nils-herrmann/clean_scirepeval huggingface dataset:
Transfer characteristics and high frequency modeling of logic gates using carbon nanotube field effect transistors (CNT-FETs)

In the present work, current model equations from an analytical model for carbon nanotube field effect transistors are utilized to calculate the transfer characteristics of different logic gates such as inverters, NAND gates, and NOR gates. A small signal model has been implemented and the necessary parameters have been calculated to describe the high frequency response for these logic gates. Results show that how different diameters and wrapping angles of carbon nanotubes, determined by the chiral vector (n,m), can change the transfer characteristics, small signal parameters, and high frequency response for devices based on carbon nanotubes.
Deep into the roots of the Libyan Tuareg: a genetic survey of their paternal heritage.

Recent genetic studies of the Tuareg have begun to uncover the origin of this semi-nomadic northwest African people and their relationship with African populations. For centuries they were caravan traders plying the trade routes between the Mediterranean coast and south-Saharan Africa. Their origin most likely coincides with the fall of the Garamantes who inhabited the Fezzan (Libya) between the 1st millennium BC and the 5th century AD. In this study we report novel data on the Y-chromosome variation in the Libyan Tuareg from Al Awaynat and Tahala, two villages in Fezzan, whose maternal genetic pool was previously characterized. High-resolution investigation of 37 Y-chromosome STR loci and analysis of 35 bi-allelic markers in 47 individuals revealed a predominant northwest African component (E-M81, haplogroup E1b1b1b) which likely originated in the second half of the Holocene in the same ancestral population that contributed to the maternal pool of the Libyan Tuareg. A significant paternal contribution from south-Saharan Africa (E-U175, haplogroup E1b1a8) was also detected, which may likely be due to recent secondary introduction, possibly through slavery practices or fusion between different tribal groups. The difference in haplogroup composition between the villages of Al Awaynat and Tahala suggests that founder effects and drift played a significant role in shaping the genetic pool of the Libyan Tuareg.
Quantitative footprinting analysis of the chromomycin A3--DNA interaction.

Chromomycin A3 (CHR) binding to the duplex d(CAAGTCTGGCCATCAGTC).d(GACTGATGGCCAGACTTG) has been studied using quantitative footprinting methods. Previous NMR studies indicated CHR binds as a dimer in the minor groove. Analysis of autoradiographic spot intensities derived from DNase I cleavage of the 18-mer in the presence of various amounts of CHR revealed that the drug binds as a dimer to the sequence 5'-TGGCCA-3',3'-ACCGGT-5' in the 18-mer with a binding constant of (2.7 +/- 1.4) x 10(7) M-1. Footprinting and fluorescence data indicate that the dimerization constant for the drug in solution is approximately 10(5) M-1. Since it has been suggested that CHR binding alters DNA to the A configuration, quantitative footprinting studies using dimethyl sulfate, which alkylates at N-7 of guanine in the major groove, were also carried out. Apparently, any drug-induced alteration in DNA structure does not affect cleavage by DMS enough to be observed by these experiments.
Trend-following Hedge Funds and Multi-period Asset Allocation

Selected hedge funds employ trend-following strategies in an attempt to achieve superior risk adjusted returns. We employ a lookback straddle approach for evaluating the return characteristics of a trend following strategy. The strategies can improve investor performance in the context of a multi-period dynamic portfolio model. The gains are achieved by taking advantage of the funds’ high level of volatility. A set of empirical results confirms the advantages of the lookback straddle for investors at the top end of the multi-period efficient frontier.
Community detection in networks: Structural communities versus ground truth

Algorithms to find communities in networks rely just on structural information and search for cohesive subsets of nodes. On the other hand, most scholars implicitly or explicitly assume that structural communities represent groups of nodes with similar (nontopological) properties or functions. This hypothesis could not be verified, so far, because of the lack of network datasets with information on the classification of the nodes. We show that traditional community detection methods fail to find the metadata groups in many large networks. Our results show that there is a marked separation between structural communities and metadata groups, in line with recent findings. That means that either our current modeling of community structure has to be substantially modified, or that metadata groups may not be recoverable from topology alone.
Comparison of 68Ga-DOTATATE and 68Ga-DOTANOC PET/CT imaging in the same patient group with neuroendocrine tumours

