nils-herrmann/clean_scirepeval huggingface dataset:
Domain Adversarial Training of Neural Networks
Venue: J. Mach. Learn. Res.

We introduce a new representation learning approach for domain adaptation, in which data at training and test time come from similar but different distributions. Our approach is directly inspired by the theory on domain adaptation suggesting that, for effective domain transfer to be achieved, predictions must be made based on features that cannot discriminate between the training (source) and test (target) domains. The approach implements this idea in the context of neural network architectures that are trained on labeled data from the source domain and unlabeled data from the target domain (no labeled target domain data is necessary) As the training progresses, the approach promotes the emergence of features that are (i) discriminative for the main learning task on the source domain and (ii) indiscriminate with respect to the shift between the domains. We show that this adaptation behaviour can be achieved in almost any feed forward model by augmenting it with few standard layers and a new gradient reversal layer. The resulting augmented architecture can be trained using standard backpropagation and stochastic gradient descent, and can thus be implemented with little effort using any of the deep learning packages. We demonstrate the success of our approach for two distinct classification problems (document sentiment analysis and image classification) where state of the art domain adaptation performance on standard benchmarks is achieved. We also validate the approach for descriptor learning task in the context of person re identification application.
Multiple Factors Affect Socioeconomics and Wellbeing of Artisanal Sea Cucumber Fishers
Venue: PloS one

Small scale fisheries are important to livelihoods and subsistence seafood consumption of millions of fishers. Sea cucumbers are fished worldwide for export to Asia, yet few studies have assessed factors affecting socioeconomics and wellbeing among fishers. We interviewed 476 men and women sea cucumber fishers at multiple villages within multiple locations in Fiji, Kiribati, Tonga and New Caledonia using structured questionnaires. Low rates of subsistence consumption confirmed a primary role of sea cucumbers in income security. Prices of sea cucumbers sold by fishers varied greatly among countries, depending on the species. Gender variation in landing prices could be due to women catching smaller sea cucumbers or because some traders take advantage of them. Dissatisfaction with fishery income was common (44% of fishers) especially for i Kiribati fishers, male fishers, and fishers experiencing difficulty selling their catch, but was uncorrelated with sale prices. Income dissatisfaction worsened with age. The number of livelihood activities averaged 2.2 2.5 across countries, and varied significantly among locations. Sea cucumbers were often a primary source of income to fishers, especially in Tonga. Other common livelihood activities were fishing other marine resources, copra production in Kiribati, agriculture in Fiji, and salaried jobs in New Caledonia. Fishing other coastal and coral reef resources was the most common fall back livelihood option if fishers were forced to exit the fishery. Our data highlight large disparities in subsistence consumption, gender related price equity, and livelihood diversity among parallel artisanal fisheries. Improvement of supply chains in dispersed small scale fisheries appears as a critical need for enhancing income and wellbeing of fishers. Strong evidence for co dependence among small scale fisheries, through fall back livelihood preferences of fishers, suggests that resource managers must mitigate concomitant effects on other fisheries when considering fishery closures. That is likely to depend on livelihood diversification programs to take pressure off co dependent fisheries.
Dynamic Constraints for Large Signal Measurements on Arbitrary Grids
Venue: IEEE Transactions on Microwave Theory and Techniques

This paper presents a novel method of dynamic constraints, which allows safe large signal measurements on arbitrary grids of samples. The method is a generalization of popular sweeps, and alike, it is based on the monotonic relationships linking some of the input variables with the setup degradation level. However, the knowledge of exact physical mechanisms is not required, which makes dynamic constraints applicable to wide range of experiments. The detailed explanation of the dynamic constraints principles and implementation is provided. Two cases are analyzed depending whether the degradation level is a monotonic function of all the experiment's input variables or only some of them. The method is assessed on 6 x 50 mm GaAs HEMT large signal measurements with the sparse grid of samples generated with adaptive sampling methods. Thanks to dynamic constraints, none of the samples seriously violated the constraints, even though no a priori knowledge about the device behavior was available. No significant device degradation is observed in the measurements.
Iso thermal and iso dynamic direct charge function characterization of GaN FET with single large signal measurement
Venue: 2016 IEEE MTT S International Microwave Symposium (IMS)

A fast and simple method for the direct characterization of nonlinear charge functions of electron devices is presented. The input and output transistor ports are simultaneously excited through single tone sources at different frequencies and calibrated large signal waveforms are measured by means of an advanced NVNA based setup. Proper choice of the two frequencies guarantees an almost complete coverage of the voltages domain in a single and very fast measurement set and allows the extraction of the charge functions by direct integration of currents in the frequency domain, since, contrary to other methods, the measured waveforms are both iso thermal and iso dynamic (i.e. at fixed charge trapping status) The method is validated by characterizing the gate charge function of a 5W 8x125mm GaN FET and implementing a simple table based model of the transistor input port. Very good results are achieved by comparison with large signal measurements under conditions different than the ones used for the characterization.
Iris recognition through machine learning techniques: A survey
Venue: Pattern Recognit. Lett.

