nils-herrmann/clean_scirepeval huggingface dataset:
discovery and design using machine learning

The screening of novel materials with good performance and the modelling of quantitative structureactivity relationships (QSARs), among other issues, are hot topics in the field of materials science. Traditional experiments and computational modelling often consume tremendous time and resources and are limited by their experimental conditions and theoretical foundations. Thus, it is imperative to develop a new method of accelerating the discovery and design process for novel materials. Recently, materials discovery and design using machine learning have been receiving increasing attention and have achieved great improvements in both time efficiency and prediction accuracy. In this review, we first outline the typical mode of and basic procedures for applying machine learning in materials science, and we classify and compare the main algorithms. Then, the current research status is reviewed with regard to applications of machine learning in material property prediction, in new materials discovery and for other purposes. Finally, we discuss problems related to machine learning in materials science, propose possible solutions, and forecast potential directions of future research. By directly combining computational studies with experiments, we hope to provide insight into the parameters that affect the properties of materials, thereby enabling more efficient and target-oriented research on materials dis-
n-Gram-based classification and unsupervised hierarchical clustering of genome sequences

In this paper we address the problem of automated classification of isolates, i.e., the problem of determining the family of genomes to which a given genome belongs. Additionally, we address the problem of automated unsupervised hierarchical clustering of isolates according only to their statistical substring properties. For both of these problems we present novel algorithms based on nucleotide n-grams, with no required preprocessing steps such as sequence alignment. Results obtained experimentally are very positive and suggest that the proposed techniques can be successfully used in a variety of related problems. The reported experiments demonstrate better performance than some of the state-of-the-art methods. We report on a new distance measure between n-gram profiles, which shows superior performance compared to many other measures, including commonly used Euclidean distance.
Low-power LVDS receiver for 1.3Gbps physical layer (PHY) interface

This paper presents the design of a low voltage differential signaling (LVDS) receiver for a 1.3 Gb/s physical layer (PRY) interface. The receiver supports a wide input common mode range of 0.05 V to 2.35 V and a minimum input differential signal of 100 mV as specified by the IEEE LVDS standard. The design is implemented in 0.13 /spl mu/m CMOS technology using both thick (3.3 V) and thin (1.2 V) gate oxide devices and the receiver consumes 11 mW of power. The receiver provides the interface between PHY and media access control (MAC) sub-layers.
Model-Checking Algorithms for Continuous-Time Markov Chains

Continuous-time Markov chains (CTMCs) have been widely used to determine system performance and dependability characteristics. Their analysis most often concerns the computation of steady-state and transient-state probabilities. This paper introduces a branching temporal logic for expressing real-time probabilistic properties on CTMCs and presents approximate model checking algorithms for this logic. The logic, an extension of the continuous stochastic logic CSL of Aziz et al., contains a time-bounded until operator to express probabilistic timing properties over paths as well as an operator to express steady-state probabilities. We show that the model checking problem for this logic reduces to a system of linear equations (for unbounded until and the steady-state operator) and a Volterra integral equation system (for time-bounded until). We then show that the problem of model-checking timebounded until properties can be reduced to the problem of computing transient state probabilities for CTMCs. This allows the verification of probabilistic timing properties by efficient techniques for transient analysis for CTMCs such as uniformization. Finally, we show that a variant of lumping equivalence (bisimulation), a well-known notion for aggregating CTMCs, preserves the validity of all formulas in the logic.
Average Modeling and Control Design for VIENNA-Type Rectifiers Considering the DC-Link Voltage Balance

This paper presents a new average d- q model and a control approach with a carrier-based pulsewidth modulation (PWM) implementation for nonregenerative three-phase three-level boost (VIENNA-type) rectifiers. State-space analysis and an averaging technique are used to derive the relationship between the controlled duty cycle and the dc-link neutral-point voltage, based on which an optimal zero-sequence component is found for dc-link voltage balance. By utilizing this zero-sequence component, the behavior of the dc-link voltage unbalance can be modeled in d-q coordinates using averaging over a switching cycle. Therefore, the proposed model is valid for up to half of the switching frequency. With the proposed model, a new control algorithm is developed with carrier-based PWM implementation, which features great simplicity and good dc-link neutral-point regulation. Space vector representation is also utilized to analyze the voltage balancing mechanism and the region of feasible operation. Simulation and experimental results validated the proposed model and control approach.
Order-Free RNN With Visual Attention for Multi-Label Classification

