Miracle-dz/UniFAR-FTUs huggingface dataset:
Nonrigid Registration Methods for Multimodal Carotid Artery Imaging

The purpose of this paper is to present a methodology to estimate the carotid artery lumen centerlines in ultrasound (US) images obtained in a free-hand examination. Challenging aspects here are speckle noise in US images, artifacts, and the lack of contrast in the direction orthogonal to the US beam direction. An algorithm based on a rough lumen segmentation obtained by robust ellipse fitting was developed to deal with these conditions and estimate the lumen center in 2D B-mode scans. In a free-hand sweep examination, continuous image acquisitions are performed through time when the radiologist moves the probe on the patient’s neck. The result is a series of images that show 2D cross-sections of the carotid’s morphology. A tracking sensor (Flock of Birds) was attached to the probe and both were connected to a PC executing the Stradwin software, which relates spatial information to the acquisition data of the US probe. The spatial information was combined with the 2D lumen center estimates to provide a centerline in 3D. For validation, 19 carotid scans from 15 different patients were scanned, their centerlines calculated by the algorithm and compared with results acquired by manual annotations. The average Euclidean distance between both among all the examinations was 0.82 mm. For each examination, the percentage of these Euclidean distances below 2 mm was calculated; the average over all examinations was 92%. Automated 3D estimation of carotid artery lumen centerlines in free-hand real-time ultrasound is feasible and can be performed with high accuracy. The algorithm is robust enough to keep the centerlines inside the vessel, even in the absence of contrast in parts of the vessel wall. Based upon: Diego D. B. Carvalho, Stefan Klein, Zeynettin Akkus, Gerrit L. ten Kate, Arend F. L. Schinkel, Johan G. Bosch, Aad van der Lugt, Wiro J. Niessen ,“Estimating 3D Lumen Centerline of Carotid Arteries in Free-hand Acquisition Ultrasound”, published in International Journal of Computer Assisted Radiology and Surgery 20 2 Estimating 3D Lumen Centerlines of Carotid Arteries in US
Capturing OO Software Metrics to attain Quality Attributes - A case study

This paper is an attempt to study the role of various object oriented software metrics with regard to software quality and implementing them on an open source Java based operating system to evaluate its design traits. Captured metrics have been mapped to higher level quality attributes characterized by the QMOOD quality model. Prior to mapping, metric values have been normalized to enable us to interpret the meaning of quantitative figures calculated to indicate quality factors. This one offers a concrete model resulting in computation of measures such as reusability, flexibility, understandability, functionality and alike using composite metrics. Also it is economical to implement this type of model and can be effectively used in observing many aspects of software product quality. This would enable the project managers to foresee the problem areas and refactor the code to eliminate anomalies and undesirable complexities which may crop up post-deployment. Besides early assessment, it should considerably facilitate in better project planning and efficient resource-allocation.
A Control-Flow-Sensitive Analysis and Optimization Framework for the KESO Multi-JVM

The field of embedded systems is experiencing a trend towards an advancing degree of integration. As microprocessors become increasingly powerful, more and more applications are run as tasks on a single microcontroller instead of being deployed separately on different chips. This development brings along new challenges in terms of safety, security, and reliability. The use of hardware memory protection to isolate tasks from each other is often either not feasible or not desirable. A software-based mechanism can both compensate a missing and supplement an existing MPU. KESO is a Java Virtual Machine implementation for embedded systems that provides constructive memory protection through the use of the strictly type-safe Java programming language. It consists of an ahead-of-time compiler that translates Java bytecode into C, inserting runtime checks where necessary, and a minimal runtime environment. KESO applications are configured statically, allowing the compiler to make a number of assumptions that can be utilized to make the resulting machine code more light-weight and efficient. In this thesis, a new analysis framework was developed in order to gather comprehensive and accurate static information about a program. The analysis works in an interprocedural and control-flow-sensitive manner and provides value, type, and reachability information, among others. Based on the analysis, a set of optimization passes was implemented that exploit the collected knowledge to eliminate dead code, statically evaluate predictable expressions, and eliminate redundant runtime checks. Detailed evaluations of the enhancements made to the compiler showed that considerable improvements could be achieved in many cases.
Seroprevalence and predominant serogroups of Leptospira sp. in serological tests of ruminants in northeastern Brazil

