Reaction systems are a model of interactive computation, where the interaction between a system — itself built up of a number of reactions — and its environment is modelled through context sequences ...provided by the environment. The standard execution semantics of reaction systems is synchronous, i.e., at each computational step all the enabled reactions are executed. In this paper, we ‘de-synchronise’ such an execution model by allowing only a subset of enabled reactions to be executed. We then study the resulting asynchronous model assuming two fundamental execution policies. The first one allows any subset of reactions to be executed, and the second one draws each subset from a pre-defined pool. We also introduce and discuss the notion of persistence of reactions and sets of reactions in the resulting models of asynchronous reaction systems. In particular, we demonstrate that reaction persistence can be implemented.
Industry 4.0 aims to establish highly flexible production, enabling effective and efficient mass customisation of products. Modelling techniques and simulation of production processes are among the ...core techniques of the manufacturing industry that facilitate flexibility and automation of a shop floor in the era of Industry 4.0. In this paper, we present an approach to support production process modelling and process model management. The approach is based on Model-Driven (MD) principles and comprises a Domain-Specific Modelling Language (DSML) named Multi-Level Production Process Modelling Language (MultiProLan). MultiProLan uses a set of concepts to specify production process models suitable for automatic instruction generation and execution of the instructions in a simulation or on a shop floor. By using MultiProLan, process designers may create process models independent of the specific production system. Such process models can either be automatically enriched by matching and scheduling algorithms or manually enriched by a process designer via MultiProLan's modelling tool. In this paper, we also present an application of our approach in the assembly industry to showcase its dynamic resource management, generation of production documentation, error handling and process monitoring.
This paper presents an active learning strategy for robotic systems that takes into account task information, enables fast learning, and allows control to be readily synthesized by taking advantage ...of the Koopman operator representation. We first motivate the use of representing nonlinear systems as linear Koopman operator systems by illustrating the improved model-based control performance with an actuated Van der Pol system. Information-theoretic methods are then applied to the Koopman operator formulation of dynamical systems where we derive a controller for active learning of robot dynamics. The active learning controller is shown to increase the rate of information about the Koopman operator. In addition, our active learning controller can readily incorporate policies built on the Koopman dynamics, enabling the benefits of fast active learning and improved control. Results using a quadcopter illustrate single-execution active learning and stabilization capabilities during free fall. The results for active learning are extended for automating Koopman observables and we implement our method on real robotic systems.
Criminal penal subsidair imprisonment imposed by the panel of judges used as a gap that is used by the convicted person to be free from the obligation to pay fines. Besides that, there are obstacles ...for the prosecutor to implement the execution of criminal fines, because the criminal fines imposed by the panel of judges are subsidair or can be replaced with confinement bodies. The problem of this research is formulated: how is the practice of the execution of criminal fines against the prepetrators of excise crime, namely the sale of goods without the marking of excise payment. The approach used is normative juridicial and empirical juridicial. Data were collected through literature studies and field studies, then analyzed qualitatively. In accordance with the description of the results of the study shows that: The practice of the execution of fines against the perpetrators of excise criminal acts, namely the sale of goods without the marking of excise payment carried out by the Bandar Lampung District Attorney after receiving a copy of the court's decision from the Registrar no later than one week after the verdict was read. Furthermore, the Head of the Bandar Lampung District Prosecutor's Office issued a Court Decision Execution Order ordering the Prosecutors Team to execute a criminal fine against a convicted tax officer. The results of the execution are then compiled and reported in the Minutes of the Execution of Court Decisions.
At 7:30 a.m. on June 16, 1944, George Junius Stinney Jr. was
escorted by four guards to the death chamber. Wearing socks but no
shoes, the 14-year-old Black boy walked with his Bible tucked under
his ...arm. The guards strapped his slight, five-foot-one-inch frame
into the electric chair. His small size made it difficult to affix
the electrode to his right leg and the face mask, which was clearly
too large, fell to the floor when the executioner flipped the
switch. That day, George Stinney became, and today remains, the
youngest person executed in the United States during the twentieth
century.
How was it possible, even in Jim Crow South Carolina, for a
child to be convicted, sentenced to death, and executed based on
circumstantial evidence in a trial that lasted only a few hours?
Through extensive archival research and interviews with Stinney's
contemporaries-men and women alive today who still carry
distinctive memories of the events that rocked the small town of
Alcolu and the entire state-Eli Faber pieces together the chain of
events that led to this tragic injustice.
The first book to fully explore the events leading to Stinney's
death, The Child in the Electric Chair offers a compelling
narrative with a meticulously researched analysis of the world in
which Stinney lived-the era of lynching, segregation, and racist
assumptions about Black Americans. Faber explains how a
systemically racist system, paired with the personal ambitions of
powerful individuals, turned a blind eye to human decency and one
of the basic tenets of the American legal system that individuals
are innocent until proven guilty.
As society continues to grapple with the legacies of racial
injustice, the story of George Stinney remains one that can teach
us lessons about our collective past and present. By ably placing
the Stinney case into a larger context, Faber reveals how this case
is not just a travesty of justice locked in the era of the Jim Crow
South but rather one that continues to resonate in our own
time.
A foreword is provided by Carol Berkin, Presidential Professor
of History Emerita at Baruch College at the City University of New
York and author of several books including Civil War Wives: The
Lives and Times of Angelina Grimke Weld, Varina Howell Davis, and
Julia Dent Grant.
Graph embedding maps graph nodes to low-dimensional vectors and is widely used in machine learning tasks. The increasing availability of billion-edge graphs underscores the importance of learning ...efficient and effective embeddings on large graphs, such as link prediction on Twitter with over one billion edges. Most existing graph embedding methods fall short of reaching high data scalability. In this paper, we present a general-purpose, distributed, information-centric random walk-based, and pipeline-optimized graph embedding framework, <inline-formula><tex-math>{\sf DistGER-Pipe}</tex-math></inline-formula>, which scales to embed billion-edge graphs. <inline-formula><tex-math>{\sf DistGER-Pipe}</tex-math></inline-formula> incrementally computes information-centric random walks to reduce redundant computations for more effective and efficient graph embedding. It further leverages a multi-proximity-aware, streaming, parallel graph partitioning strategy, simultaneously achieving high local partition quality and excellent workload balancing across machines. <inline-formula><tex-math>{\sf DistGER-Pipe}</tex-math></inline-formula> also improves the distributed <inline-formula><tex-math>{\sf Skip-Gram}</tex-math></inline-formula> learning model to generate node embeddings by optimizing access locality, CPU throughput, and synchronization efficiency. Finally, <inline-formula><tex-math>{\sf DistGER-Pipe}</tex-math></inline-formula> designs pipelined execution that decouples the operators in sampling and training procedures with an inter-round serial and intra-round parallel processing, attaining optimal utilization of computing resources. Experiments on real-world graphs demonstrate that compared to state-of-the-art distributed graph embedding frameworks, including <inline-formula><tex-math>{\sf KnightKing}</tex-math></inline-formula>, <inline-formula><tex-math>{\sf DistDGL}</tex-math></inline-formula>, <inline-formula><tex-math>{\sf Pytorch-BigGraph}</tex-math></inline-formula>, and <inline-formula><tex-math>{\sf DistGER}</tex-math></inline-formula>, <inline-formula><tex-math>{\sf DistGER-Pipe}</tex-math></inline-formula> exhibits 3.15×-1053× acceleration, 45% reduction in cross-machines communication, <inline-formula><tex-math notation="LaTeX">\gt </tex-math></inline-formula> 10% effectiveness improvement in downstream tasks, and 38% enhancement in CPU utilization.
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