 I am sure that we will all make great use of it.
Hello Leslie and Sherri!
In answer to your question about the next fund raiser I am not sure that has been announced that’s a question for our FI 2016 Directors Lauren and Evan. Stay tuned.
Looking ahead Jay and I are planning future blogposts and Fran is working with us to ensure we don’t miss anything from the newsletter.
Hope to see you at the Potluck on Saturday!And last but not least I’m having way to much fun building the universe of my dreams for everyone to adventure in! I’ve been down the big company acquisition route twice before and there’s a reason I am making Star Citizen totally independently!
Translation: I'm still waiting for the right offer so I can fly off to Belize once this whole thing crashes down as the scam it'll be.
Our ships are digital: We'll ream you for the glow ship of the month after we get all free submissions from suckers, errr contributing artists, and only say thanks for letting us keep the money from your idea. Building the universe of my dreams? I'm sitting back while coders code and artist design while I'll slap my name on it for some reason before they realize they don't need me.
Push notifications now working - make sure to enable under Settings!It depends on why you don't believe in it. I'd researching the Bible and it's claims which are awesome. Also listen to some good sermons by someone who can clearly explain the gospel. I recommend Dr. Thomas M. Cucuzza. At the end of the day though, after death do you want there to be nothing, no meaning to this life? Or to be seperated from the one God who created everything?  Eternal life with an all powerful and loving God without pain or suffering is all the reason I need to   to believe.
John 3:16 is probably the best verse to explain the gospel.
For God so loved the world, that he gave his only begotten Son, that whoever believeth in him should not perish, but have everlasting lifeREQA: Coarse-to-fine Assessment of Image Quality to Alleviate the Range Effect

Blind image quality assessment (BIQA) of user generated content (UGC) suffers from the range effect which indicates that on the overall quality range, mean opinion score (MOS) and predicted MOS (pMOS) are well correlated; focusing on a particular range, the correlation is lower. The reason for the range effect is that the predicted deviations both in a wide range and in a narrow range destroy the uniformity between MOS and pMOS. To tackle this problem, a novel method is proposed from coarse-grained metric to fine-grained prediction. Firstly, we design a rank-and-gradient loss for coarse-grained metric. The loss keeps the order and grad consistency between pMOS and MOS, thereby reducing the predicted deviation in a wide range. Secondly, we propose multi-level tolerance loss to make fine-grained prediction. The loss is constrained by a decreasing threshold to limite the predicted deviation in narrower and narrower ranges. Finally, we design a feedback network to conduct the coarse-to-fine assessment. On the one hand, the network adopts feedback blocks to process multi-scale distortion features iteratively and on the other hand, it fuses non-local context feature to the output of each iteration to acquire more quality-aware feature representation. Experimental results demonstrate that the proposed method can alleviate the range effect compared to the state-of-the-art methods effectively.


