300 research outputs found
RON: Reverse Connection with Objectness Prior Networks for Object Detection
We present RON, an efficient and effective framework for generic object
detection. Our motivation is to smartly associate the best of the region-based
(e.g., Faster R-CNN) and region-free (e.g., SSD) methodologies. Under fully
convolutional architecture, RON mainly focuses on two fundamental problems: (a)
multi-scale object localization and (b) negative sample mining. To address (a),
we design the reverse connection, which enables the network to detect objects
on multi-levels of CNNs. To deal with (b), we propose the objectness prior to
significantly reduce the searching space of objects. We optimize the reverse
connection, objectness prior and object detector jointly by a multi-task loss
function, thus RON can directly predict final detection results from all
locations of various feature maps. Extensive experiments on the challenging
PASCAL VOC 2007, PASCAL VOC 2012 and MS COCO benchmarks demonstrate the
competitive performance of RON. Specifically, with VGG-16 and low resolution
384X384 input size, the network gets 81.3% mAP on PASCAL VOC 2007, 80.7% mAP on
PASCAL VOC 2012 datasets. Its superiority increases when datasets become larger
and more difficult, as demonstrated by the results on the MS COCO dataset. With
1.5G GPU memory at test phase, the speed of the network is 15 FPS, 3X faster
than the Faster R-CNN counterpart.Comment: Project page will be available at https://github.com/taokong/RON, and
formal paper will appear in CVPR 201
Microtube's Tapers Affect its Subwavelength Focusing Effect
AbstractA tapered microtube fabricated from a glass capillary tube can achieve subwavelength focusing at optical frequencies. This focusing effect is influenced by many factures. The role of taper ratios is invested in this paper with theoretical analysis and numerical simulation. It revealed that when the taper ratio is larger, the focusing spot has higher intensity and smaller FWHM
A Scalable Neural Network for DSIC Affine Maximizer Auction Design
Automated auction design aims to find empirically high-revenue mechanisms
through machine learning. Existing works on multi item auction scenarios can be
roughly divided into RegretNet-like and affine maximizer auctions (AMAs)
approaches. However, the former cannot strictly ensure dominant strategy
incentive compatibility (DSIC), while the latter faces scalability issue due to
the large number of allocation candidates. To address these limitations, we
propose AMenuNet, a scalable neural network that constructs the AMA parameters
(even including the allocation menu) from bidder and item representations.
AMenuNet is always DSIC and individually rational (IR) due to the properties of
AMAs, and it enhances scalability by generating candidate allocations through a
neural network. Additionally, AMenuNet is permutation equivariant, and its
number of parameters is independent of auction scale. We conduct extensive
experiments to demonstrate that AMenuNet outperforms strong baselines in both
contextual and non-contextual multi-item auctions, scales well to larger
auctions, generalizes well to different settings, and identifies useful
deterministic allocations. Overall, our proposed approach offers an effective
solution to automated DSIC auction design, with improved scalability and strong
revenue performance in various settings.Comment: NeurIPS 2023 (spotlight
Metabolism and Excretion Study of Daphnoretin in Rats after Oral Administration
The metabolism and excretion profile in rats were investigated after a single dose of daphnoretin. Metabolites of daphnoretin in rats were characterized by HPLC-MS n analysis. A HPLC-UV method was developed to determine the concentration of daphnoretin in rat urine, feces and bile. Daphnoretin was biotransformed via conjunctive and oxidative pathways to three detected metabolites. The structures of these metabolites were tentatively identified. The cumulative excretion percentage of daphnoretin in urine, feces and bile of rats was 0.13, 52.7, and 0.018 %, respectively. All the metabolites and excretion data are reported for the first time.Colegio de Farmacéuticos de la Provincia de Buenos Aire
The Role of Histone Acetyltransferases in Normal and Malignant Hematopoiesis
Histone or non-histone protein acetylation plays important roles in all kinds of cellular events, including the normal and abnormal development of blood cells, through changing the epigenetic status of chromatin and regulating non-histone protein’s function. Histone acetyltransferases (HATs), which are the enzymes responsible for the histone or non-histone protein acetylation, contain p300/CBP, MYST and GNAT family etc. HATs are not only the protein modifiers and epigenetic factors, but also the critical regulators of cell development and cancerogenesis. Here we will review the function of HATs such as p300/CBP, Tip60, MOZ/MORF and GCN5/PCAF in the normal hematopoiesis and the pathogenesis of hematological malignancies. The inhibitors that have been developed to target HATs will also be reviewed here. Understanding the roles of HATs in normal/malignant hematopoiesis and the underlying mechanism will provide the potential therapeutic targets for the hematological malignancies
Coordinated Dynamic Bidding in Repeated Second-Price Auctions with Budgets
In online ad markets, a rising number of advertisers are employing bidding
agencies to participate in ad auctions. These agencies are specialized in
designing online algorithms and bidding on behalf of their clients. Typically,
an agency usually has information on multiple advertisers, so she can
potentially coordinate bids to help her clients achieve higher utilities than
those under independent bidding.
In this paper, we study coordinated online bidding algorithms in repeated
second-price auctions with budgets. We propose algorithms that guarantee every
client a higher utility than the best she can get under independent bidding. We
show that these algorithms achieve maximal coalition welfare and discuss
bidders' incentives to misreport their budgets, in symmetric cases. Our proofs
combine the techniques of online learning and equilibrium analysis, overcoming
the difficulty of competing with a multi-dimensional benchmark. The performance
of our algorithms is further evaluated by experiments on both synthetic and
real data. To the best of our knowledge, we are the first to consider bidder
coordination in online repeated auctions with constraints.Comment: 43 pages, 12 figure
Direct numerical simulation of Taylor-Couette flow with vertical asymmetric rough walls
Direct numerical simulations are performed to explore the effects of rotating
direction of the vertical asymmetric rough wall on the transport properties of
Taylor-Couette (TC) flow up to a Taylor number of . It is shown that compared to the smooth wall, the rough wall with
vertical asymmetric strips can enhance the dimensionless torque
\textit{Nu}, and more importantly, at high \textit{Ta} clockwise
rotation of the inner rough wall (the fluid is sheared by the steeper slope
side of the strips) results in a significantly bigger torque enhancement as
compared to the counter-clockwise rotation (the fluid is sheared by the smaller
slope side of the strips) due to the larger convective contribution to the
angular velocity flux, although the rotating direction has a negligible effect
on the torque at low \textit{Ta}. The larger torque enhancement caused by the
clockwise rotation of vertical asymmetric rough wall at high \textit{Ta} is
then explained by the stronger coupling between the rough wall and the bulk due
to the larger biased azimuthal velocity towards the rough wall at the mid-gap
of TC system, the increased intensity of turbulence manifesting by larger
Reynolds stress and thinner boundary layer, and the more significant
contribution of the pressure force on the surface of rough wall to the torque.Comment: 17 pages,11 figure
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