nils-herrmann/clean_scirepeval huggingface dataset:
Design and process variation analysis of CNTFET-based ternary memory cells

Two novel ternary CNTFET-based SRAM cells are proposed in this paper. The first proposed CNTFET SRAM uses additional CNTFETs to sink the bit lines to ground; its operation is nearly independent of the ternary values. The second cell utilizes the traditional voltage controller (or supply) of a binary SRAM in a ternary SRAM; it consists of adding two CNTFETs to the first proposed cell. CNTFET features (such as sizing and density) and performance metrics (such as SNM and PDP) and write/read times are considered and assessed in detail. The impact of different features (such as chirality and CNT density) is also analyzed with respect to the operations of the memory cells. The effects of different process variations (such as lithography and density/number of CNTs) are extensively evaluated with respect to performance metrics. In nearly all cases, the proposed cells outperform existing CNTFET-based cells by showing a small standard deviation in the simulated memory circuits. & 2016
Recognizing Visual Signatures of Spontaneous Head Gestures

Head movements are an integral part of human nonverbal communication. As such, the ability to detect various types of head gestures from video is important for robotic systems that need to interact with people or for assistive technologies that may need to detect conversational gestures to aid communication. To this end, we propose a novel Multi-Scale Deep Convolution-LSTM architecture, capable of recognizing short and long term motion patterns found in head gestures, from video data of natural and unconstrained conversations. In particular, our models use Convolutional Neural Networks (CNNs) to learn meaningful representations from short time windows over head motion data. To capture longer term dependencies, we use Recurrent Neural Networks (RNNs) that extract temporal patterns across the output of the CNNs. We compare against classical approaches using discriminative and generative graphical models and show that our model is able to significantly outperform baseline models.
Spine loading at different lumbar levels during pushing and pulling.

As the nature of many materials handling tasks have begun to change from lifting to pushing and pulling, it is important that one understands the biomechanical nature of the risk to which the lumbar spine is exposed. Most previous assessments of push-pull tasks have employed models that may not be sensitive enough to consider the effects of the antagonistic cocontraction occurring during complex pushing and pulling motions in understanding the risk to the spine and the few that have considered the impact of cocontraction only consider spine load at one lumbar level. This study used an electromyography-assisted biomechanical model sensitive to complex motions to assess spine loadings throughout the lumbar spine as 10 males and 10 females pushed and pulled loads at three different handle heights and of three different load magnitudes. Pulling induced greater spine compressive loads than pushing, whereas the reverse was true for shear loads at the different lumbar levels. The results indicate that, under these conditions, anterior-posterior (A/P) shear loads were of sufficient magnitude to be of concern especially at the upper lumbar levels. Pushing and pulling loads equivalent to 20% of body weight appeared to be the limit of acceptable exertions, while pulling at low and medium handle heights (50% and 65% of stature) minimised A/P shear. These findings provide insight to the nature of spine loads and their potential risk to the low back during modern exertions.
Controller Development and Implementation for Path Planning and Following in an Autonomous Urban Vehicle

Autonomous Land Vehicles (ALVs), due to their considerable potential applications in areas such as mining and defence, are currently the focus of intense research at robotics institutes worldwide. Control systems that provide reliable navigation, often in complex or previously unknown environments, is a core requirement of any ALV implementation. Three key aspects for the provision of such autonomous systems are: 1) path planning, 2) obstacle avoidance, and 3) path following. The work presented in this thesis, under the general umbrella of the ACFR’s own ALV project, the ‘High Speed Vehicle Project’, addresses these three mobile robot competencies in the context of an ALV based system. As such, it develops both the theoretical concepts and the practical components to realise an initial, fully functional implementation of such a system. This system, which is implemented on the ACFR’s (ute) test vehicle, allows the user to enter a trajectory and follow it, while avoiding any detected obstacles along the path.
Compact Broadband Circularly Polarized Antenna With Parasitic Patches

A broadband circularly polarized (CP) antenna with compact size is proposed. The antenna is composed of a loop feeding structure which provides sequential phase, four driven patches, and four parasitic patches. The driven patches, which are capacitively coupled by the feeding loop, generate one CP mode due to the sequentially rotated structure and four parasitic patches are introduced to produce additional CP mode. By combining with the CP mode of the feeding loop, the axial ratio (AR) bandwidth is greatly broadened. An antenna prototype is fabricated to validate the simulated results. Experimental results show that the antenna achieves a broad impedance bandwidth of 19.5% from 5.13 to 6.24 GHz and a 3-dB AR bandwidth of 12.9% (5.38–6.12 GHz). In addition, the proposed antenna also has a flat gain within the operating frequency band and a compact size of  $0.92\lambda _{0}\times 0.92\lambda _{0}\times 0.028\lambda _{0}$  at 5.5 GHz.
Three-dimensional modelling of helical flux-compression generators

