nils-herrmann/clean_scirepeval huggingface dataset:
A study of near-field direct antenna modulation systems using convex optimization

This paper studies the constellation diagram design for a class of communication systems known as near-field direct antenna modulation (NFDAM) systems. The modulation is carried out in a NFDAM system by means of a control unit that switches among a number of pre-designed passive controllers such that each controller generates a desired voltage signal at the far field. To find an optimal number of signals that can be transmitted and demodulated reliably in a NFDAM system, the coverage area of the signal at the far field should be identified. It is shown that this coverage area is a planar convex region in general and simply a circle in the case when no constraints are imposed on the input impedance of the antenna and the voltage received at the far field. A convex optimization method is then proposed to find a polygon that is able to approximate the coverage area of the signal constellation diagram satisfactorily. A similar analysis is provided for the identification of the coverage area of the antenna input impedance, which is beneficial for designing an energy-efficient NFDAM system.
Designing and Packaging Wide-Band PAs: Wideband PA and Packaging, History, and Recent Advances: Part 1

In current applications such as communication, aerospace and defense, electronic warfare (EW), electromagnetic compatibility (EMC), and sensing, among others, there is an ever-growing demand for more linear power with increasingly greater bandwidth and efficiency. Critical for these applications are the design and packaging of wide-band, high-power amplifiers that are both compact in size and low in cost [1]. Most such applications, including EW, radar, and EMC testers, require high 1-dB compression point (P1dB) power with good linearity across a wide band (multi-octave to decade bandwidth) . In addition to linear power, wide bandwidth is essential for high-data-rate communication and high resolution in radar and active imagers . In modern electronics equipment such as automated vehicles, rapidly increasing complexity imposes strict EMC regulations for human safety and security . This often requires challenging specifications for the power amplifier (PA), such as very high P1dB power [to kilowatt, continuous wave (CW)] across approximately a decade bandwidth with high linearity, reliability, and long life, even for 100% load mismatch .
Machine Learning Methods for Gender Identification

The task of identifying the gender identity of the author of a specific text has applications throughout social sciences and commercial areas. We study the effectiveness of many prominent methods of machine learning for solving the gender identification problem. Many of these methods are unable to handle the implicitly temporal nature of texts. We explore two prominent approaches for better handling these temporal dependencies. We introduce a finely tuned recurrent neural network for the problem of gender identification. As well, we consider another promising approach known as document vectors. Experimental results show that the proposed neural network outperforms the other classifiers which were considered, including a state of the art neural network for gender identification.
Towards efficient content-aware search over encrypted outsourced data in cloud

With the increasing adoption of cloud computing, a growing number of users outsource their datasets into cloud. The datasets usually are encrypted before outsourcing to preserve the privacy. However, the common practice of encryption makes the effective utilization difficult, for example, search the given keywords in the encrypted datasets. Many schemes are proposed to make encrypted data searchable based on keywords. However, keyword-based search schemes ignore the semantic representation information of users retrieval, and cannot completely meet with users search intention. Therefore, how to design a content-based search scheme and make semantic search more effective and context-aware is a difficult challenge. In this paper, we proposed an innovative semantic search scheme based on the concept hierarchy and the semantic relationship between concepts in the encrypted datasets. More specifically, our scheme first indexes the documents and builds trapdoor based on the concept hierarchy. To further improve the search efficiency, we utilize a tree-based index structure to organize all the document index vectors. Our experiment results based on the real world datasets show the scheme is more efficient than previous scheme. We also study the threat model of our approach and prove it does not introduce any security risk.
Skeletal muscle buffering capacity and endurance performance after high-intensity interval training by well-trained cyclists

Skeletal muscle buffering capacity (βm), enzyme activities and exercise performance were measured before and after 4 weeks of high-intensity, sub maximal␣interval training (HIT) undertaken by six well-trained competitive cyclists [mean maximal oxygen consumption ( O2max) = 66.2 ml · kg−1 · min−1]. HIT replaced a portion of habitual endurance training and consisted of six sessions, each of six to eight repetitions of 5 min duration at 80% of peak sustained power output (PPO) separated by 1 min of recovery. βm increased from 206.6 (17.9) to 240.4 (34.1) μmol H+ · g muscle dw−1 · pH−1 after HIT (P = 0.05). PPO, time to fatigue at 150% PPO (TF150) and 40-km cycle time trial performance (TT40) all significantly improved after HIT (P < 0.05). In contrast, there was no change in the activity of either phosphofructokinase or citrate synthase. In addition, βm correlated significantly with TT40 performance before HIT (r = −0.82, P < 0.05) and the relationship between change in βm and change in TT40 was close to significance (r = −0.74). βm did not correlate with TF150. These results indicate that βm may be an important determinant of relatively short-duration (< 60 min) endurance cycling activity and responds positively to just six sessions of high-intensity, submaximal interval training.
Time - motion analysis of professional rugby union players during match-play.

