831 research outputs found
The Value of Information Concealment
We consider a revenue optimizing seller selling a single item to a buyer, on
whose private value the seller has a noisy signal. We show that, when the
signal is kept private, arbitrarily more revenue could potentially be extracted
than if the signal is leaked or revealed. We then show that, if the seller is
not allowed to make payments to the buyer, the gap between the two is bounded
by a multiplicative factor of 3, if the value distribution conditioning on each
signal is regular. We give examples showing that both conditions are necessary
for a constant bound to hold.
We connect this scenario to multi-bidder single-item auctions where bidders'
values are correlated. Similarly to the setting above, we show that the revenue
of a Bayesian incentive compatible, ex post individually rational auction can
be arbitrarily larger than that of a dominant strategy incentive compatible
auction, whereas the two are no more than a factor of 5 apart if the auctioneer
never pays the bidders and if each bidder's value conditioning on the others'
is drawn according to a regular distribution. The upper bounds in both settings
degrade gracefully when the distribution is a mixture of a small number of
regular distributions
VideoControlNet: A Motion-Guided Video-to-Video Translation Framework by Using Diffusion Model with ControlNet
Recently, diffusion models like StableDiffusion have achieved impressive
image generation results. However, the generation process of such diffusion
models is uncontrollable, which makes it hard to generate videos with
continuous and consistent content. In this work, by using the diffusion model
with ControlNet, we proposed a new motion-guided video-to-video translation
framework called VideoControlNet to generate various videos based on the given
prompts and the condition from the input video. Inspired by the video codecs
that use motion information for reducing temporal redundancy, our framework
uses motion information to prevent the regeneration of the redundant areas for
content consistency. Specifically, we generate the first frame (i.e., the
I-frame) by using the diffusion model with ControlNet. Then we generate other
key frames (i.e., the P-frame) based on the previous I/P-frame by using our
newly proposed motion-guided P-frame generation (MgPG) method, in which the
P-frames are generated based on the motion information and the occlusion areas
are inpainted by using the diffusion model. Finally, the rest frames (i.e., the
B-frame) are generated by using our motion-guided B-frame interpolation (MgBI)
module. Our experiments demonstrate that our proposed VideoControlNet inherits
the generation capability of the pre-trained large diffusion model and extends
the image diffusion model to the video diffusion model by using motion
information. More results are provided at our project page
Entanglement generation via single-qubit rotations in a teared Hilbert space
We propose an efficient yet simple protocol to generate arbitrary symmetric
entangled states with only global single-qubit rotations in a teared Hilbert
space. The system is based on spin-1/2 qubits in a resonator such as atoms in
an optical cavity or superconducting qubits coupled to a metal microwave
resonator. By sending light or microwave into the resonator, it induces AC
Stark shifts on particular angular-momentum eigenstates (Dicke states) of
qubits. Then we are able to generate barriers that hinder transitions between
adjacent Dicke states and tear the original Hilbert space into pieces.
Therefore, a simple global single-qubit rotation becomes highly non-trivial,
and thus generates entanglement among the many-body system. By optimal control
of energy shifts on Dicke states, we are able to generate arbitrary symmetric
entangled states. We also exemplify that we can create varieties of useful
states with near-unity fidelities in only one or very few steps, including W
states, spin-squeezed states (SSS), and Greenberger-Horne-Zeilinger (GHZ)
states. Particularly, the SSS can be created by only one step with a squeezing
parameter approaching the Heisenberg limit (HL). Our
finding establishes a way for universal entanglement generations with only
single-qubit drivings where all the multiple-qubit controls are integrated into
simply switching on/off microwave. It has direct applications in the
variational quantum optimizer which is available with existing technology.Comment: 12 pages, 10 figure
Neighborhood VAR: Efficient estimation of multivariate timeseries with neighborhood information
In data science, vector autoregression (VAR) models are popular in modeling
multivariate time series in the environmental sciences and other applications.
However, these models are computationally complex with the number of parameters
scaling quadratically with the number of time series.
In this work, we propose a so-called neighborhood vector autoregression
(NVAR) model to efficiently analyze large-dimensional multivariate time series.
We assume that the time series have underlying neighborhood relationships,
e.g., spatial or network, among them based on the inherent setting of the
problem. When this neighborhood information is available or can be summarized
using a distance matrix, we demonstrate that our proposed NVAR method provides
a computationally efficient and theoretically sound estimation of model
parameters. The performance of the proposed method is compared with other
existing approaches in both simulation studies and a real application of stream
nitrogen study
FunCodec: A Fundamental, Reproducible and Integrable Open-source Toolkit for Neural Speech Codec
This paper presents FunCodec, a fundamental neural speech codec toolkit,
which is an extension of the open-source speech processing toolkit FunASR.
FunCodec provides reproducible training recipes and inference scripts for the
latest neural speech codec models, such as SoundStream and Encodec. Thanks to
the unified design with FunASR, FunCodec can be easily integrated into
downstream tasks, such as speech recognition. Along with FunCodec, pre-trained
models are also provided, which can be used for academic or generalized
purposes. Based on the toolkit, we further propose the frequency-domain codec
models, FreqCodec, which can achieve comparable speech quality with much lower
computation and parameter complexity. Experimental results show that, under the
same compression ratio, FunCodec can achieve better reconstruction quality
compared with other toolkits and released models. We also demonstrate that the
pre-trained models are suitable for downstream tasks, including automatic
speech recognition and personalized text-to-speech synthesis. This toolkit is
publicly available at https://github.com/alibaba-damo-academy/FunCodec.Comment: 5 pages, 3 figures, submitted to ICASSP 202
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