nils-herrmann/clean_scirepeval huggingface dataset:
Deep Learning for End-to-End Automatic Target Recognition from Synthetic Aperture Radar Imagery

The standard architecture of synthetic aperture radar (SAR) automatic target recognition (ATR) consists of three stages: detection, discrimination, and classification. In recent years, convolutional neural networks (CNNs) for SAR ATR have been proposed, but most of them classify target classes from a target chip extracted from SAR imagery, as a classification for the third stage of SAR ATR. In this report, we propose a novel CNN for end-to-end ATR from SAR imagery. The CNN named verification support network (VersNet) performs all three stages of SAR ATR end-to-end. VersNet inputs a SAR image of arbitrary sizes with multiple classes and multiple targets, and outputs a SAR ATR image representing the position, class, and pose of each detected target. This report describes the evaluation results of VersNet which trained to output scores of all 12 classes: 10 target classes, a target front class, and a background class, for each pixel using the moving and stationary target acquisition and recognition (MSTAR) public dataset.
Accomplishments and challenges of computer stereo vision

Segmentation and grouping of image elements is required to proceed with image recognition. Due to the fact that the images are two dimensional (2D) representations of the real three dimensional (3D) scenes, the information of the third dimension, like geometrical relations between the objects that are important for reasonable segmentation and grouping, are lost in 2D image representations. Computer stereo vision implies on understanding information stored in 3D-scene. Techniques for stereo computation are observed in this paper. The methods for solving the correspondence problem in stereo image matching are presented. The process of 3D-scene reconstruction from stereo image pairs and extraction of parameters important for image understanding are described. Occluded and surrounding areas in stereo image pairs are stressed out as important for image understanding.
Autophagy in the Pathogenesis of Disease

Autophagy is a lysosomal degradation pathway that is essential for survival, differentiation, development, and homeostasis. Autophagy principally serves an adaptive role to protect organisms against diverse pathologies, including infections, cancer, neurodegeneration, aging, and heart disease. However, in certain experimental disease settings, the self-cannibalistic or, paradoxically, even the prosurvival functions of autophagy may be deleterious. This Review summarizes recent advances in understanding the physiological functions of autophagy and its possible roles in the causation and prevention of human diseases.
RT-Xen: Towards real-time hypervisor scheduling in Xen

As system integration becomes an increasingly important challenge for complex real-time systems, there has been a significant demand for supporting real-time systems in virtualized environments. This paper presents RT-Xen, the first real-time hypervisor scheduling framework for Xen, the most widely used open-source virtual machine monitor (VMM). RT-Xen bridges the gap between real-time scheduling theory and Xen, whose wide-spread adoption makes it an attractive platform for integrating a broad range of real-time and embedded systems. Moreover, RT-Xen provides an open-source platform for researchers and integrators to develop and evaluate real-time scheduling techniques, which to date have been studied predominantly via analysis and simulations.
 Extensive experimental results demonstrate the feasibility, efficiency, and efficacy of fixed-priority hierarchical real-time scheduling in RT-Xen. RT-Xen instantiates a suite of fixed-priority servers (Deferrable Server, Periodic Server, Polling Server, and Sporadic Server). While the server algorithms are not new, this empirical study represents the first comprehensive experimental comparison of these algorithms within the same virtualization platform. Our empirical evaluation shows that RT-Xen can provide effective real-time scheduling to guest Linux operating systems at a 1ms quantum, while incurring only moderate overhead for all the fixed-priority server algorithms. While more complex algorithms such as Sporadic Server do incur higher overhead, none of the overhead differences among different server algorithms are significant. Deferrable Server generally delivers better soft real-time performance than the other server algorithms, while Periodic Server incurs high deadline miss ratios in overloaded situations.
ON THE USE OF ZERO-CROSSING RATE FOR AN APPLICATION OF CLASSIFICATION OF PERCUSSIVE SOUNDS

