and enter a resting state known as persistence to weather stress. Hence, persister cells are metabolically dormant cells that arise predominantly as a response to stress through elegant gene regulation that includes toxin/antitoxin systems. In this review, an analysis is made of the genetic pathways that lead to persistence, of cell signaling via the interspecies and interkingdom signal indole that leads to persistence, and the means found to date for combatting these cells which are frequently tolerant to a range of antibiotics.
Linguistic description of relative positions in images

Fuzzy set methods have been used to model and manage uncertainty in various aspects of image processing, pattern recognition, and computer vision. High-level computer vision applications hold a great potential for fuzzy set theory because of its links to natural language. Linguistic scene description, a language-based interpretation of regions and their relationships, is one such application that is starting to bear the fruits of fuzzy set theoretic involvement. In this paper, we are expanding on two earlier endeavors. We introduce new families of fuzzy directional relations that rely on the computation of histograms of forces. These families preserve important relative position properties. They provide inputs to a fuzzy rule base that produces logical linguistic descriptions along with assessments as to the validity of the descriptions. Each linguistic output uses hedges from a dictionary of about 30 adverbs and other terms that can be tailored to individual users. Excellent results from several synthetic and real image examples show the applicability of this approach.
Domain Transfer Learning for MCI Conversion Prediction

In recent studies of Alzheimer's disease (AD), it has increasing attentions in identifying mild cognitive impairment (MCI) converters (MCI-C) from MCI non-converters (MCI-NC). Note that MCI is a prodromal stage of AD, with possibility to convert to AD. Most traditional methods for MCI conversion prediction learn information only from MCI subjects (including MCI-C and MCI-NC), not from other related subjects, e.g., AD and normal controls (NC), which can actually aid the classification between MCI-C and MCI-NC. In this paper, we propose a novel domain-transfer learning method for MCI conversion prediction. Different from most existing methods, we classify MCI-C and MCI-NC with aid from the domain knowledge learned with AD and NC subjects as auxiliary domain to further improve the classification performance. Our method contains two key components: (1) the cross-domain kernel learning for transferring auxiliary domain knowledge, and (2) the adapted support vector machine (SVM) decision function construction for cross-domain and auxiliary domain knowledge fusion. Experimental results on the Alzheimer's Disease neuroimaging initiative (ADNI) database show that the proposed method can significantly improve the classification performance between MCI-C and MCI-NC, with aid of domain knowledge learned from AD and NC subjects.
Design of a slot antenna for future 5G wireless communication systems

In this study, a square-shaped slot antenna is designed for the future fifth generation (5G) wireless applications. The antenna has a compact size of 0.64λg × 0.64λg at 38 GHz, which consists of ellipse shaped radiating patch fed by a 50 Q micro-strip line on the Rogers RT5880 substrates. A rectangle shaped slot is etched in the ground plane to enhance the antenna bandwidth. In order to obtain better impedance matching bandwidth of the antennas, some small circular radiating patches are added to the square-shaped slot. Simulations show that the measured impedance bandwidth of the proposed antenna ranges from 20 to 42 GHz for a reflection coefficient of Su less than −10dB which is cover 5G bands (28/38GHz). The proposed antenna provides almost omni-directional patterns, relatively flat gain, and high radiation efficiency through the frequency band.
A Survey on Various Approaches for Taxonomy Construction

To analysis different approaches for taxonomy construction to improve the knowledge classification, information retrieval and other data mining process.  Taxonomies learning keep getting more important process for knowledge sharing about a domain. It is also used for application development such as knowledge searching, information retrieval. The taxonomy can be build manually but it is a complex process when the data are so large and it also produce some errors while taxonomy construction. There is various automatic taxonomy construction techniques are used to learn taxonomy based on keyword phrases, text corpus and from domain specific concepts etc. So it is required to build taxonomy with less human effort and with less error rate. This paper provides detailed information about those techniques.  The methods such as lexico-syntatic pattern, semi supervised methods, graph based methods, ontoplus, TaxoLearn, Bayesian approach, two-step method, ontolearn and Automatic Taxonomy Construction from Text are analyzed in this paper.  The findings of this work prove that the TaxoFinder approach provides better result than other approaches.
Improve Uncertainty Estimation for Unknown Classes in Bayesian Neural Networks with Semi-Supervised /One Set Classification