The aim of this study was to quantify the movement patterns of various playing positions during professional rugby union match-play, such that the relative importance of aerobic and anaerobic energy pathways to performance could be estimated. Video analysis was conducted of individual players (n=29) from the Otago Highlanders during six "Super 12" representative fixtures. Each movement was coded as one of six speeds of locomotion (standing still, walking, jogging, cruising, sprinting, and utility), three states of non-running intensive exertion (rucking/mauling, tackling, and scrummaging), and three discrete activities (kicking, jumping, passing). The results indicated significant demands on all energy systems in all playing positions, yet implied a greater reliance on anaerobic glycolytic metabolism in forwards, due primarily to their regular involvement in non-running intense activities such as rucking, mauling, scrummaging, and tackling. Positional group comparisons indicated that while the greatest differences existed between forwards and backs, each positional group had its own unique demands. Front row forwards were mostly involved in activities involving gaining/retaining possession, back row forwards tended to play more of a pseudo back-line role, performing less rucking/mauling than front row forwards, yet being more involved in aspects of broken play such as sprinting and tackling. While outside backs tended to specialize in the running aspects of play, inside backs tended to show greater involvement in confrontational aspects of play such as rucking/mauling and tackling. These results suggest that rugby training and fitness testing should be tailored specifically to positional groups rather than simply differentiating between forwards and backs.
Global Sensory Qualities and Aesthetic Experience in Music

A well-known tradition in the study of visual aesthetics holds that the experience of visual beauty is grounded in global computational or statistical properties of the stimulus, for example, scale-invariant Fourier spectrum or self-similarity. Some approaches rely on neural mechanisms, such as efficient computation, processing fluency, or the responsiveness of the cells in the primary visual cortex. These proposals are united by the fact that the contributing factors are hypothesized to be global (i.e., they concern the percept as a whole), formal or non-conceptual (i.e., they concern form instead of content), computational and/or statistical, and based on relatively low-level sensory properties. Here we consider that the study of aesthetic responses to music could benefit from the same approach. Thus, along with local features such as pitch, tuning, consonance/dissonance, harmony, timbre, or beat, also global sonic properties could be viewed as contributing toward creating an aesthetic musical experience. Several such properties are discussed and their neural implementation is reviewed in the light of recent advances in neuroaesthetics.
Rookie: A unique approach for exploring news archives

News archives are an invaluable primary source for placing current events in historical context. But current search engine tools do a poor job at uncovering broad themes and narratives across documents. We present Rookie: a practical soware system which uses natural language processing (NLP) to help readers, reporters and editors uncover broad stories in news archives. Unlike prior work, Rookie’s design emerged from 18 months of iterative development in consultation with editors and computational journalists. is process lead to a dramatically dierent approach from previous academic systems with similar goals. Our eorts oer a generalizable case study for others building real-world journalism soware using NLP.
Toward machines that behave ethically better than humans do

With the increasing dependence on autonomous operating agents and robots the need for ethical machine behavior rises. This paper presents a moral reasoner that combines connectionism, utilitarianism and ethical theory about moral duties. The moral decision-making matches the analysis of expert ethicists in the health domain. This may be useful in many applications, especially where machines interact with humans in a medical context. Additionally, when connected to a cognitive model of emotional intelligence and affective decision making, it can be explored how moral decision making impacts affective behavior.
Genetic engineering of algae for enhanced biofuel production.

There are currently intensive global research efforts aimed at increasing and modifying the accumulation of lipids, alcohols, hydrocarbons, polysaccharides, and other energy storage compounds in photosynthetic organisms, yeast, and bacteria through genetic engineering. Many improvements have been realized, including increased lipid and carbohydrate production, improved H(2) yields, and the diversion of central metabolic intermediates into fungible biofuels. Photosynthetic microorganisms are attracting considerable interest within these efforts due to their relatively high photosynthetic conversion efficiencies, diverse metabolic capabilities, superior growth rates, and ability to store or secrete energy-rich hydrocarbons. Relative to cyanobacteria, eukaryotic microalgae possess several unique metabolic attributes of relevance to biofuel production, including the accumulation of significant quantities of triacylglycerol; the synthesis of storage starch (amylopectin and amylose), which is similar to that found in higher plants; and the ability to efficiently couple photosynthetic electron transport to H(2) production. Although the application of genetic engineering to improve energy production phenotypes in eukaryotic microalgae is in its infancy, significant advances in the development of genetic manipulation tools have recently been achieved with microalgal model systems and are being used to manipulate central carbon metabolism in these organisms. It is likely that many of these advances can be extended to industrially relevant organisms. This review is focused on potential avenues of genetic engineering that may be undertaken in order to improve microalgae as a biofuel platform for the production of biohydrogen, starch-derived alcohols, diesel fuel surrogates, and/or alkanes.
Understanding Consumer Interaction on Instagram: The Role of Satisfaction, Hedonism, and Content Characteristics

