This paper presents an in-memory, RDMA-enabled, highly-available, transactional Key-Value Store (KVS), dubbed Dandelion, that significantly improves performance in small deployments (e.g., 5–10 machines). Small deployments are motivated by the anticipated memory expansion (e.g., through CXL), which enables the deployment of in-memory KVSes with few machines but lots of memory. A small deployment presents locality opportunities that have not been examined by related work. Specifically, it is more likely that at any given time, we must send multiple messages to the same recipient. We leverage this by transparently batching multiple requests in the same network packet. Similarly, there is a greater chance of having multiple requests that can be served by the local hashtable without going through the network. Sending all requests to the hashtable as a batch allows it to overlap their memory latencies through software prefetching. Finally, it is more likely that the node that requests a key is itself a backup of that key. We leverage this by allowing strongly-consistent local reads from backups. Our evaluation shows that these optimizations result in up to 6.5x throughput improvement over a state-of-the-art system, FaSST, in OLTP workloads in a 5-machine deployment. We characterize the impact and scalability of each of these optimizations with up to 10 machines - where Dandelion still offers as much as 3.5× higher throughput than FaSST.

Obstructive sleep apnea (OSA) is a high-prevalence disease in the general population, often underdiagnosed. The gold standard in clinical practice for its diagnosis and severity assessment is the polysomnography, although in-home approaches have been proposed in recent years to overcome its limitations. Today's ubiquitously presence of wearables may become a powerful screening tool in the general population and pulse-oximetry-based techniques could be used for early OSA diagnosis. In this work, the peripheral oxygen saturation together with the pulse-to-pulse interval (PPI) series derived from photoplethysmography (PPG) are used as inputs for OSA diagnosis. Different models are trained to classify between normal and abnormal breathing segments (binary decision), and between normal, apneic and hypopneic segments (multiclass decision). The models obtained 86.27% and 73.07% accuracy for the binary and multiclass segment classification, respectively. A novel index, the cyclic variation of the heart rate index (CVHRI), derived from PPI's spectrum, is computed on the segments containing disturbed breathing, representing the frequency of the events. CVHRI showed strong Pearson's correlation (r) with the apnea-hypopnea index (AHI) both after binary (r=0.94, p 0.001) and multiclass (r=0.91, p 0.001) segment classification. In addition, CVHRI has been used to stratify subjects with AHI higher/lower than a threshold of 5 and 15, resulting in 77.27% and 79.55% accuracy, respectively. In conclusion, patient stratification based on the combination of oxygen saturation and PPI analysis, with the addition of CVHRI, is a suitable, wearable friendly and low-cost tool for OSA screening at home.

Socio-technical processes have come to the forefront of recent analysis of the open source software development (OSSD) world. Interest in making these processes explicit is mounting, from industry and the software process community, as well as among those who may become contributors to OSSD organization. This paper serves to close this gap by providing an empirical analysis of the role migration and project career advancement process, and role-sets within, that we have observed through comparative case studies within three large OSSD project organizations: Mozilla.org, Apache.org, and NetBeans.org.

We describe the desire for a black box approach to pattern classification: a generic Autonomous Pattern Recognizer, which is capable of self-adapting to specific alphabets without human intervention. The CellNet software system is introduced, an evolutionary system that optimizes a set of pattern-recognizing agents relative to a provided set of features and a given pattern database. CellNet utilizes a new genetic operator designed to facilitate a canalization of development: Merger. CellNet utilizes our own set of arbitrarily chosen features, and is applied to the CEDAR Database of handwritten Latin characters, as well as to a database of handwritten Indian digits provided by CENPARMI. CellNet's cooperative co-evolutionary approach shows significant improvement over a more standard Genetic Algorithm, both in terms of efficiency and in nearly eliminating over-fitting (to the training set). Additionally, the binary classifiers autonomously evolved by CellNet return validation accuracies approaching 98% for both Latin and Indian digits, with no global changes to the system between the two trials.