nils-herrmann/clean_scirepeval huggingface dataset:
A 2.2 \mu\text{W} $12 dBm RF Powered Wireless Current Sensing Readout Interface IC With Injection Locking Clock Generation
Venue: IEEE Transactions on Circuits and Systems I: Regular Papers

This paper presents a wireless powering current sensing readout system on a CMOS platform for portable electrochemical measurement. The wireless sensing system includes energy efficient power management circuitry, a sensor readout interface, and a backscattering wireless communication scheme. For power and area constrained bio sensing applications, the proposed readout circuitry incorporates an ultra low power potentiostatic system that generates a current according to the electrochemical reaction, as well as an oscillator based time to digital converter instead of a voltage domain analog to digital converter. To avoid a bulky battery and power hungry clock reference, the chip is wirelessly powered and injection locked by the modulated radio waves, which includes a 918 MHz carrier signal mixed with a 3.2 MHz modulated signal. The chip, implemented using a 0.18 mm CMOS process, occupies a silicon area of 1 mm2. The proposed design achieves a sensitivity of 289 Hz/nA and an R2 linearity of 0.997 over a current range of 200 nA while consuming 2.2 mW at a supply voltage of 0.8 V. The chip, integrated with a PCB antenna, has minimum sensitivities of 12 dBm and 25 dBm for RF powering and injection locking mechanisms, respectively.
Establishing Wildflower Pollinator Habitats in Agricultural Farmland to Provide Multiple Ecosystem Services
Venue: Front. Plant Sci.

Beneficial insects provide critical ecosystem services and in agriculture their contribution in pollination and pest control is widely evident (Losey and Vaughan, 2006; Kremen and Chaplin Kramer, 2007) Globally, 35% of food production benefits from pollinator services (Klein et al. 2007) In many systems, pollination has been provided by the domesticated honey bee (primarily Apis mellifera) but the reliability of pollination services by wild pollinators is becoming increasingly valued (Garibaldi et al. 2013, 2014) These wild pollinators, the majority of which are bee species, persist independently in the ecosystem by relying on multiple resources to complete their lifecycles (Bohart, 1972) Similarly, natural enemies, such as insect predators and parasitoids, provide vital pest control and also persist independently in the farmscape. Although these beneficial insects are not directly managed for their ecosystem services, the farm landscape surrounding targeted crop fields can be modified to increase their abundance and diversity resulting in increased ecosystem services to support a sustainable agricultural system (Landis et al. 2000; Hannon and Sisk, 2009; Holzschuh et al. 2012) Managing farmscapes for these wild beneficial insects is especially critical as insects are threatened by human mediated landscape disturbances (Tscharntke et al. 2005) With wild bee populations in decline (Potts et al. 2010) there is increasing interest in managing for wild bees by incorporating pollinator habitat into farmland. The concept of setting aside land specifically for wildlife within a farmscape is not new (Baudry et al. 2000) however, the addition of wildflower plantings or saving natural wildflower areas is a specific strategy that can be adopted for its multi functionality in supporting both pollinators and natural enemies. It is especially valuable in that it can be modified and designed to fit specific cropping systems, landscapes, and support the lifecycles of a community of unmanaged beneficial insects. Here we consider how these variables have been examined in recent pollinator habitat studies, and discuss additional considerations to optimize wildflower plantings to benefit multiple ecosystem services.
Dynamic Memory Networks for Visual and Textual Question Answering
Venue: ICML

Neural network architectures with memory and attention mechanisms exhibit certain reasoning capabilities required for question answering. One such architecture, the dynamic memory network (DMN) obtained high accuracy on a variety of language tasks. However, it was not shown whether the architecture achieves strong results for question answering when supporting facts are not marked during training or whether it could be applied to other modalities such as images. Based on an analysis of the DMN, we propose several improvements to its memory and input modules. Together with these changes we introduce a novel input module for images in order to be able to answer visual questions. Our new DMN+ model improves the state of the art on both the Visual Question Answering dataset and the \babi 10k text question answering dataset without supporting fact supervision.
TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data
Venue: Nucleic acids research

