MOTIVATION: Algorithms for ligand-receptor network inference have emerged as commonly used tools to estimate cell-cell communication from reference single-cell data. Many studies employ these algorithms to compare signaling between conditions and lack methods to statistically identify signals that are significantly different. We previously developed the cell communication inference algorithm Domino, which considers ligand and receptor gene expression in association with downstream transcription factor activity scoring. We developed the dominoSignal software to innovate upon Domino and extend its functionality to test statistically differential cellular signaling. RESULTS: This new functionality includes the compilation of active signals as linkages from multiple subjects in a single-cell data set and testing condition-dependent signaling linkage. The software is applicable for analysis of single-cell data sets with multiple subjects as biological replicates as well as with bootstrapped replicates from data sets with few or pooled subjects. We use simulation studies to benchmark the number of subjects in compared groups and cells within an annotated cell type sufficient to accurately identify differential linkages. We demonstrate the application of the Differential Cell Signaling Test (DCST) in the dominoSignal software to investigate consequences of cancer cell phenotypes and immunotherapy on cell-cell communication in tumor microenvironments. These applications in cancer studies demonstrate the ability of differential cell signaling analysis to infer changes to cell communication networks from therapeutic or experimental perturbations, which is broadly applicable across biological systems. AVAILABILITY: dominoSignal is available through Bioconductor at https://www.bioconductor.org/packages/release/bioc/html/dominoSignal.html.

During chip development, engineers must target different technologies, such as simulation and various ASIC and FPGA technologies. Conventionally, they split parts of the code (e.g., memories) into separate technology-specialized blocks implementing the same high-level behavior. This leads to brittle code, with multiple but subtly different blocks describing the same semantic behavior, harming verification, agility, and extensibility. We propose fungible memories, an HDL-level "write once, map anywhere" memory abstraction with rich enough semantics to automatically target all relevant technologies using a single generic interface. We incorporate fungible memories into a compiler called Memo. For designs without a specific technology mapping, we also present a memory decompiler which lifts memories from an existing gate-level design to Memo, enabling automated technology re-targeting, which is a holy grail for digital designers. We present a structure-aware equality saturation technique which scales to netlists with millions of cells and identifies memories that the state of the art cannot. We demonstrate that Memo effectively targets backends across different technology platforms (simulation, ASIC, and FPGA) over a suite of representative designs, including a RISC-V multicore SoC.

Harmful algal blooms (HABs) pose significant threats to aquatic ecosystems, public health, and local economies by degrading water quality, causing fish kills, and producing harmful toxins. Traditional methods of detecting and mitigating HABs are often labor-intensive and expensive, targeting only smaller water bodies. Additionally, many detection systems currently work on a microbial scale or focus only on detection rather than classification. Existing research indicates that ultrasonic waves at specific frequencies can effectively destroy algal cells, but upscaling is necessary before implementation in the field becomes practical. We address this by leveraging machine learning to locate and classify multiple types of oceanic HABs and then outputting ultrasonic emissions at varied frequencies corresponding to each targeted HAB. The system analyzes satellite images with a convolutional neural network (CNN) to identify and categorize different types of harmful algae, allowing for rapid, large-scale monitoring of water bodies and providing timely data for environmental management. To train the CNN, we also construct a novel dataset containing 4998 satellite images of algal blooms along the west coast of North America. After the visual processing, we identify the optimal ultrasonic wave frequency for each identified HAB. Our model successfully identifies eight different algal genera with an accuracy of 98.2%. To tackle practical application, we propose a prototype to implement our machine learning model for algal classification with an ultrasonic irradiation device. It consists of two parts: a drone for autonomous scanning across the ocean, and an underwater device for treatment. Thus, we demonstrate the potential of machine learning to advance ultrasonic mitigation towards large-scale implementation.

Abstract Recent research and articles in popular press have raised concerns about the privacy risks that smart home devices can create for incidental users—people who encounter smart home devices that are owned, controlled, and configured by someone else. In this work, we present the results of a user-centered investigation that explores incidental users’ experiences and the tensions that arise between device owners and incidental users. We conducted five focus group sessions through which we identified specific contexts in which someone might encounter other people’s smart home devices and the main concerns device owners and incidental users have in such situations. We used these findings to inform the design of a survey instrument, which we deployed to a demographically representative sample of 386 adults in the United States. Through this survey, we can better understand which contexts and concerns are most bothersome and how often device owners are willing to accommodate incidental users’ privacy preferences. We found some surprising trends in terms of what people are most worried about and what actions they are willing to take. For example, while participants who did not own devices themselves were often uncomfortable imagining them in their own homes, they were not as concerned about being affected by such devices in homes that they entered as part of their jobs. Participants showed interest in privacy solutions that might have a technical implementation component, but also frequently envisioned an open dialogue between incidental users and device owners to negotiate privacy accommodations.

Text entry is an increasingly important activity for mobile device users. As a result, increasing text entry speed of expert typists is an important design goal for physical and soft keyboards. Mathematical models that predict text entry speed can help with keyboard design and optimization. Making typing errors when entering text is inevitable. However, current models do not consider how typists themselves reduce the risk of making typing errors (and lower error frequency) by typing more slowly. We demonstrate that users respond to costly typing errors by reducing their typing speed to minimize typing errors. We present a model that estimates the effects of risk aversion to errors on typing speed. We estimate the magnitude of this speed change, and show that disregarding the adjustments to typing speed that expert typists use to reduce typing errors leads to overly optimistic estimates of maximum errorless expert typing speeds.