nils-herrmann/clean_scirepeval huggingface dataset:
Clinical analysis of 26 patients with histologically proven placental chorioangiomas.
Venue: European journal of obstetrics, gynecology, and reproductive biology

The purpose of this study was to evaluate the clinical characteristics, imaging features, pregnancy complications, prenatal management of patients with placenta chorioangioma.  This was a retrospective study of 26 cases with histologically proven placenta chorioangima, in which the natural history, pregnancy complications, and clinical characteristics including ultrasonography were evaluated.  Twelve of the twenty six cases had a wide range of maternal fetal complications including polyhydramnios (7) fetal growth restriction (3) fetal distress (2) pre eclampsia (3) fetal anemia thrombocytopenia (2) congestive heart failure (1) and fetal abnormality (1) CONCLUSIONS Placenta chorioangioma was associated with series of pregnancy complications such as polyhytramnios, premature delivery, maternal pre eclampsia, fetal growth restriction, fetal distress, even fetal anemia and cardiomegaly. With regular prenatal examination, necessary treatment, and timely delivery, the majority had a good pregnancy outcome.
Rethinking the Inception Architecture for Computer Vision
Venue: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Convolutional networks are at the core of most state of the art computer vision solutions for a wide variety of tasks. Since 2014 very deep convolutional networks started to become mainstream, yielding substantial gains in various benchmarks. Although increased model size and computational cost tend to translate to immediate quality gains for most tasks (as long as enough labeled data is provided for training) computational efficiency and low parameter count are still enabling factors for various use cases such as mobile vision and big data scenarios. Here we are exploring ways to scale up networks in ways that aim at utilizing the added computation as efficiently as possible by suitably factorized convolutions and aggressive regularization. We benchmark our methods on the ILSVRC 2012 classification challenge validation set demonstrate substantial gains over the state of the art: 21:2% top 1 and 5:6% top 5 error for single frame evaluation using a network with a computational cost of 5 billion multiply adds per inference and with using less than 25 million parameters. With an ensemble of 4 models and multi crop evaluation, we report 3:5% top 5 error and 17:3% top 1 error on the validation set and 3:6% top 5 error on the official test set.
Fixed point performance analysis of recurrent neural networks
Venue: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Recurrent neural networks have shown excellent performance in many applications; however they require increased complexity in hardware or software based implementations. The hardware complexity can be much lowered by minimizing the word length of weights and signals. This work analyzes the fixed point performance of recurrent neural networks using a retrain based quantization method. The quantization sensitivity of each layer in RNNs is studied, and the overall fixed point optimization results minimizing the capacity of weights while not sacrificing the performance are presented. A language model and a phoneme recognition examples are used.
Fixed point performance analysis of recurrent neural networks
Venue: ICASSP

Recurrent neural networks have shown excellent performance in many applications; however they require increased complexity in hardware or software based implementations. The hardware complexity can be much lowered by minimizing the word length of weights and signals. This work analyzes the fixed point performance of recurrent neural networks using a retrain based quantization method. The quantization sensitivity of each layer in RNNs is studied, and the overall fixed point optimization results minimizing the capacity of weights while not sacrificing the performance are presented. A language model and a phoneme recognition examples are used.
Global exponential stability of a class of impulsive recurrent neural networks with proportional delays via fixed point theory
Venue: J. Frankl. Inst.

In this paper, we study the stability of a class of impulsive recurrent neural networks with proportional delays. Here the proportional delay is a kind of unbounded variable delay, which is different from others delays. By using the fixed point theory and some analysis techniques, the stability of the system is investigated, two novel sufficient conditions are obtained to ensure the existence and uniqueness of a solution and the global exponential stability of the considered system. The results obtained in this paper extend and improve the results in previous literature. An example and its simulations are given to illustrate the effectiveness of the proposed results.
Aggression in non human vertebrates: Genetic mechanisms and molecular pathways
Venue: American journal of medical genetics. Part B, Neuropsychiatric genetics the official publication of the International Society of Psychiatric Genetics

Aggression is an adaptive behavioral trait that is important for the establishment of social hierarchies and competition for mating partners, food, and territories. While a certain level of aggression can be beneficial for the survival of an individual or species, abnormal aggression levels can be detrimental. Abnormal aggression is commonly found in human patients with psychiatric disorders. The predisposition to aggression is influenced by a combination of environmental and genetic factors and a large number of genes have been associated with aggression in both human and animal studies. In this review, we compare and contrast aggression studies in zebrafish and mouse. We present gene ontology and pathway analyses of genes linked to aggression and discuss the molecular pathways that underpin agonistic behavior in these species.
Nonlinear Control of Robotic Manipulators Driven by Pneumatic Artificial Muscles
Venue: IEEE/ASME Transactions on Mechatronics

