4,626 research outputs found
Parallel implementation of the TRANSIMS micro-simulation
This paper describes the parallel implementation of the TRANSIMS traffic
micro-simulation. The parallelization method is domain decomposition, which
means that each CPU of the parallel computer is responsible for a different
geographical area of the simulated region. We describe how information between
domains is exchanged, and how the transportation network graph is partitioned.
An adaptive scheme is used to optimize load balancing. We then demonstrate how
computing speeds of our parallel micro-simulations can be systematically
predicted once the scenario and the computer architecture are known. This makes
it possible, for example, to decide if a certain study is feasible with a
certain computing budget, and how to invest that budget. The main ingredients
of the prediction are knowledge about the parallel implementation of the
micro-simulation, knowledge about the characteristics of the partitioning of
the transportation network graph, and knowledge about the interaction of these
quantities with the computer system. In particular, we investigate the
differences between switched and non-switched topologies, and the effects of 10
Mbit, 100 Mbit, and Gbit Ethernet. keywords: Traffic simulation, parallel
computing, transportation planning, TRANSIM
A stochastic and dynamical view of pluripotency in mouse embryonic stem cells
Pluripotent embryonic stem cells are of paramount importance for biomedical
research thanks to their innate ability for self-renewal and differentiation
into all major cell lines. The fateful decision to exit or remain in the
pluripotent state is regulated by complex genetic regulatory network. Latest
advances in transcriptomics have made it possible to infer basic topologies of
pluripotency governing networks. The inferred network topologies, however, only
encode boolean information while remaining silent about the roles of dynamics
and molecular noise in gene expression. These features are widely considered
essential for functional decision making. Herein we developed a framework for
extending the boolean level networks into models accounting for individual
genetic switches and promoter architecture which allows mechanistic
interrogation of the roles of molecular noise, external signaling, and network
topology. We demonstrate the pluripotent state of the network to be a broad
attractor which is robust to variations of gene expression. Dynamics of exiting
the pluripotent state, on the other hand, is significantly influenced by the
molecular noise originating from genetic switching events which makes cells
more responsive to extracellular signals. Lastly we show that steady state
probability landscape can be significantly remodeled by global gene switching
rates alone which can be taken as a proxy for how global epigenetic
modifications exert control over stability of pluripotent states.Comment: 11 pages, 7 figure
Parameterized Model Checking of Token-Passing Systems
We revisit the parameterized model checking problem for token-passing systems
and specifications in indexed .
Emerson and Namjoshi (1995, 2003) have shown that parameterized model checking
of indexed in uni-directional token
rings can be reduced to checking rings up to some \emph{cutoff} size. Clarke et
al. (2004) have shown a similar result for general topologies and indexed
, provided processes cannot choose the
directions for sending or receiving the token.
We unify and substantially extend these results by systematically exploring
fragments of indexed with respect to
general topologies. For each fragment we establish whether a cutoff exists, and
for some concrete topologies, such as rings, cliques and stars, we infer small
cutoffs. Finally, we show that the problem becomes undecidable, and thus no
cutoffs exist, if processes are allowed to choose the directions in which they
send or from which they receive the token.Comment: We had to remove an appendix until the proofs and notations there is
cleare
A Review of Fault Diagnosing Methods in Power Transmission Systems
Transient stability is important in power systems. Disturbances like faults need to be segregated to restore transient stability. A comprehensive review of fault diagnosing methods in the power transmission system is presented in this paper. Typically, voltage and current samples are deployed for analysis. Three tasks/topics; fault detection, classification, and location are presented separately to convey a more logical and comprehensive understanding of the concepts. Feature extractions, transformations with dimensionality reduction methods are discussed. Fault classification and location techniques largely use artificial intelligence (AI) and signal processing methods. After the discussion of overall methods and concepts, advancements and future aspects are discussed. Generalized strengths and weaknesses of different AI and machine learning-based algorithms are assessed. A comparison of different fault detection, classification, and location methods is also presented considering features, inputs, complexity, system used and results. This paper may serve as a guideline for the researchers to understand different methods and techniques in this field
Efficient Synthesis of Network Updates
Software-defined networking (SDN) is revolutionizing the networking industry,
but current SDN programming platforms do not provide automated mechanisms for
updating global configurations on the fly. Implementing updates by hand is
challenging for SDN programmers because networks are distributed systems with
hundreds or thousands of interacting nodes. Even if initial and final
configurations are correct, naively updating individual nodes can lead to
incorrect transient behaviors, including loops, black holes, and access control
violations. This paper presents an approach for automatically synthesizing
updates that are guaranteed to preserve specified properties. We formalize
network updates as a distributed programming problem and develop a synthesis
algorithm based on counterexample-guided search and incremental model checking.
We describe a prototype implementation, and present results from experiments on
real-world topologies and properties demonstrating that our tool scales to
updates involving over one-thousand nodes
Evolving Neural Networks through a Reverse Encoding Tree
NeuroEvolution is one of the most competitive evolutionary learning
frameworks for designing novel neural networks for use in specific tasks, such
as logic circuit design and digital gaming. However, the application of
benchmark methods such as the NeuroEvolution of Augmenting Topologies (NEAT)
remains a challenge, in terms of their computational cost and search time
inefficiency. This paper advances a method which incorporates a type of
topological edge coding, named Reverse Encoding Tree (RET), for evolving
scalable neural networks efficiently. Using RET, two types of approaches --
NEAT with Binary search encoding (Bi-NEAT) and NEAT with Golden-Section search
encoding (GS-NEAT) -- have been designed to solve problems in benchmark
continuous learning environments such as logic gates, Cartpole, and Lunar
Lander, and tested against classical NEAT and FS-NEAT as baselines.
Additionally, we conduct a robustness test to evaluate the resilience of the
proposed NEAT algorithms. The results show that the two proposed strategies
deliver improved performance, characterized by (1) a higher accumulated reward
within a finite number of time steps; (2) using fewer episodes to solve
problems in targeted environments, and (3) maintaining adaptive robustness
under noisy perturbations, which outperform the baselines in all tested cases.
Our analysis also demonstrates that RET expends potential future research
directions in dynamic environments. Code is available from
https://github.com/HaolingZHANG/ReverseEncodingTree.Comment: Accepted to IEEE Congress on Evolutionary Computation (IEEE CEC)
2020. Lecture Presentatio
Verification and Synthesis of Symmetric Uni-Rings for Leads-To Properties
This paper investigates the verification and synthesis of parameterized
protocols that satisfy leadsto properties on symmetric
unidirectional rings (a.k.a. uni-rings) of deterministic and constant-space
processes under no fairness and interleaving semantics, where and are
global state predicates. First, we show that verifying for
parameterized protocols on symmetric uni-rings is undecidable, even for
deterministic and constant-space processes, and conjunctive state predicates.
Then, we show that surprisingly synthesizing symmetric uni-ring protocols that
satisfy is actually decidable. We identify necessary and
sufficient conditions for the decidability of synthesis based on which we
devise a sound and complete polynomial-time algorithm that takes the predicates
and , and automatically generates a parameterized protocol that
satisfies for unbounded (but finite) ring sizes. Moreover, we
present some decidability results for cases where leadsto is required from
multiple distinct predicates to different predicates. To demonstrate
the practicality of our synthesis method, we synthesize some parameterized
protocols, including agreement and parity protocols
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