nils-herrmann/clean_scirepeval huggingface dataset:
Dermal and subdermal tissue filling with fetal connective tissue and cartilage, collagen, and silicone: Experimental study in the pig compared with clinical results. A new technique of dermis mini-autograft injections

The early reaction to the injection of silicone, collagen, and lyophilized heterologous fetal connective and cartilage tissues into the limiting zone deep dermis-superficial subcutaneous tissue was histologically examined in the pig and compared with clinical results. The inflammatory reaction to lyophilized heterologous fetal tissue is considerably more intense than that to collagen and silicone and lasts for several weeks. Therefore, it is not recommended for soft tissue filling in the face. Admitting an inferior antigenicity of fetal tissues, the authors suggest that enzymatically denaturalized collagen should be manufactured from heterologous fetal connective tissue, to be then further tested. The reaction of tissue to silicone and collagen is minimal. Silicone is preferred for dermal injections since in clinical experience it remains in the site of injection much longer. For subdermal injections, however, collagen is preferred. Based on experience with over 600 patients since 1958, the first author continues using liquid silicone. The lack of complications is probably a result of the fact that only small amounts (milliliters) of silicone were used in wrinkles or small depressions in the dermal layer and that from the beginning injection into the subcutaneous tissue was avoided. Since 1988 a new technique for the treatment of wrinkles and skin depressions with injections of dermal miniautografts has been used with satisfactory results.
The reduction of graphene oxide

Graphene has attracted great interest for its excellent mechanical, electrical, thermal and optical properties. It can be produced by micro-mechanical exfoliation of highly ordered pyrolytic graphite, epitaxial growth, chemical vapor deposition, and the reduction of graphene oxide (GO). The first three methods can produce graphene with a relatively perfect structure and excellent properties, while in comparison, GO has two important characteristics: (1) it can be produced using inexpensive graphite as raw material by cost-effective chemical methods with a high yield, and (2) it is highly hydrophilic and can form stable aqueous colloids to facilitate the assembly of macroscopic structures by simple and cheap solution processes, both of which are important to the large-scale uses of graphene. A key topic in the research and applications of GO is the reduction, which partly restores the structure and properties of graphene. Different reduction processes result in different properties of reduced GO (rGO), which in turn affect the final performance of materials or devices composed of rGO. In this contribution, we review the state-of-art status of the reduction of GO on both techniques and mechanisms. The development in this field will speed the applications of graphene.
Improving the intrinsic calibration of a Velodyne LiDAR sensor

LiDAR (Light detection and ranging) sensors are widely used in research and development. As such, they build the base for the evaluation of newly developed ADAS (Advanced Driver Assistance Systems) functions in the automotive field where they are used for ground truth establishment. However, the factory calibration provided for the sensors is not able to satisfy the high accuracy requirements by such applications. In this paper we propose a concept to easily improve the existing calibration of a Velodyne LiDAR sensor without the need for special calibration setups which can even be used to enhance already recorded data.
What does the Acceptance and Action Questionnaire (AAQ-II) really measure?

The present study seeks to investigate the extent to which the Acceptance and Action Questionnaire (AAQ-II) is successful in discriminating between experiential avoidance/psychological flexibility on the one hand and the supposed outcomes in terms of psychological well-being of having this trait on the other. This was done using exploratory factor analysis on an item pool containing the AAQ-II items, and items designed for the present study to measure distress and acceptance/non-acceptance, to see what factors are identified and on which factor(s) the AAQ-II items had the highest factor loadings. Interestingly, the analysis found the items of the AAQ-II to be more strongly related to items designed to measure distress than items designed to measure acceptance/nonacceptance with minimal references to functional outcomes. The results of the study are interpreted and discussed in relation to the widespread use of the AAQ in both clinical and scientific contexts and given the centrality of the measure in empirically validating the ACT model of psychopathology and treatment.
Fast image-based localization using direct 2D-to-3D matching

Recently developed Structure from Motion (SfM) reconstruction approaches enable the creation of large scale 3D models of urban scenes. These compact scene representations can then be used for accurate image-based localization, creating the need for localization approaches that are able to efficiently handle such large amounts of data. An important bottleneck is the computation of 2D-to-3D correspondences required for pose estimation. Current stateof- the-art approaches use indirect matching techniques to accelerate this search. In this paper we demonstrate that direct 2D-to-3D matching methods have a considerable potential for improving registration performance. We derive a direct matching framework based on visual vocabulary quantization and a prioritized correspondence search. Through extensive experiments, we show that our framework efficiently handles large datasets and outperforms current state-of-the-art methods.
Curvature preserving fingerprint ridge orientation smoothing using Legendre polynomials

