nils-herrmann/clean_scirepeval huggingface dataset:
Novel pharmacological approaches to the treatment of type 2 diabetes.

The huge increase in type 2 diabetes is a burden worldwide. Many marketed compounds do not address relevant aspects of the disease; they may already compensate for defects in insulin secretion and insulin action, but loss of secreting cells (β-cell destruction), hyperglucagonemia, gastric emptying, enzyme activation/inhibition in insulin-sensitive cells, substitution or antagonizing of physiological hormones and pathways, finally leading to secondary complications of diabetes, are not sufficiently addressed. In addition, side effects for established therapies such as hypoglycemias and weight gain have to be diminished. At present, nearly 1000 compounds have been described, and approximately 180 of these are going to be developed (already in clinical studies), some of them directly influencing enzyme activity, influencing pathophysiological pathways, and some using G-protein-coupled receptors. In addition, immunological approaches and antisense strategies are going to be developed. Many compounds are derived from physiological compounds (hormones) aiming at improving their kinetics and selectivity, and others are chemical compounds that were obtained by screening for a newly identified target in the physiological or pathophysiological machinery. In some areas, great progress is observed (e.g., incretin area); in others, no great progress is obvious (e.g., glucokinase activators), and other areas are not recommended for further research. For all scientific areas, conclusions with respect to their impact on diabetes are given. Potential targets for which no chemical compound has yet been identified as a ligand (agonist or antagonist) are also described.
Transelliptical Graphical Models

We advocate the use of a new distribution family—the transelliptical—for robust inference of high dimensional graphical models. The transelliptical family is an extension of the nonparanormal family proposed by Liu et al. (2009). Just as the nonparanormal extends the normal by transforming the variables using univariate functions, the transelliptical extends the elliptical family in the same way. We propose a nonparametric rank-based regularization estimator which achieves the parametric rates of convergence for both graph recovery and parameter estimation. Such a result suggests that the extra robustness and flexibility obtained by the semiparametric transelliptical modeling incurs almost no efficiency loss. We also discuss the relationship between this work with the transelliptical component analysis proposed by Han and Liu (2012).
An effective support vector machines (SVMs) performance using hierarchical clustering

The training time for SVMs to compute the maximal marginal hyper-plane is at least O(N/sup 2/) with the data set size N, which makes it nonfavorable for large data sets. This work presents a study for enhancing the training time of SVMs, specifically when dealing with large data sets, using hierarchical clustering analysis. We use the dynamically growing self-organizing tree (DGSOT) algorithm for clustering because it has proved to overcome the drawbacks of traditional hierarchical clustering algorithms. Clustering analysis helps find the boundary points, which are the most qualified data points to train SVMs, between two classes. We present a new approach of combination of SVMs and DGSOT, which starts with an initial training set and expands it gradually using the clustering structure produced by the DGSOT algorithm. We compare our approach with the Rocchio Bundling technique in terms of accuracy loss and training time gain using two benchmark real data sets.
Design of a gain-boosted telescopic fully differential amplifier with CMFB circuit

Based on Chartered 0.18μ 1.8V 1P5M CMOS process, a gain-boosted telescopic fully differential amplifier with CMFB circuit is designed and used in the sampling and holding circuit of a 14-bit, pipelined A/D converter which is supplied by 1.8 V power. In order to meet specifications, gain-boosting technique is used to improve the gain of OTA and common-mode feedback circuit is elaborately designed. Two-stages CMFB have a stable transient output of 710mV and 1V, respectively. At last, simulation results showed that 106dB loop gain, 1.13GHz unity-gain bandwidth and a phase margin of 71.75° have been achieved for the specifications of S/H circuit. The designed circuit has been applied to the front end of a 14-bit, 100MS/s pipelined ADC.
IRREDUCIBLE AND WEAKLY REDUCIBLE MINIMAL GENUS HEEGAARD SPLITTINGS

