53 research outputs found
Optimal Save-Then-Transmit Protocol for Energy Harvesting Wireless Transmitters
In this paper, the design of a wireless communication device relying
exclusively on energy harvesting is considered. Due to the inability of
rechargeable energy sources to charge and discharge at the same time, a
constraint we term the energy half-duplex constraint, two rechargeable energy
storage devices (ESDs) are assumed so that at any given time, there is always
one ESD being recharged. The energy harvesting rate is assumed to be a random
variable that is constant over the time interval of interest. A
save-then-transmit (ST) protocol is introduced, in which a fraction of time
{\rho} (dubbed the save-ratio) is devoted exclusively to energy harvesting,
with the remaining fraction 1 - {\rho} used for data transmission. The ratio of
the energy obtainable from an ESD to the energy harvested is termed the energy
storage efficiency, {\eta}. We address the practical case of the secondary ESD
being a battery with {\eta} < 1, and the main ESD being a super-capacitor with
{\eta} = 1. The optimal save-ratio that minimizes outage probability is
derived, from which some useful design guidelines are drawn. In addition, we
compare the outage performance of random power supply to that of constant power
supply over the Rayleigh fading channel. The diversity order with random power
is shown to be the same as that of constant power, but the performance gap can
be large. Furthermore, we extend the proposed ST protocol to wireless networks
with multiple transmitters. It is shown that the system-level outage
performance is critically dependent on the relationship between the number of
transmitters and the optimal save-ratio for single-channel outage minimization.
Numerical results are provided to validate our proposed study.Comment: This is the longer version of a paper to appear in IEEE Transactions
on Wireless Communication
NCART: Neural Classification and Regression Tree for Tabular Data
Deep learning models have become popular in the analysis of tabular data, as
they address the limitations of decision trees and enable valuable applications
like semi-supervised learning, online learning, and transfer learning. However,
these deep-learning approaches often encounter a trade-off. On one hand, they
can be computationally expensive when dealing with large-scale or
high-dimensional datasets. On the other hand, they may lack interpretability
and may not be suitable for small-scale datasets. In this study, we propose a
novel interpretable neural network called Neural Classification and Regression
Tree (NCART) to overcome these challenges. NCART is a modified version of
Residual Networks that replaces fully-connected layers with multiple
differentiable oblivious decision trees. By integrating decision trees into the
architecture, NCART maintains its interpretability while benefiting from the
end-to-end capabilities of neural networks. The simplicity of the NCART
architecture makes it well-suited for datasets of varying sizes and reduces
computational costs compared to state-of-the-art deep learning models.
Extensive numerical experiments demonstrate the superior performance of NCART
compared to existing deep learning models, establishing it as a strong
competitor to tree-based models
TRBoost: A Generic Gradient Boosting Machine based on Trust-region Method
Gradient Boosting Machines (GBMs) are derived from Taylor expansion in
functional space and have achieved state-of-the-art results on a variety of
problems. However, there is a dilemma for GBMs to maintain a balance between
performance and generality. Specifically, gradient descent-based GBMs employ
the first-order Taylor expansion to make them appropriate for all loss
functions. And Newton's method-based GBMs use the positive hessian information
to achieve better performance at the expense of generality. In this paper, a
generic Gradient Boosting Machine called Trust-region Boosting (TRBoost) is
presented to maintain this balance. In each iteration, we apply a constrained
quadratic model to approximate the objective and solve it by the Trust-region
algorithm to obtain a new learner. TRBoost offers the benefit that we do not
need the hessian to be positive definite, which generalizes GBMs to suit
arbitrary loss functions while keeping up the good performance as the
second-order algorithm. Several numerical experiments are conducted to confirm
that TRBoost is not only as general as the first-order GBMs but also able to
get competitive results with the second-order GBMs
Tacchi: A Pluggable and Low Computational Cost Elastomer Deformation Simulator for Optical Tactile Sensors
Simulation is widely applied in robotics research to save time and resources.
There have been several works to simulate optical tactile sensors that leverage
either a smoothing method or Finite Element Method (FEM). However, elastomer
deformation physics is not considered in the former method, whereas the latter
requires a massive amount of computational resources like a computer cluster.