We propose a recurrent neural network (RNN) based model for image multi-label classification. Our model uniquely integrates and learning of visual attention and Long Short Term Memory (LSTM) layers, which jointly learns the labels of interest and their co-occurrences, while the associated image regions are visually attended. Different from existing approaches utilize either model in their network architectures, training of our model does not require pre-defined label orders. Moreover, a robust inference process is introduced so that prediction errors would not propagate and thus affect the performance. Our experiments on NUS-WISE and MS-COCO datasets confirm the design of our network and its effectiveness in solving multi-label classification problems.
Piezoelectric transformer for integrated MOSFET and IGBT gate driver

In this paper, a new complementary gate driver for power metal-oxide semiconductor field-effect transistors and insulated gate bipolar transistors is presented based on the use of a piezoelectric transformer (PT). This type of transformer has a high integration capability. Its design is based on a multilayer structure working in the second thickness resonance mode. A new design method has been used based on an analytical Mason model in order to optimize the efficiency, the available power at the transformer secondary ends, and the total volume. This design method takes into account mechanical losses and heating of the piezoelectric material; it can be extended to predict the characteristics of the PT: gain, transmitted power, efficiency, and heating of piezoelectric materials according to load resistance. A prototype of a PT rated for an inverter-leg gate driver was fabricated and tested experimentally. All calculated characteristics have been confirmed by measurements. Satisfactory results have been obtained in driving a 10-A/300-V/10-kHz chopper. Moreover, a study has been carried out about the propagation of common mode currents between the top-switch and the bottom-switch of the inverter leg throughout the driver in order to avoid cross-talking failures.
A Fast Approximate Nearest Neighbor Search Algorithm in the Hamming Space

A fast approximate nearest neighbor search algorithm for the (binary) Hamming space is proposed. The proposed Error Weighted Hashing (EWH) algorithm is up to 20 times faster than the popular locality sensitive hashing (LSH) algorithm and works well even for large nearest neighbor distances where LSH fails. EWH significantly reduces the number of candidate nearest neighbors by weighing them based on the difference between their hash vectors. EWH can be used for multimedia retrieval and copy detection systems that are based on binary fingerprinting. On a fingerprint database with more than 1,000 videos, for a specific detection accuracy, we demonstrate that EWH is more than 10 times faster than LSH. For the same retrieval time, we show that EWH has a significantly better detection accuracy with a 15 times lower error rate.
Authorship Attribution for Twitter in 140 Characters or Less

Authorship attribution is a growing field, moving from beginnings in linguistics to recent advances in text mining. Through this change came an increase in the capability of authorship attribution methods both in their accuracy and the ability to consider more difficult problems. Research into authorship attribution in the 19th century considered it difficult to determine the authorship of a document of fewer than 1000 words. By the 1990s this values had decreased to less than 500 words and in the early 21st century it was considered possible to determine the authorship of a document in 250 words. The need for this ever decreasing limit is exemplified by the trend towards many shorter communications rather than fewer longer communications, such as the move from traditional multi-page handwritten letters to shorter, more focused emails. This trend has also been shown in online crime, where many attacks such as phishing or bullying are performed using very concise language. Cybercrime messages have long been hosted on Internet Relay Chats (IRCs) which have allowed members to hide behind screen names and connect anonymously. More recently, Twitter and other short message based web services have been used as a hosting ground for online crimes. This paper presents some evaluations of current techniques and identifies some new preprocessing methods that can be used to enable authorship to be determined at rates significantly better than chance for documents of 140 characters or less, a format popularised by the micro-blogging website Twitter1. We show that the SCAP methodology performs extremely well on twitter messages and even with restrictions on the types of information allowed, such as the recipient of directed messages, still perform significantly higher than chance. Further to this, we show that 120 tweets per user is an important threshold, at which point adding more tweets per user gives a small but non-significant increase in accuracy.
A Kernel Fuzzy c-Means Clustering-Based Fuzzy Support Vector Machine Algorithm for Classification Problems With Outliers or Noises

The support vector machine (SVM) has provided higher performance than traditional learning machines and has been widely applied in real-world classification problems and nonlinear function estimation problems. Unfortunately, the training process of the SVM is sensitive to the outliers or noises in the training set. In this paper, a common misunderstanding of Gaussian-function-based kernel fuzzy clustering is corrected, and a kernel fuzzy c-means clustering-based fuzzy SVM algorithm (KFCM-FSVM) is developed to deal with the classification problems with outliers or noises. In the KFCM-FSVM algorithm, we first use the FCM clustering to cluster each of two classes from the training set in the high-dimensional feature space. The farthest pair of clusters, where one cluster comes from the positive class and the other from the negative class, is then searched and forms one new training set with membership degrees. Finally, we adopt FSVM to induce the final classification results on this new training set. The computational complexity of the KFCM-FSVM algorithm is analyzed. A set of experiments is conducted on six benchmarking datasets and four artificial datasets for testing the generalization performance of the KFCM-FSVM algorithm. The results indicate that the KFCM-FSVM algorithm is robust for classification problems with outliers or noises.
Dynamic analysis and control of a zone-control induction heating system

