Blind Multimodal Quality Assessment of Low-light Images

Blind image quality assessment (BIQA) aims at automatically and accurately forecasting objective scores for visual signals, which has been widely used to monitor product and service quality in low-light applications, covering smartphone photography, video surveillance, autonomous driving, etc. Recent developments in this field are dominated by unimodal solutions inconsistent with human subjective rating patterns, where human visual perception is simultaneously reflected by multiple sensory information. In this article, we present a unique blind multimodal quality assessment (BMQA) of low-light images from subjective evaluation to objective score. To investigate the multimodal mechanism, we first establish a multimodal low-light image quality (MLIQ) database with authentic low-light distortions, containing image-text modality pairs. Further, we specially design the key modules of BMQA, considering multimodal quality representation, latent feature alignment and fusion, and hybrid self-supervised and supervised learning. Extensive experiments show that our BMQA yields state-of-the-art accuracy on the proposed MLIQ benchmark database. In particular, we also build an independent single-image modality Dark-4K database, which is used to verify its applicability and generalization performance in mainstream unimodal applications. Qualitative and quantitative results on Dark-4K show that BMQA achieves superior performance to existing BIQA approaches as long as a pre-trained model is provided to generate text description. The proposed framework and two databases as well as the collected BIQA methods and evaluation metrics are made publicly available on here.Comment: 15 page

Upper bounds of dual flagged Weyl characters

For a subset $D$ of boxes in an $n\times n$ square grid, let $\chi_{D}(x)$ denote the dual character of the flagged Weyl module associated to $D$. It is known that $\chi_{D}(x)$ specifies to a Schubert polynomial (resp., a key polynomial) in the case when $D$ is the Rothe diagram of a permutation (resp., the skyline diagram of a composition). One can naturally define a lower and an upper bound of $\chi_{D}(x)$. M{\'e}sz{\'a}ros, St. Dizier and Tanjaya conjectured that $\chi_{D}(x)$ attains the upper bound if and only if $D$ avoids a certain subdiagram. We provide a proof of this conjecture

Distributed Deep Learning Optimization of Heat Equation Inverse Problem Solvers

The inversion problem of partial differential equation plays a crucial role in cyber-physical systems applications. This paper presents a novel deep learning optimization approach to constructing a solver of heat equation inversion. To improve the computational efficiency in large-scale industrial applications, data and model parallelisms are incorporated on a platform of multiple GPUs. The advanced Ring-AllReduce architecture is harnessed to achieve an acceleration ratio of 3.46. Then a new multi-GPUs distributed optimization method GradReduce is proposed based on Ring-AllReduce architecture. This method optimizes the original data communication mechanism based on mechanical time and frequency by introducing the gradient transmission scheme solved by linear programming. The experimental results show that the proposed method can achieve an acceleration ratio of 3.84 on a heterogeneous system platform with two CPUs and four GPUs

How Did Order-Flow Impact Bond Prices During the European Sovereign Debt Crisis?

The impact of trades on price dynamics in the European sovereign debt markets is of significant importance to policy makers and market participants. This paper uses high-frequency quote and transaction data from the MTS European sovereign bond inter-dealer platform to investigate price-order-flow dynamics from July 2005 until December 2011 for Germany, France, Portugal, Italy, Ireland, Spain and Greece. We find that order-flow had a larger impact on quote revision in a relatively low-intensity trading environment than in a relatively high-intensity trading environment implying that informed traders should only execute in low-intensity trading environments when they value immediacy over discretion. This analysis is consistent with the limited prior literature for European debt markets. Our analysis indicates that this relationship persists during turbulent market conditions. Also, we find that the impact of order-flow on subsequent trades was larger during periods of high-trading intensity implying that market participants use order splitting trading strategies

Histone demethylase PHF8 drives neuroendocrine prostate cancer progression by epigenetically upregulating FOXA2.