The Cancer Genome Atlas (TCGA) research network has made public a large collection of clinical and molecular phenotypes of more than 10 000 tumor patients across 33 different tumor types. Using this cohort, TCGA has published over 20 marker papers detailing the genomic and epigenomic alterations associated with these tumor types. Although many important discoveries have been made by TCGA's research network, opportunities still exist to implement novel methods, thereby elucidating new biological pathways and diagnostic markers. However, mining the TCGA data presents several bioinformatics challenges, such as data retrieval and integration with clinical data and other molecular data types (e.g. RNA and DNA methylation) We developed an R/Bioconductor package called TCGAbiolinks to address these challenges and offer bioinformatics solutions by using a guided workflow to allow users to query, download and perform integrative analyses of TCGA data. We combined methods from computer science and statistics into the pipeline and incorporated methodologies developed in previous TCGA marker studies and in our own group. Using four different TCGA tumor types (Kidney, Brain, Breast and Colon) as examples, we provide case studies to illustrate examples of reproducibility, integrative analysis and utilization of different Bioconductor packages to advance and accelerate novel discoveries.
Analysis of speech signals' characteristics based on MF DFA with moving overlapping windows

In this paper, multi fractal characteristics of speech signals are analyzed based on MF DFA, and it is found that the multi fractal features are influenced greatly by frame length and noise, besides, there is a little difference between them among speech frames. Secondly, motivated by framing and using frame shift to ensure the continuity and smooth transition of speech in speech signals processing, an advanced MF DFA (MF DFA with forward moving overlapping windows) is proposed. The length of moving overlapping windows is determined by parameter th. Given the value of time scale s, we have MF DFA with the maximum moving overlapping windows and MF DFA with half overlapping windows when th=1/s and th=1/2 respectively. Moreover, when th=1 we exactly have MF DFA. Numerical experiments and analysis illustrate that the multi fractal characteristics based on AMF DFA outperform MF DFA and MF DMA in stability, noise immunity and discrimination.
Blockchain Platform for Industrial Internet of Things

Internet of Things (IoT) are being adopted for industrial and manufacturing applications such as manufacturing automation, remote machine diagnostics, prognostic health management of industrial machines and supply chain management. Cloud Based Manufacturing is a recent on demand model of manufacturing that is leveraging IoT technologies. While Cloud Based Manufacturing enables on demand access to manufacturing resources, a trusted intermediary is required for transactions between the users who wish to avail manufacturing services. We present a decentralized, peer to peer platform called BPIIoT for Industrial Internet of Things based on the Block chain technology. With the use of Blockchain technology, the BPIIoT platform enables peers in a decentralized, trustless, peer to peer network to interact with each other without the need for a trusted intermediary.
The role of renewable energy consumption in economic growth: Evidence from asymmetric causality

This paper investigates the causality among economic growth, renewable energy consumption, capital and labor for new EU member countries for the period of 1990 2009, by using asymmetric causality test approach and autoregressive distributed lag (ARDL) approach. The empirical results support that renewable energy consumption has positive impacts on economic growth for all investigated countries. But only for Bulgaria, Estonia, Poland, and Slovenia there is statistically significant impact on economic growth has found. And also supports neutrality hypothesis for Cyprus, Estonia, Hungary, Poland and Slovenia while the conservation hypothesis is present for Czech Republic. The fact that there is a causal relationship from economic growth to renewable energy consumption and the growth hypothesis is supported for Bulgaria, referring to causality from energy consumption to economic growth.
Self management of Challenging Behaviours Associated with Autism Spectrum Disorder: A Meta Analysis

