nils-herrmann/clean_scirepeval huggingface dataset:
SAO network analysis of patents for technology trends identification: a case study of polymer electrolyte membrane technology in proton exchange membrane fuel cells

This paper suggests a method for Subject–Action–Object (SAO) network analysis of patents for technology trends identification by using the concept of function. The proposed method solves the shortcoming of the keyword-based approach to identification of technology trends, i.e., that it cannot represent how technologies are used or for what purpose. The concept of function provides information on how a technology is used and how it interacts with other technologies; the keyword-based approach does not provide such information. The proposed method uses an SAO model and represents “key concept” instead of “key word”. We present a procedure that formulates an SAO network by using SAO models extracted from patent documents, and a method that applies actor network theory to analyze technology implications of the SAO network. To demonstrate the effectiveness of the SAO network this paper presents a case study of patents related to Polymer Electrolyte Membrane technology in Proton Exchange Membrane Fuel Cells.
Firmament: Fast, Centralized Cluster Scheduling at Scale

Centralized datacenter schedulers can make high-quality placement decisions when scheduling tasks in a cluster. Today, however, high-quality placements come at the cost of high latency at scale, which degrades response time for interactive tasks and reduces cluster utilization. This paper describes Firmament, a centralized scheduler that scales to over ten thousand machines at subsecond placement latency even though it continuously reschedules all tasks via a min-cost max-flow (MCMF) optimization. Firmament achieves low latency by using multiple MCMF algorithms, by solving the problem incrementally, and via problem-specific optimizations. Experiments with a Google workload trace from a 12,500-machine cluster show that Firmament improves placement latency by 20× over Quincy [22], a prior centralized scheduler using the same MCMF optimization. Moreover, even though Firmament is centralized, it matches the placement latency of distributed schedulers for workloads of short tasks. Finally, Firmament exceeds the placement quality of four widely-used centralized and distributed schedulers on a real-world cluster, and hence improves batch task response time by 6×.
A Survey of Audio-Based Music Classification and Annotation

Music information retrieval (MIR) is an emerging research area that receives growing attention from both the research community and music industry. It addresses the problem of querying and retrieving certain types of music from large music data set. Classification is a fundamental problem in MIR. Many tasks in MIR can be naturally cast in a classification setting, such as genre classification, mood classification, artist recognition, instrument recognition, etc. Music annotation, a new research area in MIR that has attracted much attention in recent years, is also a classification problem in the general sense. Due to the importance of music classification in MIR research, rapid development of new methods, and lack of review papers on recent progress of the field, we provide a comprehensive review on audio-based classification in this paper and systematically summarize the state-of-the-art techniques for music classification. Specifically, we have stressed the difference in the features and the types of classifiers used for different classification tasks. This survey emphasizes on recent development of the techniques and discusses several open issues for future research.
Exploring Semantic Representation in Brain Activity Using Word Embeddings

In this paper, we utilize distributed word representations (i.e., word embeddings) to analyse the representation of semantics in brain activity. The brain activity data were recorded using functional magnetic resonance imaging (fMRI) when subjects were viewing words. First, we analysed the functional selectivity of different cortex areas by calculating the correlations between neural responses and several types of word representations, including skipgram word embeddings, visual semantic vectors, and primary visual features. The results demonstrated consistency with existing neuroscientific knowledge. Second, we utilized behavioural data as the semantic ground truth to measure their relevance with brain activity. A method to estimate word embeddings under the constraints of brain activity similarities is further proposed based on the semantic word embedding (SWE) model. The experimental results show that the brain activity data are significantly correlated with the behavioural data of human judgements on semantic similarity. The correlations between the estimated word embeddings and the semantic ground truth can be effectively improved after integrating the brain activity data for learning, which implies that semantic patterns in neural representations may exist that have not been fully captured by state-of-the-art word embeddings derived from text corpora.
The McPAT Framework for Multicore and Manycore Architectures: Simultaneously Modeling Power, Area, and Timing

