nils-herrmann/clean_scirepeval huggingface dataset:
Personalized Page Rank for Named Entity Disambiguation

The task of Named Entity Disambiguation is to map entity mentions in the document to their correct entries in some knowledge base. We present a novel graph-based disambiguation approach based on Personalized PageRank (PPR) that combines local and global evidence for disambiguation and effectively filters out noise introduced by incorrect candidates. Experiments show that our method outperforms state-of-the-art approaches by achieving 91.7% in microand 89.9% in macroaccuracy on a dataset of 27.8K named entity mentions.
Structural optimization using sensitivity analysis and a level-set method q

In the context of structural optimization we propose a new numerical method based on a combination of the classical shape derivative and of the level-set method for front propagation. We implement this method in two and three space dimensions for a model of linear or nonlinear elasticity. We consider various objective functions with weight and perimeter constraints. The shape derivative is computed by an adjoint method. The cost of our numerical algorithm is moderate since the shape is captured on a fixed Eulerian mesh. Although this method is not specifically designed for topology optimization, it can easily handle topology changes. However, the resulting optimal shape is strongly dependent on the initial guess.
Surface displacements and source parameters of the 2003 Bam ( Iran ) earthquake from Envisat advanced synthetic aperture radar imagery

The Mw 6.6, 26 December 2003 Bam (Iran) earthquake was one of the first earthquakes for which Envisat advanced synthetic aperture radar (ASAR) data were available. Using interferograms and azimuth offsets from ascending and descending tracks, we construct a three-dimensional displacement field of the deformation due to the earthquake. Elastic dislocation modeling shows that the observed deformation pattern cannot be explained by slip on a single planar fault, which significantly underestimates eastward and upward motions SE of Bam. We find that the deformation pattern observed can be best explained by slip on two subparallel faults. Eighty-five percent of moment release occurred on a previously unknown strike-slip fault running into the center of Bam, with peak slip of over 2 m occurring at a depth of 5 km. The remainder occurred as a combination of strike-slip and thrusting motion on a southward extension of the previously mapped Bam Fault 5 km to the east.
Brand in the hand : A cross-market investigation of consumer acceptance of mobile marketing

Coinciding with the widespread adoption of 3G and 4G smartphones among consumers, mobile marketing has increasingly become a staple tactic in brands’ advertising and promotional efforts. Target,  Asia that have begun to aggressively adopt untethered mobile marketing platforms to forge closer and more relevant connections with specific audiences. In the U.S. alone, companies’ spending on mobile advertising and promotions and their ability to deliver brands to consumers is forecast to grow approximately 600% from $9.3 billion in 2010 to $56.5 billion by 2015 (Marketing Charts, 2011). Business Horizons (2012) 55, 485—493
PolyPUF: Physically Secure Self-Divergence

Physically unclonable functions (PUFs) provide a device-unique challenge-response mapping and are employed for authentication and encryption purposes. Unpredictability and reliability are the core requirements of PUFs: unpredictability implies that an adversary cannot sufficiently predict future responses from previous observations. Reliability is important as it increases the reproducibility of PUF responses and hence allows validation of expected responses. However, advanced machine-learning algorithms have been shown to be a significant threat to the practical validity of PUFs, as they are able to accurately model PUF behavior. The most effective technique was shown to be the XOR-based combination of multiple PUFs, but as this approach drastically reduces reliability, it does not scale well against software-based machine-learning attacks. In this paper, we analyze threats to PUF security and propose PolyPUF, a scalable and secure architecture to introduce polymorphic PUF behavior. This architecture significantly increases model-building resistivity while maintaining reliability. An extensive experimental evaluation and comparison demonstrate that the PolyPUF architecture can secure various PUF configurations and is the only evaluated approach to withstand highly complex neural network machine-learning attacks. Furthermore, we show that PolyPUF consumes less energy and has less implementation overhead in comparison to lightweight reference architectures.
Multiagent teamwork: analyzing the optimality and complexity of key theories and models

Despite the significant progress in multiagent teamwork, existing research does not address the optimality of its prescriptions nor the complexity of the teamwork problem. Thus, we cannot determine whether the assumptions and approximations made by a particular theory gain enough efficiency to justify the losses in overall performance. To provide a tool for evaluating this tradeoff, we present a unified framework, the COMmunicative Multiagent Team Decision Problem (COM-MTDP) model, which is general enough to subsume many existing models of multiagent systems. We analyze use the COM-MTDP model to provide a breakdown of the computational complexity of constructing optimal teams under problem domains divided along the dimensions of observability and communication cost. We then exploit the COM-MTDP's ability to encode existing teamwork theories and models to encode two instantiations of joint intentions theory, including STEAM. We then derive a domain-independent criterion for optimal communication and provide a comparative analysis of the two joint intentions instantiations. We have implemented a reusable, domain-independent software package based COM-MTDPs to analyze teamwork coordination strategies, and we demonstrate its use by encoding and evaluating the two joint intentions strategies within an example domain.
Complementary filter design on the special orthogonal group SO(3)

