nils-herrmann/clean_scirepeval huggingface dataset:
Grammar Based Music Composition

L-Systems have traditionally been used as a popular method for the modelling of spacefilling curves, biological systems and morphogenesis. In this paper, we adapt string rewriting grammars based on L-Systems into a system for music composition. Representation of pitch, duration and timbre are encoded as grammar symbols, upon which a series of re-writing rules are applied. Parametric extensions to the grammar allow the specification of continuous data for the purposes of modulation and control. Such continuous data is also under control of the grammar. Using non-deterministic grammars with context sensitivity allows the simulation of Nth-order Markov models with a more economical representation than transition matrices and greater flexibility than previous composition models based on finite state automata or Petri nets. Using symbols in the grammar to represent relationships between notes, (rather than absolute notes) in combination with a hierarchical grammar representation, permits the emergence of complex music compositions from a relatively simple grammars.
Modular Cascaded H-Bridge Multilevel PV Inverter With Distributed MPPT for Grid-Connected Applications

This paper presents a modular cascaded H-bridge multilevel photovoltaic (PV) inverter for single- or three-phase grid-connected applications. The modular cascaded multilevel topology helps to improve the efficiency and flexibility of PV systems. To realize better utilization of PV modules and maximize the solar energy extraction, a distributed maximum power point tracking control scheme is applied to both single- and three-phase multilevel inverters, which allows independent control of each dc-link voltage. For three-phase grid-connected applications, PV mismatches may introduce unbalanced supplied power, leading to unbalanced grid current. To solve this issue, a control scheme with modulation compensation is also proposed. An experimental three-phase seven-level cascaded H-bridge inverter has been built utilizing nine H-bridge modules (three modules per phase). Each H-bridge module is connected to a 185-W solar panel. Simulation and experimental results are presented to verify the feasibility of the proposed approach.
Medical image fusion: A survey of the state of the art

Medical image fusion is the process of registering and combining multiple images from single or multiple imaging modalities to improve the imaging quality and reduce randomness and redundancy in order to increase the clinical applicability of medical images for diagnosis and assessment of medical problems. Multi-modal medical image fusion algorithms and devices have shown notable achievements in improving clinical accuracy of decisions based on medical images. This review article provides a factual listing of methods and summarizes the broad scientific challenges faced in the field of medical image fusion. We characterize the medical image fusion research based on (1) the widely used image fusion methods, (2) imaging modalities, and (3) imaging of organs that are under study. This review concludes that even though there exists several open ended technological and scientific challenges, the fusion of medical images has proved to be useful for advancing the clinical reliability of using medical imaging for medical diagnostics and analysis, and is a scientific discipline that has the potential to significantly grow in the coming years.
FlexRAN: A Flexible and Programmable Platform for Software-Defined Radio Access Networks

Although the radio access network (RAN) part of mobile networks offers a significant opportunity for benefiting from the use of SDN ideas, this opportunity is largely untapped due to the lack of a software-defined RAN (SD-RAN) platform. We fill this void with FlexRAN, a flexible and programmable SD-RAN platform that separates the RAN control and data planes through a new, custom-tailored southbound API. Aided by virtualized control functions and control delegation features, FlexRAN provides a flexible control plane designed with support for real-time RAN control applications, flexibility to realize various degrees of coordination among RAN infrastructure entities, and programmability to adapt control over time and easier evolution to the future following SDN/NFV principles. We implement FlexRAN as an extension to a modified version of the OpenAirInterface LTE platform, with evaluation results indicating the feasibility of using FlexRAN under the stringent time constraints posed by the RAN. To demonstrate the effectiveness of FlexRAN as an SD-RAN platform and highlight its applicability for a diverse set of use cases, we present three network services deployed over FlexRAN focusing on interference management, mobile edge computing and RAN sharing.
A microstrip-fed modified printed bow-tie antenna for simultaneous operation in the C and X-bands

This paper presents a modified printed bow-tie antenna designed to simultaneously operate in the C and X-bands from 5.5 to 12.5 GHz. This new antenna design provides an end fire radiation pattern suitable for integration in single and dual polarized phased array systems. The antenna exhibits small size and wide bandwidth approaching 91%. The radiation characteristics are presented for a single element and for a linear array configuration.
Stacked Denoising Autoencoder-Based Deep Collaborative Filtering Using the Change of Similarity

Recommender systems based on deep learning technology pay huge attention recently. In this paper, we propose a collaborative filtering based recommendation algorithm that utilizes the difference of similarities among users derived from different layers in stacked denoising autoencoders. Since different layers in a stacked autoencoder represent the relationships among items with rating at different levels of abstraction, we can expect to make recommendations more novel, various and serendipitous, compared with a normal collaborative filtering using single similarity. The results of experiments using MovieLens dataset show that the proposed recommendation algorithm can improve the diversity of recommendation lists without great loss of accuracy.
Broadly Improving User Classification via Communication-Based Name and Location Clustering on Twitter

