nils-herrmann/clean_scirepeval huggingface dataset:
A Small Dual-Band CPW-Fed Monopole Antenna for GSM and WLAN Applications

A unique and miniaturized dual-band coplanar waveguide (CPW)-fed antenna is presented. The proposed antenna comprises a rectangular patch that is surrounded by upper and lower ground-plane sections that are interconnected by a high-impedance microstrip line. The proposed antenna structure generates two separate impedance bandwidths to cover frequency bands of GSM and Wi-Fi/WLAN. The antenna realized is relatively small in size $(17\times 20\ {\hbox{mm}}^{2})$ and operates over frequency ranges 1.60–1.85 and 4.95–5.80 GHz, making it suitable for GSM and Wi-Fi/WLAN applications. In addition, the antenna is circularly polarized in the GSM band. Experimental results show the antenna exhibits monopole-like radiation characteristics and a good antenna gain over its operating bands. The measured and simulated results presented show good agreement.
Automatic Ship Detection of Remote Sensing Images from Google Earth in Complex Scenes Based on Multi-Scale Rotation Dense Feature Pyramid Networks

Ship detection has been playing a significant role in the field of remote sensing for a long time but it is still full of challenges. The main limitations of traditional ship detection methods usually lie in the complexity of application scenarios, the difficulty of intensive object detection and the redundancy of detection region. In order to solve such problems above, we propose a framework called Rotation Dense Feature Pyramid Networks (R-DFPN) which can effectively detect ship in different scenes including ocean and port. Specifically, we put forward the Dense Feature Pyramid Network (DFPN), which is aimed at solving the problem resulted from the narrow width of the ship. Compared with previous multi-scale detectors such as Feature Pyramid Network (FPN), DFPN builds the high-level semantic feature-maps for all scales by means of dense connections, through which enhances the feature propagation and encourages the feature reuse. Additionally, in the case of ship rotation and dense arrangement, we design a rotation anchor strategy to predict the minimum circumscribed rectangle of the object so as to reduce the redundant detection region and improve the recall. Furthermore, we also propose multi-scale ROI Align for the purpose of maintaining the completeness of semantic and spatial information. Experiments based on remote sensing images from Google Earth for ship detection show that our detection method based on RDFPN representation has a state-of-the-art performance.
CMOS Voltage and Current Reference Circuits consisting of Subthreshold MOSFETs

The development of ultra-low power LSIs is a promising area of research in microelectronics. Such LSIs would be suitable for use in power-aware LSI applications such as portable mobile devices, implantable medical devices, and smart sensor networks . These devices have to operate with ultra-low power, i.e., a few microwatts or less, because they will probably be placed under conditions where they have to get the necessary energy from poor energy sources such as microbatteries or energy scavenging devices . As a step toward such LSIs, we first need to develop voltage and current reference circuits that can operate with an ultra-low current, several tens of nanoamperes or less, i.e., sub-microwatt operation. To achieve such low-power operation, the circuits have to be operated in the subthreshold region, i.e., a region at which the gate-source voltage of MOSFETs is lower than the threshold voltage [3; 4]. Voltage and current reference circuits are important building blocks for analog, digital, and mixed-signal circuit systems in microelectronics, because the performance of these circuits is determined mainly by their bias voltages and currents. The circuits generate a constant reference voltage and current for various other components such as operational amplifiers, comparators, AD/DA converters, oscillators, and PLLs. For this purpose, bandgap reference circuits with CMOS-based vertical bipolar transistors are conventionally used in CMOS LSIs [5; 6]. However, they need resistors with a high resistance of several hundred megaohms to achieve low-current, subthreshold operation. Such a high resistance needs a large area to be implemented, and this makes conventional bandgap references unsuitable for use in ultra-low power LSIs. Therefore, modified voltage and current reference circuits for lowpower LSIs have been reported (see [7]-[12], [14]-[17]). However, these circuits have various problems. For example, their power dissipations are still large, their output voltages and currents are sensitive to supply voltage and temperature variations, and they have complex circuits with many MOSFETs; these problems are inconvenient for practical use in ultra-low power LSIs. Moreover, the effect of process variations on the reference signal has not been discussed in detail. To solve these problems, I and my colleagues reported new voltage and current reference circuits [13; 18] that can operate with sub-microwatt power dissipation and with low sensitivity to temperature and supply voltage. Our circuits consist of subthreshold MOSFET circuits and use no resistors.
A simulated annealing approach to define the genetic structure of populations.