The objective of this study was to determine the seroprevalence of leptospirosis and the predominant serogroups in serological tests of ruminants performed at the Transmissible Diseases Laboratory of the Federal University of Campina Grande, Patos, Paraíba, northeastern Brazil, between 2010 and 2017. This research included the records of serological tests for leptospirosis from 5594 animals, including 1527 cattle, 1761 goats, 2170 sheep, and 136 buffaloes from four Brazilian states (Paraíba, Pernambuco, Maranhão, and Rio Grande do Norte). Of the 5594 serum samples, 662 were serologically positive, corresponding to a frequency of 11.8%. Serjoe (30.6%), Autumnalis (13.6%), and Icterohaemorrhagiae (11.3%) were the most frequent subgroups among all species. The individual frequencies in cattle, goats, sheep, and buffaloes were 20% (306/1527), 8.3% (147/1761), 7.9% (171/2170), and 27.9% (38/136), respectively, with titers ranging from 1:100 to 1:3200. The most frequent serogroups according to animal species were as follows: Serjoe predominated in cattle (62%), followed by Icterohaemorrhagiae (12.5%) and Tarassovi (6.6%); Autumnalis was the most frequent in goats and sheep (29.4% and 26.9%, respectively), followed by Seramanga (12.5%) in goats and Icterohaemmorrhagiae (13.5%) in sheep; and Australis predominated in buffaloes (39.5%), followed by Pomona (31.6%) and Canicola (21.1%). Leptospira sp. infection, determined by serology, is widespread in ruminants (cattle, goats, sheep, and buffaloes) in northeastern Brazil, suggesting the existence of alternative transmission routes that are less dependent on environmental factors. Identification of the most frequent serogroups highlights the need to improve sanitary conditions, and implement efficient and targeted control measures directed at the primary infection sources.
Improved Reduced Flash Calculations Using Two-Parameter BIP Equation

Phase equilibrium calculations constitute a significant percentage of computational time in compositional simulation, especially as the number of components and phases increase. Reduced methods address this problem by carrying out phase equilibrium calculations using a reduced number of parameters. These methods have shown to speed up batch flash calculations, decrease simulation times, and also improve convergence behaviour. In this paper, we present new reduced parameters using the two-parameter binary interaction parameter formula originally proposed by Li and Johns (2006). The new reduced parameters are applied to solve twophase flash calculations for several five different fluid descriptions. The results show significant reduction in the number of iterations required to achieve convergence compared to the Li and Johns original approach. We further compare the results to other reduced methods, including the dominant eigenvalue method and a recently improved version of that method.
Online Semi-Supervised Learning with Bandit Feedback

We formulate a new problem at the intersectionof semi-supervised learning and contextual bandits,motivated by several applications including clini-cal trials and ad recommendations. We demonstratehow Graph Convolutional Network (GCN), a semi-supervised learning approach, can be adjusted tothe new problem formulation. We also propose avariant of the linear contextual bandit with semi-supervised missing rewards imputation. We thentake the best of both approaches to develop multi-GCN embedded contextual bandit. Our algorithmsare verified on several real world datasets.
Bi-Static Asteroid Radar Processing from Very Long Baseline Interferometry

Radar observation of Near Earth Asteroids using bi-static radar allows scientists to accurately measure the asteroid trajectory to predict whether the object poses a threat to Earth and provides an understanding of its shape, size and composition, thereby increasing our understanding of the formation of the Solar System. Current methods to perform analysis consist of a fixed Radio Science Receiver (RSR) and a Portable Radio Science Receiver (PRSR). Both methods are limited by the size, lack of mobility and cost of operation and upkeep of the equipment. In addition, they are controlled under United States International Traffic in Arms Regulations (ITAR). ITAR control increases the, cost, administrative burden and time taken to plan observations. Here we report on a developed open source software package that provides results with equivalent accuracy utilizing the standard recording formats already used in radio astronomy for Very Long Baseline Interferometry (VLBI). The key advantage of this technique is that many radio telescopes can provide such recordings without modification. This analysis tool facilitates robust and auditable processing of the recordings from anywhere in the world in near real-time and was developed using the two fundamental signal processing techniques of coherent and non-coherent integration. Once processed, the data is standardized to produce an output where the magnitude is in terms of standard deviations from the noise floor. To validate the program, processing of several observations was performed and compared to the data of the same observations obtained using the current methods. The results show that the developed software can produce comparable outputs to that of the RSR and PRSR. Presentation of the software package and the processed data from the 2017 observation of asteroid “Florence” to the Commonwealth Scientific and Industrial Research Organization (CSIRO) resulted in a request for its use as lead processor of data obtained from asteroid TC4 on the 12th October 2017.
Improving VPN Security Performance Based on One-Time Password Technique Using Quantum Keys

Network encryption technology has become an essential factor for organizational security. Virtual Private Network (VPN) or VPN encryption technology is the most popular technique used to prevent unauthorized users access to private network. This technique normally relies on mathematical function in order to generate periodic key. As a result, it may decrease security performance and vulnerable system, if high performance computing make rapid progress to reverse mathematicaln calculation to find out the next secret key pattern.The main contribution of this paper emphasizes on improving VPN performance by adopting quantum keys as a seed value into one-time password technique to encompasses the whole process of authentication, data confidentiality and security key management methodology in order to protect against eavesdroppers during data transmission over insecure network.
Single Phase D-Q Theory Based Control of DER Inverters for Power Quality Improvement