We address the issue of automatically extracting rhythm descriptors from audio signals, to be eventually used in content-based musical applications such as in the context of MPEG7. Our aim is to approach the comprehension of auditory scenes in raw polyphonic audio signals without preliminary source separation. As a first step towards the automatic extraction of rhythmic structures out of signals taken from the popular music repertoire, we propose an approach for automatically extracting time indexes of occurrences of different percussive timbres in an audio signal. Within this framework, we found that a particular issue lies in the classification of percussive sounds. In this paper, we report on the method currently used to deal with this problem.
The DSM-5 Self-Rated Level 1 Cross-Cutting Symptom Measure as a Screening Tool

The DSM-5 Self-Rated Level 1 Cross-Cutting Symptom Measure was developed to aid clinicians with a dimensional assessment of psychopathology; however, this measure resembles a screening tool for several symptomatic domains. The objective of the current study was to examine the basic parameters of sensitivity, specificity, positive and negative predictive power of the measure as a screening tool. One hundred and fifty patients in a correctional community center filled out the measure prior to a psychiatric evaluation, including the Mini International Neuropsychiatric Interview screen. The above parameters were calculated for the domains of depression, mania, anxiety, and psychosis. The results showed that the sensitivity and positive predictive power of the studied domains was poor because of a high rate of false positive answers on the measure. However, when the lowest threshold on the Cross-Cutting Symptom Measure was used, the sensitivity of the anxiety and psychosis domains and the negative predictive values for mania, anxiety and psychosis were good. In conclusion, while it is foreseeable that some clinicians may use the DSM-5 Self-Rated Level 1 Cross-Cutting Symptom Measure as a screening tool, it should not be relied on to identify positive findings. It functioned well in the negative prediction of mania, anxiety and psychosis symptoms.
A survey of Context-aware Recommender System and services

The advancement of the Internet, mobile and wireless technologies have produced a rapid change of information in terms of volume and accessibility. The enormous volume of information can be devastating especially to mobile users exceeding human ability to differentiate information which is relevant and that which is irrelevant. For many yzears now, recommender system have become well-known, and have been studied in various domains such as e-learning, online shopping, tourism to help overcome information overload. Recommendations are produced based on users who have interests in a particular thing or item. This recommendation process was further enhanced by incorporating context such as time, weather, and location to make recommendations more accurate and efficient. However, these systems have introduced context-aware recommender systems. This paper presents a survey of Context-aware recommender systems, the background and algorithms of Context-aware Recommender System, and also discusses the open issues of context-aware recommender systems.
A split-capacitor vcm-based capacitor-switching scheme for low-power SAR ADCs

A split-capacitor Vcm-based capacitor-switching scheme is proposed for successive approximation register (SAR) analog-to-digital converters (ADCs) to reduce the capacitor-switching energy. By rearranging the structure and procedure of the capacitive array, the scheme can save the capacitor-switching energy by about 92% than the conventional scheme with better monotonicity. Meanwhile, a two-segment DC offset correction scheme for the comparator is also proposed to meet the speed and accuracy requirements. These techniques are utilized in the design of a 10b 70MS/s SAR ADC in 65nm 1P9M CMOS technology. Measurement results show a peak signal-to-noise-and-distortion ratio (SNDR) of 53.2dB, while consuming 960μW from 1.2V supply. The figure of merit (FoM) is 36.8fJ/Conversion-step and the total active area is 220×220μm2.
Modeling avalanche breakdown for ESD diodes in integrated circuits

Usually device compact models do not include breakdown mechanisms which are fundamental for ESD protection devices. This work proposes a novel spice-compatible modeling of breakdown phenomena for ESD diodes. The developed physics based approach includes minority carriers propagation and can be embedded in the simulation of parasitic substrate noise of power devices. The model implemented in VerilogA has been validated with device simulations for a simple structure at different temperatures showing good agreement and robust convergence.
Adaptive Multiprecision Path Tracking

This article treats numerical methods for tracking an implicitly defined path. The numerical precision required to successfully track such a path is difficult to predict a priori, and indeed, it may change dramatically through the course of the path. In current practice, one must either choose a conservatively large numerical precision at the outset or re-run paths multiple times in successively higher precision until success is achieved. To avoid unnecessary computational cost, it would be preferable to adaptively adjust the precision as the tracking proceeds in response to the local conditioning of the path. We present an algorithm that can be set to either reactively adjust precision in response to step failure or proactively set the precision using error estimates. We then test the relative merits of reactive and proactive adaptation on several examples arising as homotopies for solving systems of polynomial equations.
Reinforcement Learning Dynamics in Social Dilemmas

