nils-herrmann/clean_scirepeval huggingface dataset:
中国科技论文在线 Modeling , Analysis and Simulation of a Buck Converter Under Cascade Control

Zengshi Chen earned his Ph.D. degree in systems and controls at The Ohio State University in 2006. He is an automation research scientist in the automation division of Lufkin Industries, Inc. His research interests include variable structure systems, sliding mode control, nonlinear control and their applications to power converters, inverters and renewable energy machines.  In this paper, a cascade controller is designed and analyzed for a buck converter. The fast inner current loop uses sliding mode control (SMC). The slow outer voltage loop uses the proportional-integral (PI) control. Stability analysis and selection of PI gains are based on the closed-loop error dynamics. The closed-loop system is proven to have a minimum phase structure. The voltage transients due to step changes of input voltage and load resistance are predictable. The controller is validated by a simulation circuit with non-ideal circuit factors. The simulation results show that the reference output voltage is well tracked under system uncertainties or disturbances, confirming the validity of the proposed controller.
The Creation of a Large-Scale LFG-Based Gold Parsebank

Systems for syntactically parsing sentences have long been recognized as a priority in Natural Language Processing. Statistics-based systems require large amounts of high quality syntactically parsed data. Using the XLE toolkit developed at PARC and the LFG Parsebanker interface developed at Bergen, the Parsebank Project at Powerset has generated a rapidly increasing volume of syntactically parsed data. By using these tools, we are able to leverage the LFG framework to provide richer analyses via both constituent (c-) and functional (f-) structures. Additionally, the Parsebanking Project uses source data from Wikipedia rather than source data limited to a specific genre, such as the Wall Street Journal. This paper outlines the process we used in creating a large-scale LFG-Based Parsebank to address many of the shortcomings of previously-created parse banks such as the Penn Treebank. While the Parsebank corpus is still in progress, preliminary results using the data in a variety of contexts already show promise.
Wideband Millimeter-Wave Surface Micromachined Tapered Slot Antenna

A millimeter-wave surface micromachined tapered slot antenna (TSA) fed by an air-filled rectangular coaxial line is proposed. Detailed parametric study is conducted to determine the effects of the TSA's key structural features on its VSWR and gain. Selected exponential taper with determined growth rate and corrugation length resulted in an antenna with VSWR <; 2.5, gain > 2.75 dBi, and stable patterns over a 43-140-GHz range. The TSA is fabricated, and good correlation between modeling and W-band measurements confirms its wideband performance.
Clinical Concept Extraction with Contextual Word Embedding

Automatic extraction of clinical concepts is an essential step for turning the unstructured data within a clinical note into structured and actionable information. In this work, we propose a clinical concept extraction model for automatic annotation of clinical problems, treatments and tests in clinical notes utilizing domainspecific contextual word embedding. A contextual word embedding model is first trained on a corpus with a mixture of clinical reports and relevant Wikipedia pages in the clinical domain. Next, a bidirectional LSTM-CRF model is trained for clinical concept extraction using the contextual word embedding model. We tested our proposed model on the I2B2 2010 challenge dataset. Our proposed model achieved the best performance among reported baseline models and outperformed the state-of-the-art models by 3.4% in terms of F1-score.
Learning Relational Event Models from Video

Event models obtained automatically from video can be used in applications ranging from abnormal event detection to content based video retrieval. When multiple agents are involved in the events, characterizing events naturally suggests encoding interactions as relations. Learning event models from this kind of relational spatio-temporal data using relational learning techniques such as Inductive Logic Programming (ILP) hold promise, but have not been successfully applied to very large datasets which result from video data. In this paper, we present a novel framework remind (Relational Event Model INDuction) for supervised relational learning of event models from large video datasets using ILP. Efficiency is achieved through the learning from interpretations setting and using a typing system that exploits the type hierarchy of objects in a domain. The use of types also helps prevent over generalization. Furthermore, we also present a type-refining operator and prove that it is optimal. The learned models can be used for recognizing events from previously unseen videos. We also present an extension to the framework by integrating an abduction step that improves the learning performance when there is noise in the input data. The experimental results on several hours of video data from two challenging real world domains (an airport domain and a physical action verbs domain) suggest that the techniques are suitable to real world scenarios.
Pupillary Responses to Robotic and Human Emotions: The Uncanny Valley and Media Equation Confirmed

