nils-herrmann/clean_scirepeval huggingface dataset:
Projection Space Maximum A Posterior Method for Low Photon Counts PET Image Reconstruction

In this paper, we proposed a new MAP method more suitable for low signal to noise (SNR) measurements. Different from conventional MAP method, we assume the projection space as a Gibbs field and the penalty term we used was defined in projection space. The spatial resolution of our method was studied and we furthermore modified our method to obtain nearly spatial invariant resolution. Both simulated data and real clinical data were used to testify our method, and future work was discussed at the end of the paper.
Self-Adaptive Software

The traditional approaches to building survivable systems assume a framework of absolute trust requiring a provably impenetrable and incorruptible Trusted Computing Base (TCB). Unfortunately, we don’t have TCB’s, and experience suggests that we never will. We must instead concentrate on software systems that can provide useful services even when computational resource are compromised. Such a system will 1) Estimate the degree to which a computational resources may be trusted using models of possible compromises. 2) Recognize that a resource is compromised by relying on a system for long term monitoring and analysis of the computational infrastructure. 3) Engage in self-monitoring, diagnosis and adaptation to best achieve its purposes within the available infrastructure. All this, in turn, depends on the ability of the application, monitoring, and control systems to engage in rational decision making about what resources they should use in order to achieve the best ratio of expected benefit to risk.
A Case for Object Compositionality in Deep Generative Models of Images

Deep generative models seek to recover the process with which the observed data was generated. They may be used to synthesize new samples or to subsequently extract representations. Successful approaches in the domain of images are driven by several core inductive biases. However, a bias to account for the compositional way in which humans structure a visual scene in terms of objects has frequently been overlooked. In this work we propose to structure the generator of a GAN to consider objects and their relations explicitly, and generate images by means of composition. This provides a way to efficiently learn a more accurate generative model of real-world images, and serves as an initial step towards learning corresponding object representations. We evaluate our approach on several multi-object image datasets, and find that the generator learns to identify and disentangle information corresponding to different objects at a representational level. A human study reveals that the resulting generative model is better at generating images that are more faithful to the reference distribution.
Sequence-to-Label Script Identification for Multilingual OCR

We describe a novel line-level script identification method. Previous work repurposed an OCR model generating per-character script codes, counted to obtain line-level script identification. This has two shortcomings. First, as a sequence-to-sequence model it is more complex than necessary for the sequence-to-label problem of line script identification. This makes it harder to train and inefficient to run. Second, the counting heuristic may be suboptimal compared to a learned model. Therefore we reframe line script identification as a sequence-to-label problem and solve it using two components, trained end-toend: Encoder and Summarizer. The encoder converts a line image into a feature sequence. The summarizer aggregates the sequence to classify the line. We test various summarizers with identical inception-style convolutional networks as encoders. Experiments on scanned books and photos containing 232 languages in 30 scripts show 16% reduction of script identification error rate compared to the baseline. This improved script identification reduces the character error rate attributable to script misidentification by 33%.
Fourier collision detection

In this paper, we investigate a new approach to narrowphase collision detection for rigid objects based on the Fourier transform. This new collision test scales with respect to accuracy, and we are able to rigorously establish an upper bound on the error of our test relating the Hausdorff metric to the number of Fourier coefficients used. Because our new form of the collision test is also a smooth inequality, it can be used as a holonomic unilateral constraint in many applications, such as path planning, rigid body dynamics, nesting or tool placement, replacing the need for more ad-hoc normal/contact based constraint solvers. Moreover, we also show how this constraint can be directly differentiated via Fourier multipliers with only a constant factor overhead, which leads to a simple method for constructing a Jacobian for both normal forces and rotational torques.
Model based clustering for mixed data: clustMD

Amodel based clustering procedure for data of mixed type, clustMD, is developed using a latent variable model. It is proposed that a latent variable, following a mixture of Gaussian distributions, generates the observed data of mixed type. The observed data may be any combination of continuous, binary, ordinal or nominal variables. clustMD employs a parsimonious covariance structure for the latent variables, leading to a suite of six clustering models that vary in complexity and provide an elegant and unified approach to clustering mixed data. An expectation maximisation (EM) algorithm is used to estimate clustMD; in the presence of nominal data a Monte Carlo EM algorithm is required. The clustMD model is illustrated by clustering simulated mixed type data and prostate cancer patients, on whom mixed data have been recorded.
Power, Area, and Performance Optimization of Standard Cell Memory Arrays Through Controlled Placement

