nils-herrmann/clean_scirepeval huggingface dataset:
Performance Analysis and Optimization of a Hybrid Seismic Imaging Application
Venue: ICCS

Applications to process seismic data are computationally expensive and, therefore, employ scalable parallel systems to produce timely results. Here we describe our experiences of using performance analysis tools to gain insight into an MPI+OpenMP code developed by Shell that performs Reverse Time Migration on a cluster to produce models of the subsurface. Tuning MPI+OpenMP programs for modern platforms is difficult, and, therefore, assistance is required from performance analysis tools. These tools provided us with insights into the effectiveness of the domain decomposition strategy, the use of threaded parallelism, and functional unit utilization in individual cores. By applying insights obtained from Rice University's HPCToolkit and hardware performance counters, we were able to improve the performance of Shell's prototype distributed memory Reverse Time Migration code by roughly 30 percent.
Application of a Database Independent Approach To Assess the Quality of Operational Taxonomic Unit Picking Methods
Venue: mSystems

Assignment of 16S rRNA gene sequences to operational taxonomic units (OTUs) allows microbial ecologists to overcome the inconsistencies and biases within bacterial taxonomy and provides a strategy for clustering similar sequences that do not have representatives in a reference database.  Assignment of 16S rRNA gene sequences to operational taxonomic units (OTUs) allows microbial ecologists to overcome the inconsistencies and biases within bacterial taxonomy and provides a strategy for clustering similar sequences that do not have representatives in a reference database. I have applied the Matthews correlation coefficient to assess the ability of 15 reference independent and dependent clustering algorithms to assign sequences to OTUs. This metric quantifies the ability of an algorithm to reflect the relationships between sequences without the use of a reference and can be applied to any data set or method. The most consistently robust method was the average neighbor algorithm; however, for some data sets, other algorithms matched its performance.
Strategic self presentation on Facebook: Personal motives and audience response to online behavior
Venue: Comput. Hum. Behav.

People have always been concerned with their social image and engaged in strategic self presentation (Goffman, 1959) but the growth of social networking sites (SNS) has had a major influence on such social endeavors. When people choose how to present themselves online, they use strategies and make choices that influence the liking and respect they receive from others. We examine a model that links: (a) motives for self presentation, (b) actual online behavior on Facebook, and (c) feedback: social network members' responses "likes," comments) One hundred and fifty six undergraduates (37 males, 119 females, mean age??24.5) reported on their last three Facebook status updates (468 status updates overall) Based on structural equation modeling, we found that performance goals predict increased levels of self enhancement on Facebook and increased levels of social feedback "likes" and comments by others. Mastery goals do not predict increased levels of self derogation (posting negative contents about self) but those who do choose to derogate also receive increased numbers of "likes" and comments from their network friends. We examine a model for self presentation on Facebook.We link achievement motivation, self presentation goals, online behavior response.Performance goals are associated with increased online self enhancement, not self derogation.Self enhancement and self derogation may be effective for increasing online feedback.
Review of Action Recognition and Detection Methods
Venue: ArXiv

In computer vision, action recognition refers to the act of classifying an action that is present in a given video and action detection involves locating actions of interest in space and/or time. Videos, which contain photometric information (e.g. RGB, intensity values) in a lattice structure, contain information that can assist in identifying the action that has been imaged. The process of action recognition and detection often begins with extracting useful features and encoding them to ensure that the features are specific to serve the task of action recognition and detection. Encoded features are then processed through a classifier to identify the action class and their spatial and/or temporal locations. In this report, a thorough review of various action recognition and detection algorithms in computer vision is provided by analyzing the two step process of a typical action recognition and detection algorithm: (i) extraction and encoding of features, and (ii) classifying features into action classes. In efforts to ensure that computer vision based algorithms reach the capabilities that humans have of identifying actions irrespective of various nuisance variables that may be present within the field of view, the state of the art methods are reviewed and some remaining problems are addressed in the final chapter.
RGB D based action recognition datasets: A survey
Venue: Pattern Recognit.

Human action recognition from RGB D (Red, Green, Blue and Depth) data has attracted increasing attention since the first work reported in 2010. Over this period, many benchmark datasets have been created to facilitate the development and evaluation of new algorithms. This raises the question of which dataset to select and how to use it in providing a fair and objective comparative evaluation against state of the art methods. To address this issue, this paper provides a comprehensive review of the most commonly used action recognition related RGB D video datasets, including 27 single view datasets, 10 multi view datasets, and 7 multi person datasets. The detailed information and analysis of these datasets is a useful resource in guiding insightful selection of datasets for future research. In addition, the issues with current algorithm evaluation vis a vis limitations of the available datasets and evaluation protocols are also highlighted; resulting in a number of recommendations for collection of new datasets and use of evaluation protocols. HighlightsA detailed review and in depth analysis of 44 publicly available RGB D based action datasets.Recommendations on the selection of datasets and evaluation protocols for use in future research.Identification of some limitations of these datasets and evaluation protocols.Recommendations on future creation of datasets and use of evaluation protocols.
COMMD7 as a novel NEMO interacting protein involved in the termination of NF kB signaling
Venue: Journal of cellular physiology