Two-dimensional modelling techniques using a filamentary-mesh approach are very well established for use in helical flux-compression generator design, and provide results that have been verified experimentally. However, none of the published numerical codes appear to describe adequately certain details involved in calculating the resistance of the helical stator coil. In particular, the skin and proximity effects that arise from diffusion of the magnetic field into the conductor are clearly 3-dimensional phenomena, and their influence is only very approximately represented by either existing formulae or a 1-dimensional description. In most published codes, the formulae that are used provide merely a steady-state approximation to the proximity effect. The present paper outlines a 3-dimensional approach that has been developed for use with an existing 2-dimensional model, and which forms the first element of an overall 3-dimensional modelling code. It is shown that the scale of the 3-dimensional proximity effects in a helical coil carrying a pulsed current can be many times greater than those predicted by the conventional formulae.
Cannabinoid signaling and liver therapeutics.

Over the last decade, the endocannabinoid system has emerged as a pivotal mediator of acute and chronic liver injury, with the description of the role of CB1 and CB2 receptors and their endogenous lipidic ligands in various aspects of liver pathophysiology. A large number of studies have demonstrated that CB1 receptor antagonists represent an important therapeutic target, owing to beneficial effects on lipid metabolism and in light of its antifibrogenic properties. Unfortunately, the brain-penetrant CB1 antagonist rimonabant, initially approved for the management of overweight and related cardiometabolic risks, was withdrawn because of an alarming rate of mood adverse effects. However, the efficacy of peripherally-restricted CB1 antagonists with limited brain penetrance has now been validated in preclinical models of NAFLD, and beneficial effects on fibrosis and its complications are anticipated. CB2 receptor is currently considered as a promising anti-inflammatory and antifibrogenic target, although clinical development of CB2 agonists is still awaited. In this review, we highlight the latest advances on the impact of the endocannabinoid system on the key steps of chronic liver disease progression and discuss the therapeutic potential of molecules targeting cannabinoid receptors.
On Spatial Pattern Matching

In this paper, we study the spatial pattern matching (SPM) query. Given a set D of spatial objects (e.g., houses and shops), each with a textual description, we aim at finding all combinations of objects from D that match a user-defined spatial pattern P. A pattern P is a graph where vertices represent spatial objects, and edges denote distance relationships between them. The SPM query returns the instances that satisfy P. An example of P can be "a house within 10-minute walk from a school, which is at least 2km away from a hospital". The SPM query can benefit users such as house buyers, urban planners, and archaeologists. We prove that answering such queries is computationally intractable, and propose two efficient algorithms for their evaluation. Extensive experimental evaluation and cases studies on four real datasets show that our proposed solutions are highly effective and efficient.
Bullying in the digital age: a critical review and meta-analysis of cyberbullying research among youth.

Although the Internet has transformed the way our world operates, it has also served as a venue for cyberbullying, a serious form of misbehavior among youth. With many of today's youth experiencing acts of cyberbullying, a growing body of literature has begun to document the prevalence, predictors, and outcomes of this behavior, but the literature is highly fragmented and lacks theoretical focus. Therefore, our purpose in the present article is to provide a critical review of the existing cyberbullying research. The general aggression model is proposed as a useful theoretical framework from which to understand this phenomenon. Additionally, results from a meta-analytic review are presented to highlight the size of the relationships between cyberbullying and traditional bullying, as well as relationships between cyberbullying and other meaningful behavioral and psychological variables. Mixed effects meta-analysis results indicate that among the strongest associations with cyberbullying perpetration were normative beliefs about aggression and moral disengagement, and the strongest associations with cyberbullying victimization were stress and suicidal ideation. Several methodological and sample characteristics served as moderators of these relationships. Limitations of the meta-analysis include issues dealing with causality or directionality of these associations as well as generalizability for those meta-analytic estimates that are based on smaller sets of studies (k < 5). Finally, the present results uncover important areas for future research. We provide a relevant agenda, including the need for understanding the incremental impact of cyberbullying (over and above traditional bullying) on key behavioral and psychological outcomes.
Lexical and Machine Learning Approaches Toward Online Reputation Management

With the popularity of social media, people are more and more interested in mining opinions from it. Learning from social media not only has value for research, but also good for business use. RepLab 2012 had Profiling task and Monitoring task to understand the company related tweets. Profiling task aims to determine the Ambiguity and Polarity for tweets. In order to determine this Ambiguity and Polarity for the tweets in RepLab 2012 Profiling task, we built Google Adwords Filter for Ambiguity and several approaches like SentiWordNet, Happiness Score and Machine Learning for Polarity. We achieved good performance in the training set, and the performance in test set is also acceptable.
Advocacy: the cornerstone of cancer survivorship.

