1,769 research outputs found
Federated Learning with Server Learning: Enhancing Performance for Non-IID Data
Federated Learning (FL) has emerged as a means of distributed learning using
local data stored at clients with a coordinating server. Recent studies showed
that FL can suffer from poor performance and slower convergence when training
data at clients are not independent and identically distributed. Here we
consider a new complementary approach to mitigating this performance
degradation by allowing the server to perform auxiliary learning from a small
dataset. Our analysis and experiments show that this new approach can achieve
significant improvements in both model accuracy and convergence time even when
the server dataset is small and its distribution differs from that of the
aggregated data from all clients.Comment: 22 pages, 11 figures, 3 table
A robust real-time facial alignment system with facial landmarks detection and rectification for multimedia applications
Β© 2020, Springer Science+Business Media, LLC, part of Springer Nature. Face detection often plays the first step in various visual applications. Large variants of facial deformations due to head movements and facial expression make it difficult to identify appropriate face region. In this paper, a robust real-time face alignment system, including facial landmarks detection and face rectification, is proposed. A facial landmarks detection model based on regression tree is utilized in the proposed system. In face rectification framework, 2-D geometrical analysis based on pitch, yaw and roll movements is designed to solve the misalignment problem in face detection. The experiments on the two datasets verify the performance significantly improved by the proposed method in the facial recognition task and outperform than those obtained by other alignment methods. Furthermore, the proposed method can achieve robust recognition results even if the amount of training images is not large
Primary extraskeletal osteosarcoma of omentum majus
Extraskeletal osteosarcoma is a rare malignant soft tissue tumor. Here we present a case of a primary extraskeletal osteosarcoma arising from omentum majus in a 40-year-old Chinese woman. Ultrasonography of the pelvic cavity showed a large soft tissue mass with marked calcification. Complete surgical resection of the primary tumor was performed and the histopathological diagnosis was extraskeletal osteosarcoma of omentum majus. She was followed up without adjuvant radiotherapy and chemotherapy, and died from widespread intra-abdominal, lung and liver metastases 7 months postoperatively
Colored Resonant Signals at the LHC: Largest Rate and Simplest Topology
We study the colored resonance production at the LHC in a most general
approach. We classify the possible colored resonances based on group theory
decomposition, and construct their effective interactions with light partons.
The production cross section from annihilation of valence quarks or gluons may
be on the order of 400 - 1000 pb at LHC energies for a mass of 1 TeV with
nominal couplings, leading to the largest production rates for new physics at
the TeV scale, and simplest event topology with dijet final states. We apply
the new dijet data from the LHC experiments to put bounds on various possible
colored resonant states. The current bounds range from 0.9 to 2.7 TeV. The
formulation is readily applicable for future searches including other decay
modes.Comment: 29 pages, 9 figures. References updated and additional K-factors
include
Determining the neurotransmitter concentration profile at active synapses
Establishing the temporal and concentration profiles of neurotransmitters during synaptic release is an essential step towards understanding the basic properties of inter-neuronal communication in the central nervous system. A variety of ingenious attempts has been made to gain insights into this process, but the general inaccessibility of central synapses, intrinsic limitations of the techniques used, and natural variety of different synaptic environments have hindered a comprehensive description of this fundamental phenomenon. Here, we describe a number of experimental and theoretical findings that has been instrumental for advancing our knowledge of various features of neurotransmitter release, as well as newly developed tools that could overcome some limits of traditional pharmacological approaches and bring new impetus to the description of the complex mechanisms of synaptic transmission
Warm-Start AlphaZero Self-Play Search Enhancements
Recently, AlphaZero has achieved landmark results in deep reinforcement
learning, by providing a single self-play architecture that learned three
different games at super human level. AlphaZero is a large and complicated
system with many parameters, and success requires much compute power and
fine-tuning. Reproducing results in other games is a challenge, and many
researchers are looking for ways to improve results while reducing
computational demands. AlphaZero's design is purely based on self-play and
makes no use of labeled expert data ordomain specific enhancements; it is
designed to learn from scratch. We propose a novel approach to deal with this
cold-start problem by employing simple search enhancements at the beginning
phase of self-play training, namely Rollout, Rapid Action Value Estimate (RAVE)
and dynamically weighted combinations of these with the neural network, and
Rolling Horizon Evolutionary Algorithms (RHEA). Our experiments indicate that
most of these enhancements improve the performance of their baseline player in
three different (small) board games, with especially RAVE based variants
playing strongly
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