We thank the reviewers for the constructive feedback, which will significantly improve the paper. We elaborate on this first and address specific comments and questions from the reviewers below. RWS, etc.) applications assumes the data is generated iid and and achieve scalability through use of subsampling and The current discussion in Section 3.5 for MSC on the other hand focuses on the more challenging case, We will clarify this in the revision. We compare the base versions of the respective algorithms. We will add these references to the related work section.

Developing accurate hand gesture perception models is critical for various robotic applications, enabling effective communication between humans and machines and directly impacting neurorobotics and interactive robots. Recently, surface electromyography (sEMG) has been explored for its rich informational context and accessibility when combined with advanced machine learning approaches and wearable systems. The literature presents numerous approaches to boost performance while ensuring robustness for neurorobots using sEMG, often resulting in models requiring high processing power, large datasets, and less scalable solutions. This paper addresses this challenge by proposing the decoding of muscle synchronization rather than individual muscle activation. We study coherence-based functional muscle networks as the core of our perception model, proposing that functional synchronization between muscles and the graph-based network of muscle connectivity encode contextual information about intended hand gestures. This can be decoded using shallow machine learning approaches without the need for deep temporal networks. Our technique could impact myoelectric control of neurorobots by reducing computational burdens and enhancing efficiency. The approach is benchmarked on the Ninapro database, which contains 12 EMG signals from 40 subjects performing 17 hand gestures. It achieves an accuracy of 85.1%, demonstrating improved performance compared to existing methods while requiring much less computational power. The results support the hypothesis that a coherence-based functional muscle network encodes critical information related to gesture execution, significantly enhancing hand gesture perception with potential applications for neurorobotic systems and interactive machines.

The underlying idea is to get real interoperation among those Information Systems in order to enlarge the benefits that users can get from the Web by increasing machines' processable tasks. Although agent technology and Web Services technology have been developed in separate ways, there exists a recent work (Greenwood and M.Lyell, 2007) which tries to consolidate their approaches into a common specification describing how to seamlessly interconnect FIPA compliant agent systems (FIPA, 2005) with W3C compliant Web Services. The purpose of specifying an infrastructure for integrating these two technologies is to provide a common means of allowing each to discover and invoke instances of the other. Considering the previous approach, in the rest of this paper we will only concentrate on aspects of inter-agent communication. In general, communication among agents is based on the interchange of messages, which in this context are also known as communication acts.

Reza Pahlavi, exiled crown prince of Iran, weighs in on Iranian threats to shut the Mediterranean Sea amid war with Israel and the threat America faces from the country and its proxies. The U.S. Navy fired anti-ship ballistic missiles on Tuesday against incoming Iran-backed Houthi missiles in the Red Sea, signaling a significant escalation in the region, a senior defense official told Fox News. The Navy engaged three ballistic missiles provided to Yemen's Houthis by Iran. It was the first time the Navy shot down an incoming ballistic missile using an anti-ship ballistic missile. The USS Laboon and assets from the Eisenhower Carrier Strike Group shot down 12 one-way attack drones, three anti-ship ballistic missiles and two land attack missiles fired by the Houthis over a 12-hour period, U.S. Central Command said.

Magnetic Soft Catheters (MSCs) are capable of miniaturization due to the use of an external magnetic field for actuation. Through careful design of the magnetic elements within the MSC and the external magnetic field, the shape along the full length of the catheter can be precisely controlled. However, modeling of the magnetic-soft material is challenging due to the complex relationship between magnetic and elastic stresses within the material. Approaches based on traditional Finite Element Methods (FEM) lead to high computation time and rely on proprietary implementations. In this work, we showcase the use of our recently presented open-source simulation framework based on the Material Point Method (MPM) for the computational design of magnetic soft catheters to realize arbitrary shapes in 3D, and to facilitate follow-the-leader shape-forming insertion.

There is a wide variety of message-passing communication models, ranging from synchronous ''rendez-vous'' communications to fully asynchronous/out-of-order communications. For large-scale distributed systems, the communication model is determined by the transport layer of the network, and a few classes of orders of message delivery (FIFO, causally ordered) have been identified in the early days of distributed computing. For local-scale message-passing applications, e.g., running on a single machine, the communication model may be determined by the actual implementation of message buffers and by how FIFO queues are used. While large-scale communication models, such as causal ordering, are defined by logical axioms, local-scale models are often defined by an operational semantics. In this work, we connect these two approaches, and we present a unified hierarchy of communication models encompassing both large-scale and local-scale models, based on their concurrent behaviors. We also show that all the communication models we consider can be axiomatized in the monadic second order logic, and may therefore benefit from several bounded verification techniques based on bounded special treewidth.

The Master of Data Science and Machine Learning is a 12-month program offered by Queen's School of Computing addressing the growing demand for graduates with a Data Science and Machine Learning background from leading technology firms, healthcare companies, automobile manufacturers, research labs, and government agencies. Data Science and Machine Learning play a critical role in understanding customers, making effective decisions, recommending relevant information, detecting cyber-intrusions or financial fraud, and much more. The creation of this professional program will help to distinguish Computing graduates and increase their competitiveness for these highly skilled positions. The Program is offered remotely to up to 100 Egyptian students. There will be three cohorts of students.