In this work, we propose a pluggable and low computational cost simulator using
the Taichi programming language for simulating optical tactile sensors, named
as Tacchi . It reconstructs elastomer deformation using particles, which allows
deformed elastomer surfaces to be rendered into tactile images and reveals
contact information without suffering from high computational costs. Tacchi
facilitates creating realistic tactile images in simulation, e.g., ones that
capture wear-and-tear defects on object surfaces. In addition, the proposed
Tacchi can be integrated with robotics simulators for a robot system
simulation. Experiment results showed that Tacchi can produce images with
better similarity to real images and achieved higher Sim2Real accuracy compared
to the existing methods. Moreover, it can be connected with MuJoCo and Gazebo
with only the requirement of 1G memory space in GPU compared to a computer
cluster applied for FEM. With Tacchi, physical robot simulation with optical
tactile sensors becomes possible. All the materials in this paper are available
at https://github.com/zixichen007115/Tacchi .Comment: 8 pages, 6 figures, accepted by IEEE Robotics and Automation Letter
Analysis of the Impacts of Environmental Factors on Rat Hole Density in the Northern Slope of the Tienshan Mountains with Satellite Remote Sensing Data
Understanding the impacts of environmental factors on spatial–temporal and large-scale rodent distribution is important for rodent damage prevention. Investigating rat hole density (RHD) is one of the most effective methods to obtain the intensity of rodent damage. However, most of the previous field surveys or UAV-based remote sensing methods can only evaluate small-scale RHD and its influencing factors. However, these studies did not consider large-scale temporal and spatial heterogeneity. Therefore, we collected small-scale and in situ measurement records of RHD on the northern slope of the Tien Shan Mountains in Xinjiang (NTXJ), China, from 1982 to 2015, and then used correlation analysis and Bayesian network (BN) to analyze the environmental impacts on large-scale RHD with satellite remote sensing data such as the GIMMS NDVI product. The results show that the built BN can better quantify causality in the environmental mechanism modeling of RHD. The NDVI and LAI data from satellite remote sensing are important to the spatial–temporal RHD distribution and the mapping in the future. In regions with an elevation higher than 600 m (UPR) and lower than 600 m (LWR) of NTXJ, there are significant differences in the driving mechanism patterns of RHD, which are dependent on the elevation variation. In LWR, vegetation conditions have a weaker impact on RHD than UPR. It is possibly due to the Artemisia eaten by the dominant species Lagurus luteus (LL) in UPR being more sensitive to precipitation and temperature if compared with the Haloxylon ammodendron eaten by the Rhombomys opimus (RO) in LWR. In LWR, grazing intensity is more strongly and positively correlated to RHD than UPR, possibly due to both winter grazing and RO dependency on vegetation distribution; moreover, in UPR, sheep do not feed Artemisia as the main food, and the total vegetation is sufficient for sheep and LL to coexist. Under the different conditions of water availability of LWR and UPR, grazing may affect the ratio of aboveground and underground biomass by photosynthate allocation, thereby affecting the distribution of RHD. In extremely dry years, the RHD of LWR and UPR may have an indirect interactive relation due to changes in grazing systems
Age-related decline in hippocampal tyrosine phosphatase PTPRO is a mechanistic factor in chemotherapy-related cognitive impairment.
Chemotherapy-related cognitive impairment (CRCI) or chemo brain is a devastating neurotoxic sequela of cancer-related treatments, especially for the elderly individuals. Here we show that PTPRO, a tyrosine phosphatase, is highly enriched in the hippocampus, and its level is tightly associated with neurocognitive function but declined significantly during aging. To understand the protective role of PTPRO in CRCI, a mouse model was generated by treating Ptpro-/- female mice with doxorubicin (DOX) because Ptpro-/- female mice are more vulnerable to DOX, showing cognitive impairments and neurodegeneration. By analyzing PTPRO substrates that are neurocognition-associated tyrosine kinases, we found that SRC and EPHA4 are highly phosphorylated/activated in the hippocampi of Ptpro-/- female mice, with increased sensitivity to DOX-induced CRCI. On the other hand, restoration of PTPRO in the hippocampal CA3 region significantly ameliorate CRCI in Ptpro-/- female mice. In addition, we found that the plant alkaloid berberine (BBR) is capable of ameliorating CRCI in aged female mice by upregulating hippocampal PTPRO. Mechanistically, BBR upregulates PTPRO by downregulating miR-25-3p, which directly targeted PTPRO. These findings collectively demonstrate the protective role of hippocampal PTPRO against CRCI
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