nils-herrmann/clean_scirepeval huggingface dataset:
Spatio Temporal Modeling of Data Imputation for Daily Rainfall Series in Homogeneous Zones

Spatio temporal modelling is an area of increasing importance in which models and methods have often been developed to deal with specific applications. In this study, a spatio temporal model was used to estimate daily rainfall data. Rainfall records from several weather stations, obtained from the Agritempo system for two climatic homogeneous zones, were used. Rainfall values obtained for two fixed dates (January 1 and May 1, 2012) using the spatio temporal model were compared with the geostatisticals techniques of ordinary kriging and ordinary cokriging with altitude as auxiliary variable. The spatio temporal model was more than 17% better at producing estimates of daily precipitation compared to kriging and cokriging in the first zone and more than 18% in the second zone. The spatio temporal model proved to be a versatile technique, adapting to different seasons and dates.
Margin distribution explanation on metric learning for nearest neighbor classification
Venue: Neurocomputing

The importance of metrics in machine learning and pattern recognition algorithms has led to an increasing interest for optimizing distance metrics in recent years. Most of the state of the art methods focus on learning Mahalanobis distances and the learned metrics are in turn heavily used for the nearest neighbor based classification (NN) However, until now no theoretical link has been established between the learned metrics and their performance in NN. Although some existing methods such as large margin nearest neighbor (LMNN) have employed the concept of large margin to learn a data dependent metric, the link between the margin and the generalization performance for the metric is not fully understood. Though the recent work has indeed provided tenable margin distribution explanation on Boosting, the margin used in metric learning is quite different from that in Boosting. Thus, in this paper we try to analyze the effectiveness of metric learning algorithms for NN from the perspective of the margin distribution and provide a general and effective evaluation criterion for metric learning. On the one hand, we derive the generalization error upper bound for NN with respect to the Mahalanobis metric. On the other hand, the experiments on several benchmark datasets using existing metric learning algorithms demonstrate that large margin distribution can be obtained by these algorithms. Motivated by our analysis above, we also present a novel margin based metric learning algorithm for NN, which explicitly enlarges the margin distribution on various datasets and achieves very competitive results with the existing metric learning algorithms.
Large Margin Nearest Neighbor Classification using Curved Mahalanobis Distances
Venue: ArXiv

We consider the supervised classification problem of machine learning in Cayley Klein projective geometries: We show how to learn a curved Mahalanobis metric distance corresponding to either the hyperbolic geometry or the elliptic geometry using the Large Margin Nearest Neighbor (LMNN) framework. We report on our experimental results, and further consider the case of learning a mixed curved Mahalanobis distance. Besides, we show that the Cayley Klein Voronoi diagrams are affine, and can be built from an equivalent (clipped) power diagrams, and that Cayley Klein balls have Mahalanobis shapes with displaced centers.
A hybrid language model based on a recurrent neural network and probabilistic topic modeling
Venue: Pattern Recognition and Image Analysis

A language model based on features extracted from a recurrent neural network language model and semantic embedding of the left context of the current word based on probabilistic semantic analysis (PLSA) is developed. To calculate such embedding, the context is considered as a document. The effect of vanishing gradients in a recurrent neural network is reduced by this method. The experiment has shown that adding topic based features reduces perplexity by 10%
Context Aware Sequential Recommendation
Venue: 2016 IEEE 16th International Conference on Data Mining (ICDM)

Since sequential information plays an important role in modeling user behaviors, various sequential recommendation methods have been proposed. Methods based on Markov assumption are widely used, but independently combine several most recent components. Recently, Recurrent Neural Networks (RNN) based methods have been successfully applied in several sequential modeling tasks. However, for real world applications, these methods have difficulty in modeling the contextual information, which has been proved to be very important for behavior modeling. In this paper, we propose a novel model, named Context Aware Recurrent Neural Networks (CA RNN) Instead of using the constant input matrix and transition matrix in conventional RNN models, CA RNN employs adaptive context specific input matrices and adaptive context specific transition matrices. The adaptive context specific input matrices capture external situations where user behaviors happen, such as time, location, weather and so on. And the adaptive context specific transition matrices capture how lengths of time intervals between adjacent behaviors in historical sequences affect the transition of global sequential features. Experimental results show that the proposed CA RNN model yields significant improvements over state of the art sequential recommendation methods and context aware recommendation methods on two public datasets, i.e. the Taobao dataset and the Movielens 1M dataset.
The role of the educational leader in long day care how do they perceive their role?

