Miracle-dz/UniFAR-FTUs huggingface dataset:
Scan-Based Volume Animation Driven by Locally Adaptive Articulated Registrations

This paper describes a complete system to create anatomically accurate example-based volume deformation and animation of articulated body regions, starting from multiple in vivo volume scans of a specific individual. In order to solve the correspondence problem across volume scans, a template volume is registered to each sample. The wide range of pose variations is first approximated by volume blend deformation (VBD), providing proper initialization of the articulated subject in different poses. A novel registration method is presented to efficiently reduce the computation cost while avoiding strong local minima inherent in complex articulated body volume registration. The algorithm highly constrains the degrees of freedom and search space involved in the nonlinear optimization, using hierarchical volume structures and locally constrained deformation based on the biharmonic clamped spline. Our registration step establishes a correspondence across scans, allowing a data-driven deformation approach in the volume domain. The results provide an occlusion-free person-specific 3D human body model, asymptotically accurate inner tissue deformations, and realistic volume animation of articulated movements driven by standard joint control estimated from the actual skeleton. Our approach also addresses the practical issues arising in using scans from living subjects. The robustness of our algorithms is tested by their applications on the hand, probably the most complex articulated region in the body, and the knee, a frequent subject area for medical imaging due to injuries.
Retrieval of Remote Sensing Images Using Color , Texture and Spectral Features

Abstract: The remote sensing images are increasing day by day therefore storage and retrieval of these images is of significant importance. In this paper a prototype model for retrieval of remote sensing images on the basis of color moment and gray level co-occurrence matrix feature is developed and different vegetation index feature is extractor as color, texture and spectral. These three features plays important role in determining contents in remote sensing images. The results obtained are promising and these results can further be used for detecting change in different areas of remote sensing images.
Defining and Measuring Supercomputer Reliability, Availability, and Serviceability (RAS)

The absence of agreed definitions and metrics for supercomputer RAS obscures meaningful discussion of the issues involved and hinders their solution. This paper seeks to foster a common basis for communication about supercomputer RAS, by proposing a system state model, definitions, and measurements. These are modeled after the SEMI-E10 [1] specification which is widely used in the semiconductor manufacturing industry.
Robust urban road image segmentation

Urban road detection with on-board monocular camera in vehicle is still a difficult problem due to its complexity. Combining the superpixel scene segmentation with the classification of texture and structure information of the road scenes, this paper explores the method of detection the road from a single image. Processing the road texture information on a large scale and the structural information around the road on a small scale, and obtaining accurate information about the road edge with scene segmentation, the method is proved to be robust by experiments. Experimental data show that our approach achieved relatively good results in a variety of complex urban environment.
Using Continuous Code Change Analysis to Understand the Practice of Refactoring

Despite the enormous success that manual and automated refactoring has enjoyed during the last decade, we know little about the practice of refactoring. Understanding the refactoring practice is important for developers, refactoring tool builders, and researchers. Many previous approaches to study refactorings are based on comparing code snapshots, which is imprecise, incomplete, and does not allow answering research questions that involve time or compare manual and automated refactoring. We present the first extended empirical study that considers both manual and automated refactoring. This study is enabled by our algorithm, which infers refactorings from continuous changes. We implemented and applied this algorithm to the code evolution data collected from 23 developers working in their natural environment for 1,520 hours. Using a corpus of 5,371 refactorings, we reveal several new facts about manual and automated refactorings. For example, more than a half of the refactorings were performed manually. The popularity of automated and manual refactorings differs. More than one third of the refactorings performed by developers are clustered. For some refactoring kinds, up to 64% of performed refactorings do not reach the Version Control System.
Implementation of combinational circuits via material implication using memristors