This article describes a cancer-related advocacy skill set that can be acquired through a learning process.


OVERVIEW
Cancer survivorship is a process rather than a stage or time point, and it involves a continuum of events from diagnosis onward. There exists little consensus about what underlying processes explain different levels of long term functioning, but skills necessary for positive adaptation to cancer have been identified from both the professional literature and from the rich experiences of cancer survivors.



Healthcare practitioners need to be more creative and assertive in fostering consumer empowerment and should incorporate advocacy training into care plans. Strategies that emphasize personal competency and increase self-advocacy capabilities enable patients to make the best possible decisions for themselves regarding their cancer care. In addition, oncology practitioners must become informed advocacy partners with their patients in the public debate about healthcare and cancer care delivery.
3D Semantic Segmentation with Submanifold Sparse Convolutional Networks

Convolutional networks are the de-facto standard for analyzing spatio-temporal data such as images, videos, and 3D shapes. Whilst some of this data is naturally dense (e.g., photos), many other data sources are inherently sparse. Examples include 3D point clouds that were obtained using a LiDAR scanner or RGB-D camera. Standard "dense" implementations of convolutional networks are very inefficient when applied on such sparse data. We introduce new sparse convolutional operations that are designed to process spatially-sparse data more efficiently, and use them to develop spatially-sparse convolutional networks. We demonstrate the strong performance of the resulting models, called submanifold sparse convolutional networks (SS-CNs), on two tasks involving semantic segmentation of 3D point clouds. In particular, our models outperform all prior state-of-the-art on the test set of a recent semantic segmentation competition.
Grasping known objects with humanoid robots: A box-based approach

Autonomous grasping of household objects is one of the major skills that an intelligent service robot necessarily has to provide in order to interact with the environment. In this paper, we propose a grasping strategy for known objects, comprising an off-line, box-based grasp generation technique on 3D shape representations. The complete system is able to robustly detect an object and estimate its pose, flexibly generate grasp hypotheses from the assigned model and perform such hypotheses using visual servoing. We will present experiments implemented on the humanoid platform ARMAR-III.
Mobile activity recognition for a whole day: recognizing real nursing activities with big dataset

In this paper, we provide a real nursing data set for mobile activity recognition that can be used for supervised machine learning, and big data combined the patient medical records and sensors attempted for 2 years, and also propose a method for recognizing activities for a whole day utilizing prior knowledge about the activity segments in a day. Furthermore, we demonstrate data mining by applying our method to the bigger data with additional hospital data. In the proposed method, we 1) convert a set of segment timestamps into a prior probability of the activity segment by exploiting the concept of importance sampling, 2) obtain the likelihood of traditional recognition methods for each local time window within the segment range, and, 3) apply Bayesian estimation by marginalizing the conditional probability of estimating the activities for the segment samples. By evaluating with the dataset, the proposed method outperformed the traditional method without using the prior knowledge by 25.81% at maximum by balanced classification rate. Moreover, the proposed method significantly reduces duration errors of activity segments from 324.2 seconds of the traditional method to 74.6 seconds at maximum. We also demonstrate the data mining by applying our method to bigger data in a hospital.
Chronic impairment of the vagus nerve function leads to inhibition of dopamine but not serotonin neurons in rat brain structures.

Recent clinical studies have shown that the dorsal motor nucleus of the vagus nerve is one of the brain areas that are the earliest affected by α-synuclein and Lewy body pathology in Parkinson's disease. This observation raises the question: how the vagus nerve dysfunction affects the dopamine system in the brain?



The rats underwent surgical implantation of the microchip (MC) in the abdominal region of the vagus. In this study, we examined the effect of chronic, unilateral electrical stimulation of the left nerve vagus, of two different types: low-frequency (MCL) and physiological stimulation (MCPh) on the dopamine and serotonin metabolism determined by high-pressure chromatography with electrochemical detection in rat brain structures.



MCL electrical stimulation of the left nerve vagus in contrast to MCPh stimulation, produced a significant inhibition of dopamine system in rat brain structures. Ex vivo biochemical experiments clearly suggest that MCL opposite to MCPh impaired the function of dopamine system similarly to vagotomy.



