3,308 research outputs found
Fluctuation-dissipation relations far from equilibrium
The fluctuation-dissipation (F-D) theorem is a fundamental result for systems
near thermodynamic equilibrium, and justifies studies between microscopic and
macroscopic properties. It states that the nonequilibrium relaxation dynamics
is related to the spontaneous fluctuation at equilibrium. Most processes in
Nature are out of equilibrium, for which we have limited theory. Common wisdom
believes the F-D theorem is violated in general for systems far from
equilibrium. Recently we show that dynamics of a dissipative system described
by stochastic differential equations can be mapped to that of a thermostated
Hamiltonian system, with a nonequilibrium steady state of the former
corresponding to the equilibrium state of the latter. Her we derived the
corresponding F-D theorem, and tested with several examples. We suggest further
studies exploiting the analogy between a general dissipative system appearing
in various science branches and a Hamiltonian system. Especially we discussed
the implications of this work on biological network studies.Comment: 12 pages, 4 figures, major revision over the first versio
Intelligent data mining using artificial neural networks and genetic algorithms : techniques and applications
Data Mining (DM) refers to the analysis of observational datasets to find
relationships and to summarize the data in ways that are both understandable
and useful. Many DM techniques exist. Compared with other DM techniques,
Intelligent Systems (ISs) based approaches, which include Artificial Neural
Networks (ANNs), fuzzy set theory, approximate reasoning, and derivative-free
optimization methods such as Genetic Algorithms (GAs), are tolerant of
imprecision, uncertainty, partial truth, and approximation. They provide
flexible information processing capability for handling real-life situations. This
thesis is concerned with the ideas behind design, implementation, testing and
application of a novel ISs based DM technique. The unique contribution of this
thesis is in the implementation of a hybrid IS DM technique (Genetic Neural
Mathematical Method, GNMM) for solving novel practical problems, the
detailed description of this technique, and the illustrations of several
applications solved by this novel technique.
GNMM consists of three steps: (1) GA-based input variable selection, (2) Multi-
Layer Perceptron (MLP) modelling, and (3) mathematical programming based
rule extraction. In the first step, GAs are used to evolve an optimal set of MLP
inputs. An adaptive method based on the average fitness of successive
generations is used to adjust the mutation rate, and hence the
exploration/exploitation balance. In addition, GNMM uses the elite group and
appearance percentage to minimize the randomness associated with GAs. In
the second step, MLP modelling serves as the core DM engine in performing
classification/prediction tasks. An Independent Component Analysis (ICA)
based weight initialization algorithm is used to determine optimal weights
before the commencement of training algorithms. The Levenberg-Marquardt
(LM) algorithm is used to achieve a second-order speedup compared to
conventional Back-Propagation (BP) training. In the third step, mathematical
programming based rule extraction is not only used to identify the premises of
multivariate polynomial rules, but also to explore features from the extracted
rules based on data samples associated with each rule. Therefore, the
methodology can provide regression rules and features not only in the
polyhedrons with data instances, but also in the polyhedrons without data
instances.
A total of six datasets from environmental and medical disciplines were used
as case study applications. These datasets involve the prediction of
longitudinal dispersion coefficient, classification of electrocorticography
(ECoG)/Electroencephalogram (EEG) data, eye bacteria Multisensor Data
Fusion (MDF), and diabetes classification (denoted by Data I through to Data VI). GNMM was applied to all these six datasets to explore its effectiveness,
but the emphasis is different for different datasets. For example, the emphasis
of Data I and II was to give a detailed illustration of how GNMM works; Data III
and IV aimed to show how to deal with difficult classification problems; the
aim of Data V was to illustrate the averaging effect of GNMM; and finally Data
VI was concerned with the GA parameter selection and benchmarking GNMM
with other IS DM techniques such as Adaptive Neuro-Fuzzy Inference System
(ANFIS), Evolving Fuzzy Neural Network (EFuNN), Fuzzy ARTMAP, and
Cartesian Genetic Programming (CGP). In addition, datasets obtained from
published works (i.e. Data II & III) or public domains (i.e. Data VI) where
previous results were present in the literature were also used to benchmark
GNMM’s effectiveness.
