nils-herrmann/clean_scirepeval huggingface dataset:
FogLearn: Leveraging Fog-based Machine Learning for Smart System Big Data Analytics

Big﻿data﻿analytics﻿with﻿the﻿cloud﻿computing﻿are﻿one﻿of﻿the﻿emerging﻿area﻿for﻿processing﻿ and﻿analytics.﻿Fog﻿computing﻿is﻿the﻿paradigm﻿where﻿fog﻿devices﻿help﻿to﻿reduce﻿latency﻿ and﻿increase﻿throughput﻿for﻿assisting﻿at﻿the﻿edge﻿of﻿the﻿client.﻿This﻿article﻿discusses﻿ the﻿emergence﻿of﻿fog﻿computing﻿for﻿mining﻿analytics﻿in﻿big﻿data﻿from﻿geospatial﻿and﻿ medical﻿health﻿applications.﻿This﻿article﻿proposes﻿and﻿develops﻿a﻿fog﻿computing-based﻿ framework,﻿i.e.﻿FogLearn.﻿This﻿is﻿for﻿the﻿application﻿of﻿K-means﻿clustering﻿in﻿Ganga﻿ River﻿Basin﻿Management﻿and﻿real-world﻿feature﻿data﻿for﻿detecting﻿diabetes﻿patients﻿ suffering﻿from﻿diabetes﻿mellitus.﻿The﻿proposed﻿architecture﻿employs﻿machine﻿learning﻿ on﻿a﻿deep﻿learning﻿framework﻿for﻿the﻿analysis﻿of﻿pathological﻿feature﻿data﻿that﻿obtained﻿ from﻿smart﻿watches﻿worn﻿by﻿the﻿patients﻿with﻿diabetes﻿and﻿geographical﻿parameters﻿ of﻿River﻿Ganga﻿basin﻿geospatial﻿database.﻿The﻿results﻿show﻿that﻿fog﻿computing﻿holds﻿ an﻿immense﻿promise﻿for﻿the﻿analysis﻿of﻿medical﻿and﻿geospatial﻿big﻿data.  Cloud Computing, Clustering, Diabetes, Fog Computing, Geospatial Big Data, Geospatial Data, K-Means, Medical Big Data, River, Visualization International Journal of Fog Computing Volume 1 • Issue 1 • January-June 2018
Visual search for an object in a 3D environment using a mobile robot

Consider the problem of visually finding an object in a mostly unknown space with a mobile robot. It is clear that all possible views and images cannot be examined in a practical system. Visual attention is a complex phenomenon; we view it as a mechanism that optimizes the search processes inherent in vision. Here, we describe a particular example of a practical robotic vision system that employs some of these attentive processes. We cast this as an optimization problem, i.e., optimizing the probability of finding the target given a fixed cost limit in terms of total number of robotic actions required to find the visual target. Due to the inherent intractability of this problem, we present an approximate solution and investigate its performance and properties. We conclude that our approach, named the SYT algorithm, is sufficient to solve this problem and has additional desirable empirical characteristics.
MTOR regulates the pro-tumorigenic senescence-associated secretory phenotype by promoting IL1A translation

The TOR (target of rapamycin) kinase limits longevity by poorly understood mechanisms. Rapamycin suppresses the mammalian TORC1 complex, which regulates translation, and extends lifespan in diverse species, including mice. We show that rapamycin selectively blunts the pro-inflammatory phenotype of senescent cells. Cellular senescence suppresses cancer by preventing cell proliferation. However, as senescent cells accumulate with age, the senescence-associated secretory phenotype (SASP) can disrupt tissues and contribute to age-related pathologies, including cancer. MTOR inhibition suppressed the secretion of inflammatory cytokines by senescent cells. Rapamycin reduced IL6 and other cytokine mRNA levels, but selectively suppressed translation of the membrane-bound cytokine IL1A. Reduced IL1A diminished NF-κB transcriptional activity, which controls much of the SASP; exogenous IL1A restored IL6 secretion to rapamycin-treated cells. Importantly, rapamycin suppressed the ability of senescent fibroblasts to stimulate prostate tumour growth in mice. Thus, rapamycin might ameliorate age-related pathologies, including late-life cancer, by suppressing senescence-associated inflammation.
All You Need is Love: Current Strategies of Mediating Intimate Relationships through Technology

