nils-herrmann/clean_scirepeval huggingface dataset:
Support vector regression with modified firefly algorithm for stock price forecasting

The support vector regression (SVR) has been employed to deal with stock price forecasting problems. However, the selection of appropriate kernel parameters is crucial to obtaining satisfactory forecasting performance. This paper proposes a novel approach for forecasting stock prices by combining the SVR with the firefly algorithm (FA). The proposed forecasting model has two stages. In the first stage, to enhance the global convergence speed, a modified version of the FA, which is termed the MFA, is developed in which the dynamic adjustment strategy and the opposition-based chaotic strategy are introduced. In the second stage, a hybrid SVR model is proposed and combined with the MFA for stock price forecasting, in which the MFA is used to optimize the SVR parameters. Finally, comparative experiments are conducted to show the applicability and superiority of the proposed methods. Experimental results show the following: (1) Compared with other algorithms, the proposed MFA algorithm possesses superior performance, and (2) The proposed MFA-SVR prediction procedure can be considered as a feasible and effective tool for forecasting stock prices.
AdaComp : Adaptive Residual Gradient Compression for Data-Parallel Distributed Training

Highly distributed training of Deep Neural Networks (DNNs) on future compute platforms (offering 100 of TeraOps/s of computational capacity) is expected to be severely communication constrained. To overcome this limitation, new gradient compression techniques are needed that are computationally friendly, applicable to a wide variety of layers seen in Deep Neural Networks and adaptable to variations in network architectures as well as their hyper-parameters. In this paper we introduce a novel technique the Adaptive Residual Gradient Compression (AdaComp) scheme. AdaComp is based on localized selection of gradient residues and automatically tunes the compression rate depending on local activity. We show excellent results on a wide spectrum of state of the art Deep Learning models in multiple domains (vision, speech, language), datasets (MNIST, CIFAR10, ImageNet, BN50, Shakespeare), optimizers (SGD with momentum, Adam) and network parameters (number of learners, minibatch-size etc.). Exploiting both sparsity and quantization, we demonstrate end-to-end compression rates of ∼200× for fully-connected and recurrent layers, and ∼40× for convolutional layers, without any noticeable degradation in model accuracies.
3 D-printed spherical dipole antenna integrated on small RF node

ELECT New three-dimensional (3D) printing techniques enable the integration of an antenna directly onto the package of a small wireless sensor node. This volume-filling approach ensures near-optimal bandwidth performance of the small antenna, increasing a system’s battery life, data rate or range. Simulated results show that the fabricated spherical antenna’s bandwidth-efficiency product is more than half of the fundamental limit and radiation pattern measurements exhibit a dipole pattern with −0.7 dBi gain.
Facebook profiles reflect actual personality, not self-idealization.

More than 700 million people worldwide now have profiles on on-line social networking sites (OSNs), such as MySpace and Facebook (ComScore, 2008); OSNs have become integrated into the milieu of modern-day social interactions and are widely used as a primary medium for communication and networking (boyd & Ellison, 2007; Valkenburg & Peter, 2009). Despite the increasing integration of OSN activity into everyday life, however, there has been no research on the most fundamental question about OSN profiles: Do they convey accurate impressions of profile owners? A widely held assumption, supported by content analyses, suggests that OSN profiles are used to create and communicate idealized selves (Manago, Graham, Greenfield, & Salim-khan, 2008). According to this idealized virtual-identity hypothesis, profile owners display idealized characteristics that do not reflect their actual personalities. Thus, personality impressions based on OSN profiles should reflect profile own-ers' ideal-self views rather than what the owners are actually like. A contrasting view holds that OSNs may constitute an extended social context in which to express one's actual personality characteristics, thus fostering accurate interpersonal perceptions. OSNs integrate various sources of personal information that mirror those found in personal environments, private thoughts, facial images, and social behavior, all of which are known to contain valid information about personality creating idealized identities should be hard to accomplish because (a) OSN profiles include information about one's reputation that is difficult to control (e.g., wall posts) and (b) friends provide accountability and subtle feedback on one's profile. Accordingly, the extended real-life hypothesis predicts that people use OSNs to communicate their real personality. If this supposition is true, lay observers should be able to accurately infer the personality characteristics of OSN profile owners. In the present study, we tested the two competing hypotheses. were recruited from the University of Texas campus, where flyers and candy were used to find volunteers for a laboratory-based study of personality judgment. Participants were compensated with a combination of money and course credit. In Germany, participants were recruited through advertisements for an on-line study on personality measurement. As compensation , they received individual feedback on their personality scores. To ensure that participants did not alter their OSN profiles, we saved their profiles before the subject of OSNs was raised. Scores on all measures were normally distributed. Measures Accuracy criteria. Accuracy criteria (i.e., indices of what profile owners were actually like) were created by aggregating across multiple personality reports, each of …
On-line monitoring of power curves

