nils-herrmann/clean_scirepeval huggingface dataset:
Cnvlutin: Ineffectual-Neuron-Free Deep Neural Network Computing

This work observes that a large fraction of the computations performed by Deep Neural Networks (DNNs) are intrinsically ineffectual as they involve a multiplication where one of the inputs is zero. This observation motivates Cnvlutin (CNV), a value-based approach to hardware acceleration that eliminates most of these ineffectual operations, improving performance and energy over a state-of-the-art accelerator with no accuracy loss. CNV uses hierarchical data-parallel units, allowing groups of lanes to proceed mostly independently enabling them to skip over the ineffectual computations. A co-designed data storage format encodes the computation elimination decisions taking them off the critical path while avoiding control divergence in the data parallel units. Combined, the units and the data storage format result in a data-parallel architecture that maintains wide, aligned accesses to its memory hierarchy and that keeps its data lanes busy. By loosening the ineffectual computation identification criterion, CNV enables further performance and energy efficiency improvements, and more so if a loss in accuracy is acceptable. Experimental measurements over a set of state-of-the-art DNNs for image classification show that CNV improves performance over a state-of-the-art accelerator from 1.24× to 1.55× and by 1.37× on average without any loss in accuracy by removing zero-valued operand multiplications alone. While CNV incurs an area overhead of 4.49%, it improves overall EDP (Energy Delay Product) and ED2P (Energy Delay Squared Product) on average by 1.47× and 2.01×, respectively. The average performance improvements increase to 1.52× without any loss in accuracy with a broader ineffectual identification policy. Further improvements are demonstrated with a loss in accuracy.
Pattern recognition computation using action potential timing for stimulus representation

A computational model is described in which the sizes of variables are represented by the explicit times at which action potentials occur, rather than by the more usual 'firing rate' of neurons. The comparison of patterns over sets of analogue variables is done by a network using different delays for different information paths. This mode of computation explains how one scheme of neuroarchitecture can be used for very different sensory modalities and seemingly different computations. The oscillations and anatomy of the mammalian olfactory systems have a simple interpretation in terms of this representation, and relate to processing in the auditory system. Single-electrode recording would not detect such neural computing. Recognition 'units' in this style respond more like radial basis function units than elementary sigmoid units.
Business-to-Business E-Commerce Frameworks

E lectronic commerce lets people purchase goods and exchange information on business transactions online. The most popular e-commerce channel is the Internet. Although the Internet's role as a business channel is a fairly recent phenomenon, its impact, financial and otherwise, has been substantially greater than that of other business channels in existence for several decades. E-commerce gives companies improved efficiency and reliability of business processes through transaction automation. There are two major types of e-commerce: business to consumer (B2C), in which consumers purchase products and services from businesses , and business to business (B2B), in which businesses buy and sell among themselves. A typical business depends on other businesses for several of the direct and indirect inputs to its end products. For example, Dell Computer depends on one company for microprocessor chips and another for hard drives. B2B e-commerce automates and streamlines the process of buying and selling these intermediate products. It provides more reliable updating of business data. For procurement transactions, buyers and sellers can meet in an electronic marketplace and exchange information. In addition, B2B makes product information available globally and updates it in real time. Hence, procuring organizations can take advantage of vast amounts of product information. B2C e-commerce is now sufficiently stable. Judging from its success, we can expect B2B to similarly improve business processes for a better return on investment. Market researchers predict that B2B transactions will amount to a few trillion dollars in the next few years, as compared to about 100 billion dollars' worth of B2C transactions. B2C was easier to achieve, given the relative simplicity of reaching its target: the individual consumer. That's not the case with B2B, which involves engineering the interactions of diverse, complex enterprises. Interoperability is therefore a key issue in B2B. To achieve interoperability, many companies have formed consortia to develop B2B frameworks—generic templates that provide functions enabling businesses to communicate efficiently over the Internet. The consor-tia aim to provide an industrywide standard that companies can easily adopt. Their work has resulted in several technical standards. Among the most popular are Open Buying on the Internet (OBI), eCo, RosettaNet, commerce XML (cXML), and BizTalk. The problem with these standards, and many others, is that they are incompatible. Businesses trying to implement a B2B framework are bewildered by a variety of standards that point in different directions. Each standard has its merits and demerits. To aid decision-makers in choosing …
Factors affecting strength of perceived foreign accent in a second language.