In this paper we replicate and advance Macy and Flache’s (2002; ) work on the dynamics of reinforcement learning in 2×2 (2-player 2-strategy) social dilemmas. In particular, we formalise the solution concepts they describe, provide analytical results on the dynamics of their model for any 2×2 game, and discuss the robustness of their results to occasional mistakes made by players in choosing their actions (i.e. trembling hands). It is shown here that the dynamics of their model are strongly dependent on the speed at which players learn. With high learning rates the system quickly reaches its asymptotic behaviour; on the other hand, when learning rates are low, two distinctively different dynamic regimes can be clearly observed before the system settles down forever. Similarly, it is shown that the inclusion of small quantities of randomness in the players’ learning algorithm can change the dynamics of the model dramatically.
Fruit juice, organic anion transporting polypeptides, and drug interactions in psychiatry.

Organic anion transporting polypeptides (OATPs) are a group of membrane transport proteins that facilitate the influx of endogenous and exogenous substances across biological membranes. OATPs are found in enterocytes and hepatocytes and in brain, kidney, and other tissues. In enterocytes, OATPs facilitate the gastrointestinal absorption of certain orally administered drugs. Fruit juices such as grapefruit juice, orange juice, and apple juice contain substances that are OATP inhibitors. These fruit juices diminish the gastrointestinal absorption of certain antiallergen, antibiotic, antihypertensive, and β-blocker drugs. While there is no evidence, so far, that OATP inhibition affects the absorption of psychotropic medications, there is no room for complacency because the field is still nascent and because the necessary studies have not been conducted. Patients should therefore err on the side of caution, taking their medications at least 4 hours distant from fruit juice intake. Doing so is especially desirable with grapefruit juice, orange juice, and apple juice; with commercial fruit juices in which OATP-inhibiting substances are likely to be present in higher concentrations; with calcium-fortified fruit juices; and with medications such as atenolol and fexofenadine, the absorption of which is substantially diminished by concurrent fruit juice intake.
Robust hair capture using simulated examples

We introduce a data-driven hair capture framework based on example strands generated through hair simulation. Our method can robustly reconstruct faithful 3D hair models from unprocessed input point clouds with large amounts of outliers. Current state-of-the-art techniques use geometrically-inspired heuristics to derive global hair strand structures, which can yield implausible hair strands for hairstyles involving large occlusions, multiple layers, or wisps of varying lengths. We address this problem using a voting-based fitting algorithm to discover structurally plausible configurations among the locally grown hair segments from a database of simulated examples. To generate these examples, we exhaustively sample the simulation configurations within the feasible parameter space constrained by the current input hairstyle. The number of necessary simulations can be further reduced by leveraging symmetry and constrained initial conditions. The final hairstyle can then be structurally represented by a limited number of examples. To handle constrained hairstyles such as a ponytail of which realistic simulations are more difficult, we allow the user to sketch a few strokes to generate strand examples through an intuitive interface. Our approach focuses on robustness and generality. Since our method is structurally plausible by construction, we ensure an improved control during hair digitization and avoid implausible hair synthesis for a wide range of hairstyles.
Landsat-based Remote Sensing of Lake Water Quality Characteristics, Including Chlorophyll and Colored Dissolved Organic Matter (CDOM)