We construct infinitely many manifolds admitting both strongly irreducible and weakly reducible minimal genus Heegaard splittings. Both closed manifolds and manifolds with boundary tori are constructed. The pioneering work of Casson and Gordon  shows that a minimal genus Heegaard splitting of an irreducible, non-Haken 3-manifold is necessarily strongly irreducible; by contrast, Haken [2] showed that a minimal genus (indeed, any) Heegaard splitting of a composite 3manifold is necessarily reducible, and hence weakly reducible. The following question of Moriah  is therefore quite natural: Question 1 (, Question 1.2). Can a 3-manifold M have both weakly reducible and strongly irreducible minimal genus Heegaard splittings? We answer this question affirmatively: Theorem 2. There exist infinitely many closed, orientable 3-manifolds of Heegaard genus 3, each admitting both strongly irreducible and weakly reducible minimal genus Heegaard splittings. Theorem 2 is proved in Section 2. In Remark 7 we offer a strategy to generalize Theorem 2 to construct examples of genus g, for each g ≥ 3; it is easy to see that no such examples can exist if g < 3. In Section 3 we give examples of manifolds with one, two or three torus boundary components, each admitting both strongly irreducible and weakly reducible minimal genus Heegaard splittings. Moreover, Date: December 24, 2008.
Neural Biomarkers for Dyslexia, ADHD, and ADD in the Auditory Cortex of Children

Dyslexia, attention deficit hyperactivity disorder (ADHD), and attention deficit disorder (ADD) show distinct clinical profiles that may include auditory and language-related impairments. Currently, an objective brain-based diagnosis of these developmental disorders is still unavailable. We investigated the neuro-auditory systems of dyslexic, ADHD, ADD, and age-matched control children (N = 147) using neuroimaging, magnetencephalography and psychoacoustics. All disorder subgroups exhibited an oversized left planum temporale and an abnormal interhemispheric asynchrony (10-40 ms) of the primary auditory evoked P1-response. Considering right auditory cortex morphology, bilateral P1 source waveform shapes, and auditory performance, the three disorder subgroups could be reliably differentiated with outstanding accuracies of 89-98%. We therefore for the first time provide differential biomarkers for a brain-based diagnosis of dyslexia, ADHD, and ADD. The method allowed not only allowed for clear discrimination between two subtypes of attentional disorders (ADHD and ADD), a topic controversially discussed for decades in the scientific community, but also revealed the potential for objectively identifying comorbid cases. Noteworthy, in children playing a musical instrument, after three and a half years of training the observed interhemispheric asynchronies were reduced by about 2/3, thus suggesting a strong beneficial influence of music experience on brain development. These findings might have far-reaching implications for both research and practice and enable a profound understanding of the brain-related etiology, diagnosis, and musically based therapy of common auditory-related developmental disorders and learning disabilities.
Balancing Accuracy and Fun: Designing Camera Based Mobile Games for Implicit Heart Rate Monitoring

Heart rate monitoring is widely used in clinical care, fitness training, and stress management. However, tracking individuals' heart rates faces two major challenges, namely equipment availability and user motivation. In this paper, we present a novel technique, LivePulse Games (LPG), to measure users' heart rates in real time by having them play games on unmodified mobile phones. With LPG, the heart rate is calculated by detecting changes in transparency of users' fingertips via the built-in camera of a mobile device. More importantly, LPG integrate users' camera lens covering actions as an essential control mechanism in game play, and detect heart rates implicitly from intermittent lens covering actions. We explore the design space and trade-offs of LPG through three rounds of iterative design. In a 12-subject user study, we found that LPG are fun to play and can measure heart rates accurately. We also report the insights for balancing measurement speed, accuracy, and entertainment value in LPG.
Civic crowdfunding: how do offline communities engage online?

