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SLOWPOKE is a new system to accurately quantify the effects of hypothetical optimizations on end-to-end throughput for microservice applications, without relying on tracing or a priori knowledge of the call graph. Microservice operators can use SLOWPOKE to ask what-if performance analysis questions of the form "What throughput could my retail application sustain if I optimized the shopping cart service from 10K req/s to 20K req/s?". Given a target service and its hypothetical optimization, SLOWPOKE employs a perfor- mance model that determines how to selectively slow down non-target services to preserve the relative effect of the optimization. It then performs profiling experiments to predict the end-to-end throughput, as if the optimization had been implemented. Applied to four real-world microservice applications, SLOWPOKE accurately quantifies optimization effects with a root mean squared error of only 2.07%. It is also effective in more complex scenarios, e.g., predicting throughput after scaling optimizations or when bottlenecks arise from mutex contention. Evaluated in large-scale deployments of 45 nodes and 108 synthetic benchmarks, SLOWPOKE further demonstrates its scalability and coverage of a wide range of microservice characteristics. 

During plate convergence, shallow subduction or underthrusting of the lower-plate lithosphere beneath an overriding plate often results in far-field intraplate deformation, as observed in the Late Cretaceous–Paleogene North American Laramide or Cenozoic Himalayan-Tibetan orogen. Perplexingly, during this shallow-slab process, wide expanses of crust between the plate boundary and intraplate orogen do not experience significant synchronous deformation. These apparently undeformed crustal regions may reflect (1) a strong, rigid plate, (2) increased gravitational potential energy (GPE) to resist shortening and uplift, or (3) decoupling of the upper-plate lithosphere from any basal tractions. Here we review the geology of three orogens that formed due to flat slab subduction or underthrusting: the Himalayan-Tibetan, Mesozoic southeast China, and Laramide orogens. These orogens all involved intraplate deformation >1000-km from the plate boundary, large regions of negligible crustal shortening between the plate-boundary and intra-plate thrust belts, hot crustal conditions within the hinterland regions, and extensive upper-plate porphyry copper mineralization. A hot and weak hinterland is inconsistent with it persisting as an undeformed rigid block. GPE analysis suggests that hinterland quiescence is not uniquely due to thickened crust and elevated GPE, as exemplified by shallow marine sedimentation with low surface elevations in SE China. Comparison of these intracontinental orogens allows us to advance a general model, where hot orogenic hinterlands with a weak, mobile lower crust allow decoupling from underlying basal tractions exerted from flat-slab or underthrusting events. This hypothesis suggests that basal tractions locally drive intraplate orogens, at least partially controlled by the strength of the upper-plate lithosphere. 

In IoT deployments, it is often necessary to replicate data in failure-prone and resource-constrained computing environments to meet the data availability requirements of smart applications. In this paper, we evaluate the impact of correlated failures on an off-the-shelf probabilistic replica placement strategy for IoT systems via trace-driven simulation. We extend this strategy to handle both correlated failures as well as resource scarcity by estimating the amount of storage capacity required to meet data availability requirements. These advancements lay the foundation for building computing systems that are capable of handling the unique challenge of reliable data access in low-resource environments. 

Looking towards 6G wireless systems, frequency bands like the sub-terahertz (sub-THz) band (100 GHz - 300 GHz) are gaining traction for their promises of large available swaths of bandwidth to support the ever-growing data demands. However, challenges with harsh channel conditions and hardware non-linearities in the sub-THz band require robust communication techniques with favorable properties, such as good spectral efficiency and low peak-to-average power ratio (PAPR). Recently, OTFS and its variants have garnered significant attention for their performance in severe conditions (like high delay and Doppler), making it a promising candidate for future communications. In this work, we implement Zak-OTFS for the over-the-air experiments with traditional point pilots and the new spread pilots. Notably, we design our spread pilot waveforms with communications and sensing coexisting in the same radio resources. We define the system model and the signal design for integration onto our state-of-the-art sub-THz wireless testbed. We show successful data transmission over-the-air at 140 GHz and 240 GHz in a variety of signal-to-noise ratio (SNR) conditions. In addition, we demonstrate integrated sensing and communications (ISAC) capabilities and show PAPR improvement of over 5 dB with spread pilots compared to point pilots. 

Abstract BackgroundCannabis sativaL. with a rich history of traditional medicinal use, has garnered significant attention in contemporary research for its potential therapeutic applications in various human diseases, including pain, inflammation, cancer, and osteoarthritis. However, the specific molecular targets and mechanisms underlying the synergistic effects of its diverse phytochemical constituents remain elusive. Understanding these mechanisms is crucial for developing targeted, effective cannabis-based therapies. MethodsTo investigate the molecular targets and pathways involved in the synergistic effects of cannabis compounds, we utilized DRIFT, a deep learning model that leverages attention-based neural networks to predict compound-target interactions. We considered both whole plant extracts and specific plant-based formulations. Predicted targets were then mapped to the Reactome pathway database to identify the biological processes affected. To facilitate the prediction of molecular targets and associated pathways for any user-specified cannabis formulation, we developed CANDI (Cannabis-derived compound Analysis and Network Discovery Interface), a web-based server. This platform offers a user-friendly interface for researchers and drug developers to explore the therapeutic potential of cannabis compounds. ResultsOur analysis using DRIFT and CANDI successfully identified numerous molecular targets of cannabis compounds, many of which are involved in pathways relevant to pain, inflammation, cancer, and other diseases. The CANDI server enables researchers to predict the molecular targets and affected pathways for any specific cannabis formulation, providing valuable insights for developing targeted therapies. ConclusionsBy combining computational approaches with knowledge of traditional cannabis use, we have developed the CANDI server, a tool that allows us to harness the therapeutic potential of cannabis compounds for the effective treatment of various disorders. By bridging traditional pharmaceutical development with cannabis-based medicine, we propose a novel approach for botanical-based treatment modalities. 

“Orientational isomerism” is a concept necessary for deeper understanding of the selective reactivities in a host-guest system. This concept has been rarely explored in the context of supramolecular host guest chemistry. We designed a model system including four cyclohexene derivatives and a water-soluble host Octa Acid (OA), with hydrophobic inner cavity. The overall length of the guest molecules (~ 12 Å) was limited by manipulating the alkyl substituents at 1- and 4-positions on the cyclohexene ring. 1D 1H /2D COSY and NOESY NMR and photooxygenation reaction were used to understand the observations with this model system. Specific packaging or “orientational isomerism” of each guest molecule, induced by the host OA led to specific, in one case enhanced product selectivity. With this model system we show the important role of “orientational isomerism” in explaining enhanced product selectivity in a host-guest supramolecular system.