A wealth of evidence suggests that love, closeness, and intimacy---in short relatedness---are important for people’s psychological well-being. Nowadays, however, couples are often forced to live apart. Accordingly, there has been a growing and flourishing interest in designing technologies that mediate (and create) a feeling of relatedness when being separated, beyond the explicit verbal communication and simple emoticons available technologies offer. This article provides a review of 143 published artifacts (i.e., design concepts, technologies). Based on this, we present six strategies used by designers/researchers to create a relatedness experience: Awareness, expressivity, physicalness, gift giving, joint action, and memories. We understand those strategies as starting points for the experience-oriented design of technology.
Kickstarting Deep Reinforcement Learning

We present a method for using previously-trained ‘teacher’ agents to kickstart the training of a new ‘student’ agent. To this end, we leverage ideas from policy distillation (Rusu et al., 2015; Parisotto et al., 2015) and population based training (Jaderberg et al., 2017). Our method places no constraints on the architecture of the teacher or student agents, and it regulates itself to allow the students to surpass their teachers in performance. We show that, on a challenging and computationally-intensive multi-task benchmark (Beattie et al., 2016), kickstarted training improves the data efficiency of new agents, making it significantly easier to iterate on their design. We also show that the same kickstarting pipeline can allow a single student agent to leverage multiple ‘expert’ teachers which specialise on individual tasks. In this setting kickstarting yields surprisingly large gains, with the kickstarted agent matching the performance of an agent trained from scratch in almost 10× fewer steps, and surpassing its final performance by 42%. Kickstarting is conceptually simple and can easily be incorporated into reinforcement learning experiments.
Dynamic sliding modes control of uncertain nonlinear MIMO three tank system

In this paper controller synthesis based on standard and dynamic sliding modes for an uncertain nonlinear MIMO Three tank System is presented. Two types of sliding mode controllers are synthesized; first controller is based on standard first order sliding modes while second controller uses dynamic sliding modes. Sliding manifolds for both controllers are designed in-order to ensure finite time convergence of sliding variable for tracking the desired system trajectories. Simulation results are presented showing the performance analysis of both sliding mode controllers. Simulations are also carried out to assess the performance of dynamic sliding mode controller against parametric uncertainties / disturbances. A comparison of designed sliding mode controllers with LMI based robust H∞ controller is also discussed. The performance of dynamic sliding mode control in terms of response time, control effort and robustness of dynamic sliding mode controller is shown to be better than standard sliding mode controller and H∞ controllers.
Does Dream Color Reflect Emotion ?

Color has been shown by researchers to stimulate human emotion in the waking state. Would it not therefore make sense that those same emotions stimulate colors in the dream state? This paper describes the results of an investigation into this question. It provides support for a hypothesis that color not only reflects emotional content within a dream image, but that the frequency of color recalled from a persons dreams over the long term can reflect the occurrence of emotional events and perhaps even be influenced by, or an indicator of, personality. Investigation into the physiology and psychology of color, established a basis for quantifying the human response to color. Investigation with individual dreamwork supported the premise that color in dreams relates directly to the waking emotional situations that stimulated the dream. This was done by correlating the results of a Gestalt role-play technique (to reveal emotional content within a dream image) with a color based emotional profile tool (the Luscher Color Test) and then with the waking life situation reflected in the dream. Further research, on the long term journaling of dream color data from approximately 8000 dream records, gave support to the premise that the frequency of colors we recall from dreams respond to emotional events in our waking life, as well as personality traits. Results presented here are preliminary in that more investigation and controlled study is required, however it is presented here as an indication of a potential relationship between dream color and emotion, as well as an invitation to join us in further research. Those who have long term journals with color content please contact the authors if you wish to participate (e-mal: robertjhoss@aol.com).
Pharmaceutical parallel trade in Europe : stakeholder and competition effects

