High-level Language Research Articles

Extraction of logical networks during decompiling transistor-level CMOS circuit descriptions

Objectives. The problem of restoring the functional description of digital VLSI devices presented at the transistor level is considered. The objective of the work is to develop means for extraction of blocks representing logical networks from two-level descriptions of CMOS circuits at the transistor level, which were obtained as a result of recognition (extraction) of subcircuits that implement logic elements.Methods. Graph based methods and software tools are proposed for extracting a connected blocks representing a logical network from two-level descriptions of a transistor circuits in SPICE format. In the graph interpretation, the task is reduced to constructing a labeled directed graph of a logical network based on a labeled undirected bipartite graph specifying a two-level description of the transistor circuit.Results. The proposed method makes it possible to identify lexicographically ranked logical networks, from which a transition is made to logical equations that specify the functions implemented at the outputs of the resulting networks. Software tools have been developed that provide the generation of a hierarchical description in SPICE format that implements the original circuit at the transistor level, as well as descriptions of found logical networks in the SF language of hierarchical structural and functional descriptions of discrete devices and in high-level languages (VHDL and Verilog).Conclusion. The developed methods are implemented in C++, included in the program for decompiling transistor CMOS circuits and tested within it on practical examples of transistor-level circuits. The paper provides examples of reverse engineering of some practical transistor circuits.

Healthy Communities for Youth: A Cost Analysis of a Community-Level Program to Prevent Youth Violence

Youth violence is a national public health concern in USA, especially in resource-constrained urban communities. Between 2018 and 2021, the Healthy Communities for Youth (HCFY) program addressed youth violence prevention in select economically marginalized urban communities, with the HCFY program reducing the likelihood of youth-involved violent crime. Leveraging costs from program expense reports, this study analyzes the costs of the HCFY program in order to inform policymaking and the program’s future ongoing implementation. Total HCFY program costs were $821,000 ($290,100 annually including program start-up costs) over the 34-month project period. Operationalization costs contributed the largest share (64.8%), with 45% attributable to intervention coordinators. In the intervention community, the program costs $100 per capita, $1100 per youth-involved crime case, and $8100 per youth-involved violent crime case. Findings were sensitive to the number of youth-involved crime or violent crime cases and costs of high-level program leadership and self-evaluation analysts, with the per youth-involved violent crime case cost ranging between $700 and $1600 over the program period. Analysis of HCFY program costs is an important step in determining the affordability of a community-level program to prevent youth violence in resource-limited urban communities.

An Additively Optimal Interpreter for Approximating Kolmogorov Prefix Complexity.

We study practical approximations of Kolmogorov prefix complexity (K) using IMP2, a high-level programming language. Our focus is on investigating the optimality of the interpreter for this language as the reference machine for the Coding Theorem Method (CTM). This method is designed to address applications of algorithmic complexity that differ from the popular traditional lossless compression approach based on the principles of algorithmic probability. The chosen model of computation is proven to be suitable for this task, and a comparison to other models and methods is conducted. Our findings show that CTM approximations using our model do not always correlate with the results from lower-level models of computation. This suggests that some models may require a larger program space to converge to Levin's universal distribution. Furthermore, we compare the CTM with an upper bound on Kolmogorov complexity and find a strong correlation, supporting the CTM's validity as an approximation method with finer-grade resolution of K.

RenderKernel: High-level programming for real-time rendering systems

Real-time rendering applications leverage heterogeneous computing to optimize performance. However, software development across multiple devices presents challenges, including data layout inconsistencies, synchronization issues, resource management complexities, and architectural disparities. Additionally, the creation of such systems requires verbose and unsafe programming models. Recent developments in domain-specific and unified shading languages aim to mitigate these issues. Yet, current programming models primarily address data layout consistency, neglecting other persistent challenges.In this paper, we introduce RenderKernel, a programming model designed to simplify the development of real-time rendering systems. Recognizing the need for a high-level approach, RenderKernel addresses the specific challenges of real-time rendering, enabling development on heterogeneous systems as if they were homogeneous. This model allows for early detection and prevention of errors due to system heterogeneity at compile-time. Furthermore, RenderKernel enables the use of common programming patterns from homogeneous environments, freeing developers from the complexities of underlying heterogeneous systems. Developers can focus on coding unique application features, thereby enhancing productivity and reducing the cognitive load associated with real-time rendering system development.

