We consider four types of benchmarks in our experiments, i.e., random COPs, scale-free networks, Therefore, to be fair and practical, we do not consider existing DNN-based BP variants. We compare our DABP with the following state-of-the-art COP solvers: (1) DBP with a damping factor of 0.9 and its splitting constraint factor graph version (DBP-SCFG) with a splitting ratio of 0.95 [ However, it can be extremely tedious and time-consuming to tune the damping factor. Figure 9: Convergence rates under different iteration limits ( | X | = 100) 4 size (i.e., 5) and the constraint functions are highly structured, which allows effective pruning and It also can be concluded that our DABP converges much faster than DBP and DBP-SCFG. To demonstrate the necessity of heterogeneous hyperparameters of Eq. (6), we conduct extensive Figure 1-12 present the results on solution quality. Table 1 presents the GPU memory footprint of our DABP .

Accurate trajectory prediction is essential for the safety and efficiency of autonomous driving. Traditional models often struggle with real-time processing, capturing non-linearity and uncertainty in traffic environments, efficiency in dense traffic, and modeling temporal dynamics of interactions. We introduce NEST (Neuromodulated Small-world Hypergraph Trajectory Prediction), a novel framework that integrates Small-world Networks and hypergraphs for superior interaction modeling and prediction accuracy. This integration enables the capture of both local and extended vehicle interactions, while the Neuromodulator component adapts dynamically to changing traffic conditions. We validate the NEST model on several real-world datasets, including nuScenes, MoCAD, and HighD. The results consistently demonstrate that NEST outperforms existing methods in various traffic scenarios, showcasing its exceptional generalization capability, efficiency, and temporal foresight. Our comprehensive evaluation illustrates that NEST significantly improves the reliability and operational efficiency of autonomous driving systems, making it a robust solution for trajectory prediction in complex traffic environments.

Federated learning (FL) enables a collaborative environment for training machine learning models without sharing training data between users. This is typically achieved by aggregating model gradients on a central server. Decentralized federated learning is a rising paradigm that enables users to collaboratively train machine learning models in a peer-to-peer manner, without the need for a central aggregation server. However, before applying decentralized FL in real-world use training environments, nodes that deviate from the FL process (Byzantine nodes) must be considered when selecting an aggregation function. Recent research has focused on Byzantine-robust aggregation for client-server or fully connected networks, but has not yet evaluated such aggregation schemes for complex topologies possible with decentralized FL. Thus, the need for empirical evidence of Byzantine robustness in differing network topologies is evident. This work investigates the effects of state-of-the-art Byzantine-robust aggregation methods in complex, large-scale network structures. We find that state-of-the-art Byzantine robust aggregation strategies are not resilient within large non-fully connected networks. As such, our findings point the field towards the development of topology-aware aggregation schemes, especially necessary within the context of large scale real-world deployment.

In decentralised autonomous systems it is the interactions between individual agents which govern the collective behaviours of the system. These local-level interactions are themselves often governed by an underlying network structure. These networks are particularly important for collective learning and decision-making whereby agents must gather evidence from their environment and propagate this information to other agents in the system. Models for collective behaviours may often rely upon the assumption of total connectivity between agents to provide effective information sharing within the system, but this assumption may be ill-advised. In this paper we investigate the impact that the underlying network has on performance in the context of collective learning. Through simulations we study small-world networks with varying levels of connectivity and randomness and conclude that totally-connected networks result in higher average error when compared to networks with less connectivity. Furthermore, we show that networks of high regularity outperform networks with increasing levels of random connectivity.

In addition to seamless connectivity and smartness, the objects in the Internet of Things (IoT) are expected to have the social capabilities -- these objects are termed as ``social objects''. In this paper, an intuitive paradigm of social interactions between these objects are argued and modeled. The impact of social behavior on the interaction pattern of social objects is studied taking Peer-to-Peer (P2P) resource sharing as an example application. The model proposed in this paper studies the implications of competitive vs. cooperative social paradigm, while peers attempt to attain the shared resources / services. The simulation results divulge that the social capabilities of the peers impart a significant increase in the quality of interactions between social objects. Through an agent-based simulation study, it is proved that cooperative strategy is more efficient than competitive strategy. Moreover, cooperation with an underpinning on real-life networking structure and mobility does not negatively impact the efficiency of the system at all; rather it helps.