Pharmaceutical parallel trade in the European Union is a large and growing phenomenon, and hope has been expressed that it has the potential to reduce prices paid by health insurance and consumers and substantially to raise overall welfare. In this paper we examine the phenomenon empirically, using data on prices and volumes of individual imported products. We have found that the gains from parallel trade accrue mostly to the distribution chain rather than to health insurance and consumers. This is because in destination countries parallel traded drugs are priced just below originally sourced drugs. We also test to see whether parallel trade has a competition impact on prices in destination countries and find that it does not. Such competition effects as there are in pharmaceuticals come mainly from the presence of generics. Accordingly, instead of a convergence to the bottom in EU pharmaceutical prices, the evidence points at ‘convergence to the top’. This is explained by the fact that drug prices are subjected to regulation in individual countries, and by the limited incentives of purchasers to respond to price differentials.
Simulation of SVPWM based FOC of CSI fed induction motor drive

The application of current source inverters (CSI)in induction motor (IM) drives offers a number of advantages, including voltage boosting capability, natural shoot-through short-circuit protection and generation of sinusoidal voltages. In this paper, an attempt to model the CSI fed IM drive is presented. The mathematical model takes into account of the inverter, and induction motor dynamics and is established in the stationary reference frame. For controlling the drive speed, a direct field-oriented control (FOC) is proposed. To counter the effects of torque pulsations at very low speeds and the rotor resistance variation, a slip angle compensation loop is included in the control law formulation. Analytical expressions for CSI fed IM with Direct FOC are derived and validated using MATLAB/SIMULINK.
Multi-Modal Coreference Resolution with the Correlation between Space Structures

Multi-modal data is becoming more common in big data background. Finding the semantically similar objects from different modality is one of the heart problems of multi-modal learning. Most of the current methods try to learn the intermodal correlation with extrinsic supervised information, while intrinsic structural information of each modality is neglected. The performance of these methods heavily depends on the richness of training samples. However, obtaining the multi-modal training samples is still a labor and cost intensive work. In this paper, we bring a extrinsic correlation between the space structures of each modalities in coreference resolution. With this correlation, a semisupervised learning model for multi-modal coreference resolution is proposed. We firstly extract high-level features of images and text, then compute the distances of each object from some reference points to build the space structure of each modality. With a shared reference point set, the space structures of each modality are correlated. We employ the correlation to build a commonly shared space that the semantic distance between multimodal objects can be computed directly. The experiments on two multi-modal datasets show that our model performs better than the existing methods with insufficient training data.
Relationship between emotion and forgetting.

A major determinant of forgetting in memory is the presence of interference in the retrieval context. Previous research has shown that proactive interference has less impact for emotional than neutral study material (Levens & Phelps, 2008). However, it is unclear how emotional content affects the impact of interference in memory. Emotional content could directly affect the buildup of interference, leading to reduced levels of interference. Alternatively, emotional content could affect the controlled processes that resolve interference. The present study employed the response deadline speed-accuracy trade-off procedure to independently test these hypotheses. Participants studied 3-item lists consisting of emotional or neutral images, immediately followed by a recognition probe. Results indicated a slower rate of accrual for interfering material (lures from previous study list) and lower levels of interference for emotional than neutral stimuli, suggesting a direct impact of emotion on the buildup of interference. In contrast to this beneficiary effect, resolution of interference for emotional material was less effective than neutral material. These findings can provide insight into the interactions of emotion and memory processes.
A Multi-Region Segmentation Method for SAR Images Based on the Multi-Texture Model With Level Sets