ASSESSMENT OF THE FINANCIAL RESULT OF PUBLIC-PRIVATE PARTNERSHIP PROJECT IMPLEMENTATION USING THE EXAMPLE OF A CONCESSION AGREEMENT

The article is aimed at developing a methodological approach to improving the efficiency of assessing the financial results of implementing Ukrainian and European business projects. In this context, the object of study is a specific individual partnership and cooperation project. The research has established that one of the key informational aspects of such a partnership is the assessment and forecasting of the financial result. The goal of the research is to present the methodological approach to assessing financial performance for an individual project in the most detailed form possible. The article presents an approach based on modern simulation modelling methods, systems analysis methodology, and the method of composite seasonal indices. All modelling processes were conducted using MATLAB software. MATLAB is a high-level programming language and interactive environment for numerical computation, visualization, and programming. This program is widely used for modelling and data processing in fields such as engineering, scientific research, and mathematics. The research result is presented as an approach to the mathematical modelling of assessing the financial performance of partnership projects of Ukraine with representatives of EU business. It is necessary to emphasize that the presented research has limitations related to changes in Ukraine's hyperdynamic external environment provoked by war. Forecasts may vary depending on the development of military actions. Therefore, the prospects for further research should be directed towards improving the informational provision in the system of partnership and cooperation with EU countries to increase their own investment security.

Real-time data processing in colorimetry camera-based single-molecule localization microscopy via CPU-GPU-FPGA heterogeneous computation.

Because conventional low-light cameras used in single-molecule localization microscopy (SMLM) do not have the ability to distinguish colors, it is often necessary to employ a dedicated optical system and/or a complicated image analysis procedure to realize multi-color SMLM. Recently, researchers explored the potential of a new kind of low-light camera called colorimetry camera as an alternative detector in multi-color SMLM, and achieved two-color SMLM under a simple optical system, with a comparable cross-talk to the best reported values. However, extracting images from all color channels is a necessary but lengthy process in colorimetry camera-based SMLM (called CC-STORM), because this process requires the sequential traversal of a massive number of pixels. By taking advantage of the parallelism and pipeline characteristics of FPGA, in this paper, we report an updated multi-color SMLM method called HCC-STORM, which integrated the data processing tasks in CC-STORM into a home-built CPU-GPU-FPGA heterogeneous computing platform. We show that, without scarifying the original performance of CC-STORM, the execution speed of HCC-STORM was increased by approximately three times. Actually, in HCC-STORM, the total data processing time for each raw image with 1024 × 1024 pixels was 26.9 ms. This improvement enabled real-time data processing for a field of view of 1024 × 1024 pixels and an exposure time of 30 ms (a typical exposure time in CC-STORM). Furthermore, to reduce the difficulty of deploying algorithms into the heterogeneous computing platform, we also report the necessary interfaces for four commonly used high-level programming languages, including C/C++, Python, Java, and Matlab. This study not only pushes forward the mature of CC-STORM, but also presents a powerful computing platform for tasks with heavy computation load.

Short-Circuit Fault Diagnosis on the Windings of Three-Phase Induction Motors through Phasor Analysis and Fuzzy Logic

An induction motor is an electric machine widely used in various industrial and commercial applications due to its efficiency and simple design. In this regard, a methodology based on the electric phasor analysis of line currents and the variations in the phase angles among these line currents is proposed. The values in degrees of the angles between every pair of line currents were introduced to a fuzzy logic algorithm based on the Mamdani model, developed using the Matlab toolbox for detection and isolation of the inter-turn short-circuit faults on the windings of an induction motor. To carry out the analysis, the induction motor was modified in its stator windings to artificially induce short-circuit faults of different magnitudes. The current signals are acquired in real time using a digital platform developed in the Delphi 7 high-level language communicating with a float point unit Digital Signal Processor (DSP) TMS320F28335 by Texas Instruments. The proposed method not only detects the short circuit faults but also isolates the faulty winding.