Hidden properties of social media users, such as their ethnicity, gender, and location, are often reflected in their observed attributes, such as their first and last names. Furthermore, users who communicate with each other often have similar hidden properties. We propose an algorithm that exploits these insights to cluster the observed attributes of hundreds of millions of Twitter users. Attributes such as user names are grouped together if users with those names communicate with other similar users. We separately cluster millions of unique first names, last names, and userprovided locations. The efficacy of these clusters is then evaluated on a diverse set of classification tasks that predict hidden users properties such as ethnicity, geographic location, gender, language, and race, using only profile names and locations when appropriate. Our readily-replicable approach and publiclyreleased clusters are shown to be remarkably effective and versatile, substantially outperforming state-of-the-art approaches and human accuracy on each of the tasks studied.
UdS-(retrain|distributional|surface): Improving POS Tagging for OOV Words in German CMC and Web Data

We present in this paper our three system submissions for the POS tagging subtask of the Empirist Shared Task: Our baseline system UdS-retrain extends a standard training dataset with in-domain training data; UdSdistributional and UdS-surface add two different ways of handling OOV words on top of the baseline system by using either distributional information or a combination of surface similarity and language model information. We reach the best performance using the distributional model.
Exploring Hierarchical Structures for Recommender Systems

Items in real-world recommender systems exhibit certain hierarchical structures. Similarly, user preferences also present hierarchical structures. Recent studies show that incorporating the hierarchy of items or user preferences can improve the performance of recommender systems. However, hierarchical structures are often not explicitly available, especially those of user preferences. Thus, there's a gap between the importance of hierarchies and their availability. In this paper, we investigate the problem of exploring the implicit hierarchical structures for recommender systems when they are not explicitly available. We propose a novel recommendation framework to bridge the gap, which enables us to explore the implicit hierarchies of users and items simultaneously. We then extend the framework to integrate explicit hierarchies when they are available, which gives a unified framework for both explicit and implicit hierarchical structures. Experimental results on real-world datasets demonstrate the effectiveness of the proposed framework by incorporating implicit and explicit structures.
A multi-level ladder converter supporting vertically-stacked digital voltage domains

Modern digital systems are severely constrained by both battery life and operating temperatures, resulting in strict limits on total power consumption and power density. To continue to scale digital throughput at constant power density, there is a need for increasing parallelism and dynamic voltage/bias scaling. This work presents an architecture and power converter implementation providing efficient power-delivery for microprocessors and other high-performance digital circuits stacked in vertical voltage domains. A multi-level DC-DC converter interfaces between a fixed DC voltage and multiple 0.7 V to 1.4 V voltage domains stacked in series. The converter implements dynamic voltage scaling (DVS) with multi-objective digital control implemented in an on-board (embedded) digital control system. We present measured results demonstrating functional multi-core DVS and performance with moderate load current steps. The converter demonstrates the use of a two-phase interleaved powertrain with coupled inductors to achieve voltage and current ripple reduction for the stacked ladder-converter architecture.
LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks

Although weight and activation quantization is an effective approach for Deep Neural Network (DNN) compression and has a lot of potentials to increase inference speed leveraging bit-operations, there is still a noticeable gap in terms of prediction accuracy between the quantized model and the full-precision model. To address this gap, we propose to jointly train a quantized, bit-operation-compatible DNN and its associated quantizers, as opposed to using fixed, handcrafted quantization schemes such as uniform or logarithmic quantization. Our method for learning the quantizers applies to both network weights and activations with arbitrary-bit precision, and our quantizers are easy to train. The comprehensive experiments on CIFAR-10 and ImageNet datasets show that our method works consistently well for various network structures such as AlexNet, VGG-Net, GoogLeNet, ResNet, and DenseNet, surpassing previous quantization methods in terms of accuracy by an appreciable margin. Code available at https://github.com/Microsoft/LQ-Nets
A review of biometric technology along with trends and prospects

Identity management through biometrics offer potential advantages over knowledge and possession based methods. A wide variety of biometric modalities have been tested so far but several factors paralyse the accuracy of mono modal biometric systems. Usually, the analysis of multiple modalities offers better accuracy. An extensive review of biometric technology is presented here. Besides the mono modal systems, the article also discusses multi modal biometric systems along with their architecture and information fusion levels. The paper along with the exemplary evidences highlights the potential for biometric technology, market value and prospects.  Biometrics, Fingerprint, Face, Iris, Retina, Behavioral biometrics, Gait, Voice, Soft biometrics, Multi-modal biometrics.
General Type-2 Fuzzy Logic Systems Made Simple: A Tutorial