Embedded memory remains a major bottleneck in current integrated circuit design in terms of silicon area, power dissipation, and performance; however, static random access memories (SRAMs) are almost exclusively supplied by a small number of vendors through memory generators, targeted at rather generic design specifications. As an alternative, standard cell memories (SCMs) can be defined, synthesized, and placed and routed as an integral part of a given digital system, providing complete design flexibility, good energy efficiency, low-voltage operation, and even area efficiency for small memory blocks. Yet implementing an SCM block with a standard digital flow often fails to exploit the distinct and regular structure of such an array, leaving room for optimization. In this article, we present a design methodology for optimizing the physical implementation of SCM macros as part of the standard design flow. This methodology introduces controlled placement, leading to a structured, noncongested layout with close to 100% placement utilization, resulting in a smaller silicon footprint, reduced wire length, and lower power consumption compared to SCMs without controlled placement. This methodology is demonstrated on SCM macros of various sizes and aspect ratios in a state-of-the-art 28nm fully depleted silicon-on-insulator technology, and compared with equivalent macros designed with the noncontrolled, standard flow, as well as with foundry-supplied SRAM macros. The controlled SCMs provide an average 25% reduction in area as compared to noncontrolled implementations while achieving a smaller size than SRAM macros of up to 1Kbyte. Power and performance comparisons of controlled SCM blocks of a commonly found 256 × 32 (1 Kbyte) memory with foundry-provided SRAMs show greater than 65% and 10% reduction in read and write power, respectively, while providing faster access than their SRAM counterparts, despite being of an aspect ratio that is typically unfavorable for SCMs. In addition, the SCM blocks function correctly with a supply voltage as low as 0.3V, well below the lower limit of even the SRAM macros optimized for low-voltage operation. The controlled placement methodology is applied within a full-chip physical implementation flow of an OpenRISC-based test chip, providing more than 50% power reduction compared to equivalently sized compiled SRAMs under a benchmark application.
Hand pose estimation using HOG features from RGB-D data

Visual based recognition of hand gestures has been an active research field in recent years due to its efficiency in helping us achieve a more natural human-computer interaction. This paper presents a new approach to hand pose estimation using combined visual and geometric information obtained in a synchronized format from a RGB-D sensor. Firstly, we track the contour of the hand and recognize the fingertip positions. Then, Kernel Principal Component Analysis is used for selecting the most relevant elements from the histograms of oriented gradients feature vectors obtained on both color and depth data. We define an observation model based on the found fingertip positions and the selected principal components and we feed it as input for a Support Vector Machine classifier. Experimental results for the proposed method show that good detection percentages can be obtained with a small training dataset of real hand images and depth masks.
Memory Engram Cells Have Come of Age

The idea that memory is stored in the brain as physical alterations goes back at least as far as Plato, but further conceptualization of this idea had to wait until the 20(th) century when two guiding theories were presented: the "engram theory" of Richard Semon and Donald Hebb's "synaptic plasticity theory." While a large number of studies have been conducted since, each supporting some aspect of each of these theories, until recently integrative evidence for the existence of engram cells and circuits as defined by the theories was lacking. In the past few years, the combination of transgenics, optogenetics, and other technologies has allowed neuroscientists to begin identifying memory engram cells by detecting specific populations of cells activated during specific learning epochs and by engineering them not only to evoke recall of the original memory, but also to alter the content of the memory.
Negative-Sequence Current Injection for Fast Islanding Detection of a Distributed Resource Unit

This paper presents an active islanding detection method for a distributed resource (DR) unit which is coupled to a utility grid through a three-phase voltage-sourced converter (VSC). The method is based on injecting a negative-sequence current through the VSC controller and detecting and quantifying the corresponding negative-sequence voltage at the point of common coupling of the VSC by means of a unified three-phase signal processor (UTSP). UTSP is an enhanced phase-locked loop system which provides high degree of immunity to noise, and thus enable islanding detection based on injecting a small (3%) negative-sequence current. The negative-sequence current is injected by a negative-sequence controller which is adopted as the complementary of the conventional VSC current controller. Based on simulation studies in the PSCAD/EMTDC environment, performance of the islanding detection method under UL1741 anti-islanding test is evaluated, and its sensitivity to noise, grid short-circuit ratio, grid voltage imbalance, and deviations in the UL1741 test parameters are presented. The studies show that based on negative-sequence current injection of about 2% to 3%, islanding can be detected within 60 ms even for the worst case scenario.
Class D Audio Amplifier Performance Relationship to MOSFET Parameters

