A Discriminatively Learned CNN Embedding for Person Re-identification

We revisit two popular convolutional neural networks (CNN) in person re-identification (re-ID), i.e, verification and classification models. The two models have their respective advantages and limitations due to different loss functions. In this paper, we shed light on how to combine the two models to learn more discriminative pedestrian descriptors. Specifically, we propose a new siamese network that simultaneously computes identification loss and verification loss. Given a pair of training images, the network predicts the identities of the two images and whether they belong to the same identity. Our network learns a discriminative embedding and a similarity measurement at the same time, thus making full usage of the annotations. Albeit simple, the learned embedding improves the state-of-the-art performance on two public person re-ID benchmarks. Further, we show our architecture can also be applied in image retrieval

Improving weak-signal identification via predetection background suppression by a pixel-level, surface-wave enabled dark-field aperture

We report the successful implementation of a surface-wave enabled dark-field aperture (SWEDA) directly on a complementary metal-oxide semiconductor sensor pixel (2.2μm). This SWEDA pixel allows predetection cancellation of a uniform coherent background. We show that the signal-to-noise ratio (SNR) of the SWEDA pixel is better than that of a single undressed pixel over a significant range of signal-to-background ratio (SBR). For a small SBR value (SBR=0.001, background intensity=3.96W/m^2, integration time=5ms), we further demonstrate that a SWEDA pixel can detect a weak localized signal buried in a high background, while conventional postdetection background subtraction cannot (improved SNR=2.2 versus SNR=0.26)

Understand spiciness: mechanism of TRPV1 channel activation by capsaicin.

Capsaicin in chili peppers bestows the sensation of spiciness. Since the discovery of its receptor, transient receptor potential vanilloid 1 (TRPV1) ion channel, how capsaicin activates this channel has been under extensive investigation using a variety of experimental techniques including mutagenesis, patch-clamp recording, crystallography, cryo-electron microscopy, computational docking and molecular dynamic simulation. A framework of how capsaicin binds and activates TRPV1 has started to merge: capsaicin binds to a pocket formed by the channel's transmembrane segments, where it takes a "tail-up, head-down" configuration. Binding is mediated by both hydrogen bonds and van der Waals interactions. Upon binding, capsaicin stabilizes the open state of TRPV1 by "pull-and-contact" with the S4-S5 linker. Understanding the ligand-host interaction will greatly facilitate pharmaceutical efforts to develop novel analgesics targeting TRPV1

On compact Hermitian manifolds with flat Gauduchon connections

Given a Hermitian manifold $(M^n,g)$, the Gauduchon connections are the one parameter family of Hermitian connections joining the Chern connection and the Bismut connection. We will call $\nabla^s = (1-\frac{s}{2})\nabla^c + \frac{s}{2}\nabla^b$ the $s$-Gauduchon connection of $M$, where $\nabla^c$ and $\nabla^b$ are respectively the Chern and Bismut connections. It is natural to ask when a compact Hermitian manifold could admit a flat $s$-Gauduchon connection. This is related to a question asked by Yau \cite{Yau}. The cases with $s=0$ (a flat Chern connection) or $s=2$ (a flat Bismut connection) are classified respectively by Boothby \cite{Boothby} in the 1950s or by Q. Wang and the authors recently \cite{WYZ}. In this article, we observe that if either $s\geq 4+2\sqrt{3} \approx 7.46$ or $s\leq 4-2\sqrt{3}\approx 0.54$ and $s\neq 0$, then $g$ is K\"ahler. We also show that, when $n=2$, $g$ is always K\"ahler unless $s=2$. Note that non-K\"ahler compact Bismut flat surfaces are exactly those isosceles Hopf surfaces by \cite{WYZ}.Comment: 9 pages. This preprint was submitted to Acta Mathematica Sinica, a special issue dedicated to Professor Qikeng L