A diagnosis of autism spectrum disorder (ASD) is often accompanied by challenging behaviour that may limit the ability to engage in education, employment, or social settings. The purpose of this review was to examine the effectiveness of self management interventions in reducing challenging behaviours for individuals diagnosed with ASD. This review included self management interventions that specifically targeted a reduction in challenging behaviours as well as studies that specifically targeted skill acquisition and described improvements in challenging behaviours as a collateral effect of the intervention Method Studies were located by conducting a systematic search of peer reviewed literature published in English prior to October 2015. The PsycINFO and ERIC databases were searched. A quality assessment was conducted using the What Works Clearinghouse (WWC) guidelines. A final data set of 12 single subject research design studies that met quality standards was subsequently analysed. Treatment effect sizes were calculated using the percentage of non overlapping data.  Results were analysed by age, gender, symptom severity, presentation of problem behaviour, and the presence of functional behaviour assessments. Results indicated that self management interventions are effective at decreasing challenging behaviour for children aged four to 18 years who may be described as either high or low functioning.  Self management of challenging behaviour was identified as an emerging treatment, and inclusion within a positive behaviour support framework was argued. Important areas for consideration in future research are described.
Transition metal/nitrogen dual doped mesoporous graphene like carbon nanosheets for the oxygen reduction and evolution reactions.
Venue: Nanoscale

The oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) have been considered as a key step in energy conversion processes. Here, a novel and simple Mg(OH)2 nanocasting method is adopted to fabricate Co and N co doped porous graphene like carbon nanosheets (Co@N PGCS) by using chitosan as both carbon and N sources. The as obtained Co@N PGCS shows a mesopore dominated structure as well as a high specific surface area (1716 cm(2) g( 1) As a bifunctional electrocatalyst towards both the ORR and OER, it shows favorable ORR performance compared with the commercial Pt/C catalyst with an onset potential of 0.075 V and a half wave potential of 0.151 V in 0.1 M KOH solutions. Furthermore, it also displays considerable OER properties compared with commercial IrO2. The effective catalytic activity could originate from the introduction of transition metal species and few layer mesoporous carbon structures.
Clustering Non Stationary Data Streams with Online Deep Learning

With more devices connected, sensor data logged and people active in social networks, the trend towards working with dynamic data is clear. The number of applications where it becomes essential to perform real time analysis on data streams grows accordingly, each with its own challenges. From this area of data stream analysis we benchmark the performance of current state of the art clustering algorithms: CluStream, DenStream and ClusTree. We also adapt a Variational Autoencoder to perform in the context of non stationary data streams and assess its generative capabilities for dimensionality reduction. From this limited lab experiment we show that while there is a significant improvement in the clustering accuracy of high dimensional datasets after a dimensionality reduction with a Variational Autoencoder, not all clustering algorithms benefit in the same way from it. Additionally we show that regardless of the clustering algorithm, no relevant improvement in the purity of the clusters could be obtained after the dimensionality reduction.
A cascade deep neuro fuzzy system for high dimensional online possibilistic fuzzy clustering
Venue: 2016 XIth International Scientific and Technical Conference Computer Sciences and Information Technologies (CSIT)

A cascade deep learning system (based on neuro fuzzy nodes) and its online learning procedure are proposed in this paper. The system is based on nodes of a special type. A goal function of a special type is used for possibilistic high dimensional fuzzy clustering. To estimate a clustering quality of data processing, an optimal value of a cluster validity index is used.
A deep cascade neuro fuzzy system for high dimensional online fuzzy clustering
Venue: 2016 IEEE First International Conference on Data Stream Mining Processing (DSMP)

A deep cascade system (based on neuro fuzzy nodes) and its online learning procedure are proposed in this paper. A number of layers can grow unlimitedly during a self learning procedure. The system is based on nodes of a special type. A goal function of a special type is used for probabilistic high dimensional fuzzy clustering. To assess a clustering quality of data processing, a neuron's architecture of a special type is introduced.
Learn 2 Learn: A Metacognition Intervention for Improving Academic Performance and Motivation on Middle School Aged Students

