Query: Tumor-associated copy number changes in the circulation of patients with prostate cancer identified through whole-genome sequencing

Relevant passages:
The Mutational Landscape of Lethal Castrate Resistant Prostate Cancer <=SEP=> Characterization of the prostate cancer transcriptome and genome has identified chromosomal rearrangements and copy number gains and losses, including ETS gene family fusions, PTEN loss and androgen receptor (AR) amplification, which drive prostate cancer development and progression to lethal, metastatic castration-resistant prostate cancer (CRPC). However, less is known about the role of mutations. Here we sequenced the exomes of 50 lethal, heavily pre-treated metastatic CRPCs obtained at rapid autopsy (including three different foci from the same patient) and 11 treatment-naive, high-grade localized prostate cancers. We identified low overall mutation rates even in heavily treated CRPCs (2.00 per megabase) and confirmed the monoclonal origin of lethal CRPC. Integrating exome copy number analysis identified disruptions of CHD1 that define a subtype of ETS gene family fusion-negative prostate cancer. Similarly, we demonstrate that ETS2, which is deleted in approximately one-third of CRPCs (commonly through TMPRSS2:ERG fusions), is also deregulated through mutation. Furthermore, we identified recurrent mutations in multiple chromatin- and histone-modifying genes, including MLL2 (mutated in 8.6% of prostate cancers), and demonstrate interaction of the MLL complex with the AR, which is required for AR-mediated signalling. We also identified novel recurrent mutations in the AR collaborating factor FOXA1, which is mutated in 5 of 147 (3.4%) prostate cancers (both untreated localized prostate cancer and CRPC), and showed that mutated FOXA1 represses androgen signalling and increases tumour growth. Proteins that physically interact with the AR, such as the ERG gene fusion product, FOXA1, MLL2, UTX (also known as KDM6A) and ASXL1 were found to be mutated in CRPC. In summary, we describe the mutational landscape of a heavily treated metastatic cancer, identify novel mechanisms of AR signalling deregulated in prostate cancer, and prioritize candidates for future study.

Circulating mutant DNA to assess tumor dynamics <=SEP=> The measurement of circulating nucleic acids has transformed the management of chronic viral infections such as HIV. The development of analogous markers for individuals with cancer could similarly enhance the management of their disease. DNA containing somatic mutations is highly tumor specific and thus, in theory, can provide optimum markers. However, the number of circulating mutant gene fragments is small compared to the number of normal circulating DNA fragments, making it difficult to detect and quantify them with the sensitivity required for meaningful clinical use. In this study, we applied a highly sensitive approach to quantify circulating tumor DNA (ctDNA) in 162 plasma samples from 18 subjects undergoing multimodality therapy for colorectal cancer. We found that ctDNA measurements could be used to reliably monitor tumor dynamics in subjects with cancer who were undergoing surgery or chemotherapy. We suggest that this personalized genetic approach could be generally applied to individuals with other types of cancer (pages 914–915).

ETS gene fusions in prostate cancer: from discovery to daily clinical practice. <=SEP=> CONTEXT
In 2005, fusions between the androgen-regulated transmembrane protease serine 2 gene, TMPRSS2, and E twenty-six (ETS) transcription factors were discovered in prostate cancer.


OBJECTIVE
To review advances in our understanding of ETS gene fusions, focusing on challenges affecting translation to clinical application.


EVIDENCE ACQUISITION
The PubMed database was searched for reports on ETS fusions in prostate cancer.


EVIDENCE SYNTHESIS
Since the discovery of ETS fusions, novel 5' and 3' fusion partners and multiple splice isoforms have been reported. The most common fusion, TMPRSS2:ERG, is present in approximately 50% of prostate-specific antigen (PSA)-screened localized prostate cancers and in 15-35% of population-based cohorts. ETS fusions can be detected noninvasively in the urine of men with prostate cancer, with a specificity rate in PSA-screened cohorts of >90%. Reports from untreated population-based cohorts suggest an association between ETS fusions and cancer-specific death and metastatic spread. In retrospective prostatectomy cohorts, conflicting results have been published regarding associations between ETS fusions and cancer aggressiveness. In addition to serving as a potential biomarker, tissue and functional studies suggest a specific role for ETS fusions in the transition to carcinoma. Finally, recent results suggest that the 5' and 3' ends of ETS fusions as well as downstream targets may be targeted therapeutically.