The increasing relevance of Instagram and its growing adoption among top brands suggest an effort to better understand consumers' behaviors within this context. The purpose of this study is to examine the role of perceived hedonism and satisfaction in determining consumers' intentions to interact and their actual interaction behaviors (the number of likes, by tapping a heart icon, and comments) in a brand's official Instagram account. Also, we investigate the effect of consumer perceptions about the characteristics of the content generated in the account (perceived originality, quantity, and quality) on their perceived hedonism and satisfaction. Data were collected in two stages from 808 members of a fashion brand's official Instagram account. First, participants answered an online questionnaire to evaluate their perceptions, satisfaction, and interaction intentions. Second, 1 month later, we measure the number of likes and comments done by each participant in the brand's official Instagram account during that month. Using partial least squares to analyze the data, perceived hedonism is found to affect both satisfaction and the intention to interact in Instagram, which in turn influences actual behavior. Besides, perceived originality is the most relevant content characteristic to develop perceived hedonism. These findings offer managers a general vision of consumers' behaviors on Instagram, highlighting the importance of hedonism to create a satisfactory experience.
The critical role of retrieval practice in long-term retention

Learning is usually thought to occur during episodes of studying, whereas retrieval of information on testing simply serves to assess what was learned. We review research that contradicts this traditional view by demonstrating that retrieval practice is actually a powerful mnemonic enhancer, often producing large gains in long-term retention relative to repeated studying. Retrieval practice is often effective even without feedback (i.e. giving the correct answer), but feedback enhances the benefits of testing. In addition, retrieval practice promotes the acquisition of knowledge that can be flexibly retrieved and transferred to different contexts. The power of retrieval practice in consolidating memories has important implications for both the study of memory and its application to educational practice.
3 D Shape Segmentation and Labeling via Extreme Learning Machine

We propose a fast method for 3D shape segmentation and labeling via Extreme Learning Machine (ELM). Given a set of example shapes with labeled segmentation, we train an ELM classifier and use it to produce initial segmentation for test shapes. Based on the initial segmentation, we compute the final smooth segmentation through a graph-cut optimization constrained by the super-face boundaries obtained by over-segmentation and the active contours computed from ELM segmentation. Experimental results show that our method achieves comparable results against the state-of-the-arts, but reduces the training time by approximately two orders of magnitude, both for face-level and super-face-level, making it scale well for large datasets. Based on such notable improvement, we demonstrate the application of our method for fast online sequential learning for 3D shape segmentation at face level, as well as realtime sequential learning at super-face level.
A Behavioural Biometric System Based on Human Computer Interaction

In this paper we describe a new behavioural biometric technique based on human computer interaction. We developed a system that captures the user interaction via a pointing device, and uses this behavioural information to verify the identity of an individual. Using statistical pattern recognition techniques, we developed a sequential classifier that processes user interaction, according to which the user identity is considered genuine if a predefined accuracy level is achieved, and the user is classified as an impostor otherwise. Two statistical models for the features were tested, namely Parzen density estimation and a unimodal distribution. The system was tested with different numbers of users in order to evaluate the scalability of the proposal . Experimental results show that the normal user interaction with the computer via a pointing device entails behavioural information with discriminating power, that can be explored for identity authentication.
AmbiMax: Autonomous Energy Harvesting Platform for Multi-Supply Wireless Sensor Nodes

AmbiMax is an energy harvesting circuit and a supercapacitor based energy storage system for wireless sensor nodes (WSN). Previous WSNs attempt to harvest energy from various sources, and some also use supercapacitors instead of batteries to address the battery aging problem. However, they either waste much available energy due to impedance mismatch, or they require active digital control that incurs overhead, or they work with only one specific type of source. AmbiMax addresses these problems by first performing maximum power point tracking (MPPT) autonomously, and then charges supercapacitors at maximum efficiency. Furthermore, AmbiMax is modular and enables composition of multiple energy harvesting sources including solar, wind, thermal, and vibration, each with a different optimal size. Experimental results on a real WSN platform, Eco, show that AmbiMax successfully manages multiple power sources simultaneously and autonomously at several times the efficiency of the current state-of-the-art for WSNs
Compact Coplanar Waveguide (CPW)-Fed Zeroth-Order Resonant Antennas With Extended Bandwidth and High Efficiency on Vialess Single Layer

