nils-herrmann/clean_scirepeval huggingface dataset:
Facial landmark localization by enhanced convolutional neural network

Facial landmark localization is important to many facial recognition and analysis tasks, such as face attributes analysis, head pose estimation, 3D face modelling, and facial expression analysis. In this paper, we propose a new approach to localizing landmarks in facial image by deep convolutional neural network (DCNN). We make two enhancements on the CNN to adapt it to the feature localization task as follows. Firstly, we replace the commonly used max pooling by depth-wise convolution to obtain better localization performance. Secondly, we define a response map for each facial points as a 2D probability map indicating the presence likelihood, and train our model with a KL divergence loss. To obtain robust localization results, our approach first takes the expectations of the response maps of Enhanced CNN and then applies auto-encoder model to the global shape vector, which is effective to rectify the outlier points by the prior global landmark configurations. The proposed ECNN method achieves 5.32% mean error on the experiments on the 300-W dataset, which is comparable to the state-of-the-art performance on this standard benchmark, showing the effectiveness of our methods.
Face recognition using reinforcement learning

Neuroscientists believe that human beings recognize faces not only by utilizing some holistic search among all learned faces, but also through a feature analysis that aimed to specify more important features of each specific face. In this paper, we propose a hierarchical classifier that uses both holistic search and per face dominant feature analysis to recognize faces. Reinforcement learning is used to find a set of dominant features for each image in a training dataset. Wavelet transform is employed as a preprocessing tool, which results in higher discrimination among classes. Simulation studies justify the better performance of the proposed method as compared to that of eigenface method.
Reliable systems: fault tree analysis via Markov reward automata

Today’s society is characterised by the ubiquitousness of hardware and software systems on which we rely on day in, day out. They reach from transportation systems like cars, trains and planes over medical devices at a hospital to nuclear power plants. Moreover, we can observe a trend of automation and data exchange in today’s society and economy, including among others the integration of cyber-physical systems, internet of things, and cloud computing. All theses systems have one common denominator: they have to operate safe and reliable. But how can we trust that they operate safe and reliable? Model checking is a technique to check if a system fulfils a given requirement. To check if the requirements hold, a model of the system has to be created, while the requirements are stated in terms of some logic formula w.r.t. the model. Then, the model and formula are given to a model checker, which checks if the formula holds on the model. If this is the case the model checker provides a positive answer, otherwise a counterexample is provided. Note that model checking can be used to verify hardware as well as software systems and has been successfully applied to a wide range of different applications like aerospace systems, or biological systems. Reliability engineering is a well-established field with the purpose of developing methods and tools to ensure reliability, availability, maintainability and safety (RAMS) of complex systems, as well as to support engineers during the development, production, and maintenance to maintain these characteristics. However, with the advancements and ubiquitousness of new hardware and software systems in our daily life, also methods and tools for reliability engineering have to be adapted. This thesis contributes to the realm of model checking as well as reliability engineering. On the one hand we introduce a reward extension to Markov automata and present algorithms for different reward properties. On the other hand we extend fault trees with maintenance procedures. In the first half of the thesis, we introduce Markov reward automata (MRAs), supporting non-deterministic choices, discrete as well as continuous probability distributions and timed as well as instantaneous rewards. Moreover we introduce algorithms for reachability objectives for MRAs. In particular we define expected reward objectives for goal and time bounded rewards as well as for long-run average rewards. In the second half of the thesis we introduce fault maintenance trees (FMTs). They extend dynamic fault trees (DFTs) with corrective and preventive main-
Re-Stream : Real-time and energy-efficient resource scheduling in big data stream computing environments

To achieve high energy efficiency and low response time in big data stream computing environments, it is required to model an energy-efficient resource scheduling and optimization framework. In this paper, we propose a real-time and energy-efficient resource scheduling and optimization framework, termed the Re-Stream. Firstly, the Re-Stream profiles a mathematical relationship among energy consumption, response time, and resource utilization, and obtains the conditions to meet high energy efficiency and low response time. Secondly, a data stream graph is modeled by using the distributed stream computing theories, which identifies the critical path within the data stream graph. Such a methodology aids in calculating the energy consumption of a resource allocation scheme for a data stream graph at a given data stream speed. Thirdly, the Re-Stream allocates tasks by utilizing an energy-efficient heuristic and a critical path scheduling mechanism subject to the architectural requirements. This is done to optimize the scheduling mechanism online by reallocating the critical vertices on the critical path of a data stream graph to minimize the response time and system fluctuations. Moreover, the Re-Stream consolidates the non-critical vertices on the non-critical path so as to improve energy efficiency. We evaluate the Re-Stream to measure energy efficiency and response time for big data stream computing environments. The experimental results demonstrate that the ReStream has the ability to improve energy efficiency of a big data stream computing system, and to reduce average response time. The Re-Stream provides an elegant trade-off between increased energy efficiency and decreased response time effectively within big data stream computing environments.
Natural history of Ashkenazi intelligence.