Brezonik, P., K.D. Menken and M. Bauer. 2005. Landsat-based remote sensing of lake water quality characteristics, including chlorophyll and colored dissolved organic matter (CDOM). Lake and Reserv. Manage. 21(4):373-382. Ground-based measurements on 15 Minnesota lakes with wide ranges of optical properties and Landsat TM data from the same lakes were used to evaluate the effect of humic color on satellite-inferred water quality conditions. Color (C440), as measured by absorbance at 440 nm, causes only small biases in estimates of Secchi disk transparency (SDT) from Landsat TM data, except at very high values (> ~ 300 chloroplatinate units, CPU). Similarly, when chlorophyll a (chl a) levels are moderate or high (> 10 μg/L), low-to-moderate levels of humic color have only a small influence on the relationship between SDT and chl a concentration, but it has a pronounced influence at high levels of C440 (e.g., > ~200 CPU). However, deviations from the general chl a-SDT relationship occur at much lower C440 values (~ 60 CPU) when chl a levels are low. Good statistical relationships were found between optical properties of lake water generally associated with algal abundance (SDT, chl a, turbidity) and measured brightness of various Landsat TM bands. The best relationships for chl a (based on R2 and absence of statistical outliers or lakes with large leverage) were combinations of bands 1, 2, or 4 with the band ratio 1:3 (R2 = 0.88). Although TM bands 1-4 individually or as simple ratios were poor predictors of C440, multiple regression analyses between ln(C440) and combinations of bands 1-4 and band ratios yielded several relationships with R2 ≥ 0.70, suggesting that C440 can be estimated with fair reliability from Landsat TM data.
Determination of the effect of probiotic (Saccharomyces cerevisiae) on growth performance and hematological parameters of rabbits

Insufficient supply of animal protein is a major problem in developing countries including Nigeria. Rabbits are adjudged to be a convenient source of palatable and nutritious meat, high in protein, and contain low fat and cholesterol. A doe can produce more than 15 times her own weight in offspring in a year. However, its productivity may be limited by inadequate nutrition. The objective of this study was to determine the effect of probiotic (Saccharomyces cerevisiae) supplementation on growth performance and some hematological parameters of rabbit. The appropriate level of the probiotic inclusion for excellent health status and optimum productivity was also determined. A total of 40 male rabbits were randomly divided into four groups (A–D) of ten rabbits each. Each group was subdivided into two replicates of five rabbits each. They were fed pelleted grower mash ad libitum. The feed for groups A to C were supplemented with bioactive yeast (probiotic) at inclusion levels of 0.08, 0.12, and 0.16 g yeast/kg diet, respectively. Group D had no yeast (control). Daily feed intake was determined. The rabbits were weighed weekly. The packed cell volume (PCV), hemoglobin concentration, white blood cell total, and differential counts were determined at the 8th week, 16th week, and 22nd week following standard procedures. The three results which did not have any significant difference were pooled together. Group A which had 0.08 g yeast/kg of diet had a significantly lower (P ≤ 0.05) PCV than groups B (which had 0.12 g yeast/kg of diet) and C (which had 0.16 g yeast/kg of diet) as well as D (the control). Total WBC count for groups B and C (14.35 ± 0.100 × 103/μl and 14.65 ± 0.786 × 103/μl, respectively) were significantly higher (P ≤ 0.05) than groups A and D (6.33 ± 0.335 × 103/μl and 10.40 ± 0.296 × 103/μl, respectively). Also the absolute neutrophils and lymphocytes counts were significantly higher (P ≤ 0.05) in groups B and C than in groups A and D. Group B had significantly higher (P ≤ 0.05) weight gain (1.025 ± 0.006 kg/rabbit) followed by group A (0.950 ± 0.092 kg/rabbit). The control (group D) had the least weight gain of 0.623 ± 0.0.099 kg/rabbit. These results showed that like most probiotics, bioactive yeast at an appropriate level of inclusion had a significant beneficial effect on health status and growth rate of rabbit. Probiotic supplementation level of 0.12 g yeast/kg of diet was recommended for optimum rabbit production.
The genus Leptoplana (Leptoplanidae, Polycladida) in the Mediterranean basin. Redescription of the species Leptoplana mediterranea (Bock, 1913) comb. nov

