nils-herrmann/clean_scirepeval huggingface dataset:
Durable write cache in flash memory SSD for relational and NoSQL databases

In order to meet the stringent requirements of low latency as well as high throughput, web service providers with large data centers have been replacing magnetic disk drives with flash memory solid-state drives (SSDs). They commonly use relational and NoSQL database engines to manage OLTP workloads in the warehouse-scale computing environments. These modern database engines rely heavily on redundant writes and frequent cache flushes to guarantee the atomicity and durability of transactional updates. This has become a serious bottleneck of performance in both relational and NoSQL database engines. This paper presents a new SSD prototype called DuraSSD equipped with tantalum capacitors. The tantalum capacitors make the device cache inside DuraSSD durable, and additional firmware features of DuraSSD take advantage of the durable cache to support the atomicity and durability of page writes. It is the first time that a flash memory SSD with durable cache has been used to achieve an order of magnitude improvement in transaction throughput without compromising the atomicity and durability. Considering that the simple capacitors increase the total cost of an SSD no more than one percent, DuraSSD clearly provides a cost-effective means for transactional support. DuraSSD is also expected to alleviate the problem of high tail latency by minimizing write stalls.
Handling Multiword Expressions in Causality Estimation

Previous studies on causality estimation mainly aquire causal event pairs from a large corpus based on lexico-syntactic patterns and coreference relations, and estimate causality by a statistical method. However, most of the previous studies assume event pairs can be represented by a pair of single words, therefore they cannot estimate multiword causality correctly (e.g.“tired”-“give up”) . In this paper, we create a list of multiword expressions and extend an existing method. Our evaluation demonstrates that the proper treatment of multiword expression events is effective and the proposed method outperforms the state-of-the-art causality estimation model.
MOTHERS WHO KILL THEIR CHILDREN A literature review

Maternal filicide, the murder of a child by its mother, is a complex phenomenon with various causes and characteristics. Research, by means of the development of several classification systems and in identifying particular risk factors, has been conducted with the aim of better prevention of this emotionally evocative crime. Various disciplines have offered a wide range of perspectives on why women kill their biological children. These are intended to yield a better understanding of the aetiology of this crime. This literature review delineates three dominant perspectives: psychiatric, psychological, and sociological. The main findings of each perspective are discussed. However, these three perspectives frequently operate in conjunction with each other in that both intrapsychic and interpersonal dynamics play a role in acts of maternal filicide. The most vulnerable women appear to be those who have had a severely deficient developmental history (trauma and/or grossly inadequate parenting), those who experience current difficult psychosocial circumstances, and those who have been diagnosed with a psychiatric illness. However, not all women who experience such problems kill their children. In this regard, individual differences have an important role to play and more carefully delineated future research is suggested. One of the most significant findings of this literature review is that there appears to be a paucity of systematic research on the South African phenomenon of parental child homicide.
Genetic Improvement of Software: A Comprehensive Survey

Genetic improvement (GI) uses automated search to find improved versions of existing software. We present a comprehensive survey of this nascent field of research with a focus on the core papers in the area published between 1995 and 2015. We identified core publications including empirical studies, 96% of which use evolutionary algorithms (genetic programming in particular). Although we can trace the foundations of GI back to the origins of computer science itself, our analysis reveals a significant upsurge in activity since 2012. GI has resulted in dramatic performance improvements for a diverse set of properties such as execution time, energy and memory consumption, as well as results for fixing and extending existing system functionality. Moreover, we present examples of research work that lies on the boundary between GI and other areas, such as program transformation, approximate computing, and software repair, with the intention of encouraging further exchange of ideas between researchers in these fields.
The Flipped Classroom : An Opportunity To Engage Millennial Students Through Active Learning Strategies

"Flipping" the classroom employs easy-to-use, readily accessihle technology in order to free class time from lecture. This allows for an expanded range of learning activities during class time. Using class time for active learning versus lecture provides opportunities for greater teacher-to-student mentoring, peer-to-peer collaboration and cross-disciplinary engagement. This review of literature addresses the challenges of engaging today's students in lecture-based classrooms and presents an argument for application of the "flipped classroom" model hy educators in the disciplines of family and consumer sciences.
Accuracy of topographic maps derived from ERS-1 interferometric radar

