Search

In computational biology, biological database search has been playing a very important role. Since the COVID-19 outbreak, it has provided significant help in identifying common characteristics of viruses and developing vaccines and drugs. Sequence alignment, a method finding similarity, homology and other information between gene/protein sequences, is the usual tool in the database search. With the explosive growth of biological databases, the search process has become extremely time-consuming. However, existing parallel sequence alignment algorithms cannot deliver efficient database search due to low utilization of the resources such as cache memory and performance issues such as load imbalance and high communication overhead. In this paper, we propose an efficient sequence alignment algorithm on Sunway TaihuLight, called ESA, for biological database search. ESA adopts a novel hybrid alignment algorithm combining local and global alignments, which has higher accuracy than other sequence alignment algorithms. Further, ESA has several optimizations including cache-aware sequence alignment, capacity-aware load balancing and bandwidth-aware data transfer. They are implemented in a heterogeneous processor SW26010 adopted in the world's 6th fastest supercomputer, Sunway TaihuLight. The implementation of ESA is evaluated with the Swiss-Prot database on Sunway TaihuLight and other platforms. Our experimental results show that ESA has a speedup of 34.5 on a single core group (with 65 cores) of Sunway TaihuLight. The strong and weak scalabilities of ESA are tested with 1 to 1024 core groups of Sunway TaihuLight. The results show that ESA has linear weak scalability and very impressive strong scalability. For strong scalability, ESA achieves a speedup of 338.04 with 1024 core groups compared with a single core group. We also show that our proposed optimizations are also applicable to GPU, Intel multicore processors, and heterogeneous computing platforms. • In this paper, we propose and implement an efficient sequence alignment algorithm, ESA, for biological database search on SW26010 heterogeneous processors. This algorithm adopts both local and global alignments for biological database search with several optimizations. ESA achieves high computational performance without sacrificing accuracy. To the best of our knowledge, this is the first attempt to parallelize hybrid sequence alignment on Sunway TaihuLight using multi-level optimizations. • We propose three optimization strategies in ESA: cache-aware sequence alignment, capacity-aware load balancing and bandwidth-aware data transfer. Cache-aware sequence alignment effectively reduces the size of the data structure for sequence alignment and fully utilizes the vectorization of the slave cores of SW26010. With capacity-aware load balancing, we distribute the workload evenly among the cores of SW26010. With bandwidth-aware data transfer, ESA reduces the communication overhead by using asynchronous DMA transmission and RLC. • We evaluate the performance of ESA using the Swiss-Prot database on Sunway TaihuLight. Our experimental results show that ESA achieves a speedup of 34.5 times on a single CG over the manager core. Compared with a serial implementation on Intel (R) Xeon (R) CPU E5-2620 v4 processor, ESA achieves a speedup of 21.6 on a single CG. We also demonstrate that ESA has linear weak scalability and very competitive strong scalability. Finally, we compare ESA with mainstream algorithms on the CPU+GPU platform and achieve the highest GCUPS of 228.91. [ABSTRACT FROM AUTHOR]

Neural Radiance Fields (NeRF) can achieve state-of-the-art new view results when given a sufficient number of training views. However, NeRF's rendering is based on minimizing photometric consistency loss, and during the optimization process, it can suffer from overfitting due to factors such as lighting and texture, resulting in poor geometric and color reconstruction. The less data there is in the overlapping areas of the images, the greater the impact on the results, such as insufficient data caused by occlusion relationships. In this paper, we observed through experiments that introducing low-frequency images into NeRF during training can quickly obtain approximate geometric structures, which can guide NeRF to achieve more stable view synthesis results. Therefore, we propose a progressive wavelet mask to assist in the training of neural radiance fields. By first constructing a gaussian pyramid for the training images and then applying wavelet decomposition to them, we can obtain a series of low-frequency region masks to guide the neural radiance field to focus on learning low-frequency pixel regions and gradually introduce high-frequency lighting and texture changes. Our experiments on the LLFF and Blender datasets show that using a progressive wavelet mask in NeRF training can achieve more realistic generation effects with almost no additional computational overhead. At the same time, we also tested our method in sparse scenes, where it still performs well in avoiding overfitting. • NeRF get more stable geometric structures and rendering quality with blur image. • Designed a new training strategy to enhance the rendering performance of NeRF. • NeRF model with neural network is influenced by the ray training strategy. • The method is more effective in NeRF with neural network. [ABSTRACT FROM AUTHOR]

