nils-herrmann/clean_scirepeval huggingface dataset:
Electrical Design Considerations for a Bearingless Axial-Force/Torque Motor

A axial-force/torque motor (AFTM) establishes a completely new bearingless drive concept. The presented Lorentz-force-type actuator features a compact and integrated design using a very specific permanent-magnet excitation system and a concentric nonoverlapping air-gap stator winding. The end windings of the bent air-core coils, which are shaped in a circumferential rotor direction, provide active axial suspension forces. Thus, no additional (bearing) coils are needed for stable axial levitation. The four remaining degrees of freedom of the rotor are stabilized by passive magnetic ring bearings. This paper concentrates on the determination of the lumped parameters for the dynamic system modeling of the AFTM. After introducing a coordinate transformation for the decoupling of the control variables, the axial suspension force, and the drive torque, the relations for coil dimensioning are developed, followed by a discussion of the coil turn number selection process. Active levitation forces and drive torque specifications both must be concurrently fulfilled at a nominal rotor speed with only one common winding system, respecting several electrical, thermal, and mechanical boundaries likewise. Provided that the stator winding topology is designed properly, a simple closed-loop control strategy permits the autonomous manipulation of both control variables. A short presentation of the first experimental setup highlights the possible fields of application for the compact drive concept.
Development and Applications of Biomimetic Neuronal Networks Toward BrainMorphic Artificial Intelligence

This brief presents the brainmorphic artificial intelligence (BMAI) project. The main goal is to generate a novel type of neuromorphic computing systems including novel algorithms and devices, which can achieve very high performance of artificial intelligence (AI) information processing with low power consumption and which can be very close to biological systems. To reach this goal, we developed biomimetic neural networks. Two systems have been designed, one analog chip and one digital system into FPGA. We present in this brief two applications of these biomimetic neural networks, one in AI with a pattern recognition algorithm and one in neuroscience for understanding the hearing system of Drosophila for designing a new asynchronous low-power sensor system in the future.
A comparison of 18 different x-ray detectors currently used in dentistry.

There has been a proliferation of available dental x-ray detectors over the recent past. The purpose of this short technical report is to provide a basic comparison of spatial resolution, contrast perceptibility, and relative exposure latitudes of 18 current dental x-ray detectors, including solid-state systems (CCD and CMOS), photostimulable phosphors, and analog film.



Spatial resolution was measured using a 0.025 mm Pb phantom test grid with a measurement range from 1.5 to 20 lp/mm. For contrast perceptibility, a 7-mm thick aluminum perceptibility test device with wells of 0.1-0.9 mm depth at 0.1 mm intervals and 1 defect of 1.5 mm was used. The relative exposure latitude was determined by expert consensus using clear discrimination of the enamel-dentin junction as the lower limit and pixel blooming or unacceptable levels of cervical burnout as the upper limit.



The highest spatial resolution was found with Kodak InSight film, RVG-ui (CCD), and RVG 6000 (CMOS) detectors, each of which achieved 20 lp/mm, followed by the Planmeca Dixi2 v3 at > or =16 lp/mm. Contrast resolution was at least to 0.2 mm through 7 mm aluminum for all 18 detectors, with the best results being found for the Visualix HDI, RVG-ui, RVG 5000, and RVG 6000 detectors and the Schick CDR wired and wireless systems. The greatest exposure ranges were found with photostimulable phosphors and the Kodak RVG 6000 and RVG 5000 detectors.