CONCLUSIONS
Recent studies suggest that the first clinical applications of ETS fusions are likely to be in noninvasive detection of prostate cancer and in aiding with difficult diagnostic cases. Additional studies are needed to clarify the association between gene fusions and cancer aggressiveness, particularly those studies that take into account the multifocal and heterogeneous nature of localized prostate cancer. Multiple promising strategies have been identified to potentially target ETS fusions. Together, these results suggest that ETS fusions will affect multiple aspects of prostate cancer diagnosis and management.

The genomic landscapes of human breast and colorectal cancers. <=SEP=> Human cancer is caused by the accumulation of mutations in oncogenes and tumor suppressor genes. To catalog the genetic changes that occur during tumorigenesis, we isolated DNA from 11 breast and 11 colorectal tumors and determined the sequences of the genes in the Reference Sequence database in these samples. Based on analysis of exons representing 20,857 transcripts from 18,191 genes, we conclude that the genomic landscapes of breast and colorectal cancers are composed of a handful of commonly mutated gene "mountains" and a much larger number of gene "hills" that are mutated at low frequency. We describe statistical and bioinformatic tools that may help identify mutations with a role in tumorigenesis. These results have implications for understanding the nature and heterogeneity of human cancers and for using personal genomics for tumor diagnosis and therapy.

Advanced sequencing technologies: methods and goals <=SEP=> Nearly three decades have passed since the invention of electrophoretic methods for DNA sequencing. The exponential growth in the cost-effectiveness of sequencing has been driven by automation and by numerous creative refinements of Sanger sequencing, rather than through the invention of entirely new methods. Various novel sequencing technologies are being developed, each aspiring to reduce costs to the point at which the genomes of individual humans could be sequenced as part of routine health care. Here, we review these technologies, and discuss the potential impact of such a 'personal genome project' on both the research community and on society.

Word sense disambiguation as a traveling salesman problem <=SEP=> Word sense disambiguation (WSD) is a difficult problem in Computational Linguistics, mostly because of the use of a fixed sense inventory and the deep level of granularity. This paper formulates WSD as a variant of the traveling salesman problem (TSP) to maximize the overall semantic relatedness of the context to be disambiguated. Ant colony optimization, a robust nature-inspired algorithm, was used in a reinforcement learning manner to solve the formulated TSP. We propose a novel measure based on the Lesk algorithm and Vector Space Model to calculate semantic relatedness. Our approach to WSD is comparable to state-of-the-art knowledge-based and unsupervised methods for benchmark datasets. In addition, we show that the combination of knowledge-based methods is superior to the most frequent sense heuristic and significantly reduces the difference between knowledge-based and supervised methods. The proposed approach could be customized for other lexical disambiguation tasks, such as Lexical Substitution or Word Domain Disambiguation.

Brain structures differ between musicians and non-musician <=SEP=> From an early age, musicians learn complex motor and auditory skills (e.g., the translation of visually perceived musical symbols into motor commands with simultaneous auditory monitoring of output), which they practice extensively from childhood throughout their entire careers. Using a voxel-by-voxel morphometric technique, we found gray matter volume differences in motor, auditory, and visual-spatial brain regions when comparing professional musicians (keyboard players) with a matched group of amateur musicians and non-musicians. Although some of these multiregional differences could be attributable to innate predisposition, we believe they may represent structural adaptations in response to long-term skill acquisition and the repetitive rehearsal of those skills. This hypothesis is supported by the strong association we found between structural differences, musician status, and practice intensity, as well as the wealth of supporting animal data showing structural changes in response to long-term motor training. However, only future experiments can determine the relative contribution of predisposition and practice.

An improved k-medoids clustering algorithm <=SEP=> In this paper, we present an improved k-medoids clustering algorithm based on CF-Tree. The algorithm based on the clustering features of BIRCH algorithm, the concept of k-medoids algorithm has been improved. We preserve all the training sample data in an CF-Tree, then use k-medoids method to cluster the CF in leaf nodes of CF-Tree. Eventually, we can get k clusters from the root of the CF-Tree. This algorithm improves obviously the drawbacks of the k-medoids algorithm, such as the time complexity, scalability on large dataset, and can't find the clusters of sizes different very much and the convex shapes. Experiments show that this algorithm enhances the quality and scalability of clustering.

