nils-herrmann/clean_scirepeval huggingface dataset:
High-Sensitivity Low-Noise Miniature Fluxgate Magnetometers Using a Flip Chip Conceptual Design

This paper presents a novel class of miniature fluxgate magnetometers fabricated on a print circuit board (PCB) substrate and electrically connected to each other similar to the current "flip chip" concept in semiconductor package. This sensor is soldered together by reversely flipping a 5 cm × 3 cm PCB substrate to the other identical one which includes dual magnetic cores, planar pick-up coils, and 3-D excitation coils constructed by planar Cu interconnections patterned on PCB substrates. Principles and analysis of the fluxgate sensor are introduced first, and followed by FEA electromagnetic modeling and simulation for the proposed sensor. Comprehensive characteristic experiments of the miniature fluxgate device exhibit favorable results in terms of sensitivity (or "responsivity" for magnetometers) and field noise spectrum. The sensor is driven and characterized by employing the improved second-harmonic detection technique that enables linear V-B correlation and responsivity verification. In addition, the double magnitude of responsivity measured under very low frequency (1 Hz) magnetic fields is experimentally demonstrated. As a result, the maximum responsivity of 593 V/T occurs at 50 kHz of excitation frequency with the second harmonic wave of excitation; however, the minimum magnetic field noise is found to be 0.05 nT/Hz(1/2) at 1 Hz under the same excitation. In comparison with other miniature planar fluxgates published to date, the fluxgate magnetic sensor with flip chip configuration offers advances in both device functionality and fabrication simplicity. More importantly, the novel design can be further extended to a silicon-based micro-fluxgate chip manufactured by emerging CMOS-MEMS technologies, thus enriching its potential range of applications in modern engineering and the consumer electronics market.
A vehicular rooftop, shark-fin, multiband antenna for the GPS/LTE/cellular/DSRC systems

A shark-fin rooftop multiband antenna for the automotive industry is proposed. The antenna system receives Right Hand Circularly Polarized (RHCP) satellite signals for the Global Positioning System's (GPS) L1-Band and Vertically Linearly Polarized (VLP) signals for the Long Term Evolution (LTE), the cellular frequencies, the Universal Mobile Telecommunications Systems (UMTS) and the Dedicated Short Range Communications (DSRC) system. A ceramic patch antenna element was used for the GPS signals and a printed monopole utilizing printed ground sleeve technique for the cellular and LTE signals. A study of four potential DSRC antenna elements to determine the appropriate antenna element location within the antenna cover to obtain optimum performance is also presented and discussed in detail. The antenna module was measured on a 1-meter diameter ground plane and on a vehicle roof model. The design and measurements results are presented and discussed below.
Fast Computation of the Difference of Low-Pass Transform

This paper defines the difference of low-pass (DOLP) transform and describes a fast algorithm for its computation. The DOLP is a reversible transform which converts an image into a set of bandpass images. A DOLP transform is shown to require O(N2) multiplies and produce O(N log(N)) samples from an N sample image. When Gaussian low-pass filters are used, the result is a set of images which have been convolved with difference of Gaussian (DOG) filters from an exponential set of sizes. A fast computation technique based on ``resampling'' is described and shown to reduce the DOLP transform complexity to O(N log(N)) multiplies and O(N) storage locations. A second technique, ``cascaded convolution with expansion,'' is then defined and also shown to reduce the computational cost to O(N log(N)) multiplies. Combining these two techniques yields an algorithm for a DOLP transform that requires O(N) storage cells and requires O(N) multiplies.
ULA-OP: an advanced open platform for ultrasound research

The experimental test of novel ultrasound (US) investigation methods can be made difficult by the lack of flexibility of commercial US machines. In the best options, these only provide beamformed radiofrequency or demodulated echo-signals for acquisition by an external PC. More flexibility is achieved in high-level research platforms, but these are typically characterized by high cost and large size. This paper presents a powerful but portable US system, specifically developed for research purposes. The system design has been based on high-level commercial integrated circuits to obtain the maximum flexibility and wide data access with minimum of electronics. Preliminary applications involving nonstandard imaging transmit/receive strategies and simultaneous B-mode and multigate spectral Doppler mode are discussed.
3D Mesh Compression: Survey, Comparisons, and Emerging Trends

3D meshes are commonly used to represent virtual surface and volumes. However, their raw data representations take a large amount of space. Hence, 3D mesh compression has been an active research topic since the mid 1990s. In 2005, two very good review articles describing the pioneering works were published. Yet, new technologies have emerged since then. In this article, we summarize the early works and put the focus on these novel approaches. We classify and describe the algorithms, evaluate their performance, and provide synthetic comparisons. We also outline the emerging trends for future research.
A case study of algorithm-assisted decision making in child maltreatment hotline screening decisions