A select number of scientific communities have been quite successful in evolving the culture within their community to encourage publishing and to provide resources for re-using well-documented data. These data have great potential for analysis and knowledge generation beyond the purposes for which they were collected and intended. However, there are still barriers in this process. To explore this problem, we have developed a prototype tool: the Experiment Dashboard (ED), with the objective of demonstrating the ability and potential of enabling automated data ingestion from typical research laboratories. This innovative prototype was developed to create a novel system and artifact to explore the possibilities of allowing researchers in laboratories across the nation to link their data acquisition systems directly to structured data repositories for data and metadata ingestion. The prototype functions with commonly used data acquisition software at the data source and the HUBzero scientific gateway at the data sink. ED can be set up with minimal effort and expertise. In this paper, we describe the motivation and purposes for the prototype, the architecture we devised and functionality of this tool, and provide a demonstration of the tool for optical measurements in a structural engineering laboratory. The goal of this paper is to articulate and show through our prototype a vision for future cyberinfrastructure for empirical disciplines that rely on the rapid collection, analysis, and dissemination of valuable experimental data. We also discuss lessons learned that may be useful for others seeking to solve similar problems. [ABSTRACT FROM AUTHOR]

OnTrack is a tool that supports workflows for railway verification that has been implemented using model driven engineering frameworks. Starting with graphical scheme plans and finishing with automatically generated formal models set-up for verification, OnTrack allows railway engineers to interact with verification procedures through encapsulating formal methods. OnTrack is grounded on a domain specification language (DSL) capturing scheme plans and supports generation of various formal models using model transformations. In this paper, we detail the role model driven engineering takes within OnTrack and reflect on the use of model driven engineering concepts for developing domain specific formal methods toolsets. [ABSTRACT FROM AUTHOR]

Children and adolescents with intellectual disability are known to experience mental health disorders, but anxiety disorders in this population have received relatively little attention. Firstly, this paper provides a review of published studies reporting prevalence rates of anxiety disorders in children and adolescents with intellectual disability. Secondly, the paper reviews measures of anxiety that have been evaluated in children/adolescents with intellectual disability, and details the associated psychometric properties. Seven studies reporting prevalence rates of anxiety disorders in this population were identified, with reported rates varying from 3% to 22%. Two-one studies evaluating a measure of anxiety in a sample of children/adolescents with intellectual disability were identified. While these studies indicate that several measures show promise, further evaluation studies are needed; particularly those that evaluate the capacity of measures to screen for anxiety disorders, not only measure symptoms. [ABSTRACT FROM AUTHOR]

Smart contracts have rapidly gained popularity as self-contained pieces of code, especially those run on the Ethereum blockchain. On the one hand, smart contracts are immutable, have transparent workings, and execute autonomously. On the other hand, these qualities make it essential to properly test the behavior of a smart contract before deploying it. In this paper, we introduce SolAR , a tool and approach for Sol idity A utomated Test Suite Gene R ation. SolAR allows smart contract developers to generate test suites for Solidity smart contracts optimized automatically for branch coverage using either a state-of-the-art genetic algorithm or a fuzzing approach. It enables a novel way to handle blockchain operations—or ChainOps—from a pipeline perspective, entailing a larger-scale as well as more manageable and maintainable service continuity. [ABSTRACT FROM AUTHOR]