We present a new approach for defining groups of populations that are geographically homogeneous and maximally differentiated from each other. As a by-product, it also leads to the identification of genetic barriers between these groups. The method is based on a simulated annealing procedure that aims to maximize the proportion of total genetic variance due to differences between groups of populations (spatial analysis of molecular variance; samova). Monte Carlo simulations were used to study the performance of our approach and, for comparison, the behaviour of the Monmonier algorithm, a procedure commonly used to identify zones of sharp genetic changes in a geographical area. Simulations showed that the samova algorithm indeed finds maximally differentiated groups, which do not always correspond to the simulated group structure in the presence of isolation by distance, especially when data from a single locus are available. In this case, the Monmonier algorithm seems slightly better at finding predefined genetic barriers, but can often lead to the definition of groups of populations not differentiated genetically. The samova algorithm was then applied to a set of European roe deer populations examined for their mitochondrial DNA (mtDNA) HVRI diversity. The inferred genetic structure seemed to confirm the hypothesis that some Italian populations were recently reintroduced from a Balkanic stock, as well as the differentiation of groups of populations possibly due to the postglacial recolonization of Europe or the action of a specific barrier to gene flow.
Resnet in Resnet: Generalizing Residual Architectures

ResNets have recently achieved state-of-the-art results on challenging computer vision tasks. In this paper, we create a novel architecture that improves ResNets by adding the ability to forget and by making the residuals more expressive, yielding excellent results. ResNet in ResNet outperforms architectures with similar amounts of augmentation on CIFAR-10 and establishes a new state-of-the-art on CIFAR-100.
Deep Unsupervised Representation Learning for Remote Sensing Images

Scene classification plays a key role in interpreting the remotely sensed high-resolution images. With the development of deep learning, supervised learning in classification of Remote Sensing with convolutional networks (CNNs) has been frequently adopted. However, researchers paid less attention to unsupervised learning in remote sensing with CNNs. In order to filling the gap, this paper proposes a set of CNNs called Multiple lAyeR feaTure mAtching(MARTA) generative adversarial networks (GANs) to learn representation using only unlabeled data. There will be two models of MARTA GANs involved: (1) a generative model G that captures the data distribution and provides more training data; (2) a discriminative model D that estimates the possibility that a sample came from the training data rather than G and in this way a well-formed representation of dataset can be learned. Therefore, MARTA GANs obtain the state-of-the-art results which outperform the results got from UC-Merced Land-use dataset and Brazilian Coffee Scenes dataset.
Digital footprints: predicting personality from temporal patterns of technology use

Psychometric modeling using digital data traces is a growing field of research with a breadth of potential applications in marketing, personalization and psychological assessment. We present a novel form of digital traces for user modeling: temporal patterns of smartphone and personal computer activity. We show that some temporal activity metrics are highly correlated with certain Big Five personality metrics. We then present a machine learning method for binary classification of each Big Five personality trait using these temporal activity patterns of both computer and smartphones as model features. Our initial findings suggest that Extroversion, Openness, Agreeableness, and Neuroticism can be classified using temporal patterns of digital traces at a similar accuracy to previous research that classified personality traits using different types of digital traces.
A global controller for flying wing tailsitter vehicles

We present a global controller for tracking nominal trajectories with a flying wing tailsitter vehicle. The control strategy is based on a first-principles model of the vehicle dynamics that captures all relevant aerodynamic effects, and we apply an onboard parameter learning scheme in order to estimate unknown aerodynamic parameters. A cascaded control architecture is used: Based on position and velocity errors an outer control loop computes a desired attitude keeping the vehicle in coordinated flight, while an inner control loop tracks the desired attitude using a lookup table with precomputed optimal attitude trajectories. The proposed algorithms can be implemented on a typical microcontroller and the performance is demonstrated in various experiments.
Critical Power: An Important Fatigue Threshold in Exercise Physiology.

: The hyperbolic form of the power-duration relationship is rigorous and highly conserved across species, forms of exercise, and individual muscles/muscle groups. For modalities such as cycling, the relationship resolves to two parameters, the asymptote for power (critical power [CP]) and the so-called W' (work doable above CP), which together predict the tolerable duration of exercise above CP. Crucially, the CP concept integrates sentinel physiological profiles-respiratory, metabolic, and contractile-within a coherent framework that has great scientific and practical utility. Rather than calibrating equivalent exercise intensities relative to metabolically distant parameters such as the lactate threshold or V˙O2max, setting the exercise intensity relative to CP unifies the profile of systemic and intramuscular responses and, if greater than CP, predicts the tolerable duration of exercise until W' is expended, V˙O2max is attained, and intolerance is manifested. CP may be regarded as a "fatigue threshold" in the sense that it separates exercise intensity domains within which the physiological responses to exercise can (CP) be stabilized. The CP concept therefore enables important insights into 1) the principal loci of fatigue development (central vs. peripheral) at different intensities of exercise and 2) mechanisms of cardiovascular and metabolic control and their modulation by factors such as O2 delivery. Practically, the CP concept has great potential application in optimizing athletic training programs and performance as well as improving the life quality for individuals enduring chronic disease.
Privacy Enhanced Recommender System