Physiological responses during human-robots interaction are useful alternatives to subjective measures of uncanny feelings for nearly humanlike robots (uncanny valley) and comparable emotional responses between humans and robots (media equation). However, no studies have employed the easily accessible measure of pupillometry to confirm the uncanny valley and media equation hypotheses, evidence in favor of the existence of these hypotheses in interaction with emotional robots is scarce, and previous studies have not controlled for low level image statistics across robot appearances. We therefore recorded pupil size of 40 participants that viewed and rated pictures of robotic and human faces that expressed a variety of basic emotions. The robotic faces varied along the dimension of human likeness from cartoonish to humanlike. We strictly controlled for confounding factors by removing backgrounds, hair, and color, and by equalizing low level image statistics. After the presentation phase, participants indicated to what extent the robots appeared uncanny and humanlike, and whether they could imagine social interaction with the robots in real life situations. The results show that robots rated as nearly humanlike scored higher on uncanniness, scored lower on imagined social interaction, evoked weaker pupil dilations, and their emotional expressions were more difficult to recognize. Pupils dilated most strongly to negative expressions and the pattern of pupil responses across emotions was highly similar between robot and human stimuli. These results highlight the usefulness of pupillometry in emotion studies and robot design by confirming the uncanny valley and media equation hypotheses.
Building Standard Dataset for Quran Tafseer

The growing number of scholars and students of the Quran Tafseer and its science has led to the increase of computer-based researches. Additionally, the evaluation of these researches requires computer-based experiments to be performed with a real Quran Tafseer documents. The available Tafseer documents are in text file or image file and are in Arabic language whereas the majority of the computer programming is in English language. Furthermore, the researchers will have difficulty to compare their research with other researchers. Therefore, the requirement for standard Tafseer dataset is very necessary for the current researches. This paper proposed a Tafseer dataset that will be used by researchers in Quran Tafseer and information processing fields. The dataset is organized in XML format to provide simple access and usage from the computer applications. A computer program is developed to create the dataset in XML structure. By applying the XML language computer applications users will be able to access the dataset and manipulate the content in easily.
Knowledge-Based Reasoning in a Unified Feature Modeling Scheme

Feature-based modeling is an accepted approach to include high-level geometric information in product models as well as to facilitate a parameterized and constraint-based product development process. Moreover, features are suitable as an intermediate layer between a product’s knowledge model and its geometry model for effective and efficient information management. To achieve this, traditional feature technology must be extended to align with the approach of knowledge-based reasoning. In this paper, based on a previously proposed unified feature modeling scheme, feature definitions are extended to support knowledge-based reasoning. In addition, a communication mechanism between the knowledge-based system and the feature model is established. The methods to embed and use knowledge for information consistency control are described.
The thoracolumbar fascia: anatomy, function and clinical considerations.

In this overview, new and existent material on the organization and composition of the thoracolumbar fascia (TLF) will be evaluated in respect to its anatomy, innervation biomechanics and clinical relevance. The integration of the passive connective tissues of the TLF and active muscular structures surrounding this structure are discussed, and the relevance of their mutual interactions in relation to low back and pelvic pain reviewed. The TLF is a girdling structure consisting of several aponeurotic and fascial layers that separates the paraspinal muscles from the muscles of the posterior abdominal wall. The superficial lamina of the posterior layer of the TLF (PLF) is dominated by the aponeuroses of the latissimus dorsi and the serratus posterior inferior. The deeper lamina of the PLF forms an encapsulating retinacular sheath around the paraspinal muscles. The middle layer of the TLF (MLF) appears to derive from an intermuscular septum that developmentally separates the epaxial from the hypaxial musculature. This septum forms during the fifth and sixth weeks of gestation. The paraspinal retinacular sheath (PRS) is in a key position to act as a 'hydraulic amplifier', assisting the paraspinal muscles in supporting the lumbosacral spine. This sheath forms a lumbar interfascial triangle (LIFT) with the MLF and PLF. Along the lateral border of the PRS, a raphe forms where the sheath meets the aponeurosis of the transversus abdominis. This lateral raphe is a thickened complex of dense connective tissue marked by the presence of the LIFT, and represents the junction of the hypaxial myofascial compartment (the abdominal muscles) with the paraspinal sheath of the epaxial muscles. The lateral raphe is in a position to distribute tension from the surrounding hypaxial and extremity muscles into the layers of the TLF. At the base of the lumbar spine all of the layers of the TLF fuse together into a thick composite that attaches firmly to the posterior superior iliac spine and the sacrotuberous ligament. This thoracolumbar composite (TLC) is in a position to assist in maintaining the integrity of the lower lumbar spine and the sacroiliac joint. The three-dimensional structure of the TLF and its caudally positioned composite will be analyzed in light of recent studies concerning the cellular organization of fascia, as well as its innervation. Finally, the concept of a TLC will be used to reassess biomechanical models of lumbopelvic stability, static posture and movement.
Spatial ray features for real-time ego-lane extraction

