ReactiveFnJ: A Choreographed Model for Fork-Join Workflow in Serverless Computing

Function-as-a-Service (FaaS) is an event-based reactive programming model where functions run in ephemeral stateless containers for short duration. For building complex serverless applications, function composition is crucial to coordinate and synchronize the workflow of an application. Some serverless orchestration systems exist, but they are in their primitive state and do not provide inherent support for non-trivial workflows like, Fork-Join. To address this gap, we propose a fully serverless and scalable design model ReactiveFnJ for Fork-Join workflow. The intent of this work is to illustrate a design which is completely choreographed, reactive, asynchronous, and represents a dynamic composition model for serverless applications. Our design uses two innovative patterns, namely, Relay Composition and Master-Worker Composition to solve execution time-out challenges. As a Proof-of-Concept (PoC), the prototypical implementation of Split-Sort-Merge use case, based on Fork-Join workflow is discussed and evaluated. The ReactiveFnJ handles embarrassingly parallel computations, and its design does not depend on any external orchestration services, messaging services, and queue services. ReactiveFnJ facilitates in designing fully automated pipelines for distributed data processing systems, satisfying the Serverless Trilemma in true essence. A file of any size can be processed using our effective and extensible design without facing execution time-out challenges. The proposed model is generic and can be applied to a wide range of serverless applications that are based on the Fork-Join workflow pattern. It fosters the choreographed serverless composition for complex workflows. The proposed design model is useful for software engineers and developers in industry and commercial organizations, total solution vendors and academic researchers.

Dark Acupuncture

This paper outlines a research methodology and design strategy aimed at realizing sustainable lighting within (sub)urban multi-functional parks. It does so by detailing the research process, as well as resultant vision and design concepts, for the Delftse Hout (a park in Delft, The Netherlands). This process included formulating value-level design requirements, undertaking a detailed site-study to understand stakeholder needs, and combining these to provide conceptual and practical grounding for the future development of a lighting masterplan. A key – and we argue generalizable – outcome of the process is the development and application of dark acupuncture, a scalable design strategy aimed at strategically-placed interventions of darkness and illumination. The paper thus provides three contributions to sustainable lighting theory and practice: a detailed case study of innovative lighting design research; the refinement of dark acupuncture as a design strategy for nature-inclusive park lighting (which itself can be more broadly applicable to urban lighting policy and design); and, as a practical example of transdisciplinary research into artificial light at night.

The Need for Transfer Learning in CRISPR-Cas Off-Target Scoring

The scalable design of safe guide RNA sequences for CRISPR gene editing depends on the computational "scoring" of DNA locations that may be edited. As there is no widely accepted benchmark dataset to compare scoring models, we present a curated "TrueOT" dataset that contains thoroughly validated datapoints to best reflect the properties of in vivo editing. Many existing models are trained on data from high throughput assays. We hypothesize that such models may suboptimally transfer to the low throughput data in TrueOT due to fundamental biological differences between proxy assays and in vivo behavior. We developed new Siamese convolutional neural networks, trained them on a proxy dataset, and compared their performance against existing models on TrueOT. Our simplest model with a single convolutional and pooling layer surprisingly exhibits state-ofthe-art performance on TrueOT. Adding subsequent layers improves performance on the proxy dataset while compromising performance on TrueOT. We demonstrate that model complexity can only improve performance on TrueOT if transfer learning techniques are employed. These results suggest an urgent need for the CRISPR community to agree upon a benchmark dataset such as TrueOT and highlight that various sources of CRISPR data cannot be assumed to be equivalent. Our codebase and datasets are available on GitHub at github.com/baolab-rice/CRISPR_OT_scoring.

Inter-database validation of a deep learning approach for automatic sleep scoring

Study objectives Development of inter-database generalizable sleep staging algorithms represents a challenge due to increased data variability across different datasets. Sharing data between different centers is also a problem due to potential restrictions due to patient privacy protection. In this work, we describe a new deep learning approach for automatic sleep staging, and address its generalization capabilities on a wide range of public sleep staging databases. We also examine the suitability of a novel approach that uses an ensemble of individual local models and evaluate its impact on the resulting inter-database generalization performance. Methods A general deep learning network architecture for automatic sleep staging is presented. Different preprocessing and architectural variant options are tested. The resulting prediction capabilities are evaluated and compared on a heterogeneous collection of six public sleep staging datasets. Validation is carried out in the context of independent local and external dataset generalization scenarios. Results Best results were achieved using the CNN_LSTM_5 neural network variant. Average prediction capabilities on independent local testing sets achieved 0.80 kappa score. When individual local models predict data from external datasets, average kappa score decreases to 0.54. Using the proposed ensemble-based approach, average kappa performance on the external dataset prediction scenario increases to 0.62. To our knowledge this is the largest study by the number of datasets so far on validating the generalization capabilities of an automatic sleep staging algorithm using external databases. Conclusions Validation results show good general performance of our method, as compared with the expected levels of human agreement, as well as to state-of-the-art automatic sleep staging methods. The proposed ensemble-based approach enables flexible and scalable design, allowing dynamic integration of local models into the final ensemble, preserving data locality, and increasing generalization capabilities of the resulting system at the same time.

A Modular Mechanism for Downhole Weight-on-Bit and Torque Reaction in Small Diameter Boreholes

Drilling systems that use downhole rotation must react torque either through the drill-string or near the motor to achieve effective drilling performance. Problems with drill-string loading such as buckling, friction, and twist become more severe as hole diameter decreases. Therefore, for small holes, reacting torque downhole without interfering with the application of weight-on-bit, is preferred. In this paper we present a novel mechanism that enables effective and controllable downhole weight on bit transmission and torque reaction. This scalable design achieves its unique performance through four key features: 1) mechanical advantage based on geometry, 2) direction dependent behavior using rolling and sliding contact, 3) modular scalability by combining modules in series, and 4) torque reaction and weight on bit that are proportional to applied axial force. As a result, simple mechanical devices can be used to react large torques while allowing controlled force to be transmitted to the drill bit. We outline our design, provide theoretical predictions of performance, and validate the results using full-scale testing. The experimental results include laboratory studies as well as limited field testing using a percussive hammer. These results demonstrate effective torque reaction, axial force transmission, favorable scaling with multiple modules, and predictable performance that is proportional to applied force.

Circulator function in a Josephson junction circuit and braiding of Majorana zero modes

We propose a scheme for the circulator function in a superconducting circuit consisting of a three-Josephson junction loop and a trijunction. In this study we obtain the exact Lagrangian of the system by deriving the effective potential from the fundamental boundary conditions. We subsequently show that we can selectively choose the direction of current flowing through the branches connected at the trijunction, which performs a circulator function. Further, we use this circulator function for a non-Abelian braiding of Majorana zero modes (MZMs). In the branches of the system we introduce pairs of MZMs which interact with each other through the phases of trijunction. The circulator function determines the phases of the trijunction and thus the coupling between the MZMs to gives rise to the braiding operation. We modify the system so that MZMs might be coupled to the external ones to perform qubit operations in a scalable design.