NEMO/IKKg is the regulatory subunit of the IkB Kinase (IKK) complex, required for the activation of the NF kB pathway, which is involved in a variety of key processes, including immunity, inflammation, differentiation, and cell survival. Termination of NF kB activity on specific kB responsive genes, which is crucial for the resolution of inflammatory responses, can be achieved by direct degradation of the chromatin bound NF kB subunit RelA/p65, a process mediated by a protein complex that contains Copper Metabolism Murr1 Domain 1 (COMMD1) In this study, we identify COMMD7, another member of the COMMDs protein family, as a novel NEMO interacting protein. We show that COMMD7 exerts an inhibitory effect on NF kB activation upon TNFa stimulation. COMMD7 interacts with COMMD1 and together they cooperate to down regulate NF kB activity. Accordingly, termination of TNFa induced NF kB activity on the kB responsive gene, Icam1, is defective in cells silenced for COMMD7 expression. Furthermore, this impairment is not greatly increased when we silence the expression of both COMMD7 and COMMD1 indicating that the two proteins participate in the same pathway of termination of TNFa induced NF kB activity. Importantly, we have demonstrated that COMMD7's binding to NEMO does not interfere with the binding to the IKKs, and that the disruption of the IKK complex through the use of the NBP competitor impairs the termination of NF kB activity. We propose that an intact IKK complex is required for the termination of NF kB dependent transcription and that COMMD7 acts as a scaffold in the IKK mediated NF kB termination.  230: 152 161, 2016.
Anomaly detection in network traffic using K mean clustering
Venue: 2016 3rd International Conference on Recent Advances in Information Technology (RAIT)

With the advancement of digital age and internet technologies cyber attacks increasingly have been prompting the news headlines. These attacks exploit a network's short comings to gain unauthorized access to the sensitive information or sometimes just create a flood to prevent legitimate users from accessing it. In any case intrusion of the network plays a key role before the execution of any attack. In this paper we will discuss a how these intrusions can be detected with k means clustering based machine learning approach using big data analytical techniques and put forward the experimental results to prevent attacks at it's very core.
Activism

Activism refers to taking direct action in support of, or in opposition to, a social or political policy. The Online Etymology Dictionary traces the roots of the word back to 1915 when Swedish 'activists' petitioned for the end of that country's neutrality in World War I (WWI) However, film activism may predate the use of this nomenclature. Suffragettes, for example, appeared on film as early as 1899, and they continued to agitate for their cause in silent features, such as What 80 Million Women Want (Will Lewis, 1913) Putative understandings of activist films, as opposed to other types of political filmmaking, place them outside journalistic reportage or specific government propaganda programs in the realm of revolutionary movements, reform initiatives and struggles for social justice. Fiction and non fiction filmmakers around the world take up the camera now more likely digital than not in order to agitate for political or social causes. Others use the motion picture medium to record these movements for posterity as narrative features, documentaries or hybrid forms. The 1919 May Fourth Movement in China nourished a generation of activists advocating for action against Japanese aggression, colonialism, capitalist greed and patriarchal excesses on the silent screen. Many were inspired by the 'agit prop' films made in the Soviet Union by filmmakers, such as Dziga Vertov, V. I. Pudovkin and Sergei Eisenstein. At the conclusion of the Pacific War, filmmakers migrated from their leftwing roots in Republican era critical realism to government sanctioned socialist realism and revolutionary romanticism in the People's Republic, and to 'healthy' realism in the Republic of China on Taiwan. With this, films became less 'activist' in the original sense and more accurately films 'about' political heroes used as propaganda for government programs. However, amateur, underground and sundry oppositional cinematic practices continued, particularly on the margins of the Chinese speaking world. In the cinema clubs that sprang up in Hong Kong to support small gauge production, filmmakers took a decidedly political turn in the wake of the 1967 riots. As Ian Aitken and Mike Ingham point out in their research on Hong Kong documentary films (Hong Kong Documentary Film, Edinburgh, 2014) key figures, such as Clifford Choi, Lau Fung kut, Law Kar, Stephen Teo and Lo King wah made films in support of specific protest movements, such as the campaign to reclaim the Diaoyu Islands, throughout the 1970s and 1980s. New Wave director Tsui Hark began his filmmaking career at Newsreel in New York (later California and Third
Microaggressions as Boundary Objects