This paper presents a quick and accurate power control method for a zone-control induction heating (ZCIH) system. The ZCIH system consists of multiple working coils connected to multiple H-bridge inverters. The system controls the amplitude and phase angle of each coil current to make the temperature distribution on the workpiece uniform. This paper proposes a new control method for the coil currents based on a circuit model using real and imaginary (Re-Im) current/voltage components. The method detects and controls the Re-Im components of the coil current instead of the current amplitude and phase angle. As a result, the proposed method enables decoupling control for the system, making the control for each working coil independent from the others. Experiments on a 6-zone ZCIH laboratory setup are conducted to verify the validity of the proposed method. It is clarified that the proposed method has a stable operation both in transient and steady states. The proposed system and control method enable system complexity reduction and control stability improvements.
Design and Implementation of a Smart LED Lighting System Using a Self Adaptive Weighted Data Fusion Algorithm

This work aims to develop a smart LED lighting system, which is remotely controlled by Android apps via handheld devices, e.g., smartphones, tablets, and so forth. The status of energy use is reflected by readings displayed on a handheld device, and it is treated as a criterion in the lighting mode design of a system. A multimeter, a wireless light dimmer, an IR learning remote module, etc. are connected to a server by means of RS 232/485 and a human computer interface on a touch screen. The wireless data communication is designed to operate in compliance with the ZigBee standard, and signal processing on sensed data is made through a self adaptive weighted data fusion algorithm. A low variation in data fusion together with a high stability is experimentally demonstrated in this work. The wireless light dimmer as well as the IR learning remote module can be instructed directly by command given on the human computer interface, and the reading on a multimeter can be displayed thereon via the server. This proposed smart LED lighting system can be remotely controlled and self learning mode can be enabled by a single handheld device via WiFi transmission. Hence, this proposal is validated as an approach to power monitoring for home appliances, and is demonstrated as a digital home network in consideration of energy efficiency.
In vivo oxidative degradation of polypropylene pelvic mesh.

Commercial polypropylene pelvic mesh products were characterized in terms of their chemical compositions and molecular weight characteristics before and after implantation. These isotactic polypropylene mesh materials showed clear signs of oxidation by both Fourier-transform infrared spectroscopy and scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM/EDS). The oxidation was accompanied by a decrease in both weight-average and z-average molecular weights and narrowing of the polydispersity index relative to that of the non-implanted material. SEM revealed the formation of transverse cracking of the fibers which generally, but with some exceptions, increased with implantation time. Collectively these results, as well as the loss of flexibility and embrittlement of polypropylene upon implantation as reported by other workers, may only be explained by in vivo oxidative degradation of polypropylene.
Coordination of Cooperative Autonomous Vehicles: Toward safer and more efficient road transportation

While intelligent transportation systems come in many shapes and sizes, arguably the most transformational realization will be the autonomous vehicle. As such vehicles become commercially available in the coming years, first on dedicated roads and under specific conditions, and later on all public roads at all times, a phase transition will occur. Once a sufficient number of autonomous vehicles is deployed, the opportunity for explicit coordination appears. This article treats this challenging network control problem, which lies at the intersection of control theory, signal processing, and wireless communication. We provide an overview of the state of the art, while at the same time highlighting key research directions for the coming decades.
End-to-End Training of Hybrid CNN-CRF Models for Stereo

We propose a novel and principled hybrid CNN+CRF model for stereo estimation. Our model allows to exploit the advantages of both, convolutional neural networks (CNNs) and conditional random fields (CRFs) in an unified approach. The CNNs compute expressive features for matching and distinctive color edges, which in turn are used to compute the unary and binary costs of the CRF. For inference, we apply a recently proposed highly parallel dual block descent algorithm which only needs a small fixed number of iterations to compute a high-quality approximate minimizer. As the main contribution of the paper, we propose a theoretically sound method based on the structured output support vector machine (SSVM) to train the hybrid CNN+CRF model on large-scale data end-to-end. Our trained models perform very well despite the fact that we are using shallow CNNs and do not apply any kind of post-processing to the final output of the CRF. We evaluate our combined models on challenging stereo benchmarks such as Middlebury 2014 and Kitti 2015 and also investigate the performance of each individual component.
Modeling of arc welding power supply

This paper gives a method of establishing small-signal model for full-bridge inverter arc welding power supply via state-space averaging treatment and linearization method. On the basis of the model, the frequency response reflecting dynamical characteristics of arc welding power supply is analyzed by means of MATLAB. The simulation result has agreement with experiment result as regards dynamical property. The small signal frequent mathematical model established in the paper can reflect basic characteristic of practical arc welding power system.
Stochastic Learning on Imbalanced Data: Determinantal Point Processes for Mini-batch Diversification

