nils-herrmann/clean_scirepeval huggingface dataset:
Women's partnered orgasm consistency is associated with greater duration of penile-vaginal intercourse but not of foreplay.

It has been asserted that women's likelihood or consistency of partnered orgasm (her orgasm as a result of sexual activities with a partner) is determined by duration of foreplay, but not by duration of penile-vaginal intercourse.



The objective was to examine the extent to which women's likelihood or consistency of partnered orgasm is associated with duration of foreplay, duration of penile-vaginal intercourse, and age.



In a representative sample of the Czech population, 2,360 women reported their consistency of orgasm with a partner (from "never" to "almost every time"), and estimates of their typical durations of foreplay and of penile-vaginal intercourse.



The association of consistency of partnered orgasm with typical durations of both foreplay and penile-vaginal intercourse.



In univariate analyses, consistency of partnered orgasm was more associated with penile-vaginal intercourse duration than with foreplay duration (consistency also correlated negatively with age). In multivariate analysis, foreplay ceased to be a significant correlate of partnered orgasm consistency (the exclusion of respondents reporting a penile-vaginal intercourse duration of 1 minute or less did not alter the results).



When both sexual activity categories are examined in tandem on a population level, women's likelihood or consistency of partnered orgasm is associated with penile-vaginal intercourse duration, but not with foreplay duration. In contrast to the assumptions of many sex therapists and educators, more attention should be given to improve the quality and duration of penile-vaginal intercourse rather than foreplay.
Tactile sensing over articulated joints with stretchable sensors

Biological organisms benefit from tactile sensing across the entire surfaces of their bodies. Robots may also be able to benefit from this type of sensing, but fully covering a robot with robust and capable tactile sensors entails numerous challenges. To date, most tactile sensors for robots have been used to cover rigid surfaces. In this paper, we focus on the challenge of tactile sensing across articulated joints, which requires sensing across a surface whose geometry varies over time. We first demonstrate the importance of sensing across joints by simulating a planar arm reaching in clutter and finding the frequency of contact at the joints. We then present a simple model of how much a tactile sensor would need to stretch in order to cover a 2 degree-of-freedom (DoF) wrist joint. Next, we describe and characterize a new tactile sensor made with stretchable fabrics. Finally, we present results for a stretchable sleeve with 25 tactile sensors that covers the forearm, 2 DoF wrist, and end effector of a humanoid robot. This sleeve enabled the robot to reach a target in instrumented clutter and reduce contact forces.
Interference management for 4G cellular standards [WIMAX/LTE UPDATE]

4G cellular standards are targeting aggressive spectrum reuse (frequency reuse 1) to achieve high system capacity and simplify radio network planning. The increase in system capacity comes at the expense of SINR degradation due to increased intercell interference, which severely impacts cell-edge user capacity and overall system throughput. Advanced interference management schemes are critical for achieving the required cell edge spectral efficiency targets and to provide ubiquity of user experience throughout the network. In this article we compare interference management solutions across the two main 4G standards: IEEE 802.16m (WiMAX) and 3GPP-LTE. Specifically, we address radio resource management schemes for interference mitigation, which include power control and adaptive fractional frequency reuse. Additional topics, such as interference management for multitier cellular deployments, heterogeneous architectures, and smart antenna schemes will be addressed in follow-up papers.
An investigation of recurrent neural network architectures for statistical parametric speech synthesis

In this paper, we investigate two different recurrent neural network (RNN) architectures: Elman RNN and recently proposed clockwork RNN  for statistical parametric speech synthesis (SPSS). Of late, deep neural networks are being used for SPSS which involve predicting every frame independent of the previous predictions, and hence requires post-processing for ensuring smooth evolution of speech parameters. RNNs, on the other hand, are intuitively better suited for the task as they inherently model temporal dependencies, but were restricted in use because of the difficulty in training. Lately, techniques such as sparse initialization, Nesterov’s accelerated gradient, gradient clipping and leaky integration (LI) have been shown to overcome this difficulty. We study the utility of these techniques for SPSS task. In addition, we show that clockwork RNN is equivalent to an Elman RNN with a particular form of LI. This perspective enables us to understand the reason why a simple Elman RNN with LI units performs well on sequential tasks.
CRIMINOL HATE CRIME VICTIMISATION IN WALES Psychological and Physical Impacts Across Seven Hate Crime Victim-Types