What do new materialist activisms and direct theory look like? Something massively distributed yet also micro somehow in relation to persons, indeed crowd sensible among personal storifyings, is emergent among and as microaggression and microaggressions. Literally we see, in data visualisation, this perhaps hyperobject (see the online Microaggressions Project) A mattering entanglement not simply micro or macro reframes anything personal, person all. And the connections with movements of re action, #Baltimore, #Ferguson, and #BlackLivesMatter, matters too along with other massive distributions: of military hardware escalating aggression in police forces, of debt peonage of whole towns, and county, regional, and national citizenry to pay to arm police, against themselves. People gathering in their person at sites of danger to confront these activating realities, as well as to enact the distributive social media and art activisms supporting them and making their actions widely available. All this at scales both intentional and not, sometimes tactical and often spontaneous, escalations fraught with danger and release, and all thoroughly neurologically entangled as mattering motivations: caring, attention, attachment, aggression. These move within and through and among bodies and infrastructural embodiments. Our holobiont/s include/s it all.
Lung surfactant: Function and composition in the context of development and respiratory physiology.
Venue: Annals of anatomy Anatomischer Anzeiger official organ of the Anatomische Gesellschaft

Lung surfactant is a complex with a unique phospholipid and protein composition. Its specific function is to reduce surface tension at the pulmonary air liquid interface. The underlying Young Laplace equation, applying to the surface of any geometrical structure, is the more important the smaller its radii are. It therefore applies to the alveoli and bronchioli of mature lungs, as well as to the tubules and saccules of immature lungs. Surfactant comprises 80% phosphatidylcholine (PC) of which dipalmitoyl PC, palmitoyl myristoyl PC and palmitoyl palmitoleoyl PC together are 75% Anionic phosphatidylglycerol and cholesterol are about 10% each, whereas surfactant proteins SP A to D comprise 2 5% Maturation of the surfactant system is not essentially due to increased synthesis but to decreased turnover of specific components. Molecular differences correlate with resting respiratory rate (RR) where PC16:0/16:0 is the lower the higher RR is. PC16:0/14:0 is increased during alveolar formation, and decreases immune reactions that might impair alveolar development. In rigid bird lungs, with air capillaries rather than alveoli, and no surface area changes during the respiratory cycle, PC16:0/16:0 is highest and PC16:0/14:0 absent. As there is no need for a surface associated surfactant reservoir, SP C is absent in birds as well. Airflow is lowest and particle sedimentation highest in the extrapulmonary air sacs, rather than in the gas exchange area. Consequently, SP A and D for particle opsonization are absent in bird surfactant. In essence, comparative analysis is consistent with the concept that surfactant is adapted to the physiologic needs of a given vertebrate species at a given developmental stage.
Feature Knowledge Based Fault Detection of Induction Motors Through the Analysis of Stator Current Data
Venue: IEEE Transactions on Instrumentation and Measurement

The fault detection of electrical or mechanical anomalies in induction motors has been a challenging problem for researchers over decades to ensure the safety and economic operations of industrial processes. To address this issue, this paper studies the stator current data obtained from inverter fed laboratory induction motors and investigates the unique signatures of the healthy and faulty motors with the aim of developing knowledge based fault detection method for performing online detection of motor fault problems, such as broken rotor bar and bearing faults. Stator current data collected from induction motors were analyzed by leveraging fast Fourier transform (FFT) and the FFT results were further analyzed by the independent component analysis (ICA) method to obtain independent components and signature features that are referred to as FFT ICA features of stator currents. The resulting FFT ICA features contain rich information on the signatures of the healthy and faulty motors, which are further analyzed to build a feature knowledge database for online fault detection. Through case studies, this paper demonstrated the high accuracy, simplicity, and robustness of the proposed fault detection scheme for fault detection of induction motors. In addition, with the integration of the feature knowledge database, prior knowledge of the motor parameters, such as rotor speed and per unit slip, which are needed by the other motor current signature analysis (MCSA) methods, is not required for the proposed method, which makes it more efficient compared with the other MCSA methods.
Predicting Socio Economic Indicators using News Events
Venue: KDD

Many socio economic indicators are sensitive to real world events. Proper characterization of the events can help to identify the relevant events that drive fluctuations in these indicators. In this paper, we propose a novel generative model of real world events and employ it to extract events from a large corpus of news articles. We introduce the notion of an event class, which is an abstract grouping of similarly themed events. These event classes are manifested in news articles in the form of event triggers which are specific words that describe the actions or incidents reported in any article. We use the extracted events to predict fluctuations in different socio economic indicators. Specifically, we focus on food prices and predict the price of 12 different crops based on real world events that potentially influence food price volatility, such as transport strikes, festivals etc. Our experiments demonstrate that incorporating event information in the prediction tasks reduces the root mean square error (RMSE) of prediction by 22% compared to the standard ARIMA model. We also predict sudden increases in the food prices (i.e. spikes) using events as features, and achieve an average 5 10% increase in accuracy compared to baseline models, including an LDA topic model based predictive model.
A Classification based Approach to Economic Event Detection in Dutch News Text
Venue: LREC

