nils-herrmann/clean_scirepeval huggingface dataset:
Aspect-level Sentiment Classification with HEAT (HiErarchical ATtention) Network

Aspect-level sentiment classification is a fine-grained sentiment analysis task, which aims to predict the sentiment of a text in different aspects. One key point of this task is to allocate the appropriate sentiment words for the given aspect.Recent work exploits attention neural networks to allocate sentiment words and achieves the state-of-the-art performance. However, the prior work only attends to the sentiment information and ignores the aspect-related information in the text, which may cause mismatching between the sentiment words and the aspects when an unrelated sentiment word is semantically meaningful for the given aspect. To solve this problem, we propose a HiErarchical ATtention (HEAT) network for aspect-level sentiment classification. The HEAT network contains a hierarchical attention module, consisting of aspect attention and sentiment attention. The aspect attention extracts the aspect-related information to guide the sentiment attention to better allocate aspect-specific sentiment words of the text. Moreover, the HEAT network supports to extract the aspect terms together with aspect-level sentiment classification by introducing the Bernoulli attention mechanism. To verify the proposed method, we conduct experiments on restaurant and laptop review data sets from SemEval at both the sentence level and the review level. The experimental results show that our model better allocates appropriate sentiment expressions for a given aspect benefiting from the guidance of aspect terms. Moreover, our method achieves better performance on aspect-level sentiment classification than state-of-the-art models.
Low-Shot Learning from Imaginary Data

Humans can quickly learn new visual concepts, perhaps because they can easily visualize or imagine what novel objects look like from different views. Incorporating this ability to hallucinate novel instances of new concepts might help machine vision systems perform better low-shot learning, i.e., learning concepts from few examples. We present a novel approach to low-shot learning that uses this idea. Our approach builds on recent progress in meta-learning ("learning to learn") by combining a meta-learner with a "hallucinator" that produces additional training examples, and optimizing both models jointly. Our hallucinator can be incorporated into a variety of meta-learners and provides significant gains: up to a 6 point boost in classification accuracy when only a single training example is available, yielding state-of-the-art performance on the challenging ImageNet low-shot classification benchmark.
Exploitation of spectral redundancy in cyclostationary signals

It is shown that the cyclostationarity attribute, as it is reflected in the periodicities of (second-order) moments of the signal, can be interpreted in terms of the property that allows generation of spectral lines from the signal by putting it through a (quadratic) nonlinear transformation. The fundamental link between the spectral-line generation property and the statistical property called spectral correlation, which corresponds to the correlation that exists between the random fluctuations of components of the signal residing in distinct spectral bands, is explained. The effects on the spectral-correlation characteristics of some basic signal processing operations, such as filtering, product modulation, and time sampling, are examined. It is shown how to use these results to derive the spectral-correlation characteristics for various types of man-made signals. Some ways of exploiting the inherent spectral redundancy associated with spectral correlation to perform various signal processing tasks involving detection and estimation of highly corrupted man-made signals are described.<<ETX>>
Detection and Discrimination of Open-Phase Fault in Permanent Magnet Synchronous Motor Drive System

Open-phase fault in permanent magnet synchronous motor (PMSM) drive system occurs as the phase winding is disconnected or one leg of the inverter bridge fails. It may generate large electromagnetic torque ripple and serious mechanical vibration. Therefore, a rapid fault detection method is greatly required to identify this fault at early stage and prevent damage to the system. This paper develops a method of the open-phase fault detection and discrimination for the PMSM drive system based on the zero-sequence voltage components, in which the discrimination of the fault types, namely internal stator winding failure and switches failure of the inverter is realized. Then, appropriate fault-tolerant measures may be taken according to the different fault types. The experimental platform is established, and the experimental results verify the effectiveness of the proposed method, showing that not only the open-phase fault can be rapidly detected, but also the fault type can be effectively discriminated.
Improving Liver Lesion Detection using CT-to-PET FCN and GAN Networks

We present a brief description of our recent work that was submitted for journal publication introducing a novel system for generation of virtual PET images using CT scans . We combine a fully convolutional network (FCN) with a conditional generative adversarial network (GAN) to synthesize PET images from given input CT images. Clinically, such solutions may reduce the need for the more expensive and radioactive PET/CT scan. Quantitative evaluation was conducted using an existing lesion detection software, combining the synthesized PET as a false positive reduction layer for the detection of malignant lesions in the liver. Current results look promising showing a reduction in the average false positive per case while keeping the same true positive rate. The suggested solution is comprehensive and can be expanded to additional body organs, and different modalities.
Millimeter-wave Gbps Broadband Evolution towards 5G: Fixed Access and Backhaul

