1,651 research outputs found
Simultaneous Feature and Body-Part Learning for Real-Time Robot Awareness of Human Behaviors
Robot awareness of human actions is an essential research problem in robotics
with many important real-world applications, including human-robot
collaboration and teaming. Over the past few years, depth sensors have become a
standard device widely used by intelligent robots for 3D perception, which can
also offer human skeletal data in 3D space. Several methods based on skeletal
data were designed to enable robot awareness of human actions with satisfactory
accuracy. However, previous methods treated all body parts and features equally
important, without the capability to identify discriminative body parts and
features. In this paper, we propose a novel simultaneous Feature And Body-part
Learning (FABL) approach that simultaneously identifies discriminative body
parts and features, and efficiently integrates all available information
together to enable real-time robot awareness of human behaviors. We formulate
FABL as a regression-like optimization problem with structured
sparsity-inducing norms to model interrelationships of body parts and features.
We also develop an optimization algorithm to solve the formulated problem,
which possesses a theoretical guarantee to find the optimal solution. To
evaluate FABL, three experiments were performed using public benchmark
datasets, including the MSR Action3D and CAD-60 datasets, as well as a Baxter
robot in practical assistive living applications. Experimental results show
that our FABL approach obtains a high recognition accuracy with a processing
speed of the order-of-magnitude of 10e4 Hz, which makes FABL a promising method
to enable real-time robot awareness of human behaviors in practical robotics
applications.Comment: 8 pages, 6 figures, accepted by ICRA'1
Effect of fluid dynamics and device mechanism on biofluid behaviour in microchannel systems: modelling biofluids in a microchannel biochip separator
Biofluid behaviour in microchannel systems is investigated in this paper through the modelling of a microfluidic biochip developed for the separation of blood plasma. Based on particular assumptions, the effects of some mechanical features of the microchannels on behaviour of the biofluid are explored. These include microchannel, constriction, bending channel, bifurcation as well as channel length ratio between the main and side channels. The key characteristics and effects of the microfluidic dynamics are discussed in terms of separation efficiency of the red blood cells with respect to the rest of the medium. The effects include the Fahraeus and Fahraeus-Lindqvist effects, the Zweifach-Fung bifurcation law, the cell-free layer phenomenon. The characteristics of the microfluid dynamics include the properties of the laminar flow as well as particle lateral or spinning trajectories. In this paper the fluid is modelled as a single-phase flow assuming either Newtonian
or Non-Newtonian behaviours to investigate the effect of the
viscosity on flow and separation efficiency. It is found that, for a flow rate controlled Newtonian flow system, viscosity and outlet pressure have little effect on velocity distribution. When the fluid is assumed to be Non-Newtonian more fluid is separated than observed in the Newtonian case, leading to reduction of the flow rate ratio between the main and side channels as well as the system pressure as a whole
Parallel-mentoring for Offline Model-based Optimization
We study offline model-based optimization to maximize a black-box objective
function with a static dataset of designs and scores. These designs encompass a
variety of domains, including materials, robots and DNA sequences. A common
approach trains a proxy on the static dataset to approximate the black-box
objective function and performs gradient ascent to obtain new designs. However,
this often results in poor designs due to the proxy inaccuracies for
out-of-distribution designs. Recent studies indicate that: (a) gradient ascent
with a mean ensemble of proxies generally outperforms simple gradient ascent,
and (b) a trained proxy provides weak ranking supervision signals for design
selection. Motivated by (a) and (b), we propose \textit{parallel-mentoring} as
an effective and novel method that facilitates mentoring among parallel
proxies, creating a more robust ensemble to mitigate the out-of-distribution
issue. We focus on the three-proxy case and our method consists of two modules.
The first module, \textit{voting-based pairwise supervision}, operates on three
parallel proxies and captures their ranking supervision signals as pairwise
comparison labels. These labels are combined through majority voting to
generate consensus labels, which incorporate ranking supervision signals from
all proxies and enable mutual mentoring. However, label noise arises due to
possible incorrect consensus. To alleviate this, we introduce an
\textit{adaptive soft-labeling} module with soft-labels initialized as
consensus labels. Based on bi-level optimization, this module fine-tunes
proxies in the inner level and learns more accurate labels in the outer level
to adaptively mentor proxies, resulting in a more robust ensemble. Experiments
validate the effectiveness of our method. Our code is available here.Comment: Accepted by NeurIPS 202
CPU-GPU Heterogeneous Code Acceleration of a Finite Volume Computational Fluid Dynamics Solver
This work deals with the CPU-GPU heterogeneous code acceleration of a
finite-volume CFD solver utilizing multiple CPUs and GPUs at the same time.
