6,201 research outputs found
PAM-HC: A Bayesian Nonparametric Construction of Hybrid Control for Randomized Clinical Trials Using External Data
It is highly desirable to borrow information from external data to augment a
control arm in a randomized clinical trial, especially in settings where the
sample size for the control arm is limited. However, a main challenge in
borrowing information from external data is to accommodate potential
heterogeneous subpopulations across the external and trial data. We apply a
Bayesian nonparametric model called Plaid Atoms Model (PAM) to identify
overlapping and unique subpopulations across datasets, with which we restrict
the information borrowing to the common subpopulations. This forms a hybrid
control (HC) that leads to more precise estimation of treatment effects
Simulation studies demonstrate the robustness of the new method, and an
application to an Atopic Dermatitis dataset shows improved treatment effect
estimation
Learning Image Demoireing from Unpaired Real Data
This paper focuses on addressing the issue of image demoireing. Unlike the
large volume of existing studies that rely on learning from paired real data,
we attempt to learn a demoireing model from unpaired real data, i.e., moire
images associated with irrelevant clean images. The proposed method, referred
to as Unpaired Demoireing (UnDeM), synthesizes pseudo moire images from
unpaired datasets, generating pairs with clean images for training demoireing
models. To achieve this, we divide real moire images into patches and group
them in compliance with their moire complexity. We introduce a novel moire
generation framework to synthesize moire images with diverse moire features,
resembling real moire patches, and details akin to real moire-free images.
Additionally, we introduce an adaptive denoise method to eliminate the
low-quality pseudo moire images that adversely impact the learning of
demoireing models. We conduct extensive experiments on the commonly-used FHDMi
and UHDM datasets. Results manifest that our UnDeM performs better than
existing methods when using existing demoireing models such as MBCNN and
ESDNet-L. Code: https://github.com/zysxmu/UnDeMComment: AAAI202
Postnatal maintenance of the 5-Ht1a-Pet1 autoregulatory loop by serotonin in the raphe nuclei of the brainstem
BACKGROUND: Despite the importance of 5-HT1A as a major target for the action of several anxiolytics/antidepressant drugs, little is known about its regulation in central serotonin (5-hydroxytryptamine, 5-HT) neurons. RESULTS: We report that expression of 5-HT1A and the transcription factor Pet1 was impaired in the rostral raphe nuclei of mice lacking tryptophan hydroxylase 2 (Tph2) after birth. The downregulation of Pet1 was recapitulated in 5-Ht1a( -/- ) mice. Using an explant culture system, we show that reduction of Pet1 and 5-HT1A was rescued in Tph2( -/- ) brainstem by exogenous 5-HT. In contrast, 5-HT failed to rescue reduced expression of Pet1 in 5-Ht1a( -/- ) brainstem explant culture. CONCLUSIONS: These results suggest a causal relationship between 5-HT1A and Pet1, and reveal a potential mechanism by which 5-HT1A-Pet1 autoregulatory loop is maintained by 5-HT in a spatiotemporal-specific manner during postnatal development. Our results are relevant to understanding the pathophysiology of certain psychiatric and developmental disorders
MultiQuant: A Novel Multi-Branch Topology Method for Arbitrary Bit-width Network Quantization
Arbitrary bit-width network quantization has received significant attention
due to its high adaptability to various bit-width requirements during runtime.
However, in this paper, we investigate existing methods and observe a
significant accumulation of quantization errors caused by frequent bit-width
switching of weights and activations, leading to limited performance. To
address this issue, we propose MultiQuant, a novel method that utilizes a
multi-branch topology for arbitrary bit-width quantization. MultiQuant
duplicates the network body into multiple independent branches and quantizes
the weights of each branch to a fixed 2-bit while retaining the input
activations in the expected bit-width. This approach maintains the
computational cost as the same while avoiding the switching of weight
bit-widths, thereby substantially reducing errors in weight quantization.
Additionally, we introduce an amortization branch selection strategy to
distribute quantization errors caused by activation bit-width switching among
branches to enhance performance. Finally, we design an in-place distillation
strategy that facilitates guidance between branches to further enhance
MultiQuant's performance. Extensive experiments demonstrate that MultiQuant
achieves significant performance gains compared to existing arbitrary bit-width
quantization methods. Code is at \url{https://github.com/zysxmu/MultiQuant}
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