A data-driven approach to the performance analysis of wind turbines is presented. Turbine performance is captured with a power curve. The power curves are constructed using historical wind turbine data. Three power curve models are developed, one by the least squares method and the other by the maximum likelihood estimation method. The models are solved by an evolutionary strategy algorithm. The power curve model constructed by the least squares method outperforms the one built by the maximum likelihood approach. The third model is non-parametric and is built with the k-nearest neighbor (k-NN) algorithm. The least squares (parametric) model and the non-parametric model are used for on-line monitoring of the power curve and their performance is analyzed.
Physiological-based emotion recognition with IRS model

A major challenge in physiology-based emotion recognition is to establish an effective emotion recognizer for multi-users in the user-independent scenario. The recognition result is not satisfied because it ignores the difference in individual response pattern, which can be attributed to IRS (Individual Response Specificity) and SRS(Stimuli Response Specificity) in psychophysiology. To improve the performance of emotion recognition, this paper proposes a Group-Based IRS model by adaptively matching a suitable recognizer in accordance with user's IRS level. Specifically, the users are put into distinct groups by using cluster analysis techniques, where users within the same group have similar IRS level than other groups. Then physiological data of users from each group is utilized to build the corresponding emotion recognizers. After categorizing a new user into one group according to his IRS level, the new user's emotion state is predicted by the corresponding emotion recognizer. To validate our model, the affective physiological data was collected from 11 subjects in four induced emotions(neutral, sadness, fear and pleasure), three-channel bio-sensors were used to measure users electrocardiogram (ECG), galvanic skin response (GSR) and photo plethysmography (PPG). The results show that the recognition precision in Group-based IRS model is higher than general model.
Steganalysis by Subtractive Pixel Adjacency Matrix

This paper presents a novel method for detection of steganographic methods that embed in the spatial domain by adding a low-amplitude independent stego signal, an example of which is LSB matching. First, arguments are provided for modeling differences between adjacent pixels using first-order and second-order Markov chains. Subsets of sample transition probability matrices are then used as features for a steganalyzer implemented by support vector machines. The accuracy of the presented steganalyzer is evaluated on LSB matching and four different databases. The steganalyzer achieves superior accuracy with respect to prior art and provides stable results across various cover sources. Since the feature set based on second-order Markov chain is high-dimensional, we address the issue of curse of dimensionality using a feature selection algorithm and show that the curse did not occur in our experiments.
Harvesting Multiple Views for Marker-Less 3D Human Pose Annotations

Recent advances with Convolutional Networks (ConvNets) have shifted the bottleneck for many computer vision tasks to annotated data collection. In this paper, we present a geometry-driven approach to automatically collect annotations for human pose prediction tasks. Starting from a generic ConvNet for 2D human pose, and assuming a multi-view setup, we describe an automatic way to collect accurate 3D human pose annotations. We capitalize on constraints offered by the 3D geometry of the camera setup and the 3D structure of the human body to probabilistically combine per view 2D ConvNet predictions into a globally optimal 3D pose. This 3D pose is used as the basis for harvesting annotations. The benefit of the annotations produced automatically with our approach is demonstrated in two challenging settings: (i) fine-tuning a generic ConvNet-based 2D pose predictor to capture the discriminative aspects of a subjects appearance (i.e.,personalization), and (ii) training a ConvNet from scratch for single view 3D human pose prediction without leveraging 3D pose groundtruth. The proposed multi-view pose estimator achieves state-of-the-art results on standard benchmarks, demonstrating the effectiveness of our method in exploiting the available multi-view information.
A Differentially-Driven Dual-Polarized Magneto-Electric Dipole Antenna

A novel differentially-driven dual-polarized antenna is proposed in this communication. It is a magneto-electric dipole antenna whose gain and beamwidth keep constant along frequency within the operation bandwidth. If the antenna is ideally symmetrical, its differential port-to-port isolation is theoretically infinite. Due to the differentially-driven scheme, its cross-polarization level can be very low. Measurement shows that the proposed antenna achieves a wide impedance bandwidth of 68% (0.95 to 1.92 GHz) for differential reflection coefficients less than -10 dB and high differential port-to-port isolation of better than 36 dB within the bandwidth. The 3-dB-gain bandwidth of the proposed antenna is 62% (1.09 to 2.08 GHz), and the radiation pattern across it is stable and unidirectional. The broadside gain within the 3-dB-gain bandwidth ranges from 6.6 to 9.6 dBi. The cross-polarization level is lower than -23 dB across the 3-dB-gain bandwidth. The proposed antenna is the first differentially-driven dual-polarized magneto-electric dipole antenna. A feeding structure is specially designed to fit the differentially-driven scheme, and also to achieve wide impedance and gain bandwidths.
Automatic C-to-CUDA Code Generation for Affine Programs