Iris recognition is one of the most promising fields in biometrics. Notwithstanding this, there are not so many research works addressing it by machine learning techniques. In this survey, we especially focus on recognition, and leave the detection and feature extraction problems in the background. However, the kind of features used to code the iris pattern may significantly influence the complexity of the methods and their performance. In other words, complexity affects learning, and iris patterns require relatively complex feature vectors, even if their size can be optimized. A cross comparison of these two parameters, feature complexity vs. learning effectiveness, in the context of different learning algorithms, would require an unbiased common benchmark. Moreover, at present it is still very difficult to reproduce techniques and experiments due to the lack of either sufficient implementation details or reliable shared code.
Literary Pattern Recognition: Modernism between Close Reading and Machine Learning
Venue: Critical Inquiry

The title of this essay announces its core ambition: to propose a model of reading literary texts that synthesizes familiar humanistic approaches with computational ones. In recent years, debates over the use of computers to interpret literature have been fierce. On one side, scholars such as Franco Moretti, Matthew Jockers, Matthew Wilkens, and Andrew Piper defend the deployment of sophisticated machine techniques, like topic modeling and network analysis, to expose macroscale patterns of language and form culled from massive digitized literary corpora.1 On the other side, scholars such as Alexander Galloway, David Golumbia, Tara McPherson, and Alan Liu, who work in the field of New Media Studies, have criticized machine techniques for reducing the complexity of literary texts to mere "data" or for being incommensurable with the goals of critical theory.2 Here we move beyond this impasse by modeling a form of literary analysis
Hierarchical clustering of time series data with parametric derivative dynamic time warping

Combination of DTW and DDTW applied in a method of time series clustering.Specific correction algorithm for the internal cluster validation measures.Parameter selection for any dataset, giving the best clustering performance. Dynamic Time Warping (DTW) is a popular and efficient distance measure used in classification and clustering algorithms applied to time series data. By computing the DTW distance not on raw data but on the time series of the (first, discrete) derivative of the data, we obtain the so called Derivative Dynamic Time Warping (DDTW) distance measure. DDTW, used alone, is usually inefficient, but there exist datasets on which DDTW gives good results, sometimes much better than DTW. To improve the performance of the two distance measures, we can combine them into a new single (parametric) distance function. The literature contains examples of the combining of DTW and DDTW in algorithms for supervised classification of time series data. In this paper, we demonstrate that combination of DTW and DDTW can also be applied in a method of time series clustering (unsupervised classification) In particular, we focus on a hierarchical clustering (with average linkage) of univariate (one dimensional) time series data. We construct a new parametric distance function, combining DTW and DDTW, where a single real number parameter controls the contribution of each of the two measures to the total value of the combined distances. The parameter is tuned in the initial phase of the clustering algorithm. Using this technique in clustering methods requires a different approach (to address certain specific problems) than for supervised methods. In the clustering process we use three internal cluster validation measures (measures which do not use labels) and three external cluster validation measures (measures which do use clustering data labels) Internal measures are used to select an optimal value of the parameter of the algorithm, where external measures give information about the overall performance of the new method and enable comparison with other distance functions. Computational experiments are performed on a large real world data base (UCR Time Series Classification Archive: 84 datasets) from a very broad range of fields, including medicine, finance, multimedia and engineering. The experimental results demonstrate the effectiveness of the proposed approach for hierarchical clustering of time series data. The method with the new parametric distance function outperforms DTW (and DDTW) on the data base used. The results are confirmed by graphical and statistical comparison.
The RUDY study platform a novel approach to patient driven research in rare musculoskeletal diseases
Venue: Orphanet Journal of Rare Diseases

BackgroundResearch into rare diseases is becoming more common, with recognition of the significant diagnostic and therapeutic care gaps. Registries are considered a key research methodology to address rare diseases. This report describes the structure of the Rare UK Diseases Study (RUDY) platform that aims to improve research processes and address many of the challenges of carrying out rare musculoskeletal disease research.RUDY is an internet based platform with online registration, initial verbal consent, online capture of patient reported outcome measures and events within a dynamic consent framework. The database structure, security and governance framework are described.ResultsThere have been 380 participants recruited into RUDY with completed questionnaire rates in excess of 50 There has been one withdrawal and two participants have amended their consent options.ConclusionsThe strengths of RUDY include low burden for the clinical team, low research administration costs with high participant recruitment and ease of data collection and access. This platform has the potential to be used as the model for other rare diseases globally.
RUDY Attack: Detection at the Network Level and Its Important Features
Venue: FLAIRS Conference

