nils-herrmann/clean_scirepeval huggingface dataset:
k-Nearest Neighbor Augmented Neural Networks for Text Classification

In recent years, many deep-learning based models are proposed for text classification. This kind of models well fits the training set from the statistical point of view. However, it lacks the capacity of utilizing instance-level information from individual instances in the training set. In this work, we propose to enhance neural network models by allowing them to leverage information from k-nearest neighbor (kNN) of the input text. Our model employs a neural network that encodes texts into text embeddings. Moreover, we also utilize k-nearest neighbor of the input text as an external memory, and utilize it to capture instance-level information from the training set. The final prediction is made based on features from both the neural network encoder and the kNN memory. Experimental results on several standard benchmark datasets show that our model outperforms the baseline model on all the datasets, and it even beats a very deep neural network model (with 29 layers) in several datasets. Our model also shows superior performance when training instances are scarce, and when the training set is severely unbalanced. Our model also leverages techniques such as semi-supervised training and transfer learning quite well.
A Design Procedure for All-Digital Phase-Locked Loops Based on a Charge-Pump Phase-Locked-Loop Analogy

In this brief, a systematic design procedure for a second-order all-digital phase-locked loop (PLL) is proposed. The design procedure is based on the analogy between a type-II second-order analog PLL and an all-digital PLL. The all-digital PLL design inherits the frequency response and stability characteristics of the analog prototype PLL
Three-dimensional analysis of maxillary changes associated with facemask and rapid maxillary expansion compared with bone anchored maxillary protraction.

Our objectives in this study were to evaluate in 3 dimensions the growth and treatment effects on the midface and the maxillary dentition produced by facemask therapy in association with rapid maxillary expansion (RME/FM) compared with bone-anchored maxillary protraction (BAMP).



Forty-six patients with Class III malocclusion were treated with either RME/FM (n = 21) or BAMP (n = 25). Three-dimensional models generated from cone-beam computed tomographic scans, taken before and after approximately 1 year of treatment, were registered on the anterior cranial base and measured using color-coded maps and semitransparent overlays.



The skeletal changes in the maxilla and the right and left zygomas were on average 2.6 mm in the RME/FM group and 3.7 mm in the BAMP group; these were different statistically. Seven RME/FM patients and 4 BAMP patients had a predominantly vertical displacement of the maxilla. The dental changes at the maxillary incisors were on average 3.2 mm in the RME/FM group and 4.3 mm in the BAMP group. Ten RME/FM patients had greater dental compensations than skeletal changes.



This 3-dimensional study shows that orthopedic changes can be obtained with both RME/FM and BAMP treatments, with protraction of the maxilla and the zygomas. Approximately half of the RME/FM patients had greater dental than skeletal changes, and a third of the RME/FM compared with 17% of the BAMP patients had a predominantly vertical maxillary displacement.
Phase Current Sensor and Short-Circuit Detection based on Rogowski Coils Integrated on Gate Driver for 1.2 kV SiC MOSFET Half-Bridge Module

Silicon-carbide (SiC) MOSFETs are enabling electrical vehicle motor drives to meet the demands of higher power density, efficiency, and lower system cost. Hence, this paper seeks to explore the benefits that a gate-driver-level intelligence can contribute to SiC-based power inverters. The intelligence is brought by PCB-embedded Rogowski switch-current sensors (RSCS) integrated on the gate driver of a 1.2 kV, 300 A SiC MOSFET half-bridge module. They collect two MOSFET switch currents in a manner of high magnitude, high bandwidth, and solid signal isolation. The switch-current signals are used for short-circuit detection under various fault impedances, as well as for phase-current reconstruction by subtracting one switch current from another. The fundamentals and noise-immunity design of the gate driver containing the RSCS are presented in the paper and can be applied to any half-bridge power module. A three-phase inverter prototype has been built and operated in continuous PWM mode. On this setup, the performance and limitations of the short-circuit detection and phase-current reconstruction are experimentally validated by comparing with commercial current probes and Hall sensors.
A New Broadband Marchand Balun Using Slot-Coupled Microstrip Lines

In this letter, a new broadband Marchand balun is proposed using slot-coupled microstrip lines. The developed balun is connected by two sections of diamond-shape slot-coupled microstrips with short- and open-circuited terminations at specified ports. To achieve the broadband operation, the simple formulas have been derived to convert the conventional rectangular-shape slot-coupled microstrip to the diamond-shape counterpart. The measured results of the developed balun not only agree with the full-wave simulation results very well, but also resemble the calculated results by ideal coupled lines. The new Marchand balun developed in this letter has a 10 dB return loss bandwidth of 90%, while in this frequency range the amplitude imbalance varies from -0.37 dB to 0.32 dB, and the phase difference in the range of -180.4° ~ -181.8°.
Optimal resource management for a model driven LTE protocol stack on a multicore platform