Recent studies have suggested that positron emission tomography (PET) imaging with 68Ga-labelled DOTA-somatostatin analogues (SST) like octreotide and octreotate is useful in diagnosing neuroendocrine tumours (NETs) and has superior value over both CT and planar and single photon emission computed tomography (SPECT) somatostatin receptor scintigraphy (SRS). The aim of the present study was to evaluate the role of 68Ga-DOTA-1-NaI3-octreotide (68Ga-DOTANOC) in patients with SST receptor-expressing tumours and to compare the results of 68Ga-DOTA-D-Phe1-Tyr3-octreotate (68Ga-DOTATATE) in the same patient population. Twenty SRS were included in the study. Patients’ age (n = 20) ranged from 25 to 75 years (mean 55.4 ± 12.7 years). There were eight patients with well-differentiated neuroendocrine tumour (WDNET) grade1, eight patients with WDNET grade 2, one patient with poorly differentiated neuroendocrine carcinoma (PDNEC) grade 3 and one patient with mixed adenoneuroendocrine tumour (MANEC). All patients had two consecutive PET studies with 68Ga-DOTATATE and 68Ga-DOTANOC. All images were evaluated visually and maximum standardized uptake values (SUVmax) were also calculated for quantitative evaluation. On visual evaluation both tracers produced equally excellent image quality and similar body distribution. The physiological uptake sites of pituitary and salivary glands showed higher uptake in 68Ga-DOTATATE images. Liver and spleen uptake values were evaluated as equal. Both 68Ga-DOTATATE and 68Ga-DOTANOC were negative in 6 (30 %) patients and positive in 14 (70 %) patients. In 68Ga-DOTANOC images only 116 of 130 (89 %) lesions could be defined and 14 lesions were missed because of lack of any uptake. SUVmax values of lesions were significantly higher on 68Ga-DOTATATE images. Our study demonstrated that the images obtained by 68Ga-DOTATATE and 68Ga-DOTANOC have comparable diagnostic accuracy. However, 68Ga-DOTATATE seems to have a higher lesion uptake and may have a potential advantage.
Hilbert-Huang Transform (HHT) Analysis of Human Activities Using Through-Wall Noise Radar

Various parts of the human body have different movements when a person is performing different physical activities. There is a need to remotely detect human heartbeat and breathing for applications involving anti-terrorism and search-and-rescue. Ultrawideband noise radar systems are attractive because they are covert and immune from interference. The conventional time-frequency analyses of human activity are not generally applicable to nonlinear and nonstationary signals. If one can decompose the noisy baseband reflected signal and extract only the human-induced Doppler from it, the identification of various human activities becomes easier. We propose a nonstationary model to describe human motion and apply the Hilbert-Huang transform (HHT), which is adaptive to nonlinear and nonstationary signals, in order to analyze frequency characteristics of the baseband signal. When used with noise-like radar data, it is useful covertly identify specific human movement.
Design of a 32 element rotman lens at 220 GHz with 20 GHz bandwidth

Applications in radar systems and communications systems require very often antennas with beam steering or multi beam capabilities. For the millimeter frequency range Rotman lenses can be useful as multiple beam forming networks for linear antennas providing the advantage of broadband performance. The design and development of Rotman lens at 220 GHz feeding an antenna array for beam steering applications is presented. The construction is completely realized in waveguide technology. Experimental results are compared with theoretical considerations and electromagnetic simulations.
Digital radar implementation with amplitude predistortion

The modern advancements in digital electronics allow waveforms to be easily synthesized and captured using only digital electronics. The synthesis of radar waveforms using only digital electronics, such as Digital-to-Analog Converters (DACs) and Analog-to-Digital Converters (ADCs) allows for a majority of the analog chain to be removed from the system. In order to create a constant amplitude waveform, the amplitude distortions must be compensated for. The method chosen to compensate for the amplitude distortions is to pre-distort the waveform so, when it is influenced by the system, the output waveform has a near constant amplitude modulus. The effects of the predistortion were observed to be successful in both range and range-Doppler radar implementations.
Failure of bromocriptine therapy to control juvenile mammary hypertrophy.

Rapid massive breast hypertrophy occasionally occurs at the time of puberty or during pregnancy, with breast size eventually becoming burdensome or incapacitating to the patient. Pregnancy-related breast hypertrophy is often arrested or reversed by reducing serum prolactin levels with bromocriptine therapy. Unfortunately, breast enlargement in our 12-year-old patient with massive juvenile mammary hypertrophy was unaffected by bromocriptine therapy despite a reduction of her prolactin to normal levels. Two reduction mammaplasties followed by subcutaneous mastectomy were required to control breast hypertrophy. Breast-tissue hypersensitivity to prolactin appears to be a characteristic of pregnancy-related gigantomastia. Our pubertal patient with juvenile mammary hypertrophy failed to respond to bromocriptine therapy, so the aetiology of this syndrome may involve breast-tissue hypersensitivity to hormones other than prolactin.
Regulation of mitochondrial dynamics and neurodegenerative diseases.