Compared to common DoS/DDoS attacks that are destructive and generate massive traffic, the application layer DoS attacks can be slow and low which means they occur at a slow rate and do not generate a massive amount of traffic. These attacks appear legitimate in terms of the protocol rules and rates. These characteristics make the detection of these attacks difficult. In this paper, we study the RUDY (R U DeadYet) attack which is one of the slow and low application layer attack types. RUDY attacks can bring down a server by creating long POST HTTP form submissions to the server at a very slow rate which results in application threads at the server side becoming stuck. The mitigation methods against RUDY attacks are mostly host based. In this paper, we use a machine learning approach for the detection of RUDY attacks as well as determining the important features for their detection at the network level. The network level detection is scalable and it provides detection for hosts that do not have their own detection mechanism. We extract features from bi directional instances of the network traffic. We then use an ensemble feature selection approach containing 10 different feature ranker methods in order to extract the most important features for the detection of RUDY attacks at the network level.
Population Pharmacokinetics and Exploratory Pharmacodynamics of Lorazepam in Pediatric Status Epilepticus
Venue: Clinical Pharmacokinetics

BackgroundLorazepam is one of the preferred agents used for intravenous treatment of status epilepticus (SE) We combined data from two pediatric clinical trials to characterize the population pharmacokinetics of intravenous lorazepam in infants and children aged 3 months to 17 years with active SE or a history of SE.MethodsWe developed a population pharmacokinetic model for lorazepam using the NONMEM software. We then assessed exploratory exposure response relationships using the overall efficacy and safety study endpoints, and performed dosing simulations. total of 145 patients contributed 439 pharmacokinetic samples. The median (range) age and dose were 5.4 years (0.3 17.8) and 0.10 mg/kg (0.02 0.18) respectively. A two compartment pharmacokinetic model with allometric scaling described the data well. In addition to total body weight (WT) younger age was associated with slightly higher weight normalized clearance (CL) The following relationships characterized the typical values for the central compartment volume (V1) CL, peripheral compartment volume (V2) and intercompartmental CL (Q) using individual subject WT (kg) and age (years) V1 (L) 0.879*WT; CL (L/h) 0.115*(Age/4.7)0.133*WT0.75; V2 (L) 0.542*V1; Q (L/h) 1.45*WT0.75. No pharmacokinetic parameters were associated with clinical outcomes. Simulations suggest uniform pediatric dosing (0.1 mg/kg, to a maximum of 4 mg) can be used to achieve concentrations of 50 100 ng/mL in children with SE, which have been previously associated with effective seizure control.ConclusionsThe population pharmacokinetics of lorazepam were successfully described using a sparse sampling approach and a two compartment model in pediatric patients with active SE.
A Complete Street Intervention for Walking to Transit, Nontransit Walking, and Bicycling: A Quasi Experimental Demonstration of Increased Use.
Venue: Journal of physical activity health

Complete streets require evaluation to determine if they encourage active transportation.  Data were collected before and after a street intervention provided new light rail, bike lanes, and better sidewalks in Salt Lake City, Utah. Residents living near <800 m) and far =801 to 2000 m) from the street were compared, with sensitivity tests for alternative definitions of near <600 and <1000 m) Dependent variables were accelerometer/global positioning system (GPS) measures of transit trips, nontransit walking trips, and biking trips that included the complete street corridor.  Active travel trips for Near Time 2 residents, the group hypothesized to be the most active, were compared with the other 3 groups (Near Time 1, Far Time 1, and Far Time 2) net of control variables. Near Time 2 residents were more likely to engage in complete street transit walking trips (35% adjusted) and nontransit walking trips (50% than the other 3 groups (24% to 25% and 13% to 36% respectively) Bicycling was less prevalent, with only 1 of 3 contrasts significant (10% of Near Time 2 residents had complete street bicycle trips compared with 5% of Far Time 1 residents)  Living near the complete street intervention supported more pedestrian use and possibly bicycling, suggesting complete streets are also public health interventions.
Understanding Complete Streets: How do Professionals Define a Complete Street Project?

The Complete Streets movement has become popular throughout North America. Although the concepts and overall objectives of a Complete Street are becoming increasingly recognized in the field of transportation planning, some ambiguity exists when defining such projects based on the existing built infrastructure (e.g. bicycle lanes, road diet) This research presents data gathered from transportation planning experts working at municipalities and regional municipalities across the Greater Golden Horseshoe Region in Ontario, Canada. Using a focus group discussion as the basis of the authors' analysis, they attempt to understand how a Complete Street can be defined at the project level as well as what factors may influence this definition. The findings show that the definition of a Complete Street can be largely dependent on contextual sensitivities including surrounding land use, roadway typologies, age and maturity of a road, and the quality of roadway infrastructure implemented with right of way (ROW) upgrades. The planning process is also important. Considerations relating to a ROW's feel, function and form as well as the age, mode and mobility of the road users can be taken into account to define a Complete Street. These findings will improve the knowledge and awareness among transportation planners within (and beyond) the region regarding how a Complete Street project's built form can change in response to various urban land use contexts, as well as planning priorities and processes.
Model Free Temporal Difference Learning and Dopamine in Alcohol Dependence: Examining Concepts From Theory and Animals in Human Imaging.
Venue: Biological psychiatry. Cognitive neuroscience and neuroimaging

