nils-herrmann/clean_scirepeval huggingface dataset:
Management of incompletely developed teeth requiring root canal treatment.

Endodontic management of the permanent immature tooth continues to be a challenge for both clinicians and researchers. Clinical concerns are primarily related to achieving adequate levels of disinfection as 'aggressive' instrumentation is contraindicated and hence there exists a much greater reliance on endodontic irrigants and medicaments. The open apex has also presented obturation difficulties, notably in controlling length. Long-term apexification procedures with calcium hydroxide have proven to be successful in retaining many of these immature infected teeth but due to their thin dentinal walls and perceived problems associated with long-term placement of calcium hydroxide, they have been found to be prone to cervical fracture and subsequent tooth loss. In recent years there has developed an increasing interest in the possibility of 'regenerating' pulp tissue in an infected immature tooth. It is apparent that although the philosophy and hope of 'regeneration' is commendable, recent histologic studies appear to suggest that the calcified material deposited on the canal wall is bone/cementum rather than dentine, hence the absence of pulp tissue with or without an odontoblast layer.
Sprengel deformity: pathogenesis and management.

Sprengel deformity (ie, congenital elevation of the scapula) is a rare clinical entity. However, it is the most common congenital anomaly of the shoulder. Sprengel deformity is caused by abnormal descent of the scapula during embryonic development. Sprengel deformity is associated with cosmetic deformity and decreased shoulder function. Diagnostic confusion with limited scoliosis can be dangerous to the patient because it may delay proper treatment of other abnormalities that may be present with even mild cases. Sprengel deformity is commonly linked to a variety of conditions, including Klippel-Feil syndrome, scoliosis, and rib anomalies. Nonsurgical management can be considered for mild cases. Surgical management is typically warranted for more severe cases, with the goal of improving cosmesis and function. Surgical techniques are centered on resection of the protruding portion of the scapula and inferior translation of the scapula. Recent long-term studies indicate that patients treated surgically maintain improved shoulder function and appearance.
An Improved Pulse Width Modulation Method for Chopper-Cell-Based Modular Multilevel Converters

In this paper, an improved pulse width modulation (PWM) method for chopper-cell (or half-bridge)based modular multilevel converters (MMCs) is proposed. This method can generate an output voltage with maximally 2N+1 (where N is the number of submodules in the upper or lower arm of MMC) levels, which is as great as that of the carrier-phase-shifted PWM (CPSPWM) method. However, no phase-shifted carrier is needed. Compared with the existing submodule unified pulse width modulated (SUPWM) method, the level number of the output voltage is almost doubled and the height of the step in the staircase voltage is reduced by 50%. Meanwhile, the equivalent switching frequency in the output voltage is twice that of the conventional SUPWM method. All these features lead to much reduced harmonic content in the output voltage. What is more, the voltages of the submodule capacitors can be well balanced without any close-loop voltage balancing controllers which are mandatory in the CPSPWM schemes. Simulation and experimental results on a MMC-based inverter show validity of the proposed method.
Workshop Track -iclr 2017 Neural Combinatorial Optimization with Reinforcement Learning

We present a framework to tackle combinatorial optimization problems using neural networks and reinforcement learning. We focus on the traveling salesman problem (TSP) and train a recurrent neural network that, given a set of city coordinates, predicts a distribution over different city permutations. Using negative tour length as the reward signal, we optimize the parameters of the recurrent neural network using a policy gradient method. Without much engineering and heuristic designing, Neural Combinatorial Optimization achieves close to optimal results on 2D Euclidean graphs with up to 100 nodes. These results, albeit still quite far from state-of-the-art, give insights into how neural networks can be used as a general tool for tackling combinatorial optimization problems.
Alar base reduction: the boomerang-shaped excision.

A boomerang-shaped alar base excision is described to narrow the nasal base and correct the excessive alar flare. The boomerang excision combined the external alar wedge resection with an internal vestibular floor excision. The internal excision was inclined 30 to 45 degrees laterally to form the inner limb of the boomerang. The study included 46 patients presenting with wide nasal base and excessive alar flaring. All cases were followed for a mean period of 18 months (range, 8 to 36 months). The laterally oriented vestibular floor excision allowed for maximum preservation of the natural curvature of the alar rim where it meets the nostril floor and upon its closure resulted in a considerable medialization of alar lobule, which significantly reduced the amount of alar flare and the amount of external alar excision needed. This external alar excision measured, on average, 3.8 mm (range, 2 to 8 mm), which is significantly less than that needed when a standard vertical internal excision was used ( P < 0.0001). Such conservative external excisions eliminated the risk of obliterating the natural alar-facial crease, which did not occur in any of our cases. No cases of postoperative bleeding, infection, or vestibular stenosis were encountered. Keloid or hypertrophic scar formation was not encountered; however, dermabrasion of the scars was needed in three (6.5%) cases to eliminate apparent suture track marks. The boomerang alar base excision proved to be a safe and effective technique for narrowing the nasal base and elimination of the excessive flaring and resulted in a natural, well-proportioned nasal base with no obvious scarring.
Interaction-aware driver maneuver inference in highways using realistic driver models

