Automatic BIRCH thresholding with features transformation for hierarchical breast cancer clustering

<p>Breast cancer is one of the most common diseases diagnosed in women over the world. The balanced iterative reducing and clustering using hierarchies (BIRCH) has been widely used in many applications. However, clustering the patient records and selecting an optimal threshold for the hierarchical clusters still a challenging task. In addition, the existing BIRCH is sensitive to the order of data records and influenced by many numerical and functional parameters. Therefore, this paper proposes a unique BIRCH-based algorithm for breast cancer clustering. We aim at transforming the medical records using the breast screening features into sub-clusters to group the subject cases into malignant or benign clusters. The basic BIRCH clustering is firstly fed by a set of normalized features then we automate the threshold initialization to enhance the tree-based sub-clustering procedure. Additionally, we present a thorough analysis on the performance impact of tuning BIRCH with various relevant linkage functions and similarity measures. Two datasets of the standard breast cancer wisconsin (BCW) benchmarking collection are used to evaluate our algorithm. The experimental results show a clustering accuracy of 97.7% in 0.0004 seconds only, thereby confirming the efficiency of the proposed method in clustering the patient records and making timely decisions.</p>

Endometriosis detection and localization in laparoscopic gynecology

Endometriosis is a common gynecologic condition typically treated via laparoscopic surgery. Its visual versatility makes it hard to identify for non-specialized physicians and challenging to classify or localize via computer-aided analysis. In this work, we take a first step in the direction of localized endometriosis recognition in laparoscopic gynecology videos using region-based deep neural networks Faster R-CNN and Mask R-CNN. We in particular use and further develop publicly available data for transfer learning deep detection models according to distinctive visual lesion characteristics. Subsequently, we evaluate the performance impact of different data augmentation techniques, including selected geometrical and visual transformations, specular reflection removal as well as region tracking across video frames. Finally, particular attention is given to creating reasonable data segmentation for training, validation and testing. The best performing result surprisingly is achieved by randomly applying simple cropping combined with rotation, resulting in a mean average segmentation precision of 32.4% at 50-95% intersection over union overlap (64.2% for 50% overlap).

DExIE - An IoT-Class Hardware Monitor for Real-Time Fine-Grained Control-Flow Integrity

The Dynamic Execution Integrity Engine (DExIE) is a lightweight hardware monitor that can be flexibly attached to many IoT-class processor pipelines. It is guaranteed to catch both inter- and intra-function illegal control flows in time to prevent any illegal instructions from touching memory. The performance impact of attaching DExIE to a core depends on the concrete pipeline structure. In some especially suitable cases, extending a processor with DExIE will have no performance penalty. DExIE is real-time capable, as it causes no or only up to 10.4 % additional and then predictable pipeline stalls. Depending on the monitored processor’s size and structure, DExIE is faster than software-based monitoring and often smaller than a separate guard processor. We present not just the hardware architecture, but also the automated programming flow, and discuss compact adaptable storage formats to hold fine-grained control flow information.

Strategic Alliance and Acquisition Performance: Impact of Interfirm Synergies and Motives in the Bio-Pharmaceutical Industry

This study draws on transaction cost, resource dependence, and organizational learning theories to posit relationships between transaction performance and transaction structure (alliances versus acquisitions), interfirm synergies, and motives. The study involves analysis of 66 international and intra­national alliances and acquisitions that were undertaken in the pharmaceutical industry. An initial survey was administered to firms involved in these transactions to gather information regarding motives, transaction structure, and interorganizational synergies. A second survey was administered two years after the transaction to gather information on transaction performance. Findings support the importance of transaction structure and strategic synergies between firms. Specifically, transaction structure and high levels of strategic fit between the firms had a positive impact on performance. There is also some evidence that synergies must be linked to the motives driving the transaction. The study yields meaningful results regarding factors leading to success of transactions (alliances and acquisitions) in a longitudinal study of intranational and international transactions in the biopharmaceutical industry.