Every year there are more than 3.6 million referrals made to child protection agencies across the US. The practice of screening calls is left to each jurisdiction to follow local practices and policies, potentially leading to large variation in the way in which referrals are treated across the country. Whilst increasing access to linked administrative data is available, it is difficult for welfare workers to make systematic use of historical information about all the children and adults on a single referral call. Risk prediction models that use routinely collected administrative data can help call workers to better identify cases that are likely to result in adverse outcomes. However, the use of predictive analytics in the area of child welfare is contentious. There is a possibility that some communities— such as those in poverty or from particular racial and ethnic groups—will be disadvantaged by the reliance on government administrative data. On the other hand, these analytics tools can augment or replace human judgments, which themselves are biased and imperfect. In this paper we describe our work on developing, validating, fairness auditing, and deploying a risk prediction model in Allegheny County, PA, USA. We discuss the results of our analysis to-date, and also highlight key problems and data bias issues that present challenges for model evaluation and deployment.
Brief report: vocabulary acquisition for children with autism: teacher or computer instruction.

This study examined the impact of computers on the vocabulary acquisition of young children with autism. Children's attention, motivation, and learning of words was compared in a behavioral program and an educational software program. The educational software program was designed to parallel the behavioral program, but it added perceptually salient qualities such as interesting sounds and object movement. Children with autism were more attentive, more motivated, and learned more vocabulary in the computer than in the behavioral program. Implications are considered for the development of computer software to teach vocabulary to children who have autism.
Learning Hypernymy over Word Embeddings

Word embeddings have shown promise in a range of NLP tasks; however, it is currently difficult to accurately encode categorical lexical relations in these vector spaces. We consider one such important relation – hypernymy – and investigate the feasibility of learning a function in vector space to capture it. We argue that hypernymy is significantly harder to capture than the analogy tasks word embeddings are traditionally evaluated on. We show that a simple neural network outperforms previous systems at classifying hypernymy, and present experiments for learning a function to predict the hypernym of a word in a vector space.
Evaluating different query reformulation techniques for the Geographical Information Retrieval task considering geospatial entities as textual terms

Geographic Information Retrieval (GIR) is an active and growing research area that focuses on the retrieval of textual documents according to a geographical criteria of relevance. However, since a GIR system can be treated as a traditional Information Retrieval (IR) system, it is important to pay attention to finding effective methods for query reformulation. In this way, the search results will improve their quality and recall. In this paper, we propose different Natural Language Processing (NLP) techniques of query reformulation related to the modification and/or expansion of both parts thematic and geospatial that are usually recognized in a geographical query. We have evaluated each of the reformulations proposed using GeoCLEF as an evaluation framework for GIR systems. The results obtained show that all proposed query reformulations retrieved relevant documents that were not retrieved using the original query.
The Role of Posterior Parietal Cortex in Beat-based Timing Perception: A Continuous Theta Burst Stimulation Study

There is growing interest in how the brain's motor systems contribute to the perception of musical rhythms. The Action Simulation for Auditory Prediction hypothesis proposes that the dorsal auditory stream is involved in bidirectional interchange between auditory perception and beat-based prediction in motor planning structures via parietal cortex [Patel, A. D., & Iversen, J. R. The evolutionary neuroscience of musical beat perception: The Action Simulation for Auditory Prediction (ASAP) hypothesis. Frontiers in Systems Neuroscience, 8, 57, 2014]. We used a TMS protocol, continuous theta burst stimulation (cTBS), that is known to down-regulate cortical activity for up to 60 min following stimulation to test for causal contributions to beat-based timing perception. cTBS target areas included the left posterior parietal cortex (lPPC), which is part of the dorsal auditory stream, and the left SMA (lSMA). We hypothesized that down-regulating lPPC would interfere with accurate beat-based perception by disrupting the dorsal auditory stream. We hypothesized that we would induce no interference to absolute timing ability. We predicted that down-regulating lSMA, which is not part of the dorsal auditory stream but has been implicated in internally timed movements, would also interfere with accurate beat-based timing perception. We show (n = 25) that cTBS down-regulation of lPPC does interfere with beat-based timing ability, but only the ability to detect shifts in beat phase, not changes in tempo. Down-regulation of lSMA, in contrast, did not interfere with beat-based timing. As expected, absolute interval timing ability was not impacted by the down-regulation of lPPC or lSMA. These results support that the dorsal auditory stream plays an essential role in accurate phase perception in beat-based timing. We find no evidence of an essential role of parietal cortex or SMA in interval timing.
A MATLAB toolbox for Granger causal connectivity analysis