--Deep Neural Networks (DNNs) are usually over-parameterized, causing excessive memory and interconnection cost on the hardware platform. Existing pruning approaches remove secondary parameters at the end of training to reduce the model size; but without exploiting the intrinsic network property, they still require the full interconnection to prepare the network. Inspired by the observation that brain networks follow the Small-World model, we propose a novel structural pruning scheme, which includes (1) hierarchically trimming the network into a Small-World model before training, (2) training the network for a given dataset, and (3) optimizing the network for accuracy. The new scheme effectively reduces both the model size and the interconnection needed before training, achieving a locally clustered and globally sparse model. We demonstrate our approach on LeNet-5 for MNIST and VGG-16 for CIF AR-10, decreasing the number of parameters to 2.3% and 9.02% of the baseline model, respectively. Recent developments in Deep Neural Networks (DNNs) have made them an integral part of modern day data processing which enable applications such as image recognition [1], object detection [2], speech recognition [3] and other applications.

Learning the undirected graph structure of a Markov network from data is a problem that has received a lot of attention during the last few decades. As a result of the general applicability of the model class, a myriad of methods have been developed in parallel in several research fields. Recently, as the size of the considered systems has increased, the focus of new methods has been shifted towards the high-dimensional domain. In particular, the introduction of the pseudo-likelihood function has pushed the limits of score-based methods originally based on the likelihood. At the same time, an array of methods based on simple pairwise tests have been developed to meet the challenges set by the increasingly large data sets in computational biology. Apart from being applicable on high-dimensional problems, methods based on the pseudo-likelihood and pairwise tests are fundamentally very different. In this work, we perform an extensive numerical study comparing the different types of methods on data generated by binary pairwise Markov networks. For sampling large networks, we use a parallelizable Gibbs sampler based on sparse restricted Boltzmann machines. Our results show that pairwise methods can be more accurate than pseudo-likelihood methods in settings often encountered in high-dimensional structure learning.

Bayesian networks, with structure given by a directed acyclic graph (DAG), are a popular class of graphical models. However, learning Bayesian networks from discrete or categorical data is particularly challenging, due to the large parameter space and the difficulty in searching for a sparse structure. In this article, we develop a maximum penalized likelihood method to tackle this problem. Instead of the commonly used multinomial distribution, we model the conditional distribution of a node given its parents by multi-logit regression, in which an edge is parameterized by a set of coefficient vectors with dummy variables encoding the levels of a node. To obtain a sparse DAG, a group norm penalty is employed, and a blockwise coordinate descent algorithm is developed to maximize the penalized likelihood subject to the acyclicity constraint of a DAG. When interventional data are available, our method constructs a causal network, in which a directed edge represents a causal relation. We apply our method to various simulated and real data sets. The results show that our method is very competitive, compared to many existing methods, in DAG estimation from both interventional and high-dimensional observational data.

This paper treats the problem of learning the interaction structure of a spatiotemporal graphical model for a discrete state and discrete time stochastic process known as the susceptible, infected, recovered (SIR) model. The presence of spatial interactions cause adjacent nodes in the graph to affect each others states over time. Learning the topology of this graph is known as model selection. We cast this graphical model selection problem as a penalized likelihood problem, resulting in a convex program for which convex optimization solvers can be applied. SIR spatiotemporal graphical models are commonly used in modeling the random propagation of information between nodes in large networks in bioinformatics, signal processing, public health, and national security (4; 9; 21). Knowing the network link structure allows accurate prediction of individual node states and can aid the development of control and intervention strategies for such networks. This paper develops a tractable method to estimate the topology of the network for the SIR spatiotemporal graphical model from empirical data. Exact solutions of the graphical model selection problem is NP hard due to the combinatorial nature of enumeration through the discrete space of possible graph topologies.