While System of Systems (SoS) architectures for large and complex software projects are gaining momentum, the commonly used modeling and tooling approaches are still general-purpose or oriented towards single systems. Developers could benefit from methods and tools that avoid system-centric details in favor of native SoS modeling support. This paper presents a diagram-centric modeling tool with native SoS modeling support. The tool is implemented as a plugin for the Eclipse Papyrus modeling tool. The tool was showcased as a demo at MODELS'22. The code of the plugin is freely available via Github. • The Abstract Communicating Systems (ACS) methodology can support designing complex platforms based on Systems of Systems. • ACS was mapped on UML 2.5, and ACS modeling tool (ACSmt) is the first tool implementing the ACS methodology. • ACSmt is implemented as an Eclipse Papyrus plugin, which supports UML 2.5 and is well-accepted in the industry. • ACSmt allows for verifying structural properties of the designed SoS. • The open source code of ACSmt can be used as reference when implementing plugins for Papyrus. [ABSTRACT FROM AUTHOR]

• Our MTDD model is an integrated knowledge-driven and data-driven model. This method avoids problems such as not considering expert experience or insight, not paying attention to the overall situation, and lack of interpretability brought about by only using data-driven technology. It not only utilizes the text features and the semantic features but also applies the domain knowledge to learn the representation of the depression tendency, thus making the model more robust. In other words, the model combines text features, semantic features, and domain knowledge. The Word2Vec word embedding integrates the emotional information of the words in the emotional dictionary, expands the existing emotional dictionary, extracts the TF-IDF word frequency feature, and proposes seven grammatical analysis rules to obtain the text emotional value feature making it more suitable for depression tendency detection classification task. • The MTDD model is a deep neural network hybrid model, which circumvents the weak generalization ability of a single model for identifying depression tendencies. Specifically, the MTDD model combines the advantages of CNN and Bi-LSTM networks. The CNN network offers the advantage of the ability to extract the local features of the text. In addition, BiLSTM can effectively capture bidirectional semantic information. This combination can better represent text features and improve the model's classification accuracy. • Our MTDD model is obtained based on real data, thus making the model more suitable for practical problems. As far as we know, many existing depression detection methods are only trained on some experimental data sets, so the model's generalization ability is limited and cannot even be applied in realistic scenes. In comparison, the MTDD model is trained on social platform data, making the data more objective and accurate. In addition, the data of social platforms can be obtained at a low cost. It is easy to operate and does not require a lot of laborious labeling. Moreover, our approach avoids the influence of subjective factors in the method of consultation by mental health experts and the influence of non-public and imperfect data used for depression. • We conducted extensive experiments on a Reddit data set and a Twitter data set. The results show that, compared to multiple latest depression detection models, our MTDD model detects users who may be depressed with a 95% F1 value and obtains SOTA results. [Display omitted] Background and Objective: Depression can severely impact physical and mental health and may even harm society. Therefore, detecting the early symptoms of depression and treating them on time is critical. The widespread use of social media has led individuals with depressive tendencies to express their emotions on social platforms, share their painful experiences, and seek support and help. Therefore, the massive available amounts of social platform data provide the possibility of identifying depressive tendencies. Methods: This paper proposes a neural network hybrid model MTDD to achieve this goal. Analysis of the content of users' posts on social platforms has facilitated constructing a post-level method to detect depressive tendencies in individuals. Compared with existing methods, the MTDD model uses the following innovative methods: First, this model is based on social platform data, which is objective and accurate, can be obtained at a low cost, and is easy to operate. The model can avoid the influence of subjective factors in the depressive tendency detection method based on consultation with mental health experts. In other words, it can avoid the problem of undisclosed and imperfect data in depressive tendency detection. Second, the MTDD model is based on a deep neural network hybrid model, combining the advantages of CNN and BiLSTM networks and avoiding the problem of poor generalization ability in a single model for depression tendency recognition. Third, the MTDD model is based on multimodal features for learning the vector representation of depression-prone text, including text features, semantic features, and domain knowledge, making the model more robust. Results: Extensive experimental results demonstrate that our MTDD model detects users who may have a depressive tendency with a 95 % F1 value and obtained SOTA results. Conclusions: Our MTDD model can detect depressive users on social media platforms more effectively, providing the possibility for early diagnosis and timely treatment of depression. The experiment proves that our MTDD model outperforms many of the latest depressive tendency detection models. [ABSTRACT FROM AUTHOR]

