nils-herrmann/clean_scirepeval huggingface dataset:
Self-compassion: What It Is, What It Does, and How It Relates to Mindfulness Self-compassion: What It Is, What It Does, and How It Relates to Mindfulness Self-compassion and Emotional Wellbeing Self-compassion versus Self-esteem Self-compassion and Motivation Self-compassion and Coping Self-compassi

Over the past decade self-compassion has gained popularity as a related and complementary construct to mindfulness, and research on self-compassion is growing at an exponential rate. Self-compassion involves treating yourself with the same kindness, concern and support you'd show to a good friend. When faced with difficult life struggles, or confronting personal mistakes, failures, and inadequacies, self-compassion responds with kindness rather than harsh self-judgment, recognizing that imperfection is part of the shared human experience. In order to give oneself compassion, one must be able to turn toward, acknowledge, and accept that one is suffering, meaning that mindfulness is a core component of self-compassion. This chapter provides a comprehensive description of self-compassion and a review of the empirical literature supporting its psychological benefits. Similarities and distinctions between mindfulness and self-compassion are also explored, as these have important implications for research and intervention. This chapter hopes to provide a compelling argument for the use of both self-compassion and mindfulness as important means to help individuals develop emotional resilience and wellbeing. This chapter will present a conceptual account of self-compassion and review research on its benefits. It will also consider how self-compassion relates to mindfulness, given that these constructs are both drawn from It is important to understand the similar and unique features of self-compassion and mindfulness in order to understand how they each relate to wellbeing, and to consider how these states of heart and mind might best be developed. Self-compassion has received increased research attention lately, with over 200 journal articles and dissertations examining the topic since 2003, the year that the first two articles defining and measuring self-compassion were published (Neff, 2003a; Neff, 2003b). So what is self-compassion exactly? In order to better understand what self-compassion is, it is useful to first consider what it means to feel compassion more generally. From the Buddhist point of view, compassion is given to our own as well as to others' suffering. We include ourselves in the circle of compassion because to do otherwise would construct a false sense of separate self (Salzberg, 1997). Compassion involves sensitivity to the experience of suffering, coupled with a deep desire to alleviate that suffering (Goetz, Keltner, & Simon-Thomas, 2010). This means that in order to experience compassion, you must first acknowledge the presence of pain. Rather than rushing past that homeless woman as you're walking down the busy street, for example, you must actually stop to …
A Supervised Patch-Based Approach for Human Brain Labeling

We propose in this work a patch-based image labeling method relying on a label propagation framework. Based on image intensity similarities between the input image and an anatomy textbook, an original strategy which does not require any nonrigid registration is presented. Following recent developments in nonlocal image denoising, the similarity between images is represented by a weighted graph computed from an intensity-based distance between patches. Experiments on simulated and in vivo magnetic resonance images show that the proposed method is very successful in providing automated human brain labeling.
PassiveVLC: Enabling Practical Visible Light Backscatter Communication for Battery-free IoT Applications

This paper investigates the feasibility of practical backscatter communication using visible light for battery-free IoT applications. Based on the idea of modulating the light retroreflection with a commercial LCD shutter, we effectively synthesize these off-the-shelf optical components into a sub- mW low power visible light passive transmitter along with a retroreflecting uplink design dedicated for power constrained mobile/IoT devices. On top of that, we design, implement and evaluate PassiveVLC, a novel visible light backscatter communication system. PassiveVLC system enables a battery-free tag device to perform passive communication with the illuminating LEDs over the same light carrier and thus offers several favorable features including battery-free, sniff-proof, and biologically friendly for human-centric use cases. Experimental results from our prototyped system show that PassiveVLC is flexible with tag orientation, robust to ambient lighting conditions, and can achieve up to 1 kbps uplink speed. Link budget analysis and two proof-of-concept applications are developed to demonstrate PassiveVLC's efficacy and practicality.
Scale Space Meshing of Raw Data Point Sets

This paper develops a scale space strategy for orienting and meshing exactly and completely a raw point set. The scale space is based on the intrinsic heat equation, also called mean curvature motion (MCM). A simple iterative scheme implementing MCM directly on the raw point set is described, and a mathematical proof of its consistency with MCM is given. Points evolved by this MCM implementation can be trivially backtracked to their initial raw position. Therefore, both the orientation and mesh of the data point set obtained at a smooth scale can be transported back on the original. The gain in visual accuracy is demonstrated on archaeological objects by comparison with several state of the art meshing methods.
Postural instability precedes motion sickness