Most current x-ray detectors generally perform well in terms of spatial and contrast resolutions, and in terms of exposure latitude. These findings were independent of the modality applied.
Unsupervised Text Classification for Natural Language Interactive Narratives

Natural language interactive narratives are a variant of traditional branching storylines where player actions are expressed in natural language rather than by selecting among choices. Previous efforts have handled the richness of natural language input using machine learning technologies for text classification, bootstrapping supervised machine learning approaches with human-in-the-loop data acquisition or by using expected player input as fake training data. This paper explores a third alternative, where unsupervised text classifiers are used to automatically route player input to the most appropriate storyline branch. We describe the Data-driven Interactive Narrative Engine (DINE), a web-based tool for authoring and deploying natural language interactive narratives. To compare the performance of different algorithms for unsupervised text classification, we collected thousands of user inputs from hundreds of crowdsourced participants playing 25 different scenarios, and hand-annotated them to create a goldstandard test set. Through comparative evaluations, we identified an unsupervised algorithm for narrative text classification that approaches the performance of supervised text classification algorithms. We discuss how this technology supports authors in the rapid creation and deployment of interactive narrative experiences, with authorial burdens similar to that of traditional branching storylines.
On a Future for Power Electronics

This paper presents a historical and philosophical perspective on a possible future for power electronics. Technologies have specific life cycles that are driven by internal innovation, subsequently reaching maturity. Power electronics appears to be a much more complex case, functioning as an enabling technology spanning an enormous range of power levels, functions and applications. Power electronics is also divided into many constituent technologies. Till now, the development of power electronics has been driven chiefly by internal semiconductor technology and converter circuit technology, approaching maturity in its internally set metrics, such as efficiency. This paper examines critically the fundamental functions found in electronic energy processing, the constituent technologies comprising power electronics, and the power electronics technology space in light of the internal driving philosophy of power electronics and its historical development. It is finally concluded that, although approaching the limits of its internal metrics indicates internal maturity, the external constituent technologies of packaging, manufacturing, electromagnetic and physical impact, and converter control technology still present remarkable opportunities for development. As power electronics is an enabling technology, its development, together with internal developments, such as wide bandgap semiconductors, will be driven externally by applications in the future.
Differential Learning as a Key Training Approach to Improve Creative and Tactical Behavior in Soccer.

The aim of this study was to identify the effects of a differential-learning program, embedded in small-sided games, on the creative and tactical behavior of youth soccer players. Forty players from under-13 (U13) and under-15 (U15) were allocated into control and experimental groups and were tested using a randomized pretest to posttest design using small-sided games situations.



The experimental group participated in a 5-month differential-learning program embodied in small-sided games situations, while the control group participated in a typical small-sided games training program. In-game creativity was assessed through notational analyses of the creative components, and the players' positional data were used to compute tactical-derived variables.



The findings suggested that differential learning facilitated the development of creative components, mainly concerning attempts (U13, small; U15, small), versatility (U13, moderate; U15, small), and originality (U13, unclear; U15, small) of players' actions. Likewise, the differential-learning approach provided a decrease in fails during the game in both experimental groups (moderate). Moreover, differential learning seemed to favor regularity in pitch-positioning behavior for the distance between players' dyads (U13, small; U15, small), the distance to the team target (U13, moderate; U15, small), and the distance to the opponent target (U13, moderate; U15, small).



The differential-learning program stressed creative and positional behavior in both age groups with a distinct magnitude of effects, with the U13 players demonstrating higher improvements over the U15 players. Overall, these findings confirmed that the technical variability promoted by differential learning nurtures regularity of positioning behavior.
Smoothed marginal distribution constraints for language modeling

We present an algorithm for re-estimating parameters of backoff n-gram language models so as to preserve given marginal distributions, along the lines of wellknown Kneser-Ney (1995) smoothing. Unlike Kneser-Ney, our approach is designed to be applied to any given smoothed backoff model, including models that have already been heavily pruned. As a result, the algorithm avoids issues observed when pruning Kneser-Ney models (Siivola et al., 2007; Chelba et al., 2010), while retaining the benefits of such marginal distribution constraints. We present experimental results for heavily pruned backoff ngram models, and demonstrate perplexity and word error rate reductions when used with various baseline smoothing methods.
Spatio-Temporally Consistent Correspondence for Dense Dynamic Scene Modeling