7  Self Regulated Learning 8 Metacognition. 10 Motivational Factors of Learning 12 Classroom Engagement, Positive Emotions, and Motivation 13 Disaffection, Anxiety, and Metacognition 16 Metacognitive Based Interventions. 17 Self Assessments of Metacognition and Motivation 20 Online Measures of Metacognition 23 Metacognition and Gameplay 27 Previous Intervention Research at Trinity College 28 Implications of Research 29 Current Study 29 Hypotheses 31 Metacognitive awareness.  Academic performance. 31 Online versus offline assessments.  ENGAGEMENT, AND STUDENT SUCCESS 4 Experiment 1: Intervention  Participants.  Procedure. 37 Results 48 Correlations among measures. 48 Quantitative MC5. 50 Qualitative MC5. 51 Teacher ratings of metacognition. 52 Academic performance. 52 Self efficacy. 53 Achievement values. 54 Engagement versus disaffection. 54 Test anxiety. 55 Discussion 56 Limitations and further research. 60 Experiment 2: Online Measure of Metacognition (Pilot Study) 61  ENGAGEMENT, AND STUDENT SUCCESS 5 Measures. 62 Procedure. 63 Results 64 Quantitative "Oregon Trail" online measurement. 64 Discussion 64  ENGAGEMENT, AND STUDENT SUCCESS 6  ENGAGEMENT, AND STUDENT SUCCESS 7  The current study explored the effect of metacognition training on the academic performance of middle school students. Intervention sessions for 6 and 8 graders were designed
The effect of a cluster randomised control trial on objectively measured sedentary time and parental reports of time spent in sedentary activities in Belgian preschoolers: the ToyBox study
Venue: International Journal of Behavioral Nutrition and Physical Activity

BackgroundIn preschoolers, high levels of sedentary behaviour are associated with several adverse health outcomes. The purpose of this study is to report the effects of the ToyBox intervention (a European 24 week cluster randomised controlled trial) on sedentary behaviour in preschoolers.MethodsIn Belgium, 859 preschoolers from 27 kindergartens (15 intervention and 12 control) wore an accelerometer to objectively measure their sedentary time and 1715 parents/caregivers completed a questionnaire to assess sedentary activities in which preschoolers participate at home. Main outcomes were objectively measured sedentary time, time spent watching TV, using the computer and time spent in quiet play. Multilevel repeated measures analyses were conducted to take clustering into account. Intention to treat analysis was used to handle missing data. sample of 859 (29.5 of all contacted children) preschoolers (4.4 0.6 years, 54.4 boys) provided valid accelerometer data at either baseline or follow up and parents of 1715 (58.9 of all contacted children) preschoolers (4.4 0.5 years, 52.5 boys) completed a questionnaire at either baseline or follow up. No intervention effects were found on objectively and subjectively measured total sedentary time in the total sample. However, some effects on objectively and subjectively measured sedentary time were found in specific subgroups. Preschoolers from the intervention group from high SES kindergartens and preschoolers with high levels of sedentary time at baseline decreased their sedentary time, while preschoolers from the control group increased their sedentary time. Girls in the intervention group decreased their TV viewing time during weekend days 5.83 min/day) while girls' &TV viewing in the control group increased +4.15 min/day) In low SES kindergartens, a smaller increase for computer time during weekend days was found in preschoolers in intervention kindergartens +6.06 min/day) than in control kindergartens +12.49 min/day).ConclusionWhile some small positive effects were found in some sub groups, the ToyBox intervention had no effect on objectively and subjectively measured sedentary time in the total sample. A longer period to implement the intervention and a more active involvement of parents/caregivers might enhance intervention effects.The ToyBox study is registered with the clinical trials registry clinicaltrials.gov, ID: NCT02116296.
An IoT Application for Inventory Management with a Self Adaptive Decision Model
Venue: 2016 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData)