Assessing directed functional connectivity from time series data is a key challenge in neuroscience. One approach to this problem leverages a combination of Granger causality analysis and network theory. This article describes a freely available MATLAB toolbox--'Granger causal connectivity analysis' (GCCA)--which provides a core set of methods for performing this analysis on a variety of neuroscience data types including neuroelectric, neuromagnetic, functional MRI, and other neural signals. The toolbox includes core functions for Granger causality analysis of multivariate steady-state and event-related data, functions to preprocess data, assess statistical significance and validate results, and to compute and display network-level indices of causal connectivity including 'causal density' and 'causal flow'. The toolbox is deliberately small, enabling its easy assimilation into the repertoire of researchers. It is however readily extensible given proficiency with the MATLAB language.
Solar water splitting cells.

Energy harvested directly from sunlight offers a desirable approach toward fulfilling, with minimal environmental impact, the need for clean energy. Solar energy is a decentralized and inexhaustible natural resource, with the magnitude of the available solar power striking the earth’s surface at any one instant equal to 130 million 500 MW power plants.1 However, several important goals need to be met to fully utilize solar energy for the global energy demand. First, the means for solar energy conversion, storage, and distribution should be environmentally benign, i.e. protecting ecosystems instead of steadily weakening them. The next important goal is to provide a stable, constant energy flux. Due to the daily and seasonal variability in renewable energy sources such as sunlight, energy harvested from the sun needs to be efficiently converted into chemical fuel that can be stored, transported, and used upon demand. The biggest challenge is whether or not these goals can be met in a costeffective way on the terawatt
Paying for performance: Performance incentives increase desire for the reward object.

The current research examines how exposure to performance incentives affects one's desire for the reward object. We hypothesized that the flexible nature of performance incentives creates an attentional fixation on the reward object (e.g., money), which leads people to become more desirous of the rewards. Results from 5 laboratory experiments and 1 large-scale field study provide support for this prediction. When performance was incentivized with monetary rewards, participants reported being more desirous of money (Study 1), put in more effort to earn additional money in an ensuing task (Study 2), and were less willing to donate money to charity (Study 4). We replicated the result with nonmonetary rewards (Study 5). We also found that performance incentives increased attention to the reward object during the task, which in part explains the observed effects (Study 6). A large-scale field study replicated these findings in a real-world setting (Study 7). One laboratory experiment failed to replicate (Study 3).
Coreference Resolution in a Modular, Entity-Centered Model

Coreference resolution is governed by syntactic, semantic, and discourse constraints. We present a generative, model-based approach in which each of these factors is modularly encapsulated and learned in a primarily unsupervised manner. Our semantic representation first hypothesizes an underlying set of latent entity types, which generate specific entities that in turn render individual mentions. By sharing lexical statistics at the level of abstract entity types, our model is able to substantially reduce semantic compatibility errors, resulting in the best results to date on the complete end-to-end coreference task.
Threshold-Dependent Camouflaged Cells to Secure Circuits Against Reverse Engineering Attacks

With current tools and technology, someone who has physical access to a chip can extract the detailed layout of the integrated circuit (IC). By using advanced visual imaging techniques, reverse engineering can reveal details that are meant to be kept secret, such as a secure protocol or novel implementation that offers a competitive advantage. A promising solution to defend against reverse engineering attacks is IC camouflaging. In this work, we propose a new camouflaging technique based on the threshold voltage of the transistors. We refer to these cells as threshold dependent camouflaged cells. Our work differs from current commercial solutions in that the latter use look-alike cells, with the assumption that it is difficult for the reverse engineer to identify the cell's functionality. Yet, if a structural distinction between cells exists, then these are still vulnerable, especially as reverse engineers use more advanced and precise techniques. On the other hand, the proposed threshold dependent standard cells are structurally identical regardless of the cells' functionality. Detailed circuit simulations of our proposed threshold dependent camouflaged cells demonstrate that they can be used to cost-effectively and robustly camouflage large netlists. Corner analysis of process, temperature, and supply voltage (PVT) variations show that our cells operate as expected over all PVT corners simulated.
Translating Collocations for Bilingual Lexicons: A Statistical Approach

Collocations are notoriously difficult for non-native speakers to translate, primarily because they are opaque and cannot be translated on a word-by-word basis. We describe a program named Champollion which, given a pair of parallel corpora in two different languages and a list of collocations in one of them, automatically produces their translations. Our goal is to provide a tool for compiling bilingual lexical information above the word level in multiple languages,for different domains. The algorithm we use is based on statistical methods and produces p-word translations of n-word collocations in which n and p need not be the same. For example, Champollion translates make ... decision, employment equity, and stock market into prendre ... d6cision, 6quit6 en mati6re d'emploi, and bourse respectively. Testing Champollion on three years' worth of the Hansards corpus yielded the French translations of 300 collocations for each year, evaluated at 73% accuracy on average. In this paper, we describe the statistical measures used, the algorithm, and the implementation of Champollion, presenting our results and evaluation.
A monad for deterministic parallelism