Although deep neural network (DNN) has achieved many state-of-the-art results, estimating the uncertainty presented in the DNN model and the data is a challenging task. Problems related to uncertainty such as classifying unknown classes (class which does not appear in the training data) data as known class with high confidence, is critically concerned in the safety domain area (e.g, autonomous driving, medical diagnosis). In this paper, we show that applying current Bayesian Neural Network (BNN) techniques alone does not effectively capture the uncertainty. To tackle this problem, we introduce a simple way to improve the BNN by using one class classification (in this paper, we use the term ”set classification” instead). We empirically show the result of our method on an experiment which involves three datasets: MNIST, notMNIST and FMNIST.
Pancreatic cancers depend on a non-canonical glutamine metabolism pathway

Cancer cells exhibit metabolic dependencies that distinguish them from their normal counterparts. Among these addictions is an increased utilization of the amino acid glutamine (Gln) to fuel anabolic processes. Recently, we reported the identification of a non-canonical pathway of Gln utilization in human pancreatic cancer cells that is required for tumor growth. While most cells utilize glutamate dehydrogenase (GLUD1) to convert Gln-derived glutamate (Glu) into a-ketoglutarate (aKG) in the mitochondria to fuel the tricarboxylic acid cycle, pancreatic cancer cells rely on a distinct pathway that integrates the mitochondrial and cytosolic aspartate aminotransferases GOT2 and GOT1. By generating aKG from Glu (in conjunction with the conversion of oxaloacetate into aspartate), GOT2 fuels anaplerosis in place of GLUD1. The Asp created is released into the cytosol and acted on by GOT1. This is subsequently used through a series of reactions to yield cytosolic NADPH from malic enzyme. Importantly, we have demonstrated that pancreatic cancers are strongly dependent on this series of reactions to maintain redox homeostasis which enables proliferation. Herein, we detail the subcellular compartmentalization and consequences of the aforementioned reactions on pancreatic cancer metabolism. We have also investigated the essentiality of this pathway in other contexts and find that pancreatic cancers have a uniform and unique reliance on this pathway, which may provide novel therapeutic approaches to treat these refractory tumors.
P-CNN: Pose-Based CNN Features for Action Recognition

This work targets human action recognition in video. While recent methods typically represent actions by statistics of local video features, here we argue for the importance of a representation derived from human pose. To this end we propose a new Pose-based Convolutional Neural Network descriptor (P-CNN) for action recognition. The descriptor aggregates motion and appearance information along tracks of human body parts. We investigate different schemes of temporal aggregation and experiment with P-CNN features obtained both for automatically estimated and manually annotated human poses. We evaluate our method on the recent and challenging JHMDB and MPII Cooking datasets. For both datasets our method shows consistent improvement over the state of the art.
Problem-Solving Strategies and the Writing Process

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Multi-target Regression via Random Linear Target Combinations

Multi-target regression is concerned with the simultaneous prediction of multiple continuous target variables based on the same set of input variables. It arises in several interesting industrial and environmental application domains, such as ecological modelling and energy forecasting. This paper presents an ensemble method for multi-target regression that constructs new target variables via random linear combinations of existing targets. We discuss the connection of our approach with multi-label classification algorithms, in particular RAkEL, which originally inspired this work, and a family of recent multi-label classification algorithms that involve output coding. Experimental results on 12 multi-target datasets show that it performs significantly better than a strong baseline that learns a single model for each target using gradient boosting and compares favourably to multi-objective random forest approach, which is a state-of-the-art approach. The experiments further show that our approach improves more when stronger unconditional dependencies exist among the targets.
Domain Adaptation via Transfer Component Analysis

