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Networking research has witnessed a renaissance from exploring the seemingly unlimited predictive power of machine learning (ML) models. One such promising direction is throughput prediction – accurately predicting the network bandwidth or achievable throughput of a client in real time using ML models can enable a wide variety of network applications to proactively adapt their behavior to the changing network dynamics to potentially achieve significantly improved QoE. Motivated by the key role of newer generations of cellular networks in supporting the new generation of latency-critical applications such as AR/MR, in this work, we focus on accurate throughput prediction in cellular networks at fine time-scales, e.g., in the order of 100 ms. Through a 4-day, 1000+ km driving trip, we collect a dataset of fine-grained throughput measurements under driving across all three major US operators. Using the collected dataset, we conduct the first feasibility study of predicting fine-grained application throughput in real-world cellular networks with mixed LTE/5G technologies. Our analysis shows that popular ML models previously claimed to predict well for various wireless networks scenarios (e.g., WiFi or singletechnology network such as LTE only) do not predict well under app-centric metrics such as ARE95 and PARE10. Further, we uncover the root cause for the poor prediction accuracy of ML models as the inherent conflicting sample sequences in the fine-grained cellular network throughput data. 

With faster wireless networks and server GPUs, offloading high-accuracy but compute-intensive AR tasks implemented in Deep Neural Networks (DNNs) to edge servers offers a promising way to support high-QoE Augmented/Mixed Reality (AR/MR) applications. A cost-effective way for AR app vendors to deploy such edge-assisted AR apps to support a large user base is to use commercial Machine-Learning-as-a-Service (MLaaS) deployed at the edge cloud. To maximize cost-effectiveness, such an MLaaS provider faces a key design challenge, \ie how to maximize the number of clients concurrently served by each GPU server in its cluster while meeting per-client AR task accuracy SLAs. The above AR offloading inference serving problem differs from generic inference serving or video analytics serving in one fundamental way: due to the use of local tracking which reuses the last server-returned inference result to derive results for the current frame, the offloading frequency and end-to-end latency of each AR client directly affect its AR task accuracy (for all the frames). In this paper, we present ARISE, a framework that optimizes the edge server capacity in serving edge-assisted AR clients. Our design exploits the intricate interplay between per-client offloading schedule and batched inference on the server via proactively coordinating offloading request streams from different AR clients. Our evaluation using a large set of emulated AR clients and a 10-phone testbed shows that \name supports 1.7x--6.9x more clients compared to various baselines while keeping the per-client accuracy within the client-specified accuracy SLAs. 

Immersive applications such as Augmented Reality (AR) and Mixed Reality (MR) often need to perform multiple latency-critical tasks on every frame captured by the camera, which all require results to be available within the current frame interval. While such tasks are increasingly supported by Deep Neural Networks (DNNs) offloaded to edge servers due to their high accuracy but heavy computation, prior work has largely focused on offloading one task at a time. Compared to offloading a single task, where more frequent offloading directly translates into higher task accuracy, offloading of multiple tasks competes for shared edge server resources, and hence faces the additional challenge of balancing the offloading frequencies of different tasks to maximize the overall accuracy and hence app QoE. In this paper, we formulate this accuracy-centric multitask offloading problem, and present a framework that dynamically schedules the offloading of multiple DNN tasks from a mobile device to an edge server while optimizing the overall accuracy across tasks. Our design employs two novel ideas: (1) task-specific lightweight models that predict offloading accuracy drop as a function of offloading frequency and frame content, and (2) a general two-level control feedback loop that concurrently balances offloading among tasks and adapts between offloading and using local algorithms for each task. Evaluation results show that our framework improves the overall accuracy significantly in jointly offloading two core tasks in AR — depth estimation and odometry — by on average 7.6%–14.3% over the best baselines under different accuracy weight ratios. 

An increasing number of location-based service providers are taking the advantage of cloud computing by outsourcing their Point of Interest (POI) datasets and query services to third-party cloud service providers (CSPs), which answer various location-based queries from users on their behalf. A critical security challenge is to ensure the integrity and completeness of any query result returned by CSPs. As an important type of queries, a location-based skyline query (LBSQ) asks for the POIs not dominated by any other POI with respect to a given query position, i.e., no POI is both closer to the query position and more preferable with respect to a given numeric attribute. While there have been several recent attempts on authenticating outsourced LBSQ, none of them support the shortest path distance that is preferable to the Euclidian distance in metropolitan areas. In this paper, we tackle this open challenge by introducing AuthSkySP, a novel scheme for authenticating outsourced LBSQ under the shortest path distance, which allows the user to verify the integrity and completeness of any LBSQ result returned by an untrusted CSP. We confirm the effectiveness and efficiency of our proposed solution via detailed experimental studies using both real and synthetic datasets. 

