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Low-light image enhancement aims to improve the visual quality of images captured under poor illumination and has caught much attention these years. However, existing low-light enhancement methods encounter many problems, e.g., they may not be robust to diverse low-light conditions or have to sacrifice computational efficiency for enhancement performance, which hinder their practical applications. To solve these problems, this paper proposes a novel enhancement method, called Pixel-Wise Gamma Correction Mapping (PWGCM), which combines our innovative pixel-wise Gamma Correction (GC) and deep learning. Compared with conventional GC, our pixel-wise GC is characterized by a set of gamma correction maps, which have the same size as the input image and are taken to replace the single global GC parameter of conventional GC. These gamma correction maps are generated from the low-light image input by a lightweight convolutional neural network at low computational cost. New no-reference loss functions are provided to train the network, ensuring reliable unsupervised learning. Furthermore, our PWGCM is enhanced by an iterative strategy, under which the low-light input image is iteratively enhanced based on the generated gamma correction maps and can yield visually pleasant results. Extensive experiments are done to compare our PWGCM with several state-of-the-art methods in terms of visual quality, efficiency, and auxiliary effects on high-level tasks. The comparison results confirm the superiority of our PWGCM.

Oxytocin (OXT) is a nine amino acid neuropeptide hormone that has become one of the most intensively studied molecules in the past few decades. The vast majority of OXT is synthesized in the periventricular nucleus and supraoptic nucleus of the hypothalamus, and a few are synthesized in some peripheral organs (such as the uterus, ovaries, adrenal glands, thymus, pancreas, etc.) OXT modulates a series of physiological processes, including lactation, parturition, as well as some social behaviors. In addition, more and more attention has recently been focused on the analgesic effects of oxytocin. It has been reported that OXT can relieve tension and pain without other adverse effects. However, the critical role and detailed mechanism of OXT in analgesia remain unclear. This review aims to summarize the mechanism of OXT in analgesia and some ideas about the mechanism.

In the article, a novel reflective all-fiber integrated sensor for simultaneous gas pressure and temperature sensing has been proposed and demonstrated in experiment. The proposed sensor is fabricated by combining seven core fibers (SCFs), a twin-hole and dual-core fiber (THDCF), a single-mode fiber (SMF) extrinsic Fabry-Perot interferometer (FPI) and a fiber Bragg grating (FBG). The FPI section and FBG section act as a pressure and temperature sensing unit. Especially, the MZI has the effect of filtering the FPI spectrum and is more conducive to the demodulation of the FPI reflected signal. In FPI section, the two SMFs connects with a hollow core capillary (HCC) by the ultraviolet (UV) curing glue. The sensor exhibits a high pressure and temperature sensitivity of 13.10 nm/MPa and 0.20 nm/°C in the range of 0.1-1.2 MPa and 30-90 °C, respectively. The FBG works as a temperature sensor without pressure responding, is fabricated by using the UV laser-based phase mask technology in our lab. By utilizing the dual-wavelength matrix method, both gas pressure and temperature can be measured simultaneously. In sum, with the advantage of compact size, easy fabricated, low cost, high sensitivity, reflection-type and the ability of multi-parameter measurement, our sensor is more competitive in harsh environments.

This article takes into account general repeated security games with no prior knowledge, i.e., the game payoffs and the attacker’s behavior model are unknown and limited observability. Besides the traditional “regret” criterion, “reallocation times” is introduced as an additional criterion that provides a more comprehensive evaluation of the defense strategies. For such games, a novel random-walk perturbations with uniform exploration (RWP-UE) algorithm is proposed and we deduce the corresponding upper bound of the expected regret and expected reallocation times. Theoretical analysis shows that the RWP-UE algorithm achieves not only low regret with the same magnitude as existing achievements but also fewer reallocation times. Experiments are carried out against four types of attackers, and the results illustrate that the RWP-UE algorithm achieves superior performance.