[Display omitted] • Introduce capsule network for multi-focus image fusion. • Utilize structure information to help locate focus regions. • Design structure-guided flow module to integrate structure features. Existing deep learning based methods have shown their advantages in multi-focus image fusion task. However, most methods still suffer from inaccurate focus region detection. In this paper, we employ the property of part-whole relationships embedded by the Capsule Network (CapsNet) to solve the problem. Specifically, we introduce CapsNet in multi-focus image fusion task, and design a structure-guided flow module, which fully utilizes structure information to help locate focus regions. CapsNet is introduced to extract structure features by supervising gradient information of the image. Compared with traditional convolutional neural networks (CNNs), CapsNet takes into account the correlation of features from different positions, such that it encodes more compact features. Once structure features are obtained, a flow alignment module is introduced to learn flow field between structure and image features, and propagate effectively structure features to image features to make confident focus region detection. Experimental results show the proposed method achieves robust fusion performance on three publicly available multi-focus datasets, and outperforms or is comparable to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The selective catalytic oxidation of ammonia (NH 3 -SCO) is currently the most effective method used to eliminate NH 3. However, one of the major challenges for NH 3 -SCO is the development of catalyst capable of completely converting NH 3 into harmless N 2 and water vapor. In this paper, a high N 2 selective catalyst prepared by the sol-gel method using TiO 2 as the support, Cu as the active species, and Sm as the auxiliary agent is presented. Compared to traditional Cu/TiO 2 catalyst, the 4SmCu/TiO 2 catalyst has higher catalytic activity and N 2 selectivity at low temperatures. The NH 3 conversion can reach 100%, and the selectivity of N 2 can be maintained at 100% at 275 °C. The excellent catalytic activity is attributed to the highly dispersed active species, abundant Lewis acid sites (LASs), and the generation of large amounts of surface-adsorbed oxygen. In addition, doping Sm species will cause TiO 2 lattice distortion, and at the same time load the distorted TiO 2 with more active material. Moreover, in-situ DRIFTS analysis suggests that both the 4SmCu/TiO 2 and Cu/TiO 2 catalysts follow the "internal" selective catalytic reduction (iSCR) mechanism during NH 3 -SCO reactions. The 4SmCu/TiO 2 catalyst generates more amides (-NH 2), which reduces the non-selective oxidation of the catalysts and promotes the formation of N 2. This provides a new idea and method for the selective catalytic oxidation of NH 3 to N 2 and water vapor using Cu-based catalysts. [Display omitted] • 4SmCu/TiO2 catalyst converts all NH3 into N2 and water vapor at 275 ℃. • The lattice distortion of TiO2 can support more active species. • Doping of Sm species increased the content of Lewis acid site and surface-adsorbed oxygen. • The rich amide intermediates reduce the non-selective oxidation of NH3 and O2. [ABSTRACT FROM AUTHOR]