Memristor is a new nanoelectronic device that has become very popular at recent times for its non-volatility, better alignment and excellent scalability properties. Various research works are going on designing of logic circuits, digital memory and neuromorphic systems using this nano device. Both pure and hybrid approaches are followed for the design purpose. Material implication logic brings in the motivation for exploring memristors in the area of logic design. In this work material implication logic has been applied to design complex combinational circuits like demultiplexer, encoder, priority encoder, decoder and magnitude comparator using memristors. Both serial and parallel approaches have been used for the design purpose and the parallel implementations require lesser step counts than their serial counterparts. Also the designs presented occupy lesser area than their conventional CMOS-peers.
PCR Primers to Study the Diversity of Expressed Fungal Genes Encoding Lignocellulolytic Enzymes in Soils Using High-Throughput Sequencing

Plant biomass degradation in soil is one of the key steps of carbon cycling in terrestrial ecosystems. Fungal saprotrophic communities play an essential role in this process by producing hydrolytic enzymes active on the main components of plant organic matter. Open questions in this field regard the diversity of the species involved, the major biochemical pathways implicated and how these are affected by external factors such as litter quality or climate changes. This can be tackled by environmental genomic approaches involving the systematic sequencing of key enzyme-coding gene families using soil-extracted RNA as material. Such an approach necessitates the design and evaluation of gene family-specific PCR primers producing sequence fragments compatible with high-throughput sequencing approaches. In the present study, we developed and evaluated PCR primers for the specific amplification of fungal CAZy Glycoside Hydrolase gene families GH5 (subfamily 5) and GH11 encoding endo-β-1,4-glucanases and endo-β-1,4-xylanases respectively as well as Basidiomycota class II peroxidases, corresponding to the CAZy Auxiliary Activity family 2 (AA2), active on lignin. These primers were experimentally validated using DNA extracted from a wide range of Ascomycota and Basidiomycota species including 27 with sequenced genomes. Along with the published primers for Glycoside Hydrolase GH7 encoding enzymes active on cellulose, the newly design primers were shown to be compatible with the Illumina MiSeq sequencing technology. Sequences obtained from RNA extracted from beech or spruce forest soils showed a high diversity and were uniformly distributed in gene trees featuring the global diversity of these gene families. This high-throughput sequencing approach using several degenerate primers constitutes a robust method, which allows the simultaneous characterization of the diversity of different fungal transcripts involved in plant organic matter degradation and may lead to the discovery of complex patterns in gene expression of soil fungal communities.
Association Between Insulin Resistance and Luminal B Subtype Breast Cancer in Postmenopausal Women

AbstractCurrently, there is limited information on the clinical characteristics of breast cancer patients with insulin resistance. Hence, the purpose of this study was to investigate the association between insulin resistance and clinicopathological factors in newly diagnosed breast cancer patients without diabetes.We assessed 760 patients with breast cancer treated between 2012 and 2014. We compared the clinicopathological characteristics between patients with and without insulin resistance using univariate and multivariate analyses, including after stratification by menopausal status. Insulin resistance was defined according to the homeostatic model assessment of insulin resistance.Of 760 patients, 26.4% had insulin resistance. Age, menopausal status, body mass index, tumor size, histologic grade, Ki-67 expression, and breast cancer subtype significantly differed according to the presence of insulin resistance. Multivariate analysis revealed that postmenopausal status and obesity were significantly associated with insulin resistance. In postmenopausal women, older age, obesity, larger tumor size, advanced stage, and high proliferative luminal B subtype were significantly associated with insulin resistance. In contrast, in premenopausal patients, only obesity was related to insulin resistance. Multivariate analysis indicated that insulin resistance was independently correlated with obesity, larger tumor size, and the luminal B/human epidermal growth factor receptor-2-negative subtype in postmenopausal but not premenopausal patients.Insulin resistance was significantly associated with larger tumors and proliferative luminal B subtype breast cancer in postmenopausal women only. These findings suggest that insulin resistance could mechanistically induce tumor progression and might be a good prognostic factor, and that it could represent a therapeutic target in postmenopausal patients with breast cancer.
Region based segmentation of satellite and medical imagery with level set evolution