National reforms introduced into the early childhood education and care sector across Australia have created a requirement for each service to appoint an 'educational' leader to provide curriculum direction to ensure that children achieve quality care and education to lead to positive outcomes. Leadership in the early childhood has often been contested and complex, but the addition of a new leadership for building pedagogy and practice has created additional complexities. This paper reports the findings of a small scale qualitative research study investigating how a small number of educational leaders working in long day care settings in urban Australia perceive their role. The study identified that these educational leaders came to the position unwillingly and felt poorly prepared and supported to meet the challenges presented.
The Educational Leader Without Borders: The Conceptual Frame

This chapter examines the roles of the educational leader without borders (ELWB) and the connections these leaders have to humanity in the lives of children and their families through the schooling process. The relationship between social justice complexities and global schooling practices that define education as a basic human right are explored through the field of education, as well, the social science perspective offered by Nancy Fraser's three dimensional lenses of economic, cultural and political that leads to participatory parity as social justice. Ten vignettes written by thirteen scholars from a variety of global schooling experiences document children's stories written in authentic contextual settings from Nigeria, Thailand, Iraq, Haiti, Native American Tribes, American Latinos and U.S. poverty among school age children. These vignettes portray the human conditions of schooling world wide that the educational leader without borders must learn.
Vehicle License Plate Recognition Based on Extremal Regions and Restricted Boltzmann Machines
Venue: IEEE Transactions on Intelligent Transportation Systems

This paper presents a vehicle license plate recognition method based on character specific extremal regions (ERs) and hybrid discriminative restricted Boltzmann machines (HDRBMs) First, coarse license plate detection (LPD) is performed by top hat transformation, vertical edge detection, morphological operations, and various validations. Then, character specific ERs are extracted as character regions in license plate candidates. Followed by suitable selection of ERs, the segmentation of characters and coarse to fine LPD are achieved simultaneously. Finally, an offline trained pattern classifier of HDRBM is applied to recognize the characters. The proposed method is robust to illumination changes and weather conditions during 24 h or one day. Experimental results on thorough data sets are reported to demonstrate the effectiveness of the proposed approach in complex traffic environments.
Ovarian Cystadenoma in a Trafficked Patient
Venue: Pediatrics

The topic of child sex trafficking is receiving increased attention both in the lay press and in research articles. Recently, a number of physician organizations have issued policy statements calling for the education and involvement of physicians in combating this form of "modern day slavery." Primary care and emergency medicine physicians have led these efforts, but a number of these victims may present to surgeons. Surgeons are in a unique position to identify trafficked patients; during the process of undraping, intubation, and surgical preparation, signs of trafficking such as tattoos, scars, dental injuries, and bruising may be evident. In addition, these patients may have specific needs in terms of anesthesia and postoperative care due to substance abuse. Here, we report the case of an 18 year old girl with a history of sexual exploitation who presents for cystadenoma excision. To our knowledge, this is the first report of a sex trafficked pediatric patient presenting for surgery.
Amnesty International's Empty Promises: Decriminalization, Prostituted Women, and Sex Trafficking

Through a close examination of Amnesty International's (Amnesty) own arguments and sources, this paper demonstrates that Amnesty's proposal to decriminalize prostitution or "sex work" will harm those it claims to help. It concludes that the best available evidence indicates that decriminalization of prostitution would: increase sex trafficking, leave prostituted women or "sex workers" more vulnerable to violence, and reduce access to healthcare, protection, and services. Prostituted women primarily enter the industry at a young age, often suffering from a history of sexual and physical abuse, coming from marginalized and vulnerable communities, and driven by emotional and economic desperation. It is in this context that prostituted women "consent" to sell themselves in the extremely risky and dangerous sex industry. But instead of helping victims of sexual exploitation, Amnesty's proposal will mostly benefit their abusers the mostly male consumers and organizers of the sex industry. Amnesty reaches its flawed conclusion by consistently misreading the evidence, engaging in selective skepticism, and ignoring the troubling legal and practical implications of its own policy. Amnesty rejects decades of scholarly research on the basis of few sources in one case from close to half a century ago and sometimes misreads the reports they rely on. Amnesty consulted primarily with those most likely to support its position the "sex workers" rather than the sex trafficking victims, adult survivors of prostitution and the child victims of commercial sexual exploitation who are the most harmed by the sex industry.
Retinyl Palmitate Supplementation Modulates T bet and Interferon Gamma Gene Expression in Multiple Sclerosis Patients
Venue: Journal of Molecular Neuroscience