— As wireless communication evolves towards 5G, both fixed broadband and mobile broadband will play a crucial part in providing the Gbps infrastructure for a connected society. This paper proposes a Millimeter-wave Gbps Broadband (MGB) system as the solution to two critical problems in this evolution: last-mile access for fixed broadband and small cell backhaul for mobile broadband. The key idea is to use spectrum that is already available in the millimeter wave bands for fixed wireless access with optimized dynamic beamforming and massive MIMO infrastructure to achieve high capacity with wide area coverage. This paper explains the MGB concept and describes potential array architectures for realizing the system. Simulations demonstrate that with 500 MHz of bandwidth (at 39 GHz band) and 28 dBm transmission power (55 dBm EIRP), it is possible to provide more than 11 Gbps backhaul capacity for 96 small cells within 1-km radius.
Modeling Label Ambiguity for Neural List-Wise Learning to Rank

List-wise learning to rank methods are considered to be the stateof-the-art. One of the major problems with these methods is that the ambiguous nature of relevance labels in learning to rank data is ignored. Ambiguity of relevance labels refers to the phenomenon that multiple documents may be assigned the same relevance label for a given query, so that no preference order should be learned for those documents. In this paper we propose a novel sampling technique for computing a list-wise loss that can take into account this ambiguity. We show the e ectiveness of the proposed method by training a 3-layer deep neural network. We compare our new loss function to two strong baselines: ListNet and ListMLE. We show that our method generalizes better and signi cantly outperforms other methods on the validation and test sets. CCS CONCEPTS •Computingmethodologies→Neural networks; •Information systems →Learning to rank;
Ten Lectures on Wavelets (Ingrid Daubechies)

The resulting semigroup V is of class AK, and upper bounds are given for the Hausdorff dimension and fractal dimension of a compact invariant set associated with V. One who wishes to study Ladyzhenskaya’s book more carefully will wish to consult other sources. A good new source for Hausdorff dimension and related topics is the elegant text of Evans and Gariepy . Ladyzhenskaya’s bibliography contains only sixteen items. This review has concentrated on the Navier-Stokes equations, but her book is more comprehensive than that alone. There is a huge literature on the ODE approach to PDE, and Ladyzhenskaya’s book is largely in this spirit. The recent books of Hale , Ruelle , and Temam  are good places to get much additional information and references. One should also consult Ladyzhenskaya’s recent paper , which gives "complements and corrections" to her previous work. These include estimates of Hausdorff and fractal dimensions. The reviewer is indebted to Craig Evans for this reference.
A hybrid 2D/3D user interface for immersive object modeling

Virtual environments and other applications of computer-generated scenes are emerging. This increases the demand for tools for efficient creation of the virtual worlds and interaction with the components in the virtual world. Introduction of immersiveness in the modeling process seems promising, but a number of issues related to immersive modeling still have to be investigated. The authors present a framework for an immersive modeling system with a hybrid 2D/3D user interface. The designer is immersed in the design space, which improves his awareness of the design objects under consideration. 2D and 3D human-computer interaction techniques are combined in the user interface to improve the efficiency of the modeler. While 3D interaction is natural for a number of design tasks, 2D interaction is particularly helpful for interaction with menus and dialog boxes, for precise manipulations and for editing operations that are subject to constraints.
Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets

Bayesian optimization has become a successful tool for hyperparameter optimization of machine learning algorithms, such as support vector machines or deep neural networks. But it is still costly if each evaluation of the objective requires training and validating the algorithm being optimized, which, for large datasets, often takes hours, days, or even weeks. To accelerate hyperparameter optimization, we propose a generative model for the validation error as a function of training set size, which is learned during the optimization process and allows exploration of preliminary configurations on small subsets, by extrapolating to the full dataset. We construct a Bayesian optimization procedure, dubbed FABOLAS, which models loss and training time as a function of dataset size and automatically trades off high information gain about the global optimum against computational cost. Experiments optimizing support vector machines and deep neural networks show that FABOLAS often finds high-quality solutions 10 to 100 times faster than other state-of-the-art Bayesian optimization methods.
Fully automatic detection and segmentation of abdominal aortic thrombus in post-operative CTA images using Deep Convolutional Neural Networks

