Belief Propagation Research Articles

Proud Boys on Telegram

Utilizing an original data set of public Telegram channels affiliated with a right-wing extremist group, the Proud Boys, we conduct an exploratory analysis of the structure and nature of the group’s presence on the platform. Our study considers the group’s growth, organizational structure, connectedness with other far-right and/or fringe factions, and the range of topics discussed on this alternative social media platform. The findings show that the Proud Boys have a notable presence on Telegram, with a discernable spike in activity coinciding with Facebook’s and Instagram’s 2018 deplatforming of associated pages and profiles with this and other extremist groups. Another sharp increase in activity is then precipitated by the attack on the U.S. Capitol Building on January 6, 2021. By February 2022, we identified 92 public Telegram channels explicitly affiliated with the Proud Boys, which constitute the core of a well-connected network with 131,953 subscribers. These channels, primarily from the United States, also include international presences in Australia, New Zealand, Canada, the UK, and Germany. Our data reveals substantial interaction between the Proud Boys and other fringe and/or far-right communities on Telegram, including MAGA Trumpists, QAnon, COVID-19-related misinformation, and white-supremacist communities. Content analyses of this network highlights several prominent and recurring themes, including opposition to feminism and liberals, skepticism toward official information sources, and propagation of various conspiracy beliefs. This study offers the first systematic examination of the Proud Boys on Telegram, illuminating how a far-right extremist group leverages the latitude afforded by a relatively unregulated alternative social media platform.

Designing ecosystems of intelligence from first principles

This white paper lays out a vision of research and development in the field of artificial intelligence for the next decade (and beyond). Its denouement is a cyber-physical ecosystem of natural and synthetic sense-making, in which humans are integral participants—what we call “shared intelligence.” This vision is premised on active inference, a formulation of adaptive behavior that can be read as a physics of intelligence, and which inherits from the physics of self-organization. In this context, we understand intelligence as the capacity to accumulate evidence for a generative model of one’s sensed world—also known as self-evidencing. Formally, this corresponds to maximizing (Bayesian) model evidence, via belief updating over several scales, that is, inference, learning, and model selection. Operationally, this self-evidencing can be realized via (variational) message passing or belief propagation on a factor graph. Crucially, active inference foregrounds an existential imperative of intelligent systems; namely, curiosity or the resolution of uncertainty. This same imperative underwrites belief sharing in ensembles of agents, in which certain aspects (i.e., factors) of each agent’s generative world model provide a common ground or frame of reference. Active inference plays a foundational role in this ecology of belief sharing—leading to a formal account of collective intelligence that rests on shared narratives and goals. We also consider the kinds of communication protocols that must be developed to enable such an ecosystem of intelligences and motivate the development of a shared hyper-spatial modeling language and transaction protocol, as a first—and key—step towards such an ecology.

Low Complexity Early Stopping Belief Propagation Decoder for Polar Codes

Low Complexity Early Stopping Belief Propagation Decoder for Polar Codes

基于边际概率分布重新进行单变量选取的置信传播算法求解约束满足问题

基于边际概率分布重新进行单变量选取的置信传播算法求解约束满足问题

A Belief Propagation based Tree-EP Algorithm for MIMO Detection with High-Order Constellations

A Belief Propagation based Tree-EP Algorithm for MIMO Detection with High-Order Constellations

A Belief Propagation Based Algorithm for Solving Stochastic Constraint Satisfaction Problem

A Belief Propagation Based Algorithm for Solving Stochastic Constraint Satisfaction Problem

Belief Propagation Detection with MRC Reception and MMSE Pre-Cancellation for Overloaded MIMO

Belief Propagation Detection with MRC Reception and MMSE Pre-Cancellation for Overloaded MIMO

Track Coalescence and Repulsion in Multitarget Tracking: An Analysis of MHT, JPDA, and Belief Propagation Methods

Track Coalescence and Repulsion in Multitarget Tracking: An Analysis of MHT, JPDA, and Belief Propagation Methods

Cooperative Navigation for UAV Swarm Via Simplified Gaussian Particle based Belief Propagation

Cooperative Navigation for UAV Swarm Via Simplified Gaussian Particle based Belief Propagation

High-Throughput and Flexible Belief Propagation List Decoder for Polar Codes

High-Throughput and Flexible Belief Propagation List Decoder for Polar Codes

Scalable multitarget tracking using PCL in SFN with 3D data association uncertainty

Passive coherent location (PCL) has attracted widespread attention due to its low cost, good concealment, and strong anti-stealth. When multiple illuminators operating in a single frequency network are used in a PCL system for multitarget tracking, the three-dimensional (3D) data association uncertainty among the illuminators, measurements and targets has to be dealt with because it will significantly increase computational complexity and reduce scalability. To solve this problem, a scalable multi-association belief propagation (MA-BP) approach for multitarget tracking is proposed. By factorizing the joint probability density function, the tracking problem is described by a factor graph, in which two new illuminator-target oriented and measurement oriented association variables are defined to describe the 3D data association uncertainty. Furthermore, under the Gaussian assumption, a Gaussian version of MA-BP, named as multi-association Gaussian belief propagation (MA-GaBP), is implemented with a generalized virtual measurement model. The MA-GaBP approach can further reduce memory requirements and computational complexity. Simulation results show that the proposed approach effectively solves the 3D data association uncertainty, and has satisfied tracking accuracy and scalability.