This paper considers the problem of obtaining high quality attitude extraction and gyros bias estimation from typical low cost intertial measurement units for applications in control of unmanned aerial vehiccles. Two different non-linear complementary filters are proposed: Direct complementary filter and Passive non-linear complementary filter. Both filters evolve explicity on the special orthogonal group SO(3) and can be expressed in quaternion form for easy implementation. An extension to the passive ocmplementary filter is proposed to provide adaptive gyro bias estimation.
Multilevel DC-link inverter

This paper presents a new class of multilevel inverters based on a multilevel dc link (MLDCL) and a bridge inverter to reduce the number of switches, clamping diodes, or capacitors. An MLDCL can be a diode-clamped phase leg, a flying-capacitor phase leg, or cascaded half-bridge cells with each cell having its own dc source. A multilevel voltage-source inverter can be formed by connecting one of the MLDCLs with a single-phase bridge inverter. The MLDCL provides a dc voltage with the shape of a staircase approximating the rectified shape of a commanded sinusoidal wave, with or without pulsewidth modulation, to the bridge inverter, which in turn alternates the polarity to produce an ac voltage. Compared with the cascaded H-bridge, diode-clamped, and flying-capacitor multilevel inverters, the MLDCL inverters can significantly reduce the switch count as well as the number of gate drivers as the number of voltage levels increases. For a given number of voltage levels m, the required number of active switches is 2/spl times/(m-1) for the existing multilevel inverters but is m+3 for the MLDCL inverters. Simulation and experimental results are included to verify the operating principles of the MLDCL inverters.
An information theoretical approach to prefrontal executive function

The prefrontal cortex subserves executive control--that is, the ability to select actions or thoughts in relation to internal goals. Here, we propose a theory that draws upon concepts from information theory to describe the architecture of executive control in the lateral prefrontal cortex. Supported by evidence from brain imaging in human subjects, the model proposes that action selection is guided by hierarchically ordered control signals, processed in a network of brain regions organized along the anterior-posterior axis of the lateral prefrontal cortex. The theory clarifies how executive control can operate as a unitary function, despite the requirement that information be integrated across multiple distinct, functionally specialized prefrontal regions.
The Role of Alpha-Band Brain Oscillations as a Sensory Suppression Mechanism during Selective Attention

Evidence has amassed from both animal intracranial recordings and human electrophysiology that neural oscillatory mechanisms play a critical role in a number of cognitive functions such as learning, memory, feature binding and sensory gating. The wide availability of high-density electrical and magnetic recordings (64-256 channels) over the past two decades has allowed for renewed efforts in the characterization and localization of these rhythms. A variety of cognitive effects that are associated with specific brain oscillations have been reported, which range in spectral, temporal, and spatial characteristics depending on the context. Our laboratory has focused on investigating the role of alpha-band oscillatory activity (8-14 Hz) as a potential attentional suppression mechanism, and this particular oscillatory attention mechanism will be the focus of the current review. We discuss findings in the context of intersensory selective attention as well as intrasensory spatial and feature-based attention in the visual, auditory, and tactile domains. The weight of evidence suggests that alpha-band oscillations can be actively invoked within cortical regions across multiple sensory systems, particularly when these regions are involved in processing irrelevant or distracting information. That is, a central role for alpha seems to be as an attentional suppression mechanism when objects or features need to be specifically ignored or selected against.
A comparison of statistical methods for meta-analysis.

Meta-analysis may be used to estimate an overall effect across a number of similar studies. A number of statistical techniques are currently used to combine individual study results. The simplest of these is based on a fixed effects model, which assumes the true effect is the same for all studies. A random effects model, however, allows the true effect to vary across studies, with the mean true effect the parameter of interest. We consider three methods currently used for estimation within the framework of a random effects model, and illustrate them by applying each method to a collection of six studies on the effect of aspirin after myocardial infarction. These methods are compared using estimated coverage probabilities of confidence intervals for the overall effect. The techniques considered all generally have coverages below the nominal level, and in particular it is shown that the commonly used DerSimonian and Laird method does not adequately reflect the error associated with parameter estimation, especially when the number of studies is small.
Rock Texture Retrieval Using Gray Level Co-occurrence Matrix

