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 $\textit{Ta} = 2.39 \times 10^7$. It is shown that compared to the smooth wall, the rough wall with vertical asymmetric strips can enhance the dimensionless torque \textit{Nu}$_\omega$, 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