Prophet Matching with General Arrivals

We provide prophet inequality algorithms for online weighted matching in general (nonbipartite) graphs, under two well-studied arrival models: edge arrival and vertex arrival. The weights of the edges are drawn from a priori known probability distribution. Under edge arrival, the weight of each edge is revealed on arrival, and the algorithm decides whether to include it in the matching or not. Under vertex arrival, the weights of all edges from the newly arriving vertex to all previously arrived vertices are revealed, and the algorithm decides which of these edges, if any, to include in the matching. To study these settings, we introduce a novel unified framework of batched-prophet inequalities that captures online settings where elements arrive in batches. Our algorithms rely on the construction of suitable online contention resolution scheme (OCRS). We first extend the framework of OCRS to batched-OCRS, we then establish a reduction from batched-prophet inequality to batched-OCRS, and finally we construct batched-OCRSs with selectable ratios of 0.337 and 0.5 for edge and vertex arrival models, respectively. Both results improve the state of the art for the corresponding settings. For vertex arrival, our result is tight. Interestingly, a pricing-based prophet inequality with comparable competitive ratios is unknown.

Emergence of 4-D System Fundamental Diagram in Urban Air Mobility Traffic Flow

Urban air mobility (UAM) is an emerging mode that promises to provide relief to congested urban streets. UAM relies on airspace, however, which is an exhaustible resource considering minimum aircraft separation requirements. In light of these requirements and UAM vehicle attributes, a simulation is developed to explore UAM traffic flows and congestion development. A decentralized conflict resolution scheme is employed in the form of a non-linear program (NLP) to offer improved flexibility in detours relative to past aircraft simulations. An expansion of Edie’s definitions of density and flow rate are used in conjunction with average speed to explore the relationships between traffic flow characteristics. The results find that UAM traffic flows emulate those of other modes, by following the familiar traffic patterns of build-up and breakdown captured in the macroscopic fundamental diagram. These findings also suggest the presence of a capacity of airspace that should be carefully managed by operators to achieve optimal system performance. The relationships established in this study highlight issues that UAM operators and aviation planners may face and could be used to improve the vehicle traffic modeling of other UAM models.

Robust Facial Image Super-Resolution by Kernel Locality-Constrained Coupled-Layer Regression

Super-resolution methods for facial image via representation learning scheme have become very effective methods due to their efficiency. The key problem for the super-resolution of facial image is to reveal the latent relationship between the low-resolution ( LR ) and the corresponding high-resolution ( HR ) training patch pairs. To simultaneously utilize the contextual information of the target position and the manifold structure of the primitive HR space, in this work, we design a robust context-patch facial image super-resolution scheme via a kernel locality-constrained coupled-layer regression (KLC2LR) scheme to obtain the desired HR version from the acquired LR image. Here, KLC2LR proposes to acquire contextual surrounding patches to represent the target patch and adds an HR layer constraint to compensate the detail information. Additionally, KLC2LR desires to acquire more high-frequency information by searching for nearest neighbors in the HR sample space. We also utilize kernel function to map features in original low-dimensional space into a high-dimensional one to obtain potential nonlinear characteristics. Our compared experiments in the noisy and noiseless cases have verified that our suggested methodology performs better than many existing predominant facial image super-resolution methods.

Population structure analysis and laboratory monitoring of Shigella with a standardised core-genome multilocus sequence typing scheme: a validation study

Background The laboratory surveillance of bacillary dysentery is based on a Shigella typing scheme standardised in the late 1940s. This scheme classifies Shigella strains into four serogroups and more than 50 serotypes on the basis of biochemical tests and lipopolysaccharide O-antigen serotyping. Real-time genomic surveillance of Shigella infections has been implemented in several countries, but without the use of a standardised high-resolution typing scheme. Methods We studied over 4,000 clinical isolates and reference strains of Shigella, covering all serotypes, including provisional serotypes and atypical strains, with the current serotyping scheme. These strains and isolates were also subjected to whole-genome sequencing and analysis with the EnteroBase Escherichia/Shigella 2,513-locus core-genome multilocus sequence typing scheme (cgMLST). Findings The Shigella genomes were grouped into eight phylogenetically distinct clusters, within the E. coli species. Three of these clusters contained strains from different serogroups and serotypes, the remaining five each consisting of a single serotype. The cgMLST hierarchical clustering (HC) analysis at different levels of resolution (HC2000 to HC400) recognised the natural groupings for Shigella. By contrast, the serotyping scheme was affected by horizontal gene transfer, leading to a conflation of genetically unrelated Shigella strains and a separation of some genetically related strains. We also curated the various provisional serotypes reported in the literature and described five new Shigella serotypes for addition to the typing scheme. Interpretation The EnteroBase Escherichia/Shigella cgMLST is a standardised, robust, portable, and high-resolution scheme that will enhance the laboratory surveillance of Shigella infections, particularly for Shigella flexneri. However, cgMLST data should be considered together with in silico serotyping data, to maintain backward compatibility with the current Shigella serotyping scheme.

Flow entry conflict detection and resolution scheme for software-defined networking

Software-defined networking (SDN) has the ability to flexibly configure the network and is widely used in various scenarios. In SDN, different applications require the controller to deploy corresponding flow entries to maintain the effectiveness of network. However, the matching field range covered by entries of different applications may overlap, and when actions specified by these overlapping entries are inconsistent, conflicts may occur. Such conflicts may cause the flow to match the wrong entry, thereby affecting the correct expression of application functions. The scheme we proposed in this paper will be able to detect and resolve conflicts between flow entries. Firstly, we discussed the causes of conflicts, and then classified various conflict situations. This classification will help us to adopt different ways of resolving different types of conflicts and make the resolution of conflicts more targeted. Next, we propose a conflict detection algorithm based on B+ tree. This algorithm can detect different types of conflicts. According to theoretical proof, the use of B+ tree compared with other similar structures better in the performance of the time and space complexity. Finally, for the detected conflicting entries, we propose a conflict resolution scheme based on the failure degree of the flow entry according to the characteristics of SDN services tending to be more detailed. Through experimental evaluation, our scheme can effectively detect and resolve conflicts with lower overhead.