In this work, we address the problem of lane change maneuver prediction in highway scenarios using information from sensors and perception systems widely used in automated driving. Our prediction approach is two-fold. First, a driver model learned from demonstrations via Inverse Reinforcement Learning is used to equip a host vehicle with the anticipatory behavior reasoning capability of common drivers. Second, inference on an interaction-aware augmented Switching State-Space Model allows the approach to account for the dynamic evidence observed. The use of a driver model that correctly balances the driving and risk-aversive preferences of a driver allows the computation of a planning-based maneuver prediction. Integrating this anticipatory prediction into the maneuver inference engine brings a degree of scene understanding into the estimate and leads to faster lane change detections compared to those obtained by relying on dynamics alone. The performance of the presented framework is evaluated using highway data collected with an instrumented vehicle. The combination of model-based maneuver prediction and filtering-based state and maneuver tracking is shown to outperform an Interacting Multiple Model filter in the detection of highway lane change maneuvers regarding accuracy, detection latency — by an average of 0.4 seconds- and false-positive rates.
Modified greedy permutation algorithm for low complexity encoding in LDPC codes

The low-density parity-check (LDPC) codes are used to achieve excellent performance with low encoding and decoding complexity. One major criticism concerning LDPC codes has been their apparent high encoding complexity and memory inefficient nature due to large parity check matrix. More generally, we consider the encoding problem for codes specified by sparse parity-check matrices. We show how to exploit the sparseness of the parity-check matrix to obtain efficient encoders. A new technique for efficient encoding of LDP Codes based on the known concept of approximate lower triangulation (ALT) is introduced. The algorithm computes parity check symbols by solving a set of sparse equations, and the triangular factorization is employed to solve the equations efficiently. The key of the encoding method is to get the systematic approximate lower triangular (SALT) form of the Parity Check Matrix with minimum gap g, because the smaller the gap is, the more efficient encoding will be obtained. The functions are to be coded in MATLAB.
Value Creation in Markets A Critical Area of Focus for Business-to-Business Markets

The business market has been undergoing a paradigmatic change. The rise of the Internet, market fragmentation, and increasing global competition is changing the “value” that business marketers provide. This paradigmatic transformation requires changes in the way companies are organized to create and deliver value to their customers. Business marketers have to continuously increase their contribution to the value chain. If not, value migrates from a given business paradigm (e.g., minicomputers and DEC) to alternate business paradigms (e.g., flexibly manufactured PCs and Dell). This article focuses on ways in which business marketers are creating value in the Internet and digital age. Examples from business marketers are discussed and managerial implications are highlighted.
The Disabilities of the Arm , Shoulder and Hand Questionnaire ( DASH ) can measure the International Classification of Functioning , Disability and Health ( ICF )

Disability and Health (ICF) model of the consequences of disease identifies three health outcomes, impairment, activity limitations and participation restrictions. However, few orthopaedic health outcome measures were developed with reference to the ICF. This study examined the ability of a valid and frequently used measure of upper limb function, namely the Disabilities of the Arm, Shoulder and Hand Questionnaire (DASH), to operationalise the ICF.  Twenty-four judges used the method of Discriminant Content Validation to allocate the 38 items of the DASH to the theoretical definition of one or more ICF outcome. Onesample t-tests classified each item as measuring, impairment, activity limitations, participation restrictions, or a combination thereof.  The DASH contains items able to measure each of the three ICF outcomes with discriminant validity. The DASH contains five pure impairment items, 19 pure activity limitations items and three participation restriction items. In addition, seven items measured both activity limitations and participation restrictions.  The DASH can measure the three health outcomes identified by the ICF. Consequently the DASH could be used to examine the impact of trauma and subsequent interventions on each health outcome in the absence of measurement confound.
Mobility Calculation of Ge Nanowire Junctionless and Inversion-Mode Nanowire NFETs With Size and Shape Dependence