Electrical Parameters in Class D Audio Amplifiers ....................2 Drain Source Breakdown Voltage BVDSS................................................2 Static Drain-to-Source On Resistance RDS(on).........................................4 Gate Charge Qg......................................................................................5 Body Diode Reverse Recovery Charge, Qrr ...........................................8 Internal Gate Resistance RG(int)........................................................11 MOSFET Package ..................................................................................11 Maximum Junction Temperature .............................................................12 International Rectifier Digital Audio MOSFET ...................................................13 Conclusions.........................................................................................14 References........................................................................................................
Learning Context-Sensitive Convolutional Filters for Text Processing

Convolutional neural networks (CNNs) have recently emerged as a popular building block for natural language processing (NLP). Despite their success, most existing CNN models employed in NLP share the same learned (and static) set of filters for all input sentences. In this paper, we consider an approach of using a small meta network to learn contextsensitive convolutional filters for text processing. The role of meta network is to abstract the contextual information of a sentence or document into a set of input-aware filters. We further generalize this framework to model sentence pairs, where a bidirectional filter generation mechanism is introduced to encapsulate co-dependent sentence representations. In our benchmarks on four different tasks, including ontology classification, sentiment analysis, answer sentence selection, and paraphrase identification, our proposed model, a modified CNN with context-sensitive filters, consistently outperforms the standard CNN and attention-based CNN baselines. By visualizing the learned context-sensitive filters, we further validate and rationalize the effectiveness of proposed framework.
Automated Dataset Construction from Web Resources with Tool Kayur

Many text mining tools cannot be applied directly to documents available on web pages. There are tools for fetching and preprocessing of textual data, but combining them in one working tool chain can be time consuming. The preprocessing task is even more labor-intensive if documents are located on multiple remote sources with different storage formats. In this paper we propose the simplification of data preparation process for cases when data come from wide range of web resources. We developed an open-sourced tool, called Kayur, that greatly minimizes time and effort required for routine data preprocessing steps, allowing to quickly proceed to the main task of data analysis. The datasets generated by the tool are ready to be loaded into a data mining workbench, such as WEKA or Carrot2, to perform classification, feature prediction, and other data mining tasks.
Topic representation: A novel method of tag recommendation for text

With tags widely used in organizing and searching contents in massive data era, how to automatically generate appropriate tags of resource for users became a hot issue on social networks research. Tag recommendation for text resource can be modeled as a keyword extraction problem, hence topic modeling such as LDA which extracts latent semantic topics from text is suitable for tag recommendation. However, latent topics are too coarse-grained to describe resource. Meanwhile, LDA trains corpus globally without considering context information. Besides, topics generated are difficult to be quantifiably represented. These problems lead to the poor quality of tag recommendation in topic model based method. In this paper, we propose topic representation method, which introduces embedding semantic representation into topic model. Our results of evaluation on real social networks show that the proposed method improves the quality of tag recommendation for Chinese text resource, when comparing with traditional LDA-based method, which demonstrates the effectiveness of modifying topic modeling.
Reflections of evolution and culture in children's cognition. Implications for mathematical development and instruction.

An evolution-based framework for understanding biological and cultural influences on children's cognitive and academic development is presented. The utility of this framework is illustrated within the mathematical domain and serves as a foundation for examining current approaches to educational reform in the United States. Within this framework, there are two general classes of cognitive ability, biologically primary and biologically secondary. Biologically primary cognitive abilities appear to have evolved largely by means of natural or sexual selection. Biologically secondary cognitive abilities reflect the co-optation of primary abilities for purposes other than the original evolution-based function and appear to develop only in specific cultural contexts. A distinction between these classes of ability has important implications for understanding children's cognitive development and achievement.
Effect of mechanical ventilator weaning protocols on respiratory outcomes in infants and children: a randomized controlled trial.

Ventilator management protocols shorten the time required to wean adult patients from mechanical ventilation. The efficacy of such weaning protocols among children has not been studied.



To evaluate whether weaning protocols are superior to standard care (no defined protocol) for infants and children with acute illnesses requiring mechanical ventilator support and whether a volume support weaning protocol using continuous automated adjustment of pressure support by the ventilator (ie, VSV) is superior to manual adjustment of pressure support by clinicians (ie, PSV).



Randomized controlled trial conducted in the pediatric intensive care units of 10 children's hospitals across North America from November 1999 through April 2001.



One hundred eighty-two spontaneously breathing children (<18 years old) who had been receiving ventilator support for more than 24 hours and who failed a test for extubation readiness on minimal pressure support.