A interferometric radar technique for topographic mapping of surfaces promises a high-resolution approach to the generation of digital elevation models. We present here analyses of data collected by the synthetic aperture radar instrument on-board the ERS-1 satellite on successive orbits. Use of a single satellite in a nearly repeating orbit is attractive for reducing cost and spaceborne hardware complexity; also it permits inference of changes in the surface from the correlation properties of the radar echoes. The data have been reduced to correlation maps and digital elevation models. The correlation maps show that temporal correlation decreases significantly with time, but not necessarily at a constant well-defined rate, likely depending on environmental factors. When correlation among passes remains high, however, it is possible to form digital elevation models. Analyses of noise expected in ERS-1 interferometric data collected over Alaska and the southwestern United States indicate that maps with relative errors less than 5 m rms are possible in some regions. However, orbit uncertainties imply that tie points are required in order to reduce absolute height errors to a similar magnitude. We find that about 6 tie points per 40 X 40 km scene with 5 m rms or better height accuracy are needed to keep systematic map height errors below 5 m rms. The performance of the ERS-1 radar system for topographic applications, though useful for a variety of regional and local discipline studies, may be improved with respect to temporal decorrelation errors and absolute height acuity by modifying the orbit repeat period and incorporating precise orbit determination techniques. The resulting implementation will meet many, but not all, objectives of a global mapping mission.
Effects of acute systemic administration of cannabidiol on sleep-wake cycle in rats.

Cannabidiol (CBD) is one of the main components of Cannabis sativa and has a wide spectrum of action, including effects in the sleep-wake cycle.



The objective of this paper is to assess the effects on sleep of acute systemic administration of CBD.



Adult male Wistar rats were randomly distributed into four groups that received intraperitoneal injections of CBD 2.5 mg/kg, CBD 10 mg/kg, CBD 40 mg/kg or vehicle (n=seven animals/group). Sleep recordings were made during light and dark periods for four days: two days of baseline recording, one day of drug administration (test), and one day after drug (post-test).



During the light period of the test day, the total percentage of sleep significantly increased in the groups treated with 10 and 40 mg/kg of CBD compared to placebo. REM sleep latency increased in the group injected with CBD 40 mg/kg and was significantly decreased with the dose of 10 mg/kg on the post-test day. There was an increase in the time of SWS in the group treated with CBD 40 mg/kg, although this result did not reach statistical significance.



The systemic acute administration of CBD appears to increase total sleep time, in addition to increasing sleep latency in the light period of the day of administration.
LSB Dithering in MASH Delta–Sigma D/A Converters

Theoretical sufficient conditions are presented that ensure that the quantization noise from every constituent digital delta-sigma (DeltaSigma) modulator in a multistage digital DeltaSigma modulator is asymptotically white and uncorrelated with the input. The conditions also determine if spectral shape can be imparted to the dither's contribution to the power spectral density of the multistage digital DeltaSigma modulator's output. A large class of popular multistage digital DeltaSigma modulators that satisfy the conditions are identified and tabulated for easy reference
Efficient Global Optimization of Expensive Black-Box Functions

In many engineering optimization problems, the number of function evaluations is severely limited by time or cost. These problems pose a special challenge to the field of global optimization, since existing methods often require more function evaluations than can be comfortably afforded. One way to address this challenge is to fit response surfaces to data collected by evaluating the objective and constraint functions at a few points. These surfaces can then be used for visualization, tradeoff analysis, and optimization. In this paper, we introduce the reader to a response surface methodology that is especially good at modeling the nonlinear, multimodal functions that often occur in engineering. We then show how these approximating functions can be used to construct an efficient global optimization algorithm with a credible stopping rule. The key to using response surfaces for global optimization lies in balancing the need to exploit the approximating surface (by sampling where it is minimized) with the need to improve the approximation (by sampling where prediction error may be high). Striking this balance requires solving certain auxiliary problems which have previously been considered intractable, but we show how these computational obstacles can be overcome.
Signal/Power Integrity Modeling of High-Speed Memory Modules Using Chip-Package-Board Coanalysis

Under the platform of a high-speed double-data-rate three (DDR3) memory module, a modeling method considering all the significant effects from the chip, package, and board levels is developed to identify and investigate the critical nets affecting the signal or power integrity (SI/PI). For SI part, accurate modeling strategies for signal channels are verified by experiments on samples of address lines. The following what-if analyses of eye diagrams help to identify the discontinuities of package trace to be the bottlenecks and have great effects on the eye diagrams. For PI issues, the modeling methodologies for power distribution networks of data buses are demonstrated and validated with the results of measurement. The analysis indicates that the parasitic effects of the low-cost package structure are the most critical, depicting the importance of improved package design in the next-generation DDR memory modules.
Functionally linked resting-state networks reflect the underlying structural connectivity architecture of the human brain.