The electron mobility in junctionless (JL) and inversion-mode (IM) Ge nanowire nFETs with [110] channel direction is studied by theoretical calculations. Channels with different widths and cross-sectional shapes are compared. The mobility calculation is based on the Kubo–Greenwood formulation for 1-D systems. Phonon scattering, Coulomb scattering (CS), and surface roughness scattering (SRS) are considered. With the equivalent oxide thickness of 1 nm, diamond and square channels have the higher mobility than circular channels at low channel carrier density, but the lower mobility at high channel carrier density for both JL and IM channels. This mobility trend is explained by the SRS on the sharp corners in diamond and square channels. The JL channels have the lower mobility than IM channels at low channel carrier density due to additional CS. At high channel carrier density, SRS dominates, and the JL channel has the slightly higher mobility than the IM channel due to its weaker SRS. The mobility difference between JL and IM is negligible for 5-nm narrow wires.
A Neuro-Fuzzy Development Tool for Fuzzy Controllers under MATLAB/SIMULINK

The design and optimization process of fuzzy controllers can be supported by learning techniques derived from neural networks. Such approaches are usually called neuro-fuzzy systems. In this paper we describe an updated version of the neuro-fuzzy model NEFCON. This model is able to learn and optimize the rulebase of a Mamdani like fuzzy controller online by a reinforcement learning algorithm that uses a fuzzy error measure. Therefore we also describe some methods to determine a fuzzy error measure of a dynamic system. In addition we present an implementation of the model and an application example under the MATLAB/SIMULINK development environment. The implementation uses a graphical user interface to control the learning process interactively. The optimized fuzzy controller can be detached from the development environment and can be used in real time environments. The tool is available free of charge for non-commercial purposes (http://fuzzy.cs.uni-magdeburg.de/nefcon).
SentiWords: Deriving a High Precision and High Coverage Lexicon for Sentiment Analysis

Deriving prior polarity lexica for sentiment analysis - where positive or negative scores are associated with words out of context - is a challenging task. Usually, a trade-off between precision and coverage is hard to find, and it depends on the methodology used to build the lexicon. Manually annotated lexica provide a high precision but lack in coverage, whereas automatic derivation from pre-existing knowledge guarantees high coverage at the cost of a lower precision. Since the automatic derivation of prior polarities is less time consuming than manual annotation, there has been a great bloom of these approaches, in particular based on the SentiWordNet resource. In this paper, we compare the most frequently used techniques based on SentiWordNet with newer ones and blend them in a learning framework (a so called `ensemble method'). By taking advantage of manually built prior polarity lexica, our ensemble method is better able to predict the prior value of unseen words and to outperform all the other SentiWordNet approaches. Using this technique we have built SentiWords, a prior polarity lexicon of approximately 155,000 words, that has both a high precision and a high coverage. We finally show that in sentiment analysis tasks, using our lexicon allows us to outperform both the single metrics derived from SentiWordNet and popular manually annotated sentiment lexica.
Active gate control for switching waveform shaping irrespective of the circuit stray inductance in a practical full-bridge IGBT inverter

This paper proposes an active gate control method for switching waveform shaping irrespective of circuit stray inductance and an evaluating system based on a practical full-bridge inverter equipped with digital gate drivers. The main circuit of the proposed system has 1700-V, 150-A IGBT power modules in a practical circuit layout resulting in large stray inductances. The proposed system has a capability of optimizing driving patterns for shaping the designed switching waveform. In experimental verification, active gate control can suppress the surge voltage from higher than 60% to 6% with a fast switching speed in three different circuit layout of the proposed practical 50-kVA full-bridge inverter. As a result, this paper reveals that active gate control achieves reduction in effect of stray inductance and increases a freedom of the circuit layout in power converters.
Design and Fabrication of a High-Gain 60-GHz Cavity-Backed Slot Antenna Array Fed by Inverted Microstrip Gap Waveguide

This communication deals with the design of a <inline-formula> <tex-math notation="LaTeX">$16\times 16$ </tex-math></inline-formula> slot array antenna fed by inverted microstrip gap waveguide (IMGW). The whole structure designed in this communication consists of radiating slots, a groove gap cavity layer, a distribution feeding network, and a transition from standard WR-15 waveguide to the IMGW. First, a <inline-formula> <tex-math notation="LaTeX">$2\times 2$ </tex-math></inline-formula> cavity-backed slot subarray is designed with periodic boundary condition to achieve good performances of radiation pattern and directivity. Then, a complete IMGW feeding network with a transition from WR-15 rectangular waveguide to the IMGW has been realized to excite the radiating slots. The complete antenna array is designed at 60-GHz frequency band and fabricated using Electrical Discharging Machining Technology. The measurements show that the antenna has a 16.95% bandwidth covering 54–64-GHz frequency range. The measured gain of the antenna is more than 28 dBi with the efficiency higher than 40% covering 54–64-GHz frequency range.
Semantic Communication for Simple Goals Is Equivalent to On-line Learning