We evaluated the hypothesis that postural instability precedes the onset of motion sickness. Subjects standing in a "moving room" were exposed to nearly global oscillating optical flow. In the experimental condition, the optical oscillations were a complex sum-of-sines between 0.1 and 0.3 Hz, with an excursion of 1.8 cm. This optical motion was of such low frequency and magnitude that it was sometimes not noticed by subjects. However, in two experiments, exposure to the moving room produced significant increases in scores on a standard motion sickness questionnaire. In addition, approximately half of subjects reported motion sickness. Analysis of postural motion during exposure to the moving room revealed increases in postural sway before the onset of subjective motion sickness symptoms. This confirms a key prediction of the postural instability theory of motion sickness.
Generating Focussed Molecule Libraries for Drug Discovery with Recurrent Neural Networks

In de novo drug design, computational strategies are used to generate novel molecules with good affinity to the desired biological target. In this work, we show that recurrent neural networks can be trained as generative models for molecular structures, similar to statistical language models in natural language processing. We demonstrate that the properties of the generated molecules correlate very well with the properties of the molecules used to train the model. In order to enrich libraries with molecules active towards a given biological target, we propose to fine-tune the model with small sets of molecules, which are known to be active against that target. Against Staphylococcus aureus, the model reproduced 14% of 6051 hold-out test molecules that medicinal chemists designed, whereas against Plasmodium falciparum (Malaria) it reproduced 28% of 1240 test molecules. When coupled with a scoring function, our model can perform the complete de novo drug design cycle to generate large sets of novel molecules for drug discovery.
DualNet: Domain-invariant network for visual question answering

Visual question answering (VQA) tasks use two types of images: abstract (illustrations) and real. Domain-specific differences exist between the two types of images with respect to “objectness,” “texture,” and “color.” Therefore, achieving similar performance by applying methods developed for real images to abstract images, and vice versa, is difficult. This is a critical problem in VQA, because image features are crucial clues for correctly answering the questions about the images. However, an effective, domain-invariant method can provide insight into the high-level reasoning required for VQA. We thus propose a method called DualNet that demonstrates performance that is invariant to the differences in real and abstract scene domains. Experimental results show that DualNet outperforms state-of-the-art methods, especially for the abstract images category.
Polyimide-Enhanced Stretchable Interconnects: Design, Fabrication, and Characterization

This paper discusses the optimization of a stretchable electrical interconnection between integrated circuits in terms of stretchability and fatigue lifetime. The interconnection is based on Cu stripes embedded in a polyimide-enhanced (PI-enhanced) layer. Design-of-experiment (DOE) methods and finite-element modeling were used to obtain an optimal design and to define design guidelines, concerning both stripe and layer dimensions and material selection. Stretchable interconnects with a PI-enhanced layer were fabricated based on the optimized design parameters and tested. In situ experimental observations did validate the optimal design. Statistical analysis indicated that the PI width plays the most important role among the different design parameters. By increasing the PI width, the plastic strain in the Cu stripes is reduced, and thus, the stretchability and fatigue lifetime of the system is increased. The experimental results demonstrate that the PI-enhanced stretchable interconnect enables elongations up to 250% without Cu rupture. This maximum elongation is two times larger than the one in samples without PI enhancement . Moreover, the fatigue life at 30% elongation is 470 times higher.
Intelligent Systems: Architectures and Perspectives

The integration of different learning and adaptation techniques to overcome individual limitations and to achieve synergetic effects through the hybridization or fusion of these techniques has, in recent years, contributed to a large number of new intelligent system designs. Computational intelligence is an innovative framework for constructing intelligent hybrid architectures involving Neural Networks (NN), Fuzzy Inference Systems (FIS), Probabilistic Reasoning (PR) and derivative free optimization techniques such as Evolutionary Computation (EC). Most of these hybridization approaches, however, follow an ad hoc design methodology, justified by success in certain application domains. Due to the lack of a common framework it often remains difficult to compare the various hybrid systems conceptually and to evaluate their performance comparatively. This chapter introduces the different generic architectures for integrating intelligent systems. The designing aspects and perspectives of different hybrid archirectures like NN-FIS, EC-FIS, EC-NN, FIS-PR and NN-FIS-EC systems are presented. Some conclusions are also provided towards the end.
Combining low-, high-level and empirical domain knowledge for automated segmentation of ultrasonic breast lesions