This letter presents a 27.5–46.2-GHz broadband low-noise amplifier (LNA) featuring IP3 enhancement. The LNA bandwidth (BW) is extended by implementing dual-resonant input matching and a broadband output network. The LNA IP3 is enhanced by incorporating parallel PMOS and NMOS paths in the second stage, with their output currents combined through a three-winding transformer. Implemented using the GlobalFoundries 45-nm CMOS silicon-on insulator (SOI) process, the LNA demonstrates 27.5–46.2 GHz effective BW, 2.1 dB minimum noise figure (NF), and 19.8 dB peak gain. The measured IIP3 is − 3.6 dBm at 34 GHz under 25.5 mW DC power consumption. Compared to recently reported broadband LNAs with a similar frequency range, this design achieves the state-of-the-art NF, IIP3, and figure-of-merit (FoM). 

The interstellar medium in the Milky Way’s Central Molecular Zone (CMZ) is known to be strongly magnetised, but its large-scale morphology and impact on the gas dynamics are not well understood. We explore the impact and properties of magnetic fields in the CMZ using three-dimensional non-self gravitating magnetohydrodynamical simulations of gas flow in an external Milky Way barred potential. We find that: (1) The magnetic field is conveniently decomposed into a regular time-averaged component and an irregular turbulent component. The regular component aligns well with the velocity vectors of the gas everywhere, including within the bar lanes. (2) The field geometry transitions from parallel to the Galactic plane near ɀ = 0 to poloidal away from the plane. (3) The magneto-rotational instability (MRI) causes an in-plane inflow of matter from the CMZ gas ring towards the central few parsecs of 0.01−0.1 M_⊙yr⁻¹that is absent in the unmagnetised simulations. However, the magnetic fields have no significant effect on the larger-scale bar-driven inflow that brings the gas from the Galactic disc into the CMZ. (4) A combination of bar inflow and MRI-driven turbulence can sustain a turbulent vertical velocity dispersion ofσ_ɀ= 5 km s⁻¹on scales of 20 pc in the CMZ ring. The MRI alone sustains a velocity dispersion ofσ_ɀ≃ 3 km s⁻¹. Both these numbers are lower than the observed velocity dispersion of gas in the CMZ, suggesting that other processes such as stellar feedback are necessary to explain the observations. (5) Dynamo action driven by differential rotation and the MRI amplifies the magnetic fields in the CMZ ring until they saturate at a value that scales with the average local density asB≃ 102 (n/10³cm⁻³)^0.33µG. Finally, we discuss the implications of our results within the observational context in the CMZ. 

The Milky Way’s Central Molecular Zone (CMZ) differs dramatically from our local solar neighbourhood, both in the extreme interstellar medium conditions it exhibits (e.g. high gas, stellar, and feedback density) and in the strong dynamics at play (e.g. due to shear and gas influx along the bar). Consequently, it is likely that there are large-scale physical structures within the CMZ that cannot form elsewhere in the Milky Way. In this paper, we present new results from the Atacama Large Millimeter/submillimeter Array (ALMA) large programme ACES (ALMA CMZ Exploration Survey) and conduct a multi-wavelength and kinematic analysis to determine the origin of the M0.8–0.2 ring, a molecular cloud with a distinct ring-like morphology. We estimate the projected inner and outer radii of the M0.8–0.2 ring to be 79″ and 154″, respectively (3.1 pc and 6.1 pc at an assumed Galactic Centre distance of 8.2 kpc) and calculate a mean gas density >10⁴cm⁻³, a mass of ~10⁶M_⊙, and an expansion speed of ~20 km s⁻¹, resulting in a high estimated kinetic energy (>10⁵¹erg) and momentum (>10⁷M_⊙km s⁻¹). We discuss several possible causes for the existence and expansion of the structure, including stellar feedback and large-scale dynamics. We propose that the most likely cause of the M0.8–0.2 ring is a single high-energy hypernova explosion. To viably explain the observed morphology and kinematics, such an explosion would need to have taken place inside a dense, very massive molecular cloud, the remnants of which we now see as the M0.8–0.2 ring. In this case, the structure provides an extreme example of how supernovae can affect molecular clouds.