Mitochondria are important cellular organelles in most metabolic processes and have a highly dynamic nature, undergoing frequent fission and fusion. The dynamic balance between fission and fusion plays critical roles in mitochondrial functions. In recent studies, several large GTPases have been identified as key molecular factors in mitochondrial fission and fusion. Moreover, the posttranslational modifications of these large GTPases, including phosphorylation, ubiquitination and SUMOylation, have been shown to be involved in the regulation of mitochondrial dynamics. Neurons are particularly sensitive and vulnerable to any abnormalities in mitochondrial dynamics, due to their large energy demand and long extended processes. Emerging evidences have thus indicated a strong linkage between mitochondria and neurodegenerative diseases, including Alzheimer's disease, Parkinson's disease and Huntington's disease. In this review, we will describe the regulation of mitochondrial dynamics and its role in neurodegenerative diseases.
Design of the 1.5 MW, 30-96 MHz ultra-wideband 3 dB high power hybrid coupler for Ion Cyclotron Resonance Frequency (ICRF) heating in fusion grade reactor.

Design and developmental procedure of strip-line based 1.5 MW, 30-96 MHz, ultra-wideband high power 3 dB hybrid coupler has been presented and its applicability in ion cyclotron resonance heating (ICRH) in tokamak is discussed. For the high power handling capability, spacing between conductors and ground need to very high. Hence other structural parameters like strip-width, strip thickness coupling gap, and junction also become large which can be gone upto optimum limit where various constrains like fabrication tolerance, discontinuities, and excitation of higher TE and TM modes become prominent and significantly deteriorates the desired parameters of the coupled lines system. In designed hybrid coupler, two 8.34 dB coupled lines are connected in tandem to get desired coupling of 3 dB and air is used as dielectric. The spacing between ground and conductors are taken as 0.164 m for 1.5 MW power handling capability. To have the desired spacing, each of 8.34 dB segments are designed with inner dimension of 3.6 × 1.0 × 40 cm where constraints have been significantly realized, compensated, and applied in designing of 1.5 MW hybrid coupler and presented in paper.
Thermal image human detection using Haar-cascade classifier

Haar-Cascade classifier method has been applied to detect the presence of a human on the thermal image. The evaluation was done on the performance of detection, represented by its precision and recall values. The thermal camera images were varied to obtain comprehensive results, which covered the distance of the object from the camera, the angle of the camera to the object, the number of objects, and the environmental conditions during image acquisition. The results showed that the greater the camera-object distance, the precision and recall of human detection results declined. Human objects would also be hard to detect if his/her pose was not facing frontally. The method was able to detect more than one human in the image with positions of in front of each other, side by side, or overlapped to one another. However, if there was any other object in the image that had characteristics similar to a human, the object would also be detected as a human being, resulting in a false detection. These other objects could be an infrared shadow formed from the reflection on glass or painted walls.
Identification of novel mutations in Mexican patients with Aarskog–Scott syndrome

Aarskog-Scott syndrome (AAS), also known as faciogenital dysplasia (FGD, OMIM # 305400), is an X-linked disorder of recessive inheritance, characterized by short stature and facial, skeletal, and urogenital abnormalities. AAS is caused by mutations in the FGD1 gene (Xp11.22), with over 56 different mutations identified to date. We present the clinical and molecular analysis of four unrelated families of Mexican origin with an AAS phenotype, in whom FGD1 sequencing was performed. This analysis identified two stop mutations not previously reported in the literature: p.Gln664* and p.Glu380*. Phenotypically, every male patient met the clinical criteria of the syndrome, whereas discrepancies were found between phenotypes in female patients. Our results identify two novel mutations in FGD1, broadening the spectrum of reported mutations; and provide further delineation of the phenotypic variability previously described in AAS.
HydroDesktop: Web services-based software for hydrologic data discovery, download, visualization, and analysis

Discovering and accessing hydrologic and climate data for use in research or water management can be a difficult task that consumes valuable time and personnel resources. Until recently, this task required discovering and navigating many different data repositories, each having its ownwebsite, query interface, data formats, and descriptive language. New advances in cyberinfrastructure and in semantic mediation technologies have provided the means for creating better tools supporting data discovery and access. In this paper we describe a freely available and open source software tool, called HydroDesktop, that can be used for discovering, downloading, managing, visualizing, and analyzing hydrologic data. HydroDesktop was created as a means for searching across and accessing hydrologic data services that have been published using the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) Hydrologic Information System (HIS). We describe the design and architecture of HydroDesktop, its novel contributions in web services-based hydrologic data search and discovery, and its unique extensibility interface that enables developers to create custom data analysis and visualization plug-ins. The functionality of HydroDesktop and some of its existing plug-ins are introduced in the context of a case study for discovering, downloading, and visualizing data within the Bear River Watershed in Idaho, USA.  HydroDesktop and its source code are released under the New Berkeley Software Distribution (BSD) License which allows for liberal reuse of the software and code.
Modular Continual Learning in a Unified Visual Environment

