nils-herrmann/clean_scirepeval huggingface dataset:
A 7.6 mW, 414 fs RMS-Jitter 10 GHz Phase-Locked Loop for a 40 Gb/s Serial Link Transmitter Based on a Two-Stage Ring Oscillator in 65 nm CMOS

This paper describes the design of a 10 GHz phase-locked loop (PLL) for a 40 Gb/s serial link transmitter (TX). A two-stage ring oscillator is used to provide a four-phase, 10 GHz clock for a quarter-rate TX. Several analyses and verification techniques, ranging from the clocking architectures for a 40 Gb/s TX to oscillation failures in a two-stage ring oscillator, are addressed in this paper. A tri-state-inverter-based frequency-divider and an AC-coupled clock-buffer are used for high-speed operations with minimal power and area overheads. The proposed 10 GHz PLL fabricated in the 65 nm CMOS technology occupies an active area of 0.009 mm2 with an integrated-RMS-jitter of 414 fs from 10 kHz to 100 MHz while consuming 7.6 mW from a 1.2-V supply. The resulting figure-of-merit is -238.8 dB, which surpasses that of the state-of-the-art ring-PLLs by 4 dB.
Satellite-based vessel Automatic Identification System: A feasibility and performance analysis

The Automatic Identification System (AIS) is a VHF communication system that provides identification/ location information to vessels and shore stations by exchanging data such as position, identification, course, speed, etc. Recently, the interest in detecting and tracking ships at distances from coastlines larger than what can be accomplished by normal terrestrial VHF communications has grown, driven by requirements of long-range applications such as better handling of hazardous cargo, improved security and countering illegal operations. This paper presents an extension of AIS to a long-range positioning reporting through a constellation of LEO satellites. It outlines technical challenges like the high rate of message collisions from ships in the field of view of a satellite. Technical solutions are proposed at system and subsystem level to address these challenges. A computer-based system simulator is used to assess the system performance and carry out a high-level system sizing. Results show that a relatively small constellation of LEO satellites can guarantee good ship position detection probability as well as a reporting time interval of a few hours. System performance aspects to be addressed in future work are the possible impact of terrestrial mobile communications interference as well as variations of the estimated traffic data.
Session-based Recommendations with Recurrent Neural Networks

Session-based recommendation Permanent cold start: where personalized recommendations fail •  Many sites (e.g. classifieds, video services) don’t require users to log in. Although some form of identification is possible, it is not reliable. • Intent/theme: Sessions usually have a goal or a specific theme. Different sessions of the same user center around different concepts. The entire user history may not help much in identifying the user’s current needs. • Never/rarely returning users: High percentage of the users of webshops come from search engines in search for some products and rarely return. Workaround in practice • Item-to-item recommendations: Recommend similar or frequently co-occurring items. We explore item-to-session recommendations. By modeling the whole session, more accurate recommendations can be provided. We propose an RNN-based approach to model the session and provide session-based recommendations. domonkos.tikk@gravityrd.com @domonkostikk balazs.hidasi@gravityrd.com @balazshidasi Balázs Hidasi
Multi-target tracking scheme using a track management table for automotive radar systems

In this paper, a multiple target tracking scheme for automotive radars is presented. First, a track table for efficient track management is proposed; then, a track management function is designed using a state machine. The proposed scheme can initiate, confirm, and delete all tracks of multiple targets. Moreover, it can determine whether the detected target is a real target or a ghost track, and a terminated track or a sudden missing track. The simulation results demonstrate that the proposed tracking scheme is processed normally.
Improving Variational Inference with Inverse Autoregressive Flow

The framework of normalizing flows provides a general strategy for flexible variational inference of posteriors over latent variables. We propose a new type of normalizing flow, inverse autoregressive flow (IAF), that, in contrast to earlier published flows, scales well to high-dimensional latent spaces. The proposed flow consists of a chain of invertible transformations, where each transformation is based on an autoregressive neural network. In experiments, we show that IAF significantly improves upon diagonal Gaussian approximate posteriors. In addition, we demonstrate that a novel type of variational autoencoder, coupled with IAF, is competitive with neural autoregressive models in terms of attained log-likelihood on natural images, while allowing significantly faster synthesis.
The naturalness of reproduced high dynamic range images

