Representation learning, a task of learning latent vectors to represent entities, is a key task in improving search and recommender systems in web applications. Various representation learning methods have been developed, including graph-based approaches for relationships among entities, sequence-based methods for capturing the temporal evolution of user activities, and content-based models for leveraging text and visual content. However, the development of a unifying framework that integrates these diverse techniques to support multiple applications remains a significant challenge. This paper presents OmniSage, a large-scale representation framework that learns universal representations for a variety of applications at Pinterest. OmniSage integrates graph neural networks with content-based models and user sequence models by employing multiple contrastive learning tasks to effectively process graph data, user sequence data, and content signals. To support the training and inference of OmniSage, we developed an efficient infrastructure capable of supporting Pinterest graphs with billions of nodes. The universal representations generated by OmniSage have significantly enhanced user experiences on Pinterest, leading to an approximate 2.5% increase in sitewide repins (saves) across five applications. This paper highlights the impact of unifying representation learning methods, and we make the model code publicly available at https://github.com/pinterest/atg-research/tree/main/omnisage.

HHVM is commonly developed for large online web services, yet there remains much room for optimizing HHVM performance. This paper discusses challenges and techniques in optimizing HHVM performance for Meta's web service. We begin by evaluating the effectiveness of semantic request routing, a request routing method aimed at enhancing code cache performance in HHVM, and examine its implications for optimizing HHVM performance. Second, we characterize HHVM performance for a large-scale datacenter and identify the challenges brought by uncontrollable confounding factors. Finally, we present the performance management framework for autotuning HHVM performance at scale.

Deep convolutional neural networks (DCNN) have been the dominant methodology in the field of computer vision over the last decade, using various architectural organizations of successive convolutional layers to extract and assemble low level image features into visual component detectors. One of the tradeoffs that have been made as the community has migrated to deep neural models is the loss of explainability and understanding of which salient visual components are being recognized by a model for a particular task. However, there exists a significant heritage in the remote sensing community that has developed advanced algorithms to analyze the signal and structural characteristics of anthropogenic features. One such approach is the use of morphological image processing techniques to extract objects from imagery and aid in the structural analysis of shapes. In particular, the differential morphological profile (DMP) has had great success extracting object shapes, while naturally grouping the extracted shapes into scale ranges. In this research, we present a novel architecture that integrates an explicit (definable and explainable) scaled object extraction into the network architecture, allowing shallower convolutional layers and lower complexity neural models. The architecture is evaluated on a challenging remote sensing dataset of object classes, providing insights to this approach and illuminating future directions of integrating morphology into neural architectures for enhanced explainability.

Object detection and localization within high-resolution remote sensing imagery (HR-RSI) is a challenging task for a variety of reasons, such as the complexity and clutter of the image scene and the compactness of the intermixed object classes. Even the most comprehensive training datasets cannot adequately account for the rich diversity and complexity of anthropogenic objects and their contextual settings in large-scale HR-RSI collections. Recent approaches using deep learning techniques include bounding box approaches (e.g., YOLO), object nomination then recognition (e.g., R-CNN), and post-detection object localization of deep neural network detections. Herein, we propose a novel technique that leverages heterogeneous superpixels and deep neural feature extraction to classify the superpixel segmentation through relational analysis. In this preliminary research, we demonstrate the validity of this approach for object detection and localization, as well as its suitability for identifying the irregular shapes of objects (as opposed to a bounding box). Experiments are performed using a sub-set of the xView benchmark dataset with a goal of spearheading future techniques in cluttered scene object recognition that allows deep feature extractors to have more focus on the target objects instead of the surrounding area or nearby object pixels.

Herein, a generalization of the ordered weighted average (OWA) is put forth relative to pattern recognition. The resultant linear order statistic neuron (LOSN) is unique in that it bridges fuzzy sets, specifically fuzzy data/information aggregation, with neural networks. This article discusses the gradient descent-based optimization and geometric interpretation of the LOSN. An advantage is that the LOSN is an efficient shared weight encoding of N! perceptrons, relative to N inputs. Open source codes are provided to facilitate reproducible research. Experiments are conducted to both validate the method and show its non-linear geometric expression.

Wireless sensor networks for factory automation and control will require strict latency and reliability requirements on the order of 1 millisecond end-to-end latency and 10-9total packet error rate. Current wireless sensor networks can achieve up to nine nines (10-9total packet error rate) of reliability, but without tight latency bounds. In order to meet these requirements, wireless sensor networks will require effective modes of network diversity that are also compatible with low-latency time scales. We evaluated the efficacy of time, space, and frequency diversity in cooperative IEEE 802.15.4 wireless networks. We demonstrated a wireless sensor network topology that achieved 99.99999% of reliability bounded by a worst case end - to-end latency of 3 milliseconds.

Online reviews are often our first port of call when considering products and purchases online. When evaluating a potential purchase, we may have a specific query in mind, e.g. `will this baby seat fit in the overhead compartment of a 747?' or `will I like this album if I liked Taylor Swift's 1989?'. To answer such questions we must either wade through huge volumes of consumer reviews hoping to find one that is relevant, or otherwise pose our question directly to the community via a Q/A system.

MOTIVATION: Network biology has emerged as a powerful tool to uncover the organizational properties of living systems through the application of graph theoretic approaches. However, due to limitations in underlying data models and visualization software, knowledge relating to large molecular assemblies and biologically active fragments is poorly represented. RESULTS: Here, we demonstrate a novel hypergraph implementation that better captures hierarchical structures, using components of elastic fibers and chromatin modification as models. These reveal unprecedented views of the biology of these systems, demonstrating the unique capacity of hypergraphs to resolve overlaps and uncover new insights into the subfunctionalization of variant complexes. AVAILABILITY AND IMPLEMENTATION: Hyperscape is available as a web application at http://www.compsysbio.org/hyperscape. Source code, examples and a tutorial are freely available under a GNU license. CONTACTS: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.