Smoothing fingerprint ridge orientation involves a principal discrepancy. Too little smoothing can result in noisy orientation fields (OF), too much smoothing will harm high curvature areas, especially singular points (SP). In this paper we present a fingerprint ridge orientation model based on Legendre polynomials. The motivation for the proposed method can be found by analysing the almost exclusively used method in literature for orientation smoothing, proposed by Witkin and Kass (1987) more than two decades ago. The main contribution of this paper argues that the vectorial data (sine and cosine data) should be smoothed in a coupled way and the approximation error should not be evaluated employing vectorial data. For evaluating the proposed method we use a Poincare-Index based SP detection algorithm. The experiments show, that in comparison to competing methods the proposed method has improved orientation smoothing capabilities, especially in high curvature areas.
Evolutionary reinforcement learning in FX order book and order flow analysis

As macroeconomic fundamentals based modelling of FX timeseries have been shown not to fit the empirical evidence at horizons of less than one year, interest has moved towards microstructure-based approaches. Order flow data has recently been receiving an increasing amount of attention in equity market analyses and thus increasingly in foreign exchange as well. In this paper, order flow data is coupled with order book derived indicators and we explore whether pattern recognition techniques derived from computational learning can be applied to successfully infer trading strategies on the underlying timeseries. Due to the limited amount of data available the results are preliminary. However, the approach demonstrates promise and it is shown that using order flow and order book data is usually superior to trading on technical signals alone.
Towards a psychographic user model from mobile phone usage

Knowing the users' personality can be a strategic advantage for the design of adaptive and personalized user interfaces. In this paper, we present the results of a first trial conducted with the aim of inferring people's personality traits based on their mobile phone call behavior. Initial findings corroborate the efficacy of using call detail records (CDR) and Social Network Analysis (SNA) of the call graph to infer the Big Five personality factors. On-going work includes a large-scale study that shall refine the accuracy of the models with a reduced margin of error.
Monolithic integration of trench vertical DMOS (VDMOS) power transistors into a BCD process

The monolithic integration of trench vertical DMOS (VDMOS) n-channel transistors into an IC BCD process is reported for the first time. The integration scheme for the trench VDMOS module is discussed and silicon results are compared with TCAD simulations. For a 50-V device, the integrated trench device is shown to offer at least a factor-of-two R/sub DS(ON)/ /spl times/ area advantage over its planar counterpart. An R/sub DS(ON)/ /spl times/ area value of 80 m/spl Omega/mm/sup 2/ is achieved for the integrated trench VDMOS using a minimum feature size of 1 /spl mu/m.
3 D Tracking via Body Radio Reflections by

This thesis presents WiTrack, a system that tracks the 3D motion of a user from the radio signals reflected off her body. It works even if the person is occluded from the WiTrack device or in a different room. WiTrack does not require the user to carry any wireless device, yet its accuracy exceeds current RF localization systems, which require the user to hold a transceiver. Empirical measurements with a WiTrack prototype show that, on average, it localizes the center of a human body to within a median of 10 to 13 cm in the x and y dimensions, and 21 cm in the z dimension. It also provides coarse tracking of body parts, identifying the direction of a pointing hand with a median of 11.20. WiTrack bridges a gap between RF-based localization systems which locate a user through walls and occlusions, and human-computer interaction systems like Kinect, which can track a user without instrumenting her body, but require the user to stay within the direct line of sight of the device.  Dina Katabi Title: Professor of Computer Science and Engineering
Acute Effects of Cocaine on Human Brain Activity and Emotion

We investigated brain circuitry mediating cocaine-induced euphoria and craving using functional MRI (fMRI). During double-blind cocaine (0.6 mg/kg) and saline infusions in cocaine-dependent subjects, the entire brain was imaged for 5 min before and 13 min after infusion while subjects rated scales for rush, high, low, and craving. Cocaine induced focal signal increases in nucleus accumbens/subcallosal cortex (NAc/SCC), caudate, putamen, basal forebrain, thalamus, insula, hippocampus, parahippocampal gyrus, cingulate, lateral prefrontal and temporal cortices, parietal cortex, striate/extrastriate cortices, ventral tegmentum, and pons and produced signal decreases in amygdala, temporal pole, and medial frontal cortex. Saline produced few positive or negative activations, which were localized to lateral prefrontal cortex and temporo-occipital cortex. Subjects who underwent repeat studies showed good replication of the regional fMRI activation pattern following cocaine and saline infusions, with activations on saline retest that might reflect expectancy. Brain regions that exhibited early and short duration signal maxima showed a higher correlation with rush ratings. These included the ventral tegmentum, pons, basal forebrain, caudate, cingulate, and most regions of lateral prefrontal cortex. In contrast, regions that demonstrated early but sustained signal maxima were more correlated with craving than with rush ratings; such regions included the NAc/SCC, right parahippocampal gyrus, and some regions of lateral prefrontal cortex. Sustained negative signal change was noted in the amygdala, which correlated with craving ratings. Our data demonstrate the ability of fMRI to map dynamic patterns of brain activation following cocaine infusion in cocaine-dependent subjects and provide evidence of dynamically changing brain networks associated with cocaine-induced euphoria and cocaine-induced craving.
Social cognition: thinking categorically about others.

