38 research outputs found
Machine learning for discovering laws of nature
A microscopic particle obeys the principles of quantum mechanics -- so where
is the sharp boundary between the macroscopic and microscopic worlds? It was
this "interpretation problem" that prompted Schr\"odinger to propose his famous
thought experiment (a cat that is simultaneously both dead and alive) and
sparked a great debate about the quantum measurement problem, and there is
still no satisfactory answer yet. This is precisely the inadequacy of rigorous
mathematical models in describing the laws of nature. We propose a
computational model to describe and understand the laws of nature based on
Darwin's natural selection. In fact, whether it's a macro particle, a micro
electron or a security, they can all be considered as an entity, the change of
this entity over time can be described by a data series composed of states and
values. An observer can learn from this data series to construct theories
(usually consisting of functions and differential equations). We don't model
with the usual functions or differential equations, but with a state Decision
Tree (determines the state of an entity) and a value Function Tree (determines
the distance between two points of an entity). A state Decision Tree and a
value Function Tree together can reconstruct an entity's trajectory and make
predictions about its future trajectory. Our proposed algorithmic model
discovers laws of nature by only learning observed historical data (sequential
measurement of observables) based on maximizing the observer's expected value.
There is no differential equation in our model; our model has an emphasis on
machine learning, where the observer builds up his/her experience by being
rewarded or punished for each decision he/she makes, and eventually leads to
rediscovering Newton's law, the Born rule (quantum mechanics) and the efficient
market hypothesis (financial market)
Resonant response of forced complex networks: the role of topological disorder
We investigate the effect of topological disorder on a system of forced
threshold elements, where each element is arranged on top of complex
heterogeneous networks. Numerical results indicate that the response of the
system to a weak signal can be amplified at an intermediate level of
topological disorder, thus indicating the occurrence of
topological-disorder-induced resonance. Using mean field method, we obtain an
analytical understanding of the resonant phenomenon by deriving the effective
potential of the system. Our findings might provide further insight into the
role of network topology in signal amplification in biological networks.Comment: 12 pages, 4 figure
Timelike entanglement entropy and deformation
In a previous work arXiv:1811.07758 about the deformed CFT,
from the consistency requirement of the entanglement entropy theory, we found
that in addition to the usual spacelike entanglement entropy, a timelike
entanglement entropy must be introduced and treated equally. Inspired by the
recent explicit constructions of the timelike entanglement entropy and its bulk
dual, we provide a comprehensive analysis of the timelike and spacelike
entanglement entropies in the deformed finite size system and finite
temperature system. The results confirm our prediction that in the finite size
system only the timelike entanglement entropy receives a correction, while in
the finite temperature system only the usual spacelike entanglement entropy
gets a correction. These findings affirm the necessity of a complete measure
including both spacelike and timelike entanglement entropies, called general
entanglement entropy, for characterizing deformed systems from the quantum
information perspective.Comment: v1: 12 pages, 2 figures; v2: references added; v3: typo fixe
Timelike entanglement entropy in
In the context of dS/CFT, we propose a timelike entanglement entropy
defined by the renormalization group flow. This timelike entanglement entropy
is calculated in CFT by using the Callan-Symanzik equation. We find an exact
match between this entanglement entropy and the length of a timelike geodesic
connecting two different spacelike surfaces in dS.The counterpart of this
entanglement entropy in AdS is a spacelike one, also induced by RG flow and
extends all the way into the bulk of AdS. As a result, in both
AdS/CFT and dS/CFT, there exist exactly three entanglement
entropies, providing precisely sufficient information to reconstruct the
three-dimensional bulk geometry.Comment: v1: 9 pages, 1 figure; v2: references added, fixed typo
Nucleation in scale-free networks
We have studied nucleation dynamics of the Ising model in scale-free networks
with degree distribution by using forward flux sampling
method, focusing on how the network topology would influence the nucleation
rate and pathway. For homogeneous nucleation, the new phase clusters grow from
those nodes with smaller degree, while the cluster sizes follow a power-law
distribution. Interestingly, we find that the nucleation rate decays
exponentially with the network size , and accordingly the critical nucleus
size increases linearly with , implying that homogeneous nucleation is not
relevant in the thermodynamic limit. These observations are robust to the
change of and also present in random networks. In addition, we have
also studied the dynamics of heterogeneous nucleation, wherein impurities
are initially added, either to randomly selected nodes or to targeted ones with
largest degrees. We find that targeted impurities can enhance the nucleation
rate much more sharply than random ones. Moreover, scales as and for targeted and
random impurities, respectively. A simple mean field analysis is also present
to qualitatively illustrate above simulation results.Comment: 7 pages, 5 figure
Exploring Lightweight Hierarchical Vision Transformers for Efficient Visual Tracking
Transformer-based visual trackers have demonstrated significant progress
owing to their superior modeling capabilities. However, existing trackers are
hampered by low speed, limiting their applicability on devices with limited
computational power. To alleviate this problem, we propose HiT, a new family of
efficient tracking models that can run at high speed on different devices while
retaining high performance. The central idea of HiT is the Bridge Module, which
bridges the gap between modern lightweight transformers and the tracking
framework. The Bridge Module incorporates the high-level information of deep
features into the shallow large-resolution features. In this way, it produces
better features for the tracking head. We also propose a novel dual-image
position encoding technique that simultaneously encodes the position
information of both the search region and template images. The HiT model
achieves promising speed with competitive performance. For instance, it runs at
61 frames per second (fps) on the Nvidia Jetson AGX edge device. Furthermore,
HiT attains 64.6% AUC on the LaSOT benchmark, surpassing all previous efficient
trackers.Comment: This paper was accepted by ICCV202
SeqTrack: Sequence to Sequence Learning for Visual Object Tracking
In this paper, we present a new sequence-to-sequence learning framework for
visual tracking, dubbed SeqTrack. It casts visual tracking as a sequence
generation problem, which predicts object bounding boxes in an autoregressive
fashion. This is different from prior Siamese trackers and transformer
trackers, which rely on designing complicated head networks, such as
classification and regression heads. SeqTrack only adopts a simple
encoder-decoder transformer architecture. The encoder extracts visual features
with a bidirectional transformer, while the decoder generates a sequence of
bounding box values autoregressively with a causal transformer. The loss
function is a plain cross-entropy. Such a sequence learning paradigm not only
simplifies tracking framework, but also achieves competitive performance on
benchmarks. For instance, SeqTrack gets 72.5% AUC on LaSOT, establishing a new
state-of-the-art performance. Code and models are available at here.Comment: CVPR2023 pape
The g-irradiation-induced chemical change from b-FeOOH to
Abstract The reactions of g-irradiation on 5 nm b-FeOOH in the presence of isopropanol and water have been investigated. In the initial stage of the g-irradiation, b-FeOOH turned into a-FeOOH. With the g-irradiation continued, a-FeOOH was slowly reduced to Fe 3 O 4 . After the g-irradiation with a dose of 64.3 kGy, all the bFeOOH and a-FeOOH disappeared and the product was a single phase of Fe 3 O 4 , which had an average particle size of 54 nm. The process of this reaction is discussed. The g-irradiation of b-FeOOH should be a new method of preparing magnetite.