nils-herrmann/clean_scirepeval huggingface dataset:
Common and Discriminative Subspace Kernel Based Multiblock Tensor Partial Least Squares Regression
Venue: AAAI

In this work, we introduce a new generalized nonlinear tensor regression framework called kernel based multiblock tensor partial least squares (KMTPLS) for predicting a set of dependent tensor blocks from a set of independent tensor blocks through the extraction of a small number of common and discriminative latent components. By considering both common and discriminative features, KMTPLS effectively fuses the information from multiple tensorial data sources and unifies the single and multiblock tensor regression scenarios into one general model. Moreover, in contrast to multilinear model, KMTPLS successfully addresses the nonlinear dependencies between multiple response and predictor tensor blocks by combining kernel machines with joint Tucker decomposition, resulting in a significant performance gain in terms of predictability. An efficient learning algorithm for KMTPLS based on sequentially extracting common and discriminative latent vectors is also presented. Finally, to show the effectiveness and advantages of our approach, we test it on the real life regression task in computer vision, i.e. reconstruction of human pose from multiview video sequences.
Data Mining, Fourth Edition: Practical Machine Learning Tools and Techniques

Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches. Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today's techniques coupled with the methods at the leading edge of contemporary research. Please visit the book companion website at http:/www.cs.waikato.ac.nz/ml/weka/book.html It contains Powerpoint slides for Chapters 1 12. This is a very comprehensive teaching resource, with many PPT slides covering each chapter of the bookOnline Appendix on the Weka workbench; again a very comprehensive learning aid for the open source software that goes with the book.
Prevalencia de obesidad general y obesidad abdominal en la poblacion adulta espanola (25 64 anos) 2014 2015: estudio ENPE

Resumen Introduccion y objetivos Segun el analisis de 2013 del Institute of Health Metrics, valores elevados de indice de masa corporal son el primer factor de riesgo de carga de enfermedad en Espana. Con base en este punto de interes, se describe la prevalencia de obesidad total y obesidad abdominal en la poblacion adulta espanola (25 64 anos) en 2014 2015. Metodos La muestra procede del estudio ENPE, estudio transversal en muestra representativa de la poblacion no institucionalizada (n 6.800) realizado entre mayo de 2014 y mayo de 2015. Este analisis se refiere a poblacion entre 25 y 64 anos (n 3.966) Observadores entrenados realizaron las mediciones antropometricas en los domicilios segun protocolos internacionales estandarizados. Se considero sobrepeso valores de indice de masa corporal 25 y obesidad, indice de masa corporal 30. La obesidad abdominal se tipifico para valores de cintura 102 cm en varones y 88 cm en mujeres. Resultados La prevalencia de sobrepeso estimada en la poblacion adulta espanola (25 64 anos) es del 39,3% (intervalo de confianza del 95% [IC95% 35,7 42,9% la de obesidad general, del 21,6% (IC95% 19,0 24,2% el 22,8% (IC95% 20,6 25,0% entre los varones y el 20,5% (IC95% 18,5 22,5% entre las mujeres, y aumenta con la edad. La prevalencia de obesidad abdominal se estima en el 33,4% (IC95% 31,1 35,7% mayor entre las mujeres (el 43,3% IC95% 41,1 45,8% que entre los varones (el 23,3% IC95% 20,9 25,5% y tambien aumenta con la edad. Conclusiones Las prevalencias de obesidad general y obesidad abdominal en Espana son altas, aunque con distribucion desigual por comunidades autonomas. La comparacion con datos precedentes plantea un aumento importante de la sobrecarga ponderal, lo que indica la necesidad de vigilancia sistematica y acciones integradas.
Impact of e Learning on students: A proposal and evaluation of enhanced e learning model to increase the academic performance of university students
Venue: 2016 Sixth International Conference on Digital Information Processing and Communications (ICDIPC)