This paper presents findings from the All Wales Hate Crime Project. Most hate crime research has focused on discrete victim types in isolation. For the first time, internationally, this paper examines the psychological and physical impacts of hate crime across seven victim types drawing on quantitative and qualitative data. It contributes to the hate crime debate in two significant ways: (1) it provides the first look at the problem in Wales and (2) it provides the first multi-victim-type analysis of hate crime, showing that impacts are not homogenous across victim groups. The paper provides empirical credibility to the impacts felt by hate crime victims on the margins who have routinely struggled to gain support.
Wideband Four-Way Out-of-Phase Slotline Power Dividers

Two new wideband four-way out-of-phase slotline power dividers are proposed in this paper. The half-wavelength slotlines are employed to construct the presented compact power dividers. Based on the proposed power-dividing circuit, a four-way power divider is implemented with compact size and simple structure. To obtain high isolation among the four output ports and good output impedance matching, another four-way out-of-phase slotline power divider with improved isolation performance is designed by introducing an air-bridge resistor and two slotlines with isolation resistors. The simulated and measured results of the proposed power dividers demonstrate reasonable performance of impedance matching, insertion loss, amplitude balancing, and isolation among the output ports.
On Developing One-Chip Integration of 1.2 kV SiC MOSFET and JBS Diode (JBSFET)

This paper presents the design, fabrication, and characterization of the SiC JBSFET (junction barrier Schottky (JBS) diode integrated MOSFET). The fabrication of the JBSFET adopted a novel single metal, single thermal treatment process to simultaneously form ohmic contacts on n+, p+ implanted regions, and Schottky contact on the n-4H-SiC epilayer. The presented SiC JBSFET uses 40% smaller wafer area because the diode and MOSFET share the edge termination as well as the current conducting drift region. The proposed single chip solution of MOSFET/JBS diode functionalities eliminates the parasitic inductance between separately packaged devices allowing a higher frequency operation in a power converter.
Large Scale Dense Visual Inertial SLAM

In this paper we present a novel large scale SLAM system that combines dense stereo vision with inertial tracking. The system divides space into a grid and efficiently allocates GPU memory only when there is surface information within a grid cell. A rolling grid approach allows the system to work for large scale outdoor SLAM. A dense visual inertial dense tracking pipeline incrementally localizes stereo cameras against the scene. The proposed system is tested with both a simulated data set and several real-life data in different lighting (illumination changes), motion (slow and fast), and weather (snow, sunny) conditions. Compared to structured light-RGBD systems the proposed system works indoors and outdoors and over large scales beyond single rooms or desktop scenes. Crucially, the system is able to leverage inertial measurements for robust tracking when visual measurements do not suffice. Results demonstrate effective operation with simulated and real data, and both indoors and outdoors under varying lighting conditions.
A new hybrid method based on Particle Swarm Optimization, Ant Colony Optimization and 3-Opt algorithms for Traveling Salesman Problem

The Traveling Salesman Problem (TSP) is one of the standard test problems used in performance analysis of discrete optimization algorithms. The Ant Colony Optimization (ACO) algorithm appears among heuristic algorithms used for solving discrete optimization problems. In this study, a new hybrid method is proposed to optimize parameters that affect performance of the ACO algorithm using Particle Swarm Optimization (PSO). In addition, 3-Opt heuristic method is added to proposed method in order to improve local solutions. The PSO algorithm is used for detecting optimum values of parameters  ̨ and ˇ which are used for city selection operations in the ACO algorithm and determines significance of inter-city pheromone and distances. The 3-Opt algorithm is used for the purpose of improving city selection operations, which could not be improved due to falling in local minimums by the ACO algorithm. The performance of proposed hybrid method is investigated on ten different benchmark problems taken from literature and it is compared to the performance of some well-known algorithms. Experimental results show that the performance of proposed method by using fewer ants than the number of cities for the TSPs is better than the performance of compared methods in most cases in terms of solution quality and robustness.
Transforming Question Answering Datasets Into Natural Language Inference Datasets

Existing datasets for natural language inference (NLI) have propelled research on language understanding. We propose a new method for automatically deriving NLI datasets from the growing abundance of largescale question answering datasets. Our approach hinges on learning a sentence transformation model which converts question-answer pairs into their declarative forms. Despite being primarily trained on a single QA dataset, we show that it can be successfully applied to a variety of other QA resources. Using this system, we automatically derive a new freely available dataset of over 500k NLI examples (QA-NLI), and show that it exhibits a wide range of inference phenomena rarely seen in previous NLI datasets.
Learning from Notes: Organizational Issues in Groupware Implementation

This paper explores the introduction of groupware into an organization to understand the changes in work practices and social interaction facilitated by the technology. The results suggest that people’s mental models and organizations’ structure and culture significantly influence how groupware is implemented and used. Specifically, in the absence of mental models that stressed its collaborative nature, groupwae was interpreted in terms of familiar personal, stand-alone technologies such as spreadsheets. Further, the culture and structure provided few incentives or norms for cooperating or sharing expertise, hence the groupware on its own was unlikely to engender collaboration. Recognizing the central influence of these cognitive and organizational elements is critical to developers, researchers, and practitioners of groupware,
Comparative analysis of piezoelectric power harvesting circuits for rechargeable batteries

