nils-herrmann/clean_scirepeval huggingface dataset:
Timber Tracking - Reducing Complexity of Due Diligence by using Blockchain Technology (Position Paper)

Managing and verifying forest products in a value chain is often reliant on easily manipulated document or digital tracking methods – Chain of Custody Systems. We aim to create a new means of tracking timber by developing a tamper proof digital system based on Blockchain technology. Blockchain will be combined with new digital protocols for physical verification and authentication. Deliverables include online traceability system, and procedures for entering and verifying data inputs. Certification schemes and governments gain increased legitimacy from tamper-proof means to comply with timber trade requirements. Global companies gain from efficient traceability; society at large benefits from increased tax revenues, anti-corruption, and environmental benefits from sustainable forestry. We think this potential collaboration between government and industry can help simplify a variety of current due diligence processes, in this paper we focus on timber tracking.
Review of Recent Phased Arrays for Millimeter-Wave Wireless Communication

Owing to the rapid growth in wireless data traffic, millimeter-wave (mm-wave) communications have shown tremendous promise and are considered an attractive technique in fifth-generation (5G) wireless communication systems. However, to design robust communication systems, it is important to understand the channel dynamics with respect to space and time at these frequencies. Millimeter-wave signals are highly susceptible to blocking, and they have communication limitations owing to their poor signal attenuation compared with microwave signals. Therefore, by employing highly directional antennas, co-channel interference to or from other systems can be alleviated using line-of-sight (LOS) propagation. Because of the ability to shape, switch, or scan the propagating beam, phased arrays play an important role in advanced wireless communication systems. Beam-switching, beam-scanning, and multibeam arrays can be realized at mm-wave frequencies using analog or digital system architectures. This review article presents state-of-the-art phased arrays for mm-wave mobile terminals (MSs) and base stations (BSs), with an emphasis on beamforming arrays. We also discuss challenges and strategies used to address unfavorable path loss and blockage issues related to mm-wave applications, which sets future directions.
A Dual Circularly Polarized 2.45-GHz Rectenna for Wireless Power Transmission

A 2.45-GHz rectifying antenna (rectenna) using a compact dual circularly polarized (DCP) patch antenna with an RF-dc power conversion part is presented. The DCP antenna is coupled to a microstrip line by an aperture in the ground plane and includes a bandpass filter for harmonic rejections. It exhibits a measured bandwidth of 2100 MHz (10 dB return loss) and a 705-MHz CP bandwidth (3 dB axial ratio). The maximum efficiency and dc voltage are respectively equal to 63% and 2.82 V over a resistive load of 1600 Ω for a power density of 0.525 mW/cm2.
Impact of business analytics and enterprise systems on managerial accounting

The nature of management accountants' responsibility is evolving from merely reporting aggregated historical value to also including organizational performance measurement and providing management with decision related information. Corporate information systems such as enterprise resource planning (ERP) systems have provided management accountants with both expanded data storage power and enhanced computational power. With big data extracted from both internal and external data sources, management accountants now could utilize data analytics techniques to answer the questions including: what has happened (descriptive analytics), what will happen (predictive analytics), and what is the optimized solution (prescriptive analytics). However, research shows that the nature and scope of managerial accounting has barely changed and that management accountants employ mostly descriptive analytics, some predictive analytics, and a bare minimum of prescriptive analytics. This paper proposes a Managerial Accounting Data Analytics (MADA) framework based on the balanced scorecard theory in a business intelligence context. MADA provides management accountants the ability to utilize comprehensive business analytics to conduct performance measurement and provide decision related information. With MADA, three types of business analytics (descriptive, predictive, and prescriptive) are implemented into four corporate performance measurement perspectives (financial, customer, internal process, and learning and growth) in an enterprise system environment. Other related issues that affect the successful utilization of business analytics within a corporate-wide business intelligence (BI) system, such as data quality and data integrity, are also discussed. This paper contributes to the literature by discussing the impact of business analytics on managerial accounting from an enterprise systems and BI perspective and by providing the Managerial Accounting Data Analytics (MADA) framework that incorporates balanced scorecard methodology.
An Ontology-based Context Model in Intelligent Environments