Domain adaptation allows knowledge from a source domain to be transferred to a different but related target domain. Intuitively, discovering a good feature representation across domains is crucial. In this paper, we first propose to find such a representation through a new learning method, transfer component analysis (TCA), for domain adaptation. TCA tries to learn some transfer components across domains in a reproducing kernel Hilbert space using maximum mean miscrepancy. In the subspace spanned by these transfer components, data properties are preserved and data distributions in different domains are close to each other. As a result, with the new representations in this subspace, we can apply standard machine learning methods to train classifiers or regression models in the source domain for use in the target domain. Furthermore, in order to uncover the knowledge hidden in the relations between the data labels from the source and target domains, we extend TCA in a semisupervised learning setting, which encodes label information into transfer components learning. We call this extension semisupervised TCA. The main contribution of our work is that we propose a novel dimensionality reduction framework for reducing the distance between domains in a latent space for domain adaptation. We propose both unsupervised and semisupervised feature extraction approaches, which can dramatically reduce the distance between domain distributions by projecting data onto the learned transfer components. Finally, our approach can handle large datasets and naturally lead to out-of-sample generalization. The effectiveness and efficiency of our approach are verified by experiments on five toy datasets and two real-world applications: cross-domain indoor WiFi localization and cross-domain text classification.
Large-scale image retrieval with compressed Fisher vectors

The problem of large-scale image search has been traditionally addressed with the bag-of-visual-words (BOV). In this article, we propose to use as an alternative the Fisher kernel framework. We first show why the Fisher representation is well-suited to the retrieval problem: it describes an image by what makes it different from other images. One drawback of the Fisher vector is that it is high-dimensional and, as opposed to the BOV, it is dense. The resulting memory and computational costs do not make Fisher vectors directly amenable to large-scale retrieval. Therefore, we compress Fisher vectors to reduce their memory footprint and speed-up the retrieval. We compare three binarization approaches: a simple approach devised for this representation and two standard compression techniques. We show on two publicly available datasets that compressed Fisher vectors perform very well using as little as a few hundreds of bits per image, and significantly better than a very recent compressed BOV approach.
Obfuscation-Resilient Privacy Leak Detection for Mobile Apps Through Differential Analysis

Mobile apps are notorious for collecting a wealth of private information from users. Despite significant effort from the research community in developing privacy leak detection tools based on data flow tracking inside the app or through network traffic analysis, it is still unclear whether apps and ad libraries can hide the fact that they are leaking private information. In fact, all existing analysis tools have limitations: data flow tracking suffers from imprecisions that cause false positives, as well as false negatives when the data flow from a source of private information to a network sink is interrupted; on the other hand, network traffic analysis cannot handle encryption or custom encoding. We propose a new approach to privacy leak detection that is not affected by such limitations, and it is also resilient to obfuscation techniques, such as encoding, formatting, encryption, or any other kind of transformation performed on private information before it is leaked. Our work is based on blackbox differential analysis, and it works in two steps: first, it establishes a baseline of the network behavior of an app; then, it modifies sources of private information, such as the device ID and location, and detects leaks by observing deviations in the resulting network traffic. The basic concept of black-box differential analysis is not novel, but, unfortunately, it is not practical enough to precisely analyze modern mobile apps. In fact, their network traffic contains many sources of non-determinism, such as random identifiers, timestamps, and server-assigned session identifiers, which, when not handled properly, cause too much noise to correlate output changes with input changes. The main contribution of this work is to make black-box differential analysis practical when applied to modern Android apps. In particular, we show that the network-based non-determinism can often be explained and eliminated, and it is thus possible to reliably use variations in the network traffic as a strong signal to detect privacy leaks. We implemented this approach in a tool, called AGRIGENTO, and we evaluated it on more than one thousand Android apps. Our evaluation shows that our approach works well in practice and outperforms current state-of-the-art techniques. We conclude our study by discussing several case studies that show how popular apps and ad libraries currently exfiltrate data by using complex combinations of encoding and encryption mechanisms that other approaches fail to detect. Our results show that these apps and libraries seem to deliberately hide their data leaks from current approaches and clearly demonstrate the need for an obfuscation-resilient approach such as ours.
Establishing Causal Coherence across Sentences: An ERP Study