The purpose of this tutorial paper is to make general type-2 fuzzy logic systems (GT2 FLSs) more accessible to fuzzy logic researchers and practitioners, and to expedite their research, designs, and use. To accomplish this, the paper 1) explains four different mathematical representations for general type-2 fuzzy sets (GT2 FSs); 2) demonstrates that for the optimal design of a GT2 FLS, one should use the vertical-slice representation of its GT2 FSs because it is the only one of the four mathematical representations that is parsimonious; 3) shows how to obtain set theoretic and other operations for GT2 FSs using type-1 (T1) FS mathematics (α- cuts play a central role); 4) reviews Mamdani and TSK interval type-2 (IT2) FLSs so that their mathematical operations can be easily used in a GT2 FLS; 5) provides all of the formulas that describe both Mamdani and TSK GT2 FLSs; 6) explains why center-of sets type-reduction should be favored for a GT2 FLS over centroid type-reduction; 7) provides three simplified GT2 FLSs (two are for Mamdani GT2 FLSs and one is for a TSK GT2 FLS), all of which bypass type reduction and are generalizations from their IT2 FLS counterparts to GT2 FLSs; 8) explains why gradient-based optimization should not be used to optimally design a GT2 FLS; 9) explains how derivative-free optimization algorithms can be used to optimally design a GT2 FLS; and 10) provides a three-step approach for optimally designing FLSs in a progressive manner, from T1 to IT2 to GT2, each of which uses a quantum particle swarm optimization algorithm, by virtue of which the performance for the IT2 FLS cannot be worse than that of the T1 FLS, and the performance for the GT2 FLS cannot be worse than that of the IT2 FLS.
Exploring Social Influence on Location-Based Social Networks

Recently, with the advent of location-based social networking services (LBSNs), travel planning and location-aware information recommendation based on LBSNs have attracted much research attention. In this paper, we study the impact of social relations hidden in LBSNs, i.e., The social influence of friends. We propose a new social influence-based user recommender framework (SIR) to discover the potential value from reliable users (i.e., Close friends and travel experts). Explicitly, our SIR framework is able to infer influential users from an LBSN. We claim to capture the interactions among virtual communities, physical mobility activities and time effects to infer the social influence between user pairs. Furthermore, we intend to model the propagation of influence using diffusion-based mechanism. Moreover, we have designed a dynamic fusion framework to integrate the features mined into a united follow probability score. Finally, our SIR framework provides personalized top-k user recommendations for individuals. To evaluate the recommendation results, we have conducted extensive experiments on real datasets (i.e., The Go Walla dataset). The experimental results show that the performance of our SIR framework is better than the state-of the-art user recommendation mechanisms in terms of accuracy and reliability.
Double dissociation of value computations in orbitofrontal and anterior cingulate neurons

Damage to prefrontal cortex (PFC) impairs decision-making, but the underlying value computations that might cause such impairments remain unclear. Here we report that value computations are doubly dissociable among PFC neurons. Although many PFC neurons encoded chosen value, they used opponent encoding schemes such that averaging the neuronal population extinguished value coding. However, a special population of neurons in anterior cingulate cortex (ACC), but not in orbitofrontal cortex (OFC), multiplexed chosen value across decision parameters using a unified encoding scheme and encoded reward prediction errors. In contrast, neurons in OFC, but not ACC, encoded chosen value relative to the recent history of choice values. Together, these results suggest complementary valuation processes across PFC areas: OFC neurons dynamically evaluate current choices relative to recent choice values, whereas ACC neurons encode choice predictions and prediction errors using a common valuation currency reflecting the integration of multiple decision parameters.
Fabricating BRDFs at high spatial resolution using wave optics

Recent attempts to fabricate surfaces with custom reflectance functions boast impressive angular resolution, yet their spatial resolution is limited. In this paper we present a method to construct spatially varying reflectance at a high resolution of up to 220dpi, orders of magnitude greater than previous attempts, albeit with a lower angular resolution. The resolution of previous approaches is limited by the machining, but more fundamentally, by the geometric optics model on which they are built. Beyond a certain scale geometric optics models break down and wave effects must be taken into account. We present an analysis of incoherent reflectance based on wave optics and gain important insights into reflectance design. We further suggest and demonstrate a practical method, which takes into account the limitations of existing micro-fabrication techniques such as photolithography to design and fabricate a range of reflection effects, based on wave interference.
Capacitive skin sensors for robot impact monitoring