In this paper, we proposed a novel global segmentation method for satellite images with active contour model on noisy images with ten percentage of salt and pepper. It was implemented with a special technique selective binary and Gaussian filtering regularized level set evolution. First we selectively penalize the level set function to be binary and then use a Gaussian smoothing kernel to regularize it. The advantages of our method is a new region based signed pressure force(SPF) function is proposed, which can step effectively the contour at weak or blurred edges and automatically detect the interior and exterior boundaries with the initial contour being any where in the images effected with noise. The proposed method can implement by the simple finite difference scheme. Experiments on satellite images with noise demonstrate the advantages of the proposed method over the Chan-Vase (CV) active contour in terms of the number of Iterations.
Toward User-Centred Geovisual Analytics in Maritime Surveillance

Within the domain of maritime safety and security, many operators and analysts are asked to manage huge set of real-time data. Information systems, such as Maritime Surveillance Systems, are used to visualize and analyse information of monitored vessels (e.g., AIS, RADAR). Past tracks, present states of vessels and contextual data, such as meteorology, are gathered in these visual platforms.
Design scheme of seeker equivalent device of Missile-Borne Synthetic Aperture Radar

Synthetic Aperture Radar (SAR) is an active microwave remote sensor, which could form real-time image around the target. Traditionally, the simulation of SAR imaging algorithm is based on Matlab which is totally software simulation and the final performance is difficult to guarantee. In this paper, the design of the seeker Equivalent Device of Missile-Borne Synthetic Aperture Radar is a device in hardware-in loop simulation of echo signal and imaging algorithm. Based on this device the target echo signal is simulated in a real environment which provides us a more reliable simulation platform for the echo signal and imaging algorithms. In practical testing, this device performs well in point target imaging simulation.
Robust Face Recognition Technique for a Real-Time Embedded Face Recognition System

In Chapter 8, Shan, Bigdeli, Lovell and Chen propose a pose variability compensation technique which synthesizes realistic frontal face images from non-frontal views. It is based on modeling the face via active appearance models and estimating the pose through a correlation model. The proposed technique is coupled with adaptive principal component analysis (APCA) which was previously shown to perform well in the presence of both lighting and expression variations. The proposed recognition techniques, though advanced, are not computationally intensive. So they are quite well-suited to the embedded system environment. Indeed, the authors have implemented an early prototype of a face recognition module on a mobile camera phone so that the camera could be used to identify the person holding the phone.
A Low-Power Routing Protocol for Wireless Sensor Networks Based on Data Aggregation

A data aggregation routing protocol with minimum transmission cost spanning tree facing the problem of low-power routing was proposed, to lower energy consumption of wireless sensor networks. Data from sensor nodes was transmitted to the root node to sink node within the same sensor area. Data aggregation and transmission to the in-network sink along the shortest path is in accordance with principles of least energy consumption and energy equilibrium. Results showed that this protocol produced a transmitted data volume decreased by 50%-80% and a low propagation time delay.
Linked Data for Financial Reporting

Recently, uncertainty has dominated global financial markets. Companies are struggling with their balance sheets while merges and acquisitions (M&A) happens on a more frequent basis. This calls for highly accurate corporate performance data, which serve as the ground truth for M&A decisions. Conventional financial reporting approach has drawn criticism from both investors and analysts, attribute to the inherent complexity, lack of timeliness, and inadequacy of scope. In this paper, we try to address these shortcomings with a Linked Data approach. We showcase a system for enriching corporate financial reporting with other types of proprietary and public data, the latter are part of the Linked Open Data cloud or harvested from social media sites. Our motivation is to 1) source in a variety of data so as to portray a comprehensive image of a company and 2) render tedious financial figures through an intuitive and comparative perspective. We introduce the architecture of the Linked Financial Data for Reporting framework (LFDr) and outline the details of its implementation. We also propose methodologies for the evaluation of the whole framework.
Collection of HerbalMedicinalProperty relation extracted from texts