This paper presents the design and analysis of compact coplanar waveguide (CPW)-fed zeroth-order resonant (ZOR) antennas. They are designed on a CPW single layer where vias are not required. The ZOR phenomenon is employed to reduce the antenna size. The novel composite right/left-handed (CRLH) unit cell on a vialess single layer simplifies the fabrication process. In addition, the CPW geometry provides high design freedom, so that bandwidth-extended ZOR antennas can be designed. The antenna's bandwidth is characterized by the circuit parameters. Based on the proposed bandwidth extension technique, symmetric, asymmetric, and chip-loaded antennas are designed. The ZOR characteristic and bandwidth extension are verified by a commercial EM simulator. Their performances are compared with those of previously reported metamaterial resonant antennas. They provide further size reduction, higher efficiency, easier manufacturing, and extended bandwidth.
Dynamic modeling of Mckibben pneumatic artificial muscles for antagonistic actuation

This paper presents the dynamic modeling of Mckibben pneumatic artificial muscles. The air flow model of a valve orifice and the air volume model of a pneumatic muscle are incorporated into the proposed dynamic model to estimate precisely the pressure variance of a pneumatic muscle when Mckibben muscles are in inflating and deflating. Coefficient parameters of the proposed model are determined optimally through well-coordinated experiments. Frequency response test is performed on an antagonistic structure consisting of Mckibben muscles manufactured in our laboratory and a pneumatic circuit with fast pneumatic control valves. Comparing with experimental results, simulations revealed that the proposed model gave good performance in estimating motions of the antagonistic actuation as well as the pressure variance of the Mckibben muscles.
A high-performance overlay architecture for pipelined execution of data flow graphs

A major issue facing the widespread use of FPGAs as accelerators is their programmability wall: the difficulty of hardware design and the long synthesis times. Overlays-pre-synthesized FPGA circuits that are themselves reconfigurable - promise to tackle these challenges. We design and evaluate an overlay architecture, structured as a mesh of functional units, for pipelined execution of data-flow graphs (DFGs), a common abstraction for expressing parallelism in applications. We use data-driven execution based on elastic pipelines to balance pipeline latencies and achieve a high fMAX, scalability and maximum throughput. We prototype two overlays on a Stratix IV FPGA: a 355 MHz 24×16 integer overlay and a 312 MHz 18×16 floating-point overlay. We also design a tool that maps DFGs to overlays. We map 15 DFGs and show that the two overlays deliver throughputs of up to 35 GOPS and 22 GFLOPS, respectively. We also show that DFG mapping is fast, taking no more than 6 seconds for the largest DFG. Thus, our overlay architecture raises the level of abstraction of FPGA programming closer to that of software and avoids lengthy synthesis time, easing the use of these devices to accelerate applications.
Big Data Mobile Services for New York City Taxi Riders and Drivers

In recent years, taxis in New York City have been equipped with GPS sensors to record the start and end locations of every trip, in conjunction with fare collection equipment recording trip time, fare, and distance. This paper analyzes this vast dataset with big data technologies. We use MapReduce and Hive in order to understand traffic and travel patterns, discover relationships among the data, and make predictions on the taxi network. We also propose a service model combining the results of our data analysis with smartphone applications to offer users various taxi-related services based on results of our big data analysis.
An EEG-based brain-computer interface for cursor control.

This study began development of a new communication and control modality for individuals with severe motor deficits. We trained normal subjects to use the 8-12 Hz mu rhythm recorded from the scalp over the central sulcus of one hemisphere to move a cursor from the center of a video screen to a target located at the top or bottom edge. Mu rhythm amplitude was assessed by on-line frequency analysis and translated into cursor movement: larger amplitudes moved the cursor up and smaller amplitudes moved it down. Over several weeks, subjects learned to change mu rhythm amplitude quickly and accurately, so that the cursor typically reached the target in 3 sec. The parameters that translated mu rhythm amplitudes into cursor movements were derived from evaluation of the distributions of amplitudes in response to top and bottom targets. The use of these distributions was a distinctive feature of this study and the key factor in its success. Refinements in training procedures and in the distribution-based method used to translate mu rhythm amplitudes into cursor movements should further improve this 1-dimensional control. Achievement of 2-dimensional control is under study. The mu rhythm may provide a significant new communication and control option for disabled individuals.
Statistical Phrase-Based Translation

We propose a new phrase-based translation model and decoding algorithm that enables us to evaluate and compare several, previously proposed phrase-based translation models. Within our framework, we carry out a large number of experiments to understand better and explain why phrase-based models outperform word-based models. Our empirical results, which hold for all examined language pairs, suggest that the highest levels of performance can be obtained through relatively simple means: heuristic learning of phrase translations from word-based alignments and lexical weighting of phrase translations. Surprisingly, learning phrases longer than three words and learning phrases from high-accuracy wordlevel alignment models does not have a strong impact on performance. Learning only syntactically motivated phrases degrades the performance of our systems.