This study examined neural activity associated with establishing causal relationships across sentences during on-line comprehension. ERPs were measured while participants read and judged the relatedness of three-sentence scenarios in which the final sentence was highly causally related, intermediately related, and causally unrelated to its context. Lexico-semantic co-occurrence was matched across the three conditions using a Latent Semantic Analysis. Critical words in causally unrelated scenarios evoked a larger N400 than words in both highly causally related and intermediately related scenarios, regardless of whether they appeared before or at the sentence-final position. At midline sites, the N400 to intermediately related sentence-final words was attenuated to the same degree as to highly causally related words, but otherwise the N400 to intermediately related words fell in between that evoked by highly causally related and intermediately related words. No modulation of the late positivity/P600 component was observed across conditions. These results indicate that both simple and complex causal inferences can influence the earliest stages of semantically processing an incoming word. Further, they suggest that causal coherence, at the situation level, can influence incremental word-by-word discourse comprehension, even when semantic relationships between individual words are matched.
Influence of teacher empowerment on teachers ’ organizational commitment , professional commitment and organizational citizenship behavior in schools

The present study focuses on the relationship between teacher empowerment and teachers’ organizational commitment, professional commitment (PC) and organizational citizenship behavior (OCB). It examines which subscales of teacher empowerment can best predict these outcomes. The data were collected through a questionnaire returned by a sample of 983 teachers in Israeli middle and high schools. Pearson correlations and multiple regression analyses indicated that teachers’ perceptions of their level of empowerment are significantly related to their feelings of commitment to the organization and to the profession, and to their OCBs. Among the six subscales of empowerment, professional growth, status and self-efficacy were significant predictors of organizational and PC, while decisionmaking, self-efficacy, and status were significant predictors of OCB. Practical implications of the study are discussed in relation to teachers, principals and policy-makers. r 2004
Advances in light microscopy for neuroscience.

Since the work of Golgi and Cajal, light microscopy has remained a key tool for neuroscientists to observe cellular properties. Ongoing advances have enabled new experimental capabilities using light to inspect the nervous system across multiple spatial scales, including ultrastructural scales finer than the optical diffraction limit. Other progress permits functional imaging at faster speeds, at greater depths in brain tissue, and over larger tissue volumes than previously possible. Portable, miniaturized fluorescence microscopes now allow brain imaging in freely behaving mice. Complementary progress on animal preparations has enabled imaging in head-restrained behaving animals, as well as time-lapse microscopy studies in the brains of live subjects. Mouse genetic approaches permit mosaic and inducible fluorescence-labeling strategies, whereas intrinsic contrast mechanisms allow in vivo imaging of animals and humans without use of exogenous markers. This review surveys such advances and highlights emerging capabilities of particular interest to neuroscientists.
Encoding Markov logic networks in Possibilistic Logic

Markov logic uses weighted formulas to compactly encode a probability distribution over possible worlds. Despite the use of logical formulas, Markov logic networks (MLNs) can be difficult to interpret, due to the often counter-intuitive meaning of their weights. To address this issue, we propose a method to construct a possibilistic logic theory that exactly captures what can be derived from a given MLN using maximum a posteriori (MAP) inference. Unfortunately, the size of this theory is exponential in general. We therefore also propose two methods which can derive compact theories that still capture MAP inference, but only for specific types of evidence. These theories can be used, among others, to make explicit the hidden assumptions underlying an MLN or to explain the predictions it makes.
A Review : Flexible Removable Partial Dentures Dr

In an era of implants and fixed prosthesis, removable partial dentures are still a treatment of choice for a wide range of patients and clinicians. As every other treatment option, removable prosthesis also has some disadvantages. But with introduction of flexible partial dentures, many of the disadvantages of conventional acrylic and cast partial dentures are taken care of. This article discusses about the material used in fabrication, indications and compares the flexible partial dentures with other removable partial denture options along with design and insertion technique of the flexible removable partial dentures.