The Long Term Evolution (LTE) is the successor technology of the 3G wireless system. The high data rates enabled by LTE can benefit from a strong computational power provided by today's high-performance embedded processors. In this work we therefore utilize a multicore processor to increase the LTE system throughput in the mobile terminal. We investigate the dynamic memory allocation scheme for the LTE protocol stack, modeled using Specification and Description Language (SDL), as the underlying issue with migrating from single to multiple cores. We discover that, under some schemes, multicore performance becomes inferior to a single-core, especially in case of intensive dynamic memory allocation and deallocation. By modifying the SDL system's run time kernel we implement a static memory management scheme. This is supplemented by a selective usage of resource protection in single- and dual-core situations. As a result, an increase of the system throughput by about 75% can be observed when migrating from one core to two cores.
Design of a Novel Broadband Skeletal Discone Antenna With a Compact Configuration

A novel broadband skeletal discone antenna with a compact configuration is proposed in this letter. The antenna consists of three components including a circular metal disc, a small upside-down cone, and a skeletal cone. The operating principle of this antenna is investigated by simulation, and the corresponding simulated results are also demonstrated in this letter. According to the simulation, the antenna was fabricated and followed by the measurement. The comparison between the simulated and measured results is made for validating the design strategy. Although some discrepancies can be obviously found at some discrete frequencies, both the simulated and measured results indicate that this antenna is capable of operating in the frequency range of 400 MHz to almost 16.4 GHz with VSWR below 2.5, while maintaining stable omnidirectional radiation. Meanwhile, this antenna also has a distinct advantage over the traditional discone antenna for its compact configuration, which makes it a more competitive candidate for applications as wireless communication.
An Engineering Model for Color Difference as a Function of Size

This work describes a first step towards the creation of an engineering model for the perception of color difference as a function of size. Our approach is to non-uniformly rescale CIELAB using data from crowdsourced experiments, such as those run on Amazon Mechanical Turk. In such experiments, the inevitable variations in viewing conditions reflect the environment many applications must run in. Our goal is to create a useful model for design applications where it is important to make colors distinct, but for which a small set of highly distinct colors is inadequate.
Discrete-Time Methods for the Analysis of Event Histories

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Air-Gap Convection in Rotating Electrical Machines

This paper reviews the convective heat transfer within the air gap of both cylindrical and disk geometry rotating electrical machines, including worked examples relevant to fractional horsepower electrical machines. Thermal analysis of electrical machines is important because torque density is limited by maximum temperature. Knowledge of surface convective heat transfer coefficients is necessary for accurate thermal modeling, for example, using lumped parameter models. There exists a wide body of relevant literature, but much of it has traditionally been in other application areas, dominated by mechanical engineers, such as gas turbine design. Particular attention is therefore given to the explanation of the relevant nondimensional parameters and to the presentation of measured convective heat transfer correlations for a wide variety of situations from laminar to turbulent flow at small and large gap sizes for both radial-flux and axial-flux electrical machines.
Modeling and Mitigating Impact of False Data Injection Attacks on Automatic Generation Control

This paper studies the impact of false data injection (FDI) attacks on automatic generation control (AGC), a fundamental control system used in all power grids to maintain the grid frequency at a nominal value. Attacks on the sensor measurements for AGC can cause frequency excursion that triggers remedial actions, such as disconnecting customer loads or generators, leading to blackouts, and potentially costly equipment damage. We derive an attack impact model and analyze an optimal attack, consisting of a series of FDIs that minimizes the remaining time until the onset of disruptive remedial actions, leaving the shortest time for the grid to counteract. We show that, based on eavesdropped sensor data and a few feasible-to-obtain system constants, the attacker can learn the attack impact model and achieve the optimal attack in practice. This paper provides essential understanding on the limits of physical impact of the FDIs on power grids, and provides an analysis framework to guide the protection of sensor data links. For countermeasures, we develop efficient algorithms to detect the attack, estimate which sensor data links are under attack, and mitigate attack impact. Our analysis and algorithms are validated by experiments on a physical 16-bus power system test bed and extensive simulations based on a 37-bus power system model.
Phantom-limb pain as a perceptual correlate of cortical reorganization following arm amputation

