nils-herrmann/clean_scirepeval huggingface dataset:
Interpretable Representation Learning for Healthcare via Capturing Disease Progression through Time

Various deep learning models have recently been applied to predictive modeling of Electronic Health Records (EHR). In medical claims data, which is a particular type of EHR data, each patient is represented as a sequence of temporally ordered irregularly sampled visits to health providers, where each visit is recorded as an unordered set of medical codes specifying patient's diagnosis and treatment provided during the visit. Based on the observation that different patient conditions have different temporal progression patterns, in this paper we propose a novel interpretable deep learning model, called Timeline. The main novelty of Timeline is that it has a mechanism that learns time decay factors for every medical code. This allows the Timeline to learn that chronic conditions have a longer lasting impact on future visits than acute conditions. Timeline also has an attention mechanism that improves vector embeddings of visits. By analyzing the attention weights and disease progression functions of Timeline, it is possible to interpret the predictions and understand how risks of future visits change over time. We evaluated Timeline on two large-scale real world data sets. The specific task was to predict what is the primary diagnosis category for the next hospital visit given previous visits. Our results show that Timeline has higher accuracy than the state of the art deep learning models based on RNN. In addition, we demonstrate that time decay factors and attentions learned by Timeline are in accord with the medical knowledge and that Timeline can provide a useful insight into its predictions.
A Shared Vision for Machine Learning in Neuroscience.

With ever-increasing advancements in technology, neuroscientists are able to collect data in greater volumes and with finer resolution. The bottleneck in understanding how the brain works is consequently shifting away from the amount and type of data we can collect and toward what we actually do with the data. There has been a growing interest in leveraging this vast volume of data across levels of analysis, measurement techniques, and experimental paradigms to gain more insight into brain function. Such efforts are visible at an international scale, with the emergence of big data neuroscience initiatives, such as the BRAIN initiative (Bargmann et al., 2014), the Human Brain Project, the Human Connectome Project, and the National Institute of Mental Health's Research Domain Criteria initiative. With these large-scale projects, much thought has been given to data-sharing across groups (Poldrack and Gorgolewski, 2014; Sejnowski et al., 2014); however, even with such data-sharing initiatives, funding mechanisms, and infrastructure, there still exists the challenge of how to cohesively integrate all the data. At multiple stages and levels of neuroscience investigation, machine learning holds great promise as an addition to the arsenal of analysis tools for discovering how the brain works.
Carotid intima-media thickness and plaque in cardiovascular risk assessment.

Carotid intima-media thickness (CIMT) has been shown to predict cardiovascular (CV) risk in multiple large studies. Careful evaluation of CIMT studies reveals discrepancies in the comprehensiveness with which CIMT is assessed-the number of carotid segments evaluated (common carotid artery [CCA], internal carotid artery [ICA], or the carotid bulb), the type of measurements made (mean or maximum of single measurements, mean of the mean, or mean of the maximum for multiple measurements), the number of imaging angles used, whether plaques were included in the intima-media thickness (IMT) measurement, the report of adjusted or unadjusted models, risk association versus risk prediction, and the arbitrary cutoff points for CIMT and for plaque to predict risk. Measuring the far wall of the CCA was shown to be the least variable method for assessing IMT. However, meta-analyses suggest that CCA-IMT alone only minimally improves predictive power beyond traditional risk factors, whereas inclusion of the carotid bulb and ICA-IMT improves prediction of both cardiac risk and stroke risk. Carotid plaque appears to be a more powerful predictor of CV risk compared with CIMT alone. Quantitative measures of plaques such as plaque number, plaque thickness, plaque area, and 3-dimensional assessment of plaque volume appear to be progressively more sensitive in predicting CV risk than mere assessment of plaque presence. Limited data show that plaque characteristics including plaque vascularity may improve CV disease risk stratification further. IMT measurement at the CCA, carotid bulb, and ICA that allows inclusion of plaque in the IMT measurement or CCA-IMT measurement along with plaque assessment in all carotid segments is emerging as the focus of carotid artery ultrasound imaging for CV risk prediction.
GaN-on-Si Vertical Schottky and p-n Diodes