In order to support driver assistance systems in unconstrained environments, we propose to extend local appearance-based road classification with a spatial feature generation and classification. Therefore, a hierarchical approach consisting of multiple low level base classifiers, the novel spatial feature generation, as well as a final road terrain classification, is used. The system perceives a variety of local properties of the environment by means of base classifiers operating on patches extracted from monocular camera images, each represented in a metric confidence map. The core of the proposed approach is the computation of spatial ray features (SPRAY) from these confidence maps. With this, the road-terrain classifier can decide based on local visual properties and their spatial layout in the scene. In order to show the feasibility of the approach, the extraction and evaluation of the metric ego-lane driving corridor on an inner city stream is demonstrated. This is a challenging task because on a local appearance level, ego-lane is not distinguishable from other asphalt parts on the road. However, by incorporating the proposed SPRAY features the distinction is possible without requiring an explicit lane model. Due to the parallel structure of this bottom-up approach, the implemented system operates in real-time with approximately 25 Hz on a GPU.
Fast Eigenspace Approximation using Random Signals

We focus in this work on the estimation of the first k eigenvectors of any graph Laplacian using filtering of Gaussian random signals. We prove that we only need k such signals to be able to exactly recover as many of the smallest eigenvectors, regardless of the number of nodes in the graph. In addition, we address key issues in implementing the theoretical concepts in practice using accurate approximated methods. We also propose fast algorithms both for eigenspace approximation and for the determination of the kth smallest eigenvalue λk. The latter proves to be extremely efficient under the assumption of locally uniform distribution of the eigenvalue over the spectrum. Finally, we present experiments which show the validity of our method in practice and compare it to state-of-the-art methods for clustering and visualization both on synthetic smallscale datasets and larger real-world problems of millions of nodes. We show that our method allows a better scaling with the number of nodes than all previous methods while achieving an almost perfect reconstruction of the eigenspace formed by the first k eigenvectors. Graph signal processing, low-rank reconstruction, partitionning, spectral graph theory, spectrum analysis, subspace approximation, visualization
Ranging RFID Tags With Ultrasound

Indoor localization and tracking of persons and assets with centimeter-level accuracy for inventory, security, medical monitoring, and training, as well as gesture interfaces for domotics, is highly desirable in the framework of the emerging IoT paradigm. Low cost, tiny, battery, or batteryless operated position sensors are required. 3-D localization can be computed by combining three or more distance measurements between sensor and reference points. Our aim is to give the capability of measuring the distance from a reference point to radio frequency identification (RFID) tags. The main challenge is in the estimation of the distances with millimeter accuracy in presence of both size and power supply strict constraints, and thus with very limited computational power. An accurate ranging technique using cross-correlation and small RFID-based sensors is proposed. Its originality resides in moving the main computational efforts from the sensor to an external processing unit with sufficient computational and supply power, thus overcoming the sensor limits. The system is composed of a beacon that emits ultrasound chirps and RF sync signals, a RFID-based distance sensor, a commercial RFID reader, and a processing unit. Main advantages are the high miniaturization and low power consumption of the remote sensor, and its compatibility with existing RFID standards.
The Electronic McPhail Trap

Certain insects affect cultivations in a detrimental way. A notable case is the olive fruit ﬂy (Bactrocera oleae (Rossi)), that in Europe alone causes billions of euros in crop-loss/per year. Pests can be controlled with aerial and ground bait pesticide sprays, the efficiency of which depends on knowing the time and location of insect infestations as early as possible. The inspection of traps is currently carried out manually. Automatic monitoring traps can enhance efficient monitoring of flying pests by identifying and counting targeted pests as they enter the trap. This work deals with the hardware setup of an insect trap with an embedded optoelectronic sensor that automatically records insects as they fly in the trap. The sensor responsible for detecting the insect is an array of phototransistors receiving light from an infrared LED. The wing-beat recording is based on the interruption of the emitted light due to the partial occlusion from insect's wings as they fly in the trap. We show that the recordings are of high quality paving the way for automatic recognition and transmission of insect detections from the field to a smartphone. This work emphasizes the hardware implementation of the sensor and the detection/counting module giving all necessary implementation details needed to construct it.
Derivative Dynamic Time Warping