E Learning has become an important means of gaining education/knowledge with the help of electronic media, typically on the Internet. This research paper identifies and analyses the impacts of e learning on the academic performance of students. It also identifies existing solutions to address the impacts. In addition it proposes an enhanced e learning model by selecting two best solutions. Finally evaluates the new model. The method of evaluation was based on conducting a survey to around 50 students 3 different universities overseas and here in Australia. The result of the analysis showed that the majority of the students had positive perceptions of the proposed model with regard to its impact on student's academic performance. Furthermore steps were taken to improve the proposed model accordingly by focusing on considering the major factors involved, minimizing the drawbacks.
The Effect of Learning Strategy and Cognitive Style toward Mathematical Problem Solving Learning Outcomes

This study aims to analyze and determine the effect of learning strategies (The differences between Problem Based Learning and Direct Instruction) and cognitive style on mathematical problems solving learning outcomes. This study used a quasi experimental research design (nonequivalent control group designs) The research subjects were students from state elementary school of V grade in Gading I Surabaya, east java. The totals of elementary school population in east Surabaya were 141 elementary schools. The selected schools are the schools which have some parallel classes, therefore the circumstances are relatively similar. Four classes are selected, they were from SDN Gading 1 state elementary school. These schools were randomly chosen to implement different learning strategies, they are implementing problem based learning and hands on learning. The data collection techniques were mathematical problems solving learning pre tests and post test, Group embedded figures test (GEFT) for cognitive style, assessment performance for learning achievement. Data analysis techniques in this study use ANOVA technique (analysis of variance) two lines. The results of this study showed that (1) there is a difference in learning achievement between the students' groups who are taught by PBL and direct learning; (2) there are differences in achieving learning outcomes toward students' group and different cognitive styles; (3) there are significant interactions between the use of learning strategies and cognitive style on learning outcomes.
Formal design of robot Integrated Task and Motion Planning
Venue: 2016 IEEE 55th Conference on Decision and Control (CDC)

Integrated Task and Motion Planning (ITMP) for mobile robots becomes a new trend. Most existing methods for ITMP either restrict to static environments or lack performance guarantees. This motivates us to use formal design methods for mobile robots ITMP in a dynamic environment with moving obstacles. Our basic idea is to synthesize a global integrated task and motion plan through composing simple local moves and actions, and to achieve its performance guarantee through modular and incremental verifications. The design consists of two steps. First, reactive motion controllers are designed and verified locally. Then, a global plan is built upon these certified controllers by concatenating them together. In particular, we model the controllers and verify their safety through formulating them as Differential Dynamic Logic (dL) formula. Furthermore, these proven safe controllers are abstracted in Counter Linear Temporal Logic over Constraint System CLTLB(D) and composed based on an encoding to Satisfiability Modulo Theories (SMT) that takes into account the geometric constraints. Since dL allows compositional verification, the sequential composition of the safe motion primitives also preserves safety properties. Illustrative examples are presented to show the effectiveness of the method.
Integrated task and motion planning using physics based heuristics

This work presents a knowledge based task and motion planning framework based on a version of the FastForward task planner. A reasoning process on symbolic literals in terms of knowledge and geometric information about the workspace, together with the use of a physics based motion planner, is proposed to evaluate the applicability and feasibility of manipulation actions and to compute the heuristic values that guide the search. The proposal results in low cost physically feasible plans
Efficient Geometric Predicates for Integrated Task and Motion Planning

Integrated robot task and motion planning requires an interface for the symbolic planner to query and sample in the geometric world. This work introduces a new set of bounded geometric predicates for collision and inclusion queries on a single sided approximation of the geometry, generated by the bounding mesh algorithm. Integration leads to an efficient task and motion planner, which is evaluated on different robot systems, including tasks with branched plans, sensing actions, complex assembly actions, and bimanual manipulation.
Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks
Venue: NIPS