Topological belief space planning for active SLAM with pairwise Gaussian potentials and performance guarantees

Determining a globally optimal solution of belief space planning (BSP) in high-dimensional state spaces directly is computationally expensive, as it involves belief propagation and objective function evaluation for each candidate action. However, many problems of interest, such as active SLAM, exhibit structure that can be harnessed to expedite planning. Also, in order to choose an optimal action, an exact value of the objective function is not required as long as the actions can be sorted in the same way. In this paper we leverage these two key aspects and present the topological belief space planning (t-bsp) concept that uses topological signatures to perform this ranking for information-theoretic cost functions, considering only topologies of factor graphs that correspond to future posterior beliefs. In particular, we propose a highly efficient topological signature based on the von Neumann graph entropy that is a function of graph node degrees and supports an incremental update. We analyze it in the context of active pose SLAM and derive error bounds between the proposed topological signature and the original information-theoretic cost function. These bounds are then used to provide performance guarantees for t-bsp with respect to a given solver of the original information-theoretic BSP problem. Realistic and synthetic simulations demonstrate drastic speed-up of the proposed method with respect to the state-of-the-art methods while retaining the ability to select a near-optimal solution. A proof of concept of t-bsp is given in a small-scale real-world active SLAM experiment.

Research on the Era Value and Communication Path of Human Destiny Community and Socialist Core Values in the Context of Big Data

Abstract This paper analyzes the socialist core values and explores the contemporary characteristics and values of socialist core values under the background of big data. On this basis, in order to better practice the socialist core values, through the communication path of big data, combined with the belief propagation algorithm and the association effect, it constructs a node influence propagation NIP model based on the improvement of knowledge graph to realize the propagation of socialist core values. At the same time, the algorithm constructed in this paper is subjected to performance verification experiments, and the effect of socialist core value dissemination and the value of the times are explored. The results show that the influence range of the national level and the individual level are roughly the same as 1200 and 1190, respectively, both slightly higher than the individual influence range of 1000, and the propagation time of the state, society and individual is within 150s, 200s and 300s respectively. Resistance to Western ideology rose by 0.13, the deepening of socialist theory grew by 0.09, and the process of constructing a world value system increased by 0.06. Based on this paper, the study of the value of time and the path of dissemination has a positive impact on the development of socialism.

Gauging tensor networks with belief propagation

Effectively compressing and optimizing tensor networks requires reliable methods for fixing the latent degrees of freedom of the tensors, known as the gauge. Here we introduce a new algorithm for gauging tensor networks using belief propagation, a method that was originally formulated for performing statistical inference on graphical models and has recently found applications in tensor network algorithms. We show that this method is closely related to known tensor network gauging methods. It has the practical advantage, however, that existing belief propagation implementations can be repurposed for tensor network gauging, and that belief propagation is a very simple algorithm based on just tensor contractions so it can be easier to implement, optimize, and generalize. We present numerical evidence and scaling arguments that this algorithm is faster than existing gauging algorithms, demonstrating its usage on structured, unstructured, and infinite tensor networks. Additionally, we apply this method to improve the accuracy of the widely used simple update gate evolution algorithm.

Polar-Coded Cooperation With Optimized Relay Selection in Multi-Satellite and Wireless Integrated Systems

Optimized polar-coded cooperation with an approximate prior probability-based threshold decision is proposed to improve the reliability and complexity in selective decode-and-forward (SDF) cooperation of multiple satellite constellation systems. First, an SDF scheme with polar coding is adopted in cooperative communications for efficient code construction and relay forwarding. Second, in the logarithmic belief propagation (BP) polar decoding, the variable iterative threshold is derived with independent and variable signal-to-noise ratio (SNR)-based approximate prior probability. This threshold reduces complexity by eliminating active nodes with high reliability in each decoding iteration. Third, the information detection factor is introduced to evaluate the accuracy of decoded bits, thereby limiting the average decoding iterations to improve decoding efficiency. Simulation results show that the proposed polar-coded SDF cooperation obtains 1.5 dB performance gain at a bit error rate (BER) of 10-3 compared with that of existing LDPC-related schemes. The complexity of the optimized logarithmic BP decoding is reduced by 25%–50% compared with that of the traditional BP decoding. Therefore, the proposed scheme has good BER performance and low complexity in cooperative communications of multi-satellite and wireless integrated systems.