Nowadays, as the computational power increases, the role of automatic visual inspection becomes more important. Therefore, also visual quality control has gained in popularity. This paper presents an application of gray level co-occurrence matrix (GLCM) to texturebased similarity evaluation of rock images. Retrieval results were evaluated for two databases, one consisting of the whole images and the other with blocks obtained by splitting the original images. Retrieval results for both databases were obtained by calculating distance between the feature vector of the query image and other feature vectors in the database. Performance of the cooccurrence matrices was also compared to that of Gabor wavelet features. Co-occurrence matrices performed better for the given rock image dataset. This similarity evaluation application could reduce the cost of geological investigations by allowing improved accuracy in automatic rock sample selection.
Novel Deep Trench Buried-Body-Contact (DBBC) of 4F2 cell for sub 30nm DRAM technology

Novel Deep Trench Buried-Body-Contact (DBBC) has been successfully developed for 4F2 DRAM cells on sub-30nm technology node. The critical requirements of thermal stability, shallow junction depth, and conformal source-drain doping profile for the contact are achieved by using an ultra thin Ti silicide ohmic layer and PLAD technique, which also show excellent electrical performance and process feasibility for the development of 4F2 DRAM cell on the 30nm node and beyond.
One-Shot-Learning Gesture Recognition using HOG-HOF Features

The purpose of this thesis is to describe one-shot-learning gesture recognition systems developed on the ChaLearn Gesture Dataset . We use RGB and depth images and combine appearance (Histograms of Oriented Gradients) and motion descriptors (Histogram of Optical Flow) for parallel temporal segmentation and recognition. The Quadratic-Chi distance family is used to measure differences between histograms to capture cross-bin relationships. We also propose a new algorithm for trimming videos — to remove all the unimportant frames from videos. Our two methods both outperform other published methods and help narrow down the gap between human performance and algorithms on this task. The code has been made publicly available in the MLOSS repository.
A Practical and Configurable Lip Sync Method for Games

We demonstrate a lip animation (lip sync) algorithm for real-time applications that can be used to generate synchronized facial movements with audio generated from natural speech or a text-to-speech engine. Our method requires an animator to construct animations using a canonical set of visemes for all pairwise combinations of a reduced phoneme set (phone bigrams). These animations are then stitched together to construct the final animation, adding velocity and lip-pose constraints. This method can be applied to any character that uses the same, small set of visemes. Our method can operate efficiently in multiple languages by reusing phone bigram animations that are shared among languages, and specific word sounds can be identified and changed on a per-character basis. Our method uses no machine learning, which offers two advantages over techniques that do: 1) data can be generated for non-human characters whose faces can not be easily retargeted from a human speaker's face, and 2) the specific facial poses or shapes used for animation can be specified during the setup and rigging stage, and before the lip animation stage, thus making it suitable for game pipelines or circumstances where the speech targets poses are predetermined, such as after acquisition from an online 3D marketplace.
Minimum Translation Modeling with Recurrent Neural Networks

We introduce recurrent neural networkbased Minimum Translation Unit (MTU) models which make predictions based on an unbounded history of previous bilingual contexts. Traditional back-off n-gram models suffer under the sparse nature of MTUs which makes estimation of highorder sequence models challenging. We tackle the sparsity problem by modeling MTUs both as bags-of-words and as a sequence of individual source and target words. Our best results improve the output of a phrase-based statistical machine translation system trained on WMT 2012 French-English data by up to 1.5 BLEU, and we outperform the traditional n-gram based MTU approach by up to 0.8 BLEU.
An Event-Driven Classifier for Spiking Neural Networks Fed with Synthetic or Dynamic Vision Sensor Data

This paper introduces a novel methodology for training an event-driven classifier within a Spiking Neural Network (SNN) System capable of yielding good classification results when using both synthetic input data and real data captured from Dynamic Vision Sensor (DVS) chips. The proposed supervised method uses the spiking activity provided by an arbitrary topology of prior SNN layers to build histograms and train the classifier in the frame domain using the stochastic gradient descent algorithm. In addition, this approach can cope with leaky integrate-and-fire neuron models within the SNN, a desirable feature for real-world SNN applications, where neural activation must fade away after some time in the absence of inputs. Consequently, this way of building histograms captures the dynamics of spikes immediately before the classifier. We tested our method on the MNIST data set using different synthetic encodings and real DVS sensory data sets such as N-MNIST, MNIST-DVS, and Poker-DVS using the same network topology and feature maps. We demonstrate the effectiveness of our approach by achieving the highest classification accuracy reported on the N-MNIST (97.77%) and Poker-DVS (100%) real DVS data sets to date with a spiking convolutional network. Moreover, by using the proposed method we were able to retrain the output layer of a previously reported spiking neural network and increase its performance by 2%, suggesting that the proposed classifier can be used as the output layer in works where features are extracted using unsupervised spike-based learning methods. In addition, we also analyze SNN performance figures such as total event activity and network latencies, which are relevant for eventual hardware implementations. In summary, the paper aggregates unsupervised-trained SNNs with a supervised-trained SNN classifier, combining and applying them to heterogeneous sets of benchmarks, both synthetic and from real DVS chips.
Nonmonotonic Generalization Bias of Gaussian Mixture Models