A Prospective, Open-Label Study of Hyaluronic Acid-Based Filler With Lidocaine (VYC-15L) Treatment for the Correction of Infraorbital Skin Depressions. <=SEP=> BACKGROUND
Infraorbital skin depressions are one of the most troublesome facial areas for aesthetically aware patients.


OBJECTIVE
Evaluate effectiveness and safety of Juvéderm Volbella with Lidocaine (VYC-15L; Allergan plc, Dublin, Ireland) for correction of bilateral infraorbital depressions.


METHODS
In this 12-month, prospective, uncontrolled, open-label study, subjects aged ≥18 years with infraorbital depressions rated ≥1 on the Allergan Infra-oRbital Scale (AIRS) received injections of VYC-15L with optional touch-up treatment on Day 14. The primary efficacy measure was ≥1 AIRS grade improvement from baseline at month 1.


RESULTS
Of 80 subjects initially treated with VYC-15L, 75 (94%) completed the study. All injections were intentionally deep, most using multiple microbolus technique. At 1 month, 99.3% of eyes achieved ≥1 AIRS grade improvement. The responder rate (subjects with ≥1 AIRS grade improvement in both eyes) was 99% at month 1, 92% at month 6, and 54% at month 12. Most injection site reactions (e.g., bruising, redness, irregularities/bumps) were mild and resolved by day 14. Late-onset mild to moderate edema was observed in 11% of eyes at month 6% and 4% of eyes at month 12.


CONCLUSION
VYC-15L is effective and safe for the treatment of infraorbital depressions, with effectiveness lasting up to 12 months.

Spreadsheet Validation and Analysis through Content Visualization <=SEP=> Visualizing spreadsheet content provides analytic insight and visual validation of large amounts of spreadsheet data. Oculus Excel Visualizer is a point and click data visualization experiment which directly visualizes Excel data and re-users the layout and formatting already present in the spreadsheet.

A power-efficient 4-2 Adder Compressor topology <=SEP=> The addition is the most used arithmetic operation in Digital Signal Processing (DSP) algorithms, such as filters, transforms and predictions. These algorithms are increasingly present in audio and video processing of battery-powered mobile devices having, therefore, energy constraints. In the context of addition operation, the efficient 4-2 adder compressor is capable to performs four additions simultaneously. This higher order of parallelism reduces the critical path and the internal glitching, thus reducing mainly the dynamic power dissipation. This work proposes two CMOS+ gate-based topologies to further reduce the power, area and delay of the 4-2 adder compressor. The proposed CMOS+ 4-2 adder compressor circuits topologies were implemented with Cadence Virtuoso tool at 45 nm technology and simulated in both electric and layout levels. The results show that a proper choice of gates in 4-2 adder compressor realization can reduce the power, delay and area about 22.41%, 32.45% and 7.4%, respectively, when compared with the literature.

IoTScanner: Detecting and Classifying Privacy Threats in IoT Neighborhoods <=SEP=> In the context of the emerging Internet of Things (IoT), a proliferation of wireless connectivity can be expected. That ubiquitous wireless communication will be hard to centrally manage and control, and can be expected to be opaque to end users. As a result, owners and users of physical space are threatened to lose control over their digital environments. In this work, we propose the idea of an IoTScanner. The IoTScanner integrates a range of radios to allow local reconnaissance of existing wireless infrastructure and participating nodes. It enumerates such devices, identifies connection patterns, and provides valuable insights for technical support and home users alike. Using our IoTScanner, we attempt to classify actively streaming IP cameras from other non-camera devices using simple heuristics. We show that our classification approach achieves a high accuracy in an IoT setting consisting of a large number of IoT devices. While related work usually focuses on detecting either the infrastructure, or eavesdropping on traffic from a specific node, we focus on providing a general overview of operations in all observed networks. We do not assume prior knowledge of used SSIDs, preshared passwords, or similar.