We study a mini-batch diversification scheme for stochastic gradient descent (SGD). While classical SGD relies on uniformly sampling data points to form a mini-batch, we propose a non-uniform sampling scheme based on the Determinantal Point Process (DPP). The DPP relies on a similarity measure between data points and gives low probabilities to mini-batches which contain redundant data, and higher probabilities to mini-batches with more diverse data. This simultaneously balances the data and leads to stochastic gradients with lower variance. We term this approach Balanced Mini-batch SGD (BM-SGD). We show that regular SGD and stratified sampling emerge as special cases. Furthermore, BM-SGD can be considered a generalization of stratified sampling to cases where no discrete features exist to bin the data into groups. We show experimentally that our method results more interpretable and diverse features in unsupervised setups, and in better classification accuracies in supervised setups.
Design of compact stacked-patch antennas in LTCC multilayer packaging modules for wireless applications

A simple procedure for the design of compact stacked-patch antennas is presented based on LTCC multilayer packaging technology. The advantage of this topology is that only one parameter, i.e., the substrate thickness (or equivalently the number of LTCC layers), needs to be adjusted in order to achieve an optimized bandwidth performance. The validity of the new design strategy is verified through applying it to practical compact antenna design for several wireless communication bands, including ISM 2.4-GHz band, IEEE 802.11a 5.8-GHz, and LMDS 28-GHz band. It is shown that a 10-dB return-loss bandwidth of 7% can be achieved for the LTCC (/spl epsiv//sub r/=5.6) multilayer structure with a thickness of less than 0.03 wavelengths, which can be realized using a different number of laminated layers for different frequencies (e.g., three layers for the 28-GHz band).
Robust Video Super-Resolution with Learned Temporal Dynamics

Video super-resolution (SR) aims to generate a highresolution (HR) frame from multiple low-resolution (LR) frames in a local temporal window. The inter-frame temporal relation is as crucial as the intra-frame spatial relation for tackling this problem. However, how to utilize temporal information efficiently and effectively remains challenging since complex motion is difficult to model and can introduce adverse effects if not handled properly. We address this problem from two aspects. First, we propose a temporal adaptive neural network that can adaptively determine the optimal scale of temporal dependency. Filters on various temporal scales are applied to the input LR sequence before their responses are adaptively aggregated. Second, we reduce the complexity of motion between neighboring frames using a spatial alignment network which is much more robust and efficient than competing alignment methods and can be jointly trained with the temporal adaptive network in an end-to-end manner. Our proposed models with learned temporal dynamics are systematically evaluated on public video datasets and achieve state-of-the-art SR results compared with other recent video SR approaches. Both of the temporal adaptation and the spatial alignment modules are demonstrated to considerably improve SR quality over their plain counterparts.
Intrinsic shape signatures: A shape descriptor for 3D object recognition

This paper presents a new approach for recognition of 3D objects that are represented as 3D point clouds. We introduce a new 3D shape descriptor called Intrinsic Shape Signature (ISS) to characterize a local/semi-local region of a point cloud. An intrinsic shape signature uses a view-independent representation of the 3D shape to match shape patches from different views directly, and a view-dependent transform encoding the viewing geometry to facilitate fast pose estimation. In addition, we present a highly efficient indexing scheme for the high dimensional ISS shape descriptors, allowing for fast and accurate search of large model databases. We evaluate the performance of the proposed algorithm on a very challenging task of recognizing different vehicle types using a database of 72 models in the presence of sensor noise, obscuration and scene clutter.
Sigma-Point Kalman Filters for Integrated Navigation

Core to integrated navigation systems is the concept of fusing noisy observations from GPS, Inertial Measurement Units (IMU), and other available sensors. The current industry standard and most widely used algorithm for this purpose is the extended Kalman filter (EKF) . The EKF combines the sensor measurements with predictions coming from a model of vehicle motion (either dynamic or kinematic), in order to generate an estimate of the current navigational state (position, velocity, and attitude). This paper points out the inherent shortcomings in using the EKF and presents, as an alternative, a family of improved derivativeless nonlinear Kalman filters called sigma-point Kalman filters (SPKF). We demonstrate the improved state estimation performance of the SPKF by applying it to the problem of loosely coupled GPS/INS integration. A novel method to account for latency in the GPS updates is also developed for the SPKF (such latency compensation is typically inaccurate or not practical with the EKF). A UAV (rotor-craft) test platform is used to demonstrate the results. Performance metrics indicate an approximate 30% error reduction in both attitude and position estimates relative to the baseline EKF implementation.