This letter demonstrates GaN vertical Schottky and p-n diodes on Si substrates for the first time. With a total GaN drift layer of only 1.5-μm thick, a breakdown voltage (BV) of 205 V was achieved for GaN-on-Si Schottky diodes, and a soft BV higher than 300 V was achieved for GaN-on-Si p-n diodes with a peak electric field of 2.9 MV/cm in GaN. A trap-assisted space-charge-limited conduction mechanism determined the reverse leakage and breakdown mechanism for GaN-on-Si vertical p-n diodes. The ON-resistance was 6 and 10 mQ · cm2 for the vertical Schottky and p-n diode, respectively. These results show the promising performance of GaN-on-Si vertical devices for future power applications.
Structural Neighborhood Based Classification of Nodes in a Network

Classification of entities based on the underlying network structure is an important problem. Networks encountered in practice are sparse and have many missing and noisy links. Statistical learning techniques have been used in intra-network classification; however, they typically exploit only the local neighborhood, so may not perform well. In this paper, we propose a novel structural neighborhood-based classifier learning using a random walk. For classifying a node, we take a random walk from the node and make a decision based on how nodes in the respective k^th-level neighborhood are labeled. We observe that random walks of short length are helpful in classification. Emphasizing role of longer random walks may cause the underlying Markov chain to converge to a stationary distribution. Considering this, we take a lazy random walk based approach with variable termination probability for each node, based on the node's structural properties including its degree. Our experimental study on real world datasets demonstrates the superiority of the proposed approach over the existing state-of-the-art approaches.
Youth Top Problems: using idiographic, consumer-guided assessment to identify treatment needs and to track change during psychotherapy.

To complement standardized measurement of symptoms, we developed and tested an efficient strategy for identifying (before treatment) and repeatedly assessing (during treatment) the problems identified as most important by caregivers and youths in psychotherapy.



A total of 178 outpatient-referred youths, 7-13 years of age, and their caregivers separately identified the 3 problems of greatest concern to them at pretreatment and then rated the severity of those problems weekly during treatment. The Top Problems measure thus formed was evaluated for (a) whether it added to the information obtained through empirically derived standardized measures (e.g., the Child Behavior Checklist [CBCL; Achenbach & Rescorla, 2001] and the Youth Self-Report [YSR; Achenbach & Rescorla, 2001]) and (b) whether it met conventional psychometric standards.



The problems identified were significant and clinically relevant; most matched CBCL/YSR items while adding specificity. The top problems also complemented the information yield of the CBCL/YSR; for example, for 41% of caregivers and 79% of youths, the identified top problems did not correspond to any items of any narrowband scales in the clinical range. Evidence on test-retest reliability, convergent and discriminant validity, sensitivity to change, slope reliability, and the association of Top Problems slopes with standardized measure slopes supported the psychometric strength of the measure.



The Top Problems measure appears to be a psychometrically sound, client-guided approach that complements empirically derived standardized assessment; the approach can help focus attention and treatment planning on the problems that youths and caregivers consider most important and can generate evidence on trajectories of change in those problems during treatment.
Activated carbon adsorption of PAHs from vegetable oil used in soil remediation.