Local Rigidity Test Problem. Dense 3D reconstruction of a dynamic foreground subject from a pair of unsynchronized videos with unknown temporal overlap. Challenges: 1. How to identify temporal overlap in terms of estimated dynamic geometry . 2. How to robustly estimate geometry without knowledge of temporal overlap.  1. Define the cardinality of the maximal set of locally rigid feature tracks as a measure of spatio-temporal consistency of a pair of video sub-sequences. 2. Develop a closed-loop track correspondence refinement process to find the maximal set of rigid tracks.  1. We exploit the correlation between temporal alignment errors and geometric estimation errors. 2. We provide a joint solution to the geometry estimation and temporal the video alignment problems. 3. Model-free (i.e. data-driven) framework with wide applicability.
Highly Directive Hybrid Metal-Dielectric Yagi-Uda Nanoantennas.

A hybrid metal-dielectric nanoantenna promises to harness the large Purcell factor of metallic nanostructures while taking advantage of the high scattering directivity and low dissipative losses of dielectric nanostructures. Here, we investigate a compact hybrid metal-dielectric nanoantenna that is inspired by the Yagi-Uda design. It comprises a metallic gold bowtie nanoantenna feed element and three silicon nanorod directors, exhibiting high unidirectional in-plane directivity and potential beam redirection capability in the visible spectral range. The entire device has a footprint of only 0.38 λ2, and its forward directivity is robust against fabrication imperfections. We use the photoluminescence from the gold bowtie nanoantenna itself as an elegant emitter to characterize the directivity of the device and experimentally demonstrate a directivity of ∼49.2. In addition, we demonstrate beam redirection with our device, achieving a 5° rotation of the main emission lobe with a feed element displacement of only 16 nm. These results are promising for various applications, including on-chip wireless communications, quantum computing, display technologies, and nanoscale alignment.
Development of Acute Megakaryoblastic Leukemia with Isochromosome (12p) after a Primary Mediastinal Germ Cell Tumor in Korea

The association of hematological malignancies with a mediastinal germ cell tumor (GCT) is very rare. We report one case of a young adult male with primary mediastinal GCT who subsequently developed acute megakaryoblastic leukemia involving isochromosome (12p). A 25-yr-old man had been diagnosed with a mediastinal GCT and underwent surgical resection and adjuvant chemotherapy. At 1 week after the last cycle of chemotherapy, his peripheral blood showed leukocytosis with blasts. A bone marrow study confirmed the acute megakaryoblastic leukemia. A cytogenetic study revealed a complex karyotype with i(12p). Although additional chemotherapy was administered, the patient could not attain remission and died of septic shock. This case was definitely distinct from therapy-related secondary leukemia in terms of clinical, morphologic, and cytogenetic features. To our knowledge, this is the first case report of a patient with mediastinal GCT subsequently developing acute megakaryoblastic leukemia involving i(12p) in Korea.
Task-based limb optimization for legged robots

The design of legged robots is often inspired by animals evolved to excel at different tasks. However, while mimicking morphological features seen in nature can be very powerful, robots may need to perform motor tasks that their living counterparts do not. In the absence of designs that can be mimicked, an alternative is to resort to mathematical models that allow the relationship between a robot's form and function to be explored. In this paper, we propose such a model to co-design the motion and leg configurations of a robot such that a measure of performance is optimized. The framework begins by planning trajectories for a simplified model consisting of the center of mass and feet. The framework then optimizes the length of each leg link while solving for associated full-body motions. Our model was successfully used to find optimized designs for legged robots performing tasks that include jumping, walking, and climbing up a step. Although our results are preliminary and our analysis makes a number of simplifying assumptions, our findings indicate that the cost function, the sum of squared joint torques over the duration of a task, varies substantially as the design parameters change.
Intelligent traffic management system