The problem of visualizing high dynamic range images on the devices with restricted dynamic range has recently gained a lot of interest in the computer graphics community. Various so-called tone mapping operators have been proposed to face this issue. The field of tone mapping assumes thorough knowledge of both the objective and subjective attributes of an image. However, there no published analysis of such attributes exists so far. In this paper, we present an overview of image attributes which are used extensively in different tone mapping methods. Furthermore, we propose a scheme of relationships between these attributes, leading to the definition of an overall quality measure which we call naturalness. We present results of the subjective psychophysical testing that we have performed to prove the proposed relationship scheme. Our effort sets the stage for well-founded quality comparisons between tone mapping operators. By providing good definitions of the different attributes, comparisons, be they user-driven or fully automatic, are made possible at all.
Signature Verification Competition for Online and Offline Skilled Forgeries (SigComp2011)

The Netherlands Forensic Institute and the Institute for Forensic Science in Shanghai are in search of a signature verification system that can be implemented in forensic casework and research to objectify results. We want to bridge the gap between recent technological developments and forensic casework. In collaboration with the German Research Center for Artificial Intelligence we have organized a signature verification competition on datasets with two scripts (Dutch and Chinese) in which we asked to compare questioned signatures against a set of reference signatures. We have received 12 systems from 5 institutes and performed experiments on online and offline Dutch and Chinese signatures. For evaluation, we applied methods used by Forensic Handwriting Examiners (FHEs) to assess the value of the evidence, i.e., we took the likelihood ratios more into account than in previous competitions. The data set was quite challenging and the results are very interesting.
Fast Ego-motion Estimation with Multi-rate Fusion of Inertial and Vision

This paper presents a tracking system for ego-motion estimation which fuses vision and inertial measurements using EKF and UKF (Extended and Unscented Kalman Filters), where a comparison of their performance has been done. It also considers the multi-rate nature of the sensors: inertial sensing is sampled at a fast sampling frequency while the sampling frequency of vision is lower. the proposed approach uses a constant linear acceleration model and constant angular velocity model based on quaternions, which yields a non-linear model for states and a linear model in measurement equations. Results show that a significant improvement is obtained on the estimation when fusing both measurements with respect to just vision or just inertial measurements. It is also shown that the proposed system can estimate fast-motions even when vision system fails. Moreover, a study of the influence of the noise covariance is also performed, which aims to select their appropriate values at the tuning process. The setup is an end-effector mounted camera, which allow us to pre-define basic rotational and translational motions for validating results. vision and inertial, multi-rate systems, sensor fusion,
The neuroscience of social decision-making.

Given that we live in highly complex social environments, many of our most important decisions are made in the context of social interactions. Simple but sophisticated tasks from a branch of experimental economics known as game theory have been used to study social decision-making in the laboratory setting, and a variety of neuroscience methods have been used to probe the underlying neural systems. This approach is informing our knowledge of the neural mechanisms that support decisions about trust, reciprocity, altruism, fairness, revenge, social punishment, social norm conformity, social learning, and competition. Neural systems involved in reward and reinforcement, pain and punishment, mentalizing, delaying gratification, and emotion regulation are commonly recruited for social decisions. This review also highlights the role of the prefrontal cortex in prudent social decision-making, at least when social environments are relatively stable. In addition, recent progress has been made in understanding the neural bases of individual variation in social decision-making.
Gimli: open source and high-performance biomedical name recognition

Automatic recognition of biomedical names is an essential task in biomedical information extraction, presenting several complex and unsolved challenges. In recent years, various solutions have been implemented to tackle this problem. However, limitations regarding system characteristics, customization and usability still hinder their wider application outside text mining research. We present Gimli, an open-source, state-of-the-art tool for automatic recognition of biomedical names. Gimli includes an extended set of implemented and user-selectable features, such as orthographic, morphological, linguistic-based, conjunctions and dictionary-based. A simple and fast method to combine different trained models is also provided. Gimli achieves an F-measure of 87.17% on GENETAG and 72.23% on JNLPBA corpus, significantly outperforming existing open-source solutions. Gimli is an off-the-shelf, ready to use tool for named-entity recognition, providing trained and optimized models for recognition of biomedical entities from scientific text. It can be used as a command line tool, offering full functionality, including training of new models and customization of the feature set and model parameters through a configuration file. Advanced users can integrate Gimli in their text mining workflows through the provided library, and extend or adapt its functionalities. Based on the underlying system characteristics and functionality, both for final users and developers, and on the reported performance results, we believe that Gimli is a state-of-the-art solution for biomedical NER, contributing to faster and better research in the field. Gimli is freely available at http://bioinformatics.ua.pt/gimli .
Modular equalization architecture using inter-module and switchless intra-module equalizer for energy storage system

