Miracle-dz/UniFAR-FTUs huggingface dataset:
FOR THE SOUTH POLE FROM SURFACE-BASED INFRARED RADIATION MEASUREMENTS

Cloud cover at high latitudes is not well known (Hahn et al. 1995; Rossow and Schiffer 1999). Visual observations of fractional cloud cover (FCCvis) are acceptable during times of daylight but are unreliable during the polar night because of the difficult nature of the observation. Observers are forced to estimate the FCC of optically thin clouds in the dark and extreme cold. Hahn et al. (1995) concluded that at the South Pole during the winter there may not even be enough moonlight when the moon is above the horizon for observers to adequately estimate FCC. Satellite retrievals of polar FCC have a completely different set of caveats. During the polar day, satellites must radiatively distinguish clouds and snow of similar temperatures and albedos. The task becomes a little easier during the polar night due to the persistent near-surface temperature inversion which raises the brightness temperature (TB) of some clouds above the snow surface temperature. ISCCP retrievals of FCC (FCCISCCP) over the poles are notoriously low during the polar day (Rossow and Schiffer 1999). In fact, Hahn et al. (1995, Figure 13) showed that the climatological retrievals of FCCISCCP over the South Pole have a seasonal cycle opposite that of FCCvis. Until recently there has been no reliable ground truth for wintertime FCC in Antarctica. There is now evidence that FCC estimated from satellite retrievals may not be as poor as suspected (Town et al. 2004). Town et al. (2004) compared one year of FCC retrieved from Polar Atmospheric Emitted Radiance Interferometer (PAERI) data (FCCPAERI) and pyrgeometer data (FCCpyr) taken at the South Pole during 2001 with climatological FCCISCCP and FCC estimated from AVHRR Polar Pathfinder data (FCCAPP-x) (Key 2002, Pavolonis and Key 2003) over that region with some success. Here we present FCC from ground-based pyrgeometer measurements taken at the South Pole for 9 years (1994-2003). Monthly mean FCCpyr is compared with concurrent measurements of FCCPAERI, FCCvis, FCCISCCP and FCCAPP-x. It is
Performance and Energy Monitoring Tools for Modern Processor Architectures

Accurate on-the-fly characterization of application behavior requires assessing a set of execution-related parameters at run-time, including performance, power and energy consumption. These parameters can be obtained by relying on hardware measurement facilities built-in modern multi-core architectures, such as performance and energy counters. However, current operating systems do not provide the means to directly obtain these characterization data. Thus, the user needs to rely on complex custom-built libraries with limited capabilities, which might introduce significant execution and measurement overheads. In this work, we propose two different tools for efficient performance, power and energy monitoring of systems with modern multi-core CPUs, that allow capturing the run-time behavior of a wide range of applications at different system levels: i) at the user-space level, and ii) at kernel-level, by using the OS scheduler to directly capture this information. Although the importance of the proposed monitoring facilities is patent for many purposes, we focus herein on their employment for application characterization with the Cache-aware Roofline model. The experimental results show the capabilities of the proposed tools to deliver detailed and accurate information about the behavior of real-world applications on the underlying architectural resources. Moreover, they allow reconstructing and identifying the execution patterns of the profiled benchmarks from standard suites (SPEC CPU2006), while introducing negligible overheads.
ROPNN: Detection of ROP Payloads Using Deep Neural Networks

Return-oriented programming (ROP) is a code reuse attack that chains short snippets of existing code to perform arbitrary operations on target machines. Existing detection methods against ROP exhibit unsatisfactory detection accuracy and/or have high runtime overhead. In this paper, we present ROPNN, which innovatively combines address space layout guided disassembly and deep neural networks to detect ROP payloads. The disassembler treats application input data as code pointers and aims to find any potential gadget chains, which are then classified by a deep neural network as benign or malicious. Our experiments show that ROPNN has high detection rate (99.3%) and a very low false positive rate (0.01%). ROPNN successfully detects all of the 100 real-world ROP exploits that are collected in-the-wild, created manually or created by ROP exploit generation tools. Additionally, ROPNN detects all 10 ROP exploits that can bypass Bin-CFI. ROPNN is non-intrusive and does not incur any runtime overhead to the protected program.
AN ENTROPY VARIABLE-BASED GRIDFREE SOLVER WITH LU-SGS ACCELERATOR