Vegetable oil has been proven to be advantageous as a non-toxic, cost-effective and biodegradable solvent to extract polycyclic aromatic hydrocarbons (PAHs) from contaminated soils for remediation purposes. The resulting vegetable oil contained PAHs and therefore required a method for subsequent removal of extracted PAHs and reuse of the oil in remediation processes. In this paper, activated carbon adsorption of PAHs from vegetable oil used in soil remediation was assessed to ascertain PAH contaminated oil regeneration. Vegetable oils, originating from lab scale remediation, with different PAH concentrations were examined to study the adsorption of PAHs on activated carbon. Batch adsorption tests were performed by shaking oil-activated carbon mixtures in flasks. Equilibrium data were fitted with the Langmuir and Freundlich isothermal models. Studies were also carried out using columns packed with activated carbon. In addition, the effects of initial PAH concentration and activated carbon dosage on sorption capacities were investigated. Results clearly revealed the effectiveness of using activated carbon as an adsorbent to remove PAHs from the vegetable oil. Adsorption equilibrium of PAHs on activated carbon from the vegetable oil was successfully evaluated by the Langmuir and Freundlich isotherms. The initial PAH concentrations and carbon dosage affected adsorption significantly. The results indicate that the reuse of vegetable oil was feasible.
A Rare Case of Annular Pustular Psoriasis Associated with Pemphigus Foliaceus

260  2013, Revised April 11, 2013, Accepted for publication April 19, 2013 *These authors equally contributed as first authors.  Ken Igawa, Department of Dermatology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita-shi, Osaka, 565-0871,  E-mail: igawa@derma.med.osaka-u.ac.jp
Corn (Zea mays L.) growth, leaf pigment concentration, photosynthesis and leaf hyperspectral reflectance properties as affected by nitrogen supply

Plant nitrogen (N) deficiency often limits crop productivity. Early detection of plant N deficiency is important for improving fertilizer N-use efficiency and crop yield. An experiment was conducted in sunlit, controlled environment chambers in the 2001 growing season to determine responses of corn (Zea mays L. cv. 33A14) growth and leaf hyperspectral reflectance properties to varying N supply. Four N treatments were: (1) half-strength Hoagland's nutrient solution applied throughout the experiment (control); (2) 20% of control N starting 15 days after emergence (DAE); (3) 0% N starting 15 DAE; and (4) 0% N starting 23 DAE (0% NL). Plant height, the number of leaves, and leaf lengths were examined for nine plants per treatment every 3–4 days. Leaf hyperspectral reflectance, concentrations of chlorophyll a, chlorophyll b,and carotenoids, leaf and canopy photosynthesis, leaf area, and leaf N concentration were also determined during the experiment. The various N treatments led to a wide range of N concentrations (11 – 48 g kg−1 DW) in uppermost fully expanded leaves. Nitrogen deficiency suppressed plant growth rate and leaf photosynthesis. At final harvest (42 DAE), plant height, leaf area and shoot biomass were 64–66% of control values for the 20% N treatment, and 46-56% of control values for the 0% N treatment. Nitrogen deficit treatments of 20% N and 0% N (Treatment 3) could be distinguished by changes in leaf spectral reflectance in wavelengths of 552 and 710 nm 7 days after treatment. Leaf reflectance at these two wavebands was negatively correlated with either leaf N (r = –0.72 and –0.75**) or chlorophyll (r = –0.60 and –0.72**) concentrations. In addition, higher correlations were found between leaf N concentration and reflectance ratios. The identified N-specific spectral algorithms may be used for image interpretation and diagnosis of corn N status for site-specific N management.
Study and Simulation of SOGI PLL for Single Phase Grid Connected System

Algorithms for synchronization of grid are highly important in control of grid interfaced power converter. In order to execute stout control strategies for the interconnection of renewable energy systems with grid, rapid and precise detection of grid parameters such as amplitude, phase and frequency is critical. This paper propounds Phase Lock Loop technique for grid synchronization of single phase Distributed Generation System. Second Order Generalized Integrator (SOGI) PLL serves as Quadrature Signal Generator (QSG) which revolves around SOGI ,Parks Transform, Low pass filter (LF) and frequency phase shift generator (FPG). The QSG-SOGI and Parks Transform system is used for enhancing the performance of prevalent multiplier based phase detector (PD). Model of SOGIPLL in MATLAB/Simulink environment is shown here.
Irrigation management system with micro-controller application