Safety storage in large warehouse is an urgent issue to be addressed by both the local authorities and businesses, especially after the Tianjin explosions. This paper proposes an inventory management system for a warehousing company. The system integrates RFID technology and a self Adaptive distributed decision support model for inbound and outbound actives, inventory location suggestions and incident handling. The model consists of three major components: environment recognition, knowledge merging and the decision making. In addition, a 'self adaptive' feature is adopted for adjusting the knowledge used in decision making procedure. An experiment is also outlined to validate the utilisations of our model and the proposed system.
Feasibility and Efficacy of BLE Beacon IoT Devices in Inventory Management at the Shop Floor

Inventory Management is a key area for customer service and cost optimization in any manufacturing setup. As companies turn global and have thousands of components and hundreds of warehouses the inventory becomes a nightmare and a lot of time is spend in tracking inventory and ensuring right shipments. Traditional systems of robotic arms for inventory pick and drop have been based on premises of marking areas of the warehouse and tracking it. However with the advent of IoT all this is set to change as inventory objects become more self aware and self broadcasting. This paper technically suggests an approach of managing inventory using low energy blue tooth beacons and also does a statistical case research on two groups of the same organization one before the pilot run where traditional barcode scanners are used to track inventory and other one where the pilot trial BLE beacon technology was used. Statistically the IoT beacon users are much more efficient and accurate and save lot of time and costs in the short run itself.
Trichodynia: a review of the literature
Venue: International journal of dermatology

Trichodynia designates a painful sensation on the scalp sometimes reported by patients with hair loss. Its best description dates back to 1960, when Sulzberger et al. ( 556 560) reported it in a proportion of women complaining of an unexplained diffuse alopecia. Sulzberger et al.'s remark that trichodynia may also be circumscribed "spotty" is an important detail missed by most ensuing observers. Women are mostly affected or, at least, more likely to report it. The quality and intensity vary greatly, the "burning" variety being most severe. Whether trichodynia is prevalent in telogen effluvium or it may also be present in androgenetic alopecia remains a crucial question. Hairs shed only from painful sites, and this observation suggests that the severity of trichodynia is directly related to hair shedding and intensity. Trichodynia seems to be related to the release of substance P and is present in alopecia areata. Both observations suggest that peripilar inflammation may be a causative agent.
Efficient fully homomorphic encryption with circularly secure key switching process
Venue: Int. J. High Perform. Comput. Netw.

Fully homomorphic encryption (FHE) has important applications in cloud computing. However, almost all fully homomorphic encryption schemes share two common flaws that they all use large scale secret keys and some operations inefficient. In this paper, the "special b" variant of the Learning With Errors problem (bLWE) is presented, and helps us construct the first circularly secure key switching process which can replace the key switching process and similar re linearization process used by the existing FHE schemes. Then, we present an efficient FHE. Compared with Brakerski's scheme, our scheme reduces L secret keys to one and is more efficient. Finally, we prove the chosen plaintext attack (CPA) security of the fully homomorphic scheme and the circular security of key switching process in standard model under the learning with errors problem (LWE) assumption.
Efficient fully homomorphic encryption with circularly secure key switching process
Venue: HiPC 2016

Fully homomorphic encryption FHE has important applications in cloud computing. However, almost all fully homomorphic encryption schemes share two common flaws that they all use large scale secret keys and some operations are inefficient. In this paper, the 'special b' variant of the learning with errors problem bLWE is presented, and helps us construct the first circularly secure key switching process which can replace the key switching process and similar re linearisation process used by the existing FHE schemes. Then, we present an efficient FHE. Compared with Brakerski's scheme, our scheme reduces L secret keys to one and is more efficient. Finally, we prove the chosen plaintext attack CPA security of the fully homomorphic scheme and the circular security of key switching process in standard model under the learning with errors problem LWE assumption.