A core aspect of human intelligence is the ability to learn new tasks quickly and switch between them flexibly. Here, we describe a modular continual reinforcement learning paradigm inspired by these abilities. We first introduce a visual interaction environment that allows many types of tasks to be unified in a single framework. We then describe a reward map prediction scheme that learns new tasks robustly in the very large state and action spaces required by such an environment. We investigate how properties of module architecture influence efficiency of task learning, showing that a module motif incorporating specific design principles (e.g. early bottlenecks, low-order polynomial nonlinearities, and symmetry) significantly outperforms more standard neural network motifs, needing fewer training examples and fewer neurons to achieve high levels of performance. Finally, we present a meta-controller architecture for task switching based on a dynamic neural voting scheme, which allows new modules to use information learned from previouslyseen tasks to substantially improve their own learning efficiency.
Understanding bag-of-words model: a statistical framework

The bag-of-words model is one of the most popular representation methods for object categorization. The key idea is to quantize each extracted key point into one of visual words, and then represent each image by a histogram of the visual words. For this purpose, a clustering algorithm (e.g., K-means), is generally used for generating the visual words. Although a number of studies have shown encouraging results of the bag-of-words representation for object categorization, theoretical studies on properties of the bag-of-words model is almost untouched, possibly due to the difficulty introduced by using a heuristic clustering process. In this paper, we present a statistical framework which generalizes the bag-of-words representation. In this framework, the visual words are generated by a statistical process rather than using a clustering algorithm, while the empirical performance is competitive to clustering-based method. A theoretical analysis based on statistical consistency is presented for the proposed framework. Moreover, based on the framework we developed two algorithms which do not rely on clustering, while achieving competitive performance in object categorization when compared to clustering-based bag-of-words representations.
Neural multi-atlas label fusion: Application to cardiac MR images

Multi-atlas segmentation approach is one of the most widely-used image segmentation techniques in biomedical applications. There are two major challenges in this category of methods, i.e., atlas selection and label fusion. In this paper, we propose a novel multi-atlas segmentation method that formulates multi-atlas segmentation in a deep learning framework for better solving these challenges. The proposed method, dubbed deep fusion net (DFN), is a deep architecture that integrates a feature extraction subnet and a non-local patch-based label fusion (NL-PLF) subnet in a single network. The network parameters are learned by end-to-end training for automatically learning deep features that enable optimal performance in a NL-PLF framework. The learned deep features are further utilized in defining a similarity measure for atlas selection. By evaluating on two public cardiac MR datasets of SATA-13 and LV-09 for left ventricle segmentation, our approach achieved 0.833 in averaged Dice metric (ADM) on SATA-13 dataset and 0.95 in ADM for epicardium segmentation on LV-09 dataset, comparing favorably with the other automatic left ventricle segmentation methods. We also tested our approach on Cardiac Atlas Project (CAP) testing set of MICCAI 2013 SATA Segmentation Challenge, and our method achieved 0.815 in ADM, ranking highest at the time of writing.
Credit Scoring using Machine Learning Techniques

Lenders such as banks and credit card companies while reviewing a client’s request for loan use credit scores. Credit scores help measure the creditworthiness of the client using a numerical score. Now it has been found out that the problem can be optimized by using various statistical models. In this study a wide range of statistical methods in machine learning have been applied, though the datasets available to the public is limited due to confidentiality concerns. Problems particular to the context of credit scoring are examined and the statistical methods are reviewed.
Ant colony Optimization: A Solution of Load balancing in Cloud

As the cloud computing is a new style of computing over internet. It has many advantages along with some crucial issues to be resolved in order to improve reliability of cloud environment. These issues are related with the load management, fault tolerance and different security issues in cloud environment. In this paper the main concern is load balancing in cloud computing. The load can be CPU load, memory capacity, delay or network load. Load balancing is the process of distributing the load among various nodes of a distributed system to improve both resource utilization and job response time while also avoiding a situation where some of the nodes are heavily loaded while other nodes are idle or doing very little work. Load balancing ensures that all the processor in the system or every node in the network does approximately the equal amount of work at any instant of time. Many methods to resolve this problem has been came into existence like Particle Swarm Optimization, hash method, genetic algorithms and several scheduling based algorithms are there. In this paper we are proposing a method based on Ant Colony optimization to resolve the problem of load balancing in cloud environment.
The Multiphasic and Dynamic Nature of Flow in Adventure Experiences

This study evaluated phases of adventure experiences by identifying flow and reversal theory states over a 3-day white-water river surfing course. Data were col­ lected with novice river surfers (n = S) via in-depth qualitative interviews using head-mounted video cameras. Findings suggested that "opposing" experiential phases (i.e., telic and paratelic) may be symbiotic in adventure experiences and may facilitate flow experiences. These results may account for the dynamic nature of enjoyment, flow, and motivational states within adventure experiences. Future research should seek to validate the phasic models presented herein and evaluate their potential applicability to other adventurous activities.