We present weight normalization: a reparameterization of the weight vectors in a neural network that decouples the length of those weight vectors from their direction. By reparameterizing the weights in this way we improve the conditioning of the optimization problem and we speed up convergence of stochastic gradient descent. Our reparameterization is inspired by batch normalization but does not introduce any dependencies between the examples in a minibatch. This means that our method can also be applied successfully to recurrent models such as LSTMs and to noise sensitive applications such as deep reinforcement learning or generative models, for which batch normalization is less well suited. Although our method is much simpler, it still provides much of the speed up of full batch normalization. In addition, the computational overhead of our method is lower, permitting more optimization steps to be taken in the same amount of time. We demonstrate the usefulness of our method on applications in supervised image recognition, generative modelling, and deep reinforcement learning.
Character Aware Neural Language Models
Venue: AAAI

We describe a simple neural language model that relies only on character level inputs. Predictions are still made at the word level. Our model employs a convolutional neural network (CNN) and a highway network over characters, whose output is given to a long short term memory (LSTM) recurrent neural network language model (RNN LM) On the English Penn Treebank the model is on par with the existing state of the art despite having 60% fewer parameters. On languages with rich morphology (Arabic, Czech, French, German, Spanish, Russian) the model outperforms word level/morpheme level LSTM baselines, again with fewer parameters. The results suggest that on many languages, character inputs are sufficient for language modeling. Analysis of word representations obtained from the character composition part of the model reveals that the model is able to encode, from characters only, both semantic and orthographic information.
Videoconferencing and telementoring about dementia care: evaluation of a pilot model for sharing scarce old age psychiatry resources
Venue: International Psychogeriatrics

While videoconferencing, telementoring, and peer support have been shown to enhance services in some instances, there has been no research investigating the use of these technologies in supporting professionals managing clients with dementia. The objective of this research was to evaluate expansion of an old age psychiatry consultation service and pilot test a model to improve medical supervision and clinical governance for staff within regional and remote areas using remote information technology.  The design was a mixed methods (qualitative and quantitative) evaluation using before, mid point and post implementation semi structured interviews and questionnaires to examine orientation, acceptance, and impact underpinned by theoretical approaches to evaluation. Education evaluations used a Likert style response template. Participants were 18 dementia service staff, including staff from linked services and old age psychiatrists. Qualitative interviews addressed the pilot implementation including: expectations, experiences, strategies for improving the pilot, and perceived impact on work practice and professional development opportunities.  There was high satisfaction with the program. The case conference process contributed to perceived improved outcomes for clients, family, and staff. Clinicians perceived improvement in family carer and staff carer stress and their confidence in managing clients with behavioral and psychological symptoms of dementia (BPSD) Thematic analysis indicated that the pilot enhanced professional development, decreased travel time, and improved team cohesion.  Given the increasing aging population in regional, rural, and remote areas, initiatives using videoconferencing and telementoring will help to develop a confident and skilled workforce. This pilot program was found to be acceptable and feasible. Potential benefits for clients and family carers should be examined in future resesarch.
Dynamic User Clustering and Power Allocation for Uplink and Downlink Non Orthogonal Multiple Access (NOMA) Systems
Venue: IEEE Access