This paper elaborates the hypothesis that the unique demography and sociology of Ashkenazim in medieval Europe selected for intelligence. Ashkenazi literacy, economic specialization, and closure to inward gene flow led to a social environment in which there was high fitness payoff to intelligence, specifically verbal and mathematical intelligence but not spatial ability. As with any regime of strong directional selection on a quantitative trait, genetic variants that were otherwise fitness reducing rose in frequency. In particular we propose that the well-known clusters of Ashkenazi genetic diseases, the sphingolipid cluster and the DNA repair cluster in particular, increase intelligence in heterozygotes. Other Ashkenazi disorders are known to increase intelligence. Although these disorders have been attributed to a bottleneck in Ashkenazi history and consequent genetic drift, there is no evidence of any bottleneck. Gene frequencies at a large number of autosomal loci show that if there was a bottleneck then subsequent gene flow from Europeans must have been very large, obliterating the effects of any bottleneck. The clustering of the disorders in only a few pathways and the presence at elevated frequency of more than one deleterious allele at many of them could not have been produced by drift. Instead these are signatures of strong and recent natural selection.
Development of a Wearable Controller for Gesture-Recognition-Based Applications Using Polyvinylidene Fluoride

This paper reports on a wearable gesture-based controller fabricated using the sensing capabilities of the flexible thin-film piezoelectric polymer polyvinylidene fluoride (PVDF) which is shown to repeatedly and accurately discern, in real time, between right and left hand gestures. The PVDF is affixed to a compression sleeve worn on the forearm to create a wearable device that is flexible, adaptable, and highly shape conforming. Forearm muscle movements, which drive hand motions, are detected by the PVDF which outputs its voltage signal to a developed microcontroller-based board and processed by an artificial neural network that was trained to recognize the generated voltage profile of right and left hand gestures. The PVDF has been spatially shaded (etched) in such a way as to increase sensitivity to expected deformations caused by the specific muscles employed in making the targeted right and left gestures. The device proves to be exceptionally accurate both when positioned as intended and when rotated and translated on the forearm.
Task Variant Allocation in Distributed Robotics

We consider the problem of assigning software processes (or tasks) to hardware processors in distributed robotics environments. We introduce the notion of a task variant, which supports the adaptation of software to specific hardware configurations. Task variants facilitate the trade-off of functional quality versus the requisite capacity and type of target execution processors. We formalise the problem of assigning task variants to processors as a mathematical model that incorporates typical constraints found in robotics applications; the model is a constrained form of a multi-objective, multi-dimensional, multiple-choice knapsack problem. We propose and evaluate three different solution methods to the problem: constraint programming, a constructive greedy heuristic and a local search metaheuristic. Furthermore, we demonstrate the use of task variants in a real instance of a distributed interactive multi-agent navigation system, showing that our best solution method (constraint programming) improves the system’s quality of service, as compared to the local search metaheuristic, the greedy heuristic and a randomised solution, by an average of 16%, 41% and 56% respectively.
Examining the nutritional quality of breakfast cereals marketed to children.

There are both public health and food industry initiatives aimed at increasing breakfast consumption among children, particularly the consumption of ready-to-eat cereals. The purpose of this study was to determine whether there were identifiable differences in nutritional quality between cereals that are primarily marketed to children and cereals that are not marketed to children. Of the 161 cereals identified between January and February 2006, 46% were classified as being marketed to children (eg, packaging contained a licensed character or contained an activity directed at children). Multivariate analyses of variance were used to compare children's cereals and nonchildren's cereals with respect to their nutritional content, focusing on nutrients required to be reported on the Nutrition Facts panel (including energy). Compared to nonchildren's cereals, children's cereals were denser in energy, sugar, and sodium, but were less dense in fiber and protein. The proportion of children's and nonchildren's cereals that did and did not meet national nutritional guidelines for foods served in schools were compared using chi2analysis. The majority of children's cereals (66%) failed to meet national nutrition standards, particularly with respect to sugar content. t tests were used to compare the nutritional quality of children's cereals with nutrient-content claims and health claims to those without such claims. Although the specific claims were generally justified by the nutritional content of the product, there were few differences with respect to the overall nutrition profile. Overall, there were important differences in nutritional quality between children's cereals and nonchildren's cereals. Dietary advice for children to increase consumption of ready-to-eat breakfast cereals should identify and recommend those cereals with the best nutrient profiles.
Modular Q-learning based multi-agent cooperation for robot soccer