StarPlat: A versatile DSL for graph analytics

Graphs model several real-world phenomena. With the growth of unstructured and semi-structured data, parallelization of graph algorithms is inevitable. Unfortunately, due to inherent irregularity of computation, memory access, and communication, graph algorithms are traditionally challenging to parallelize. To tame this challenge, several libraries, frameworks, and domain-specific languages (DSLs) have been proposed to reduce the parallel programming burden of the users, who are often domain experts. However, existing frameworks to model graph algorithms typically target a single architecture. In this paper, we present a graph DSL, named StarPlat, that allows programmers to specify graph algorithms in a high-level format, but generates code for three different backends from the same algorithmic specification. In particular, the DSL compiler generates OpenMP for multi-core systems, MPI for distributed systems, and CUDA for many-core GPUs. Since these three are completely different parallel programming paradigms, binding them together under the same language is challenging. We share our experience with the language design. Central to our compiler is an intermediate representation which allows a common representation of the high-level program, from which individual backend code generations begin. We demonstrate the expressiveness of StarPlat by specifying four graph algorithms: betweenness centrality computation, page rank computation, single-source shortest paths, and triangle counting. Using a suite of ten large graphs, we illustrate the effectiveness of our approach by comparing the performance of the generated codes with that obtained with hand-crafted library codes. We find that the generated code is competitive to library-based codes in many cases. More importantly, we show the feasibility to generate efficient codes for different target architectures from the same algorithmic specification of graph algorithms.

Results of a Needs Assessment for a DEI Workshop for GME Program Coordinators.

Competency in diversity, equity, and inclusion skills is critical for graduate medical education program coordinators. Coordinators contribute to high-level personnel and program management while also providing informal social support to residents. However, little has been reported about program coordinator learning needs, interest, and self-reported skills and attitudes regarding diversity, equity, and inclusion initiatives in graduate medical education. This study sought to describe the characteristics, job tasks, attitudes, and learning needs of program coordinators attending a professional development session about diversity, equity, and inclusion. Participants registered for a September 2022 program coordinator professional development workshop on diversity, equity, inclusion, and bias were invited to complete an electronic needs assessment prior to the workshop. Items were based on expert opinion and literature review. We performed descriptive and comparative analysis. The response rate was 54% (106/198); 90% (94/104) of respondents identified as female; 42% (44/104) identified as an underrepresented minority. Fifty-seven percent (63/104) received mandatory training on bias while 13% (14/104) were previously trained on bias at a conference specific to the role of a coordinator. Eighty-nine percent (86/104) of coordinators reported having contact with applicants during recruiting; 67% (63/104) offer informal resident evaluations. Most participants agreed it is the coordinator's professional responsibility to confront colleagues who display signs of discrimination toward women (66%; 62/104) or based on cultural/ethnic identity (65%; 61/104). Program coordinators report visible and impactful roles in the residency leadership and management team. Few coordinators have received diversity, equity, and inclusion training related to their complex work in graduate medical education. Future graduate medical education diversity, equity, inclusion, and bias competency programs should specifically include program coordinators.

CyNetDiff: A Python Library for Accelerated Implementation of Network Diffusion Models

In recent years, there has been increasing interest in network diffusion models and related problems. The most popular of these are the independent cascade and linear threshold models. Much of the recent experimental work done on these models requires a large number of simulations conducted on large graphs, a computationally expensive task suited for low-level languages. However, many researchers prefer the use of higher-level languages (such as Python) for their flexibility and shorter development times. Moreover, in many research tasks, these simulations are the most computationally intensive task, so it would be desirable to have a library for these with an interface to a high-level language with the performance of a low-level language. To fill this niche, we introduce CyNetDiff, a Python library with components written in Cython to provide improved performance for these computationally intensive diffusion tasks.

