109 research outputs found
Compression via Compressive Sensing : A Low-Power Framework for the Telemonitoring of Multi-Channel Physiological Signals
Telehealth and wearable equipment can deliver personal healthcare and
necessary treatment remotely. One major challenge is transmitting large amount
of biosignals through wireless networks. The limited battery life calls for
low-power data compressors. Compressive Sensing (CS) has proved to be a
low-power compressor. In this study, we apply CS on the compression of
multichannel biosignals. We firstly develop an efficient CS algorithm from the
Block Sparse Bayesian Learning (BSBL) framework. It is based on a combination
of the block sparse model and multiple measurement vector model. Experiments on
real-life Fetal ECGs showed that the proposed algorithm has high fidelity and
efficiency. Implemented in hardware, the proposed algorithm was compared to a
Discrete Wavelet Transform (DWT) based algorithm, verifying the proposed one
has low power consumption and occupies less computational resources.Comment: 2013 International Workshop on Biomedical and Health Informatic
Learning Optimal Deterministic Auctions with Correlated Valuation Distributions
In mechanism design, it is challenging to design the optimal auction with
correlated values in general settings. Although value distribution can be
further exploited to improve revenue, the complex correlation structure makes
it hard to acquire in practice. Data-driven auction mechanisms, powered by
machine learning, enable to design auctions directly from historical auction
data, without relying on specific value distributions. In this work, we design
a learning-based auction, which can encode the correlation of values into the
rank score of each bidder, and further adjust the ranking rule to approach the
optimal revenue. We strictly guarantee the property of strategy-proofness by
encoding game theoretical conditions into the neural network structure.
Furthermore, all operations in the designed auctions are differentiable to
enable an end-to-end training paradigm. Experimental results demonstrate that
the proposed auction mechanism can represent almost any strategy-proof auction
mechanism, and outperforms the auction mechanisms wildly used in the correlated
value settings
Quantum corrections to the magnetoconductivity of surface states in three-dimensional topological insulators
The interplay between quantum interference, electron-electron interaction (EEI), and disorder is one of the central themes of condensed matter physics. Such interplay can cause high-order magnetoconductance (MC) corrections in semiconductors with weak spin-orbit coupling (SOC). However, it remains unexplored how the magnetotransport properties are modified by the high-order quantum corrections in the electron systems of symplectic symmetry class, which include topological insulators (TIs), Weyl semimetals, graphene with negligible intervalley scattering, and semiconductors with strong SOC. Here, we extend the theory of quantum conductance corrections to two-dimensional (2D) electron systems with the symplectic symmetry, and study experimentally such physics with dual-gated TI devices in which the transport is dominated by highly tunable surface states. We find that the MC can be enhanced significantly by the second-order interference and the EEI effects, in contrast to the suppression of MC for the systems with orthogonal symmetry. Our work reveals that detailed MC analysis can provide deep insights into the complex electronic processes in TIs, such as the screening and dephasing effects of localized charge puddles, as well as the related particle-hole asymmetry
Truthful Auctions for Automated Bidding in Online Advertising
Automated bidding, an emerging intelligent decision making paradigm powered
by machine learning, has become popular in online advertising. Advertisers in
automated bidding evaluate the cumulative utilities and have private financial
constraints over multiple ad auctions in a long-term period. Based on these
distinct features, we consider a new ad auction model for automated bidding:
the values of advertisers are public while the financial constraints, such as
budget and return on investment (ROI) rate, are private types. We derive the
truthfulness conditions with respect to private constraints for this
multi-dimensional setting, and demonstrate any feasible allocation rule could
be equivalently reduced to a series of non-decreasing functions on budget.
However, the resulted allocation mapped from these non-decreasing functions
generally follows an irregular shape, making it difficult to obtain a
closed-form expression for the auction objective. To overcome this design
difficulty, we propose a family of truthful automated bidding auction with
personalized rank scores, similar to the Generalized Second-Price (GSP)
auction. The intuition behind our design is to leverage personalized rank
scores as the criteria to allocate items, and compute a critical ROI to
transform the constraints on budget to the same dimension as ROI. The
experimental results demonstrate that the proposed auction mechanism
outperforms the widely used ad auctions, such as first-price auction and
second-price auction, in various automated bidding environments
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