• We present ChampKit, a Python-based software package that enables rapid exploration and evaluation of deep learning models for patch-level classification of histopathology data. It is open source and available at https://github.com/SBU-BMI/champkit. ChampKit is designed to be highly reproducible and enables systematic, unbiased evaluation of patch-level histopathology classification. It incorporates public datasets for six clinically important tasks and access to hundreds of (pre-trained) deep learning models. It can easily be extended to custom patch classification datasets and custom deep learning architectures. • The users are intended to be (1) biomedical research groups interested in finding and fine-tuning the best models to analyze a broad collection of whole slide images, and (2) deep learning methods research groups interested in systematically and quickly evaluating their methods against a set of state-of-the-art methods with different pretraining and transfer learning configurations. • We demonstrate the utility of ChampKit by evaluating two ResNet models and one vision transformer on the six diverse classification tasks for patch-level histopathology datasets. We did not find consistent benefits from pretrained models versus random initialization across the different datasets, which suggests that a thorough exploration of model architectures is important to identify optimal models for a given dataset. Histopathology is the gold standard for diagnosis of many cancers. Recent advances in computer vision, specifically deep learning, have facilitated the analysis of histopathology images for many tasks, including the detection of immune cells and microsatellite instability. However, it remains difficult to identify optimal models and training configurations for different histopathology classification tasks due to the abundance of available architectures and the lack of systematic evaluations. Our objective in this work is to present a software tool that addresses this need and enables robust, systematic evaluation of neural network models for patch classification in histology in a light-weight, easy-to-use package for both algorithm developers and biomedical researchers. Here we present ChampKit (Comprehensive Histopathology Assessment of Model Predictions toolKit): an extensible, fully reproducible evaluation toolkit that is a one-stop-shop to train and evaluate deep neural networks for patch classification. ChampKit curates a broad range of public datasets. It enables training and evaluation of models supported by timm directly from the command line, without the need for users to write any code. External models are enabled through a straightforward API and minimal coding. As a result, Champkit facilitates the evaluation of existing and new models and deep learning architectures on pathology datasets, making it more accessible to the broader scientific community. To demonstrate the utility of ChampKit, we establish baseline performance for a subset of possible models that could be employed with ChampKit, focusing on several popular deep learning models, namely ResNet18, ResNet50, and R26-ViT, a hybrid vision transformer. In addition, we compare each model trained either from random weight initialization or with transfer learning from ImageNet pretrained models. For ResNet18, we also consider transfer learning from a self-supervised pretrained model. The main result of this paper is the ChampKit software. Using ChampKit, we were able to systemically evaluate multiple neural networks across six datasets. We observed mixed results when evaluating the benefits of pretraining versus random intialization, with no clear benefit except in the low data regime, where transfer learning was found to be beneficial. Surprisingly, we found that transfer learning from self-supervised weights rarely improved performance, which is counter to other areas of computer vision. Choosing the right model for a given digital pathology dataset is nontrivial. ChampKit provides a valuable tool to fill this gap by enabling the evaluation of hundreds of existing (or user-defined) deep learning models across a variety of pathology tasks. Source code and data for the tool are freely accessible at https://github.com/SBU-BMI/champkit. [ABSTRACT FROM AUTHOR]

Dengue is a disease transmitted by the Aedes Aegypti mosquito, which also transmits the Zika virus and Chikungunya. Unfortunately, the population of different countries has been suffering from the diseases transmitted by this mosquito. The communities should play an important role in combating and preventing the mosquito-borne diseases. However, due to the limited engagement of the population, new solutions need to be used to strengthen the mosquito surveillance. VazaDengue is one of these solutions, which offers the users a web and mobile platform for preventing and combating mosquito-borne diseases. The system relies on social actions of citizens reporting mosquito breeding sites and dengue cases, in which the reports are made available to the community and health agencies. In order to address the limited population engagement, the system proactively monitors social media network as Twitter to enrich the information provided by the system. It processes the natural language text from the network to classify the tweets according to a set of predefined categories. After the classification, the relevant tweets are provided to the users as reports. In this paper, we describe the VazaDengue features including its ability to harvest and classify tweets. Since the VazaDengue system aims to strengthen the entomological surveillance of the mosquito that transmits Dengue, Zika, and Chikungunya by providing geolocated reports, we present here two studies to evaluate its potential contributions. The first evaluation uses a survey conducted in the Brazilian community of health agents. The goal is to evaluate the relevance of the classified tweets according to the health agents’ perspective. The second study compares the official reports of the 2015–2016 epidemic waves in Brazil with the concentration of mosquito-related tweets found by VazaDengue. The goal is to verify if the concentration of tweets can be used for monitoring the mosquito manifestation in big cities. The results of these two evaluations are encouraging. For instance, we have found that the health agents tend to agree with the relevance of the classified tweets. Moreover, the concentration of tweets is likely to be effective for monitoring big cities. The results of these evaluations are helping us to improve the VazaDengue system further. These improvements will make the VazaDengue system even more useful for combating and preventing the mosquito-borne diseases. [ABSTRACT FROM AUTHOR]