A new intra-disk redundancy scheme for high-reliability RAID storage systems in the presence of unrecoverable errors <=SEP=> Today's data storage systems are increasingly adopting low-cost disk drives that have higher capacity but lower reliability, leading to more frequent rebuilds and to a higher risk of unrecoverable media errors. We propose an efficient intradisk redundancy scheme to enhance the reliability of RAID systems. This scheme introduces an additional level of redundancy inside each disk, on top of the RAID redundancy across multiple disks. The RAID parity provides protection against disk failures, whereas the proposed scheme aims to protect against media-related unrecoverable errors. In particular, we consider an intradisk redundancy architecture that is based on an interleaved parity-check coding scheme, which incurs only negligible I/O performance degradation. A comparison between this coding scheme and schemes based on traditional Reed--Solomon codes and single-parity-check codes is conducted by analytical means. A new model is developed to capture the effect of correlated unrecoverable sector errors. The probability of an unrecoverable failure associated with these schemes is derived for the new correlated model, as well as for the simpler independent error model. We also derive closed-form expressions for the mean time to data loss of RAID-5 and RAID-6 systems in the presence of unrecoverable errors and disk failures. We then combine these results to characterize the reliability of RAID systems that incorporate the intradisk redundancy scheme. Our results show that in the practical case of correlated errors, the interleaved parity-check scheme provides the same reliability as the optimum, albeit more complex, Reed--Solomon coding scheme. Finally, the I/O and throughput performances are evaluated by means of analysis and event-driven simulation.

Understanding and improving recurrent networks for human activity recognition by continuous attention <=SEP=> Deep neural networks, including recurrent networks, have been successfully applied to human activity recognition. Unfortunately, the final representation learned by recurrent networks might encode some noise (irrelevant signal components, unimportant sensor modalities, etc.). Besides, it is difficult to interpret the recurrent networks to gain insight into the models' behavior. To address these issues, we propose two attention models for human activity recognition: temporal attention and sensor attention. These two mechanisms adaptively focus on important signals and sensor modalities. To further improve the understandability and mean Fl score, we add continuity constraints, considering that continuous sensor signals are more robust than discrete ones. We evaluate the approaches on three datasets and obtain state-of-the-art results. Furthermore, qualitative analysis shows that the attention learned by the models agree well with human intuition.

WASSA-2017 Shared Task on Emotion Intensity <=SEP=> We present the first shared task on detecting the intensity of emotion felt by the speaker of a tweet. We create the first datasets of tweets annotated for anger, fear, joy, and sadness intensities using a technique called best–worst scaling (BWS). We show that the annotations lead to reliable fine-grained intensity scores (rankings of tweets by intensity). The data was partitioned into training, development, and test sets for the competition. Twenty-two teams participated in the shared task, with the best system obtaining a Pearson correlation of 0.747 with the gold intensity scores. We summarize the machine learning setups, resources, and tools used by the participating teams, with a focus on the techniques and resources that are particularly useful for the task. The emotion intensity dataset and the shared task are helping improve our understanding of how we convey more or less intense emotions through language.

The Structure and Dynamics of Co-Citation Clusters: A Multiple-Perspective Co-Citation Analysis <=SEP=> A multiple-perspective co-citation analysis method is introduced for characterizing and interpreting the structure and dynamics of co-citation clusters. The method facilitates analytic and sense making tasks by integrating network visualization, spectral clustering, automatic cluster labeling, and text summarization. Co-citation networks are decomposed into co-citation clusters. The interpretation of these clusters is augmented by automatic cluster labeling and summarization. The method focuses on the interrelations between a co-citation cluster’s members and their citers. The generic method is applied to a three-part analysis of the field of Information Science as defined by 12 journals published between 1996 and 2008: 1) a comparative author co-citation analysis (ACA), 2) a progressive ACA of a time series of co-citation networks, and 3) a progressive document co-citation analysis (DCA). Results show that the multipleperspective method increases the interpretability and accountability of both ACA and DCA networks.