During rest, multiple cortical brain regions are functionally linked forming resting-state networks. This high level of functional connectivity within resting-state networks suggests the existence of direct neuroanatomical connections between these functionally linked brain regions to facilitate the ongoing interregional neuronal communication. White matter tracts are the structural highways of our brain, enabling information to travel quickly from one brain region to another region. In this study, we examined both the functional and structural connections of the human brain in a group of 26 healthy subjects, combining 3 Tesla resting-state functional magnetic resonance imaging time-series with diffusion tensor imaging scans. Nine consistently found functionally linked resting-state networks were retrieved from the resting-state data. The diffusion tensor imaging scans were used to reconstruct the white matter pathways between the functionally linked brain areas of these resting-state networks. Our results show that well-known anatomical white matter tracts interconnect at least eight of the nine commonly found resting-state networks, including the default mode network, the core network, primary motor and visual network, and two lateralized parietal-frontal networks. Our results suggest that the functionally linked resting-state networks reflect the underlying structural connectivity architecture of the human brain.
Comparing Two IRT Models for Conjunctive Skills

A step in ITS often involve multiple skills. Thus a step requiring a conjunction of skills is harder than steps that require requiring each individual skill only. We developed two Item-Response Models – Additive Factor Model (AFM) and Conjunctive Factor Model (CFM) – to model the conjunctive skills in the student data sets. Both models are compared on simulated data sets and a real assessment data set. We showed that CFM was as good as or better than AFM in the mean cross validation errors on the simulated data. In the real data set CFM is not clearly better. However, AFM is essentially performing as a conjunctive model.
Exploring Deep Learning and Transfer Learning for Colonic Polyp Classification

Recently, Deep Learning, especially through Convolutional Neural Networks (CNNs) has been widely used to enable the extraction of highly representative features. This is done among the network layers by filtering, selecting, and using these features in the last fully connected layers for pattern classification. However, CNN training for automated endoscopic image classification still provides a challenge due to the lack of large and publicly available annotated databases. In this work we explore Deep Learning for the automated classification of colonic polyps using different configurations for training CNNs from scratch (or full training) and distinct architectures of pretrained CNNs tested on 8-HD-endoscopic image databases acquired using different modalities. We compare our results with some commonly used features for colonic polyp classification and the good results suggest that features learned by CNNs trained from scratch and the "off-the-shelf" CNNs features can be highly relevant for automated classification of colonic polyps. Moreover, we also show that the combination of classical features and "off-the-shelf" CNNs features can be a good approach to further improve the results.
Position-based impedance control of an industrial hydraulic manipulator

This paper addresses the problem of impedance control in hydraulic robots. Whereas most impedance and hybrid force/position control formulations have focussed on electrically-driven robots with controllable actuator torques, torque control of hydraulic actuators is a difficult task. Motivated by the previous works [2,9, lo], a position-based impedance controller is proposed and demonstrated on an existing industrial hydraulic robot (a Unimate MKII-2000). A nonlinear proportional-integral (NPI) controller is first developed to meet the accurate positioning requirements of this impedance control formulation. The NPI controller is shown to make the manipulator match a range of second-order target impedances. Various experiments in free space and in environmental contact, including a simple impedance modulation experiment, demonstrate the feasibility and the promise of the technique.
A Review on a Emotion Detection and Recognization from Text Using Natural Language Processing

In today’s world communication is done on vast area along with growth of Internet. Communication and human interaction is carried out using social sites, applications etc. in textual form. Emotion plays a vital role in human day-to-day life conveying their feelings and situation in their life. Emotions can be expressed using speech, gestures facial expression, or text. The Ekman model includes happy or joy, sad, disgust, anger, fear and surprise as basic emotion. Emotion Detection from textual source can be done using concepts of Natural Language Processing. In this paper emotion detection using NLP and its naive algorithm are described.
Approach and avoidance strength during goal attainment: regulatory focus and the "goal looms larger" effect.

Three studies tested the hypothesis that regulatory focus, both as a chronic person variable and as a manipulated situational variable, differentially affects the strength of participants' approach and avoidance strategic motivations as they get closer to the goal. In 2 studies, flexion and extension arm pressure were used as on-line measures of approach and avoidance intensity, respectively. As predicted, the approach gradient was steeper for participants with a promotion focus on aspirations and gains than for participants with a prevention focus on responsibilities and nonlosses, whereas the reverse was true for the avoidance gradient. In a third study, the same pattern of results was found on a persistence measure of motivational strength. Participants with a promotion focus worked longer on anagrams closer to the goal when they were approach means for goal attainment than when they were avoidance means, whereas the reverse was true for participants with a prevention focus.
A novel level shifter for practical IoT applications