Computing becomes increasingly mobile and pervasive today; these changes imply that applications and services must be aware of and adapt to their changing contexts in highly dynamic environments. Today, building context-aware systems is a complex task due to lack of an appropriate infrastructure support in intelligent environments. A context-aware infrastructure requires an appropriate context model to represent, manipulate and access context information. In this paper, we propose a formal context model based on ontology using OWL to address issues including semantic context representation, context reasoning and knowledge sharing, context classification, context dependency and quality of context. The main benefit of this model is the ability to reason about various contexts. Based on our context model, we also present a Service-Oriented Context-Aware Middleware (SOCAM) architecture for building of context-aware services.
Reinforcement learning and dopamine in the striatum: A modeling perspective

The recent research evidences show that dopamine (DA) system in the brain is involved in various functions like reward-related learning, exploration, preparation, and execution in goal directed behavior. It is suggested that dopaminergic neurons provide a prediction error akin to the error computed in the temporal difference learning (TDL) models of reinforcement learning (RL). Houk et al. (1995) proposed a biochemical model in the spine head of neurons at the striatum in the basal ganglia which generates and uses neural signals to predict reinforcement. The model explains how the DA neurons are able to predict reinforcement and how output from these neurons might then be used to reinforce the behaviors that lead to primary reinforcement. They proposed a scheme drawing parallels between actor-critic architecture and dopamine activity in the basal ganglia. Houk et al. (1995) also proposed a biochemical model of interactions between protein molecules which supports learning earlier predictions of reinforcement in the spine head of medium spiny neurons at the striatum. However, Houk’s proposed cellular model fails to account for the time delay between the dopaminergic and glutamatergic activity required for reward-related learning and also fails to explain the ‘eligibility trace’ condition needed in delayed tasks of associative conditioning in which a memory trace of the antecedent signal is needed at the time of a succeeding reward. In this article, we review various models of RL with an emphasis on the cellular models of RL. In particular, we emphasize biochemical models of RL, and point out the future directions.
pypet: A Python Toolkit for Data Management of Parameter Explorations

pypet (Python parameter exploration toolkit) is a new multi-platform Python toolkit for managing numerical simulations. Sampling the space of model parameters is a key aspect of simulations and numerical experiments. pypet is designed to allow easy and arbitrary sampling of trajectories through a parameter space beyond simple grid searches. pypet collects and stores both simulation parameters and results in a single HDF5 file. This collective storage allows fast and convenient loading of data for further analyses. pypet provides various additional features such as multiprocessing and parallelization of simulations, dynamic loading of data, integration of git version control, and supervision of experiments via the electronic lab notebook Sumatra. pypet supports a rich set of data formats, including native Python types, Numpy and Scipy data, Pandas DataFrames, and BRIAN(2) quantities. Besides these formats, users can easily extend the toolkit to allow customized data types. pypet is a flexible tool suited for both short Python scripts and large scale projects. pypet's various features, especially the tight link between parameters and results, promote reproducible research in computational neuroscience and simulation-based disciplines.
Replicating viral vector platform exploits alarmin signals for potent CD8+ T cell-mediated tumour immunotherapy

Viral infections lead to alarmin release and elicit potent cytotoxic effector T lymphocyte (CTLeff) responses. Conversely, the induction of protective tumour-specific CTLeff and their recruitment into the tumour remain challenging tasks. Here we show that lymphocytic choriomeningitis virus (LCMV) can be engineered to serve as a replication competent, stably-attenuated immunotherapy vector (artLCMV). artLCMV delivers tumour-associated antigens to dendritic cells for efficient CTL priming. Unlike replication-deficient vectors, artLCMV targets also lymphoid tissue stroma cells expressing the alarmin interleukin-33. By triggering interleukin-33 signals, artLCMV elicits CTLeff responses of higher magnitude and functionality than those induced by replication-deficient vectors. Superior anti-tumour efficacy of artLCMV immunotherapy depends on interleukin-33 signalling, and a massive CTLeff influx triggers an inflammatory conversion of the tumour microenvironment. Our observations suggest that replicating viral delivery systems can release alarmins for improved anti-tumour efficacy. These mechanistic insights may outweigh safety concerns around replicating viral vectors in cancer immunotherapy.
The directory-based cache coherence protocol for the DASH multiprocessor