Theories of learning and generalization hold that the generalization bias, defined as the difference between the training error and the generalization error, increases on average with the number of adaptive parameters. This article, however, shows that this general tendency is violated for a gaussian mixture model. For temperatures just below the first symmetry breaking point, the effective number of adaptive parameters increases and the generalization bias decreases. We compute the dependence of the neural information criterion on temperature around the symmetry breaking. Our results are confirmed by numerical cross-validation experiments.
A Metamodeling Approach for Reasoning about Requirements

In requirements engineering, there are several approaches for requirements modeling such as goal-oriented, aspect-driven, and system requirements modeling. In practice, companies often customize a given approach to their specific needs. Thus, we seek a solution that allows customization in a systematic way. In this paper, we propose a metamodel for requirements models (called core metamodel) and an approach for customizing this metamodel in order to support various requirements modeling approaches. The core metamodel represents the common concepts extracted from some prevalent approaches. We define the semantics of the concepts and the relations in the core metamodel. Based on this formalization, we can perform reasoning on requirements that may detect implicit relations and inconsistencies. Our approach for customization keeps the semantics of the core concepts intact and thus allows reuse of tools and reasoning over the customized metamodel. We illustrate the customization of our core metamodel with SysML concepts. As a case study, we apply the reasoning on requirements of an industrial mobile service application based on this customized core requirements metamodel.
A fully-differential capacitive touch controller with input common-mode feedback for symmetric display noise cancellation

A fully-differential capacitive touch sensing method is proposed in which common-mode noise currents are symmetrically subtracted at the differential input of the first stage such that it doesn't contribute to dynamic range reduction in the later stages. And, for better sensitivity, the proposed method could accumulate signal charges in continuous time domain, and does not suffer from aliasing issues observed in many discrete-time charge integrating methods. Measurement results showed 42 dB SNR for a 1-mm diameter stylus on a 5-inch full-HD on-cell touch display panel.
Modular Deep Q Networks for Sim-to-real Transfer of Visuo-motor Policies

While deep learning has had significant successes in computer vision thanks to the abundance of visual data, collecting sufficiently large real-world datasets for robot learning can be costly. To increase the practicality of these techniques on real robots, we propose a modular deep reinforcement learning method capable of transferring models trained in simulation to a real-world robotic task. We introduce a bottleneck between perception and control, enabling the networks to be trained independently, but then merged and fine-tuned in an end-to-end manner to further improve hand-eye coordination. On a canonical, planar visually-guided robot reaching task a fine-tuned accuracy of 1.6 pixels is achieved, a significant improvement over naive transfer (17.5 pixels), showing the potential for more complicated and broader applications. Our method provides a technique for more efficient learning and transfer of visuomotor policies for real robotic systems without relying entirely on large real-world robot datasets.
Understanding the bias of call detail records in human mobility research

In recent years, call detail records (CDRs) have been widely used in human mobility research. Although CDRs are originally collected for billing purposes, the vast amount of digital footprints generated by calling and texting activities provide useful insights into population movement. However, can we fully trust CDRs given the uneven distribution of people’s phone communication activities in space and time? In this article, we investigate this issue using a mobile phone location dataset collected from over one million subscribers in Shanghai, China. It includes CDRs (~27%) plus other cellphone-related logs (e.g., tower pings, cellular handovers) generated in a workday. We extract all CDRs into a separate dataset in order to compare human mobility patterns derived from CDRs vs. from the complete dataset. From an individual perspective, the effectiveness of CDRs in estimating three frequently used mobility indicators is evaluated. We find that CDRs tend to underestimate the total travel distance and the movement entropy, while they can provide a good estimate to the radius of gyration. In addition, we observe that the level of deviation is related to the ratio of CDRs in an individual’s trajectory. From a collective perspective, we compare the outcomes of these two datasets in terms of the distance decay effect and urban community detection. The major differences are closely related to the habit of mobile phone usage in space and time. We believe that the event-triggered nature of CDRs does introduce a certain degree of bias in human mobility research and we suggest that researchers use caution to interpret results derived from CDR data. ARTICLE HISTORY Received 21 July 2015  21 December 2015