First, a high-level description of the CFD solver called SENSEI, the
discretization of SENSEI, and the CPU-GPU heterogeneous computing workflow in
SENSEI leveraging MPI and OpenACC are given. Then, a performance model for
CPU-GPU heterogeneous computing requiring ghost cell exchange is proposed to
help estimate the performance of the heterogeneous implementation. The scaling
performance of the CPU-GPU heterogeneous computing and its comparison with the
pure multi-CPU/GPU performance for a supersonic inlet test case is presented to
display the advantages of leveraging the computational power of both the CPU
and the GPU. Using CPUs and GPUs as workers together, the performance can be
improved further compared to using pure CPUs or GPUs, and the advantages can be
fairly estimated by the performance model proposed in this work. Finally,
conclusions are drawn to provide 1) suggestions for application users who have
an interest to leverage the computational power of the CPU and GPU to
accelerate their own scientific computing simulations and 2) feedback for
hardware architects who have an interest to design a better CPU-GPU
heterogeneous system for heterogeneous computing
Slo3 K+ Channels: Voltage and pH Dependence of Macroscopic Currents
The mouse Slo3 gene (KCNMA3) encodes a K+ channel that is regulated by changes in cytosolic pH. Like Slo1 subunits responsible for the Ca2+ and voltage-activated BK-type channel, the Slo3 α subunit contains a pore module with homology to voltage-gated K+ channels and also an extensive cytosolic C terminus thought to be responsible for ligand dependence. For the Slo3 K+ channel, increases in cytosolic pH promote channel activation, but very little is known about many fundamental properties of Slo3 currents. Here we define the dependence of macroscopic conductance on voltage and pH and, in particular, examine Slo3 conductance activated at negative potentials. Using this information, the ability of a Horrigan-Aldrich–type of general allosteric model to account for Slo3 gating is examined. Finally, the pH and voltage dependence of Slo3 activation and deactivation kinetics is reported. The results indicate that Slo3 differs from Slo1 in several important ways. The limiting conductance activated at the most positive potentials exhibits a pH-dependent maximum, suggesting differences in the limiting open probability at different pH. Furthermore, over a 600 mV range of voltages (−300 to +300 mV), Slo3 conductance shifts only about two to three orders of magnitude, and the limiting conductance at negative potentials is relatively voltage independent compared to Slo1. Within the context of the Horrigan-Aldrich model, these results indicate that the intrinsic voltage dependence (zL) of the Slo3 closed–open equilibrium and the coupling (D) between voltage sensor movement are less than in Slo1. The kinetic behavior of Slo3 currents also differs markedly from Slo1. Both activation and deactivation are best described by two exponential components, both of which are only weakly voltage dependent. Qualitatively, the properties of the two kinetic components in the activation time course suggest that increases in pH increase the fraction of more rapidly opening channels
A creature with a hundred waggly tails: intrinsically disordered proteins in the ribosome
This article is made available for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.Intrinsic disorder (i.e., lack of a unique 3-D structure) is a common phenomenon, and many biologically active proteins are disordered as a whole, or contain long disordered regions. These intrinsically disordered proteins/regions constitute a significant part of all proteomes, and their functional repertoire is complementary to functions of ordered proteins. In fact, intrinsic disorder represents an important driving force for many specific functions. An illustrative example of such disorder-centric functional class is RNA-binding proteins. In this study, we present the results of comprehensive bioinformatics analyses of the abundance and roles of intrinsic disorder in 3,411 ribosomal proteins from 32 species. We show that many ribosomal proteins are intrinsically disordered or hybrid proteins that contain ordered and disordered domains. Predicted globular domains of many ribosomal proteins contain noticeable regions of intrinsic disorder. We also show that disorder in ribosomal proteins has different characteristics compared to other proteins that interact with RNA and DNA including overall abundance, evolutionary conservation, and involvement in protein–protein interactions. Furthermore, intrinsic disorder is not only abundant in the ribosomal proteins, but we demonstrate that it is absolutely necessary for their various functions
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