Neuroendocrine prostate cancer (NEPC) is a more aggressive subtype of castration-resistant prostate cancer (CRPC). Although it is well established that PHF8 can enhance prostate cancer cell proliferation, whether PHF8 is involved in prostate cancer initiation and progression is relatively unclear. By comparing the transgenic adenocarcinoma of the mouse prostate (TRAMP) mice with or without Phf8 knockout, we systemically examined the role of PHF8 in prostate cancer development. We found that PHF8 plays a minimum role in initiation and progression of adenocarcinoma. However, PHF8 is essential for NEPC because not only is PHF8 highly expressed in NEPC but also animals without Phf8 failed to develop NEPC. Mechanistically, PHF8 transcriptionally upregulates FOXA2 by demethylating and removing the repressive histone markers on the promoter region of the FOXA2 gene, and the upregulated FOXA2 subsequently regulates the expression of genes involved in NEPC development. Since both PHF8 and FOXA2 are highly expressed in NEPC tissues from patients or patient-derived xenografts, the levels of PHF8 and FOXA2 can either individually or in combination serve as NEPC biomarkers and targeting either PHF8 or FOXA2 could be potential therapeutic strategies for NEPC treatment. © 2020 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland

Digital survey of the Cliff-Burial sites with consumer-level UAV photogrammetry: a case study of Mt. Wuyi.

The cliff-burial was an ancient funerary ritual once popular in the Far East, in which the dead were buried high on the cliffs overlooking rivers in the log coffins (the “hanging coffins”) left in the natural caves, excavated grottoes, or on some woodpiles. Hence, the conventional 3D survey means is less practical. This study explores the possibility of using UAV (consumer–level drones) in the survey with substantial improvement in accuracy, range and flight control, and compares the accuracy effects of different measurement methods

A Student’s t Mixture Probability Hypothesis Density Filter for Multi-Target Tracking with Outliers

In multi-target tracking, the outliers-corrupted process and measurement noises can reduce the performance of the probability hypothesis density (PHD) filter severely. To solve the problem, this paper proposed a novel PHD filter, called Student’s t mixture PHD (STM-PHD) filter. The proposed filter models the heavy-tailed process noise and measurement noise as a Student’s t distribution as well as approximates the multi-target intensity as a mixture of Student’s t components to be propagated in time. Then, a closed PHD recursion is obtained based on Student’s t approximation. Our approach can make full use of the heavy-tailed characteristic of a Student’s t distribution to handle the situations with heavy-tailed process and the measurement noises. The simulation results verify that the proposed filter can overcome the negative effect generated by outliers and maintain a good tracking accuracy in the simultaneous presence of process and measurement outliers

Multi-Branch Detection Network Based on Trigger Attention for Pedestrian Detection Under Occlusion

Center and Scale Prediction (CSP) first introduced the Anchor-free method to the field of pedestrian detection. Pedestrian detection often occurs in complex scenes subject to occlusion, and it is difficult to extract pedestrian features in a single centre point prediction in CSP. To solve this problem, this paper presents a multi-branch detection network (MBDN) based on trigger attention. Firstly, a multi-centre point prediction branch feature extraction model (multi-centre) is proposed to solve the problem of CSP missed detections in occlusion scenarios. Secondly, a novel trigger attention module is designed. The module uses visible parts as triggers to automatically learn the weight relationships of multiple branches, let the network automatically learn the confidence of the centre points of different branches, and automatically strengthen the branch where the visible area on the feature map is located. Finally, a channel non-maximum suppression (NMS) module is used in the MBDN network to reduce the redundant bounding boxes. Then experiments results show that the log-average missing rate (MR−2) of the heavy subset is reduced from 49.63% to 45.51% while maintaining the performance on a reasonable subset. Code and models can be accessed at (https://github.com/weidalin/MBDN)

Application of consumer–level uav photogrammetry in digital survey of cliff-burial culture relics: a case study of mount Wuyi.

cliffs with log coffins (the “hanging coffins”) left in the natural caves, excavated grottoes or on some wood piles. In view of the fact that the cliff-burial sites are usually located on the escarpments overlooking the rivers and with great slope, where the conventional 3D survey means, such as ground laser scanning and traditional aerial photogrammetry, is out of option. This study explores the possibility of using UAV (consumer–level drone) with substantial improvement in survey accuracy, range and flight control (Figure 2), which aims to lay the foundation for the global cliff-burial culture heritage research. A survey method extendible on other excavated funeral sites would be tested and codified, as a possible follow up of the research on some study cases in Far East Asia and Europe