nils-herrmann/clean_scirepeval huggingface dataset:
Zebrafish Genome Engineering Using the CRISPR Cas9 System.
Venue: Trends in genetics TIG

Geneticists have long sought the ability to manipulate vertebrate genomes by directly altering the information encoded in specific genes. The recently discovered clustered regularly interspaced short palindromic repeats (CRISPR) Cas9 endonuclease has the ability to bind single loci within vertebrate genomes and generate double strand breaks (DSBs) at those sites. These DSBs induce an endogenous DSB repair response that results in small insertions or deletions at the targeted site. Alternatively, a template can be supplied, in which case homology directed repair results in the generation of engineered alleles at the break site. These changes alter the function of the targeted gene facilitating the analysis of gene function. This tool has been widely adopted in the zebrafish model; we discuss the development of this system in the zebrafish and how it can be manipulated to facilitate genome engineering.
Genome engineering in the yeast pathogen Candida glabrata using the CRISPR Cas9 system
Venue: Scientific reports

Among Candida species, the opportunistic fungal pathogen Candida glabrata has become the second most common causative agent of candidiasis in the world and a major public health concern. Yet, few molecular tools and resources are available to explore the biology of C. glabrata and to better understand its virulence during infection. In this study, we describe a robust experimental strategy to generate loss of function mutants in C. glabrata. The procedure is based on the development of three main tools: (i) a recombinant strain of C. glabrata constitutively expressing the CRISPR Cas9 system, (ii) an online program facilitating the selection of the most efficient guide RNAs for a given C. glabrata gene, and (iii) the identification of mutant strains by the Surveyor technique and sequencing. As a proof of concept, we have tested the virulence of some mutants in vivo in a Drosophila melanogaster infection model. Our results suggest that yps11 and a previously uncharacterized serine/threonine kinase are involved, directly or indirectly, in the ability of the pathogenic yeast to infect this model host organism.
Forecasting of stock return prices with sparse representation of financial time series over redundant dictionaries
Venue: Expert Syst. Appl.

A new predictive model for time series within the financial sector is presented.The method is based on learned redundant dictionaries for sparse representation of financial time series.The overall return gain generated by the predictive model exceeds the gain generated by the market.Untrained dictionaries outperform dictionaries trained with the KSV D method.Untrained dictionaries require a reduced number of atoms to achieve successful results. This paper presents the theory, methodology and application of a new predictive model for time series within the financial sector, specifically data from 20 companies listed on the U.S. stock exchange market. The main impact of this article is (1) the proposal of a recommender system for financial investment to increase the cumulative gain; (2) an artificial predictor that beats the market in most cases; and (3) the fact that, to the best of our knowledge, this is the first effort to predict time series by learning redundant dictionaries to sparsely reconstruct these signals. The methodology is conducted by finding the optimal set of predicting model atoms through two directions for dictionaries generation: the first one by extracting atoms from past daily return price values in order to build untrained dictionaries; and the second one, by atom extraction followed by training of dictionaries though K SVD. Prediction of financial time series is a periodic process where each cycle consists of two stages: (1) training of the model to learn the dictionary that maximizes the probability of occurrence of an observation sequence of return values, (2) prediction of the return value for the next coming trading day. The motivation for such research is the fact that a tool, which might generate confidence of the potential benefits obtained from using formal financial services, would encourage more participation in a formal system such as the stock market. Theory, issues, challenges and results related to the application of sparse representation to the prediction of financial time series, as well as the performance of the method, are presented.
Proposal of a New Similarity Measure Based on Delay Embedding for Time Series Classification

Time series data is abundant in many areas of practical life such as medical and health related applications, biometric or process industry, financial or economical analysis etc. The categorization of multivariate time series (MTS) data poses problem due to its dynamical nature and conventional machine learning algorithms for static data become unsuitable for time series data processing. For classification or clustering, a similarity measure to assess similarity between two MTS data is needed. Though various similarity measures have been developed so far, dynamic time warping (DTW) and its variants have been found to be the most popular. An approach of time series classification with a similarity measure (Cross Translational Error CTE) based on multidimensional delay vector (MDV) representation of time series has been proposed previously. In this work another new similarity measure (Dynamic Translational Error DTE) an improved version of CTE, and its two variants are proposed and the performance study of DTE(1) and DTE(2) in comparison to several other currently available similarity measures have been done using 43 publicly available bench mark data sets with simulation experiments. It has been found that the new measures produce the best recognition accuracy in larger number of data sets compared to other measures.
Generalization of metric classification algorithms for sequences classification and labelling
Venue: ArXiv