This research aims to collect the extracted HerbalMedicinalProperty relations from downloaded herbal-plant documents for creating the herbal-medicinal-property-network based representation. An HerbalMedicinalProperty relation is a semantic relation between one herbal-plant-component concept and several herbal-medicinal-property-concept expressions on texts and vice versa. An herbal-plant-component occurrence is a noun-phrase expression and each herbal-medicinal-property- concept occurrence is an event expression by a verb-phrase of EDU (an Elementary Discourse Unit or a simple sentence). The herbal-medicinal-property-network based representation benefits a recommendation system of solving health-problems on web-boards. The research has two main problems: 1) how to extract HerbalMedicinalProperty relations from the documents, and 2) how to collect the HerbalMedicinalProperty relations for creating the herbal-medicinal-property-network based representation. Therefore, we propose applying a co-occurrence of N-Words (or N-Word-Co) including N-Word-Co size learning on the verb phrase to identify several medicinal-property-concept EDU occurrences over the documents after the linguistic phenomena has been applied to solve the herbal-plant-component concepts. The extracted HerbalMedicinalProperty relations are then collected as a matrix of herbal-plant names, herbal-plant components, and herbal-medicinal properties for creating the herbal-medicinal-property-network based representation. The research results provide the high precision of the HerbalMedicinalProperty-relation extraction from the documents.
A peer-to-peer, value-oriented XML Store

This paper presents and evaluates XML Store, a distributed and highly scalable storage system for XML documents that builds on the Chord peer-to-peer protocol developed at MIT. The interface (API) to the storage system is simple and value-oriented, permitting no destructive updates, which permits simple solutions to replication, caching, and transaction management. Our experimental implementation of XML Store and the Chord protocol in Java performs satisfactorily and demonstrates the advantages of value-oriented programming.
Coordinating Application and System Adaptation for Performance , Power , and Accuracy Management

This paper presents PowerDial++, a system for dynamically adapting both application and system configuration in response to unpredictable changes in application workload, available power, or user goals. PowerDial++ coordinates the management of application parameters governing performance versus accuracy tradeoffs with system parameters governing performance versus power tradeoffs. Our results show that PowerDial++’s coordinated approach provides a greater range of operating points than application or system configuration alone. For example, PowerDial++ can meet energy efficiency goals with greater accuracy or meet accuracy goals with reduced power consumption compared to prior approaches. Additionally, PowerDial++’s ability to manage goals in multiple dimensions provides greater efficiency in response to unpredictable changes in application input or user goals.
Effects of Ranolazine on Torsades de Pointes Tachycardias in a Healthy Isolated Rabbit Heart Model

Purpose Torsades de pointes (TdP) tachycardias are triggered, polymorphic ventricular arrhythmias arising from early afterdepolarizations (EADs) and increased dispersion of repolarization. Ranolazine is a new agent which reduces pathologically elevated late INa but also IKr. Aim of this study was to evaluate the effects of ranolazine in a validated isolated Langendorff-perfused rabbit heart model. Methods TdP was reproducibly induced with d-sotalol (10−4 mol/L) and low potassium (K) (1.0 mmol/L for 5 min, pacing at CL 1000 ms). In 10 hearts, ECG and 8 epi- and endocardial monophasic action potentials were recorded. Action potential duration (APD) was measured at 90% repolarization and dispersion defined as APD max–min. Results D-sotalol prolonged APD90 and increased dispersion of APD90, simultaneously causing EADs and induction of TdP. The combination of d-sotalol and two concentrations of ranolazine did not increase dispersion of ventricular APD90 as compared to vehicle. Ranolazine at 5 μmol/L did not cause additional induction of EADs and/or TdP but also did not significantly suppress arrhythmogenic triggers. The higher concentration of ranolazine (10 μmol/L) in combination with d-sotalol caused further prolongation of APD90, at the same time reduction in APD90 dispersion. In parallel, the incidence of EADs was reduced and an antitorsadogenic effect was seen. Conclusions In the healthy isolated rabbit heart (where late INa is not elevated), ranolazine does not cause proarrhythmia but exerts antiarrhythmic effects in a dose-dependent manner against d-sotalol/low K-induced TdP. This finding—despite additional APD prolongation—supports the safety of a combined use of both drugs and merits clinical investigation.
Preexisting Japanese Encephalitis Virus Neutralizing Antibodies and Increased Symptomatic Dengue Illness in a School-Based Cohort in Thailand