Civic crowdfunding is a sub-type of crowdfunding whereby citizens contribute to funding community-based projects ranging from physical structures to amenities. Though civic crowdfunding has great potential for impact, it remains a developing field in terms of project success and widespread adoption. To explore how technology shapes interactions and outcomes within civic projects, our research addresses two interrelated questions: how do offline communities engage online across civic crowdfunding projects, and, what purpose does this activity serve both projects and communities? These questions are explored through discussion of types of offline communities and description of online activity across civic crowdfunding projects. We conclude by considering the implications of this knowledge for civic crowdfunding and its continued research.
Combining local appearance and holistic view: Dual-Source Deep Neural Networks for human pose estimation

We propose a new learning-based method for estimating 2D human pose from a single image, using Dual-Source Deep Convolutional Neural Networks (DS-CNN). Recently, many methods have been developed to estimate human pose by using pose priors that are estimated from physiologically inspired graphical models or learned from a holistic perspective. In this paper, we propose to integrate both the local (body) part appearance and the holistic view of each local part for more accurate human pose estimation. Specifically, the proposed DS-CNN takes a set of image patches (category-independent object proposals for training and multi-scale sliding windows for testing) as the input and then learns the appearance of each local part by considering their holistic views in the full body. Using DS-CNN, we achieve both joint detection, which determines whether an image patch contains a body joint, and joint localization, which finds the exact location of the joint in the image patch. Finally, we develop an algorithm to combine these joint detection/localization results from all the image patches for estimating the human pose. The experimental results show the effectiveness of the proposed method by comparing to the state-of-the-art human-pose estimation methods based on pose priors that are estimated from physiologically inspired graphical models or learned from a holistic perspective.
nGraph-HE: A Graph Compiler for Deep Learning on Homomorphically Encrypted Data

Homomorphic encryption (HE)—the ability to perform computations on encrypted data—is an attractive remedy to increasing concerns about data privacy in the field of machine learning. However, building models that operate on ciphertext is currently labor-intensive and requires simultaneous expertise in deep learning, cryptography, and software engineering. Deep learning frameworks, together with recent advances in graph compilers, have greatly accelerated the training and deployment of deep learning models to a variety of computing platforms. Here, we introduce nGraph-HE, an extension of the nGraph deep learning compiler, which allows data scientists to deploy trained models with popular frameworks like TensorFlow, MXNet and PyTorch directly, while simply treating HE as another hardware target. This combination of frameworks and graph compilers greatly simplifies the development of privacy-preserving machine learning systems, provides a clean abstraction barrier between deep learning and HE, allows HE libraries to exploit HE-specific graph optimizations, and comes at a low cost in runtime overhead versus native HE operations.
A definition of the intima of human arteries and of its atherosclerosis-prone regions. A report from the Committee on Vascular Lesions of the Council on Arteriosclerosis, American Heart Association.

T his report is a concise review of current knowledge of the structure and function of the intima of the aorta and the major distributing arteries. The main purpose of the review is to delineate normal arterial intima from atherosclerotic lesions and, in particular, to distinguish physiological adaptations from atherosclerotic increases in intimal thickness. To characterize normal intima, including the adaptive intimal thickenings, some of which represent locations in which atherosclerotic lesions are prone to develop, the structure, composition, and functions of the arterial intima in young people as well as in laboratory animals not subjected to known atherogenic stimuli are reviewed. This report on arterial intima is the first in a series of four. The second report will review and define initial, fatty streak, and intermediate types of atherosclerotic lesions, and the third report will review all types of advanced (i.e., potentially clinical and clinical) lesions. The overall objective is to define arterial intima and all types of atherosclerotic lesions, and then to postulate, in a fourth and final report, a valid and up-to-date pathobiological nomenclature and classification of atherosclerotic lesions.
The Detector-Based Candela Scale and Related Photometric Calibration Procedures at NIST