In a multi-agent system, action selection is important for the cooperation and coordination among agents. As the environment is dynamic and complex, modular Q-learning, which is one of the reinforcement learning schemes, is employed in assigning a proper action to an agent in the multi-agent system. The architecture of modular Q-learning consists of learning modules and a mediator module. The mediator module of the modular Q-learning system selects a proper action for the agent based on the Q-value obtained from each learning module. To obtain better performance, along with the Q-value, the mediator module also considers the state information in the action selection process. A uni-vector field is used for robot navigation. In the robot soccer environment, the effectiveness and applicability of modular Q-learning and the uni-vector field method are verified by real experiments using five micro-robots.
Complications of the neovagina in male-to-female transgender surgery: A systematic review and meta-analysis with discussion of management.

Gender-affirmation surgery is often the final gender-confirming medical intervention sought by those patients suffering from gender dysphoria. In the male-to-female (MtF) transgendered patient, the creation of esthetic and functional external female genitalia with a functional vaginal channel is of the utmost importance. The aim of this review and meta-analysis is to evaluate the epidemiology, presentation, management, and outcomes of neovaginal complications in the MtF transgender reassignment surgery patients. PUBMED was searched in accordance with PRISMA guidelines for relevant articles (n = 125). Ineligible articles were excluded and articles meeting all inclusion criteria went on to review and analysis (n = 13). Ultimately, studies reported on 1,684 patients with an overall complication rate of 32.5% and a reoperation rate of 21.7% for non-esthetic reasons. The most common complication was stenosis of the neo-meatus (14.4%). Wound infection was associated with an increased risk of all tissue-healing complications. Use of sacrospinous ligament fixation (SSL) was associated with a significantly decreased risk of prolapse of the neovagina. Gender-affirmation surgery is important in the treatment of gender dysphoric patients, but there is a high complication rate in the reported literature. Variability in technique and complication reporting standards makes it difficult to assess the accurately the current state of MtF gender reassignment surgery. Further research and implementation of standards is necessary to improve patient outcomes.  2018.
Achievement of localization system for humanoid robots with virtual horizontal scan relative to improved odometry fusing internal sensors and visual information

To achieve tasks in unknown environments with high reliability, highly accurate localization during task execution is necessary for humanoid robots. In this paper, we discuss a localization system which can be applied to a humanoid robot when executing tasks in the real world. During such tasks, humanoid robots typically do not possess a referential to a constant horizontal plane which can in turn be used as part of fast and cost efficient localization methods. We solve this problem by first computing an improved odometry estimate through fusing visual odometry, feedforward commands from gait generator and orientation from inertia sensors. This estimate is used to generate a 3D point cloud from the accumulation of successive laser scans and such point cloud is then properly sliced to create a constant height horizontal virtual scan. Finally, this slice is used as an observation base and fed to a 2D SLAM method. The fusion process uses a velocity error model to achieve greater accuracy, which parameters are measured on the real robot. We evaluate our localization system in a real world task execution experiment using the JAXON robot and show how our system can be used as a practical solution for humanoid robots localization during complex tasks execution processes.
Diagnostic evaluation of the erectile function domain of the International Index of Erectile Function.

To evaluate the erectile function (EF) domain of the International Index of Erectile Function (IIEF) as a diagnostic tool to discriminate between men with and without erectile dysfunction (ED) and to develop a clinically meaningful gradient of severity for ED.



One thousand one hundred fifty-one men (1035 with and 116 without ED) who reported attempting sexual activity were evaluated using data from four clinical trials of sildenafil citrate (Viagra) and two control samples. The statistical program Classification and Regression Trees was used to determine optimal cutoff scores on the EF domain (range 6 to 30) to distinguish between men with and without ED and to determine levels of ED severity on the EF domain using the IIEF item on sexual intercourse satisfaction.



For a 0.5 prevalence rate of ED, the optimal cutoff score was 25, with men scoring less than or equal to 25 classified as having ED and those scoring above 25 as not having ED (sensitivity 0.97, specificity 0.88). Sensitivity analyses revealed a robust statistical solution that was well supported with different assumed prevalence rates and several cross-validations. The severity of ED was classified into five categories: no ED (EF score 26 to 30), mild (EF score 22 to 25), mild to moderate (EF score 17 to 21), moderate (EF score 11 to 16), and severe (EF score 6 to 10). Substantial agreement was shown between these predicted and "true" classes (weighted kappa 0.80).