Synthetic aperture radar (SAR) image segmentation is a difficult problem due to the presence of strong multiplicative noise. To attain multi-region segmentation for SAR images, this paper presents a parametric segmentation method based on the multi-texture model with level sets. Segmentation is achieved by solving level set functions obtained from minimizing the proposed energy functional. To fully utilize image information, edge feature and region information are both included in the energy functional. For the need of level set evolution, the ratio of exponentially weighted averages operator is modified to obtain edge feature. Region information is obtained by the improved edgeworth series expansion, which can adaptively model a SAR image distribution with respect to various kinds of regions. The performance of the proposed method is verified by three high resolution SAR images. The experimental results demonstrate that SAR images can be segmented into multiple regions accurately without any speckle pre-processing steps by the proposed method.
Emerging roles of p53 and other tumour-suppressor genes in immune regulation

Tumour-suppressor genes are indispensable for the maintenance of genomic integrity. Recently, several of these genes, including those encoding p53, PTEN, RB1 and ARF, have been implicated in immune responses and inflammatory diseases. In particular, the p53 tumour- suppressor pathway is involved in crucial aspects of tumour immunology and in homeostatic regulation of immune responses. Other studies have identified roles for p53 in various cellular processes, including metabolism and stem cell maintenance. Here, we discuss the emerging roles of p53 and other tumour-suppressor genes in tumour immunology, as well as in additional immunological settings, such as virus infection. This relatively unexplored area could yield important insights into the homeostatic control of immune cells in health and disease and facilitate the development of more effective immunotherapies. Consequently, tumour-suppressor genes are emerging as potential guardians of immune integrity.
Evaluation of Green Dot: an active bystander intervention to reduce sexual violence on college campuses.

Using a cross-sectional survey of a random sample of 7,945 college undergraduates, we report on the association between having received Green Dot active bystander behavior training and the frequency of actual and observed self-reported active bystander behaviors as well as violence acceptance norms. Of 2,504 students aged 18 to 26 who completed the survey, 46% had heard a Green Dot speech on campus, and 14% had received active bystander training during the past 2 years. Trained students had significantly lower rape myth acceptance scores than did students with no training. Trained students also reported engaging in significantly more bystander behaviors and observing more self-reported active bystander behaviors when compared with nontrained students. When comparing self-reported active bystander behavior scores of students trained with students hearing a Green Dot speech alone, the training was associated with significantly higher active bystander behavior scores. Those receiving bystander training appeared to report more active bystander behaviors than those simply hearing a Green Dot speech, and both intervention groups reported more observed and active bystander behaviors than nonexposed students.
Multimodal Deep Boltzmann Machines for feature selection on gene expression data

In this paper, multimodal Deep Boltzmann Machines (DBM) is employed to learn important genes (biomarkers) on gene expression data from human carcinoma colorectal. The learning process involves gene expression data and several patient phenotypes such as lymph node and distant metastasis occurrence. The proposed framework in this paper uses multimodal DBM to train records with metastasis occurrence. Later, the trained model is tested using records with no metastasis occurrence. After that, Mean Squared Error (MSE) is measured from the reconstructed and the original gene expression data. Genes are ranked based on the MSE value. The first gene has the highest MSE value. After that, k-means clustering is performed using various number of genes. Features that give the highest purity index are considered as the important genes. The important genes obtained from the proposed framework and two sample t-test are being compared. From the accuracy of metastasis classification, the proposed framework gives higher results compared to the top genes from two sample t-test.
DeepThin: A Self-Compressing Library for Deep Neural Networks