Vitamin A derivatives such as retinoic acid may improve the impaired balance of CD4+ T cells in autoimmune and inflammatory diseases. This study is a double blind randomized trial to evaluate the effect of vitamin A (as form of retinyl palmitate) supplementation on multiple sclerosis (MS) patients. Thirty nine patients were enrolled and randomly assigned to two groups. Both groups were followed for 6 months. The experimental group received 25,000 IU of retinyl palmitate daily, while the control group received a placebo. Before and after the study, the expression of interferon gamma (IFN g) and T bet genes was evaluated in peripheral blood mononuclear cells of patients by RT PCR. The results showed that after 6 months of supplementation, expression of IFN g and T bet was significantly decreased. These data suggest that retinyl palmitate supplementation can modulate the impaired balance of Th1 and Th2 cells and vitamin A products that may be involved in the therapeutic mechanism of vitamin A in MS patients. This study provides information regarding the decreased gene expression of IFN g and T bet in MS by retinyl palmitate supplementation.
DEVELOPMENT OF NANOSTRUCTURED LIPID CARRIER FORMULATION CONTAINING OF RETINYL PALMITATE

The purpose of this research is to develop a Nanostructure Lipid Carrier (NLC) formulation containing of Retinyl Palmitate.  Preparation of NLC was carried out by ultrasonication method. The formulas of NLC were developed by using virgin coconut oil and oleic acid as a liquid lipids, cetyl palmitate, and stearic acid as solid lipids, Tween 80 and Poloxamer as a surfactant and glycerine as co surfactant. Characterization of NLC consisted of visual appearance, morphology, particle size, polydispersity index (PI) and physical stability test using freeze thaw, centrifugation, and accelerate stability test method.  Obtained NLC revealed a good characterization with the formulation of 7.2% of cetyl palmitate, 4.8% of oleic acid, 10% of Tween 80, 10% of glycerin, and 2% of retinyl palmitate. This NLC has a pale yellow appearance, globule size of 258+ 15.85 nm; and a polydispersity index of 0.31+ 0.09. It was also physically stable after centrifuged at 13,000 rpm for 30 min, during 4 cycles of freeze thaw and storage at room temperature for 28 d. During storage for 28 d, retinyl palmitate in NLC had been degraded only about 15% in comparison with macroemulsion destroyed almost 50% of retinyl palmitate in the same time.  NLC formulations with 7.2% cetyl palmitate, 4.8% of oleic acid, 10% of Tween 80, 10% of glycerol, and 2% of retinyl palmitate is the most optimal formula that showed a good characteristic. Stability study revealed that NLC provided better stability than macroemulsion.  Retinyl palmitate, NLC, Ultrasonication Methode
2015 American Thyroid Association Management Guidelines for Adult Patients with Thyroid Nodules and Differentiated Thyroid Cancer: The American Thyroid Association Guidelines Task Force on Thyroid Nodules and Differentiated Thyroid Cancer.
Venue: Thyroid official journal of the American Thyroid Association

Thyroid nodules are a common clinical problem, and differentiated thyroid cancer is becoming increasingly prevalent.  guidelines for the management of these disorders were revised in 2009, significant scientific advances have occurred in the field. The aim of these guidelines is to inform clinicians, patients, researchers, and health policy makers on published evidence relating to the diagnosis and management of thyroid nodules and differentiated thyroid cancer.  The specific clinical questions addressed in these guidelines were based on prior versions of the guidelines, stakeholder input, and input of task force members. Task force panel members were educated on knowledge synthesis methods, including electronic database searching, review and selection of relevant citations, and critical appraisal of selected studies. Published English language articles on adults were eligible for inclusion. The American College of Physicians Guideline Grading System was used for critical appraisal of evidence and grading strength of recommendations for therapeutic interventions. We developed a similarly formatted system to appraise the quality of such studies and resultant recommendations. The guideline panel had complete editorial independence from the ATA. Competing interests of guideline task force members were regularly updated, managed, and communicated to the ATA and task force members.  The revised guidelines for the management of thyroid nodules include recommendations regarding initial evaluation, clinical and ultrasound criteria for fine needle aspiration biopsy, interpretation of fine needle aspiration biopsy results, use of molecular markers, and management of benign thyroid nodules. Recommendations regarding the initial management of thyroid cancer include those relating to screening for thyroid cancer, staging and risk assessment, surgical management, radioiodine remnant ablation and therapy, and thyrotropin suppression therapy using levothyroxine. Recommendations related to long term management of differentiated thyroid cancer include those related to surveillance for recurrent disease using imaging and serum thyroglobulin, thyroid hormone therapy, management of recurrent and metastatic disease, consideration for clinical trials and targeted therapy, as well as directions for future research.  We have developed evidence based recommendations to inform clinical decision making in the management of thyroid nodules and differentiated thyroid cancer. They represent, in our opinion, contemporary optimal care for patients with these disorders.
Hypergraph Partitioning for Sparse Matrix Matrix Multiplication
Venue: TOPC