The article deals with the issue of modification of metric classification algorithms. In particular, it studies the algorithm k Nearest Neighbours for its application to sequential data. A method of generalization of metric classification algorithms is proposed. As a part of it, there has been developed an algorithm for solving the problem of classification and labelling of sequential data. The advantages of the developed algorithm of classification in comparison with the existing one are also discussed in the article. There is a comparison of the effectiveness of the proposed algorithm with the algorithm of CRF in the task of chunking in the open data set CoNLL2000.
Design and Analysis of Novel Fractal Linear and Planar Array Antennas for Satellite Applications

This article proposed a new geometric design methodology for the systematic expansion of fractal linear and planar array antennas. Using this proposed geometric design methodology any deterministic polygon shape can be constructed. In this article, two element fractal linear and triangular array antennas are examined using proposed methodology up to four iterations of two expansion factors. Due to the repetitive nature of the proposed geometric design methodology, both linear and planar fractal arrays shows multi beam behavior with excellent array factor properties. The behavior of the proposed arrays shows better performance than linear and planar fractal array antennas generated by concentric circular ring sub array geometric generator. Triangular planar fractal array of expansion factor two at fourth iteration achieved a single valued beam width of 3.80 with 31.6 side lobe level. The suggested fractal arrays are analyzed and simulated by MATLAB 13 programming.
The Status of Financial Inclusion, Regulation, and Education in India

India's financial inclusion agenda has witnessed a paradigm shift over the last decade, away from an emphasis on credit to a more comprehensive approach toward financial services (e.g. opening bank accounts and offering basic financial products, such as insurance) This paper describes the structure of banking and microfinance institutions in India relevant to the developing model of financial inclusion, as well as relevant regulatory structure and modes of delivery. It explains the current state of financial inclusion, as well as regulatory changes necessary to make the new architecture for inclusion viable, including a critique of some of the recommendations of the Mor Committee on Comprehensive Financial Services for Small Businesses and Low Income Households. The paper then reviews modes of delivery and the regulatory structure being contemplated or recently introduced. It assesses the suitability objective envisaged as critical for inclusion, associated challenge of revamping consumer protection laws, and imperative of improving financial literacy. The paper also discusses the case of micro, small, and medium sized enterprises in the given context.
From radical transparency to radical disclosure: reconfiguring (in)voluntary transparency through the management of visibilities

This article challenges how the concepts of voluntary and involuntary transparency are understood in the digital age by focusing on the management of involuntary and voluntary disclosure. We tend to understand radical transparency through new forms of involuntary networked data dissemination, spread without the consent or knowledge of whoever held the data. This view conflates the politics of exclusion with crucial questions of compulsion. At the same time, radical transparency's promise to end secrecy has not materialized. Instead, the social material relations underpinning digital disclosures suggest they function to reconfigure visibilities of control and recognition rather than reveal extant objects. Thus, the article introduces a typology of disclosure to better understand the involuntary and autonomous and inclusive and exclusionary dimensions of managing visibility in the digital era. It then explores two sets of empirical "radical disclosure" practices, made with purpose but without consent, to articulate how digital disclosures reconfigure visibility and set limits and opportunities in society. The article concludes with a suggestion toward ramifications for governance and autonomy.
High performance load acceleration control based on singular spectrum analysis for industrial robot
Venue: 2016 IEEE International Power Electronics and Motion Control Conference (PEMC)

Rapid and accurate motion control of industrial robots has a vital demand on the precise load torque estimation. To achieve highly robust motion control against dynamic torque, our previous research had introduced the Kalman filter based instantaneous state observer (ISOB) to attain the instantaneous load torque estimation of a robot arm. This paper proposes the load torque estimation by ISOB based on singular spectrum analysis (SSA) to improve the torque estimation performance. The proposed method achieves highly precise load torque estimation, especially in impulsive responses, with effective noise suppression. The proposed method is targeted to realize the high performance load acceleration control for industrial robots. The validity of the proposed method is verified by numerical simulation results based on the speed control experimental data of a robot arm.
Dyeability of the Fabrics dyed with Herb Extracts