Computerized Tomography Angiography (CTA) based follow-up of Abdominal Aortic Aneurysms (AAA) treated with Endovascular Aneurysm Repair (EVAR) is essential to evaluate the progress of the patient and detect complications. In this context, accurate quantification of post-operative thrombus volume is required. However, a proper evaluation is hindered by the lack of automatic, robust and reproducible thrombus segmentation algorithms. We propose a new fully automatic approach based on Deep Convolutional Neural Networks (DCNN) for robust and reproducible thrombus region of interest detection and subsequent fine thrombus segmentation. The DetecNet detection network is adapted to perform region of interest extraction from a complete CTA and a new segmentation network architecture, based on Fully Convolutional Networks and a Holistically-Nested Edge Detection Network, is presented. These networks are trained, validated and tested in 13 post-operative CTA volumes of different patients using a 4-fold cross-validation approach to provide more robustness to the results. Our pipeline achieves a Dice score of more than 82% for post-operative thrombus segmentation and provides a mean relative volume difference between ground truth and automatic segmentation that lays within the experienced human observer variance without the need of human intervention in most common cases.
Adapted Neuro-Fuzzy Inference System on indirect approach TSK fuzzy rule base for stock market analysis

Nowadays because of the complicated nature of making decision in stock market and making real-time strategy for buying and selling stock via portfolio selection and maintenance, many research papers has involved stock price prediction issue. Low accuracy resulted by models may increase trade cost such as commission cost in more sequenced buy and sell signals because of insignificant alarms and otherwise bad diagnosis in price trend do not satisfy trader’s expectation and may involved him/her in irrecoverable cost. Therefore, in this paper, Neuro-Fuzzy Inference System adopted on a Takagi–Sugeno–Kang (TSK) type Fuzzy Rule Based System is developed for stock price prediction. The TSK fuzzy model applies the technical index as the input variables and the consequent part is a linear combination of the input variables. Fuzzy C-Mean clustering implemented for identifying number of rules. Initial membership function of the premise part approximately defined as Gaussian function. TSK parameters tuned by Adaptive NeroFuzzy Inference System (ANFIS). Proposed model is tested on the Tehran Stock Exchange Indexes (TEPIX). This index with high accuracy near by 97.8% has successfully forecasted with several experimental tests from different sectors.
An integrative imputation method based on multi-omics datasets

Integrative analysis of multi-omics data is becoming increasingly important to unravel functional mechanisms of complex diseases. However, the currently available multi-omics datasets inevitably suffer from missing values due to technical limitations and various constrains in experiments. These missing values severely hinder integrative analysis of multi-omics data. Current imputation methods mainly focus on using single omics data while ignoring biological interconnections and information imbedded in multi-omics data sets. In this study, a novel multi-omics imputation method was proposed to integrate multiple correlated omics datasets for improving the imputation accuracy. Our method was designed to: 1) combine the estimates of missing value from individual omics data itself as well as from other omics, and 2) simultaneously impute multiple missing omics datasets by an iterative algorithm. We compared our method with five imputation methods using single omics data at different noise levels, sample sizes and data missing rates. The results demonstrated the advantage and efficiency of our method, consistently in terms of the imputation error and the recovery of mRNA-miRNA network structure. We concluded that our proposed imputation method can utilize more biological information to minimize the imputation error and thus can improve the performance of downstream analysis such as genetic regulatory network construction.
Using Ontology-Based Traffic Models for More Efficient Decision Making of Autonomous Vehicles

The paper describes how a high-level abstract world model can be used to support the decision-making process of an autonomous driving system. The approach uses a hierarchical world model and distinguishes between a low-level model for the trajectory planning and a high-level model for solving the traffic coordination problem. The abstract world model used in the CyberCars-2 project is presented. It is based on a topological lane segmentation and introduces relations to represent the semantic context of the traffic scenario. This makes it much easier to realize a consistent and complete driving control system, and to analyze, evaluate and simulate such a system.
On Evaluation of 6D Object Pose Estimation

A pose of a rigid object has 6 degrees of freedom and its full knowledge is required in many robotic and scene understanding applications. Evaluation of 6D object pose estimates is not straightforward. Object pose may be ambiguous due to object symmetries and occlusions, i.e. there can be multiple object poses that are indistinguishable in the given image and should be therefore treated as equivalent. The paper defines 6D object pose estimation problems, proposes an evaluation methodology and introduces three new pose error functions that deal with pose ambiguity. The new error functions are compared with functions commonly used in the literature and shown to remove certain types of non-intuitive outcomes. Evaluation tools are provided at: https : //github.com/thodan/obj pose eval
New clock-gating techniques for low-power flip-flops

Two novel low power flip-flops are presented in the paper. Proposed flip-flops use new gating techniques that reduce power dissipation deactivating the clock signal. Presented circuits overcome the clock duty-cycle limitation of previously reported gated flip-flops.
Circuit simulations with the inclusion of parasitics show that sensible power dissipation reduction is possible if input signal has reduced switching activity. A 16-bit counter is presented as a simple low power application.
Real-time pose estimation of a dynamic quadruped in GPS-denied environments for 24-hour operation

