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 $O(n ^ {-2})$ 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 63.4 m. After heightening, the new dam is a concrete-faced rockfill dam with a maximum height of 98.1 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 τ demonstrate the suppression effect of rotation. Two scaling laws between the mixing layer width h ( t ) and dimensionless time t / τ 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 τ

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