MotorNet, a Python toolbox for controlling differentiable biomechanical effectors with artificial neural networks.

Artificial neural networks (ANNs) are a powerful class of computational models for unravelling neural mechanisms of brain function. However, for neural control of movement, they currently must be integrated with software simulating biomechanical effectors, leading to limiting impracticalities: (1) researchers must rely on two different platforms and (2) biomechanical effectors are not generally differentiable, constraining researchers to reinforcement learning algorithms despite the existence and potential biological relevance of faster training methods. To address these limitations, we developed MotorNet, an open-source Python toolbox for creating arbitrarily complex, differentiable, and biomechanically realistic effectors that can be trained on user-defined motor tasks using ANNs. MotorNet is designed to meet several goals: ease of installation, ease of use, a high-level user-friendly application programming interface, and a modular architecture to allow for flexibility in model building. MotorNet requires no dependencies outside Python, making it easy to get started with. For instance, it allows training ANNs on typically used motor control models such as a two joint, six muscle, planar arm within minutes on a typical desktop computer. MotorNet is built on PyTorch and therefore can implement any network architecture that is possible using the PyTorch framework. Consequently, it will immediately benefit from advances in artificial intelligence through PyTorch updates. Finally, it is open source, enabling users to create and share their own improvements, such as new effector and network architectures or custom task designs. MotorNet's focus on higher-order model and task design will alleviate overhead cost to initiate computational projects for new researchers by providing a standalone, ready-to-go framework, and speed up efforts of established computational teams by enabling a focus on concepts and ideas over implementation.

MotorNet, a Python toolbox for controlling differentiable biomechanical effectors with artificial neural networks

Artificial neural networks (ANNs) are a powerful class of computational models for unravelling neural mechanisms of brain function. However, for neural control of movement, they currently must be integrated with software simulating biomechanical effectors, leading to limiting impracticalities: (1) researchers must rely on two different platforms and (2) biomechanical effectors are not generally differentiable, constraining researchers to reinforcement learning algorithms despite the existence and potential biological relevance of faster training methods. To address these limitations, we developed MotorNet, an open-source Python toolbox for creating arbitrarily complex, differentiable, and biomechanically realistic effectors that can be trained on user-defined motor tasks using ANNs. MotorNet is designed to meet several goals: ease of installation, ease of use, a high-level user-friendly application programming interface, and a modular architecture to allow for flexibility in model building. MotorNet requires no dependencies outside Python, making it easy to get started with. For instance, it allows training ANNs on typically used motor control models such as a two joint, six muscle, planar arm within minutes on a typical desktop computer. MotorNet is built on PyTorch and therefore can implement any network architecture that is possible using the PyTorch framework. Consequently, it will immediately benefit from advances in artificial intelligence through PyTorch updates. Finally, it is open source, enabling users to create and share their own improvements, such as new effector and network architectures or custom task designs. MotorNet’s focus on higher-order model and task design will alleviate overhead cost to initiate computational projects for new researchers by providing a standalone, ready-to-go framework, and speed up efforts of established computational teams by enabling a focus on concepts and ideas over implementation.

PyIgH : A unified architecture of IgH EtherCAT Master based on Python considering hard real-time constraints

The increasing demand for rapid application development tools, especially those employing high-level languages such as Python, has underscored the importance of utilizing a wide array of popular libraries while addressing real-time constraints in distributed hardware systems. This paper introduces PyIgH, a unified architecture of an IgH EtherCAT master based on Python, specifically designed to satisfy hard real-time requirements in an EtherCAT network. Implemented as a Python module, PyIgH exposes the functionalities and capabilities of an open-source EtherCAT master, facilitating seamless configuration and control of EtherCAT slave devices within the Python runtime environment. Real-time adaptation of the POSIX library, encapsulated within Python, is also utilized to satisfy the timing requirements of EtherCAT. The feasibility of the proposed approach is verified by analyzing the real-time performance in terms of periodicity and in-controller delay of the EtherCAT control task with a 1 kHz cycle. Experimental results demonstrate that PyIgH is suitable for hard real-time applications and serves as a valid alternative to conventional low-level EtherCAT masters. Additionally, a practical application involving motion control of a six-axis collaborative robot showcases consistent performance of PyIgH within a real-time multi-tasking environment.