Lower-Upper Symmetric Gauss Seidel (LUSGS) implicit method has been implemented within the framework of gridfree Least Squares Kinetic Upwind Method (LSKUM). The LU-SGS method is modified to preserve the generality of the gridfree method for non-symmetric connectivity and yet retain matrix-free feature of the LU-SGS method. Moreover, in the present work kinetic boundary conditions are also implemented in the implicit framework. Implicit scheme for gridfree method is proved to be stable for all time steps and a speed-up 5 to 6 is obtained without any additional memory requirement. These aspects are demonstrated through specific examples.
The Impact of Inbound Tourism on Economic Growth of Nepal

This study examines the relationship between tourism and economic growth in Nepal by considering the relative importance of financial development. The newly developed Bayer and Hank joint test results show that tourism, economic growth and financial development are co-integration. The results also show that incoming tourism has stimulated short-term and long-term economic growth in Nepal. In addition, the analysis point out a long-term one-way Granger causality from tourism to economic growth. It is recommended that policies that fascinate more international visitors should be promoted.
Partially repair damaged Islets of diabetic rat model via insulin-producing cells differentiated from human umbilical cord mesenchymal stem cells infusion

Pancreatic islet transplantation is the most effective treatment for type 1 diabetes, but the shortage of donor islets and risk of immunological rejection prevent it from being used widely. Mesenchymal stem cells in the Wharton’s jelly of human umbilical cords are accepted as a promising alternative transplant source. The current method of inducing differentiation is time consuming. The aim of this study was to develop a novel method combining genetic and chemical approaches to tackle this problem and minimize the potential risk of rejection. Freshly isolated and expanded human umbilical cord mesenchymal stem cells (HUMSCs) were induced to differentiate into insulin-producing cells by the novel two-step method. In this method, HUMSCs were first transfected with neuronal differentiation 1 (NEUROD1) for 2 days and afterwards treated with chemical agents for 8 days to induce differentiation. The differentiated cells expressed the pancreatic cell markers, which were pancreatic and duodenal homeobox 1 (PDX1), glucagon, insulin, and NK2 homeobox 2 (NKX2-2), and released insulin both spontaneously and after high-glucose stimulation. Then, differentiated cells were transplanted via tail vein into a diabetic rat model induced by streptozotocin. Hyperglycemia, body weight and survival rate of the rats was improved, and serum insulin levels increased after transplantation. More importantly, it was histologically confirmed that transplanted cells homing to the pancreatic gland of diabetic rats and improved the morphology of pancreatic islets of the recipient rats. Overall, the novel two-step method successfully induced HUMSCs to differentiate into insulin-producing cells and effectively treat the diabetic rat via vascular delivery.
The five E ' s in festival experience in the context of Gen Y : Evidence form a small island destination 1-1-2015

The purpose of this study is threefold. First, to test the experience economy framework in the context of a small island destination (SID) in order to determine if a music festival may be used as an experiential product to lure a cohort replacement. Second, to examine a music festival’s experiential domains that may influence the overall experience of Generation Y (Gen Y) tourists. And, third to determine if the overall experience of Gen Y tourists who attend a music festival may predict their behavioral intentions to return to and recommend a SID. The study investigates whether a music festival may be used as an experiential attraction to draw the Gen Y market segment to a SID as a cohort replacement for the baby boomer generation. Structural Equation Modeling (SEM) is used to test the application of the experience economy framework to a generational cohort’s behavioral intentions after the consumption of an experiential product in the context of a SID. The study suggests that in the case of Gen Y it may be necessary to reference economic value as part of the second order construct for overall experience in order to predict future intentions.
A Comparison of Risk-Based and Traditional Auditing and their Effect on the Quality of Audit Reports