DASH is a scalable shared-memory multiprocessor currently being developed at Stanford's Computer Systems Laboratory. The architecture consists of powerful processing nodes, each with a portion of the shared-memory, connected to a scalable interconnection network. A key feature of DASH is its distributed directory-based cache coherence protocol. Unlike traditional snoopy coherence protocols, the DASH protocol does not rely on broadcast; instead it uses point-to-point messages sent between the processors and memories to keep caches consistent. Furthermore, the DASH system does not contain any single serialization or control point. While these features provide the basis for scalability, they also force a reevaluation of many fundamental issues involved in the design of a protocol. These include the issues of correctness, performance and protocol complexity. In this paper, we present the design of the DASH coherence protocol and discuss how it addresses the above issues. We also discuss our strategy for verifying the correctness of the protocol and briefly compare our protocol to the IEEE Scalable Coherent Interface protocol.
Lung Nodule Detection Using Convolutional Neural Networks

We present a novel procedure to apply deep learning techniques to medical image classification. With increasing popularity of computed tomography (CT) lung screening, fully manual diagnosis of lung cancer puts a burden on the radiologists who need to spend hours reading through CT scanned images to identify Region of Interests (ROIs) to schedule follow-ups. Accurate computer-aided diagnosis of lung cancer can effectively reduce their workload and help training new radiologists. However, lung cancer detection is challenging because of the varying size, location, shape, and density of nodules. Many studies have approached this problem using image-processing techniques with the intention of developing an optimal set of features. Convolutional neural network has demonstrated to learn discriminative visual features automatically and has beat many state-of-art algorithms in image-processing tasks, such as pattern recognition, object detection, segmentation, etc. In this report, we evaluate the feasibility of implementing deep learning algorithms for lung cancer diagnosis with the Lung Image Database Consortium (LIDC) database. We compare the performance of two 2.5D convolutional neural network models, two 3D convolutional neural network models, and one 3D convolutional neural networks with a 3D spatial transformer network module on the task of detecting lung nodules. The performance of our best model is comparable with the state-of-art results in the lung nodule detection task.
Application of Gradient Boosting through SAS Enterprise Miner TM 12 . 3 to Classify Human Activities

Using smart clothing with wearable medical sensors integrated to keep track of human health is now attracting many researchers. However, body movement caused by daily human activities inserts artificial noise into physiological data signals, which affects the output of a health monitoring/alert system. To overcome this problem, recognizing human activities, determining relationship between activities and physiological signals, and removing noise from the collected signals are essential steps. This paper focuses on the first step, which is human activity recognition. Our research shows that no other study used SAS® for classifying human activities. For this study, two data sets were collected from an open repository. Both data sets have 561 input variables and one nominal target variable with four levels. Principal component analysis along with other variable reduction and selection techniques were applied to reduce dimensionality in the input space. Several modeling techniques with different optimization parameters were used to classify human activity. The gradient boosting model was selected as the best model based on a test misclassification rate of 0.1233. That is, 87.67% of total events were classified correctly.
Absorptive SPDT switch design using cascaded switchable parallel-coupled stub resonator and switchable radial stub resonator