Time series are a ubiquitous form of data occurring in virtually every scientific discipline. A common task with time series data is comparing one sequence with another. In some domains a very simple distance measure, such as Euclidean distance will suffice. However, it is often the case that two sequences have the approximately the same overall component shapes, but these shapes do not line up in X-axis. Figure 1 shows this with a simple example. In order to find the similarity between such sequences, or as a preprocessing step before averaging them, we must "warp" the time axis of one (or both) sequences to achieve a better alignment. Dynamic time warping (DTW), is a technique for efficiently achieving this warping. In addition to data mining (Keogh & Pazzani 2000, Yi et. al. 1998, Berndt & Clifford 1994), DTW has been used in gesture recognition (Gavrila & Davis 1995), robotics (Schmill et. al 1999), speech processing (Rabiner & Juang 1993), manufacturing (Gollmer & Posten 1995) and medicine (Caiani et. al 1998).
Fingertip friction modulation due to electrostatic attraction

The human fingertip is extremely sensitive to lateral (shear) forces that arise in surface exploration. We and others have developed haptic displays that work by modulating surface friction via electrostatic attraction. Despite the demonstrated ability of these displays to render haptic environments, an understanding of the fingertip-surface interface is lacking. We have developed a tribometer for measuring the lateral frictional forces on a fingertip under well-controlled conditions. We show an expected square law dependence of frictional force (and inferred electrostatic normal force) on actuation voltage, although we observe a large person to person variability. We model an expected dependence of the frictional force on the frequency of the actuation voltage, predicting a first order cut off below about 500Hz. However, our measurements are unambiguously at odds with the model's predictions.
Towards End-to-End Lane Detection: an Instance Segmentation Approach

Modern cars are incorporating an increasing number of driver assist features, among which automatic lane keeping. The latter allows the car to properly position itself within the road lanes, which is also crucial for any subsequent lane departure or trajectory planning decision in fully autonomous cars. Traditional lane detection methods rely on a combination of highly-specialized, hand-crafted features and heuristics, usually followed by post-processing techniques, that are computationally expensive and prone to scalability due to road scene variations. More recent approaches leverage deep learning models, trained for pixel-wise lane segmentation, even when no markings are present in the image due to their big receptive field. Despite their advantages, these methods are limited to detecting a pre-defined, fixed number of lanes, e.g. ego-lanes, and can not cope with lane changes. In this paper, we go beyond the aforementioned limitations and propose to cast the lane detection problem as an instance segmentation problem - in which each lane forms its own instance - that can be trained end-to-end. To parametrize the segmented lane instances before fitting the lane, we further propose to apply a learned perspective transformation, conditioned on the image, in contrast to a fixed ”bird’s-eye view” transformation. By doing so, we ensure a lane fitting which is robust against road plane changes, unlike existing approaches that rely on a fixed, predefined transformation. In summary, we propose a fast lane detection algorithm, running at 50 fps, which can handle a variable number of lanes and cope with lane changes. We verify our method on the tuSimple dataset and achieve competitive results.
Predicting battery life from usage trajectory patterns

This paper addresses the task of predicting the battery capacity degradation ratio for a given usage pattern. This is an interesting pattern recognition task, where each usage pattern is represented as a trajectory in a feature space, and the prediction model captures the previous usage trajectory patterns. The main technical challenge here is how to build a good model from a limited number of training samples. To tackle this, we introduce a new smoothing technique in the trajectory space. The trajectory smoothing technique is shown to be equivalent of a novel regularization scheme for linear regression. Using real Li-ion battery data, we show that our approach outperforms existing methods.
Higher Order Recurrent Neural Networks

In this paper, we study novel neural network structures to better model long term dependency in sequential data. We propose to use more memory units to keep track of more preceding states in recurrent neural networks (RNNs), which are all recurrently fed to the hidden layers as feedback through different weighted paths. By extending the popular recurrent structure in RNNs, we provide the models with better short-term memory mechanism to learn long term dependency in sequences. Analogous to digital filters in signal processing, we call these structures as higher order RNNs (HORNNs). Similar to RNNs, HORNNs can also be learned using the back-propagation through time method. HORNNs are generally applicable to a variety of sequence modelling tasks. In this work, we have examined HORNNs for the language modeling task using two popular data sets, namely the Penn Treebank (PTB) and English text8. Experimental results have shown that the proposed HORNNs yield the state-of-the-art performance on both data sets, significantly outperforming the regular RNNs as well as the popular LSTMs.
A passivity based Cartesian impedance controller for flexible joint robots - part I: torque feedback and gravity compensation

In this paper a novel approach to the Cartesian impedance control problem for robots with flexible joints is presented. The proposed controller structure is based on simple physical considerations, which are motivating the extension of classical position feedback by an additional feedback of the joint torques. The torque feedback action can be interpreted as a scaling of the apparent motor inertia. Furthermore the problem of gravity compensation is addressed. Finally, it is shown that the closed loop system can be seen as a feedback interconnection of passive systems. Based on this passivity property a proof of asymptotic stability is presented.