Non orthogonal multiple access (NOMA) has recently been considered as a key enabling technique for 5G cellular systems. In NOMA, by exploiting the channel gain differences, multiple users are multiplexed into transmission power domain and then non orthogonally scheduled for transmission on the same spectrum resources. Successive interference cancellation (SIC) is then applied at the receivers to decode the message signals. In this paper, first, we briefly describe the differences in the working principles of uplink and downlink NOMA transmissions in a cellular wireless system. Then, for both uplink and downlink NOMAs, we formulate a sum throughput maximization problem in a cell such that the user clustering (i.e. grouping users into a single cluster or multiple clusters) and power allocations in NOMA clusters can be optimized under transmission power constraints, minimum rate requirements of the users, and SIC constraints. Due to the combinatorial nature of the formulated mixed integer non linear programming problem, we solve the problem in two steps, i.e. by first grouping users into clusters and then optimizing their respective power allocations. In particular, we propose a low complexity sub optimal user grouping scheme. The proposed scheme exploits the channel gain differences among users in an NOMA cluster and groups them into a single cluster or multiple clusters in order to enhance the sum throughput of the system. For a given set of NOMA clusters, we then derive the optimal power allocation policy that maximizes the sum throughput per NOMA cluster and in turn maximizes the overall system throughput. Using Karush Kuhn Tucker optimality conditions, closed form solutions for optimal power allocations are derived for any cluster size, considering both uplink and downlink NOMA systems. Numerical results compare the performances of NOMA and OMA and illustrate the significance of NOMA in various network scenarios.
A General MIMO Framework for NOMA Downlink and Uplink Transmission Based on Signal Alignment
Venue: IEEE Transactions on Wireless Communications

The application of multiple input multiple output (MIMO) techniques to nonorthogonal multiple access (NOMA) systems is important to enhance the performance gains of NOMA. In this paper, a novel MIMO NOMA framework for downlink and uplink transmission is proposed by applying the concept of signal alignment. By using stochastic geometry, closed form analytical results are developed to facilitate the performance evaluation of the proposed framework for randomly deployed users and interferers. The impact of different power allocation strategies, namely fixed power allocation and cognitive radio inspired power allocation, on the performance of MIMO NOMA is also investigated. Computer simulation results are provided to demonstrate the performance of the proposed framework and the accuracy of the developed analytical results.
Abstract WP78: The Cost of Alteplase Has More Than Doubled Over the Past Decade

Intravenous alteplase (tissue plasminogen activator) has been shown to be cost effective due to savings in long term disability. In October of 2005, an increased DRG payment to hospitals for alteplase treated stroke patients was introduced. We sought to describe the trends in the cost of alteplase over time, in comparison to trends in hospital reimbursement in the United States.  Utilizing publicly available information on the Centers for Medicare and Medicaid Services (CMS) website (www.cms.gov) we obtained CMS quarterly payment amounts between 1/1/2005 10/1/2014 for alteplase, listed as "alteplase recombinant, per mg." CMS payment amounts are the manufacturer's average sales price (ASP) 6% until 4/2014, when it was lowered to 4.3% Estimates for DRG base payments were calculated within MEDPAR for FY 2006 (DRG 559) and 2013 (MS DRGs 61,62, and 63) as: (DRG relative weights) x (standardized operating and capital amount) The Consumer Price Index (CPI) was also queried for all prescription drugs, urban areas, during the same study period as reference.  The CMS payment amount for alteplase/mg was $30.50 in 1/2005, and $64.30 in 10/2014. Trends in the CMS payment amounts for alteplase increased by 111% between 2005 2014, shown in the Figure. The CPI for all prescription drugs increased by 30.2% in the same time frame. The base payment for alteplase treated stroke admissions was $11,173 in 2006, and $12,064 in 2013, an 8% increase.  We found a striking increase in the cost of alteplase over the last decade, with a 100mg vial now with a CMS payment of $6400, a more than 100% increase over ten years. During the same timeframe, the DRG base payment to hospitals increased by only 8% and alteplase cost increased from 27% of the payment in 2006 to 53% in 2013. Researchers and stroke physicians should be aware of these changes in drug costs and their impact on cost effectiveness analyses.
Dealing with Multiple Classes in Online Class Imbalance Learning
Venue: IJCAI

Online class imbalance learning deals with data streams having very skewed class distributions in a timely fashion. Although a few methods have been proposed to handle such problems, most of them focus on two class cases. Multi class imbalance imposes additional challenges in learning. This paper studies the combined challenges posed by multiclass imbalance and online learning, and aims at a more effective and adaptive solution. First, we introduce two resampling based ensemble methods, called MOOB and MUOB, which can process multi class data directly and strictly online with an adaptive sampling rate. Then, we look into the impact of multi minority and multi majority cases on MOOB and MUOB in comparison to other methods under stationary and dynamic scenarios. Both multi minority and multi majority make a negative impact. MOOB shows the best and most stable G mean in most stationary and dynamic cases.
Diversite de la macrofaune des sols cultives sur les Hautes Terres de Madagascar

