Presents corrections to the article "A Sub-Pixel Accurate Quantification of Joint Space Narrowing Progression in Rheumatoid Arthritis".

Rheumatoid arthritis (RA) is a chronic autoimmune disease that primarily affects peripheral synovial joints, like fingers, wrists and feet. Radiology plays a critical role in the diagnosis and monitoring of RA. Limited by the current spatial resolution of radiographic imaging, joint space narrowing (JSN) progression of RA for the same reason above can be less than one pixel per year with universal spatial resolution. Insensitive monitoring of JSN can hinder the radiologist/rheumatologist from making a proper and timely clinical judgment. In this paper, we propose a novel and sensitive method that we call partial image phase-only correlation which aims to automatically quantify JSN progression in the early RA. The majority of the current literature utilizes the mean error, root-mean-square deviation and standard deviation to report the accuracy at pixel level. Our work measures JSN progression between a baseline and its follow-up finger joint images by using the phase spectrum in the frequency domain. Using this study, the mean error can be reduced to 0.0130 mm when applied to phantom radiographs with ground truth, and 0.0519 mm standard deviation for clinical radiography. With the sub-pixel accuracy far beyond usual manual measurements, we are optimistic that the proposed work is a promising scheme for automatically quantifying JSN progression.

The Log-Structured Merge Tree (LSM-Tree) is widely used in key-value (KV) stores because of its excwrite performance. But LSM-Tree-based KV stores still have the overhead of write-ahead log and write stall caused by slow L 0 flush and L 0 - L 1 compaction. New byte-addressable, persistent memory (PM) devices bring an opportunity to improve the write performance of LSM-Tree. Previous studies on PM-based LSM-Tree have not fully exploited PM’s “dual role” of main memory and external storage. In this article, we analyze two strategies of memtables based on PM and the reasons write stall problems occur in the first place. Inspired by the analysis result, we propose FlatLSM, a specially designed flat LSM-Tree for non-volatile memory based KV stores. First, we propose PMTable with separated index and data. The PM Log utilizes the Buffer Log to store KVs of size less than 256B. Second, to solve the write stall problem, FlatLSM merges the volatile memtables and the persistent L 0 into large PMTables, which can reduce the depth of LSM-Tree and concentrate I/O bandwidth on L 0 - L 1 compaction. To mitigate write stall caused by flushing large PMTables to SSD, we propose a parallel flush/compaction algorithm based on KV separation. We implemented FlatLSM based on RocksDB and evaluated its performance on Intel’s latest PM device, the Intel Optane DC PMM with the state-of-the-art PM-based LSM-Tree KV stores, FlatLSM improves the throughput 5.2× on random write workload and 2.55× on YCSB-A.

In order to overcome the problems of retrieval accuracy and time-consuming of traditional document information retrieval methods, this paper designs an intelligent retrieval method of library document information based on hidden topic mining. Firstly, LDA model is used to mine the hidden topics of library document information, and then, based on the mining results, similarity degree of document information is calculated in inference network model. Finally, the Bayesian model is constructed in the sample space to retrieve the library literature information under the maximum retrieval space coverage. Experimental results show that, compared with traditional retrieval methods, the proposed method improves the retrieval accuracy significantly, with the highest retrieval accuracy reaching 99%, and the retrieval time is significantly reduced, indicating that the proposed method effectively improves the retrieval accuracy and timeliness.

We give efficient algorithms to solve fundamental data movement problems on mesh-connected computers augmented with limited global bandwidth. Adding a small amount of global bandwidth makes a practical design that combines aspects of mesh and fully connected models to achieve the benefits of each. We give algorithms for sorting, finding the median, finding a spanning tree, and determining various graph properties to show that the small amount of global communication can significantly reduce the time, and that concurrent read helps even more. Most of these algorithms are optimal. We also extend our results to mesh-connected computers with row and column buses.

In this paper we combine aspects of the PRAM and mesh models to achieve the benefits of each. Many fast algorithms are known for the PRAM, but it is unrealistic to build. Another well-known model is the 2-dimensional mesh, of which many have been built, but the algorithms are constrained by the mesh's diameter and bisection bandwidth, forcing algorithms for nontrivial problems to take time which is at least the square root of the number of processors. Here we use a minuscule amount of shared memory to make a practical design which is significantly faster than the standard mesh. We call this the shared memory mesh. We give several algorithms which show that a small amount of shared memory can significantly reduce the time for problems such as finding the median, labeling the connected components of an image, and labeling the connected components of a graph. Further, we show that many of these algorithms are optimal.