The candela, one of the SI base units, has been realized by using absolutely calibrated detectors rather than sources. A group of eight photometers was constructed using silicon photodiodes, precision apertures, and glass filters for V (λ) match. Their absolute spectral responsivities were calibrated against the NIST absolute spectral responsivity scale. The measurement chain has been significantly shortened compared with the old scale based on a blackbody. This resulted in improving the calibration uncertainty to 0.46% (2σ), a factor-of-2 improvement. This revision has made various photometric calibrations at NIST more versatile and flexible. Luminous intensities of light sources ranging from 10-3 to 104 candelas are directly calibrated with the standard photometers, which have a linear response over that range. Illuminance meters are calibrated directly against the standard photometers. A luminance scale has also been realized on the detector base using an integrating sphere source. Total flux ranging from 10-2 to 105 lumens can be measured in a 2 m integrating sphere using a photometer with a wide dynamic range. The revisions of the calibration procedures significantly improved the calibration uncertainty.
Animal-assisted interventions for elderly patients affected by dementia or psychiatric disorders: a review.

The aim of this literature review was to assess the effects of Animal-Assisted Interventions (AAI) on elderly patients with dementia or various psychiatric disorders.



We conducted a comprehensive literature search using the online PubMed network of the US National Library of Medicine & National Institutes of Health, Embase, PsycINFO, with the purpose of investigating AAI effects on cognitive functions, mood, and behaviour.



A total of 18 articles on dementia and 5 on psychiatric disorders were included in the present review. AAI were found to have positive influences on demented patients by reducing degree of agitation and by improving degree and quality of social interaction. Few studies have assessed the effects of AAI on mood, and even fewer have assessed its consequences on cognitive functions. The results that are available indicate a positive effect on communication and coping ability, but none on cognitive performance. A substitute pet robot yielded encouraging results, but its use requires further investigation. The few studies conducted for elderly patients presenting a variety of psychiatric diagnoses produced controversial findings.



In spite of the encouraging results of AAI, much more research examining the issue of optimal AAI duration, frequency of sessions, and suitable target group is needed.
BLACKLISTED SPEAKER IDENTIFICATION USING TRIPLET NEURAL NETWORKS

We present a novel approach to the speaker recognition problem using Triplet Neural Networks. Currently, the three most popular methods includes Gaussian Mixture Model with Universal Background Model (GMM-UBM), Joint Factor Analysis (JFA), and i-vectors approach. Among them, the i-vectors approach is considered to be the state-of-the-art recognition method, as they often outperform both GMM-UBM and JFA. In this paper, we report that a Triplet Neural Network (TNN), an enhanced version of Siamese networks, in combination with different classifiers such as SVM, can outperform the state-of-the-art i-vector method. For this first Multi-target speaker detection and identification challenge at ICASP, our novel TNN approach reached an error rate (ERR) of 0.84% when combined with K-nearest-neighbor (KNN) (baseline model 2.00%) for task 1. For task 2 of this challenge, TNN with a support vector machine (SVM) is used to obtain a further improvement, improving from 444 confusion errors in the baseline model to only 359 when trying to identify the blacklisted speaker’s identity.
CryptoML: Secure outsourcing of big data machine learning applications

We present CryptoML, the first practical framework for provably secure and efficient delegation of a wide range of contemporary matrix-based machine learning (ML) applications on massive datasets. In CryptoML a delegating client with memory and computational resource constraints wishes to assign the storage and ML-related computations to the cloud servers, while preserving the privacy of its data. We first suggest the dominant components of delegation performance cost, and create a matrix sketching technique that aims at minimizing the cost by data pre-processing. We then propose a novel interactive delegation protocol based on the provably secure Shamir's secret sharing. The protocol is customized for our new sketching technique to maximize the client's resource efficiency. CryptoML shows a new trade-off between the efficiency of secure delegation and the accuracy of the ML task. Proof of concept evaluations corroborate applicability of CryptoML to datasets with billions of non-zero records.
Homeostatic Fault Tolerance in Spiking Neural Networks: A Dynamic Hardware Perspective