Dopamine potentially unites two important roles: one in addiction, being involved in most substances of abuse including alcohol, and a second one in a specific type of learning, namely model free temporal difference reinforcement learning. Theories of addiction have long suggested that drugs of abuse may usurp dopamine's role in learning. Here, we briefly review the preclinical literature to motivate specific hypotheses about model free temporal difference learning and then review the imaging evidence in the drug of abuse with the most substantial societal consequences: alcohol. Despite the breadth of the literature, only a few studies have examined the predictions directly, and these provide at best inconclusive evidence for the involvement of temporal difference learning alterations in alcohol dependence. We discuss the difficulties of testing the theory in humans, make specific suggestions, and close with a focus on the interaction with other learning mechanisms.
HIV 1 Thai B strain has spread out of former plasma donors into general population through sexual contact in Henan, China
Venue: Journal of medical virology

Henan, China is characterized by the outbreak of HIV epidemic of Thai B strain in former plasma donors in 1990s. After the forbidden of paid blood donation, whether Thai B strain will spread out of former plasma donors into sexual transmitted population is unknown. To answer the question, phylogenetic analysis was used to explore relationships of HIV strains circulating in those two populations in the study. HIV 1 sero positive drug naive patients infected through sexual contact were enrolled into the study. Full length gag and pol genes were amplified with nested RT PCR followed by sequencing and phylogenetic analysis. The genotypes of anti HIV drug resistance were also analyzed with available pol genes. HIV subtypes were determined in 249 individuals from 288 participants. Subtype B was dominant (202/249, 81.1% followed by CRF01_AE (25/249, 10.0% CRF07_BC (14/249, 5.6% C (4/249, 1.6% URF (3/249, 1.2% and CRF08_BC (1/249, 0.4% Most of subtype B strains belong to Thailand B lineage. All of Thai B strains identified in sexual transmitted population intermixed with those from former blood donors in phylogenetic tree, suggesting close phylogenetic relationship between strains epidemic in those two populations. TDR was identified in 9.9% individuals. Thai B strain has spread out of former blood donors in Henan province. The finding will contribute to understanding the distribution and evolution of HIV 1 in Henan province and also provide clue to behavior change intervention.  2016.
Vehicle detection in aerial imagery A small target detection benchmark
Venue: J. Vis. Commun. Image Represent.

We made a new database for vehicle detection in aerial imagery.We tested several state of the art detection algorithm on the database to provide a Baseline.Most of the best methods gives similar results.The best methods are still far from achieving good enough results for industrial applications. This paper introduces VEDAI: Vehicle Detection in Aerial Imagery a new database of aerial images provided as a tool to benchmark automatic target recognition algorithms in unconstrained environments. The vehicles contained in the database, in addition of being small, exhibit different variabilities such as multiple orientations, lighting/shadowing changes, specularities or occlusions. Furthermore, each image is available in several spectral bands and resolutions. A precise experimental protocol is also given, ensuring that the experimental results obtained by different people can be properly reproduce and compared. Finally, the paper also gives the performance of baseline algorithms on this dataset, for different settings of these algorithms, to illustrate the difficulties of the task and provide baseline comparisons.
Infrared small target detection using multiscale gray difference weighted image entropy
Venue: IEEE Transactions on Aerospace and Electronic Systems

We propose an effective small target detection approach based on weighted image entropy. The approach weights the local entropy measure by the multiscale grayscale difference followed by an adaptive threshold operation, which aims to improve the signal to noise ratio for cases in which jamming objects in the scene have similar thermal intensity measure with respect to the background as small target. The detection capability of the proposed approach has been validated on six real sequences, and the results demonstrate its significance and improvement.
Pen Mid Air: An Exploration of Mid Air Gestures to Complement Pen Input on Tablets
Venue: NordiCHI

In this paper, we report on a series of studies, exploring the potential of pen and mid air input on tablets. We describe a field study with an early prototype of a drawing application and follow up inquiries, such as the development and comparison of gesture sets for pen and mid air input and pen and multi touch input with users in a lab environment. Overall, our results suggest that pen and mid air input should be offered to complement traditional pen and multi touch input on tablets. We illustrate the final user defined gestures set for pen and mid air input and discuss how user preferences of mid air gestures over touch gestures seem to depend on the complexity of operations and the need of additional menus on the screen.