Connectivity Reveals the Relationships between Human Brain Areas Associated with High-Level Linguistic Processing and Macaque Brain Areas.

Cross-species research has advanced human understanding of brain regions, with cross-species comparisons using magnetic resonance imaging technology becoming increasingly common. Currently, cross-species research on human language regions has primarily focused on traditional brain areas such as the Broca region. While some studies have indicated that human language function also involves other language regions, the corresponding relationships between these brain regions in humans and macaques remain unclear. This study calculated the strength of the connections between the high-level language processing regions in human and macaque brains, identified homologous target areas based on the structural connections of white-matter fiber bundles, and compared the connectivity profiles of both species. The results of the experiment demonstrated that macaques possess brain regions which exhibit connectivity patterns resembling those found in human high-level language processing regions. This discovery suggests that while the function of a human brain region is specialized, it still maintains a structural connectivity similar to that seen in macaques.

An Implementation Evaluation of the Smartphone-Enhanced Visual Inspection with Acetic Acid (SEVIA) Program for Cervical Cancer Prevention in Urban and Rural Tanzania.

The World Health Organization (WHO) recommends visual inspection with acetic acid (VIA) for cervical cancer screening (CCS) in lower-resource settings; however, quality varies widely, and it is difficult to maintain a well-trained cadre of providers. The Smartphone-Enhanced Visual Inspection with Acetic acid (SEVIA) program was designed to offer secure sharing of cervical images and real-time supportive supervision to health care workers, in order to improve the quality and accuracy of visual assessment of the cervix for treatment. The purpose of this evaluation was to document early learnings from patients, providers, and higher-level program stakeholders, on barriers and enablers to program implementation. From 9 September to 8 December 2016, observational activities and open-ended interviews were conducted with image reviewers (n = 5), providers (n = 17), community mobilizers (n = 14), patients (n = 21), supervisors (n = 4) and implementation partners (n = 5) involved with SEVIA. Sixty-six interviews were conducted at 14 facilities, in all five of the program regions Results SEVIA was found to be a highly regarded tool for the enhancement of CCS services in Northern Tanzania. Acceptability, adoption, appropriateness, feasibility, and coverage of the intervention were highly recognized. It appeared to be an effective means of improving good clinical practice among providers and fit seamlessly into existing roles and processes. Barriers to implementation included network connectivity issues, and community misconceptions and the adoption of CCS more generally. SEVIA is a practical and feasible mobile health intervention and tool that is easily integrated into the National CCS program to enhance the quality of care.

Optimizing Three-Dimensional Stencil-Operations on Heterogeneous Computing Environments

Complex algorithms and enormous data sets require parallel execution of programs to attain results in a reasonable amount of time. Both aspects are combined in the domain of three-dimensional stencil operations, for example, computational fluid dynamics. This work contributes to the research on high-level parallel programming by discussing the generalizable implementation of a three-dimensional stencil skeleton that works in heterogeneous computing environments. Two exemplary programs, a gas simulation with the Lattice Boltzmann method, and a mean blur, are executed in a multi-node multi-graphics processing units environment, proving the runtime improvements in heterogeneous computing environments compared to a sequential program.