This paper proposes a novel modular equalization architecture using inter-module and intra-module equalizers for energy storage systems comprising multiple cells/modules connected in series. In the proposed equalization architecture, cells in a module is equalized by a series-resonant voltage multiplier (SRVM)-based intra-module equalizer while module voltages are unified by a switched capacitor converter (SCC)-based inter-module equalizer. A square wave voltage generated at a switching node of the SCC-based inter-module equalizer is utilized to drive the SRVM-based intra-module equalizer so that the SRVM-based intra-module equalizer can be totally switchless topology. The required switch count of the proposed equalization architecture can be dramatically reduced compared to conventional architectures, allowing simplified circuitry. The proposed equalization architecture is modular and allows different equalizer topologies to be used for inter- and intra-module equalizers, offering good modularity and design flexibility. The derivation procedure of the proposed equalization architecture is explained, followed by separate analyses for the SCC-based inter-module equalizer and SRVM-based intra-module equalizer. An experimental equalization test for three supercapacitor (SC) modules, each consisting of four cells in series, was performed from an initially voltage-imbalanced condition. Module and cell voltages were equalized at different rates, and the equalization performance of the proposed modular equalization architecture was successfully demonstrated.
Trends in Automotive Communication Systems

The use of networks for communications between the electronic control units (ECU) of a vehicle in production cars dates from the beginning of the 1990s. The specific requirements of the different car domains have led to the development of a large number of automotive networks such as Local Interconnect Network, J1850, CAN, TTP/C, FlexRay, media-oriented system transport, IDB1394, etc. This paper first introduces the context of in-vehicle embedded systems and, in particular, the requirements imposed on the communication systems. Then, a comprehensive review of the most widely used automotive networks, as well as the emerging ones, is given. Next, the current efforts of the automotive industry on middleware technologies, which may be of great help in mastering the heterogeneity, are reviewed. Finally, we highlight future trends in the development of automotive communication systems.
Evaluating collaborative filtering recommender systems

Recommender systems have been evaluated in many, often incomparable, ways. In this article, we review the key decisions in evaluating collaborative filtering recommender systems: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole. In addition to reviewing the evaluation strategies used by prior researchers, we present empirical results from the analysis of various accuracy metrics on one content domain where all the tested metrics collapsed roughly into three equivalence classes. Metrics within each equivalency class were strongly correlated, while metrics from different equivalency classes were uncorrelated.
Improving Distributional Similarity with Lessons Learned from Word Embeddings

Recent trends suggest that neural-network-inspired word embedding models outperform traditional count-based distributional models on word similarity and analogy detection tasks. We reveal that much of the performance gains of word embeddings are due to certain system design choices and hyperparameter optimizations, rather than the embedding algorithms themselves. Furthermore, we show that these modifications can be transferred to traditional distributional models, yielding similar gains. In contrast to prior reports, we observe mostly local or insignificant performance differences between the methods, with no global advantage to any single approach over the others.
Can we Evaluate the Quality of Generated Text?

Evaluating the output of NLG systems is notoriously difficult, and performin g assessments of text quality even more so. A range of automated and subject-based approaches to the evaluation of text quality h ave been taken, including comparison with a putative gold standard text, analysis of specific linguistic features of the output, expert review and task-based evaluation. In this paper we present the results of a variety of such approaches in the context of a case study application. We discuss the problems encountered in the implementation of each approach in the context of the literature, and propo se that a test based on the Turing test for machine intelligence offers a way forward in the evaluation of the subjective notion of text qua lity.
An Integrated Environment for the Development of Knowledge-Based Recommender Applications

The complexity of product assortments offered by online selling platforms makes the selection of appropriate items a challenging task. Customers can differ significantly in their expertise and level of knowledge regarding such product assortments. Consequently, intelligent recommender systems are required which provide personalized dialogues supporting the customer in the product selection process. In this paper we present the domainindependent, knowledge-based recommender environment CWAdvisor which assists users by guaranteeing the consistency and appropriateness of solutions, by identifying additional selling opportunities, and by providing explanations for solutions. Using examples from different application domains, we show how model-based diagnosis, personalization, and intuitive knowledge acquisition techniques support the effective implementation of customer-oriented sales dialogues. In this context, we report our experiences gained in industrial projects and present an evaluation of successfully deployed recommender applications.
Batch Normalization in Convolutional Neural Networks — A comparative study with CIFAR-10 data