This study examined the dyeability of fabrics dyed with rosemary, mint, sage, and thyme herb extracts. The herb extracts were pulverized and characterized by UV and FT IR analysis. Dyeing properties of herb colorants on cotton, silk and wool fiber and effect of dyeing conditions on dye uptake were compared. The constituents of four dif ferent herbs were shown to be similar to each other. The dye uptake of rosemary and sage were shown to be larger than those of mint and thyme regardless of the fiber types. Affinity of herb colorants to protain fibers was higher than to cellulose fiber. Compared with silk and wool, the dyeability of cotton was the worse, the dye uptake of silk and wool fibers were increased along with dyeing time and dyeing temperature. As the dye uptake increased with increasing of the dye concentration, and its isothermal adsorption curves were langmuir type, indicating that ionic bonding was involved in the adsorption of herb colorants to the fibers.
Convolutional Pose Machines
Venue: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Pose Machines provide a sequential prediction framework for learning rich implicit spatial models. In this work we show a systematic design for how convolutional networks can be incorporated into the pose machine framework for learning image features and image dependent spatial models for the task of pose estimation. The contribution of this paper is to implicitly model long range dependencies between variables in structured prediction tasks such as articulated pose estimation. We achieve this by designing a sequential architecture composed of convolutional networks that directly operate on belief maps from previous stages, producing increasingly refined estimates for part locations, without the need for explicit graphical model style inference. Our approach addresses the characteristic difficulty of vanishing gradients during training by providing a natural learning objective function that enforces intermediate supervision, thereby replenishing back propagated gradients and conditioning the learning procedure. We demonstrate state of the art performance and outperform competing methods on standard benchmarks including the MPII, LSP, and FLIC datasets.
On the right side? A longitudinal study of left versus right lateralized semantic dementia.
Venue: Brain a journal of neurology

The typical presentation of semantic dementia is associated with marked, left predominant anterior temporal lobe atrophy and with changes in language. About 30% of individuals, however, present with predominant right anterior temporal lobe atrophy, usually accompanied by behavioural changes and prosopagnosia. Here, we aimed to establish whether these initially distinct clinical presentations evolve into a similar syndrome at the neural and behavioural level. Thirty one patients who presented with predominant anterior temporal lobe atrophy were included. Based on imaging, patients were categorized as either predominant left (n 22) or right (n 9) semantic dementia. Thirty three Alzheimer's disease patients and 25 healthy controls were included for comparison. Participants completed the Addenbrooke's Cognitive Examination, a Face and Emotion Processing Battery and the Cambridge Behavioural Inventory, and underwent magnetic resonance imaging annually. Longitudinal neuroimaging analyses showed greater right temporal pole atrophy in left semantic dementia than Alzheimer's disease, whereas right semantic dementia showed greater orbitofrontal and left temporal lobe atrophy than Alzheimer's disease. Importantly, direct comparisons between semantic dementia groups revealed that over time, left semantic dementia showed progressive thinning in the right temporal pole, whereas right semantic dementia showed thinning in the orbitofrontal cortex and anterior cingulate. Behaviourally, longitudinal analyses revealed that general cognition declined in all patients. In contrast, patients with left and right semantic dementia showed greater emotion recognition decline than Alzheimer's disease. In addition, left semantic dementia showed greater motivation loss than Alzheimer's disease. Correlational analyses revealed that emotion recognition was associated with right temporal pole, right medial orbitofrontal and right fusiform integrity, while changes in motivation were associated with right temporal pole cortical thinning. While left and right semantic dementia show distinct profiles at presentation, both phenotypes develop deficits in emotion recognition and behaviour. These findings highlight the pervasive socio emotional deficits in frontotemporal dementia, even in patients with an initial language presentation. These changes reflect right anterior temporal and orbitofrontal cortex degeneration, underscoring the role of these regions in social cognition and behaviour.
A two decade dementia incidence comparison from the Cognitive Function and Ageing Studies I and II
Venue: Nature communications

Dramatic global increases in future numbers of people with dementia have been predicted. No multicentre population based study powered to detect changes over time has reported dementia incidence. MRC Cognitive Function and Ageing Study (CFAS) undertook baseline interviews in populations aged 65+ years in England and Wales (1989 1994) Three areas (CFAS I) were selected for new sampling two decades later (2008 2011) with same geographical boundaries, sampling and approach methods (CFAS II) At 2 years CFAS I interviewed 5,156 (76% response) with 5,288 interviewed in CFAS II (74% response) Here we report a 20% drop in incidence (95% CI: 0 40% driven by a reduction in men across all ages above 65. In the UK we estimate 209,600 new dementia cases per year. This study was uniquely designed to test for differences across geography and time. A reduction of age specific incidence means that the numbers of people estimated to develop dementia in any year has remained relatively stable.
Clerical marriage and buddhist modernity in early twentieth century Korea