Background Dengue viruses (DENVs) and Japanese encephalitis virus (JEV) have significant cross-reactivity in serological assays; the clinical implications of this remain undefined. An improved understanding of whether and how JEV immunity modulates the clinical outcome of DENV infection is important as large-scale DENV vaccine trials will commence in areas where JEV is co-endemic and/or JEV immunization is routine. Methods and Findings The association between preexisting JEV neutralizing antibodies (NAbs) and the clinical severity of DENV infection was evaluated in a prospective school-based cohort in Thailand that captured asymptomatic, non-hospitalized, and hospitalized DENV infections. Covariates considered included age, baseline DENV antibody status, school of attendance, epidemic year, and infecting DENV serotype. 942 children experienced at least one DENV infection between 1998 and 2002, out of 3,687 children who were enrolled for at least one full year. In crude analysis, the presence of JEV NAbs was associated with an increased occurrence of symptomatic versus asymptomatic infection (odds ratio [OR] = 1.55, 95% CI: 1.08–2.23) but not hospitalized illness or dengue hemorrhagic fever (DHF). The association was strongest in children with negative DENV serology (DENV-naive) (OR = 2.75, 95% CI: 1.12–6.72), for whom the presence of JEV NAbs was also associated with a symptomatic illness of longer duration (5.4 days for JEV NAb+ versus 2.6 days for JEV NAb-, p = 0.048). JEV NAbs were associated with increased DHF in younger children with multitypic DENV NAb profiles (OR = 4.05, 95% CI: 1.18 to 13.87). Among those with JEV NAbs, the association with symptomatic illness did not vary by antibody titer. Interpretation The prior existence of JEV NAbs was associated with an increased probability of symptomatic as compared to asymptomatic DENV illness. These findings are in contrast to previous studies suggesting an attenuating effect of heterologous flavivirus immunity on DENV disease severity.
First Order Controllers for a Petrochemical Pyrolisys Reactor

The objective of this paper is to design a real-time control solution for chemical processes, namely the ethylene reactor, the key-installation in the petrochemical industry. The controller used is lag or lead controller, a simple yet suitable control tool. This article deals with the problem of finding all first order controllers that stabilize a given LTI continuous time or discrete time plant and attain various performance specifications. The main tool for the controller design is Neimarks D-decomposition, which was originally introduced in 1949 in the Russian control literature. The process data have been measured from an assembly of pyrolysis reactors on the petrochemical platform from Pitesti, Romania.
A Reverse Engineering Role-Play to Teach Systems Engineering Methods

Students engaged in systems engineering education typically lack experience and understanding of the multidisciplinary complexity of systems engineering projects. Consequently, students struggle to understand the value, rationale, and usefulness of established systems engineering methods, often perceiving them as banal or trivial. The paper presents a learning activity based on a three-stage reverse engineering role-play developed to increase students’ awareness of the importance of correctly using systems engineering methods. The activity was developed and integrated in the Systems Engineering course given at Blekinge Institute of Technology. Its effectiveness was analyzed through semistructured self-reflection reports along with two editions of the course. The results showed the development of students’ understanding of how to use systems engineering methods. In particular, the students realized the need to deliver detailed and easy-to-read models to the decision makers. This result was in line with the achievement of some of the intended learning outcomes of the course.