This paper proposed a 0.5 V–1.32 V input to 3.3 V output voltage level shifter (LS) that meets both requirements for current IoT applications, namely a minimal 0.7 V input voltage and 500 MHz speed at nominal 1.2 V. Simulations of this LS using a commercial 55-nm process design kit demonstrated a minimal input voltage in the near-threshold region (500 mV) and speeds in the GHz range when running at 1.2 V nominally. This was achieved using an isolation control signal to time the half-latch that switches input and output voltage, and a NAND gate as an inverting buffer for data transferring in active state or a pull high logic in isolation state. Besides, the proposed LS exhibits average static power of less than 70 pW and a small area consumption which is suitable for IoT applications.
Batch Size Influence on Performance of Graphic and Tensor Processing Units during Training and Inference Phases

The impact of the maximally possible batch size (for the better runtime) on performance of graphic processing units (GPU) and tensor processing units (TPU) during training and inference phases is investigated. The numerous runs of the selected deep neural network (DNN) were performed on the standard MNIST and Fashion-MNIST datasets. The significant speedup was obtained even for extremely low-scale usage of Google TPUv2 units (8 cores only) in comparison to the quite powerful GPU NVIDIA Tesla K80 card with the speedup up to 10x for training stage (without taking into account the overheads) and speedup up to 2x for prediction stage (with and without taking into account overheads). The precise speedup values depend on the utilization level of TPUv2 units and increase with the increase of the data volume under processing, but for the datasets used in this work (MNIST and FashionMNIST with images of sizes 28x28) the speedup was observed for batch sizes >512 images for training phase and >40 000 images for prediction phase. It should be noted that these results were obtained without detriment to the prediction accuracy and loss that were equal for both GPU and TPU runs up to the 3rd significant digit for MNIST dataset, and up to the 2nd significant digit for Fashion-MNIST dataset.
IMLS-SLAM: Scan-to-Model Matching Based on 3D Data

The Simultaneous Localization And Mapping (SLAM) problem has been well studied in the robotics community, especially using mono, stereo cameras or depth sensors. 3D depth sensors, such as Velodyne LiDAR, have proved in the last 10 years to be very useful to perceive the environment in autonomous driving, but few methods exist that directly use these 3D data for odometry. We present a new low-drift SLAM algorithm based only on 3D LiDAR data. Our method relies on a scan-to-model matching framework. We first have a specific sampling strategy based on the LiDAR scans. We then define our model as the previous localized LiDAR sweeps and use the Implicit Moving Least Squares (IMLS) surface representation. We show experiments with the Velodyne HDL32 with only 0.40% drift over a 4 km acquisition without any loop closure (i.e., 16 $m$ drift after 4 km). We tested our solution on the KITTI benchmark with a Velodyne HDL64 and ranked among the best methods (against mono, stereo and LiDAR methods) with a global drift of only 0.69%.
Access Control Framework for Social Network System using Ontology

In recent years, we have seen unprecedented growth in the area of Online Social Networking (OSN) that is still keeping on day by day. Social networking websites such as Facebook, Google+, and Twitter are using widely by people to share personal and public information with friends, coworkers, colleagues, family and even with strangers . Facebook, one of the most popular social network sites, has million of active users and billions of pieces of content or data that use daily like web links, news stories, blog posts, notes, photo albums, etc. shared each month. To protect such kind for huge or big data or information need more secured and flexible access control model. There are so many access control policies are available for controlling online social network, but all social networking sites like Facebook or Twitter has their own access control mechanism that is not standard and still not more secured or flexible. To protect such kind of publically oriented user data need more dynamic access control model. In order to protect OSN, in this paper an innovative or dynamic access control framework for social networking systems using semantic web ontology has been proposed which addresses the protection of semantic-rich information in a knowledge base ontology
A two-stage training deep neural network for small pedestrian detection

In the present paper, we propose a deep network architecture in order to improve the accuracy of pedestrian detection. The proposed method contains a proposal network and a classification network that are trained separately. We use a single shot multibox detector (SSD) as a proposal network to generate the set of pedestrian proposals. The proposal network is fine-tuned from a pre-trained network by several pedestrian data sets of large input size (512 × 512 pixels) in order to improve detection accuracy of small pedestrians. Then, we use a classification network to classify pedestrian proposals. We then combine the scores from the proposal network and the classification network to obtain better final detection scores. Experiments were evaluated using the Caltech test set, and, compared to other state-of-the-art methods of pedestrian detection task, the proposed method obtains better results for small pedestrians (30 to 50 pixels in height) with an average miss rate of 42%.