This article introduces McPAT, an integrated power, area, and timing modeling framework that supports comprehensive design space exploration for multicore and manycore processor configurations ranging from 90nm to 22nm and beyond. At microarchitectural level, McPAT includes models for the fundamental components of a complete chip multiprocessor, including in-order and out-of-order processor cores, networks-on-chip, shared caches, and integrated system components such as memory controllers and Ethernet controllers. At circuit level, McPAT supports detailed modeling of critical-path timing, area, and power. At technology level, McPAT models timing, area, and power for the device types forecast in the ITRS roadmap. McPAT has a flexible XML interface to facilitate its use with many performance simulators.
 Combined with a performance simulator, McPAT enables architects to accurately quantify the cost of new ideas and assess trade-offs of different architectures using new metrics such as Energy-Delay-Area2 Product (EDA2P) and Energy-Delay-Area Product (EDAP). This article explores the interconnect options of future manycore processors by varying the degree of clustering over generations of process technologies. Clustering will bring interesting trade-offs between area and performance because the interconnects needed to group cores into clusters incur area overhead, but many applications can make good use of them due to synergies from cache sharing. Combining power, area, and timing results of McPAT with performance simulation of PARSEC benchmarks for manycore designs at the 22nm technology shows that 8-core clustering gives the best energy-delay product, whereas when die area is taken into account, 4-core clustering gives the best EDA2P and EDAP.
Optical Ethernet—Flexible Optical Metro Networks

Enhanced flexibility in optical transport networks is a key requirement to support dynamic traffic load in packet-based networks. Today, flexibility is achieved by packet switches linked by static point-to-point transport connections. Wide-stretched synchronization patterns, line coding schemes, and forward error correction (FEC) frames prohibit flexibility right at the transport layer. We introduce a new optical transport concept that combines packet aggregation with a multipoint-to-point line coding and FEC processing. This concept avoids the quadratic full mesh scalability problem of other aggregated switching technologies such as, e.g., wavelength switching. It combines the flexibility of a distributed Ethernet switch and the performance of a leading edge optical transport system.
Multiresonator based chipless RFID tag and dedicated RFID reader

A RFID system with a chipless RFID tag on a 90-µm thin Taconic TF290 laminate is presented. The chipless tag encodes data into the spectral signature in both magnitude and phase of the spectrum. The design and operation of a prototype RFID reader is also presented. The RFID reader operates between 5 – 10.7 GHz frequency band and successfully detects a chipless tag at 15 cm range. The tag design can be transferred easily to plastic and paper, making it suitable for mass deployment for low cost items and has the potential to replace trillions of barcodes printed each year. The RFID reader is suitable for mounting over conveyor belt systems.
PSDVec: A toolbox for incremental and scalable word embedding

PSDVec is a Python/Perl toolbox that learns word embeddings, i.e. the mapping of words in a natural language to continuous vectors which encode the semantic/syntactic regularities between the words. PSDVec implements a word embedding learning method based on a weighted low-rank positive semidefinite approximation. To scale up the learning process, we implement a blockwise online learning algorithm to learn the embeddings incrementally. This strategy greatly reduces the learning time of word embeddings on a large vocabulary, and can learn the embeddings of new words without re-learning the whole vocabulary. On 9 word similarity/analogy benchmark sets and 2 Natural Language Processing (NLP) tasks, PSDVec produces embeddings that has the best average performance among popular word embedding tools. PSDVec provides a new option for NLP practitioners. & 2016 Elsevier B.V. All rights reserved.
DTN routing in body sensor networks with dynamic postural partitioning

This paper presents novel store-and-forward packet routing algorithms for Wireless Body Area Networks (WBAN) with frequent postural partitioning. A prototype WBAN has been constructed for experimentally characterizing on-body topology disconnections in the presence of ultra short range radio links, unpredictable RF attenuation, and human postural mobility. On-body DTN routing protocols are then developed using a stochastic link cost formulation, capturing multi-scale topological localities in human postural movements. Performance of the proposed protocols are evaluated experimentally and via simulation, and are compared with a number of existing single-copy DTN routing protocols and an on-body packet flooding mechanism that serves as a performance benchmark with delay lower-bound. It is shown that via multi-scale modeling of the spatio-temporal locality of on-body link disconnection patterns, the proposed algorithms can provide better routing performance compared to a number of existing probabilistic, opportunistic, and utility-based DTN routing protocols in the literature.
Development and Application of Controller for Transition Flight of Tail-Sitter UAV

There is renewed interest in tail-sitter airplanes on account of their vertical takeoff and landing capability as well as their efficient horizontal flight capabilities. The transition from a vertical near-hover mode to a horizontal cruise mode is a critical component of the tail-sitter flight profile. In practice, this transition is often achieved by a stall-and-tumble maneuver, which is somewhat risky and therefore not desirable, so alternative maneuvering strategies along controlled trajectories are sought. Accordingly, this paper presents the synthesis and application of a transition controller to a tail-sitter UAV for the first time. For practical reasons, linear controllers are designed using the PID technique and linked by gain scheduling. The limits of the PID controller are complemented by a so-called L1 adaptive controller that considers the coupling effect, reduces the effort for appropriate gain selection, and improves the tracking performance at different points during operation. Each transition trajectory is controlled by the flight velocity and path angle using dynamic inversion. The transition Y. Jung (B) · D. H. Shim Department of Aerospace Engineering, KAIST, Daejeon, South Korea e-mail: jyd28@kaist.ac.kr D. H. Shim e-mail: hcshim@kaist.ac.kr control law is tested on a tail-sitter UAV, an 18kg vehicle that has a 2-m wingspan with an aspect ratio of 4.71 and is powered by a 100-cm3 gasoline engine driving an aft-mounted ducted fan. This paper describes not only the synthesis and the onboard implementation of the control law but also the flight testing of the fixed-wing UAV in hover, transition, and cruise modes.
Nonlinear random matrix theory for deep learning