This study assessed the relation between non-native subjects' age of learning (AOL) English and the overall degree of perceived foreign accent in their production of English sentences. The 240 native Italian (NI) subjects examined had begun learning English in Canada between the ages of 2 and 23 yr, and had lived in Canada for an average of 32 yr. Native English-speaking listeners used a continuous scale to rate sentences spoken by the NI subjects and by subjects in a native English comparison group. Estimates of the AOL of onset of foreign accents varied across the ten listeners who rated the sentences, ranging from 3.1 to 11.6 yr (M = 7.4). Foreign accents were evident in sentences spoken by many NI subjects who had begun learning English long before what is traditionally considered to be the end of a critical period. Very few NI subjects who began learning English after the age of 15 yr received ratings that fell within the native English range. Principal components analyses of the NI subjects' responses to a language background questionnaire were followed by multiple-regression analyses. AOL accounted for an average of 59% of variance in the foreign accent ratings. Language use factors accounted for an additional 15% of variance. Gender was also found to influence degree of foreign accent.
Stair descent in the simple hexapod 'RHex'

We present the first controller that allows our small hexapod robot, RHex, to descend a wide variety of regular sized, “real-world” stairs. After selecting one of two sets of trajectories, depending on the slope of the stairs, our open-loop, clock-driven controllers require no further operator input nor task level feedback. Energetics for stair descent is captured via specific resistance values and compared to stair ascent and other behaviors. Even though the algorithms developed and validated in this paper were developed for a particular robot, the basic motion strategies, and the phase relationships between the contralateral leg pairs are likely applicable to other hexapod robots of similar size as well.
Discovering Inconsistencies in PubMed Abstracts through Ontology-Based Information Extraction

Searching for a cure for cancer is one of the most vital pursuits in modern medicine. In that aspect microRNA research plays a key role. Keeping track of the shifts and changes in established knowledge in the microRNA domain is very important. In this paper, we introduce an Ontology-Based Information Extraction method to detect occurrences of inconsistencies in microRNA research paper abstracts. We propose a method to first use the Ontology for MIcroRNA Targets (OMIT) to extract triples from the abstracts. Then we introduce a new algorithm to calculate the oppositeness of these candidate relationships. Finally we present the discovered inconsistencies in an easy to read manner to be used by medical professionals. To our best knowledge, this study is the first ontology-based information extraction model introduced to find shifts in the established knowledge in the medical domain using research paper abstracts. We downloaded 36877 abstracts from the PubMed database. From those, we found 102 inconsistencies relevant to the microRNA domain.
Digging Digg: Comment Mining, Popularity Prediction, and Social Network Analysis

Using comment information available from Digg we define a co-participation network between users. We focus on the analysis of this implicit network, and study the behavioral characteristics of users. Using an entropy measure, we infer that users at Digg are not highly focused and participate across a wide range of topics. We also use the comment data and social network derived features to predict the popularity of online content linked at Digg using a classification and regression framework. We show promising results for predicting the popularity scores even after limiting our feature extraction to the first few hours of comment activity that follows a Digg submission.
Arabic Ontology Learning from Un-structured Text

Ontology Learning (OL) from a text is a process that consists of text processing, knowledge extraction, and ontology construction. For Arabic language, text processing, and knowledge extraction tasks are not mature as for Latin languages. They have not been integrated into the full Arabic OL pipeline. Currently, there is very little automated support for using knowledge from Arabic literature in semantically-enabled systems. This paper demonstrates the feasibility of using some existing OL methods for Arabic text and elicits proposals for further work toward building open domain OL systems for Arabic. This is done by building an OL system based on some available NLP tools for Arabic text utilizing GATE text analysis system for corpus and annotation management. The prototype is evaluated similarly to other OL systems and its performance is promising and recommended to enable more effective research and application of Arabic ontology learning.
Hyaluronidase injection for the treatment of eyelid edema: a retrospective analysis of 20 patients

Hyaluronidase (Hylase Dessau(®)) is a hyaluronic acid-metabolizing enzyme, which has been shown to loosen the extracellular matrix, thereby improving the diffusion of local anesthetics. Lower eyelid edema is a common post-interventional complication of cosmetic procedures performed in the lid region, such as the injection of hyaluronic acid fillers for tear-trough augmentation. The purpose of this study was to validate the efficacy of hyaluronidase in the management of lower eyelid edema.



We performed a retrospective analysis with 20 patients with lower eyelid edema. Most patients (n = 14) presented with edema following hyaluronic acid injection (tear-trough augmentation), whereas the minority (n = 6) were treated due to idiopathic edema (malar edema or malar mounds). Patients were treated by local infiltration of approximately 0.2 ml to 0.5 ml of hyaluronidase (Hylase Dessau(®) 20 IU to 75 IU) per eyelid. Photographs were taken prior to and seven days after infiltration.