ALTHOUGH phantom-limb pain is a frequent consequence of the amputation of an extremity, little is known about its origin1-4. On the basis of the demonstration of substantial plasticity of the somatosensory cortex after amputation5 or somatosensory deafferentation in adult monkeys6, it has been suggested that cortical reorganization could account for some non-painful phantom-limb phenomena in amputees and that cortical reorganization has an adaptive (that is, pain-preventing) function2,5,7,8. Theoretical and empirical work on chronic back pain9,10 has revealed a positive relationship between the amount of cortical alteration and the magnitude of pain, so we predicted that cortical reorganization and phantom-limb pain should be positively related. Using non-invasive neuromagnetic imaging techniques to determine cortical reorganization in humans11-13, we report a very strong direct relationship (r = 0.93) between the amount of cortical reorganization and the magnitude of phantom limb pain (but not non-painful phantom phenomena) experienced after arm amputation. These data indicate that phantom-limb pain is related to, and may be a consequence of, plastic changes in primary somatosensory cortex.
Beyond Part Models: Person Retrieval with Refined Part Pooling (and A Strong Convolutional Baseline)

Employing part-level features offers fine-grained information for pedestrian image description.A prerequisite of part discovery is that each part should be well located. Instead of using external resources like pose estimator, we consider content consistency within each part for precise part location. Specifically, we target at learning discriminative part-informed features for person retrieval and make two contributions. (i) A network named Part-based Convolutional Baseline (PCB). Given an image input, it outputs a convolutional descriptor consisting of several part-level features. With a uniform partition strategy, PCB achieves competitive results with the state-of-the-art methods, proving itself as a strong convolutional baseline for person retrieval. (ii) A refined part pooling (RPP) method. Uniform partition inevitably incurs outliers in each part, which are in fact more similar to other parts. RPP re-assigns these outliers to the parts they are closest to, resulting in refined parts with enhanced within-part consistency. Experiment confirms that RPP allows PCB to gain another round of performance boost. For instance, on the Market-1501 dataset, we achieve (77.4+4.2)% mAP and (92.3+1.5)% rank-1 accuracy, surpassing the state of the art by a large margin. Code is available at: https://github.com/syfafterzy/PCB_RPP
Single-Shot Object Detection with Enriched Semantics

We propose a novel single shot object detection network named Detection with Enriched Semantics (DES). Our motivation is to enrich the semantics of object detection features within a typical deep detector, by a semantic segmentation branch and a global activation module. The segmentation branch is supervised by weak segmentation ground-truth, i.e., no extra annotation is required. In conjunction with that, we employ a global activation module which learns relationship between channels and object classes in a self-supervised manner. Comprehensive experimental results on both PASCAL VOC and MS COCO detection datasets demonstrate the effectiveness of the proposed method. In particular, with a VGG16 based DES, we achieve an mAP of 81.7 on VOC2007 test and an mAP of 32.8 on COCO test-dev with an inference speed of 31.5 milliseconds per image on a Titan Xp GPU. With a lower resolution version, we achieve an mAP of 79.7 on VOC2007 with an inference speed of 13.0 milliseconds per image.
Existential meaning's role in the enhancement of hope and prevention of depressive symptoms.

The authors confirmed that existential meaning has a unique relationship with and can prospectively predict levels of hope and depressive symptoms within a population of college students. Baseline measures of explicit meaning (i.e., an individual's self-reported experience of a sense of coherence and purpose in life) and implicit meaning (i.e., an individual's self-reported embodiment of the factors that are normatively viewed as comprising a meaningful life) explained significant amounts of variance in hope and depressive symptoms 2 months later beyond the variance explained by baseline levels of hope/depression, neuroticism, conscientiousness, agreeableness, openness to experience, extraversion, and social desirability. The authors discuss implications of these findings for the field of mental health treatment and suggest ways of influencing individuals' experience of existential meaning.
The impact of goal achievement orientation on student technology usage in the classroom

The aim of this study was to investigate whether students with differing goal achievement orientations were more likely to engage in on-task or off-task mobile device usage, as well as whether particular devices (specifically, laptops and smartphones) have a positive or negative relationship with specific task usage. The results of this study found the mastery approach goal achievement orientation to be negatively associated with off-task device usage, and the mastery avoidance goal achievement orientation to be positively associated with on-task usage behaviors. In addition, it was found that on-task behaviors were positively associated with laptop usage, while off-task behaviors were positively associated with smartphone usage. This study focused on student behavior in a higher education classroom, although the results may be applicable in any context in which users of personal electronic and internet-enabled devices are focused on specific learning objectives.
Improvements in children's speech recognition performance