Neural network configurations with random weights play an important role in the analysis of deep learning. They define the initial loss landscape and are closely related to kernel and random feature methods. Despite the fact that these networks are built out of random matrices, the vast and powerful machinery of random matrix theory has so far found limited success in studying them. A main obstacle in this direction is that neural networks are nonlinear, which prevents the straightforward utilization of many of the existing mathematical results. In this work, we open the door for direct applications of random matrix theory to deep learning by demonstrating that the pointwise nonlinearities typically applied in neural networks can be incorporated into a standard method of proof in random matrix theory known as the moments method. The test case for our study is the Gram matrix Y Y T , Y = f(WX), where W is a random weight matrix, X is a random data matrix, and f is a pointwise nonlinear activation function. We derive an explicit representation for the trace of the resolvent of this matrix, which defines its limiting spectral distribution. We apply these results to the computation of the asymptotic performance of single-layer random feature networks on a memorization task and to the analysis of the eigenvalues of the data covariance matrix as it propagates through a neural network. As a byproduct of our analysis, we identify an intriguing new class of activation functions with favorable properties.
Design of an underactuated parallelogram crawler module for an in-pipe robot

This paper presents a design method to determine the output ratio of a differential mechanism, which is installed in an in-pipe robot with three underactuated parallelogram crawler modules. The crawler module can automatically shift its shape to a parallelogram when encountering with obstacles. To clarify the requirements for the mechanism, two outputs of each crawler mechanism (torque of the arms and front pulley) are quasi-statically analyzed, and how the environmental and design parameters affect the system are verified.
Visual Tracking Using Neuromorphic Asynchronous Event-Based Cameras

This letter presents a novel computationally efficient and robust pattern tracking method based on a time-encoded, frame-free visual data. Recent interdisciplinary developments, combining inputs from engineering and biology, have yielded a novel type of camera that encodes visual information into a continuous stream of asynchronous, temporal events. These events encode temporal contrast and intensity locally in space and time. We show that the sparse yet accurately timed information is well suited as a computational input for object tracking. In this letter, visual data processing is performed for each incoming event at the time it arrives. The method provides a continuous and iterative estimation of the geometric transformation between the model and the events representing the tracked object. It can handle isometry, similarities, and affine distortions and allows for unprecedented real-time performance at equivalent frame rates in the kilohertz range on a standard PC. Furthermore, by using the dimension of time that is currently underexploited by most artificial vision systems, the method we present is able to solve ambiguous cases of object occlusions that classical frame-based techniques handle poorly.
A Wideband Stacked Offset Microstrip Antenna With Improved Gain and Low Cross Polarization

A broadband printed microstrip antenna having cross polarization level >; 15 dB with improved gain in the entire frequency band is presented. Principle of stacking is implemented on a strip loaded slotted broadband patch antenna for enhancing the gain without affecting the broadband impedance matching characteristics and offsetting the position of the upper patch excites a lower resonance which enhances the bandwidth further. The antenna has a dimension of 42 × 55 × 4.8 mm3 when printed on a substrate of dielectric constant 4.2 and has a 2:1 VSWR bandwidth of 34.9%. The antenna exhibits a peak gain of 8.07 dBi and a good front to back ratio better than 12 dB is observed throughout the entire operating band. Simulated and experimental reflection characteristics of the antenna with and without stacking along with offset variation studies, radiation patterns and gain of the final antenna are presented.
Procedural City Modeling

We propose a method to procedurally generate a familiar yet complex human artifact: the city. We are not trying to reproduce existing cities, but to generate artificial cities that are convincing and plausible by capturing developmental behavior. In addition, our results are meant to build upon themselves, such that they ought to look compelling at any point along the transition from village to metropolis. Our approach largely focuses upon land usage and building distribution for creating realistic city environments, whereas previous attempts at city modeling have mainly focused on populating road networks. Finally, we want our model to be self automated to the point that the only necessary input is a terrain description, but other high-level and low-level parameters can be specified to support artistic contributions. With the aid of agent based simulation we are generating a system of agents and behaviors that interact with one another through their effects upon a simulated environment. Our philosophy is that as each agent follows a simple behavioral rule set, a more complex behavior will tend to emerge out of the interactions between the agents and their differing rule sets. By confining our model to a set of simple rules for each class of agents, we hope to make our model extendible not only in regard to the types of structures that are produced, but also in describing the social and cultural influences prevalent in all cities.
Evaluating search systems using result page context