Hyaluronidase was found to reduce effectively and rapidly or resolve eyelid edema after a single injection. No relevant adverse effects were observed. However, it must be noted that a hyaluronidase injection may also dissolve injected hyaluronic acid fillers and may therefore negatively affect tear-trough augmentations. While the effects of a treatment for edema due to tear-trough augmentation were permanent, malar edema and malar mounds reoccurred within two to three weeks.



The infiltration of hyaluronidase is rapid, safe and currently the only effective option for the management of eyelid edema. No relevant adverse effects were observed.
Accurate Weakly Supervised Deep Lesion Segmentation on CT Scans: Self-Paced 3D Mask Generation from RECIST

Volumetric lesion segmentation via medical imaging is a powerful means to precisely assess multiple time-point lesion/tumor changes. Because manual 3D segmentation is prohibitively time consuming and requires radiological experience, current practices rely on an imprecise surrogate called response evaluation criteria in solid tumors (RECIST). Despite their coarseness, RECIST marks are commonly found in current hospital picture and archiving systems (PACS), meaning they can provide a potentially powerful, yet extraordinarily challenging, source of weak supervision for full 3D segmentation. Toward this end, we introduce a convolutional neural network based weakly supervised self-paced segmentation (WSSS) method to 1) generate the initial lesion segmentation on the axial RECISTslice; 2) learn the data distribution on RECIST-slices; 3) adapt to segment the whole volume slice by slice to finally obtain a volumetric segmentation. In addition, we explore how super-resolution images (2 ∼ 5 times beyond the physical CT imaging), generated from a proposed stacked generative adversarial network, can aid the WSSS performance. We employ the DeepLesion dataset, a comprehensive CTimage lesion dataset of 32, 735 PACS-bookmarked findings, which include lesions, tumors, and lymph nodes of varying sizes, categories, body regions and surrounding contexts. These are drawn from 10, 594 studies of 4, 459 patients. We also validate on a lymph-node dataset, where 3D ground truth masks are available for all images. For the DeepLesion dataset, we report mean Dice coefficients of 93% on RECIST-slices and 76% in 3D lesion volumes. We further validate using a subjective user study, where an experienced ∗Indicates equal contribution. †This work is done during Jinzheng Cai’s internship at National Institutes of Health. Le Lu is now with Nvidia Corp (lel@nvidia.com). CN N Initial 2D Segmentation Self-Paced 3D Segmentation CN N CN N CN N Image Image
Word Sense Disambiguation Using WordNet Domains

In this paper we present the methodology for word sense disambiguation based on domain information. Domain is a set of words in which there is a strong semantic relation among the words. The words in the sentence contribute to determine the domain of the sentence. The availability of WordNet domains makes the domain-oriented text analysis possible. The domain of the target word can be fixed based on the domains of the content words in the local context. This approach can be effectively used to disambiguate nouns. We present the unsupervised approach to word sense disambiguation using the WordNet domains. The model determines the domain of the target word and the sense corresponding to this domain is taken as the correct sense. We have used the WordNet domains 3.1 as lexical database.
GNU Radio based software-defined FMCW radar for weather surveillance application

In this paper, a GNU Radio based software-defined FMCW (Frequency Modulated — Continuous Wave) radar is studied for weather surveillance application. The FMCW radar that has been gaining popularity due to the use of solid state microwave amplifier to generate a signal source is proposed for the design since the current weather surveillance radar is usually using a pulse-radar type that needs high power in pulse generation and high cost in the deployment. In addition, by using software-defined radar, therefore the designed FMCW radar can be implemented and configured at a reduced cost and complexity. The FMCW radar prototype is implemented using both open sources of software and hardware. The software part of the radar is realized using GNU Radio, whilst the hardware part is implemented using USRP (Universal Software Radio Peripheral) N210. Based on the design specification, an FMCW radar prototype based on GNU Radio is then realized and examined. From the performance-test, the prototype that works at a center frequency of 2.1GHz with a bandwidth of 750kHz is able to perform range detection of targets after utilizing the FFT (Fast Fourier Transform) function using MATLAB. In addition, the discussion of system design of software defined FMCW radar and performance-test of its prototype are presented.
Active Flattening of Curved Document Images via Two Structured Beams