ABSTRACT: The purpose of the present research was to compare risk-based and traditional auditing and their effect on the quality of audit reports. The sample consisted of 80 senior managers, supervisors, and auditors of Iran’s Audit Organization. A questionnaire rated on the 5-point Likert scale was used to assess the participants’ view on 4 subscales: validity, quality, reliability, and closeness to reality. The results of t-test showed that risk-based auditing creates significantly more valid and more reliable audit reports than traditional auditing. Also it was shown that risk-based auditing is not closer to reality than traditional auditing. It can thus be concluded that risk-based auditing can gain the trust and confidence of the users of audit reports.
Using the R package CoopGame for the analysis , solution and visualization of cooperative games with transferable utility

This document gives a brief and concise overview of the various functionalities of the package CoopGame and presents a few use cases. In particular, we introduce the capabilities of CoopGame to create special families of cooperative games, to check game properties and to compute set-valued and point-valued solutions. We also introduce the usage of CoopGame for visualizing set-valued and point-valued solutions in the case of three or four players. We end with a brief outlook to future developments. This vignette accompanies version 0.2. 1 of the package CoopGame.
Output : Specifications of Composite Components Input : Traces Monitoring Subcomponents Input

Specifications play an important role in many software engineering activities. Despite their usefulness, formal specifications are often unavailable in practice. Specification mining techniques try to automatically recover specifications from existing programs. Unfortunately, mined specifications are often overly general, which hampers their applications in the downstream analysis and testing. Nowadays, programmers develop software systems by utilizing existing components that usually have some available specifications. However, benefits of these available specifications are not explored by current specification miners. In this paper, we propose an approach to leverage available specifications of subcomponents to improve the precision of specifications of the composite component mined by statebased mining techniques. We monitor subcomponents against their specifications during the mining process and use states that are reached to construct abstract states of the composite component. Our approach makes subcomponents’ states encoded within their specifications visible to their composite component, and improves the precision of mined specifications by effectively increasing the number of their states. The empirical evaluation shows that our approach can significantly improve the precision of mined specifications by removing erroneous behavior without noticeable loss of recall.
In-band Full Duplex Radio : A survey

Massive development in the field of wireless communication enables utmost utilization of the precious spectrum. However, many devices are working in half-duplex (HD) mode and consume valuable resources by employing frequency-division or time-division. The recent research shows that the fullduplex (FD) system is one of the most promising solution for saving the time and frequency resource. The full duplex system allows simultaneous communication at the same frequency. Thus, the operation in full duplex reduce the spectrum requirement of a given communication system by half. In this paper we have presented the survey of selfinterference in FD communication and discussed the cooperative spectrum sensing scenario in FD Cognitive radio.
REGULARITY OF WEAK SOLUTIONS FOR A CLASS OF INFINTELY DEGENERATE ELLIPTIC SEMILINEAR EQUATIONS

where Λ = (e2 + |D|2)1/2 =< D >. If the system X satisfies the finite type of Hörmander’s condition then (1.1) holds for any real s > 0 . On the other hand (1.1) admits the infinite degeneracy of the system X, and the estimate (1.1) with s > 1 always implies the interior hypoellipticity of the second order operator 4X = ∑m j=1 X ∗ j Xj , where X ∗ j is the formal adjoint of Xj (see [7]). Some sufficient conditions for this estimate can be seen in the Appendix of [10]. The typical example for (1.1) is the system in R3 such as X1 = ∂x1 , X2 = ∂x2 , X3 = exp(−|x1|−1/s)∂x3 with s > 0 (see [5, 6, 7]). The operator 4X for this example degenerates infinitely on Γ0 = {x1 = 0}. Associated with the system of vector fields X = (X1, · · · , Xm), we define function spaces :
LUMINESCENT PROPERTIES OF RARE EARTH DOPED ZnO-B2O3-P2O5 GLASSES