This paper presents an intelligent traffic management system using RFID technology. The system is capable of providing practically important traffic data which would aid in reducing the travel time for the users. Also, it can be used for other purposes like tracing of stolen cars, vehicles that evade traffic signals/tickets, toll collection or vehicle taxes etc. The system consists of a passive tag, an RFID reader, a microcontroller, a GPRS module, a high-speed server with a database system and a user module. Using RFID technology, this system collects the required data and calculates average speed of vehicles on each road of a city under consideration. It then transmits the acquired data i.e., average speed calculated at various junctions to the central computation server which calculates the time taken by a vehicle to travel in a particular road. Through Dijkstra's algorithm, the central server computes the fastest route to all the nodes (junctions) considering each node as the initial point in the city. Therefore, the system creates a map of shortest time paths of the whole city. This data is accessed by users through an interface module placed in their vehicles.
A Study on Connected Components Labeling algorithms using GPUs

Connected Components Labeling (CCL) is a wellknown problem with many applications in Image Processing. We propose in this article an optimized version of CCL for GPUs using GPGPU (General- Computing on Graphics Processing Units) techniques and the usual UnionFind algorithm to solve the CCL problem. We compare its performance with an efficient serial algorithm and with Label Equivalence, another method proposed in the literature which uses GPUs as well. Our algorithm presented a 5-10x speedup in relation to the serial algorithm and performed similar to Label Equivalence, but proved to be more predictable in the sense that its execution time is more image-independent. Image processing; Connected Components Labeling; Connected Components Analysis; GPU;
Close to Human Quality TTS with Transformer

Although end-to-end neural text-to-speech (TTS) methods (such as Tacotron2) are proposed and achieve state-of-theart performance, they still suffer from two problems: 1) low efficiency during training and inference; 2) hard to model long dependency using current recurrent neural networks (RNNs). Inspired by the success of Transformer network in neural machine translation (NMT), in this paper, we introduce and adapt the multi-head attention mechanism to replace the RNN structures and also the original attention mechanism in Tacotron2. With the help of multi-head self-attention, the hidden states in the encoder and decoder are constructed in parallel, which improves training efficiency. Meanwhile, any two inputs at different times are connected directly by a self-attention mechanism, which solves the long range dependency problem effectively. Using phoneme sequences as input, our Transformer TTS network generates mel spectrograms, followed by a WaveNet vocoder to output the final audio results. Experiments are conducted to test the efficiency and performance of our new network. For the efficiency, our Transformer TTS network can speed up the training about 4.25 times faster compared with Tacotron2. For the performance, rigorous human tests show that our proposed model achieves state-of-the-art performance (outperforms Tacotron2 with a gap of 0.048) and is very close to human quality (4.39 vs 4.44 in MOS).
Case records of the Massachusetts General Hospital. Case 9-2011. A 37-year-old man with flushing and hypotension.

From the Divisions of Rheumatology, Allergy, and Immunology (M.R.M.) and Cardiology (D.M.D.); and the Departments of Radiology (J.Y.S.) and Pathology (R.P.H.), Massachusetts General Hospital; the Division of Rheumatology, Allergy, and Immunology, Brigham and Women’s Hospital (M.C.C.); and the Departments of Medicine (M.R.M., M.C.C., D.M.D.), Radiology (J.Y.S.), and Pathology (R.P.H.), Harvard Medical School — all in Boston.
WHATSUP: A Decentralized Instant News Recommender

We present WHATSUP, a collaborative filtering system for disseminating news items in a large-scale dynamic setting with no central authority. WHATSUP constructs an implicit social network based on user profiles that express the opinions of users about the news items they receive (like-dislike). Users with similar tastes are clustered using a similarity metric reflecting long-standing and emerging (dis)interests. News items are disseminated through a novel heterogeneous gossip protocol that (1) biases the orientation of its targets towards those with similar interests, and (2) amplifies dissemination based on the level of interest in every news item. We report on an extensive evaluation of WHATSUP through (a) simulations, (b) a ModelNet emulation on a cluster, and (c) a PlanetLab deployment based on real datasets. We show that WHATSUP outperforms various alternatives in terms of accurate and complete delivery of relevant news items while preserving the fundamental advantages of standard gossip: namely, simplicity of deployment and robustness.
27.6 A 4GS/s 13b pipelined ADC with capacitor and amplifier sharing in 16nm CMOS