I. INTRODUCTION
I MAGE quality assessment (IQA) explores how to imitate human beings to automatically assess image quality. Accurately describing the quality change has extensive applications in image restoration, image retrieval and image quality monitoring systems, etc. IQA approaches can be generally divided into three categories: full-reference IQA approach (FR-IQA), reduced-reference approach (RR-IQA) and blind IQA approach (BIQA). FR-IQA and RR-IQA measure the similarity between the distorted image and reference image [3], [18], [20]- [23]. However, in most authentic scenarios, it is hard to achieve ideal reference information. BIQA does not require any reference image as a prerequisite in predicting the perceptual quality, so it attract great attention in recent years.
How well BIQA methods perform on a narrower quality range is important for more realistic prediction on user generated content(UGC). According to [34], the distributions of image quality in the authentically distorted databases is arXiv:2209.01760v2 [eess.IV] 18 Oct 2022 narrower and peakier as compared to those synthetic ones, as most pictures captured in the real world are improved by imaging device. According to the distribution property, streaming media server can screen the images with extreme qualities to obtain images with moderate qualities. These images will be integrated as the benchmark of subsequent image enhancement and image-effect synthesis. Thus, user generated content requires that BIQA model possesses more fine-grained perception to image quality. That is, it can deal with the range effect very well.
The ground truth data of the authentically distorted databases is in the form of the mean opinion score (MOS) that is the average of quality ratings given by multiple subjects. Based on this, it can be divided into five categorical scales [11]. We make statistic analysis of the scale difference and the distance difference between the predicted MOS (pMOS) and MOS. Firstly, as is shown in the first table of Fig.1(b), quality perceptual deviation from a wide range brings outliers to a scale, which destroy the prediction performance in this scale. The reason for this is that the objective BIQA model fails to keep the prediction sequence in the order as the ground truth sequence possesses. Secondly, as is shown in the second table of Fig.1(b), the predicted values can not concentrate in smaller deviation ranges, which is further intensifies the poor accuracy in the local range. Next, we aims to fix these two problems responsible for the range effect utilizing coarse-grained metric and fine-grained prediction respectively.
In response to the first problem, metric learning can be introduced to acquire the scale membership. The state-ofthe-art metric learning methods map the samples into the multi-dimensional feature vectors, as for clustering or nearest neighbor classification utilizing a distance function that capture pair-wised similarity [37] [36]. This is effective on BIQA approaches for the synthetic task [38] [39] [40]. These methods map the synthetically distorted images into the feature vectors which represent distortion classification or distortion level. Based on these degradation, they can accurately metric the quality differences between pair-wised images. However, in authentic databases, images possess nonidentical scenes and immeasurable distortions that increase the difficult to obtain the coherence of feature distributions [31]. Even it can be achieved between a pair of images, it may not be qualityaware. In this paper, the proposed method maps a set of images to supervised scores. Based on these scores, rank-andgradient metric can be conducted as for reducing the prediction deviation from a wide range and further alleviate the order confusion of the predicted quality sequence.
To deal with the inaccuracy problem in smaller deviation ranges, we adopts a multi-stage prediction strategy. Previous BIQA approaches adopt a one-time strategy to predict MOS, with a feedforward structure. However, the way neglects feedback mechanism of human visual system (HVS) as is important for perceptual learning [41]. Neurologically speaking, feedforward hierarchy underlies implicit processing for initial vision at a glance, and feedback connections add details to explicit vision with scrutiny [42]. The same applies to BIQA that fine-grained cognition of image quality is achieved through a feedback processing where high-level and low-level features are recurrently integrated by HVS. Moreover, feedback-based learning approach has been proved more effective than the commonly employed feedforward paradigm in prediction tasks [?]. In the proposed method, MOS prediction is constantly refined under multi-level tolerance constraints through a feedback structure. Besides, coarse-grained metric is fused into the structure as the prior knowledge as it is easier compared to fine-grained prediction from the perspective of curriculum learning [68].
Patrick et al. [24] have already proposed a new evaluation criterion aiming at FR-IQA which is less dependent on the range effect. However, there has been no way to solve this problem in BIQA yet. To deal with this challenge, we first propose a novel coarse-to-fine assessment method. The flowchart of the proposed method is illustrated in Fig.2. Firstly, it takes a set of images as input. The set contains a predicted image and four anchors sampled from different quality scales randomly. And then, these images are mapped into multi-scale distortion features and context features. The former are taken as the input of feedback blocks and the latter are fused into the outputs of feedback blocks in each time step. Based on this, coarse-to-fine assessment acquires local-to-global feature representation which is more quality-aware [70]. Finally, after feature mapping, coarse-to-fine assessment is conducted. In the first time step, coarse-grained metric utilizes rank-andgradient loss to keep the order and grad of predicted sequence consistent with the ground data. In subsequent time steps, finegrained prediction adopts multi-level tolerance losses to refine the prediction accuracy.

II. RELATED WORK
In this section, we review BIQA methods for synthetically distorted images and BIQA methods for authentically distorted images respectively.

A. BIQA for Synthetically Distorted Images
In the previous development, the research of BIQA approaches for synthetic