A new generation of robots is being designed for human occupied workspaces where safety is of great concern. This research demonstrates the use of a capacitive skin sensor for collision detection. Tests demonstrate that the sensor reduces impact forces and can detect and characterize collision events, providing information that may be used in the future for force reduction behaviors. Various parameters that affect collision severity, including interface friction, interface stiffness, end tip velocity and joint stiffness irrespective of controller bandwidth are also explored using the sensor to provide information about the contact force at the site of impact. Joint stiffness is made independent of controller bandwidth limitations using passive torsional springs of various stiffnesses. Results indicate a positive correlation between peak impact force and joint stiffness, skin friction and interface stiffness, with implications for future skin and robot link designs and post-collision behaviors.
A 22nm IA multi-CPU and GPU System-on-Chip

This paper describes the 22nm Intel® processor codenamed Ivy Bridge that integrates up to four high-performance Intel Architecture (IA) cores, a power/performance optimized graphics/media processing unit (GPU), as well as memory, PCIe, and display controllers in the same die. The Ivy Bridge architecture is derived from the second-generation Intel Core™ processor. The processor has about 1.4 billion transistors in about 160mm2 in its largest incarnation. It introduces several enhancements in power, performance, and features over its predecessor. The IA core adds a pipelined divider, a next page prefetcher, additions to the ISA for 16b floating point conversion, fast string moves, and fast access of the FS/GS base registers. The Graphics/Media block provides significantly improved performance along with DX11 API support. Further, display capabilities have been augmented with a third independent display pipeline. The on-die power management control unit (PCU) and its associated firmware have added several power and thermal optimizations to improve performance and yield within the existing platform power envelopes, as well as to improve idle power relative to its predecessor. Power gates are distributed throughout the cores, enabling the PCU to independently either reduce voltage to a state retention voltage, or turn off voltage to a given core, depending on the current core usage conditions. The processor also implements power gating for portions of the DDR I/O buffers, reducing CPU power consumption when memory is in "self-refresh" mode. To optimize core sleep time, Ivy Bridge incorporates smart interrupt routing logic, which sends interrupts to active cores. The PCU also analyzes the processor's inherent voltage-frequency dependency to determine the optimum operating voltages across the entire dynamic range of operation. The processor uses die temperature to estimate power and energy consumption in order to maximize performance.
Text-aware balloon extraction from manga

Manga, a Japanese word for comics, is a worldwide popular visual entertainment. Nowadays, electronic devices boost the fast development of motion manga for the purpose of visual richness and manga promotion. To convert static manga images into motion mangas, text balloons are usually animated individually for better story telling. This needs the artists to cut out each text balloon meticulously, and therefore it is quite labor-intensive and time-consuming. In this paper, we propose an automatic approach that can extract text balloons from manga images both accurately and effectively. Our approach starts by extracting white areas that contain texts as text blobs. Different from existing text blob extraction methods that only rely on shape properties, we incorporate text properties in order to differentiate text blobs from texture blobs. Instead of heuristic parameter thresholding, we achieve text blob classification via learning-based classifiers. Along with the novel text blob classification method, we also make the first attempt in trying to tackle the boundary issue in balloon extraction. We apply our method on various styles of mangas and comics with texts in different languages, and convincing results are obtained in all cases.
SPARQL Web-Querying Infrastructure: Ready for Action?

Hundreds of public SPARQL endpoints have been deployed on the Web, forming a novel decentralised infrastructure for querying billions of structured facts from a variety of sources on a plethora of topics. But is this infrastructure mature enough to support applications? For 427 public SPARQL endpoints registered on the DataHub, we conduct various experiments to test their maturity. Regarding discoverability, we find that only one-third of endpoints make descriptive meta-data available, making it difficult to locate or learn about their content and capabilities. Regarding interoperability, we find patchy support for established SPARQL features like ORDER BY as well as (understandably) for new SPARQL 1.1 features. Regarding efficiency, we show that the performance of endpoints for generic queries can vary by up to 3–4 orders of magnitude. Regarding availability, based on a 27-month long monitoring experiment, we show that only 32.2% of public endpoints can be expected to have (monthly) “two-nines” uptimes of 99–100%.
Question Answering through Transfer Learning from Large Fine-grained Supervision Data

We show that the task of question answering (QA) can significantly benefit from the transfer learning of models trained on a different large, fine-grained QA dataset. We achieve the state of the art in two well-studied QA datasets, WikiQA and SemEval-2016 (Task 3A), through a basic transfer learning technique from SQuAD. For WikiQA, our model outperforms the previous best model by more than 8%. We demonstrate that finer supervision provides better guidance for learning lexical and syntactic information than coarser supervision, through quantitative results and visual analysis. We also show that a similar transfer learning procedure achieves the state of the art on an entailment task.