We propose a fine grained hypergraph model for sparse matrix matrix multiplication (SpGEMM) a key computational kernel in scientific computing and data analysis whose performance is often communication bound. This model correctly describes both the interprocessor communication volume along a critical path in a parallel computation and also the volume of data moving through the memory hierarchy in a sequential computation. We show that identifying a communication optimal algorithm for particular input matrices is equivalent to solving a hypergraph partitioning problem. Our approach is nonzero structure dependent, meaning that we seek the best algorithm for the given input matrices. In addition to our three dimensional fine grained model, we also propose coarse grained one dimensional and two dimensional models that correspond to simpler SpGEMM algorithms. We explore the relations between our models theoretically, and we study their performance experimentally in the context of three applications that use SpGEMM as a key computation. For each application, we find that at least one coarse grained model is as communication efficient as the fine grained model. We also observe that different applications have affinities for different algorithms. Our results demonstrate that hypergraphs are an accurate model for reasoning about the communication costs of SpGEMM as well as a practical tool for exploring the SpGEMM algorithm design space.
Mechanism driven biomarkers to guide immune checkpoint blockade in cancer therapy
Venue: Nature Reviews Cancer

With recent approvals for multiple therapeutic antibodies that block cytotoxic T lymphocyte associated antigen 4 (CTLA4) and programmed cell death protein 1 (PD1) in melanoma, non small cell lung cancer and kidney cancer, and additional immune checkpoints being targeted clinically, many questions still remain regarding the optimal use of drugs that block these checkpoint pathways. Defining biomarkers that predict therapeutic effects and adverse events is a crucial mandate, highlighted by recent approvals for two PDL1 diagnostic tests. Here, we discuss biomarkers for anti PD1 therapy based on immunological, genetic and virological criteria. The unique biology of the CTLA4 immune checkpoint, compared with PD1, requires a different approach to biomarker development. Mechanism based insights from such studies may guide the design of synergistic treatment combinations based on immune checkpoint blockade.
The development of Australian automotive design: General Motors Holden, 1923 1953

The merger of General Motors Australia and Holden Motor Body Builders in 1931 not only established Australia's leading automotive company it formalised Automotive Design in this country. This paper, for the first time, will examine and disclose the initial 30 years of Automotive design at GM H and expose the original designers, their origins and achievements. Initially located in Adelaide from 1923, Holden's design section produced unique body styles, like the streamline styled All Enclosed Coupe and provided solutions not only for dust sealing, ventilation and motor body strength but solved complex engineering problems with innovative design during WWII. The initial designers were trained internally by one man, Bert Wylie, who understood that good body design lay in precise drawings and not a chalk board. The team he built, with American input, applied considerable knowledge and expertise to ensure the Holden car, when released in 1948, was exactly what the Australian market wanted. Design at GM H evolved from American practice through a small team who were provided the opportunity to be innovative and progressive. This paper explores the emergence of the Holden design team and their place in the general design sphere. The paper will argue that by the mid 1940s the GM H design group were capable of producing a complete Australian car and that this expertise elevated the Holden design team to a position of respect within the GM Corporation.
INTEGRATION OF THE WELL BEING IN THE EARLY PHASES OF THE AUTOMOTIVE DESIGN PROCESS: FIRST APPROACH AND CONTRIBUTION OF PHYSIOLOGICAL MEASURES

In this paper is presented the opportunity to consider the user's Well being in an automotive context directly from the early phases of the design process by proposing new kinds of tools and methods. After establishing a definition of Well being, an evaluation method is suggested enabling the car designers to assess how a new system would impact the driver's Well being. More precisely, a first experiment studying the possibility to use physiological measures in order to quantify the driver's Well being is presented.
Domain Decomposition Methods for Time Harmonic Electromagnetic Waves With High Order Whitney Forms
Venue: IEEE Transactions on Magnetics

Classically, the domain decomposition methods (DDMs) for time harmonic electromagnetic wave propagation problems make use of the standard, low order, Nedelec basis functions. This paper analyzes the convergence rate of DDM when higher order finite elements are used for both volume and interface discretizations, in particular when different orders are used in the volume and on the interfaces.
Students' Decision Making About Postgraduate Education at G University in China: The Main Factors and the Role of Family and of Teachers

The paper draws on findings from a case study which explored factors influencing students' decision making of postgraduate (PG) education at G University in China. Both questionnaires and follow up interviews were used for data collection. This paper reports the main reasons for students' choices of subject and institution for PG education, and the influences of families and teachers, and of guanxi in their decision making. The findings show that both families and teachers play important roles in shaping students' decision making about PG education. It provides insights into students' decision making about higher education embedded in the Chinese culture of Confucianism.