161 research outputs found
ReBoot: Distributed statistical learning via refitting Bootstrap samples
In this paper, we study a one-shot distributed learning algorithm via
refitting Bootstrap samples, which we refer to as ReBoot. Given the local
models that are fit on multiple independent subsamples, ReBoot refits a new
model on the union of the Bootstrap samples drawn from these local models. The
whole procedure requires only one round of communication of model parameters.
Theoretically, we analyze the statistical rate of ReBoot for generalized linear
models (GLM) and noisy phase retrieval, which represent convex and non-convex
problems respectively. In both cases, ReBoot provably achieves the full-sample
statistical rate whenever the subsample size is not too small. In particular,
we show that the systematic bias of ReBoot, the error that is independent of
the number of subsamples, is in GLM, where n is the subsample
size. This rate is sharper than that of model parameter averaging and its
variants, implying the higher tolerance of ReBoot with respect to data splits
to maintain the full-sample rate. Simulation study exhibits the statistical
advantage of ReBoot over competing methods including averaging and CSL
(Communication-efficient Surrogate Likelihood) with up to two rounds of
gradient communication. Finally, we propose FedReBoot, an iterative version of
ReBoot, to aggregate convolutional neural networks for image classification,
which exhibits substantial superiority over FedAve within early rounds of
communication
Flow-Attention-based Spatio-Temporal Aggregation Network for 3D Mask Detection
Anti-spoofing detection has become a necessity for face recognition systems
due to the security threat posed by spoofing attacks. Despite great success in
traditional attacks, most deep-learning-based methods perform poorly in 3D
masks, which can highly simulate real faces in appearance and structure,
suffering generalizability insufficiency while focusing only on the spatial
domain with single frame input. This has been mitigated by the recent
introduction of a biomedical technology called rPPG (remote
photoplethysmography). However, rPPG-based methods are sensitive to noisy
interference and require at least one second (> 25 frames) of observation time,
which induces high computational overhead. To address these challenges, we
propose a novel 3D mask detection framework, called FASTEN
(Flow-Attention-based Spatio-Temporal aggrEgation Network). We tailor the
network for focusing more on fine-grained details in large movements, which can
eliminate redundant spatio-temporal feature interference and quickly capture
splicing traces of 3D masks in fewer frames. Our proposed network contains
three key modules: 1) a facial optical flow network to obtain non-RGB
inter-frame flow information; 2) flow attention to assign different
significance to each frame; 3) spatio-temporal aggregation to aggregate
high-level spatial features and temporal transition features. Through extensive
experiments, FASTEN only requires five frames of input and outperforms eight
competitors for both intra-dataset and cross-dataset evaluations in terms of
multiple detection metrics. Moreover, FASTEN has been deployed in real-world
mobile devices for practical 3D mask detection.Comment: 13 pages, 5 figures. Accepted to NeurIPS 202
Heightening of an Existing Embankment Dam: Results from Numerical Simulations
The old dam of the Zhushou Reservoir is a clay core rock-debris dam with a maximum height of 63.4Â m. After heightening, the new dam is a concrete-faced rockfill dam with a maximum height of 98.1Â m. In the initial design stage, a rigid connection is proposed between the cutoff wall and toe slab. After the concrete cutoff wall is built at the axis of the old dam, a complete cutoff system is composed of cutoff wall, toe slab, and face slab. In this paper, based on the static and dynamic tests of dam materials, the Shen Zhujiang double-yield surface elastic-plastic model is used as the static constitutive model, and the contact friction model is used as the contact surface model. The three-dimensional finite element method is used to simulate the construction filling and water storage process during operation. The simulation results show that the maximum horizontal displacement occurs in the dam body of the old dam and the maximum settlement occurs at the interface between the old and new dams. During the storage period, the cutoff wall will not be damaged, and the tensile stress of the local area at the junction of toe slab and bank slope has exceeded the allowable value for C30 plain concrete, so the reinforcement should be strengthened at this location
Electrical Tunable Spintronic Neuron with Trainable Activation Function
Spintronic devices have been widely studied for the hardware realization of
artificial neurons. The stochastic switching of magnetic tunnel junction driven
by the spin torque is commonly used to produce the sigmoid activation function.