Fault tolerance is a remarkable feature of biological systems and their self-repair capability influence modern electronic systems. In this paper, we propose a novel plastic neural network model, which establishes homeostasis in a spiking neural network. Combined with this plasticity and the inspiration from inhibitory interneurons, we develop a fault-resilient robotic controller implemented on an FPGA establishing obstacle avoidance task. We demonstrate the proposed methodology on a spiking neural network implemented on Xilinx Artix-7 FPGA. The system is able to maintain stable firing (tolerance ±10%) with a loss of up to 75% of the original synaptic inputs to a neuron. Our repair mechanism has minimal hardware overhead with a tuning circuit (repair unit) which consumes only three slices/neuron for implementing a threshold voltage-based homeostatic fault-tolerant unit. The overall architecture has a minimal impact on power consumption and, therefore, supports scalable implementations. This paper opens a novel way of implementing the behavior of natural fault tolerant system in hardware establishing homeostatic self-repair behavior.
SurfelWarp: Efficient Non-Volumetric Single View Dynamic Reconstruction

We contribute a dense SLAM system that takes a live stream of depth images as input and reconstructs nonrigid deforming scenes in real time, without templates or prior models. In contrast to existing approaches, we do not maintain any volumetric data structures, such as truncated signed distance function (TSDF) fields or deformation fields, which are performance and memory intensive. Our system works with a flat point (surfel) based representation of geometry, which can be directly acquired from commodity depth sensors. Standard graphics pipelines and general purpose GPU (GPGPU) computing are leveraged for all central operations: i.e., nearest neighbor maintenance, non-rigid deformation field estimation and fusion of depth measurements. Our pipeline inherently avoids expensive volumetric operations such as marching cubes, volumetric fusion and dense deformation field update, leading to significantly improved performance. Furthermore, the explicit and flexible surfel based geometry representation enables efficient tackling of topology changes and tracking failures, which makes our reconstructions consistent with updated depth observations. Our system allows robots maintain a scene description with nonrigidly deformed objects that potentially enables interactions with dynamic working environments.
Wireless Sensor Network Optimization: Multi-Objective Paradigm

Optimization problems relating to wireless sensor network planning, design, deployment and operation often give rise to multi-objective optimization formulations where multiple desirable objectives compete with each other and the decision maker has to select one of the tradeoff solutions. These multiple objectives may or may not conflict with each other. Keeping in view the nature of the application, the sensing scenario and input/output of the problem, the type of optimization problem changes. To address different nature of optimization problems relating to wireless sensor network design, deployment, operation, planing and placement, there exist a plethora of optimization solution types. We review and analyze different desirable objectives to show whether they conflict with each other, support each other or they are design dependent. We also present a generic multi-objective optimization problem relating to wireless sensor network which consists of input variables, required output, objectives and constraints. A list of constraints is also presented to give an overview of different constraints which are considered while formulating the optimization problems in wireless sensor networks. Keeping in view the multi facet coverage of this article relating to multi-objective optimization, this will open up new avenues of research in the area of multi-objective optimization relating to wireless sensor networks.
From A as in Aliasing to Z as in Zipper: Artifacts in MRI

A variety of artifacts may be encountered in MR images. They can be divided up into four major categories: (1) hardware-related artifacts, (2) sequence-related artifacts, (3) patient-related artifacts, and (4) artifacts of special MR techniques. This article discusses these types of artifacts, their derivations, their effects upon the MR image, and possible solutions to minimize them. In MR-Bildern kann eine Vielzahl von Artefakten auftreten. Sie können vier großen Kategorien zugeordnet werden: 1. Hardware-bedingte Artefakte, 2. Sequenz-bedingte Artefakte, 3. von Patienten verursachte Artefakte und 4. Artefakte spezieller MR-Techniken. In diesem Artikel werden die oben genannten Haupttypen von Artefakten diskutiert. Dabei werden jeweils ihre Ursache und ihre Auswirkungen auf das MR-Bild erörtert und Wege aufgezeigt, wie man sie vermeiden oder zumindest minimieren kann.