Using piezoelectric materials to harvest energy from ambient vibrations to power wireless sensors has been of great interest over the past few years. Due to the power output of the piezoelectric materials is relatively low, rechargeable battery is considered as one kind of energy storage to accumulate the harvested energy for intermittent use. Piezoelectric harvesting circuits for rechargeable batteries have two schemes: non-adaptive and adaptive ones. A non-adaptive harvesting scheme includes a conventional diode bridge rectifier and a passive circuit. In recent years, several researchers have developed adaptive schemes for the harvesting circuit. Among them, the adaptive harvesting scheme by Ottman et al. is the most promising. This paper is aimed to quantify the performances of adaptive and non-adaptive schemes and to discuss their performance characteristics.
Learning to Query, Reason, and Answer Questions On Ambiguous Texts

A key goal of research in conversational systems is to train an interactive agent to help a user with a task. Human conversation, however, is notoriously incomplete, ambiguous, and full of extraneous detail. To operate effectively, the agent must not only understand what was explicitly conveyed but also be able to reason in the presence of missing or unclear information. When unable to resolve ambiguities on its own, the agent must be able to ask the user for the necessary clarifications and incorporate the response in its reasoning. Motivated by this problem we introduce QRAQ (Query, Reason, and Answer Questions), a new synthetic domain, in which a User gives an Agent a short story and asks a challenge question. These problems are designed to test the reasoning and interaction capabilities of a learningbased Agent in a setting that requires multiple conversational turns. A good Agent should ask only non-deducible, relevant questions until it has enough information to correctly answer the User’s question. We use standard and improved reinforcement learning based memory-network architectures to solve QRAQ problems in the difficult setting where the reward signal only tells the Agent if its final answer to the challenge question is correct or not. To provide an upper-bound to the RL results we also train the same architectures using supervised information that tells the Agent during training which variables to query and the answer to the challenge question. We evaluate our architectures on four QRAQ dataset types, and scale the complexity for each along multiple dimensions.
Improving Generalization Performance by Switching from Adam to SGD

Despite superior training outcomes, adaptive optimization methods such as Adam, Adagrad or RMSprop have been found to generalize poorly compared to Stochastic gradient descent (SGD). These methods tend to perform well in the initial portion of training but are outperformed by SGD at later stages of training. We investigate a hybrid strategy that begins training with an adaptive method and switches to SGD when appropriate. Concretely, we propose SWATS, a simple strategy which Switches from Adam to SGD when a triggering condition is satisfied. The condition we propose relates to the projection of Adam steps on the gradient subspace. By design, the monitoring process for this condition adds very little overhead and does not increase the number of hyperparameters in the optimizer. We report experiments on several standard benchmarks such as: ResNet, SENet, DenseNet and PyramidNet for the CIFAR-10 and CIFAR-100 data sets, ResNet on the tiny-ImageNet data set and language modeling with recurrent networks on the PTB and WT2 data sets. The results show that our strategy is capable of closing the generalization gap between SGD and Adam on a majority of the tasks.
An Optimal Radial Profile Order Based on the Golden Ratio for Time-Resolved MRI

In dynamic magnetic resonance imaging (MRI) studies, the motion kinetics or the contrast variability are often hard to predict, hampering an appropriate choice of the image update rate or the temporal resolution. A constant azimuthal profile spacing (111.246deg), based on the Golden Ratio, is investigated as optimal for image reconstruction from an arbitrary number of profiles in radial MRI. The profile order is evaluated and compared with a uniform profile distribution in terms of signal-to-noise ratio (SNR) and artifact level. The favorable characteristics of such a profile order are exemplified in two applications on healthy volunteers. First, an advanced sliding window reconstruction scheme is applied to dynamic cardiac imaging, with a reconstruction window that can be flexibly adjusted according to the extent of cardiac motion that is acceptable. Second, a contrast-enhancing k-space filter is presented that permits reconstructing an arbitrary number of images at arbitrary time points from one raw data set. The filter was utilized to depict the T1-relaxation in the brain after a single inversion prepulse. While a uniform profile distribution with a constant angle increment is optimal for a fixed and predetermined number of profiles, a profile distribution based on the Golden Ratio proved to be an appropriate solution for an arbitrary number of profiles
A Software Architecture for Simulating IEEE 802.11e HCCA