However, the shape of the activation function in previous studies is fixed
during the training of neural network. This restricts the updating of weights
and results in a limited performance. In this work, we exploit the physics
behind the spin torque induced magnetization switching to enable the dynamic
change of the activation function during the training process. Specifically,
the pulse width and magnetic anisotropy can be electrically controlled to
change the slope of activation function, which enables a faster or slower
change of output required by the backpropagation algorithm. This is also
similar to the idea of batch normalization that is widely used in the machine
learning. Thus, this work demonstrates that the algorithms are no longer
limited to the software implementation. They can in fact be realized by the
spintronic hardware using a single device. Finally, we show that the accuracy
of hand-written digit recognition can be improved from 88% to 91.3% by using
these trainable spintronic neurons without introducing additional energy
consumption. Our proposals can stimulate the hardware realization of spintronic
neural networks.Comment: 26 pages, 9 figure
Determination of conifer age biomarker DAL1 interactome using Y2H-seq
Age is a sophisticated physiological signal that ensures the sequence of different developmental stages in organisms. The regulation of ageing pathways appears to differ between gymnosperms and angiosperms. We previously identified DAL1 as a conserved conifer age biomarker that plays a crucial role in the transition from vegetative to reproductive life-history phases in pines. Therefore, elucidating the specific interaction events related to DAL1 is key to understanding how age drives conifer development. Large-scale yeast two-hybrid (Y2H) analysis followed by next-generation high-throughput sequencing (Y2H-seq) allowed us to identify 135 PtDAL1 interacting proteins in Pinus tabuliformis. Our study found that PtDAL1 interacting proteins showed an ageing-related module, with sophisticated interacting networks composed of transcription factors (TFs), transcriptional regulators (TRs), and kinases. These interacting proteins are produced in response to a variety of phytohormones and environmental signals, and are likely involved in wood formation, needle development, oleoresin terpenoids biosynthesis, and reproductive development. In this study, we propose a novel regulation model of conifer ageing pathways whereby PtDAL1 coordinates different environmental stimuli and interacts with corresponding proteins to regulate appropriate development
Small-scale fluctuation and scaling law of mixing in three-dimensional rotating turbulent Rayleigh-Taylor instability
The effect of rotation on small-scale characteristics and scaling law in the mixing zone of the three-dimensional turbulent Rayleigh-Taylor instability (RTI) is investigated by the lattice Boltzmann method at small Atwood number. The mixing zone width
h
(
t
)
, the root mean square of small scale fluctuation, the spectra, and the structure functions are obtained to analyze the rotating effect. We mainly focus on the process of the development of plumes and discuss the physical mechanism in the mixing zone in rotating and nonrotating systems. The variation of kinetic energy spectra
E
u
and temperature energy spectra
E
θ
with the dimensionless rotation
Ω
Ï„
demonstrate the suppression effect of rotation. Two scaling laws between the mixing layer width
h
(
t
)
and dimensionless time
t
/
Ï„
are obtained at various Coriolis forces(
√
h
(
t
)
≃
t
0.9
and
√
h
(
t
)
≃
t
0.35
). The rotation increasingly suppresses the growth of the mixing layer width
h
(
t
)
. The velocity and temperature fluctuations are also suppressed by the rotation effect. The relation between the Nusselt number (Nu) and the Rayleigh number (Ra) indicates that the heat transfer is suppressed by the rotation effect in the rotating RT system. The width of the inertial subrange increasingly narrows with increasing
Ω
Ï„
Spatially Nonuniform Oscillations in Ferrimagnets Based on an Atomistic Model
The ferrimagnets, such as GdxFeCo(1-x), can produce ultrafast magnetic
switching and oscillation due to the strong exchange field. The two-sublattices
macrospin model has been widely used to explain the experimental results.
However, it fails in describing the spatial nonuniform magnetic dynamics which
gives rises to many important phenomenons such as the domain walls and
skyrmions. Here we develop the two-dimensional atomistic model and provide a
torque analysis method to study the ferrimagnetic oscillation. Under the
spin-transfer torque, the magnetization oscillates in the exchange mode or the
flipped exchange mode. When the Gd composition is increased, the exchange mode
firstly disappears, and then appears again as the magnetization compensation
point is reached. We show that these results can only be explained by analyzing
the spatial distribution of magnetization and effective fields. In particular,
when the sample is small, a spatial nonuniform oscillation is also observed in
the square film. Our work reveals the importance of spatial magnetic
distributions in understanding the ferrimagnetic dynamics. The method developed
in this paper provides an important tool to gain a deeper understanding of
ferrimagnets and antiferromagnets. The observed ultrafast dynamics can also
stimulate the development of THz oscillators.Comment: 17 pages, 4 figure
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