We introduce a method for evaluating the relevance of all visible components of a Web search results page, in the context of that results page. Contrary to Cranfield-style evaluation methods, our approach recognizes that a user's initial search interaction is with the result page produced by a search system, not the landing pages linked from it. Our key contribution is that the method allows us to investigate aspects of component relevance that are difficult or impossible to judge in isolation. Such contextual aspects include component-level information redundancy and cross-component coherence. We report on how the method complements traditional document relevance measurement and its support for comparative relevance assessment across multiple search engines. We also study possible issues with applying the method, including brand presentation effects, inter-judge agreement, and comparisons with document-based relevance judgments. Our findings show this is a useful method for evaluating the dominant user experience in interacting with search systems.
Online digital filters for biological signals: some fast designs for a small computer

After reviewing the design of a class of lowpass recursive digital filters having integer multiplier and linear phase characteristics, the possibilities for extending the class to include high pass, bandpass, and bandstop (‘notch’) filters are described. Experience with a PDP 11 computer has shown that these filters may be programmed simply using machine code, and that online operation at sampling rates up to about 8 kHz is possible. The practical application of such filters is illustrated by using a notch desgin to remove mains-frequency interference from an e.c.g. waveform. Après avoir passé en revue la conception d'un type de filtres digitaux récurrents passe-bas à multiplicateurs incorporés et à caractéristiques de phase linéaires, cet article décrit les possibilités d'extension de ce type aux filtres, passe-haut, passe-bande et à élimination de bande. Une expérience menée avec un ordinateur PDP 11 a indiqué que ces filtres peuvent être programmés de manière simple avec un code machine, et qu'il est possible d'effectuer des opérations en ligne avec des taux d'échantillonnage jusqu'à environ 8 kHz. L'application pratique de tels filtres est illustrée par un exemple dans lequel un filtre à élimination de bande est utilisé pour éliminer les interférences due à la fréquence du courant d'alimentation dans un tracé d'e.c.g. Nach einer Untersuchung der Konstruktion einer Gruppe von Rekursivdigitalfiltern mit niedrigem Durchlässigkeitsbereich und mit ganzzahligen Multipliziereinrichtungen und Linearphaseneigenschaften werden die Möglichkeiten beschrieben, die Gruppe so zu erweitern, daß sie Hochfilter, Bandpaßfilter und Bandstopfilter (“Kerbfilter”) einschließt. Erfahrungen mit einem PDP 11-Computer haben gezeigt, daß diese Filter auf einfache Weise unter Verwendung von Maschinenkode programmiert werden können und daß On-Line-Betrieb bei Entnahmegeschwindigkeiten von bis zu 8 kHz möglich ist. Die praktische Anwendung solcher Filter wird durch Verwendung einer Kerbkonstruktion zur Ausscheidung von Netzfrequenzstörungen von einer ECG-Wellenform illustriert.
Active Inference, Curiosity and Insight

This article offers a formal account of curiosity and insight in terms of active (Bayesian) inference. It deals with the dual problem of inferring states of the world and learning its statistical structure. In contrast to current trends in machine learning (e.g., deep learning), we focus on how people attain insight and understanding using just a handful of observations, which are solicited through curious behavior. We use simulations of abstract rule learning and approximate Bayesian inference to show that minimizing (expected) variational free energy leads to active sampling of novel contingencies. This epistemic behavior closes explanatory gaps in generative models of the world, thereby reducing uncertainty and satisfying curiosity. We then move from epistemic learning to model selection or structure learning to show how abductive processes emerge when agents test plausible hypotheses about symmetries (i.e., invariances or rules) in their generative models. The ensuing Bayesian model reduction evinces mechanisms associated with sleep and has all the hallmarks of “aha” moments. This formulation moves toward a computational account of consciousness in the pre-Cartesian sense of sharable knowledge (i.e., con: “together”; scire: “to know”).
Simple Baselines for Human Pose Estimation and Tracking

There has been significant progress on pose estimation and increasing interests on pose tracking in recent years. At the same time, the overall algorithm and system complexity increases as well, making the algorithm analysis and comparison more difficult. This work provides simple and effective baseline methods. They are helpful for inspiring and evaluating new ideas for the field. State-of-the-art results are achieved on challenging benchmarks. The code will be available at https://github. com/leoxiaobin/pose.pytorch.