12,421 research outputs found
-Regression in the Arbitrary Partition Model of Communication
We consider the randomized communication complexity of the distributed
-regression problem in the coordinator model, for . In this
problem, there is a coordinator and servers. The -th server receives
and and the coordinator would like to find a -approximate
solution to . Here
for convenience. This model, where the data is
additively shared across servers, is commonly referred to as the arbitrary
partition model.
We obtain significantly improved bounds for this problem. For , i.e.,
least squares regression, we give the first optimal bound of
bits.
For ,we obtain an upper bound. Notably, for sufficiently large,
our leading order term only depends linearly on rather than
quadratically. We also show communication lower bounds of for and for . Our bounds considerably improve previous bounds due to (Woodruff et al.
COLT, 2013) and (Vempala et al., SODA, 2020)
Conditioning of BPM pickup signals for operations of the Duke storage ring with a wide range of single-bunch current
The Duke storage ring is a dedicated driver for the storage ring based
oscillator free-electron lasers (FELs), and the High Intensity Gamma-ray Source
(HIGS). It is operated with a beam current ranging from about 1 mA to 100 mA
per bunch for various operations and accelerator physics studies. High
performance operations of the FEL and gamma-ray source require a stable
electron beam orbit, which has been realized by the global orbit feedback
system. As a critical part of the orbit feedback system, the electron beam
position monitors (BPMs) are required to be able to precisely measure the
electron beam orbit in a wide range of the single-bunch current. However, the
high peak voltage of the BPM pickups associated with high single-bunch current
degrades the performance of the BPM electronics, and can potentially damage the
BPM electronics. A signal conditioning method using low pass filters is
developed to reduce the peak voltage to protect the BPM electronics, and to
make the BPMs capable of working with a wide range of single-bunch current.
Simulations and electron beam based tests are performed. The results show that
the Duke storage ring BPM system is capable of providing precise orbit
measurements to ensure highly stable FEL and HIGS operations
Object segmentation in depth maps with one user click and a synthetically trained fully convolutional network
With more and more household objects built on planned obsolescence and
consumed by a fast-growing population, hazardous waste recycling has become a
critical challenge. Given the large variability of household waste, current
recycling platforms mostly rely on human operators to analyze the scene,
typically composed of many object instances piled up in bulk. Helping them by
robotizing the unitary extraction is a key challenge to speed up this tedious
process. Whereas supervised deep learning has proven very efficient for such
object-level scene understanding, e.g., generic object detection and
segmentation in everyday scenes, it however requires large sets of per-pixel
labeled images, that are hardly available for numerous application contexts,
including industrial robotics. We thus propose a step towards a practical
interactive application for generating an object-oriented robotic grasp,
requiring as inputs only one depth map of the scene and one user click on the
next object to extract. More precisely, we address in this paper the middle
issue of object seg-mentation in top views of piles of bulk objects given a
pixel location, namely seed, provided interactively by a human operator. We
propose a twofold framework for generating edge-driven instance segments.
First, we repurpose a state-of-the-art fully convolutional object contour
detector for seed-based instance segmentation by introducing the notion of
edge-mask duality with a novel patch-free and contour-oriented loss function.
Second, we train one model using only synthetic scenes, instead of manually
labeled training data. Our experimental results show that considering edge-mask
duality for training an encoder-decoder network, as we suggest, outperforms a
state-of-the-art patch-based network in the present application context.Comment: This is a pre-print of an article published in Human Friendly
Robotics, 10th International Workshop, Springer Proceedings in Advanced
Robotics, vol 7. The final authenticated version is available online at:
https://doi.org/10.1007/978-3-319-89327-3\_16, Springer Proceedings in
Advanced Robotics, Siciliano Bruno, Khatib Oussama, In press, Human Friendly
Robotics, 10th International Workshop,
CMS-RCNN: Contextual Multi-Scale Region-based CNN for Unconstrained Face Detection
Robust face detection in the wild is one of the ultimate components to
support various facial related problems, i.e. unconstrained face recognition,
facial periocular recognition, facial landmarking and pose estimation, facial
expression recognition, 3D facial model construction, etc. Although the face
detection problem has been intensely studied for decades with various
commercial applications, it still meets problems in some real-world scenarios
due to numerous challenges, e.g. heavy facial occlusions, extremely low
resolutions, strong illumination, exceptionally pose variations, image or video
compression artifacts, etc. In this paper, we present a face detection approach
named Contextual Multi-Scale Region-based Convolution Neural Network (CMS-RCNN)
to robustly solve the problems mentioned above. Similar to the region-based
CNNs, our proposed network consists of the region proposal component and the
region-of-interest (RoI) detection component. However, far apart of that
network, there are two main contributions in our proposed network that play a
significant role to achieve the state-of-the-art performance in face detection.
Firstly, the multi-scale information is grouped both in region proposal and RoI
detection to deal with tiny face regions. Secondly, our proposed network allows
explicit body contextual reasoning in the network inspired from the intuition
of human vision system. The proposed approach is benchmarked on two recent
challenging face detection databases, i.e. the WIDER FACE Dataset which
contains high degree of variability, as well as the Face Detection Dataset and
Benchmark (FDDB). The experimental results show that our proposed approach
trained on WIDER FACE Dataset outperforms strong baselines on WIDER FACE
Dataset by a large margin, and consistently achieves competitive results on
FDDB against the recent state-of-the-art face detection methods
Classification of GHZ-type, W-type and GHZ-W-type multiqubit entanglements
We propose the concept of SLOCC-equivalent basis (SEB) in the multiqubit
space. In particular, two special SEBs, the GHZ-type and the W-type basis are
introduced. They can make up a more general family of multiqubit states, the
GHZ-W-type states, which is a useful kind of entanglement for quantum
teleporatation and error correction. We completely characterize the property of
this type of states, and mainly classify the GHZ-type states and the W-type
states in a regular way, which is related to the enumerative combinatorics.
Many concrete examples are given to exhibit how our method is used for the
classification of these entangled states.Comment: 16 pages, Revte
Predictive simulations of neuromuscular coordination and joint-contact loading in human gait
We implemented direct collocation on a full-body neuromusculoskeletal model to calculate muscle forces, ground reaction forces and knee contact loading simultaneously for one cycle of human gait. A data-tracking collocation problem was solved for walking at the normal speed to establish the practicality of incorporating a 3D model of articular contact and a model of foot–ground interaction explicitly in a dynamic optimization simulation. The data-tracking solution then was used as an initial guess to solve predictive collocation problems, where novel patterns of movement were generated for walking at slow and fast speeds, independent of experimental data. The data-tracking solutions accurately reproduced joint motion, ground forces and knee contact loads measured for two total knee arthroplasty patients walking at their preferred speeds. RMS errors in joint kinematics were < 2.0° for rotations and < 0.3 cm for translations while errors in the model-computed ground-reaction and knee-contact forces were < 0.07 BW and < 0.4 BW, respectively. The predictive solutions were also consistent with joint kinematics, ground forces, knee contact loads and muscle activation patterns measured for slow and fast walking. The results demonstrate the feasibility of performing computationally-efficient, predictive, dynamic optimization simulations of movement using full-body, muscle-actuated models with realistic representations of joint function
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