We suggest a close relationship between the peripheral vagus nerve impairment and the inhibition of dopamine system in the brain structures. This is the first report of such relationship which may suggest that mental changes (pro-depressive) could occur in the first stage of Parkinson's disease far ahead of motor impairment.
Development of an FMCW LADAR Source Chip using MEMS-Electronic-Photonic Heterogeneous Integration

We present a modular integration platform, combining active and passive photonic devices with CMOS integrated electronics. Based on this integration platform, an integrated Frequency Modulated Continuous Wave (FMCW) Laser Detection and Ranging (LADAR) source chip is being developed. Such a LADAR source chip can be used in miniaturized 3D imaging systems for defense and consumer electronics applications. In this paper, we discuss the integration approach, the performance of select individual components, and experimental results on a bench top LADAR model system.
Benchmark Machine Learning Approaches with Classical Time Series Approaches on the Blood Glucose Level Prediction Challenge

There is a growing trend of applying machine learning techniques in time series prediction tasks. In the meanwhile, the classic autoregression models has been widely used in time series prediction for decades. In this paper, experiments are conducted to compare the performances of multiple popular machine learning algorithms including two major types of deep learning approaches, with the classic autoregression with exogenous inputs (ARX) model on this particular Blood Glucose Level Prediction (BGLP) Challenge. We tried two types of methods to perform multi-step prediction: recursive method and direct method. The recursive method needs future input feature information. The results show there is no significant difference between the machine learning models and the classic ARX model. In fact, the ARX model achieved the lowest average Root Mean Square Error (RMSE) across subjects in the test data when recursive method was used for multi-step prediction.
The intricacies of induced lactation for same-sex mothers of an adopted child.

The definition of a modern family is changing. In this case study, we describe the breastfeeding experience of a child receiving human milk from all 3 of his mothers: his 2 adoptive mothers, who induced lactation to nurse him, and his birth mother, who shared in his early feeding during the open adoption process and continued to pump and send milk to him for several months. We review the lactation protocol used by his adoptive mothers and the unique difficulties inherent in this multi-mother family dynamic. Both adoptive mothers successfully induced moderate milk production using a combination of hormonal birth control, domperidone, herbal supplements, and a schedule of breast pumping. However, because of the increased complexity of the immediate postpartum period and concerns with defining parental roles in a same-sex marriage, maintenance of milk production was difficult.
System-level design: orthogonalization of concerns andplatform-based design

System-level design issues become critical as implementation technology evolves toward increasingly complex integrated circuits and the time-to-market pressure continues relentlessly. To cope with these issues, new methodologies that emphasize re-use at all levels of abstraction are a “must”, and this is a major focus of our work in the Gigascale Silicon Research Center. We present some important concepts for system design that are likely to provide at least some of the gains in productivity postulated above. In particular, we focus on a method that separates parts of the design process and makes them nearly independent so that complexity could be mastered. In this domain, architecture-function co-design and communication-based design are introduced and motivated. Platforms are essential elements of this design paradigm. We define system platforms and we argue about their use and relevance. Then we present an application of the design methodology to the design of wireless systems. Finally, we present a new approach to platform-based design called modern embedded systems, compilers, architectures and languages, based on highly concurrent and software-programmable architectures and associated design tools.
Approximation algorithms for spanner problems and Directed Steiner Forest

We present an O ( √ n log n)-approximation algorithm for the problem of finding the sparsest spanner of a given directed graph G on n vertices. A spanner of a graph is a sparse subgraph that approximately preserves distances in the original graph. More precisely, given a graph G = (V , E) with nonnegative edge lengths d : E → R 0 and a stretch k 1, a subgraph H = (V , E H ) is a k-spanner of G if for every edge (s, t) ∈ E , the graph H contains a path from s to t of length at most k · d(s, t). The previous best approximation ratio was Õ (n2/3), due to Dinitz and Krauthgamer (STOC ’11). We also improve the approximation ratio for the important special case of directed 3-spanners with unit edge lengths from Õ ( √ n ) to O (n1/3 log n). The best previously known algorithms for this problem are due to Berman, Raskhodnikova and Ruan (FSTTCS ’10) and Dinitz and Krauthgamer. The approximation ratio of our algorithm almost matches Dinitz and Krauthgamer’s lower bound for the integrality gap of a natural linear programming relaxation. Our algorithm directly implies an O (n1/3 log n)-approximation for the 3-spanner problem on undirected graphs with unit lengths. An easy O ( √ n )-approximation algorithm for this problem has been the best known for decades. Finally, we consider the Directed Steiner Forest problem: given a directed graph with edge costs and a collection of ordered vertex pairs, find a minimum-cost subgraph that contains a path between every prescribed pair. We obtain an approximation ratio of O (n2/3+ ) for any constant > 0, which improves the O (n · min(n4/5,m2/3)) ratio due to Feldman, Kortsarz and Nutov (JCSS’12).