This paper presents a controller for DER inverters in the distribution side to act as shunt active filters and mitigate the power quality issues in the grid without the installation of additional FACTS devices. The inverter is able to inject the real power available at the DC link from renewable energy resource as well as perform power factor correction, harmonic elimination and balancing of the loads connected to the system at the coupling point. MATLAB/SIMULINK model of the system is developed and performance of the controller is tested and validated under varying irradiance and load conditions. Keywords-distributed generation; single phase D-Q theory; power quality; shunt active filter
Altered Gabab Receptor Thermoregulatory Function in Rats with Diet-Induced Obesity

Abstract GABAB receptors are G-protein-coupled receptors, playing a very important role in the regulation of many physiological processes. The GABAB signaling pathway could modulate neurotransmission processes at the level of the preoptic area in the anterior hypothalamus, which is thought to function as the thermoregulatory center. The present study was performed to investigate the effects of GABAB agonists and antagonists on core body temperature of rats with normal weight and diet-induced obesity. The results showed that systemic administration of the GABAB antagonist CGP35348 induced significant hyper-thermia in rats with normal weight, whereas the GABAB agonist baclofen led to a decrease in body temperature. The effects of baclofen and CGP35348 on body temperature were less pronounced in rats with diet-induced obesity compared with those with normal weight. Presently it remains unclear how obesity affects the GABAB receptor function at the level of the central thermoregulatory system.
SAIL-VOS: Semantic Amodal Instance Level Video Object Segmentation – A Synthetic Dataset and Baselines

We introduce SAIL-VOS (Semantic Amodal Instance Level Video Object Segmentation), a new dataset aiming to stimulate semantic amodal segmentation research. Humans can effortlessly recognize partially occluded objects and reliably estimate their spatial extent beyond the visible. However, few modern computer vision techniques are capable of reasoning about occluded parts of an object. This is partly due to the fact that very few image datasets and no video dataset exist which permit development of those methods. To address this issue, we present a synthetic dataset extracted from the photo-realistic game GTA-V. Each frame is accompanied with densely annotated, pixel-accurate visible and amodal segmentation masks with semantic labels. More than 1.8M objects are annotated resulting in 100 times more annotations than existing datasets. We demonstrate the challenges of the dataset by quantifying the performance of several baselines. Data and additional material is available at http://sailvos.web. illinois.edu.
Collaborative Learning of Semi-Supervised Segmentation and Classification for Medical Images

Medical image analysis has two important research areas: disease grading and fine-grained lesion segmentation. Although the former problem often relies on the latter, the two are usually studied separately. Disease severity grading can be treated as a classification problem, which only requires image-level annotations, while the lesion segmentation requires stronger pixel-level annotations. However, pixel-wise data annotation for medical images is highly time-consuming and requires domain experts. In this paper, we propose a collaborative learning method to jointly improve the performance of disease grading and lesion segmentation by semi-supervised learning with an attention mechanism. Given a small set of pixel-level annotated data, a multi-lesion mask generation model first performs the traditional semantic segmentation task. Then, based on initially predicted lesion maps for large quantities of image-level annotated data, a lesion attentive disease grading model is designed to improve the severity classification accuracy. Meanwhile, the lesion attention model can refine the lesion maps using class-specific information to fine-tune the segmentation model in a semi-supervised manner. An adversarial architecture is also integrated for training. With extensive experiments on a representative medical problem called diabetic retinopathy (DR), we validate the effectiveness of our method and achieve consistent improvements over state-of-the-art methods on three public datasets.
Decision Making under the Risk Using Assets Liability Model (ALM): Case Study on Four Assets with no Transaction Costs

This paper focuses on the Asset Liability Model (ALM) with multistage stochastic model. The model is based on four assets with no transaction costs. The initial wealth is Wo should be invested wisely to meet the liability L at the end of the planned horizon H . the best possible decision is to keep the final wealth larger than liability L. Using excel solver we try to optimize solution as best possible decision that will at least meeting the liability L at the end of the terminal wealth. The optimization case was adopted from the book (Brandimarte, 2011, pp.754-758) which has been modified with four assets and with no transaction costs. In adapting the case, we added additional returns for extra 2 stocks, while returns for the initial stock and bond remain.
The Third International Multi-Conference on Computing in the Global Information Technology