ZnO-B2O3-P2O5 (ZBP) glasses are a comparatively new material with a wide application potential for some optic devices. This is so because they are an excellent host matrix for different rear earths, especially for samarium ions, which in turn leads to intense luminescence in the visible region. Based on some previous studies of the structure, chemical durability, thermal stability, physical and optical properties it is found that this matrix is not only a good candidate as a host material for different RE ions but it is also applicable in sensing and valuable documents protection. The present communication reports results referring to synthesis of new rare earth (Eu, Gd, Tb, Nd) doped and co-doped with Gd/Nd, Gd/Sm, Nd/Eu, Nd/Sm, Tb/Sm ZBP glasses and study of their absorption and fluorescent properties. The synthesis is performed by the conventional melt quenching method at 950oC in a muffle furnace. It is found that all synthesized glasses are homogeneous (with no gases inclusion), while some of them are fully transparent. The luminescent analysis reveals strong fluorescence in the visible and near infra-red region for different doping ions. Rare earth co-doping widens the potential applications of these glasses. ZBPs are also found a good host matrix for laser-active ions.
Node-By-Node Greedy Deep Learning for Interpretable Features

Multilayer networks have seen a resurgence under the umbrella of deep learning. Current deep learning algorithms train the layers of the network sequentially, improving algorithmic performance as well as providing some regularization. We present a new training algorithm for deep networks which trains each node in the network sequentially. Our algorithm is orders of magnitude faster, creates more interpretable internal representations at the node level, while not sacrificing on the ultimate out-of-sample performance.
ドライブバイワイヤによるモバイルハプトのハプティック遠隔操作 Haptic Teleoperation of Mobile-Hapto using Drive-by-Wire

This paper proposes bilateral force feedback system which is method of teleoperation using mobile-hapto and haptic-pedal. Mobile-hapto possesses two actuators in its wheel. On the other hand, haptic-pedal possesses three actuators. Thus, bilateral control system with different configurations and degree-of-freedom is constructed. When moving, road surface information is fed back to the operator by the proposed method. As a result, improvement of reality and operationality of teleoperation is attained. The viability of the proposed method is confirmed by experiments.
1 Feasibility of Utilizing Stable-Isotope Dimethyl 2 Labeling in Liquid Chromatography-Tandem Mass 3 Spectrometry-Based Determination for Food 4 Allergens — Case of Kiwifruit 5

Stable-isotope dimethyl labeling is a highly reactive and cost-effective derivatization procedure 11 that could be utilized in proteomics analysis. In this study, a liquid chromatography-tandem mass 12 spectrometry in multiple reaction monitoring mode (LC-MS-MRM) platform for the quantification of kiwi 13 allergens was first developed using this strategy. Three signature peptides for target allergens Act d 1, Act d 14 5, and Act d 11 were determined and were derivatized with normal and deuterated formaldehyde as external 15 calibrants and internal standards, respectively. The results showed that sample preparation with the phenol 16 method provided comprehensive protein populations. Recoveries at four different levels ranging from 17 72.5–109.3% were achieved for the H-labeled signature peptides of Act d 1 (SPA1-H) and Act d 5 (SPA5-H) 18 with precision ranging from 1.86–9.92%. The limit of quantification (LOQ) was set at 8 pg mL–1 for SPA1-H 19 and at 4 ng mL–1 for SPA5-H. The developed procedure was utilized to analyze seven kinds of hand-made 20 kiwi foods containing 0.0175–0.0515 mg g–1 of Act d 1 and 0.0252–0.0556 mg g–1 of Act d 5. This study 21 extended the applicability of stable-isotope dimethyl labeling to the economical and precise determination of 22 food allergens and peptides. 23
An Interpretable Multi-scale Deep Network for Structure Localization in Medical Images