Recommender systems are widely used in online applications since they enable personalized service to the users. The underlying collaborative filtering techniques work on user’s data which are mostly privacy sensitive and can be misused by the service provider. To protect the privacy of the users, we propose to encrypt the privacy sensitive data and generate recommendations by processing them under encryption. With this approach, the service provider learns no information on any user’s preferences or the recommendations made. The proposed method is based on homomorphic encryption schemes and secure multiparty computation (MPC) techniques. The overhead of working in the encrypted domain is minimized by packing data as shown in the complexity analysis.
Cancer genomics: from discovery science to personalized medicine

Recent advances in genome technologies and the ensuing outpouring of genomic information related to cancer have accelerated the convergence of discovery science and clinical medicine. Successful examples of translating cancer genomics into therapeutics and diagnostics reinforce its potential to make possible personalized cancer medicine. However, the bottlenecks along the path of converting a genome discovery into a tangible clinical endpoint are numerous and formidable. In this Perspective, we emphasize the importance of establishing the biological relevance of a cancer genomic discovery in realizing its clinical potential and discuss some of the major obstacles to moving from the bench to the bedside.
Graph Based Convolutional Neural Network

The benefit of localized features within the regular domain has given rise to the use of Convolutional Neural Networks (CNNs) in machine learning, with great proficiency in the image classification. The use of CNNs becomes problematic within the irregular spatial domain due to design and convolution of a kernel filter being non-trivial. One solution to this problem is to utilize graph signal processing techniques and the convolution theorem to perform convolutions on the graph of the irregular domain to obtain feature map responses to learnt filters. We propose graph convolution and pooling operators analogous to those in the regular domain. We also provide gradient calculations on the input data and spectral filters, which allow for the deep learning of an irregular spatial domain problem. Signal filters take the form of spectral multipliers, applying convolution in the graph spectral domain. Applying smooth multipliers results in localized convolutions in the spatial domain, with smoother multipliers providing sharper feature maps. Algebraic Multigrid is presented as a graph pooling method, reducing the resolution of the graph through agglomeration of nodes between layers of the network. Evaluation of performance on the MNIST digit classification problem in both the regular and irregular domain is presented, with comparison drawn to standard CNN. The proposed graph CNN provides a deep learning method for the irregular domains present in the machine learning community, obtaining 94.23% on the regular grid, and 94.96% on a spatially irregular subsampled MNIST.
Deep learning

and high computational costs are being tackled. Researchers in academic settings as well as in startup companies such as Deep Genomics, launched July 22, 2015, by some of the authors of DeepBind, will increasingly apply deep learning to genome analysis and precision medicine. The goal is to predict the effect of genetic variants— both naturally occurring and introduced by genome editing—on a cell’s regulatory landscape and how this in turn affects disease development. Nicole Rusk ❯❯Deep learning
From Adversarial Training to Generative Adversarial Networks

In this paper, we are interested in two seemingly different concepts: adversarial training and generative adversarial networks (GANs). Particularly, how these techniques help to improve each other. To this end, we analyze the limitation of adversarial training as the defense method, starting from questioning how well the robustness of a model can generalize. Then, we successfully improve the generalizability via data augmentation by the “fake” images sampled from generative adversarial network. After that, we are surprised to see that the resulting robust classifier leads to a better generator, for free. We intuitively explain this interesting phenomenon and leave the theoretical analysis for future work. Motivated by these observations, we propose a system that combines generator, discriminator, and adversarial attacker in a single network. After end-to-end training and fine tuning, our method can simultaneously improve the robustness of classifiers, measured by accuracy under strong adversarial attacks, and the quality of generators, evaluated both aesthetically and quantitatively. In terms of the classifier, we achieve better robustness than the state-of-the-art adversarial training algorithm proposed in (Madry etla., 2017), while our generator achieves competitive performance compared with SN-GAN (Miyato and Koyama, 2018). Source code is publicly available online at https://github.com/anonymous.
Straw Row Position And Orientation Reconstruction Through Image Segmentation