Breast cancer is the most frequently diagnosed malignancy and the second leading cause of mortality in women . In the last decade, ultrasound along with digital mammography has come to be regarded as the gold standard for breast cancer diagnosis. Automatically detecting tumors and extracting lesion boundaries in ultrasound images is difficult due to their specular nature and the variance in shape and appearance of sonographic lesions. Past work on automated ultrasonic breast lesion segmentation has not addressed important issues such as shadowing artifacts or dealing with similar tumor like structures in the sonogram. Algorithms that claim to automatically classify ultrasonic breast lesions, rely on manual delineation of the tumor boundaries. In this paper, we present a novel technique to automatically find lesion margins in ultrasound images, by combining intensity and texture with empirical domain specific knowledge along with directional gradient and a deformable shape-based model. The images are first filtered to remove speckle noise and then contrast enhanced to emphasize the tumor regions. For the first time, a mathematical formulation of the empirical rules used by radiologists in detecting ultrasonic breast lesions, popularly known as the "Stavros Criteria" is presented in this paper. We have applied this formulation to automatically determine a seed point within the image. Probabilistic classification of image pixels based on intensity and texture is followed by region growing using the automatically determined seed point to obtain an initial segmentation of the lesion. Boundary points are found on the directional gradient of the image. Outliers are removed by a process of recursive refinement. These boundary points are then supplied as an initial estimate to a deformable model. Incorporating empirical domain specific knowledge along with low and high-level knowledge makes it possible to avoid shadowing artifacts and lowers the chance of confusing similar tumor like structures for the lesion. The system was validated on a database of breast sonograms for 42 patients. The average mean boundary error between manual and automated segmentation was 6.6 pixels and the normalized true positive area overlap was 75.1%. The algorithm was found to be robust to 1) variations in system parameters, 2) number of training samples used, and 3) the position of the seed point within the tumor. Running time for segmenting a single sonogram was 18 s on a 1.8-GHz Pentium machine.
Survey on Complex Event Processing and Predictive Analytics

Observing failures and other – desired or undesired – behavior patterns in large scale software systems of specific domains (telecommunication systems, information systems, online web applications, etc.) is difficult. Very often, it is only possible by examining the runtime behavior of these systems through operational logs or traces. However, these systems can generate data in order of gigabytes every day, which makes a challenge to process in the course of predicting upcoming critical problems or identifying relevant behavior patterns. We can say that there is a gap between the amount of information we have and the amount of information we need to make a decision. Low level data has to be processed, correlated and synthesized in order to create high level, decision helping data. The actual value of this high level data lays in its availability at the time of decision making (e.g., do we face a virus attack?). In other words high level data has to be available real-time or near real-time. The research area of event processing deals with processing such data that are viewed as events and with making alerts to the administrators (users) of the systems about relevant behavior patterns based on the rules that are determined in advance. The rules or patterns describe the typical circumstances of the events which have been experienced by the administrators. Normally, these experts improve their observation capabilities over time as they experience more and more critical events and the circumstances preceding them. However, there is a way to aid this manual process by applying the results from a related (and from many aspects, overlapping) research area, predictive analytics, and thus improving the effectiveness of event processing. Predictive analytics deals with the prediction of future events based on previously observed historical data by applying sophisticated methods like machine learning, the historical data is often collected and transformed by using techniques similar to the ones of event processing, e.g., filtering, correlating the data, and so on. In this paper, we are going to examine both research areas and offer a survey on terminology, research achievements, existing solutions, and open issues. We discuss the applicability of the research areas to the telecommunication domain. We primarily base our survey on articles published in international conferences and journals, but we consider other sources of information as well, like technical reports, tools or web-logs.
A reliable gateway for in-vehicle networks based on LIN, CAN, and FlexRay

This article describes a reliable gateway for in-vehicle networks. Such networks include local interconnect networks, controller area networks, and FlexRay. There is some latency when transferring a message from one node (source) to another node (destination). A high probability of error exists due to different protocol specifications such as baud-rate, and message frame format. Therefore, deploying a reliable gateway is a challenge to the automotive industry. We propose a reliable gateway based on the OSEK/VDX components for in-vehicle networks. We also examine the gateway system developed, and then we evaluate the performance of our proposed system.
Optimization of Nested SQL Queries Revisited

Current methods of evaluating nested queries in the SQL language can be inefficient in a variety of query and data base contexts. Previous research in the area of nested query optimization which sought methods of reducing evaluation costs is summarized, including a classification scheme for nested queries, algorithms designed to transform each type of query to a logically equivalent form which may then be evaluated more efficiently, and a description of a major bug in one of these algorithms. Further examination reveals another bug in the same algorithm. Solutions to these bugs are proposed and incorporated into a new transformation algorithm, and extensions are proposed which will allow the transformation algorithms to handle a larger class of predicates. A recursive algorithm for processing a general nested query is presented and the action of this algorithm is demonstrated. This algorithm can be used to transform any nested query.
Design of Vivaldi antenna array with end-fire beam steering function for 5G mobile terminals