In recent years, we have seen the emergence of multi-GS/s medium-to-high-resolution ADCs. Presently, SAR ADCs dominate low-speed applications and time-interleaved SARs are becoming increasingly popular for high-speed ADCs [1,2]. However the SAR architecture faces two key problems in simultaneously achieving multi-GS/s sample rates and high resolution: (1) the fundamental trade-off of comparator noise and speed is limiting the speed of single-channel SARs, and (2) highly time-interleaved ADCs introduce complex lane-to-lane mismatches that are difficult to calibrate with high accuracy. Therefore, pipelined  and pipelined-SAR [4] remain the most common architectural choices for high-speed high-resolution ADCs. In this work, a pipelined ADC achieves 4GS/s sample rate, using a 4-step capacitor and amplifier-sharing front-end MDAC architecture with 4-way sampling to reduce noise, distortion and power, while overcoming common issues for SHA-less ADCs.
Skin disease: a cardinal feature of systemic sclerosis.

Despite the heterogeneity of SSc, almost all patients have skin involvement. As such, skin manifestations are critical in the initial diagnosis of SSc and in the subsequent sub-classification into the different subsets of disease. The two principal subsets are lcSSc and dcSSc. The main difference between these two subsets is the speed of disease progression and the extent and severity of skin and visceral involvement; lcSSc has an insidious onset with skin involvement confined largely to the face and extremities. Whilst vascular manifestations of SSc such as pulmonary arterial hypertension are typically more common in lcSSc, patients in both subsets can develop ischaemic digital ulcers. In dcSSc, disease progression is very rapid, with skin thickening extending beyond the extremities and earlier, more widespread internal organ involvement. DcSSc is generally considered to be the more severe subset of the disease. Skin scores in SSc correlate inversely with survival and are considered a valuable marker of disease severity. Skin involvement is easily detectable and, using the modified Rodnan skin score, the degree of skin fibrosis can be quantified. As well as general management measures, a number of targeted therapies are commonly used for treatment of cutaneous manifestations of SSc. These include the intravenous prostanoid iloprost and the dual endothelin receptor antagonist bosentan, which is approved in Europe for the prevention of new digital ulcers.
Implementing prototype model for School Security System (SSS) using RFID

Now days, the number of crime over children is increasing day by day. the implementation of School Security System(SSS) via RFID to avoid crime, illegal activates by students and reduce worries among parents. The project is the combination of latest Technology using RFID, GPS/GSM, image processing, WSN and web based development using Php,VB.net language apache web server and SQL. By using RFID technology it is easy track the student thus enhances the security and safety in selected zone. The information about student such as in time and out time from Bus and campus will be recorded to web based system and the GPS/GSM system automatically sends information (SMS / Phone Call) toothier parents. That the student arrived to Bus/Campus safely.
Toward Real Event Detection

News agencies and other news providers or consumers are confronted with the task of extracting events from news articles. This is done i) either to monitor and, hence, to be informed about events of specific kinds over time and/or ii) to react to events immediately. In the past, several promising approaches to extracting events from text have been proposed. Besides purely statistically-based approaches there are methods to represent events in a semantically-structured form, such as graphs containing actions (predicates), participants (entities), etc. However, it turns out to be very difficult to automatically determine whether an event is real or not. In this paper, we give an overview of approaches which proposed solutions for this research problem. We show that there is no gold standard dataset where real events are annotated in text documents in a fine-grained, semantically-enriched way. We present a methodology of creating such a dataset with the help of crowdsourcing and present preliminary results.