This paper investigates a new approach in straw row position and orientation reconstruction in an open field, based on image segmentation with Fully Convolutional Networks (FCN). The model architecture consists of an encoder (for feature extraction) and decoder (produces segmentation map from encoded features) modules and similar to  except for two fully connected layers. The heatmaps produced by the FCN are used to determine orientations and spatial arrangments of the straw rows relatively to harvester via transforming the bird's eye view and Fast Hough Transform (FHT). This leads to real-time harvester trajectory optimization over treated area of the field by correction conditions calculation through the row’s directions family.
Internet Gaming Addiction: A Systematic Review of Empirical Research

The activity of play has been ever present in human history and the Internet has emerged as a playground increasingly populated by gamers. Research suggests that a minority of Internet game players experience symptoms traditionally associated with substance-related addictions, including mood modification, tolerance and salience. Because the current scientific knowledge of Internet gaming addiction is copious in scope and appears relatively complex, this literature review attempts to reduce this confusion by providing an innovative framework by which all the studies to date can be categorized. A total of 58 empirical studies were included in this literature review. Using the current empirical knowledge, it is argued that Internet gaming addiction follows a continuum, with antecedents in etiology and risk factors, through to the development of a “full-blown” addiction, followed by ramifications in terms of negative consequences and potential treatment. The results are evaluated in light of the emergent discrepancies in findings, and the consequent implications for future research.
A Composite Kernel to Extract Relations between Entities with Both Flat and Structured Features

This paper proposes a novel composite kernel for relation extraction. The composite kernel consists of two individual kernels: an entity kernel that allows for entity-related features and a convolution parse tree kernel that models syntactic information of relation examples. The motivation of our method is to fully utilize the nice properties of kernel methods to explore diverse knowledge for relation extraction. Our study illustrates that the composite kernel can effectively capture both flat and structured features without the need for extensive feature engineering, and can also easily scale to include more features. Evaluation on the ACE corpus shows that our method outperforms the previous best-reported methods and significantly outperforms previous two dependency tree kernels for relation extraction.
Handwriting Recognition with Large Multidimensional Long Short-Term Memory Recurrent Neural Networks

Multidimensional long short-term memory recurrent neural networks achieve impressive results for handwriting recognition. However, with current CPU-based implementations, their training is very expensive and thus their capacity has so far been limited. We release an efficient GPU-based implementation which greatly reduces training times by processing the input in a diagonal-wise fashion. We use this implementation to explore deeper and wider architectures than previously used for handwriting recognition and show that especially the depth plays an important role. We outperform state of the art results on two databases with a deep multidimensional network.
Research on Unstructured Text Data Mining and Fault Classification Based on RNN-LSTM with Malfunction Inspection Report

This paper documents the condition-based maintenance (CBM) of power transformers, the analysis of which relies on two basic data groups: structured (e.g., numeric and categorical) and unstructured (e.g., natural language text narratives) which accounts for 80% of data required. However, unstructured data comprised of malfunction inspection reports, as recorded by operation and maintenance of the power grid, constitutes an abundant untapped source of power insights. This paper proposes a method for malfunction inspection report processing by deep learning, which combines the text data mining–oriented recurrent neural networks (RNN) with long short-term memory (LSTM). In this paper, the effectiveness of the RNN-LSTM network for modeling inspection data is established with a straightforward training strategy in which we replicate targets at each sequence step. Then, the corresponding fault labels are given in datasets, in order to calculate the accuracy of fault classification by comparison with the original data labels and output samples. Experimental results can reflect how key parameters may be selected in the configuration of the key variables to achieve optimal results. The accuracy of the fault recognition demonstrates that the method we proposed can provide a more effective way for grid inspection personnel to deal with unstructured data.
Learning to Optimize

Algorithm design is a laborious process and often requires many iterations of ideation and validation. In this paper, we explore automating algorithm design and present a method to learn an optimization algorithm, which we believe to be the first method that can automatically discover a better algorithm. We approach this problem from a reinforcement learning perspective and represent any particular optimization algorithm as a policy. We learn an optimization algorithm using guided policy search and demonstrate that the resulting algorithm outperforms existing hand-engineered algorithms in terms of convergence speed and/or the final objective value.
Paraphrase Identification and Semantic Similarity in Twitter with Simple Features

Paraphrase Identification and Semantic Similarity are two different yet well related tasks in NLP. There are many studies on these two tasks extensively on structured texts in the past. However, with the strong rise of social media data, studying these tasks on unstructured texts, particularly, social texts in Twitter is very interesting as it could be more complicated problems to deal with. We investigate and find a set of simple features which enables us to achieve very competitive performance on both tasks in Twitter data. Interestingly, we also confirm the significance of using word alignment techniques from evaluation metrics in machine translation in the overall performance of these tasks.