This paper presents optimized irrigation scheme using automatic water control for performing crop specific irrigation activities. Level of irrigation depends on soil moisture content and type of crop considered. Different crops need different soil moisture for optimum crop yield. For optimum crop yield, smart irrigation, knowledge of soil condition, and water requirement of crop is essential. Optimal usage of water reduces overall power consumption and optimizes usage of water reserves. Smart usage of irrigation system optimizes land area productivity and conserves nature by reducing overall involved emission & losses in conversion components. Low cost microcontroller like Arduino based interfacing unit, has been used in this paper to obtain low cost smart irrigation solution.
Mobilizing Informational Social Capital in Cyber Space: Online Social Network Structural Properties and Knowledge Sharing

Online networks can be construed as social networks in which people engage in interactions, build relationships, share information, and request and extend assistance to each other using electronic communication technologies. Is social capital embedded in online networks? How is such social capital mobilized (i.e., shared)? What structural properties of cyber networks are associated with mobilization of social capital? These questions have drawn the attention of researchers in the areas of social networks, social capital, and online communication. Our research is an initial effort to touch upon these three questions. Whereas most previous research on both conventional and online social networks has favored analysis of either egocentric (i.e., individuals) or bounded (i.e., groups or organizations) networks as the primary unit of analysis, this study investigates online discussion forums that span formal boundaries of organizations, examining their structural properties and patterns of information exchange. We employ a network-based approach to the study of social capital, postulating that mobilization of social capital is contingent on social network properties. Using data from professional online forums devoted to knowledge management, we find that two network properties, core–periphery structure and centralization, are related to the mobilization of informational social capital in online networks. Implications and limitations of this study are discussed and suggestions for future research are provided.
Perceived social isolation and cognition

Social species, from Drosophila melanogaster to Homo sapiens, fare poorly when isolated. Homo sapiens, an irrepressibly meaning-making species, are, in normal circumstances, dramatically affected by perceived social isolation. Research indicates that perceived social isolation (i.e. loneliness) is a risk factor for, and may contribute to, poorer overall cognitive performance, faster cognitive decline, poorer executive functioning, increased negativity and depressive cognition, heightened sensitivity to social threats, a confirmatory bias in social cognition that is self-protective and paradoxically self-defeating, heightened anthropomorphism and contagion that threatens social cohesion. These differences in attention and cognition impact on emotions, decisions, behaviors and interpersonal interactions that can contribute to the association between loneliness and cognitive decline and between loneliness and morbidity more generally.
HYDROPONICS WORLDWIDE - A TECHNICAL OVERVIEW

Growing greenhouse vegetables is one of the most exacting and intense forms of all agricultural enterprises. In combination with greenhouses, hydroponics is becoming increasingly popular, especially in the United States, Canada, western Europe, and Japan. It is high technology and capital intensive. It is highly productive, conservative of water and land and protective of the environment. For production of leafy vegetables and herbs, deep flow hydroponics is common. For growing row crops such as tomato, cucumber, and pepper, the two most popular artificial growing media are rockwool and perlite. Computers today operate hundreds of devices within a greenhouse by utilizing dozens of input parameters, to maintain the most desired growing environment. The technology of greenhouse food production is changing rapidly with systems today producing yields never before realized. The future for hydroponic/soilless cultured systems appears more positive today than any time over the last 50 years.
Orthodontics. Part 6: Risks in orthodontic treatment

Orthodontics has the potential to cause significant damage to hard and soft tissues. The most important aspect of orthodontic care is to have an extremely high standard of oral hygiene before and during orthodontic treatment. It is also essential that any carious lesions are dealt with before any active treatment starts. Root resorption is a common complication during orthodontic treatment but there is some evidence that once appliances are removed this resorption stops. Some of the risk pointers for root resorption are summarised. Soft tissue damage includes that caused by archwires but also the more harrowing potential for headgears to cause damage to eyes. It is essential that adequate safety measures are included with this type of treatment.
Three-dimensional knee joint moments during performance of the bodyweight squat: effects of stance width and foot rotation.