An Evaluation of Sleepiness, Performance, and Workload Among Operators During a Real-Time Reactive Telerobotic Lunar Mission Simulation

Objective We assessed operator performance during a real-time reactive telerobotic lunar mission simulation to understand how daytime versus nighttime operations might affect sleepiness, performance, and workload. Background Control center operations present factors that can influence sleepiness, neurobehavioral performance, and workload. Each spaceflight mission poses unique challenges that make it difficult to predict how long operators can safely and accurately conduct operations. We aimed to evaluate the performance impact of time-on-task and time-of-day using a simulated telerobotic lunar rover to better inform staffing and scheduling needs for the upcoming Volatiles Investigating Polar Exploration Rover (VIPER) mission. Methods We studied seven trained operators in a simulated mission control environment. Operators completed two five-hour simulations in a randomized order, beginning at noon and midnight. Performance was evaluated every 25 minutes using the Karolinska Sleepiness Scale, Psychomotor Vigilance Task, and NASA Task Load Index. Results Participants rated themselves as sleepier (5.06 ± 2.28) on the midnight compared to the noon simulation (3.12 ± 1.44; p < .001). Reaction time worsened over time during the midnight simulation but did not vary between simulations. Workload was rated higher during the noon (37.93 ± 20.09) compared to the midnight simulation (32.09 ± 21.74; p = .007). Conclusion Our findings suggest that work shifts during future operations should be limited in duration to minimize sleepiness. Our findings also suggest that working during the day, when distractions are present, increases perceived workload. Further research is needed to understand how working consecutive shifts and taking breaks within a shift influence performance.

Lightweight Dynamic Redundancy Control with Adaptive Encoding for Server-based Storage

With the recent performance improvements in commodity hardware, low-cost commodity server-based storage has become a practical alternative to dedicated-storage appliances. Because of the high failure rate of commodity servers, data redundancy across multiple servers is required in a server-based storage system. However, the extra storage capacity for this redundancy significantly increases the system cost. Although erasure coding (EC) is a promising method to reduce the amount of redundant data, it requires distributing and encoding data among servers. There remains a need to reduce the performance impact of these processes involving much network traffic and processing overhead. Especially, the performance impact becomes significant for random-intensive applications. In this article, we propose a new lightweight redundancy control for server-based storage. Our proposed method uses a new local filesystem-based approach that avoids distributing data by adding data redundancy to locally stored user data. Our method switches the redundancy method of user data between replication and EC according to workloads to improve capacity efficiency while achieving higher performance. Our experiments show up to 230% better online-transaction-processing performance for our method compared with CephFS, a widely used alternative system. We also confirmed that our proposed method prevents unexpected performance degradation while achieving better capacity efficiency.

FluidSMR: Adaptive Management for Hybrid SMR Drives

Hybrid Shingled Magnetic Recording (H-SMR) drives are the most recently developed SMR drives, which allow dynamic conversion of the recording format between Conventional Magnetic Recording (CMR) and SMR on a single disk drive. We identify the unique opportunities of H-SMR drives to manage the tradeoffs between performance and capacity, including the possibility of adjusting the SMR area capacity based on storage usage and the flexibility of dynamic data swapping between the CMR area and SMR area. We design and implement FluidSMR, an adaptive management scheme for hybrid SMR Drives, to fully utilize H-SMR drives under different workloads and capacity usages. FluidSMR has a two-phase allocation scheme to support a growing usage of the H-SMR drive. The scheme can intelligently determine the sizes of the CMR and the SMR space in an H-SMR drive based on the dynamic changing of workloads. Moreover, FluidSMR uses a cache in the CMR region, managed by a proposed loop-back log policy, to reduce the overhead of updates to the SMR region. Evaluations using enterprise traces demonstrate that FluidSMR outperforms baseline schemes in various workloads by decreasing the average I/O latency and effectively reducing/controlling the performance impact of the format conversion between CMR and SMR.

Strategic management accounting and performance implications: a literature review and research agenda

The important role that management accounting plays in driving organisational performance has been reiterated in the literature. In line with that importance, the call for more effort to enhance knowledge on strategic management accounting has increased over the years. Responding to that call, this study utilised a qualitative approach that involved a systematic review to synthesise existing literature towards understanding the strategic management accounting foundation, contingency factors, and organisational performance impact. Based on the evidence in reviewed literature, we flag key directions for advancing this theoretical premise towards providing further insights that would enable practitioners strategically align their strategic management accounting practices for optimal organisational performance. The limitations of this study have been acknowledged.