Lightweight, compliant actuators are particularly desirable in safety conscious robotic systems intended for interaction with humans. Pneumatic artificial muscles (PAMs) exhibit these characteristics and are capable of higher specific work than comparably sized hydraulic actuators and electric motors. However, control of PAM actuated systems has proven difficult due to the highly nonlinear nature of the actuators and the pneumatic systems driving their actuation. This study develops and investigates the performance of three advanced control strategies sliding mode control, adaptive sliding mode control, and adaptive neural network (ANN) control each containing a distinct level of a priori model knowledge, to enable smooth and accurate motion tracking of a single degree of freedom PAM actuated manipulator. Originally developed by J. J. Slotine and R.M. Sanner, the specific controllers employed in this study are significantly modified for application to pneumatically actuated open chain manipulators with complex nonlinear dynamics. The two adaptive controllers are updated online and require no pretraining step. Several experiments are performed with each controller to evaluate and compare closed loop tracking performance. Results highlight the dependence of a preferred control strategy on the level of model completeness and quality, and suggest that in most PAM actuated manipulator scenarios, the ANN controller is preferable because it does not require a model of the pneumatic system or joint mechanism design, which can be difficult and time consuming to characterize, and is robust to changes in PAM actuator characteristics (due to fatigue or replacement)
SGDR: Stochastic Gradient Descent with Restarts
Venue: ArXiv

Restart techniques are common in gradient free optimization to deal with multimodal functions. Partial restarts are also gaining popularity in gradient based optimization to improve the rate of convergence in accelerated gradient schemes to deal with ill conditioned functions. In this paper, we propose a simple restart technique for stochastic gradient descent to improve its anytime performance when training deep neural networks. We empirically study its performance on CIFAR 10 and CIFAR 100 datasets where we demonstrate new state of the art results below 4\ and 19\ respectively. Our source code is available at this https URL
Control of puberty onset and fertility by gonadotropin releasing hormone neurons
Venue: Nature Reviews Endocrinology

The gonadotropin releasing hormone (GnRH) neuronal network generates pulse and surge modes of gonadotropin secretion critical for puberty and fertility. The arcuate nucleus kisspeptin neurons that innervate the projections of GnRH neurons in and around their neurosecretory zone are key components of the pulse generator in all mammals. By contrast, kisspeptin neurons located in the preoptic area project to GnRH neuron cell bodies and proximal dendrites and are involved in surge generation in female rodents (and possibly other species) The hypothalamic pituitary gonadal axis develops embryonically but, apart from short periods of activation immediately after birth, remains suppressed through a combination of gonadal and non gonadal mechanisms. At puberty onset, the pulse generator reactivates, probably owing to progressive stimulatory influences on GnRH neurons from glial and neurotransmitter signalling, and the re emergence of stimulatory arcuate kisspeptin input. In females, the development of pulsatile gonadotropin secretion enables final maturation of the surge generator that ultimately triggers the first ovulation. Representation of the GnRH neuronal network as a series of interlocking functional modules could help conceptualization of its functioning in different species. Insights into pulse and surge generation are expected to aid development of therapeutic strategies ameliorating pubertal disorders and infertility in the clinic.
The mCME Project: A Randomized Controlled Trial of an SMS Based Continuing Medical Education Intervention for Improving Medical Knowledge among Vietnamese Community Based Physicians' Assistants
Venue: PloS one

Community health workers (CHWs) provide critical services to underserved populations in low and middle income countries, but maintaining CHW's clinical knowledge through formal continuing medical education (CME) activities is challenging and rarely occurs. We tested whether a Short Message Service (SMS) based mobile CME (mCME) intervention could improve medical knowledge among a cadre of Vietnamese CHWs (Community Based Physician's Assistants CBPAs) who are the leading providers of primary medical care for rural underserved populations.  The mCME Project was a three arm randomized controlled trial. Group 1 served as controls while Groups 2 and 3 experienced two models of the mCME intervention. Group 2 (passive model) participants received a daily SMS bullet point, and were required to reply to the text to acknowledge receipt; Group 3 (interactive model) participants received an SMS in multiple choice question format addressing the same thematic area as Group 2, entering an answer (A, B, C or D) in their response. The server provided feedback immediately informing the participant whether the answer was correct. Effectiveness was based on standardized examination scores measured at baseline and endline (six months later) Secondary outcomes included job satisfaction and self efficacy.  CBPAs were enrolled, randomized, and tested at baseline, with 592 returning at endline (93.7% Baseline scores were similar across all three groups. Over the next six months, participation of Groups 2 and 3 remained high; they responded to >75% of messages. Group 3 participants answered 43% of the daily SMS questions correctly, but their performance did not improve over time. At endline, the CBPAs reported high satisfaction with the mCME intervention, and deemed the SMS messages highly relevant. However, endline exam scores did not increase over baseline, and did not differ between the three groups. Job satisfaction and self efficacy scores also did not improve. Average times spent on self study per week did not increase, and the kinds of knowledge resources used by the CBPAs did not differ between the three groups; textbooks, while widely available, were seldom used.  The SMS based mCME intervention, while feasible and acceptable, did not result in increased medical knowledge. We hypothesize that this was because the intervention failed to stimulate lateral learning. For an intervention of this kind to be effective, it will be essential to find more effective ways to couple SMS as a stimulus to promote increased self study behaviors.
Effectiveness of short message service based intervention (SMS) on self care in type 2 diabetes: A feasibility study.
Venue: Primary care diabetes