A revision of the genus Leptoplana Ehrenberg, 1831 in the Mediterranean basin is undertaken. This revision deals with the distribution and validity of the species of Leptoplana known for the area. The Mediterranean sub-species polyclad, Leptoplana tremellaris forma mediterranea Bock, 1913 is elevated to the specific level. Leptoplana mediterranea comb. nov. is redescribed from the Lake of Tunis, Tunisia. This flatworm is distinguished from Leptoplana tremellaris mainly by having a prostatic vesicle provided with a long diverticulum attached ventrally to the seminal vesicle, a genital pit closer to the male pore than to the female one and a twelve-eyed hatching juvenile instead of the four-eyed juvenile of L. tremellaris. The direct development in L. mediterranea is described at 15 °C.
Three-Port Series-Resonant DC–DC Converter to Interface Renewable Energy Sources With Bidirectional Load and Energy Storage Ports

In this paper, a three-port converter with three active full bridges, two series-resonant tanks, and a three-winding transformer is proposed. It uses a single power conversion stage with high-frequency link to control power flow between batteries, load, and a renewable source such as fuel cell. The converter has capabilities of bidirectional power flow in the battery and the load port. Use of series-resonance aids in high switching frequency operation with realizable component values when compared to existing three-port converter with only inductors. The converter has high efficiency due to soft-switching operation in all three bridges. Steady-state analysis of the converter is presented to determine the power flow equations, tank currents, and soft-switching region. Dynamic analysis is performed to design a closed-loop controller that will regulate the load-side port voltage and source-side port current. Design procedure for the three-port converter is explained and experimental results of a laboratory prototype are presented.
The End of the “ Modern Corporation ” : Deregulation and Ownership of Electric Utilities

Berle and Means (1932) claimed that “modern” corporations in the United States had diffuse ownership. Their claim characterizes regulated electric utilities until the mid1990s. But following the 1992 EPACT deregulation, block ownership in utilities increased sharply, relative to a matched sample of non-utilities, which already had large blocks by the 90s. The post-EPACT blocks in utilities were not long-term investments by monitors but short duration speculative investments. With the financial crisis of 2008, large active investors are replaced by asset managers. In sum, dispersed ownership arose through the regulatory protections introduced in the 1930s and disappeared following their repeal. 1 We owe special thanks to Nicholas Beauchamp, Katrina Evtimova, Daniel Kangmin Ko, Umberto Mignozzetti, Jin Nie, Peter Ryan, Becci Weiss, and Wei Xiong for superb research assistance. We are also grateful to Jas Sekhon and John Henderson for advice on matching. We thank Colin Mayer and Wei Jiang for their suggestions at early stages of our project. We are grateful to the following research centers at Columbia University for their financial support: the Center for Global Economic Governance, the Chazen Institute, the Institute for Social and Economic Research and Policy, and the Richman Center.
Convolutional Recurrent Neural Networks for Glucose Prediction

Glucose control serves as the primary method of diabetes management. Current digital therapeutic approaches for subjects with Type 1 diabetes mellitus (T1DM) such as the artificial pancreas and bolus calculators leverage machine learning techniques for predicting subcutaneous glucose for improved control. Deep learning has recently been applied in healthcare and medical research to achieve state-of-the-art results in a range of tasks including disease diagnosis, and patient state prediction among others. In this work, we present a deep learning model that is capable of predicting glucose levels over a 30-minute horizon with leading accuracy for simulated patient cases (RMSE = 10.02±1.28 [mg/dl] and MARD = 5.95±0.64%) and real patient cases (RMSE = 21.23±1.15 [mg/dl] and MARD = 10.53±1.28%). In addition, the model also provides competitive performance in forecasting adverse glycaemic events with minimal time lag both in a simulated patient dataset (MCChyperglycaemia = 0.82±0.06 and MCChypoglycaemia = 0.76±0.13) and in a real patient dataset (MCChyperglycaemia = 0.79±0.04 and MCChypoglycaemia = 0.28±0.11). This approach is evaluated on a dataset of 10 simulated cases generated from the UVa/Padova simulator and a clinical dataset of 5 real cases each containing glucose readings, insulin bolus, and meal (carbohydrate) data. Performance of the recurrent convolutional neural network is benchmarked against four state-of-the-art algorithms: support vector regression (SVR), latent variable (LVX) model, autoregressive model (ARX), and neural network for predicting glucose algorithm (NNPG).