Related papers: Direct Acyclic Graph based Ledger for Internet of …
This paper presents the current state of the art on attack and defense modeling approaches that are based on directed acyclic graphs (DAGs). DAGs allow for a hierarchical decomposition of complex scenarios into simple, easily understandable…
To satisfy the increasing performance needs of modern cyber-physical systems, multiprocessor architectures are increasingly utilized. To efficiently exploit their potential parallelism in hard real-time systems, appropriate task models and…
A communication network modelled by a directed acyclic graph (DAG) is considered, over which a source wishes to send a specified number of bits to a destination node. Each node of the DAG is powered by a separate renewable energy source,…
Many real-world scientific workflows can be represented by a Directed Acyclic Graph (DAG), where each node represents a task and a directed edge signifies a dependency between two tasks. Due to the increasing computational resource…
This paper investigates the issue of fairness in Distributed Ledger Technology (DLT), specifically focusing on the shortcomings observed in current blockchain systems due to Miner Extractable Value (MEV) phenomena and systemic…
Trustless systems, such as those blockchain enpowered, provide trust in the system regardless of the trust of its participants, who may be honest or malicious. Proof-of-stake (PoS) protocols and DAG-based approaches have emerged as a better…
We study a distributed learning problem in which learning agents are embedded in a directed acyclic graph (DAG). There is a fixed and arbitrary distribution over feature/label pairs, and each agent or vertex in the graph is able to directly…
Directed acyclic graphs (DAGs) constitute a central modeling tool to enable principled reasoning about cause-effect interactions in complex systems. However, since the causal structure underlying a group of variables is often unknown and…
Learning the structure of causal directed acyclic graphs (DAGs) is useful in many areas of machine learning and artificial intelligence, with wide applications. However, in the high-dimensional setting, it is challenging to obtain good…
Directed acyclic graph (DAG) tasks are currently adopted in the real-time domain to model complex applications from the automotive, avionics, and industrial domains that implement their functionalities through chains of intercommunicating…
Assuming a directed acyclic graph (DAG) that represents prior knowledge of causal relationships between variables is a common starting point for cause-effect estimation. Existing literature typically invokes hypothetical domain expert…
Recently continuous relaxations have been proposed in order to learn Directed Acyclic Graphs (DAGs) from data by backpropagation, instead of using combinatorial optimization. However, a number of techniques for fully discrete…
This paper explores the conditions for determining fixed nodes in structured networks, specifically focusing on directed acyclic graphs (DAGs). We introduce several necessary and sufficient conditions for determining fixed nodes in…
We propose a novel score-based approach to learning a directed acyclic graph (DAG) from observational data. We adapt a recently proposed continuous constrained optimization formulation to allow for nonlinear relationships between variables…
Blockchain consensus protocols enable participants to agree on consistent views of the blockchain that may be ahead or behind relative to each other but do not fork into different chains. A number of recently popular…
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…
Avalanche is a blockchain consensus protocol with exceptionally low latency and high throughput. This has swiftly established the corresponding token as a top-tier cryptocurrency. Avalanche achieves such remarkable metrics by substituting…
This work addresses the problem of learning directed acyclic graphs (DAGs) from nodal observations generated by a linear structural equation model. DAG learning is a central task in signal processing, machine learning, and causal inference,…
Recent progress in large language models has renewed interest in how multi-step reasoning is represented internally. While prior work often treats reasoning as a linear chain, many reasoning problems are more naturally modeled as directed…
In order to fully unlock the transformative power of distributed ledgers and blockchains, it is crucial to develop innovative consensus algorithms that can overcome the obstacles of security, scalability, and interoperability, which…