151 research outputs found
Strong Revenue (Non-)Monotonicity of Single-parameter Auctions
Consider Myerson's optimal auction with respect to an inaccurate prior, e.g.,
estimated from data, which is an underestimation of the true value
distribution. Can the auctioneer expect getting at least the optimal revenue
w.r.t. the inaccurate prior since the true value distribution is larger? This
so-called strong revenue monotonicity is known to be true for single-parameter
auctions when the feasible allocations form a matroid. We find that strong
revenue monotonicity fails to generalize beyond the matroid setting, and
further show that auctions in the matroid setting are the only downward-closed
auctions that satisfy strong revenue monotonicity. On the flip side, we recover
an approximate version of strong revenue monotonicity that holds for all
single-parameter auctions, even without downward-closedness. As applications,
we get sample complexity upper bounds for single-parameter auctions under
matroid constraints, downward-closed constraints, and general constraints. They
improve the state-of-the-art upper bounds and are tight up to logarithmic
factors
A 0.1–5.0 GHz flexible SDR receiver with digitally assisted calibration in 65 nm CMOS
© 2017 Elsevier Ltd. All rights reserved.A 0.1–5.0 GHz flexible software-defined radio (SDR) receiver with digitally assisted calibration is presented, employing a zero-IF/low-IF reconfigurable architecture for both wideband and narrowband applications. The receiver composes of a main-path based on a current-mode mixer for low noise, a high linearity sub-path based on a voltage-mode passive mixer for out-of-band rejection, and a harmonic rejection (HR) path with vector gain calibration. A dual feedback LNA with “8” shape nested inductor structure, a cascode inverter-based TCA with miller feedback compensation, and a class-AB full differential Op-Amp with Miller feed-forward compensation and QFG technique are proposed. Digitally assisted calibration methods for HR, IIP2 and image rejection (IR) are presented to maintain high performance over PVT variations. The presented receiver is implemented in 65 nm CMOS with 5.4 mm2 core area, consuming 9.6–47.4 mA current under 1.2 V supply. The receiver main path is measured with +5 dB m/+5dBm IB-IIP3/OB-IIP3 and +61dBm IIP2. The sub-path achieves +10 dB m/+18dBm IB-IIP3/OB-IIP3 and +62dBm IIP2, as well as 10 dB RF filtering rejection at 10 MHz offset. The HR-path reaches +13 dB m/+14dBm IB-IIP3/OB-IIP3 and 62/66 dB 3rd/5th-order harmonic rejection with 30–40 dB improvement by the calibration. The measured sensitivity satisfies the requirements of DVB-H, LTE, 802.11 g, and ZigBee.Peer reviewedFinal Accepted Versio
Implicit Ray-Transformers for Multi-view Remote Sensing Image Segmentation
The mainstream CNN-based remote sensing (RS) image semantic segmentation
approaches typically rely on massive labeled training data. Such a paradigm
struggles with the problem of RS multi-view scene segmentation with limited
labeled views due to the lack of considering 3D information within the scene.
In this paper, we propose ''Implicit Ray-Transformer (IRT)'' based on Implicit
Neural Representation (INR), for RS scene semantic segmentation with sparse
labels (such as 4-6 labels per 100 images). We explore a new way of introducing
multi-view 3D structure priors to the task for accurate and view-consistent
semantic segmentation. The proposed method includes a two-stage learning
process. In the first stage, we optimize a neural field to encode the color and
3D structure of the remote sensing scene based on multi-view images. In the
second stage, we design a Ray Transformer to leverage the relations between the
neural field 3D features and 2D texture features for learning better semantic
representations. Different from previous methods that only consider 3D prior or
2D features, we incorporate additional 2D texture information and 3D prior by
broadcasting CNN features to different point features along the sampled ray. To
verify the effectiveness of the proposed method, we construct a challenging
dataset containing six synthetic sub-datasets collected from the Carla platform
and three real sub-datasets from Google Maps. Experiments show that the
proposed method outperforms the CNN-based methods and the state-of-the-art
INR-based segmentation methods in quantitative and qualitative metrics
A Mixed-Integer SDP Solution Approach to Distributionally Robust Unit Commitment with Second Order Moment Constraints
A power system unit commitment (UC) problem considering uncertainties of
renewable energy sources is investigated in this paper, through a
distributionally robust optimization approach. We assume that the first and
second order moments of stochastic parameters can be inferred from historical
data, and then employed to model the set of probability distributions. The
resulting problem is a two-stage distributionally robust unit commitment with
second order moment constraints, and we show that it can be recast as a
mixed-integer semidefinite programming (MI-SDP) with finite constraints. The
solution algorithm of the problem comprises solving a series of relaxed MI-SDPs
and a subroutine of feasibility checking and vertex generation. Based on the
verification of strong duality of the semidefinite programming (SDP) problems,
we propose a cutting plane algorithm for solving the MI-SDPs; we also introduce
a SDP relaxation for the feasibility checking problem, which is an intractable
biconvex optimization. Experimental results on a IEEE 6-bus system are
presented, showing that without any tunings of parameters, the real-time
operation cost of distributionally robust UC method outperforms those of
deterministic UC and two-stage robust UC methods in general, and our method
also enjoys higher reliability of dispatch operation
Adapt On-the-Go: Behavior Modulation for Single-Life Robot Deployment
To succeed in the real world, robots must cope with situations that differ
from those seen during training. We study the problem of adapting on-the-fly to
such novel scenarios during deployment, by drawing upon a diverse repertoire of
previously learned behaviors. Our approach, RObust Autonomous Modulation
(ROAM), introduces a mechanism based on the perceived value of pre-trained
behaviors to select and adapt pre-trained behaviors to the situation at hand.
Crucially, this adaptation process all happens within a single episode at test
time, without any human supervision. We provide theoretical analysis of our
selection mechanism and demonstrate that ROAM enables a robot to adapt rapidly
to changes in dynamics both in simulation and on a real Go1 quadruped, even
successfully moving forward with roller skates on its feet. Our approach adapts
over 2x as efficiently compared to existing methods when facing a variety of
out-of-distribution situations during deployment by effectively choosing and
adapting relevant behaviors on-the-fly.Comment: 19 pages, 6 figure
Understanding the structure and rheological properties of potato starch induced by hot-extrusion 3D printing
This work investigates the 3D printability of potato starch (PS). For this purpose, the structure and rheological properties of the PS-based ink under hot-extrusion 3D printing (HE-3DP) at different PS concentrations and printing temperatures were studied. PS concentration was found to determine the structure and rheological properties of the PS gel. The printing temperature was shown to influence the structural transformation of PS and closely linked to the rheological properties of the gel. PS samples of 15–25% concentration at 70 °C presented optimal printability, which showed the absence of the original granule, crystalline and lamellar structures, with the formation of a uniform and compact gel network. In this case, the rheological properties were in a suitable range for HE-3DP including G′ (615.72–1057.63 Pa), τy (89.389–263.25 Pa) and τf (490.00–1104.97 Pa), which provided the PS-based ink with smooth extrusion, excellent printing accuracy and high structural strength, suitable for applications such as food and biomedical materials
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