An absorptive Single Pole Double Throw (SPDT) switch using cascaded switchable parallel-coupled stub resonator and switchable radial stub resonator is proposed in this paper. By using mathematical model, theories of the switchable resonators are discussed. The switchable resonators are reconfigured between bandstop and allpass response in the absorptive SPDT switch design. The allpass response is operated in wide bandwidth centered at resonant frequency. Frequency spectrum in 3.5 GHz band has been chosen to demonstrate the design where the targeted application is Time Division Duplex (TDD) switching in wideband wireless data communication systems. The proposed absorptive SPDT switch is simulated in an electronic design automation software for analysis in terms of insertion loss, return loss and isolation. Result in simulation shows less than 2 dB of insertion loss, more than 14 dB of return loss at absorptive ports and more than 39 dB of isolation from 3.4 to 3.6 GHz.
Correlation analysis of MQTT loss and delay according to QoS level

protocol developed and released by IBM. To ensure the reliability of message transmission, MQTT supports three levels of QoS. In this paper, we analyze MQTT message transmission process which consists of real wired/wireless publish client, broker server and Subscribe client. By transmitting messages through 3 levels of QoS with various sizes of payloads, we have captured packets to analyze end-to-end delays and message loss.
Differential effects of attentional focus strategies during long-term resistance training.

The purpose of this study was to investigate the effects of using an internal versus external focus of attention during resistance training on muscular adaptations. Thirty untrained college-aged men were randomly assigned to an internal focus group (INTERNAL) that focused on contracting the target muscle during training (n = 15) or an external focus group (EXTERNAL) that focused on the outcome of the lift (n = 15). Training for both routines consisted of 3 weekly sessions performed on non-consecutive days for 8 weeks. Subjects performed 4 sets of 8-12 repetitions per exercise. Changes in strength were assessed by six repetition maximum in the biceps curl and isometric maximal voluntary contraction in knee extension and elbow flexion. Changes in muscle thickness for the elbow flexors and quadriceps were assessed by ultrasound. Results show significantly greater increases in elbow flexor thickness in INTERNAL versus EXTERNAL (12.4% vs. 6.9%, respectively); similar changes were noted in quadriceps thickness. Isometric elbow flexion strength was greater for INTERNAL while isometric knee extension strength was greater for EXTERNAL, although neither reached statistical significance. The findings lend support to the use of a mind-muscle connection to enhance muscle hypertrophy.
Strength Training for Endurance Athletes: Theory to Practice

THE PURPOSE OF THIS REVIEW IS TWOFOLD: TO ELUCIDATE THE UTILITY OF RESISTANCE TRAINING FOR ENDURANCE ATHLETES, AND PROVIDE THE PRACTITIONER WITH EVIDENCED-BASED PERIODIZATION STRATEGIES FOR CONCURRENT STRENGTH AND ENDURANCE TRAINING IN ATHLETIC POPULATIONS. BOTH LOW-INTENSITY EXERCISE ENDURANCE (LIEE) AND HIGH-INTENSITY EXERCISE ENDURANCE (HIEE) HAVE BEEN SHOWN TO IMPROVE AS A RESULT OF MAXIMAL, HIGH FORCE, LOW VELOCITY (HFLV) AND EXPLOSIVE, LOW-FORCE, HIGH-VELOCITY STRENGTH TRAINING.
Conceptualizing Context for Pervasive Advertising

Profile-driven personalization based on socio-demographics is currently regarded as the most convenient base for successful personalized advertising. However, signs point to the dormant power of context recognition: Advertising systems that can adapt to the situational context of a consumer will rapidly gain importance. While technologies that can sense the environment are increasingly advanced, questions such as what to sense and how to adapt to a consumer’s context are largely unanswered. In this chapter, we analyze the purchase context of a retail outlet and conceptualize it such that adaptive pervasive advertising applications really deliver on their potential: showing the right message at the right time to the right recipient.  In Müller, Jörg, Alt, Florian, & Michelis, Daniel (Eds.), Pervasive Advertising (pp. 159-183).
Detection and recognition of urban road markings using images