Breaking news on economic events such as stock splits or mergers and acquisitions has been shown to have a substantial impact on the financial markets. As it is important to be able to automatically identify events in news items accurately and in a timely manner, we present in this paper proof of concept experiments for a supervised machine learning approach to economic event detection in newswire text. For this purpose, we created a corpus of Dutch financial news articles in which 10 types of company specific economic events were annotated. We trained classifiers using various lexical, syntactic and semantic features. We obtain good results based on a basic set of shallow features, thus showing that this method is a viable approach for economic event detection in news text.
Ultrasound detrusor wall thickness measurement in combination with bladder capacity can safely detect detrusor underactivity in adult men
Venue: World Journal of Urology

PurposeDetrusor underactivity (DU) has lately gained increasing interest because this bladder condition is an important cause of post void residual urine and lower urinary tract symptoms (LUTS) in adult men. Until now, DU can only be diagnosed by pressure flow measurement. Therefore, the aim of this study was to search for noninvasive tests which can safely predict DU in adult men.MethodsUnselected, treatment naive male patients aged =40 years with uncomplicated, non neurogenic LUTS were prospectively evaluated. All men received after standard assessment of male LUTS ultrasound detrusor wall thickness (DWT) measurements at a bladder filling =250 ml and computer urodynamic investigation. DU was defined as incomplete bladder emptying >30 ml) in the absence of bladder outlet obstruction or dysfunctional voiding. Classification and regression tree (CART) analysis was used to determine parameters and threshold values for DU.ResultsThe study population consisted of 143 consecutive men with medians of 62 years, IPSS 16, and prostate volume 35 ml. In total, 33 patients (23.1 had DU. CART analysis showed that all men with DWT 1.23 mm plus bladder capacity >445 ml had DU. This multivariate model has a sensitivity of 42 specificity of 100 positive predictive value of 100 and negative predictive value of 85 .ConclusionsThis study showed that all men with ultrasound DWT 1.23 mm bladder capacity >445 ml have DU. Combination of these two tests could help physicians to diagnose DU noninvasively in clinical practice. A prospective independent study should confirm these results.
Deep Compression: Compressing Deep Neural Network with Pruning, Trained Quantization and Huffman Coding
Venue: ICLR

Neural networks are both computationally intensive and memory intensive, making them difficult to deploy on embedded systems with limited hardware resources. To address this limitation, we introduce "deep compression" a three stage pipeline: pruning, trained quantization and Huffman coding, that work together to reduce the storage requirement of neural networks by 35x to 49x without affecting their accuracy. Our method first prunes the network by learning only the important connections. Next, we quantize the weights to enforce weight sharing, finally, we apply Huffman coding. After the first two steps we retrain the network to fine tune the remaining connections and the quantized centroids. Pruning, reduces the number of connections by 9x to 13x; Quantization then reduces the number of bits that represent each connection from 32 to 5. On the ImageNet dataset, our method reduced the storage required by AlexNet by 35x, from 240MB to 6.9MB, without loss of accuracy. Our method reduced the size of VGG 16 by 49x from 552MB to 11.3MB, again with no loss of accuracy. This allows fitting the model into on chip SRAM cache rather than off chip DRAM memory. Our compression method also facilitates the use of complex neural networks in mobile applications where application size and download bandwidth are constrained. Benchmarked on CPU, GPU and mobile GPU, compressed network has 3x to 4x layerwise speedup and 3x to 7x better energy efficiency.
Molecular screening strategies for NF1 like syndromes with cafe au lait macules
Venue: Molecular medicine reports

Multiple cafe au lait macules (CALM) are usually associated with neurofibromatosis type 1 (NF1) one of the most common hereditary disorders. However, a group of genetic disorders presenting with CALM have mutations that are involved in human skin pigmentation regulation signaling pathways, including KIT ligand/KIT proto oncogene receptor tyrosine kinase and Ras/mitogen activated protein kinase. These disorders, which include Legius syndrome, Noonan syndrome with multiple lentigines or LEOPARD syndrome, and familial progressive hyperpigmentation) are difficult to distinguish from NF1 at early stages, using skin appearance alone. Furthermore, certain syndromes are clinically overlapping and molecular testing is a vital diagnostic method. The present review aims to provide an overview of these 'NF1 like' inherited diseases and recommend a cost effective strategy for making a clear diagnosis among these diseases with an ambiguous borderline.