Patients were randomized to a PSV protocol (n = 62), VSV protocol (n = 60), or no protocol (n = 60).



Duration of weaning time (from randomization to successful extubation); extubation failure (any invasive or noninvasive ventilator support within 48 hours of extubation).



Extubation failure rates were not significantly different for PSV (15%), VSV (24%), and no protocol (17%) (P =.44). Among weaning successes, median duration of weaning was not significantly different for PSV (1.6 days), VSV (1.8 days), and no protocol (2.0 days) (P =.75). Male children more frequently failed extubation (odds ratio, 7.86; 95% confidence interval, 2.36-26.2; P<.001). Increased sedative use in the first 24 hours of weaning predicted extubation failure (P =.04) and, among extubation successes, duration of weaning (P<.001).



In contrast with adult patients, the majority of children are weaned from mechanical ventilator support in 2 days or less. Weaning protocols did not significantly shorten this brief duration of weaning.
Design and optimization of magnetic wheel for wall and ceiling climbing robot

Magnetic wall and ceiling climbing robots have been proposed in many industrial applications where robots must move over ferromagnetic material surfaces. The magnetic circuit design with magnetic attractive force calculation of permanent magnetic wheel plays an important role which significantly affects the system reliability, payload ability and power consumption of the robot. In this paper, a flexible wall and ceiling climbing robot with six permanent magnetic wheels is proposed to climb along the vertical wall and overhead ceiling of steel cargo containers as part of an illegal contraband inspection system. The permanent magnetic wheels are designed to apply to the wall and ceiling climbing robot, whilst finite element method is employed to estimate the permanent magnetic wheels with various wheel rims. The distributions of magnetic flux lines and magnetic attractive forces are compared on both plane and corner scenarios so that the robot can adaptively travel through the convex and concave surfaces of the cargo container. Optimisation of wheel rims is presented to achieve the equivalent magnetic adhesive forces along with the estimation of magnetic ring dimensions in the axial and radial directions. Finally, the practical issues correlated with the applications of the techniques are discussed and the conclusions are drawn with further improvement and prototyping.
Macro- and micro-expression spotting in long videos using spatio-temporal strain

We propose a method for the automatic spotting (temporal segmentation) of facial expressions in long videos comprising of macro- and micro-expressions. The method utilizes the strain impacted on the facial skin due to the non-rigid motion caused during expressions. The strain magnitude is calculated using the central difference method over the robust and dense optical flow field observed in several regions (chin, mouth, cheek, forehead) on each subject's face. This new approach is able to successfully detect and distinguish between large expressions (macro) and rapid and localized expressions (micro). Extensive testing was completed on a dataset containing 181 macro-expressions and 124 micro-expressions. The dataset consists of 56 videos collected at USF, 6 videos from the Canal-9 political debates, and 3 low quality videos found on the internet. A spotting accuracy of 85% was achieved for macro-expressions and 74% of all micro-expressions were spotted.
Input Match and Output Balance Improvement of Marchand Balun With Connecting Line

A novel method allowing for improvement of input match and output balance of Marchand balun with connecting line has been presented. The proposed compensation technique together with the ideal Marchand balun design formulas allow for achieving an arbitrary impedance transformation ratio even for the circuit having a long connecting line. The theoretical analysis has been verified by EM simulations and measurements of a Marchand balun composed of 3 dB coupled-line sections and 30° transmission line operating at the center frequency f 0 = 1.15 GHz and terminated at input and outputs with 50 Ω and 100 Ω, respectively.
A non-temporal texture driven approach to real-time fire detection

Here we investigate the automatic detection of fire pixel regions in conventional video (or still) imagery within realtime bounds. As an extension to prior, established approaches within this field we specifically look to extend the primary use of threshold-driven colour spectroscopy to the combined use of colour-texture feature descriptors as an input to a trained classification approach that is independent of temporal information. We show the limitations of such spectroscopy driven approaches on simple, real-world examples and propose our novel extension as a robust, real-time solution within this field by combining simple texture descriptors to illustrate maximal ∼98% fire region detection.
Unsupervised Word Segmentation in Context

This paper extends existing word segmentation models to take non-linguistic context into account. It improves the token F-score of a top performing segmentation models by 2.5% on a 27k utterances dataset. We posit that word segmentation is easier in-context because the learner is not trying to access irrelevant lexical items. We use topics from a Latent Dirichlet Allocation model as a proxy for “activities” contexts, to label the Providence corpus. We present Adaptor Grammar models that use these context labels, and we study their performance with and without context annotations at test time.