We present a real-time system that enables a highly capable dynamic quadruped robot to maintain an accurate six-degreeof-freedom pose estimate (within a 1.0% error of distance traveled) over long distances traversed through complex, dynamic outdoor terrain, during day and night, in the presence of camera occlusion and saturation, and occasional large external disturbances, such as slips or falls. The system fuses a stereo-camera sensor, inertial measurement unit, leg odometry, and optional intermittent GPS position updates with an extended Kalman filter to ensure robust, low-latency performance. To maintain a six-degree-of-freedom local positioning accuracy alongside the global positioning knowledge, two reference frames are used; a local reference frame and a global reference frame, with the former benefiting obstacle detection and mapping and the latter for operator-specified and autonomous way-point following. Extensive experimental results obtained from multiple field tests are presented to illustrate the performance and robustness of the system over hours of continuous runs and hundreds of kilometers of distance traveled in a wide variety of terrains and conditions.
Optimum Design of Traveling-Wave SIW Slot Array Antennas

This paper presents an optimum design method based on the Method of Least Squares (MLS) for the traveling-wave substrate integrated waveguide (SIW) slot array antenna. Elliot's design formulas for dielectric filled waveguide including external and internal mutual coupling are used for the design by means of the equivalent waveguide. In order to achieve an efficient antenna, design formulas are extended to nonuniformly spaced slots. An error function consisting of four terms is formulated and then minimized with respect to the design parameters namely lengths, offsets, spacings and excitations of slots using combination of genetic algorithm and conjugate gradient method (GA-CG). A linear slot array at $X$ -band is designed, simulated and fabricated for verification. Results of the proposed method, simulation software and measurement are in good agreement.
The Evolution to Modern Phased Array Architectures

Phased array technology has been evolving steadily with advances in solid-state microwave integrated circuits, analysis and design tools, and reliable fabrication practices. With significant government investments, the technologies have matured to a point where phased arrays are widely used in military systems. Next-generation phased arrays will employ high levels of digitization, which enables a wide range of improvements in capability and performance. Digital arrays leverage the rapid commercial evolution of digital processor technology. The cost of phased arrays can be minimized by utilizing high-volume commercial microwave manufacturing and packaging techniques. Dramatic cost reductions are achieved by employing a tile array architecture, which greatly reduces the number of printed circuit boards and connectors in the array.
A Logic-based Approach for Recognizing Textual Entailment Supported by Ontological Background Knowledge

We present the architecture and the evaluation of a new system for recognizing textual entailment (RTE). In RTE we want to identify automatically the type of a logical relation between two input texts. In particular, we are interested in proving the existence of an entailment between them. We conceive our system as a modular environment allowing for a high-coverage syntactic and semantic text analysis combined with logical inference. For the syntactic and semantic analysis we combine a deep semantic analysis with a shallow one supported by statistical models in order to increase the quality and the accuracy of results. For RTE we use logical inference of first-order employing model-theoretic techniques and automated reasoning tools. The inference is supported with problem-relevant background knowledge extracted automatically and on demand from external sources like, e.g., WordNet, YAGO, and OpenCyc, or other, more experimental sources with, e.g., manually defined presupposition resolutions, or with axiomatized general and common sense knowledge. The results show that fine-grained and consistent knowledge coming from diverse sources is a necessary condition determining the correctness and traceability of results.
Child Care Quality and Child Development

Development in early childhood predicts schooling and labor market outcomes in adulthood. In this paper we use a fixed effects identification strategy to assess how differences in the quality of child care affect the communication, fine motor, and problem solving skills of infants and toddlers. We show that children have significantly better development outcomes in classrooms with more experienced caregivers, and classrooms with caregivers who demonstrate higher-quality interactions with children. There is substantial heterogeneity in the effects of caregiver quality on child development. Parents either cannot observe, or do not value, the quality of care.
Augment and Adapt: A Simple Approach to Image Tampering Detection

Convolutional Neural Networks have been shown to be promising for image tampering detection in the recent years. However, the number of tampered images available to train a network is still small. This is mainly due to the cumbersomeness involved in creating lots of tampered images. As a result, the potential offered by these networks is not completely exploited. In this work, we propose a simple method to address this problem by augmenting data using inpainting and compositing schemes. We consider different forms of inpainting like simple inpainting and semantic inpainting as well as compositing schemes like feathering in order to augment the data. A domain adaptation technique is employed to reduce the domain shift between the augmented data and the data available using proprietary softwares. We demonstrate that this method of augmentation is effective in improving the detection accuracies. We present experimental evaluation on two popular datasets for image tampering detection to demonstrate the effectiveness of the proposed approach.