Deep learning is an emerging field of computational science that involves large quantity of data for training a model. In this paper, we have performed a comparative study of various state-of-the-art Convolutional Networks viz. DenseNet, VGG, Inception (v3) Network and Residual Network with different activation function, and demonstrate the importance of Batch Normalization. It is shown that Batch Normalization is not only important in improving the performance of the neural networks, but are essential for being able to train a deep convolutional networks. In this work state-ofthe-art convolutional neural networks viz. DenseNet, VGG, Residual Network and Inception (v3) Network are compared on a standard dataset, CIFAR-10 with batch normalization for 200 epochs. The conventional RELU activation results in accuracy of 82.68%, 88.79%, 81.01%, and 84.92% respectively. With ELU activation Residual and VGG Networks’ performance increases to 84.59% and 89.91%, this is highly significant.
Discriminative Transfer Learning with Tree-based Priors

High capacity classifiers, such as deep neural networks, often struggle on classes that have very few training examples. We propose a method for improving classification performance for such classes by discovering similar classes and transferring knowledge among them. Our method learns to organize the classes into a tree hierarchy. This tree structure imposes a prior over the classifier’s parameters. We show that the performance of deep neural networks can be improved by applying these priors to the weights in the last layer. Our method combines the strength of discriminatively trained deep neural networks, which typically require large amounts of training data, with tree-based priors, making deep neural networks work well on infrequent classes as well. We also propose an algorithm for learning the underlying tree structure. Starting from an initial pre-specified tree, this algorithm modifies the tree to make it more pertinent to the task being solved, for example, removing semantic relationships in favour of visual ones for an image classification task. Our method achieves state-of-the-art classification results on the CIFAR-100 image data set and the MIR Flickr image-text data set.
Teaching nonlinear modeling, simulation, and control of electronic power converters using MATLAB/SIMULINK

This paper describes an efficient method to teach analysis and simulation of power electronic converters to undergraduate students, using system level nonlinear state-space models. System-level modeling of power electronic converters reproduces only the ideal switching behavior of the semiconductors and is a useful concept for the numerical simulation of power converters, since simulations present no convergence problems and require little computational time. Switched state-space models, programmed in the MATLAB/SIMULINK software package, can be advantageously used to simulate power converters at the system level and also to design and study their controllers. Switched state-space nonlinear models should be obtained using a theoretical framework suitable for the enhanced control of variable structure power systems. Since the method is inherently nonlinear, no approximated linear models are needed; and since state-space models are used, modern control techniques (sliding mode, neural networks, fuzzy logic) for power converters can easily be used. This paper summarizes the proposed methodology and gives some examples.
Conversation Trees: A Grammar Model for Topic Structure in Forums

Online forum discussions proceed differently from face-to-face conversations and any single thread on an online forum contains posts on different subtopics. This work aims to characterize the content of a forum thread as a conversation tree of topics. We present models that jointly perform two tasks: segment a thread into subparts, and assign a topic to each part. Our core idea is a definition of topic structure using probabilistic grammars. By leveraging the flexibility of two grammar formalisms, Context-Free Grammars and Linear Context-Free Rewriting Systems, our models create desirable structures for forum threads: our topic segmentation is hierarchical, links non-adjacent segments on the same topic, and jointly labels the topic during segmentation. We show that our models outperform a number of tree generation baselines.
Perceived risk of information security and privacy in online shopping : A study of environmentally sustainable products

Consumers’ information security and privacy for online shopping is a fundamental concern of decisionmaking for purchase. In this study, the technology acceptance model was used as a framework to explore website characteristics related to perceived risk of information security and purchase intention. A survey was conducted with a sample size of 387 online shoppers, methodology was done using LISREL 8.54 and SPSS 12.0 to perform factor analysis in order to obtain the factors of the construct and to measure their validity and reliability, the study revealed that website characteristics positively influence perceived risk of information security and privacy and further, have an impact on purchase intention. This study showed that perceived risk of information security and privacy on a website is strongly related to purchase intention. Website management must therefore be strengthenedfirst, by upgrading the network security of e-commerce technology, and second, by developing e-commerce security management systems. Moreover, a website platform should be constructed to improve awareness of consumer information security and to ensure a secure environment for online shopping. Finally, it is important to strengthen integrity management in order to enable consumers to securely buy goods online.
Towards meaningful physics from generative models

In several physical systems, important properties characterizing the system itself are theoretically related with specific degrees of freedom. Although standard Monte Carlo simulations provide an effective tool to accurately reconstruct the physical configurations of the system, they are unable to isolate the different contributions corresponding to different degrees of freedom. Here we show that unsupervised deep learning can become a valid support to MC simulation, coupling useful insights in the phases detection task with good reconstruction performance. As a testbed we consider the 2D XY model, showing that a deep neural network based on variational autoencoders can detect the continuous Kosterlitz-Thouless (KT) transitions, and that, if endowed with the appropriate constrains, they generate configurations with meaningful physical content.