Global consequences of land use. <=SEP=> Land use has generally been considered a local environmental issue, but it is becoming a force of global importance. Worldwide changes to forests, farmlands, waterways, and air are being driven by the need to provide food, fiber, water, and shelter to more than six billion people. Global croplands, pastures, plantations, and urban areas have expanded in recent decades, accompanied by large increases in energy, water, and fertilizer consumption, along with considerable losses of biodiversity. Such changes in land use have enabled humans to appropriate an increasing share of the planet's resources, but they also potentially undermine the capacity of ecosystems to sustain food production, maintain freshwater and forest resources, regulate climate and air quality, and ameliorate infectious diseases. We face the challenge of managing trade-offs between immediate human needs and maintaining the capacity of the biosphere to provide goods and services in the long term.

Plasmacytoid dendritic cells sense self-DNA coupled with antimicrobial peptide <=SEP=> Plasmacytoid dendritic cells (pDCs) sense viral and microbial DNA through endosomal Toll-like receptors to produce type 1 interferons. pDCs do not normally respond to self-DNA, but this restriction seems to break down in human autoimmune disease by an as yet poorly understood mechanism. Here we identify the antimicrobial peptide LL37 (also known as CAMP) as the key factor that mediates pDC activation in psoriasis, a common autoimmune disease of the skin. LL37 converts inert self-DNA into a potent trigger of interferon production by binding the DNA to form aggregated and condensed structures that are delivered to and retained within early endocytic compartments in pDCs to trigger Toll-like receptor 9. Thus, our data uncover a fundamental role of an endogenous antimicrobial peptide in breaking innate tolerance to self-DNA and suggest that this pathway may drive autoimmunity in psoriasis.

Urban Computing: Concepts, Methodologies, and Applications <=SEP=> Urbanization's rapid progress has modernized many people's lives but also engendered big issues, such as traffic congestion, energy consumption, and pollution. Urban computing aims to tackle these issues by using the data that has been generated in cities (e.g., traffic flow, human mobility, and geographical data). Urban computing connects urban sensing, data management, data analytics, and service providing into a recurrent process for an unobtrusive and continuous improvement of people's lives, city operation systems, and the environment. Urban computing is an interdisciplinary field where computer sciences meet conventional city-related fields, like transportation, civil engineering, environment, economy, ecology, and sociology in the context of urban spaces. This article first introduces the concept of urban computing, discussing its general framework and key challenges from the perspective of computer sciences. Second, we classify the applications of urban computing into seven categories, consisting of urban planning, transportation, the environment, energy, social, economy, and public safety and security, presenting representative scenarios in each category. Third, we summarize the typical technologies that are needed in urban computing into four folds, which are about urban sensing, urban data management, knowledge fusion across heterogeneous data, and urban data visualization. Finally, we give an outlook on the future of urban computing, suggesting a few research topics that are somehow missing in the community.

RaceTrack: efficient detection of data race conditions via adaptive tracking <=SEP=> Bugs due to data races in multithreaded programs often exhibit non-deterministic symptoms and are notoriously difficult to find. This paper describes RaceTrack, a dynamic race detection tool that tracks the actions of a program and reports a warning whenever a suspicious pattern of activity has been observed. RaceTrack uses a novel hybrid detection algorithm and employs an adaptive approach that automatically directs more effort to areas that are more suspicious, thus providing more accurate warnings for much less over-head. A post-processing step correlates warnings and ranks code segments based on how strongly they are implicated in potential data races. We implemented RaceTrack inside the virtual machine of Microsoft's Common Language Runtime (product version v1.1.4322) and monitored several major, real-world applications directly out-of-the-box,without any modification. Adaptive tracking resulted in a slowdown ratio of about 3x on memory-intensive programs and typically much less than 2x on other programs,and a memory ratio of typically less than 1.2x. Several serious data race bugs were revealed, some previously unknown.

ESTIMATION METHODS FOR STOCHASTIC VOLATILITY MODELS : A SURVEY <=SEP=> Although stochastic volatility (SV) models have an intuitive appeal, their empirical application has been limited mainly due to difficulties involved in their estimation. The main problem is that the likelihood function is hard to evaluate. However, recently, several new estimation methods have been intro duced and the literature on SV models has grown substantially. In this article, we review this literature. We describe the main estimators of the parameters and the underlying volatilities focusing on their advantages and limitations both from the theoretical and empirical point of view. We complete the survey with an application of the most important procedures to the S&P 500 stock price index.