In attempting to make sense of other people, perceivers regularly construct and use categorical representations to simplify and streamline the person perception process. Noting the importance of categorical thinking in everyday life, our emphasis in this chapter is on the cognitive dynamics of categorical social perception. In reviewing current research on this topic, three specific issues are addressed: (a) When are social categories activated by perceivers, (b) what are the typical consequences of category activation, and (c) can perceivers control the influence and expression of categorical thinking? Throughout the chapter, we consider how integrative models of cognitive functioning may inform our understanding of categorical social perception.
An Operational Amplifier with Recycling Folded Cascode Topology and Adaptive Biasing

This paper presents a highly adaptive operational amplifier with high gain, high bandwidth, high speed and low power consumption. By adopting the recycling folded cascode topology along with an adaptivebiasing circuit, this design achieves high performance in terms of gain-bandwidth product (GBW) and slew rate (SR). This single stage op-amp has been designed in 0.18μm technology with a power supply of 1.8V and a 5pF load. The simulation results show that the amplifier achieved a GBW of 335.5MHz, Unity Gain Bandwidth of 247.1MHz and a slew rate of 92.8V/μs.
Cyber Security Cryptography and Machine Learning

A fuzzy extractor (FE) enables reproducible generation of high-quality randomness from noisy inputs having sufficient min-entropy. FEs have been proposed for deriving cryptographic keys from biometric data. FEs rely in their operation on a public “helper string” that is guaranteed not to leak too much information about the original input. Unfortunately, this guarantee may not hold when multiple independent helper strings are generated from correlated inputs; reusable FEs are needed in that case. Although the notion of reusable FEs was introduced in 2004, it has received little attention since then. In this paper, we first analyze an FE proposed by Fuller et al. (Asiacrypt 2013) based on the learning-with-errors (LWE) assumption, and show that it is not reusable. This is interesting as the first natural example of a non-reusable FE. We then show how to adapt their construction to obtain reusable FEs. Of independent interest, we show a generic technique for strengthening the notion of reusability achieved by an FE in the random-oracle model.
DESIGN HIGH GAIN DC-DC BOOST CONVERTER WITH COUPLING INDUCTOR AND SIMULATION IN PSIM

The high gain DC-DC converter with coupling inductor is design to boost low voltages to voltages into high range of 30 to 50 times input voltage. It is especially useful in boosting low solar panel voltage (12V) to high voltage, so that 230V ac can be generated. At the time the efficiency is also high and it is cost effective. It is a transformer less topology. This converter will work with input voltage of 12Vdc, and generate constant output voltage of 360Vdc with the help of PI controller. To achieve high voltage output gain the converter output terminal and boost output terminal are connected in serially with the isolated inductor with less voltage stress on controlled power switch and power diodes. PSIM software has been used for simulation.
Benchmarking Scalability and Elasticity of Distributed Database Systems

Distributed database system performance benchmarks are an important source of information for decision makers who must select the right technology for their data management problems. Since important decisions rely on trustworthy experimental data, it is necessary to reproduce experiments and verify the results. We reproduce performance and scalability benchmarking experiments of HBase and Cassandra that have been conducted by previous research and compare the results. The scope of our reproduced experiments is extended with a performance evaluation of Cassandra on different Amazon EC2 infrastructure configurations, and an evaluation of Cassandra and HBase elasticity by measuring scaling speed and performance impact while scaling.
Stealthy Denial of Service Strategy in Cloud Computing

The success of the cloud computing paradigm is due to its on-demand, self-service, and pay-by-use nature. According to this paradigm, the effects of Denial of Service (DoS) attacks involve not only the quality of the delivered service, but also the service maintenance costs in terms of resource consumption. Specifically, the longer the detection delay is, the higher the costs to be incurred. Therefore, a particular attention has to be paid for stealthy DoS attacks. They aim at minimizing their visibility, and at the same time, they can be as harmful as the brute-force attacks. They are sophisticated attacks tailored to leverage the worst-case performance of the target system through specific periodic, pulsing, and low-rate traffic patterns. In this paper, we propose a strategy to orchestrate stealthy attack patterns, which exhibit a slowly-increasing-intensity trend designed to inflict the maximum financial cost to the cloud customer, while respecting the job size and the service arrival rate imposed by the detection mechanisms. We describe both how to apply the proposed strategy, and its effects on the target system deployed in the cloud.
Classification of sleep apnea using cross wavelet transform