We present a new programming model for deterministic parallel computation in a pure functional language. The model is monadic and has explicit granularity, but allows dynamic construction of dataflow networks that are scheduled at runtime, while remaining deterministic and pure. The implementation is based on monadic concurrency, which has until now only been used to simulate concurrency in functional languages, rather than to provide parallelism. We present the API with its semantics, and argue that parallel execution is deterministic. Furthermore, we present a complete work-stealing scheduler implemented as a Haskell library, and we show that it performs at least as well as the existing parallel programming models in Haskell.
A compact printed monopole antenna with symmetrical i and rectangular shaped slots for bluetooth/WLAN/WIMAX applications

A compact dual band microstrip fed printed monopole antenna is designed and presented in this paper for Bluetooth/WLAN/WiMAX applications. Two I with rectangular shape slot have been etched on a Roger RT/duroid substrate to form desired monopole antenna. The radiator of the antenna is very compact with an area of only 9.1 × 10.2 mm2. The size of the I & rectangular slots of dual radiating elements were adjusted so as to achieve two different resonant modes for dual band operation. The simulation result shows that the proposed antenna has 10-dB impedance bandwidth of 219.6 MHz and 3.20 GHz which cover the Bluetooth, WLAN 2.4/5.8 GHz and the WiMAX 2.5/3.5 GHz bands. The overall proposed antenna size is 15 × 17 × 0.762 mm 3 with the obtained VSWR less than 1.5.
Cognitive Wireless Powered Network: Spectrum Sharing Models and Throughput Maximization

The recent advance in radio-frequency (RF) wireless energy transfer (WET) has motivated the study of wireless powered communication network (WPCN) in which distributed wireless devices are powered via dedicated WET by the hybrid access-point (H-AP) in the downlink (DL) for uplink (UL) wireless information transmission (WIT). In this paper, by utilizing the cognitive radio (CR) technique, we study a new type of CR enabled secondary WPCN, called cognitive WPCN, under spectrum sharing with the primary wireless communication system. In particular, we consider a cognitive WPCN, consisting of one single H-AP with constant power supply and distributed wireless powered users, shares the same spectrum for its DL WET and UL WIT with an existing primary communication link, where the WPCN's WET/WIT and the primary link's WIT may interfere with each other. Under this new setup, we propose two coexisting models for spectrum sharing of the two systems, namely underlay and overlay-based cognitive WPCNs, depending on different types of knowledge on the primary user transmission available for the cognitive WPCN. For each model, we maximize the sum-throughput of the cognitive WPCN by optimizing its transmission under different constraints applied to protect the primary user transmission. Analysis and simulation results are provided to compare the sum-throughput of the cognitive WPCN versus the achievable rate of the primary user under two proposed coexisting models. It is shown that the overlay based cognitive WPCN outperforms the underlay based counterpart, thanks to its fully co-operative WET/WIT design with the primary WIT, while also requiring higher complexity for implementation.
Improved Automatic Keyword Extraction Given More Linguistic Knowledge

In this paper, experiments on automatic extraction of keywords from abstracts using a supervised machine learning algorithm are discussed. The main point of this paper is that by adding linguistic knowledge to the representation (such as syntactic features), rather than relying only on statistics (such as term frequency and ngrams), a better result is obtained as measured by keywords previously assigned by professional indexers. In more detail, extracting NP-chunks gives a better precision than n-grams, and by adding the POS tag(s) assigned to the term as a feature, a dramatic improvement of the results is obtained, independent of the term selection approach applied.
Efficient Verification of Holograms Using Mobile Augmented Reality

Paper documents such as passports, visas and banknotes are frequently checked by inspection of security elements. In particular, optically variable devices such as holograms are important, but difficult to inspect. Augmented Reality can provide all relevant information on standard mobile devices. However, hologram verification on mobiles still takes long and provides lower accuracy than inspection by human individuals using appropriate reference information. We aim to address these drawbacks by automatic matching combined with a special parametrization of an efficient goal-oriented user interface which supports constrained navigation. We first evaluate a series of similarity measures for matching hologram patches to provide a sound basis for automatic decisions. Then a re-parametrized user interface is proposed based on observations of typical user behavior during document capture. These measures help to reduce capture time to approximately 15 s with better decisions regarding the evaluated samples than what can be achieved by untrained users.