This manuscript proposes a new design of phased array antenna for future fifth generation (5G) cellular communications. The proposed phased array antenna is designed on a low-cost N9000 PTFE substrate with overall size of 60×130×0.8 mm3. It consists of eight 28-GHz Vivaldi antenna elements used to form a linear phased array in the edge region (top-side) on a mobile phone PCB. The simulated results show that the antenna has the reflection coefficient (S11) less than -10 dB in the frequency range of 27.4 to 28.6 GHz. The proposed phased array antenna has good gain, efficiency, and 3D beam steering characteristics in the entire operation band, which makes it suitable for millimeter-wave 5G communications. In addition, the performance of the antenna in the vicinity of user's hand has been investigated in this study.
Cognitive , metacognitive , and motivational aspects of problem solving

This article examines the role of cognitive, metacognitive, and motivational skills in problem solving. Cognitive skills include instructional objectives, components in a learning hierarchy, and components in information processing. Metacognitive skills include strategies for reading comprehension, writing, and mathematics. Motivational skills include motivation based on interest, self-efficacy, and attributions. All three kinds of skills are required for successful problem solving in academic settings.
FeelSleeve: Haptic Feedback to Enhance Early Reading

Engaging children with traditional approaches in education, especially reading, grows ever more difficult in the face of their attachment to tablets and computer games. We explore the possibility of making the story reading experience more interesting and memorable for children using haptic augmentation. In this paper, we present FeelSleeve, an interface that allows children to feel story events in their hands while they are reading on a mobile device. FeelSleeve uses transducers and audio output from the tablet within a gloved attachment to create vibratory effects that are meaningfully related to story content. We describe a study investigating whether embedding such haptic feedback into stories enhances reading for six to nine year olds. Our results indicate that story events accompanied by haptic feedback are better comprehended and appear to be more salient in memory. These results provide evidence that haptic effects have the potential to improve children's reading experience and make it more memorable.
Review of charging power levels and infrastructure for plug-in electric and hybrid vehicles

This paper reviews the current status and implementation of battery chargers, charging power levels and infrastructure for plug-in electric vehicles and hybrids. Battery performance depends both on types and design of the batteries, and on charger characteristics and charging infrastructure. Charger systems are categorized into off-board and on-board types with unidirectional or bidirectional power flow. Unidirectional charging limits hardware requirements and simplifies interconnection issues. Bidirectional charging supports battery energy injection back to the grid. Typical onboard chargers restrict the power because of weight, space and cost constraints. They can be integrated with the electric drive for avoiding these problems. The availability of a charging infrastructure reduces on-board energy storage requirements and costs. On-board charger systems can be conductive or inductive. While conductive chargers use direct contact, inductive chargers transfer power magnetically. An off-board charger can be designed for high charging rates and is less constrained by size and weight. Level 1 (convenience), Level 2 (primary), and Level 3 (fast) power levels are discussed. These system configurations vary from country to country depending on the source and plug capacity standards. Various power level chargers and infrastructure configurations are presented, compared, and evaluated based on amount of power, charging time and location, cost, equipment, effect on the grid, and other factors.
Template Based Inference in Symmetric Relational Markov Random Fields

Relational Markov Random Fields are a general and flexible framework for reasoning about the joint distribution over attributes of a large number of interacting entities. The main computational difficulty in learning such models is inference. Even when dealing with complete data, where one can summarize a large domain by sufficient statistics, learning requires one to compute the expectation of the sufficient statistics given different parameter choices. The typical solution to this problem is to resort to approximate inference procedures, such as loopy belief propagation. Although these procedures are quite efficient, they still require computation that is on the order of the number of interactions (or features) in the model. When learning a large relational model over a complex domain, even such approximations require unrealistic running time. In this paper we show that for a particular class of relational MRFs, which have inherent symmetry, we can perform the inference needed for learning procedures using a template-level belief propagation. This procedure’s running time is proportional to the size of the relational model rather than the size of the domain. Moreover, we show that this computational procedure is equivalent to sychronous loopy belief propagation. This enables a dramatic speedup in inference and learning time. We use this procedure to learn relational MRFs for capturing the joint distribution of large protein-protein interaction networks.