Previous works [11, 6] introduced a model of semantic communication between a “user” and a “server,” in which the user attempts to achieve a given goal for communication. They show that whenever the user can sense progress, there exist universal user strategies that can achieve the goal whenever it is possible for any other user to reliably do so. A drawback of the actual constructions is that the users are inefficient: they enumerate protocols until they discover one that is successful, leading to the potential for exponential overhead in the length of the desired protocol. Goldreich et al.  conjectured that this overhead could be reduced to a polynomial dependence if we restricted our attention to classes of sufficiently simple user strategies and goals. In this work, we are able to obtain such universal strategies for some reasonably general special cases by establishing an equivalence between these special cases and the usual model of mistake-bounded on-line learning [3, 15]. This equivalence also allows us to see the limits of constructing universal users based on sensing and motivates the study of sensing with richer kinds of feedback. Along the way, we also establish a new lower bound for the “beliefs model” , which demonstrates that constructions of efficient users in that framework rely on the existence of a common “belief” under which all of the servers in a class are designed to be efficient.
Multi-View Learning With Incomplete Views

One underlying assumption of the conventional multi-view learning algorithms is that all examples can be successfully observed on all the views. However, due to various failures or faults in collecting and pre-processing the data on different views, we are more likely to be faced with an incomplete-view setting, where an example could be missing its representation on one view (i.e., missing view) or could be only partially observed on that view (i.e., missing variables). Low-rank assumption used to be effective for recovering the random missing variables of features, but it is disabled by concentrated missing variables and has no effect on missing views. This paper suggests that the key to handling the incomplete-view problem is to exploit the connections between multiple views, enabling the incomplete views to be restored with the help of the complete views. We propose an effective algorithm to accomplish multi-view learning with incomplete views by assuming that different views are generated from a shared subspace. To handle the large-scale problem and obtain fast convergence, we investigate a successive over-relaxation method to solve the objective function. Convergence of the optimization technique is theoretically analyzed. The experimental results on toy data and real-world data sets suggest that studying the incomplete-view problem in multi-view learning is significant and that the proposed algorithm can effectively handle the incomplete views in different applications.
Automatic clothes segmentation for soft biometrics

During the last decade, researchers have verified that clothing can provide information for gender recognition. However, before extracting features, it is necessary to segment the clothing region. We introduce a new clothes segmentation method based on the application of the GrabCut technique over a trixel mesh, obtaining very promising results for a close to real time system. Finally, the clothing features are combined with facial and head context information to outperform previous results in gender recognition with a public database.
Performance Analysis of GWO , GA and PSO Optimized FOPID and PSS for SMIB System

The designing of power transmission network is a difficult task due to the complexity of power system. Due to complexity in the power system there is always a loss of the stability due to the fault. Whenever a fault is intercepted in system, the whole system goes to severe transients. These transients cause oscillation in phase angle which leads poor power quality. The nature of oscillation is increasing instead being sustained, which leads system failure in form of generator damage. To reduce and eliminate the unstable oscillations one needs to use a stabilizer which can generate a perfect compensatory signal in order to minimize the harmonics generated due to instability. This paper presents a Power System stabilizer to reduce oscillations due to small signal disturbance. Additionally, a hybrid approach is proposed using FOPID stabilizer with the PSS connected SMIB. Genetic algorithm (GA), Particle swarm optimization (PSO) and Grey Wolf Optimization (GWO) are used for the parameter tuning of the stabilizer. Reason behind the use of GA, PSO and GWO instead of conventional methods is that it search the parameter heuristically, which leads better results. The efficiency of proposed approach is observed by rotor angle and power angle deviations in the SMIB system.
Improved impedance matching for SuperDARN TTFD antennas

This paper presents a novel antenna impedance matching system used in the latest Australian SuperDARN class HF radar, at Buckland Park, South Australia. Earlier radar designs used an off-the-shelf log-periodic wideband antenna that is significantly easier to match over the SuperDARN frequency band, but expensive to buy and mount, and had limited capability for azimuthal beamforming. The newer TTFD antenna, used in many recent SuperDARN radars, offers improvement in these areas, but is in essence a narrow band antenna. It is capable of wideband operation at the cost of being difficult to match, frequency dependant, high-impedance and complex load. Previous TTFD matching transformers utilising toroids have been measured and evaluated for their suitability for the Buckland Park radar. A new system based on an LC matching network circuit has been devised to replace them. The design approach and results of the new matching circuit are detailed.
Feature-based vector simulation of water waves

We present a method for simulating local water waves caused by obstacles in water streams for real-time graphics applications. Given a low-resolution water surface and velocity field, our method is able to decorate the input water surface with high resolution detail for the animated waves around obstacles. We construct and animate a vector representation of the waves. It is then converted to feature-aligned meshes for capturing the surfaces of the waves. Results demonstrate that our method has the benefits of real-time performance and easy controllability. The method also fits well into a state-of-the-art river animation system.