In this paper, a novel approach for classifying sleep apneas using cross wavelet transform has been proposed. This is the first time that cross wavelet transform has ever been applied to sleep apnea type classification. The developed method takes the airflow and thoracic effort signals, as an in-put, which are then transformed to time-frequency and phase plane in order to extract the information of correlation between the two signals during different apnea condition. As the cross-wavelet returns large number of coefficients, which may be difficult to handle in some automated detection system, therefore dimension reduction was necessary. In the work, kernel principal component analysis (KPCA) based dimension reduction technique has been applied, and four Eigen values from each of the cross-wavelet amplitude and phase coefficients found to be effective for detection of apnea into three categories i.e., obstructive, central and mixed. The proposed system has been tested on the recordings obtained from 23 subjects. The average classification rate obtained using simple threshold technique was 85% ± 0.78%, and the values for each class were 85.2% (obstructive), 86.4% (central) and 83.6% (mixed). The results show that cross-wavelet is useful in order to distinguish the apneas, as it looks into the phase and amplitude coherence between the two signals.
Robust Latent Subspace Learning for Image Classification

This paper proposes a novel method, called robust latent subspace learning (RLSL), for image classification. We formulate an RLSL problem as a joint optimization problem over both the latent SL and classification model parameter predication, which simultaneously minimizes: 1) the regression loss between the learned data representation and objective outputs and 2) the reconstruction error between the learned data representation and original inputs. The latent subspace can be used as a bridge that is expected to seamlessly connect the origin visual features and their class labels and hence improve the overall prediction performance. RLSL combines feature learning with classification so that the learned data representation in the latent subspace is more discriminative for classification. To learn a robust latent subspace, we use a sparse item to compensate error, which helps suppress the interference of noise via weakening its response during regression. An efficient optimization algorithm is designed to solve the proposed optimization problem. To validate the effectiveness of the proposed RLSL method, we conduct experiments on diverse databases and encouraging recognition results are achieved compared with many state-of-the-arts methods.
A Biologically Plausible Learning Rule for Deep Learning in the Brain

Intelligence is our ability to learn appropriate responses to new stimuli and situations. Recent theoretical insights have allowed a preliminary understanding of how animals learn to represent and memorize the key features of sensory stimuli for the guidance of action, and how this learning can proceed by trial-and-error. However, our understanding of how organisms can learn more complex tasks is still in its infancy. A number of researchers have proposed that deep learning, which is providing important progress in a wide range of high complexity tasks, might inspire new insights into learning processes in the brain. However, the methods used for deep learning in artificial neural networks are biologically unrealistic and would need to be replaced by biologically realistic counterparts. Previously, some biologically plausible learning rules were devised [Roelfsema and Ooyen (2005); Rombouts et al. (2012)], showing promising results. However, these previous studies focused on shallow networks with three layers, and only mentioned the possibility of generalizing the biologically plausible learning schemes to deeper networks with an arbitrary number of layers. Will these learning rules also generalize to networks with more layers and can they handle tasks of higher complexity? Here, we demonstrate that these learning schemes indeed generalize to networks with an arbitrary number of layers if we include an attention network that propagates information about the selected action to lower network levels. The resulting learning rule is equivalent to a particular form of error-backpropagation that trains one output unit at anyone time. To demonstrate the utility of the learning scheme in larger problems we include simulations with a network with two -hiddenlayers that is trained on the MNIST dataset, i.e. a standard and interesting Machine Learning task. Our results demonstrate that the capability of the new biologically plausible learning rule (called Q-AGREL) is comparable to error backpropagation. However, its learning rate is 1.5-2 times slower because the network trains one output unit at a time in a reinforcement learning setting and it therefore has to discover the appropriate outputs by trial-and-error, unlike supervised backpropagation where the correct response is signaled by an external teacher and the connections to all output units are updated at the same time. Our results provide new insights into how deep learning can be implemented in the brain.
An Empirical Investigation on the Impact of Crowd Participation on the Degree of Project Success: The Perspective of Crowd Capital

Fundraisers expect to raise as much funds as possible even after they have reached initial threshold of funding goal. This study focuses on the degree of project success defined as the total amount of funds a project can obtain after it is already successful (reached the initial threshold of funding goal). Drawing upon the theory of crowd capital, this study aims to explore the effect of the crowds—represented as crowd participation—on the degree of project success. Three types of crowd participation are identified, namely funds pledge, popularity creation, and on-site communication. We postulate that funds pledge will have an inverse U-shaped relationship with the degree of project success; while the other two factors will positively influence the degree of project success. Our empirical data from a reward-based crowdfunding platform supported our predictions for funds pledge and on-site communication. Future research and implications are discussed.