The purpose of this investigation was to assess the effects of stance width and foot rotation angle on three-dimensional knee joint moments during bodyweight squat performance. Twenty-eight participants performed 8 repetitions in 4 conditions differing in stance or foot rotation positions. Knee joint moment waveforms were subjected to principal component analysis. Results indicated that increasing stance width resulted in a larger knee flexion moment magnitude, as well as larger and phase-shifted adduction moment waveforms. The knee's internal rotation moment magnitude was significantly reduced with external foot rotation only under the wide stance condition. Moreover, squat performance with a wide stance and externally rotated feet resulted in a flattening of the internal rotation moment waveform during the middle portion of the movement. However, it is speculated that the differences observed across conditions are not of clinical relevance for young, healthy participants.
Synthesis of mesoporous-silica-coated Gd2O3:Eu@silica particles as cell imaging and drug delivery agents.

Mesoporous-silica-coated Gd2O3:Eu/silica nanoparticles were synthesized by a multistep chemical process and characterized by XRD, TEM and N2 adsorption/desorption isotherms in terms of size, morphology and porosity. The core Gd2O3:Eu obtained by this method was highly luminescent upon excitation, giving the function of cell imaging upon incubation with the human cervical carcinoma (HeLa) cells. The outer porous silica shell is able to load the anticancer drug with a relatively high loading efficiency and release the loaded drugs at a sustained rate. The HeLa cells can be killed effectively on incubation with the core-shell porous particles loaded with the anticancer drug DOX. Meanwhile, the accumulation of mesoporous nanoparticles loaded with drugs in the target location could be monitored via fluorescence imaging. Therefore, the core-shell hybrid nanoparticles presented in this work are potential multifunctional biomaterials for smart detection or diagnosis and therapy in future biomedical engineering.
A Comparison of Multi-label Feature Selection Methods using the Problem Transformation Approach

Feature selection is an important task in machine learning, which can effectively reduce the dataset dimensionality by removing irrelevant and/or redundant features. Although a large body of research deals with feature selection in single-label data, in which measures have been proposed to filter out irrelevant features, this is not the case for multi-label data. This work proposes multi-label feature selection methods which use the filter approach. To this end, two standard multi-label feature selection approaches, which transform the multi-label data into single-label data, are used. Besides these two problem transformation approaches, we use ReliefF and Information Gain to measure the goodness of features. This gives rise to four multi-label feature selection methods. A thorough experimental evaluation of these methods was carried out on 10 benchmark datasets. Results show that ReliefF is able to select fewer features without diminishing the quality of the classifiers constructed using the features selected.
Decentralized configuration of embedded web services for smart home applications

Web Services can be used as a communication structure for embedded devices. Out of numerous Web Service specifications there is a subset (Profile) to implement Web Services on resource constrained devices, called Devices Profile for Web Services (DPWS). The resulting service oriented architecture enables the user to discover new devices, e.g., in the local smart home network. The open standards for Web Services further define a metadata format for service description. Besides the simple invocation of service operations, an eventing mechanism is specified. A client which subscribes to an event will receive notifications. Those features and the inherent Plug&Play capability provided by Web Services are suitable to connect smart home devices in a user-friendly manner. However, DPWS specifies no standard procedure to combine multiple devices with each other to build more complex applications. Therefore, new concepts for embedded Web Service orchestration are needed. Some commercial solutions use a central hub, which represents a Single Point of Failure (SPoF). Hence, a failure would lead to a breakdown of all smart Internet of Things (IoT) applications and decrease the user acceptance. We propose a concept where no central broker is needed by defining a Configuration Service that runs on multiple devices. Based on that service a smartphone app is used to establish trigger-action rules between devices. A prototype is implemented as a proof-of-concept for different smart home devices. Furthermore, a mobile Android application to find and orchestrate the devices is presented.