QR Codes (2D barcodes) are used to encode and decode data at a rapid rate. Using camera phones to read two dimensional barcodes for various purposes is currently a popular topic in both research and in practical applications. But until now, the information provided by QR Codes was solely static. What you see is exactly what was encoded. This paper proposes the notion of contextual QR Codes that merge a public QR Code and private information, in order to provide data related to a particular context. For example, a public tag will be decoded and translated into "Good evening John Smith" or "Bonjour Jean Dupond", if the system is able to obtain data such as the name of the user, the language used on the machine (English or French for instance) and the moment when the interaction takes place.
Gene Expression Analysis of Jasmonic Acid Related Genes during Root Infection of a Sugarbeet Cyst Nematode (Heterodera schachtii) on the Arabidopsis thaliana Plant

In several plant species, the establishment of systemically acquired resistance is associated with enhanced transcription of a large number of genes. This study aimed to analyze the expression of Jasmonic Acid (JA) related genes (COI 1, JAR 1, PDF 1.2A) in roots and shoots during cyst nematode infection on the Arabidopsis thaliana roots. The gene expression analysis results showed that while PDF1.2A was down-regulated in the roots, the reverse occurred in the leaf samples 24 hours after the sugarbeet cyst nematode inoculation. This indicates that the shoot defense system was activated during nematode root infection, thus, there is a systemic acquired resistance occurring in the shoots.
Outdoor augmented reality using optical see-through HMD system for visualizing building information

With the development of Information Technologies (IT), a need for new methods to visualize digital information has appeared in architectural and urban field. Augmented Reality (AR) technology, that overlays computer graphics on real world scenes, can be used to satisfy the need. AR-systems can use a wide variety of displays with different characteristics and limitations. For example, most common AR mobile applications, that use devices such as smartphones, occupy both hands of the operator. On the other hand, Head Mounted Displays (HMD) allow more freedom of movement to the user. In this paper, an outdoor AR system for visualizing building information is proposed and implemented. The proposed system uses optical see-through HMD to facilitate visualization, conception and communication about building information by augmenting virtual models on real scenes while giving the users free movement capability. To realize the system, a marker-less AR, which was based on local feature image registration and Structure from Motion (SfM) technologies, was developed. In our proposed method, in the preprocessing step, using a number of photographs from several viewpoints, a point cloud representation of a building is reconstructed by SfM. Then, in the main process, the local features of the live video image of HMD are extracted in real time and are associated with photographs for SfM using image recognition algorithms (such as SURF). Finally, a label which has information for assessing and rating the environmental performance are rendered in the optical see-thorough HMD.
Fisheries impact on trophic levels: North of the Persian Gulf case study, 2002-2011

There are few documented reports about the trophic structure and catch composition of trawl fishery in the Persian Gulf. In this study, the changes in the marine food webs in the North of Persian Gulf along the Bushehr province coastal water of Iran was determined based on the estimates of the two indicators, mean trophic level (MTI) and Fishing in Balanced (FiB) index for the last decade (2002-2011). The data indicated an increase in total landings (of 49 exploited species), and moderate increasing trend in both MTL and FiB-index was observed. Even though the Iranian coastal fishing has increased over time, it has a low impact on the trophic structure of marine communities in this region. The study suggested that it may be due to occurrence of ‘‘fishing up’’ phenomenon in this area and there is a need to expand fishing to offshore and deep waters for discovery of new and high-TL species.
Minimized Communication Protocol Based on a Multi-LOD Meta-Model for Adaptive Detailing of BIM Models

A continuous generation and evaluation of design variants characterize the conceptual architectural design stages. Variants comparison is a crucial process for making design-detailing decisions. Objectifiable criteria, including results of simulations and analysis, used for evaluation and comparison of design variants can legitimize decisions as the design process proceeds. A major challenge today is the management of design information and collaboration among several actors in a building project. Yet, a large portion of the Architecture, Engineering and Construction (AEC) industry deals with conventional methods to exchange design information. The growing use of building information models is promising but even the most recent developments and practices still rely heavily on human-readable protocols and issue management systems. Considering the potential of schematized computer-readable communications to be analyzed and used for future references and case-based reasoning systems. This paper proposes a novel minimized communication protocol that aims to introduce a computer-readable, yet adaptive universal method which works on schematized information exchange requirements (templates) for different use cases. The concept is demonstrated using an example scenario.
Analyzing Nanjing Tones and Sandhi: Statistical Modelling Methods

In this study, we used growth curve analysis to model the surface f0 contours of the sandhi tones, and we also modelled the underlying pitch target of sandhi tones and their corresponding citation tones in Nanjing Chinese. The underlying pitch targets of reported sandhi tones showed neutralization with reported base tones in most cases, but the surface f0 contours of each pair were not neutralized. Our results provided acoustic evidence for the debate over the existence of some tone sandhi rules in Nanjing Chinese based on impressionistic data. Finally, transcription modification of some monosyllabic Nanjing Chinese tones was recommended based on the transformation of the results of the statistical modelling of the acoustic data.