We present a software framework for simulating the HCF Controlled Channel Access (HCCA) in an IEEE 802.11e system. The proposed approach allows for flexible integration of different scheduling algorithms with the MAC. The 802.11e system consists of three modules: Classifier, HCCA Scheduler, MAC. We define a communication interface exported by the MAC module to the HCCA Scheduler. A Scheduler module implementing the reference scheduler defined in the draft IEEE 802.11e document is also described. The software framework reported in this paper has been implemented using the Network Simulator 2 platform. A preliminary performance analysis of the reference scheduler is also reported.
Team Knowledge and Coordination in Geographically Distributed Software Development

Coordination is important in software development because it leads to benefi ts such as cost savings, shorter development cycles, and better-integrated products. Team cognition research suggests that members coordinate through team knowledge, but this perspective has only been investigated in real-time collocated tasks and we know little about which types of team knowledge best help coordination in the most geographically distributed software work. In this fi eld study, we investigate the coordination needs of software teams, how team knowledge affects coordination, and how this effect is infl uenced by geographic dispersion. Our fi ndings show that software teams have three distinct types of coordination needs—technical, temporal, and process—and that these needs vary with the members’ role; geographic distance has a negative effect on coordination, but is mitigated by shared knowledge of the team and presence awareness; and shared task knowledge is more important for coordination among collocated members. We articulate propositions for future research in this area based on our analysis.
Permission based Android security: Issues and countermeasures

Android security has been a hot spot recently in both academic research and public concerns due to numerous instances of security attacks and privacy leakage on Android platform. Android security has been built upon a permission based mechanism which restricts accesses of third-party Android applications to critical resources on an Android device. Such permission based mechanism is widely criticized for its coarse-grained control of application permissions and difficult management of permissions by developers, marketers, and end-users. In this paper, we investigate the arising issues in Android security, including coarse granularity of permissions, incompetent permission administration, insufficient permission documentation, over-claim of permissions, permission escalation attack, and TOCTOU (Time of Check to Time of Use) attack. We illustrate the relationships among these issues, and investigate the existing countermeasures to address these issues. In particular, we provide a systematic review on the development of these countermeasures, and compare them according to their technical features. Finally, we propose several methods to further mitigate the risk in Android security. a 2014 Elsevier Ltd. All rights reserved.
An Introduction to Light Interaction with Human Skin

Despite the notable progress in physically-based rendering, there is still a long way to go before one can automatically generate predictable images of organic materials such as human skin. In this tutorial, the main physical and biological aspects involved in the processes of propagation and absorption of light by skin tissues are examined. These processes affect not only skin appearance, but also its health. For this reason, they have also been the object of study in biomedical research. The models of light interaction with human skin developed by the biomedical community are mainly aimed at the simulation of skin spectral properties which are used to determine the concentration and distribution of various substances. In computer graphics, the focus has been on the simulation of light scattering properties that affect skin appearance. Computer models used to simulate these spectral and scattering properties are described in this tutorial, and their strengths and limitations discussed.
Test-retest reliability and validity of the Pittsburgh Sleep Quality Index in primary insomnia.

Psychometric evaluation of the Pittsburgh Sleep Quality Index (PSQI) for primary insomnia.



The study sample consisted of 80 patients with primary insomnia (DSM-IV). The length of the test-retest interval was either 2 days or several weeks. Validity analyses were calculated for PSQI data and data from sleep diaries, as well as polysomnography. To evaluate the specificity of the PSQI, insomnia patients were compared with a control group of 45 healthy subjects.



In primary insomnia patients, the overall PSQI global score correlation coefficient for test-retest reliability was .87. Validity analyses showed high correlations between PSQI and sleep log data and lower correlations with polysomnography data. A PSQI global score > 5 resulted in a sensitivity of 98.7 and specificity of 84.4 as a marker for sleep disturbances in insomnia patients versus controls.



The PSQI has a high test-retest reliability and a good validity for patients with primary insomnia.
Building Chatbots from Forum Data: Model Selection Using Question Answering Metrics

We propose to use question answering (QA) data from Web forums to train chatbots from scratch, i.e., without dialog training data. First, we extract pairs of question and answer sentences from the typically much longer texts of questions and answers in a forum. We then use these shorter texts to train seq2seq models in a more efficient way. We further improve the parameter optimization using a new model selection strategy based on QA measures. Finally, we propose to use extrinsic evaluation with respect to a QA task as an automatic evaluation method for chatbots. The evaluation shows that the model achieves a MAP of 63.5% on the extrinsic task. Moreover, it can answer correctly 49.5% of the questions when they are similar to questions asked in the forum, and 47.3% of the questions when they are more conversational in style.