Jacdac: Service-Based Prototyping of Embedded Systems

The traditional approach to programming embedded systems is monolithic: firmware on a microcontroller contains both application code and the drivers needed to communicate with sensors and actuators, using low-level protocols such as I2C, SPI, and RS232. In comparison, software development for the cloud has moved to a service-based development and operation paradigm: a service provides a discrete unit of functionality that can be accessed remotely by an application, or other service, but is independently managed and updated. We propose, design, implement, and evaluate a service-based approach to prototyping embedded systems called Jacdac. Jacdac defines a service specification language, designed especially for embedded systems, along with a host of specifications for a variety of sensors and actuators. With Jacdac, each sensor/actuator in a system is paired with a low-cost microcontroller that advertises the services that represent the functionality of the underlying hardware over an efficient and low-cost single-wire bus protocol. A separate microcontroller executes the user's application program, which is a client of the Jacdac services on the bus. Our evaluation shows that Jacdac supports a service-based abstraction for sensors/actuators at low cost and reasonable performance, with many benefits for prototyping: ease of use via the automated discovery of devices and their capabilities, substitution of same-service devices for each other, as well as high-level programming, monitoring, and debugging. We also report on the experience of bringing Jacdac to commercial availability via third-party manufacturers.

The Renoir Dataflow Platform: Efficient Data Processing without Complexity

Today, data analysis drives the decision-making process in virtually every human activity. This demands for software platforms that offer simple programming abstractions to express data analysis tasks and that can execute them in an efficient and scalable way. State-of-the-art solutions range from low-level programming primitives, which give control to the developer about communication and resource usage, but require significant effort to develop and optimize new algorithms, to high-level platforms that hide most of the complexities of parallel and distributed processing, but often at the cost of reduced efficiency.To reconcile these requirements, we developed Renoir, a novel distributed data processing platform written in Rust. Renoir provides a high-level dataflow programming model as mainstream data processing systems. It supports static and streaming data, it enables data transformations, grouping, aggregation, iterative computations, and time-based analytics, and it provides all these features incurring in a low overhead.In this paper, we present the programming model and the implementation details of Renoir. We evaluate it under heterogeneous workloads. We compare it with state-of-the-art solutions for data analysis and high-performance computing, as well as alternative research products, which offer different programming abstractions and implementation strategies. Renoir programs are compact and easy to write: developers need not care about low-level concerns such as resource usage, data serialization, concurrency control, and communication. At the same time, Renoir consistently presents comparable or better performance than competing solutions, by a large margin in several scenarios.We conclude that Renoir offers a good tradeoff between simplicity and performance, allowing developers to easily express complex data analysis tasks and achieve high performance and scalability.

Devising a High-Level Command Language for the Teleoperation of Autonomous Vehicles

Autonomous vehicles (AVs) are rapidly evolving as a novel way of transportation. Nevertheless, there is a consensus that AVs cannot address all traffic scenarios independently. Consequently, there arises a need for remote human intervention. To pave the way for large-scale deployment of AVs onto public roadways, innovative models of remote operation must evolve. Such a paradigm is Tele-assistance, which posits that the low-level control of AVs should be delegated through high-level commands. Our work explores how such a command language should be constructed as a first step in designing a Tele-assistance user interface. Through interviews with 17 experienced teleoperators, we elicit a set of discrete commands that a remote operator can use to resolve various road scenarios. Subsequently, we create a scenario-command mapping and a thematic classification of the defined commands. Finally, we present an initial Tele-assistance interface design based on these commands.

Edge architecture for the automation and control of flexible manufacturing lines

The automation and control of industrial machines and processes is a task traditionally performed by PLCs and SCADA systems. However, with the advent of Industry 4.0 paradigm, which merges OT and IT domains, this task can also be performed inside the IT domain. This paper proposes an edge architecture where a flexible manufacturing line is led by a state machine programmed and running on the IT domain inside the Manufacturing Execution System. Each industrial asset has been modelled as a black-box, where security and hard real-time functionalities are programmed and controlled inside each black-box by traditional OT tools. Functionalities and variables required to synchronize each black-box, have been modelled as inputs and outputs. These input and output variables and methods are available using Industry 4.0 industrial communication protocols, such as OPC UA or MQTT. Then, a state machine programmed on a high-level IT language model inside the MES, automates the manufacturing line reacting to the inputs and outputs of each black-box. This architecture has been successfully validated automating a manufacturing line composed by a conveyor, a robot and a vision based quality control system.