The desperate need for tools with features similar to the Content Management Systems (CMS), offering Application Programming Interfaces (APIs) and providing flexibility for specific business and user demands, is the key motivation behind the work presented in this paper. A novel Content Management Framework (CMF), called mtCMF, is introduced. It offers user-friendly wizards and state-of-the-art adaptive structure. It does not have any constraints for use in terms of a predefined website structure, data types or database tables. Business objects can be easily defined by using the proposed mtCMF, and database tables are created from these objects automatically. mtCMF has an adaptive scaffolding architecture to allow generating Create, Read, Update, and Delete (CRUD) screens, based on the novel on-the-fly approach for all types of database tables. It has a flexible localization option to support multiple languages, and delivers Representational State Transfer (REST) services for mobile clients and remote application development. Its simple structure and novel wizard-based user-friendly interface make it superior to traditional CMS tools and web development frameworks. Performance analysis of mtCMF was carried out by using Blackfire Profiler. The results clearly have proved that mtCMF achieves better performance than its counterparts Laravel, Symfony, WordPress and Joomla. [ABSTRACT FROM AUTHOR]

This paper describes MLweb, an open source software toolkit for machine learning on the web. The specificity of MLweb is that all computations are performed on the client side without the need to send data to a third-party server. MLweb includes three main components: a JavaScript API for scientific computing (LALOLib), an extension of this library with machine learning tools (ML.js) and an online development environment (LALOLab) with many examples. [ABSTRACT FROM AUTHOR]

Decimal multiplication is a ubiquitous operation which is inherently complex in terms of partial product generation and accumulation. In this paper, the authors propose a generalized design approach and architectural framework for ‘digit-by-digit’ multiplication. Decimal partial products are generated in parallel using fast and area efficient BCD digit multipliers and their reduction is achieved using hybrid multi-operand binary-to-decimal converters. In contrast to most of the previous implementations, which propose changes either in partial product generation or reduction, this work proposes modifications at both partial product generation and reduction stages resulting in an improved performance. A comprehensive analysis of synthesis results (consistent with IEEE-compliant 16-digit decimal multiplier architecture) indicates an improvement in delay of 8–29% and a reduced area-delay product of 4–38% compared to similar work published previously. [ABSTRACT FROM AUTHOR]

A geological model (geomodel) in three-dimensional (3D) space is a digital representation of the Earth's subsurface, recognized by geologists and stored in resultant geological data (geodata). The increasing demand for data management and interoperable applications of geomodelscan be addressed by developing standard-based exchange formats for the representation of not only a single geological object, but also holistic geomodels. However, current standards such as GeoSciML cannot incorporate all the geomodel-related information. This paper presents Geo3DML for the exchange of 3D geomodels based on the existing Open Geospatial Consortium (OGC) standards. Geo3DML is based on a unified and formal representation of structural models, attribute models and hierarchical structures of interpreted resultant geodata in different dimensional views, including drills, cross-sections/geomaps and 3D models, which is compatible with the conceptual model of GeoSciML. Geo3DML aims to encode all geomodel-related information integrally in one framework, including the semantic and geometric information of geoobjects and their relationships, as well as visual information. At present, Geo3DML and some supporting tools have been released as a data-exchange standard by the China Geological Survey (CGS). [ABSTRACT FROM AUTHOR]

The consensual agreement that ground motion record selection plays an important role in the non-linear dynamic structural response has contributed to numerous research studies seeking the definition of accurate ground motion record selection techniques. However, most of the available tools only allow for record selection based on spectral compatibility between the mean response spectrum of a record suite and a target response spectrum. This paper presents SelEQ, a fully integrated framework that implements state-of-the art procedures for ground motion record selection and scaling. In addition to typical record selection procedures, SelEQ allows obtaining the Conditional Mean Spectrum (CMS) for the European territory, the latter making use of the open source platform OpenQuake and the recently proposed SHARE seismic hazard model. This important feature allows state-of-the-art record selection for probabilistic-based assessment and risk analysis. SelEQ incorporates a number of procedures available in the literature that facilitate preliminary record selection (e.g. disaggregation for a specific site) and that allow advanced selection criteria (e.g. control of mismatch of individual ground motion records). The framework makes use of the Adaptive Harmony Search meta-heuristic optimization algorithm in order to significantly minimize computational cost and analysis time, whilst still meeting the imposed selection constraints. Application examples of the framework indicate that it can accurately select suites of ground motion records for code-based and probabilistic-based seismic assessment. [ABSTRACT FROM AUTHOR]