ii PREFACE iv TABLE OF CONTENTS v LIST OF TABLES viii ACKNOWLEDGEMENTS ix DEDICATION x INTRODUCTION 1 Contemporary Scholarship in Both Korean and English Related to the Issue of Clerical Marriage 1 Thesis 5 Sources 8 Theoretical Framework 10 Organization 16 CHAPTER ONE: RE THINKING LATE CHOSON BUDDHISM 22  Vinayas and Pure Rules in the Buddhist Traditions in China and Korea 25 Early Vinaya Tradition and Pure Rules in Chinese Buddhism 25 Vinaya and Pure Rules in Korean Buddhism from Silla to Koryo 27 Vinayas, Precepts, and Ordination of Choson Buddhism 30 General Buddhist Policies of the Choson Court 30 Vinaya and Pure Rules in the Choson Period 33 Ordination, Precepts, and Monk Certificates 38 Prohibition of Clerical Marriage 45 Re thinking Late Choson Buddhism 47 Formation of Dharma Lineage 47 Buddhist Clergy's Private Property 53 Revival of Ordination Lineage. 57 Prelude to Clerical Marriage in Colonial Buddhism. 62  CHAPTER TWO: TEMPLE REGULATIONS AND THE ISSUE OF CLERICAL MARRIAGE 65  Korean Buddhism in the Transition Period 66 The 1902 Temple Regulations 66 The Wonjong and the Imjejong 68
Perception of Monastic Slaves by Scholar Officials and Monks in the Late Koryo and Early Choson Periods

This paper is an attempt at re envisioning pre modern Korean Buddhism by engaging the issue of monastic slaves during the late Koryo and early Choson. The approach is to accept "mundane provisions" adopted by temples as a closer reflection of Buddhism in its practiced form than doctrine focused Buddhism. Consequently, this paper concludes that we cannot infer from doctrine, as we often do, the actual actions of monks. Our doctrinal understanding of Buddhism is far from being an accurate depiction of monastic Buddhism; it is rather a rhetorical representation. If we accept the voices of the late Koryo and early Choson monks, the custom of monastic slavery did not transgress from any monastic regulations nor any Buddhist ideals. To interpret the actions of the monks as transgressions of the Vinayic codes or some highly held ideals reduces the issue at hand to a unidimensional matter and leads to an oversimplification of the complex dynamics of socio economic and historical forces that have shaped and re formed Buddhism in Korea.
Static race detection for device drivers: The Goblint approach
Venue: 2016 31st IEEE/ACM International Conference on Automated Software Engineering (ASE)

Device drivers rely on fine grained locking to ensure safe access to shared data structures. For human testers, concurrency makes such code notoriously hard to debug; for automated reasoning, dynamically allocated memory and low level pointer manipulation poses significant challenges. We present a flexible approach to data race analysis, implemented in the open source Goblint static analysis framework that combines different pointer and value analyses in order to handle a wide range of locking idioms, including locks allocated dynamically as well as locks stored in arrays. To the best of our knowledge, this is the most ambitious effort, having lasted well over ten years, to create a fully automated static race detection tool that can deal with most of the intricate locking schemes found in Linux device drivers. Our evaluation shows that these analyses are sufficiently precise, but practical use of these techniques requires inferring environmental and domain specific assumptions.
Implementing an Antibiotic Stewardship Program: Guidelines by the Infectious Diseases Society of America and the Society for Healthcare Epidemiology of America.
Venue: Clinical infectious diseases an official publication of the Infectious Diseases Society of America

Evidence based guidelines for implementation and measurement of antibiotic stewardship interventions in inpatient populations including long term care were prepared by a multidisciplinary expert panel of the Infectious Diseases Society of America and the Society for Healthcare Epidemiology of America. The panel included clinicians and investigators representing internal medicine, emergency medicine, microbiology, critical care, surgery, epidemiology, pharmacy, and adult and pediatric infectious diseases specialties. These recommendations address the best approaches for antibiotic stewardship programs to influence the optimal use of antibiotics.
Feature Detection for Vehicle Localization in Urban Environments Using a Multilayer LIDAR
Venue: IEEE Transactions on Intelligent Transportation Systems

Localization is an important component of autonomous vehicles, as it enables the accomplishment of tasks, such as path planning and navigation. Although vehicle position can be obtained by GNSS devices, they are susceptible to errors and satellite signal unavailability in urban scenarios. Several map aided localization solution methods have been proposed in the literature, but mostly for indoor environments. Maps used for localization store relevant environmental features that are extracted by a detection method. However, many feature detection methods do not consider the presence of dynamic obstacles or occlusions in the environment, which can impair the localization performance. In order to detect curbs even in occluding scenes, we developed a method based on ring compression analysis and least trimmed squares. For road marking detection, we developed a modified version of the Otsu thresholding method to segment road painting from road surfaces. Finally, the feature detection methods were integrated with a Monte Carlo localization method to estimate the vehicle position. Experimental tests in urban streets have been used to validate the proposed approach with favorable results.