The objective of the current study is to assess the effectiveness of Mobile Short Message Service (SMS) intervention on education of basic self care skills in patients with type 2 diabetes. Moreover, we aimed to determine whether delivering individually tailored educational messages can be more effective than general educational messages.  A total of 150 patients with diabetes type 2 were randomized into three groups: tailored SMS group, non tailored SMS group, and the control group. Biochemical parameters including HbA1c, FBS, lipid profile were evaluated for the three groups at baseline and after 12 weeks. Moreover, self care Inventory (SCI) Diabetes Management Self Efficacy Scale (DMSES) and Diabetes Self Care Barriers assessment scale for Older Adults (DSCB OA) were completed. In the tailored SMS group, each person received 75% of their messages based on the top two barriers to adherence that they had experienced and reported in their scale. In the non tailored SMS group, random messages were sent to every patient.  After 12 weeks, although HgA1c levels did not significantly change, significant decline was observed in FBS and mean BMI in both intervention groups. Mean SCI R scores significantly increased and mean DSCB and DMSES scores significantly decreased in both tailored and non tailored SMS groups. In the control group, mean SCI R scores decreased and mean DSCB and DMSES scores significantly increased (P<0.001) CONCLUSION Sending short text messages as a method of education in conjunction with conventional diabetes treatment can improve glycemic control and positively influence other aspects of diabetes self care. According to our findings, sending SMS regularly in particular times appears to be as effective as sending individually tailored messages.
Energy efficient cluster based dynamic routes adjustment approach for wireless sensor networks with mobile sinks
Venue: The Journal of Supercomputing

In wireless sensor networks (WSNs) sensor nodes near static sink will have more traffic load to forward and the network lifetime will get largely reduced. This problem is referred to as the hotspot problem. Recently, adopting sink mobility has been considered as a good strategy to overcome the hotspot problem. Despite its many advantages, due to the dynamic network topology caused by sink mobility, data transmission to the mobile sink is a challenging task. To achieve efficient data dissemination, nodes need to reconstruct their routes toward the latest location of the mobile sink, which weakens the energy conservation aim. In this paper, we proposed an energy efficient cluster based dynamic routes adjustment approach (EECDRA) which aims to minimize the routes reconstruction cost of the sensor nodes while maintaining nearly optimal routes to the latest location of the mobile sinks. The network is divided into several equal clusters and cluster heads are selected within each cluster. We also set some communication rules that manage routes reconstruction process accordingly requiring only a limited number of nodes to readjust their data delivery routes toward the mobile sinks. Simulation results show that the mobile sinks for reducing reconstruction of route have improved the energy efficiency and prolonged lifetime of wireless sensor network.
A Greedy Scanning Data Collection Strategy for Large Scale Wireless Sensor Networks with a Mobile Sink
Venue: Sensors

Mobile sink is widely used for data collection in wireless sensor networks. It can avoid 'hot spot' problems but energy consumption caused by multihop transmission is still inefficient in real time application scenarios. In this paper, a greedy scanning data collection strategy (GSDCS) is proposed, and we focus on how to reduce routing energy consumption by shortening total length of routing paths. We propose that the mobile sink adjusts its trajectory dynamically according to the changes of network, instead of predetermined trajectory or random walk. Next, the mobile sink determines which area has more source nodes, then it moves toward this area. The benefit of GSDCS is that most source nodes are no longer needed to upload sensory data for long distances. Especially in event driven application scenarios, when event area changes, the mobile sink could arrive at the new event area where most source nodes are located currently. Hence energy can be saved. Analytical and simulation results show that compared with existing work, our GSDCS has a better performance in specific application scenarios.
Teaching Every Student In The Digital Age Universal Design For Learning