As a closely integrated system GNMM has the merit that it needs little human
interaction. With some predefined parameters, such as GA’s crossover
probability and the shape of ANNs’ activation functions, GNMM is able to
process raw data until some human-interpretable rules being extracted. This is
an important feature in terms of practice as quite often users of a DM system
have little or no need to fully understand the internal components of such a
system. Through case study applications, it has been shown that the GA-based
variable selection stage is capable of: filtering out irrelevant and noisy
variables, improving the accuracy of the model; making the ANN structure less
complex and easier to understand; and reducing the computational complexity
and memory requirements. Furthermore, rule extraction ensures that the MLP
training results are easily understandable and transferrable
PLIT: An alignment-free computational tool for identification of long non-coding RNAs in plant transcriptomic datasets
Long non-coding RNAs (lncRNAs) are a class of non-coding RNAs which play a significant role in several biological processes. RNA-seq based transcriptome sequencing has been extensively used for identification of lncRNAs. However, accurate identification of lncRNAs in RNA-seq datasets is crucial for exploring their characteristic functions in the genome as most coding potential computation (CPC) tools fail to accurately identify them in transcriptomic data. Well-known CPC tools such as CPC2, lncScore, CPAT are primarily designed for prediction of lncRNAs based on the GENCODE, NONCODE and CANTATAdb databases. The prediction accuracy of these tools often drops when tested on transcriptomic datasets. This leads to higher false positive results and inaccuracy in the function annotation process. In this study, we present a novel tool, PLIT, for the identification of lncRNAs in plants RNA-seq datasets. PLIT implements a feature selection method based on L1 regularization and iterative Random Forests (iRF) classification for selection of optimal features. Based on sequence and codon-bias features, it classifies the RNA-seq derived FASTA sequences into coding or long non-coding transcripts. Using L1 regularization, 31 optimal features were obtained based on lncRNA and protein-coding transcripts from 8 plant species. The performance of the tool was evaluated on 7 plant RNA-seq datasets using 10-fold cross-validation. The analysis exhibited superior accuracy when evaluated against currently available state-of-the-art CPC tools
YOWOv2: A Stronger yet Efficient Multi-level Detection Framework for Real-time Spatio-temporal Action Detection
Designing a real-time framework for the spatio-temporal action detection task
is still a challenge. In this paper, we propose a novel real-time action
detection framework, YOWOv2. In this new framework, YOWOv2 takes advantage of
both the 3D backbone and 2D backbone for accurate action detection. A
multi-level detection pipeline is designed to detect action instances of
different scales. To achieve this goal, we carefully build a simple and
efficient 2D backbone with a feature pyramid network to extract different
levels of classification features and regression features. For the 3D backbone,
we adopt the existing efficient 3D CNN to save development time. By combining
3D backbones and 2D backbones of different sizes, we design a YOWOv2 family
including YOWOv2-Tiny, YOWOv2-Medium, and YOWOv2-Large. We also introduce the
popular dynamic label assignment strategy and anchor-free mechanism to make the
YOWOv2 consistent with the advanced model architecture design. With our
improvement, YOWOv2 is significantly superior to YOWO, and can still keep
real-time detection. Without any bells and whistles, YOWOv2 achieves 87.0 %
frame mAP and 52.8 % video mAP with over 20 FPS on the UCF101-24. On the AVA,
YOWOv2 achieves 21.7 % frame mAP with over 20 FPS. Our code is available on
https://github.com/yjh0410/YOWOv2
Can Hubbard model resist electric current?
It is claimed by a recent quantum Monte Carlo simulation that the
linear-in-temperature DC resistivity observed in the high- cuprate
superconductors can be reproduced in the pure two dimensional Hubbard
model\cite{Huang}. Here we show perturbatively that such a translational
invariant electronic model can not support a steady state current in the
presence of a uniform electric field at any finite temperature. Instead, the
Hubbard model is perfectly conducting in the linear response regime and will
undergo Bloch oscillation at finite electric field for any finite temperature.
Nevertheless, the quantum Monte Carlo simulation can provide us the key
information on the temperature dependence of the Drude weight, a quantity of
central importance in the holographic description of the transport properties
of the strange metal phase.Comment: 5 pages, 0 figures, new arguments based on emergent symmetry and
related anomaly adde
Instability of the spin liquid with a large spinon Fermi surface in the Heisenberg-ring exchange model on the triangular lattice
It is widely believed that the spin liquid with a large spinon Fermi
surface(SFS state) can be realized in the spin-
model on the triangular lattice, when the ring exchange coupling is
sufficiently strong to suppress the 120 degree magnetic ordered state. This
belief is supported by many variational studies on this model and seems to be
consistent with observations on the organic spin liquid materials such as
-(BEDT-TTF)Cu(CN), a system that is close to Mott
transition and thus has large ring exchange coupling. Here we show through a
systematic variational search that such a state is never favored in the
model on the triangular lattice. Instead, a state with broken
spatial symmetry is favored in the most probable parameter regime for the SFS
state, with an energy much lower than that of the SFS state and other
previously proposed variational states. More specifically, we find that for
, the system favors a valence bond solid state with a
period in its local spin correlation pattern, with a variational
energy that is about lower than that of the SFS state. This state is
separated form the -flux state for by an
intermediate valence bond solid state with a zigzag pattern in its local spin
correlation. We find that the variational phase digram is in qualitative
agreement with that obtained from exact diagonalization on a
cluster.Comment: 16 pages, 17 figure
Strong relevance of Zinc impurity in the spin- Kagome quantum antiferromagnets: a variational study
Copper hydroxyhalide materials herbertsmithite ZnCu(OH)Cl
and Zn-barlowite ZnCu(OH)FrBr are thought to be the best
realizations of the spin- Kagome quantum antiferromagnetic
Heisenberg model and are widely believed to host a spin liquid ground state.
However, the exact nature of such a novel state of matter is still under strong
debate, partly due to the complication related to the occupation disorder
between the Zinc and the Copper ions in these systems. In particular, recent
nuclear magnetic resonance measurements indicate that the magnetic response of
the Kagome plane is significantly spatial inhomogeneous, even though the
content of the misplaced Zinc or Copper ions is believed to be very small. Here
we use extensive variational optimization to show that the well known
-Dirac spin liquid state is extremely sensitive to the introduction of
the nonmagnetic Zinc impurity in the Kagome plane. More specifically, we find
that the Zinc impurities can significantly reorganize the local spin
correlation pattern around them and induce strong spatial oscillation in the
magnetic response of the system. We argue that this is a general trend in
highly frustrated quantum magnet systems, in which the nonmagnetic impurity may
act as strongly relevant perturbation on the emergent resonating valence bond
structure in their spin liquid ground state. We also argue that the strong
spatial oscillation in the magnetic response should be attributed to the free
moment released by the doped Zinc ions and may serve as the smoking gun
evidence for the Dirac node in the Dirac spin liquid state on the Kagome
lattice.Comment: 12 pages, 10 figure
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