Graphics Processing Units (GPUs) offer tremendous computational power. CUDA (Compute Unified Device Architecture) provides a multi-threaded parallel programming model, facilitating high performance implementations of general-purpose computations. However, the explicitly managed memory hierarchy and multi-level parallel view make manual development of high-performance CUDA code rather complicated. Hence the automatic transformation of sequential input programs into efficient parallel CUDA programs is of considerable interest. This paper describes an automatic code transformation system that generates parallel CUDA code from input sequential C code, for regular (affine) programs. Using and adapting publicly available tools that have made polyhedral compiler optimization practically effective, we develop a C-to-CUDA transformation system that generates two-level parallel CUDA code that is optimized for efficient data access. The performance of automatically generated code is compared with manually optimized CUDA code for a number of benchmarks. The performance of the automatically generated CUDA code is quite close to hand-optimized CUDA code and considerably better than the benchmarks’ performance on a multicore CPU.
Few-shot Autoregressive Density Estimation: Towards Learning to Learn Distributions

Deep autoregressive models have shown state-of-the-art performance in density estimation for natural images on large-scale datasets such as ImageNet. However, such models require many thousands of gradient-based weight updates and unique image examples for training. Ideally, the models would rapidly learn visual concepts from only a handful of examples, similar to the manner in which humans learns across many vision tasks. In this paper, we show how 1) neural attention and 2) meta learning techniques can be used in combination with autoregressive models to enable effective few-shot density estimation. Our proposed modifications to PixelCNN result in state-of-the art few-shot density estimation on the Omniglot dataset. Furthermore, we visualize the learned attention policy and find that it learns intuitive algorithms for simple tasks such as image mirroring on ImageNet and handwriting on Omniglot without supervision. Finally, we extend the model to natural images and demonstrate few-shot image generation on the Stanford Online Products dataset.
Low-cost backdrivable motor control based on feed-forward/feed-back friction compensation

A low-cost but accurate and backdrivable motor control system running on a DC motor and a harmonic drive gear is proposed. It compensates internal friction of the gear and the counter-electromotive torque by combining a model-based feed-forward method and a disturbance observer using a cheap torque sensor. A complementary use of those techniques lowers requirements to their performances i.e. precision, bandwidth, etc, while it is equipped with a flexible property against the external torque. A 2-DOF servo controller is also built upon the system in order to simultaneously achieve smooth responses and robust convergences to the reference.
Robust Distant Supervision Relation Extraction via Deep Reinforcement Learning

Distant supervision has become the standard method for relation extraction. However, even though it is an efficient method, it does not come at no cost—The resulted distantly-supervised training samples are often very noisy. To combat the noise, most of the recent state-of-theart approaches focus on selecting onebest sentence or calculating soft attention weights over the set of the sentences of one specific entity pair. However, these methods are suboptimal, and the false positive problem is still a key stumbling bottleneck for the performance. We argue that those incorrectly-labeled candidate sentences must be treated with a hard decision, rather than being dealt with soft attention weights. To do this, our paper describes a radical solution—We explore a deep reinforcement learning strategy to generate the false-positive indicator, where we automatically recognize false positives for each relation type without any supervised information. Unlike the removal operation in the previous studies, we redistribute them into the negative examples. The experimental results show that the proposed strategy significantly improves the performance of distant supervision comparing to state-of-the-art systems.
Cannabidiol--recent advances.

The aim of this review is to present some of the recent publications on cannabidiol (CBD; 2), a major non-psychoactive constituent of Cannabis, and to give a general overview. Special emphasis is laid on biochemical and pharmacological advances, and on novel mechanisms recently put forward, to shed light on some of the pharmacological effects that can possibly be rationalized through these mechanisms. The plethora of positive pharmacological effects observed with CBD make this compound a highly attractive therapeutic entity.
A Wide-band Circular Polarization Stacked Patch Antenna for the Wireless Communication Applications

A wide-band ‘corners-truncated rectangular’ stacked patch antenna for use in the circular polarization applications was proposed. The antenna proposed in this paper an axial ratio of less than 3 dB and a VSWR of less than 2 : 1 were shown to be achievable over a 25% bandwidth for use in the wireless communication applications, and this antenna can achieves higher gain, lower side lobes and wider bandwidth compared to the traditional microstrip patch antenna.
DeepSim: deep learning code functional similarity