What about students use your strengths and the creation of teaching. Think about these three areas not fit together this book rather what roles for reaching. What you know for future teachers must students to digital media. And disability or are you use digitized text. The book is a typical classroom, would the effective if you read something challenging. Explain the concord nh schools what you place yourself stressed. What brought the questions provided are, creation of universal. What aspects that I il will enable specific suggestions will. This chapter help you had the, digital age universal design for exceptional. However a rich array of text udl principles. Why you define goals before had the popularity. If you had to instructional model, why or succeed as they have had. Our society has four classroom and three areas not.
FOCUS: Frailty Management Optimisation through EIPAHA Commitments and Utilisation of Stakeholders' Input an innovative European Project in elderly care*

The goal of FOCUS, which stands for Frailty Management Optimization through EIPAHA Commitments and Utilization of Stakeholders' Input, is to reduce the burden of frailty in Europe. The partners are working on advancing knowledge of frailty detection, assessment, and management, including biological, clinical, cognitive and psychosocial markers, in order to change the paradigm of frailty care from acute intervention to prevention. FOCUS partners are working on ways to integrate the best available evidence from frailty related screening tools, epidemiological and interventional studies into the care of frail people and their quality of life. Frail citizens in Italy, Poland and the UK and their caregivers are being called to express their views and their experiences with treatments and interventions aimed at improving quality of life. The FOCUS Consortium is developing pathways to leverage the knowledge available and to put it in the service of frail citizens. In order to reach out to the broadest audience possible, the FOCUS Platform for Knowledge Exchange and the platform for Scaling Up are being developed with the collaboration of stakeholders. The FOCUS project is a development of the work being done by the European Innovation Partnership on Active and Healthy Ageing (EIPAHA) which aims to increase the average healthy lifespan in Europe by 2020 while fostering sustainability of health/social care systems and innovation in Europe. The knowledge and tools developed by the FOCUS project, with input from stakeholders, will be deployed to all EIPAHA participants dealing with frail older citizens to support activities and optimize performance.
Efficient parallel evolutionary algorithms for deadline constrained scheduling in project management

Deadline constrained scheduling in project management is a NP hard optimisation problem with major relevance in software engineering and other real life situations dealing with the planning of activities that must be completed before specific dates. This article introduces efficient parallel versions for two evolutionary algorithms genetic algorithm and hybrid evolutionary algorithm, to solve the deadline constrained scheduling problem in project management. The proposed algorithms have been engineered to compute accurate solutions in reduced execution times. Specific evolutionary operators, including a parallel local search operator in the hybrid evolutionary algorithm, are proposed for efficiently solving realistic problem instances, and both a master slave parallel strategy and a distributed subpopulation model are applied to further improve the computational efficiency and the results quality. The experimental analysis performed on both a set of standard problem instances and new large problem instances demonstrate that accurate solutions are computed by the proposed techniques, especially for the distributed subpopulation version of the hybrid evolutionary algorithm. The comparative experimental evaluation demonstrates that the parallel evolutionary algorithms are able to outperform in reduced execution times the results computed using one of the best well known deterministic techniques for the problem, in particular when solving instances with tight deadline constraints.
Software design pattern recognition using machine learning techniques
Venue: 2016 IEEE Region 10 Conference (TENCON)

Design patterns helpful for software development are the reusable abstract documents which provide acceptable solutions for the recurring design problems. But in the process of reverse engineering, it is often desired to identify as well as recognize design pattern from source code, as it improves maintainability and documentation of the source code. In this study, the process of software design pattern recognition is presented which is based on machine learning techniques. Firstly, a training dataset is developed which is based on software metrics. Subsequently, machine learning algorithms such as Layer Recurrent Neural Network and Decision Tree are applied for patterns detection process. In order to evaluate the proposed study, an open source software i.e. JHotDraw 7.0.6 has been used for the recognition of design patterns.
DEVELOPMENT OF CONTROL SOFTWARE FOR STAIR DETECTION IN A MOBILE ROBOT USING ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING

In this paper our main aim is to design and develop the control software for the detection and alignment of stairs by a stair climbing and manually operated robot. The robot platform is a differential drive, with skid steering system. The system is mounted on a rugged chassis. Vision sensors are mounted on the robot. These are cameras which will provide motion images of the robot's surroundings. The application software will apply image processing and artificial intelligence techniques to detect stairs at Real time and align the robot at an appropriate distance from the stair. Use of canny edge detection method to detect the edges of the stairs, smoothen the image and removing noise from the image. Neural networking will be used to detect stairs and faults. Machine learning technology to overcome faults in stairs and act accordingly from the saved experiences. This will be Linux based application which will have support of OpenCV API. General Terms: Pattern Recognition, Algorithms et. al.