As the industry deploys increasingly large and complex neural networks to mobile devices, more pressure is put on the memory and compute resources of those devices. Deep compression, or compression of deep neural network weight matrices, is a technique to stretch resources for such scenarios. Existing compression methods cannot effectively compress models smaller than 1-2% of their original size. We develop a new compression technique, DeepThin, building on existing research in the area of low rank factorization. We identify and break artificial constraints imposed by low rank approximations by combining rank factorization with a reshaping process that adds nonlinearity to the approximation function.We deploy DeepThin as a plug-gable library integratedwith TensorFlow that enables users to seamlessly compress models at different granularities. We evaluate DeepThin on two state-of-the-art acoustic models, TFKaldi and DeepSpeech, comparing it to previous compression work (Pruning, HashNet, and Rank Factorization), empirical limit study approaches, and hand-tuned models. For TFKaldi, our DeepThin networks show better word error rates (WER) than competing methods at practically all tested compression rates, achieving an average of 60% relative improvement over rank factorization, 57% over pruning, 23% over hand-tuned same-size networks, and 6% over the computationally expensive HashedNets. For DeepSpeech, DeepThin-compressed networks achieve better test loss than all other compression methods, reaching a 28% better result than rank factorization, 27% better than pruning, 20% better than hand-tuned same-size networks, and 12% better than HashedNets. DeepThin also provide inference performance benefits in two ways: (1) by shrinking the application working sets, allowing the model to fit in a level of the ∗Work done while at Intel. arXiv, February, 2018 cache/memory hierarchy where the original network was too large, and (2) by exploiting unique features of the technique to reuse many intermediate computations, reducing the total compute operations necessary. We evaluate the performance of DeepThin inference across three Haswelland Broadwell-based platforms with varying cache sizes. Speedups range from 2X to 14X, depending on the compression ratio and platform cache sizes.
Nuclei Segmentation with Recurrent Residual Convolutional Neural Networks based U-Net (R2U-Net)

Bio-medical image segmentation is one of the promising sectors where nuclei segmentation from high-resolution histopathological images enables extraction of very high-quality features for nuclear morphometrics and other analysis metrics in the field of digital pathology. The traditional methods including Otsu thresholding and watershed methods do not work properly in different challenging cases. However, Deep Learning (DL) based approaches are showing tremendous success in different modalities of bio-medical imaging including computation pathology. Recently, the Recurrent Residual U-Net (R2U-Net) has been proposed, which has shown state-of-the-art (SOTA) performance in different modalities (retinal blood vessel, skin cancer, and lung segmentation) in medical image segmentation. However, in this implementation, the R2U-Net is applied to nuclei segmentation for the first time on a publicly available dataset that was collected from the Data Science Bowl Grand Challenge in 2018. The R2U-Net shows around 92.15% segmentation accuracy in terms of the Dice Coefficient (DC) during the testing phase. In addition, the qualitative results show accurate segmentation, which clearly demonstrates the robustness of the R2U-Net model for the nuclei segmentation task.
Meditation, learning, organizational innovation and performance

This study attempts to investigate (1) the effect of meditation experience on employees’ self-directed learning (SDL) readiness and organizational innovative (OI) ability as well as organizational performance (OP), and (2) the relationships among SDL, OI, and OP. This study conducts an empirical study of 15 technological companies (n = 412) in Taiwan, utilizing the collected survey data to test the relationships among the three dimensions. Results show that: (1) The employees’ meditation experience significantly and positively influenced employees’ SDL readiness, companies’ OI capability and OP; (2) The study found that SDL has a direct and significant impact on OI; and OI has direct and significant influences on OP. Research limitation/implications-The generalization of the present study is constrained by (1) the existence of possible biases of the participants, (2) the variations of length, type and form of meditation demonstrated by the employees in these high tech companies, and (3) the fact that local data collection in Taiwan may present different cultural characteristics which may be quite different from those in other areas or countries. Managerial implications are presented at the end of the work. The findings indicate that SDL can only impact organizational innovation through employees “openness to a challenge”, “inquisitive nature”, self-understanding and acceptance of responsibility for learning. Such finding implies better organizational innovative capability under such conditions, thus organizations may encourage employees to take risks or accept new opportunities through various incentives, such as monetary rewards or public recognitions. More specifically, the present study discovers that while administration innovation is the most important element influencing an organization’s financial performance, market innovation is the key component in an organization’s market performance. The present study discovers that meditation experience positively