Partial IDS decoding based on the base graph of protograph LDPC codes

AbstractThe residual belief propagation (RBP) algorithm, which is the most classic informed dynamic scheduling strategy, achieves outstanding performance in error correction and can drastically accelerate convergence speed. However, the greedy algorithmic property of this iterative decoding will inevitably cause loss of decoding performance. To address this, a novel algorithm called the partial average bundle residual belief propagation (PABRBP) is proposed in this paper. According to the construction characteristics of a base matrix of protograph‐LDPC codes, informed dynamic scheduling (IDS) strategies are applied to an edge bundle of base matrices for the first time. This edge bundle of the base matrix can be applied to a corresponding cyclic permutation matrix. Furthermore, the update level of each bundle is determined by the value of the Partially Average Bundle Residual (PABR). Therefore, the edge message with the maximum residual in each bundle is updated in order, and the process of iterative decoding is less likely to become trapped in a local optimum. Additionally, the generation of silent nodes is reduced as much as possible. To further improve the PABRBP decoding performance for medium and long codes over the fading channel, the adjusted compensation term is periodically introduced. Analysis and simulation results show that PABRBP demonstrates a notable convergence quality and decoding performance improvement over the fading channels compared to existing state‐of‐art IDS algorithms.

Distributed Inference Over Linear Models Using Alternating Gaussian Belief Propagation

We consider the problem of maximum likelihood estimation in linear models represented by factor graphs and solved via the Gaussian belief propagation algorithm. Motivated by massive internet of things (IoT) networks and edge computing, we set the above problem in a clustered scenario, where the factor graph is divided into clusters and assigned for processing in a distributed fashion across a number of edge computing nodes. For these scenarios, we show that an alternating Gaussian belief propagation (AGBP) algorithm that alternates between inter-and intra-cluster iterations, demonstrates superior performance in terms of convergence properties compared to the existing solutions in the literature. We present a comprehensive framework and introduce appropriate metrics to analyse AGBP algorithm across a wide range of linear models characterised by symmetric and non-symmetric, square, and rectangular matrices. We extend the analysis to the case of dynamic linear models by introducing dynamic arrival of new data over time. Using a combination of analytical and extensive numerical results, we show the efficiency and scalability of AGBP algorithm, making it a suitable solution for large-scale inference in massive IoT networks.

Matrix Product Belief Propagation for reweighted stochastic dynamics over graphs.

Stochastic processes on graphs can describe a great variety of phenomena ranging from neural activity to epidemic spreading. While many existing methods can accurately describe typical realizations of such processes, computing properties of extremely rare events is a hard task, particularly so in the case of recurrent models, in which variables may return to a previously visited state. Here, we build on the matrix product cavity method, extending it fundamentally in two directions: First, we show how it can be applied to Markov processes biased by arbitrary reweighting factors that concentrate most of the probability mass on rare events. Second, we introduce an efficient scheme to reduce the computational cost of a single node update from exponential to polynomial in the node degree. Two applications are considered: inference of infection probabilities from sparse observations within the SIRS epidemic model and the computation of both typical observables and large deviations of several kinetic Ising models.

Generalization of graph network inferences in higher-order graphical models

AbstractProbabilistic graphical models provide a powerful tool to describe complex statistical structure, with many real-world applications in science and engineering from controlling robotic arms to understanding neuronal computations. A major challenge for these graphical models is that inferences such as marginalization are intractable for general graphs. These inferences are often approximated by a distributed message-passing algorithm such as Belief Propagation, which does not always perform well on graphs with cycles, nor can it always be easily specified for complex continuous probability distributions. Such difficulties arise frequently in expressive graphical models that include intractable higher-order interactions. In this paper we define the Recurrent Factor Graph Neural Network (RF-GNN) to achieve fast approximate inference on graphical models that involve many-variable interactions. Experimental results on several families of graphical models demonstrate the out-of-distribution generalization capability of our method to different sized graphs, and indicate the domain in which our method outperforms Belief Propagation (BP). Moreover, we test the RF-GNN on a real-world Low-Density Parity-Check dataset as a benchmark along with other baseline models including BP variants and other GNN methods. Overall we find that RF-GNNs outperform other methods under high noise levels.

Deep-Learning-Based Adaptive Error-Correction Decoding for Spin-Torque Transfer Magnetic Random Access Memory (STT-MRAM)

Spin-torque transfer magnetic random access memory (STT-MRAM) is a promising emerging non-volatile memory (NVM) technology with wide applications. However, the data recovery of STT-MRAM is affected by the diversity of channel raw bit error rate (BER) across different dies caused by process variations, as well as the unknown resistance offset due to temperature change. Therefore, it is critical to develop effective decoding algorithms of error correction codes for STT-MRAM. In this paper, we first propose a neural bit-flipping (BF) decoding algorithm, which can share the same trellis representation as the state-of-the-art neural decoding algorithms, such as the neural belief propagation (NBP) and neural offset min-sum (NOMS) algorithm. Hence a neural network (NN) decoder with a uniform architecture but different NN parameters can realize all these neural decoding algorithms. Based on such a unified NN decoder architecture, we further propose a novel deep learning (DL)-based adaptive decoding algorithm whose decoding complexity can be adjusted according to the change of the channel conditions of STT-MRAM. Extensive experimental evaluation results demonstrate that the proposed neural decoders can greatly improve the performance over the standard decoders, with similar decoding latency and energy consumption. Moreover, the DL-based adaptive decoder can work well over different channel conditions of STT-MRAM irrespective of the unknown resistance offset, with a 50% reduction of the decoding latency and energy consumption compared to the fixed decoder.