There are several reasons why conventional speech recognition systems modeled on adult data fail to perform satisfactorily on children's speech input. For instance, children's vocal characteristics differ signi cantly from those of adults. In addition, their choices of vocabulary and sentence construction modalities usually do not conform to adult patterns. We describe comparative studies demonstrating the performance gain realized by adopting to children's acoustic and language model data to construct a children's speech recognition system.
Wheat Leaf Disease Detection Using Machine Learning Method-A Review

This paper is highlighting the outliers about the wheat leaf disease detection. India is the second larger producer of wheat after china. The wheat diseases are harmful to wheat production, but there are algorithms that can effectively identify common diseases of wheat leaves. The wheat diseases are generally viral, bacterial, fungal, insects, rust etc. There are many types of disease which are presents in wheat leaf. Recently, wheat disease detection through leaf image and data processing techniques are used extensively and in expensive system especially for assisting farmers in monitoring the big plantation area. Machine learning techniques are described for wheat leaf disease detection and its classification also. The key issues and challenges in wheat leaf disease detection are also highlighted. A vast collection of papers, books and standards are listed in the reference list, which gives useful information to the researchers and farmers in agriculture.
Efficient Programmable Pulse Shaping for $X$ -Band GaN MMIC Radar Power Amplifiers

This paper presents a supply modulated <inline-formula> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula>-band 12-W peak power transmitter that maintains an average efficiency greater than 50% for various shapes of amplitude-modulated pulses. The main power amplifier is a two-stage GaN-on-SiC MMIC with a peak efficiency of 65%, while the pulse envelope modulator is a 95% efficient hybrid 3-b power DAC implemented with GaN-on-Si transistor switches. Envelope shaping of a pulsed waveform results in improved spectral confinement of greater than 15 dB for the first sideband compared with constant-envelope pulses, with over 20 points improvement in total efficiency. The combination of supply modulation and digital predistortion is shown to result in high composite (total) efficiency of over 55%, with simultaneous high dynamic range and with flexible digitally programmable pulse shaping.
Data investigation based on XFS file system metadata

At present, as the amount of digitized information is increasing geometrically, the importance of digitized information as critical clues from the perspective of criminal investigation is also increasing. The importance of digital forensics has also magnified accordingly. As a fundamental technique of digital forensics, recovery of data deleted to conceal information is extremely important and needs to be studied. Although file system types and versions are diversified, sufficient studies have not been performed. The XFS file system was designed to process huge files, and thus it is expected to be applied in server systems. In this paper, we analyze the XFS file system using its structure and metadata information; then, we propose a technique to recover the deleted files through previous analysis results, and verify it through testing.
Please Biofeed the Zombies: Enhancing the Gameplay and Display of a Horror Game Using Biofeedback

This paper describes an investigation into how real-time but low-cost biometric information can be interpreted by computer games to enhance gameplay without fundamentally changing it. We adapted a cheap sensor, (the Lightstone mediation sensor device by Wild Divine), to record and transfer biometric information about the player (via sensors that clip over their fingers) into a commercial game engine, Half-Life 2. During game play, the computer game was dynamically modified by the player’s biometric information to increase the cinematically augmented “horror” affordances. These included dynamic changes in the game shaders, screen shake, and the creation of new spawning points for the game’s non-playing characters (zombies), all these features were driven by the player’s biometric data. To evaluate the usefulness of this biofeedback device, we compared it against a control group of players who also had sensors clipped on their fingers, but for the second group the gameplay was not modified by the biometric information of the players. While the evaluation results indicate biometric data can improve the situated feeling of horror, there are many design issues that will need to be investigated by future research, and the judicious selection of theme and appropriate interaction is vital.
Data Imbalance Problem in Text Classification

Aimming at the ever-present problem of imbalanced data in text classification, the authors study on several forms of imbalanced data, such as text number, class size, subclass and class fold. Some useful conclusions are gotten from a series of correlative experiments: first, when the text of two class is almost the same number, the difference of word number become major factor to affect the accuracy of the classification, second, to improve the accuracy of the classification through increasing the small class size is limited, third, in the case of unbalanced data, the same words which are appeared in two class often carry strong class information, that is, class overlap will not affect the classification accuracy.