The EF domain possesses favorable statistical properties as a diagnostic tool, not only in distinguishing between men with and without ED, but also in classifying levels of ED severity. Clinical validation with self-rated assessments of ED severity is warranted.
BLISS 2.0: a web-based tool for predicting conserved regulatory modules in distantly-related orthologous sequences

BLISS 2.0 is a web-based application for identifying conserved regulatory modules in distantly related orthologous sequences. Unlike existing approaches, it performs the cross-genome comparison at the binding site level. Experimental results on simulated and real world data indicate that BLISS 2.0 can identify conserved regulatory modules from sequences with little overall similarity at the DNA sequence level.


AVAILABILITY
Non-linear Convolution Filters for CNN-Based Learning

During the last years, Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in image classification. Their architectures have largely drawn inspiration by models of the primate visual system. However, while recent research results of neuroscience prove the existence of non-linear operations in the response of complex visual cells, little effort has been devoted to extend the convolution technique to non-linear forms. Typical convolutional layers are linear systems, hence their expressiveness is limited. To overcome this, various non-linearities have been used as activation functions inside CNNs, while also many pooling strategies have been applied. We address the issue of developing a convolution method in the context of a computational model of the visual cortex, exploring quadratic forms through the Volterra kernels. Such forms, constituting a more rich function space, are used as approximations of the response profile of visual cells. Our proposed second-order convolution is tested on CIFAR-10 and CIFAR-100. We show that a network which combines linear and non-linear filters in its convolutional layers, can outperform networks that use standard linear filters with the same architecture, yielding results competitive with the state-of-the-art on these datasets.
Classifying the Histology Image of Uterine Cervical Cancer

This paper presents an image segmentation method for the manual identification of squamous epithelium from cervical cancer images. The present study was utilizing the various feature extraction techniques including texture feature, triangle feature and profile based correlation features. Generated features are used to classifying the squamous epithelium into normal, Cervical Intraepithelial Neoplasia (CIN1), CIN2 and CIN3. The results are used to classify the images into: 1) normal, 2) pre-cancer. The final system will take as input a biopsy image of the cervix containing the epithelium layer and provide the classification using our approach, to assist the pathologist in cervical cancer diagnosis.  Histology, Cervical Intraepithelial Neoplasia, Human Papillomavirus.
Context-Dependent Regression Testing for Natural Language Processing

Regression testing of natural language systems is problematic for two main reasons: component input and output is complex, and system behaviour is context-dependent. We have developed a generic approach which solves both of these issues. We describe our regression tool, CONTEST, which supports context-dependent testing of dialogue system components, and discuss the regression test sets we developed, designed to effectively isolate components from changes and problems earlier in the pipeline. We believe that the same approach can be used in regression testing for other dialogue systems, as well as in testing any complex NLP system containing multiple components.
Fully Homomorphic Encryption without Bootstrapping

We present a radically new approach to fully homomorphic encryption (FHE) that dramatically improves performance and bases security on weaker assumptions. A central conceptual contribution in our work is a new way of constructing leveled fully homomorphic encryption schemes (capable of evaluating arbitrary polynomial-size circuits), without Gentry’s bootstrapping procedure. Specifically, we offer a choice of FHE schemes based on the learning with error (LWE) or ring-LWE (RLWE) problems that have 2 security against known attacks. For RLWE, we have: • A leveled FHE scheme that can evaluate L-level arithmetic circuits with Õ(λ · L) per-gate computation – i.e., computation quasi-linear in the security parameter. Security is based on RLWE for an approximation factor exponential in L. This construction does not use the bootstrapping procedure. • A leveled FHE scheme that uses bootstrapping as an optimization, where the per-gate computation (which includes the bootstrapping procedure) is Õ(λ), independent of L. Security is based on the hardness of RLWE for quasi-polynomial factors (as opposed to the sub-exponential factors needed in previous schemes). We obtain similar results for LWE, but with worse performance. We introduce a number of further optimizations to our schemes. As an example, for circuits of large width – e.g., where a constant fraction of levels have width at least λ – we can reduce the per-gate computation of the bootstrapped version to Õ(λ), independent of L, by batching the bootstrapping operation. Previous FHE schemes all required Ω̃(λ) computation per gate. At the core of our construction is a much more effective approach for managing the noise level of lattice-based ciphertexts as homomorphic operations are performed, using some new techniques recently introduced by Brakerski and Vaikuntanathan (FOCS 2011). ∗Sponsored by the Air Force Research Laboratory (AFRL).  This material is based on research sponsored by DARPA under agreement number FA8750-11-C-0096 and FA8750-11-2-0225. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of DARPA or the U.S. Government. Approved for Public Release, Distribution Unlimited. †This material is based on research sponsored by DARPA under Agreement number FA8750-11-2-0225. All disclaimers as above apply.