Generative Attention Model with Adversarial Self-learning for Visual Question Answering <=SEP=> Visual question answering (VQA) is arguably one of the most challenging multimodal understanding problems as it requires reasoning and deep understanding of the image, the question, and their semantic relationship. Existing VQA methods heavily rely on attention mechanisms to semantically relate the question words with the image contents for answering the related questions. However, most of the attention models are simplified as a linear transformation, over the multimodal representation, which we argue is insufficient for capturing the complex nature of the multimodal data. In this paper we propose a novel generative attention model obtained by adversarial self-learning. The proposed adversarial attention produces more diverse visual attention maps and it is able to generalize the attention better to new questions. The experiments show the proposed adversarial attention leads to a state-of-the-art VQA model on the two VQA benchmark datasets, VQA v1.0 and v2.0.

HMDB: A large video database for human motion recognition <=SEP=> With nearly one billion online videos viewed everyday, an emerging new frontier in computer vision research is recognition and search in video. While much effort has been devoted to the collection and annotation of large scalable static image datasets containing thousands of image categories, human action datasets lag far behind. Current action recognition databases contain on the order of ten different action categories collected under fairly controlled conditions. State-of-the-art performance on these datasets is now near ceiling and thus there is a need for the design and creation of new benchmarks. To address this issue we collected the largest action video database to-date with 51 action categories, which in total contain around 7,000 manually annotated clips extracted from a variety of sources ranging from digitized movies to YouTube. We use this database to evaluate the performance of two representative computer vision systems for action recognition and explore the robustness of these methods under various conditions such as camera motion, viewpoint, video quality and occlusion.

ParaPIM: a parallel processing-in-memory accelerator for binary-weight deep neural networks <=SEP=> Recent algorithmic progression has brought competitive classification accuracy despite constraining neural networks to binary weights (+1/-1). These findings show remarkable optimization opportunities to eliminate the need for computationally-intensive multiplications, reducing memory access and storage. In this paper, we present ParaPIM architecture, which transforms current Spin Orbit Torque Magnetic Random Access Memory (SOT-MRAM) sub-arrays to massively parallel computational units capable of running inferences for Binary-Weight Deep Neural Networks (BWNNs). ParaPIM's in-situ computing architecture can be leveraged to greatly reduce energy consumption dealing with convolutional layers, accelerate BWNNs inference, eliminate unnecessary off-chip accesses and provide ultra-high internal bandwidth. The device-to-architecture co-simulation results indicate ~4x higher energy efficiency and 7.3x speedup over recent processing-in-DRAM acceleration, or roughly 5x higher energy-efficiency and 20.5x speedup over recent ASIC approaches, while maintaining inference accuracy comparable to baseline designs.

Automated detection of smuggled high-risk security threats using Deep Learning <=SEP=> The security infrastructure is ill-equipped to detect and deter the smuggling of non-explosive devices that enable terror attacks such as those recently perpetrated in western Europe. The detection of so-called “small metallic threats” (SMTs) in cargo containers currently relies on statistical risk analysis, intelligence reports, and visual inspection of X-ray images by security officers. The latter is very slow and unreliable due to the difficulty of the task: objects potentially spanning less than 50 pixels have to be detected in images containing more than 2 million pixels against very complex and cluttered backgrounds. In this contribution, we demonstrate for the first time the use of Convolutional Neural Networks (CNNs), a type of Deep Learning, to automate the detection of SMTs in fullsize X-ray images of cargo containers. Novel approaches for dataset augmentation allowed to train CNNs from-scratch despite the scarcity of data available. We report fewer than 6% false alarms when detecting 90% SMTs synthetically concealed in streamof-commerce images, which corresponds to an improvement of over an order of magnitude over conventional approaches such as Bag-of-Words (BoWs). The proposed scheme offers potentially super-human performance for a fraction of the time it would take for a security officers to carry out visual inspection (processing time is approximately 3.5s per container image).