Anatomical structure localization plays an important role in image-based medical diagnosis. Despite recent progress achieved by deep learning methods, most existing neural networks for localization lack an interpretable inference process and thus are diﬃcult to use for critical diagnosis in practice. In this paper, we propose an interpretable multi-scale convolutional networks for structure localization in medical images. Our network employs a modularized architecture consisting of a local branch to encode structure features and a context branch to capture global cues. The local branch adopts a coarse-to-ﬁne strategy to reﬁne the localization. And within each step, it learns a linear voting scheme based on a set of visual landmarks. The context branch uses a deformable pooling to encode contextual anatomical structures for reducing local ambiguities in localization. Given a prediction, we are able to trace back and determine which features are involved and their importance. We validate the proposed strategy on a Nuchal Translucency (NT) dataset, and the results demonstrate that our method is capable of generating an interpretable localization process and achieves the state-of-the-art detection performance.
AN INVESTIGATION INTO THE DIVERSIFICATION-PERFORMANCE RELATIONSHIP AMONG PROPERTY-LIABILITY INSURERS

Extensive research exists on the relationship between diversification and financial performance of firms. Very few of these studies focus on the insurance industry. This paper seeks to provide empirical evidence on the relationship between propertyliability insurers’ product diversification and their risk-adjusted return using crosssection and time-series data for the years 1992 through 2000. The study findings indicate a U-shaped relationship between the extent of product diversification and risk-adjusted returns. In the first stage, the initial benefits of diversification are outweighed by the cost of diversification and hence result in a reduction in financial performance. However, as firms expand further, greater benefits of diversification are achieved, leading to a positive impact on financial returns. The results of the study show a consistent pattern using Herfindahl Index and Entropy Measures of diversification.
Multi-atlas Approaches for Image Segmentation across Modality , Species and Application Area

Volumetric measurements derived from non-invasive medical imaging data provide valuable biomarkers for drug discovery and development. While human observers can segment regions-of-interest from an individual scan using manual approaches, this process is slow and prone to interand intra-observer variability. Given the size of many clinical and preclinical studies, semi-automated segmentation approaches are necessary to match research timelines and minimize costs. Additionally, one of the largest challenges facing machine-learning and artificial intelligence tasks is the ability to efficiently and accurately generate labels (identify anatomical structures) for pre-existing and new data. Tools that are capable of such labeling in 2D and 3D data sets across a variety of anatomical structures and imaging modalities are indispensable for the downstream generation of features utilized in machine learning pipelines. A MAS tool is presented to accurately and reproducibly segment image data. MAS methods utilize image registration techniques to map a population of reference subjects and their associated subject-specific region segmentations to a test subject. The tool leverages linear and non-linear registrations of a reference library of gold-standard segmentations to a test dataset, building a probabilistic atlas that is mapped to a determined region of interest (ROI) [1,2].
Use of NLP Techniques for an Enhanced Mobile Personal Assistant: The Case of Turkish

This article introduces a Turkish mobile assistant application which produces state-of-the art results for the Turkish language by using natural language processing (NLP) techniques. The voice-enabled mobile assistant application allows users to enter queries for nine pre-defined tasks; namely, making calls, sending sms messages and emails, getting directions, querying exchange rates, weather forecast and traffic information, searching on the internet and launching applications on the phone. Users’ queries are processed in a multi-stage approach (viz., NLP, query classification and parameter extraction). Either the requested task is performed or the requested information is displayed as the response of the application. The article presents the architecture of the introduced system, its comparison with some prominent mobile assistants as well as the newly created data resources (viz., two query datasets annotated for classification and parameter extraction, two specific datasets for domain adaptation of named entity recognition and syntactic parsing NLP modules) to be used in further research. The evaluations on the impact of NLP preprocessing layers to the query classification performances reveal that the added value by NLP may range from 0.2 to 10.7 percentage points depending on the preferred machine learning algorithm for the query classification stage. The impact of NLP for the parameter extraction stage is also crucial since the outputs of NLP modules are used systematically by the extraction rules. The overall performance of the introduced approach is measured as 70.8% which is very promising under the fact that the system is trained with very limited-size of annotated data. The technology introduced in this article is basically designed for the case of a mobile assistant but it can also be used for every voice-enabled control system to improve the user experience, such as smart homes or smart televisions.