Measuring code similarity is fundamental for many software engineering tasks, e.g., code search, refactoring and reuse. However, most existing techniques focus on code syntactical similarity only, while measuring code functional similarity remains a challenging problem. In this paper, we propose a novel approach that encodes code control flow and data flow into a semantic matrix in which each element is a high dimensional sparse binary feature vector, and we design a new deep learning model that measures code functional similarity based on this representation. By concatenating hidden representations learned from a code pair, this new model transforms the problem of detecting functionally similar code to binary classification, which can effectively learn patterns between functionally similar code with very different syntactics. 
 We have implemented our approach, DeepSim, for Java programs and evaluated its recall, precision and time performance on two large datasets of functionally similar code. The experimental results show that DeepSim significantly outperforms existing state-of-the-art techniques, such as DECKARD, RtvNN, CDLH, and two baseline deep neural networks models.
Designing Algorithms To Aid Discovery by Chemical
Robots

Recently, automated robotic systems have become very efficient, thanks to improved coupling between sensor systems and algorithms, of which the latter have been gaining significance thanks to the increase in computing power over the past few decades. However, intelligent automated chemistry platforms for discovery orientated tasks need to be able to cope with the unknown, which is a profoundly hard problem. In this Outlook, we describe how recent advances in the design and application of algorithms, coupled with the increased amount of chemical data available, and automation and control systems may allow more productive chemical research and the development of chemical robots able to target discovery. This is shown through examples of workflow and data processing with automation and control, and through the use of both well-used and cutting-edge algorithms illustrated using recent studies in chemistry. Finally, several algorithms are presented in relation to chemical robots and chemical intelligence for knowledge discovery.
Hierarchical Control of Droop-Controlled AC and DC Microgrids—A General Approach Toward Standardization

AC and dc microgrids (MGs) are key elements for integrating renewable and distributed energy resources as well as distributed energy-storage systems. In the last several years, efforts toward the standardization of these MGs have been made. In this sense, this paper presents the hierarchical control derived from ISA-95 and electrical dispatching standards to endow smartness and flexibility to MGs. The hierarchical control proposed consists of three levels: 1) The primary control is based on the droop method, including an output-impedance virtual loop; 2) the secondary control allows the restoration of the deviations produced by the primary control; and 3) the tertiary control manages the power flow between the MG and the external electrical distribution system. Results from a hierarchical-controlled MG are provided to show the feasibility of the proposed approach.
Accurate dB-Linear Variable Gain Amplifier With Gain Error Compensation

This paper describes use of a novel exponential approximation for designing dB-linear variable gain amplifiers (VGAs). The exponential function is accurately generated using a simple error-compensation technique. The dB-linear gain is controlled linearly by the gate voltage, resulting in a simple and robust VGA. The proposed dB-linear VGA fabricated in a 65-nm CMOS process achieves a total variable gain range of 76 dB and dB-linear range greater than 50 dB with ±0.5-dB gain error. Under a 1.2-V supply voltage, the current consumption of the VGA is 1.8 mA and that of the output buffer is 1.4 mA. The input-referred in-band noise density is 3.5 nV/√{Hz} and the in-band OIP3 is 11.5 dBm. Due to the very simple circuit topology, the total active area of the VGA and the output buffer is extremely small, 0.01 mm2.
Machine Learning for Seismic Signal Processing: Phase Classification on a Manifold

In this research, we consider the supervised learning problem of seismic phase classification. In seismology, knowledge of the seismic activity arrival time and phase leads to epicenter localization and surface velocity estimates useful in developing seismic early warning systems and detecting man-made seismic events. Formally, the activity arrival time refers to the moment at which a seismic wave is first detected and the seismic phase classifies the physics of the wave. We propose a new perspective for the classification of seismic phases in three-channel seismic data collected within a network of regional recording stations. Our method extends current techniques and incorporates concepts from machine learning. Machine learning techniques attempt to leverage the concept of "learning'' the patterns associated with different types of data characteristics. In this case, the data characteristics are the seismic phases. This concept makes sense because the characteristics of the phase types are dictated by the physics of wave propagation. Thus by "learning'' a signature for each type of phase, we can apply classification algorithms to identify the phase of incoming data from a database of known phases observed over the recording network. Our method first uses a multi-scale feature extraction technique for clustering seismic data on low-dimensional manifolds. We then apply kernel ridge regression on each feature manifold for phase classification. In addition, we have designed an information theoretic measure used to merge regression scores across the multi-scale feature manifolds. Our approach complements current methods in seismic phase classification and brings to light machine learning techniques not yet fully examined in the context of seismology. We have applied our technique to a seismic data set from the Idaho, Montana, Wyoming, and Utah regions collected during 2005 and 2006. This data set contained compression wave and surface wave seismic phases. Through cross-validation, our method achieves a 74.6% average correct classification rate when compared to analyst classifications.