Trends in Automotive Communication Systems <=SEP=> The use of networks for communications between the electronic control units (ECU) of a vehicle in production cars dates from the beginning of the 1990s. The specific requirements of the different car domains have led to the development of a large number of automotive networks such as Local Interconnect Network, J1850, CAN, TTP/C, FlexRay, media-oriented system transport, IDB1394, etc. This paper first introduces the context of in-vehicle embedded systems and, in particular, the requirements imposed on the communication systems. Then, a comprehensive review of the most widely used automotive networks, as well as the emerging ones, is given. Next, the current efforts of the automotive industry on middleware technologies, which may be of great help in mastering the heterogeneity, are reviewed. Finally, we highlight future trends in the development of automotive communication systems.

Bank Customer Credit Scoring by Using Fuzzy Expert System <=SEP=> Granting banking facility is one of the most important parts of the financial supplies for each bank. So this activity becomes more valuable economically and always has a degree of risk. These days several various developed Artificial Intelligent systems like Neural Network, Decision Tree, Logistic Regression Analysis, Linear Discriminant Analysis and etc, are used in the field of granting facilities that each of this system owns its advantages and disadvantages. But still studying and working are needed to improve the accuracy and performance of them. In this article among other AI methods, fuzzy expert system is selected. This system is based on data and also extracts rules by using data. Therefore the dependency to experts is omitted and interpretability of rules is obtained. Validity of these rules could be confirmed or rejected by banking affair experts. For investigating the performance of proposed system, this system and some other methods were performed on various datasets. Results show that the proposed algorithm obtained better performance among the others.

Semantics matters : cognitively plausible delineation of city centres from point of interest data <=SEP=> We sketch a workflow for cognitively plausible recognition of vague geographical concepts, such as a city centre. Our approach imitates a pedestrian strolling through the streets, and comparing his/her internal cognitive model of a city centre with the stimulus from the external world to decide whether he/she is in the city centre or outside. The cognitive model of a British city centre is elicited through an online questionnaire survey and used to delineate referents of city centre from point of interest data. We first compute a measure of ‘city centre-ness’ at each location within a city, and then merge the area of high city centre-ness to a contiguous region. The process is illustrated on the example of the City of Bristol, and the computed city centre area for Bristol is evaluated by comparison to reference areas derived from alternative sources. The evaluation suggests that our approach performs well and produces a representation of a city centre that is near to people’s conceptualisation. The benefits of our work are better (and user-driven) descriptions of complex geographical concepts. We see such models as a prerequisite for generalisation over large changes in detail, and for very specific purposes.

Label Information Guided Graph Construction for Semi-Supervised Learning <=SEP=> In the literature, most existing graph-based semi-supervised learning methods only use the label information of observed samples in the label propagation stage, while ignoring such valuable information when learning the graph. In this paper, we argue that it is beneficial to consider the label information in the graph learning stage. Specifically, by enforcing the weight of edges between labeled samples of different classes to be zero, we explicitly incorporate the label information into the state-of-the-art graph learning methods, such as the low-rank representation (LRR), and propose a novel semi-supervised graph learning method called semi-supervised low-rank representation. This results in a convex optimization problem with linear constraints, which can be solved by the linearized alternating direction method. Though we take LRR as an example, our proposed method is in fact very general and can be applied to any self-representation graph learning methods. Experiment results on both synthetic and real data sets demonstrate that the proposed graph learning method can better capture the global geometric structure of the data, and therefore is more effective for semi-supervised learning tasks.

Flexible sensor for Mckibben pneumatic actuator <=SEP=> A flexible electro-conductive rubber sensor for measuring the length of a McKibben pneumatic actuator has been developed. Mckibben actuators are flexible, lightweight, and widely used to actuate power assisting devices for which compliance is required for safety. The actuator's length needs to be measured to control the devices accurately. However, the flexibility and lightweight properties might be lost if rigid sensors such as linear potentionmeters or encoders are directly attached to the actuators. To keep the desirable properties of McKibben actuators, compact and flexible sensors are necessary. In this paper, a flexible sensor using electro-conductive flexible rubber is proposed to measure the length of McKibben actuators. Using this sensor, a higher accuracy can be obtained by measuring the circumference displacement than directly measuring the axial displacement. The estimation accuracy is evaluated and the usefulness of the proposed method is verified by adopting to a multiple-link robot arm driven by the McKibben actuators.