Document images captured by a digital camera often suffer from serious geometric distortions. In this paper, we propose an active method to correct geometric distortions in a camera-captured document image. Unlike many passive rectification methods that rely on text-lines or features extracted from images, our method uses two structured beams illuminating upon the document page to recover two spatial curves. A developable surface is then interpolated to the curves by finding the correspondence between them. The developable surface is finally flattened onto a plane by solving a system of ordinary differential equations. Our method is a content independent approach and can restore a corrected document image of high accuracy with undistorted contents. Experimental results on a variety of real-captured document images demonstrate the effectiveness and efficiency of the proposed method.
Measuring team effectiveness in cyber-defense exercises: a cross-disciplinary case study

In 2010, IT-security experts from northern European governments and organizations gathered to conduct the first of a series of NATO-led cyber-defense exercises in a pilot attempt of training cyber defense. To gain knowledge on how to assess team effectiveness in cyber-defense exercises, this case study investigates the role of behavioral assessment techniques as a complement to task-based performance measurement. The collected data resulted in a massive data set including system logs, observer reports, and surveys. Six different methods were compared for feasibility in assessing the teams’ performance, including automated availability check, exploratory sequential data analysis, and network intrusion detection system attack analysis. In addition, observer reports and surveys were used to collect aspects relating to team structures and processes, aiming to discover whether these aspects can explain differences in effectiveness. The cross-disciplinary approach and multiple metrics create possibilities to study not only the performance-related outcome of the exercise, but also why this result is obtained. The main conclusions found are (1) a combination of technical performance measurements and behavioral assessment techniques are needed to assess team effectiveness, and (2) cyber situation awareness is required not only for the defending teams, but also for the observers and the game control.
A data-driven method for in-game decision making in MLB: when to pull a starting pitcher

Professional sports is a roughly $500 billion dollar industry that is increasingly data-driven. In this paper we show how machine learning can be applied to generate a model that could lead to better on-field decisions by managers of professional baseball teams. Specifically we show how to use regularized linear regression to learn pitcher-specific predictive models that can be used to help decide when a starting pitcher should be replaced. A key step in the process is our method of converting categorical variables (e.g., the venue in which a game is played) into continuous variables suitable for the regression. Another key step is dealing with situations in which there is an insufficient amount of data to compute measures such as the effectiveness of a pitcher against specific batters. 
 For each season we trained on the first 80% of the games, and tested on the rest. The results suggest that using our model could have led to better decisions than those made by major league managers. Applying our model would have led to a different decision 48% of the time. For those games in which a manager left a pitcher in that our model would have removed, the pitcher ended up performing poorly 60% of the time.
Modeling RESTful applications

Today, Representational State Transfer (REST) is becoming more and more important. RESTful web services are an alternative to Remote Procedure Call technologies like SOAP and WS-* services. There are many frameworks for implementing RESTful applications, but there is still a lack of support for the early phases of the development process, particularly analysis and design. For building formal models of RESTful applications an appropriate metamodel is needed. After analyzing existing approaches and techniques a first version of such a REST metamodel is presented and used to model an example application. Beside enabling modeling, such a metamodel offers a vocabulary for REST in practice and the basis for model driven development.
Nonhuman primates prefer slow tempos but dislike music overall

Human adults generally find fast tempos more arousing than slow tempos, with tempo frequently manipulated in music to alter tension and emotion. We used a previously published method [McDermott, J., & Hauser, M. (2004). Are consonant intervals music to their ears? Spontaneous acoustic preferences in a nonhuman primate. Cognition, 94(2), B11-B21] to test cotton-top tamarins and common marmosets, two new-World primates, for their spontaneous responses to stimuli that varied systematically with respect to tempo. Across several experiments, we found that both tamarins and marmosets preferred slow tempos to fast. It is possible that the observed preferences were due to arousal, and that this effect is homologous to the human response to tempo. In other respects, however, these two monkey species showed striking differences compared to humans. Specifically, when presented with a choice between slow tempo musical stimuli, including lullabies, and silence, tamarins and marmosets preferred silence whereas humans, when similarly tested, preferred music. Thus despite the possibility of homologous mechanisms for tempo perception in human and nonhuman primates, there appear to be motivational ties to music that are uniquely human.
Enhancing the LexVec Distributed Word Representation Model Using Positional Contexts and External Memory

In this paper we take a state-of-the-art model for distributed word representation that explicitly factorizes the positive pointwise mutual information (PPMI) matrix using window sampling and negative sampling and address two of its shortcomings. We improve syntactic performance by using positional contexts, and solve the need to store the PPMI matrix in memory by working on aggregate data in external memory. The effectiveness of both modifications is shown using word similarity and analogy tasks.