Thorough testing of safety-critical autonomous systems, such as self-driving cars, autonomous robots, and drones, is essential for detecting potential failures before deployment. One crucial testing stage is model-in-the-loop testing, where the system model is evaluated by executing various scenarios in a simulator. However, the search space of possible parameters defining these test scenarios is vast, and simulating all combinations is computationally infeasible. To address this challenge, we introduce AmbieGen, a search-based test case generation framework for autonomous systems. AmbieGen uses evolutionary search to identify the most critical scenarios for a given system, and has a modular architecture that allows for the addition of new systems under test, algorithms, and search operators. Currently, AmbieGen supports test case generation for autonomous robots and autonomous car lane keeping assist systems. In this paper, we provide a high-level overview of the framework's architecture and demonstrate its practical use cases. • AmbieGen is an evolutionary algorithm based test scenario generation tool. • The search algorithm maximizes the difficulty of test scenarios as well as their diversity. • The tool is customizable and can be used to test different robotic systems. • Current tool version includes test scenario generation for autonomous vehicles and mobile robots. • The tool can be accessed at: https://github.com/swat-lab-optimization/AmbieGen-tool. [ABSTRACT FROM AUTHOR]

• We introduce the open source simulator FLUST, as part of the UltraSound ToolBox (USTB). • FLUST produces multiple realizations of ultrasound signals from flow fields. • High integrity signals are achieved at low computational cost. • Framework includes tools for visualization and assessment of estimator performance. • Database includes customizable acquisition setups, flow phantoms and estimators. Background and objective: Ultrasound based blood velocity estimation is a continuously developing frontier, where the vast number of possible acquisition setups and velocity estimators makes it challenging to assess which combination is better suited for a given imaging application. FLUST, the Flow-Line based Ultrasound Simulation Tool, may be used to address this challenge, providing a common platform for evaluation of velocity estimation schemes on in silico data. However, the FLUST approach had some limitations in its original form, including reduced robustness for phase sensitive setups and the need for manual selection of integrity parameters. In addition, implementation of the technique and therefore also documentation of signal integrity was left to potential users of the approach. Methods: In this work, several improvements to the FLUST technique are proposed and investigated, and a robust, open source simulation framework developed. The software supports several transducer types and acquisition setups, in addition to a range of different flow phantoms. The main goal of this work is to offer a robust, computationally cheap and user-friendly framework to simulate ultrasound data from stationary blood velocity fields and thereby facilitate design and evaluation of estimation schemes, including acquisition design, velocity estimation and other post-processing steps. Results: The technical improvements proposed in this work resulted in reduced interpolation errors, reduced variability in signal power, and also automatic selection of spatial and temporal discretization parameters. Results are presented illustrating the challenges and the effectiveness of the solutions. The integrity of the improved simulation framework is validated in an extensive study, with results indicating that speckle statistics, spatial and temporal correlation and frequency content all correspond well with theoretical predictions. Finally, an illustrative example shows how FLUST may be used throughout the design and optimization process of a velocity estimator. Conclusions: The FLUST framework is available as a part of the UltraSound ToolBox (USTB), and the results in this paper demonstrate that it can be used as an efficient and reliable tool for the development and validation of ultrasound-based velocity estimation schemes. [ABSTRACT FROM AUTHOR]

Formal verification of a UML class diagram annotated with OCL constraints has been a long-standing challenge in Model-driven Engineering. In the past decades, many tools and techniques have been proposed to tackle this challenge. However, they do not scale well and are often unable to locate the conflicts when then number of OCL constraints significantly increases. In this paper, we present a new tool called QMaxUSE. This tool is designed for verifying UML class diagrams annotated with large number of OCL invariants. QMaxUSE is easy to install and deploy. It offers two distinct features. (1) A simple query language that allows users to choose parts of a UML class diagram to be verified. (2) A new procedure that is capable of performing concurrent verification. [ABSTRACT FROM AUTHOR]