Description du sujet. L'article traite de la macrofaune du sol, composante cle du fonctionnement biologique du sol. Objectifs. Cette etude, realisee en 2015, a eu pour objectif de tester l'effet de pratiques culturales et de systemes de culture sur la macrofaune du sol dans une zone d'agriculture elevage des Hautes Terres de Madagascar. Cinq systemes de culture ont ete etudies cultures annuelles labourees annuellement avec apport de fumier modere (LAMF) ou eleve (LAEF) cultures fourrageres perennes sans labour et avec apport de fumier eleve (CFEF) cultures annuelles en agriculture de conservation sans labour et apport de fumier modere (ACMF) et paturages permanents, non laboures et sans apport de fumier (PP0F) Methode. Pour chaque systeme de culture, 12 parcelles d'experimentation ou d'exploitation agricole ont fait l'objet d'un echantillonnage de la macrofaune du sol avec cinq monolithes de sol par parcelle, sur 30 cm de profondeur. Resultats. Cinquante deux especes d'invertebres, classees en 15 ordres taxonomiques et 6 groupes fonctionnels, ont ete identifiees. Pour les cultures annuelles sans labour, l'absence de travail du sol a favorise la diversite taxonomique et la densite de la macrofaune, n'a pas reduit la densite des vers blancs (larves de Coleopteres Scarabeoideae) rhizophages mais a entraine une diminution des densites de vers de terre par rapport au mode de culture fourragere. En systemes laboures, l'intensite d'application du fumier n'a eu d'effet ni sur les vers blancs ni sur les vers de terre.  Cette etude sur la macrofaune du sol dans cette region de Madagascar a permis de mieux percevoir les pratiques et les systemes les plus favorables aux invertebres utiles (comme les vers de terre) et plus defavorables aux invertebres nuisibles (comme les vers blancs) Ceci permettra d'orienter les paysans dans la gestion de leurs terres.
Glycated Albumin versus Glycated Hemoglobin as a Glycemic Indicator in Diabetic Patients on Peritoneal Dialysis
Venue: International journal of molecular sciences

Compared with glycated hemoglobin (HbA1c) glycated albumin (GA) is superior in estimating glycemic control in diabetic patients on hemodialysis (HD) However, the better index for assessment of glycemic control in diabetic patients on peritoneal dialysis (PD) and the impact of protein loss on GA are unknown. Twenty diabetic patients on HD were matched by age, sex, and baseline postprandial plasma glucose (PG) levels to 20 PD patients. PG, HbA1c, GA, and serum albumin levels were measured for six months. Protein loss in PD patients was estimated by measuring the protein concentration in the peritoneal dialysate and by 24 h urine collection. Although PG and HbA1c did not differ significantly between the groups, the PD group had significantly lower GA (17.8% versus 20.8% p 0.001) and GA/HbA1c ratio (2.95% versus 3.45% p 0.0001) than the HD group. Although the PG level correlated significantly with the GA levels in both groups, it was not correlated with the HbA1c levels in both groups. HbA1c level was negatively associated with erythropoiesis stimulating agent (ESA) dose in both groups, whereas GA was not significantly associated with serum albumin, hemoglobin concentration, ESA dose, and protein loss. Multiple regression analysis identified GA as the only independent factor associated with PG in PD patients. Our results suggested that GA was not significantly associated with protein loss, hemoglobin, serum albumin, and ESA dose. Although GA might underestimate glycemic status, it provided a significantly better measure for estimating glycemic control than HbA1c, even in PD patients.