While road lane markings detection was extensively studied, in particular for intelligent vehicle applications, the detection and recognition of all kind of marking such as arrows, crosswalks, zebras, words, pictograms, continuous and discontinuous lane markings was drastically less studied. However, it has many potential applications in the design of advanced driver assistance systems, as well as for asset management along itineraries. An algorithm is proposed which is based on the following processing steps: marking pixel extraction, detection using connected components before Inverse Perspective Mapping and recognition based on the comparison with a single pattern or with repetitive rectangular patterns. The proposed algorithm is able to detect and recognize repetitive markings (such as crosswalks) as well as single patterns (such as arrows). We believe that the proposed algorithm can be extended easily to solve the problem of the identification of all types of markings.
Design and Implementation of a GaN-Based, 100-kHz, 102-W/in3 Single-Phase Inverter

High power density is a desirable feature of power electronics design, which prompts economic incentives for industrial applications. In this paper, a gallium nitride (GaN)-based 2-kVA single-phase inverter design was developed for the Google Little Box Challenge, which achieves a 102-W/in3 power density. First, the static and dynamic temperature-dependent characteristics of multiple SiC and enhancement-mode GaN FETs are investigated and compared. Based on the device testing results, several topologies of the inverter stage and different power decoupling solutions are compared with respect to the device volume, efficiency, and thermal requirements. Moreover, some design approaches for magnetic devices and the implementation of gate drives for GaN devices are discussed in this paper, which enable a compact and robust system. Finally, a dc notch filter and a hard switching full-bridge converter are combined as the proposed design for the prototype. A 2-kVA prototype is demonstrated, which meets the volume, efficiency, and thermal requirements. The performance of the prototype is verified by the experimental results.
Immediate effect of a slow pace breathing exercise Bhramari pranayama on blood pressure and heart rate.

The study was carried out to evaluate the immediate effect Bhramari pranayama, a slow breathing exercise for 5 minutes on heart rate and blood pressure. Heart rate and blood pressure of volunteers were recorded. The subject was directed to inhale slowly up to the maximum for about 5 seconds and then to exhale slowly up to the maximum for about 15 sec keeping two thumbs on two external auditory canal, index and middle finger together on two closed eyes and ring finger on the two sides of the nose. During exhalation the subject must chant the word "O-U-Mmmma" with a humming nasal sound mimicking the sound of a humming wasp, so that the laryngeal walls and the inner walls of the nostril mildly vibrate (Bhramari pranayama, respiratory rate 3/min). After 5 minutes of this exercise, the blood pressure and heart rate were recorded again. Both the systolic and diastolic blood pressure were found to be'decreased with a slight fall in heart rate. Fall of diastolic pressure and mean pressure were significant. The result indicated that slow pace Bhramari pranayama for 5 minutes, induced parasympathetic dominance on cardiovascular system.
Reduced Number of Hypocretin Neurons in Human Narcolepsy

Murine and canine narcolepsy can be caused by mutations of the hypocretin (Hcrt) (orexin) precursor or Hcrt receptor genes. In contrast to these animal models, most human narcolepsy is not familial, is discordant in identical twins, and has not been linked to mutations of the Hcrt system. Thus, the cause of human narcolepsy remains unknown. Here we show that human narcoleptics have an 85%-95% reduction in the number of Hcrt neurons. Melanin-concentrating hormone (MCH) neurons, which are intermixed with Hcrt cells in the normal brain, are not reduced in number, indicating that cell loss is relatively specific for Hcrt neurons. The presence of gliosis in the hypocretin cell region is consistent with a degenerative process being the cause of the Hcrt cell loss in narcolepsy.
Substrate Integrated Horn Antenna Loaded With Open Parallel Transitions

The substrate integrated H-plane horn antenna has been struggling with very limited impedance bandwidth since its invention, which is coming from its natural aperture discontinuity. Apart from a variety of series transitions introduced so far, here, parallel transitions are applied to the antenna aperture in addition to narrow slots on the horn walls around the aperture to widen the antenna impedance bandwidth. It is shown that the depth of the transitions can easily determine the center frequency of the matching bandwidth. The new parallel transitions also reduce the antenna back radiation since they act as chokes around the horn aperture.