Objective: Although the neurological and olfactory symptoms of coronavirus disease 2019 have been identified, the neurotropic properties of the causative virus, severe acute respiratory syndrome-associated coronavirus 2 (SARS-CoV-2), remain unknown. We sought to identify the susceptible cell types and potential routes of SARS-CoV-2 entry into the central nervous system, olfactory system, and respiratory system. Methods: We collected single-cell RNA data from normal brain and nasal epithelium specimens, along with bronchial, tracheal, and lung specimens in public datasets. The susceptible cell types that express SARS-CoV-2 entry genes were identified using single-cell RNA sequencing and the expression of the key genes at protein levels was verified by immunohistochemistry. We compared the coexpression patterns of the entry receptor angiotensin-converting enzyme 2 (ACE2) and the spike protein priming enzyme transmembrane serine protease (TMPRSS)/cathepsin L among the specimens. Results: The SARS-CoV-2 entry receptor ACE2 and the spike protein priming enzyme TMPRSS/cathepsin L were coexpressed by pericytes in brain tissue; this coexpression was confirmed by immunohistochemistry. In the nasal epithelium, ciliated cells and sustentacular cells exhibited strong coexpression of ACE2 and TMPRSS. Neurons and glia in the brain and nasal epithelium did not exhibit coexpression of ACE2 and TMPRSS. However, coexpression was present in ciliated cells, vascular smooth muscle cells, and fibroblasts in tracheal tissue; ciliated cells and goblet cells in bronchial tissue; and alveolar epithelium type 1 cells, AT2 cells, and ciliated cells in lung tissue. Conclusion: Neurological symptoms in patients with coronavirus disease 2019 could be associated with SARS-CoV-2 invasion across the blood-brain barrier via pericytes. Additionally, SARS-CoV-2-induced olfactory disorders could be the result of localized cell damage in the nasal epithelium.

Inferring the root cause of failures among thousands of components in a data center network is challenging, especially for "gray" failures that are not reported directly by switches. Faults can be localized through end-to-end measurements, but past localization schemes are either too slow for large-scale networks or sacrifice accuracy. We describe Flock, a network fault localization algorithm and system that achieves both high accuracy and speed at datacenter scale. Flock uses a probabilistic graphical model (PGM) to achieve high accuracy, coupled with new techniques to dramatically accelerate inference in discrete-valued Bayesian PGMs. Large-scale simulations and experiments in a hardware testbed show Flock speeds up inference by >10000x compared to past PGM methods, and improves accuracy over the best previous datacenter fault localization approaches, reducing inference error by 1.19-11x on the same input telemetry, and by 1.2-55x after incorporating passive telemetry. We also prove Flock's inference is optimal in restricted settings

Heterogeneous applications and restricted bandwidth on the Internet have motivated recent works on scavenger congestion control, which yields bandwidth to competing primary traffic for increased network-wide utility. Although potential use cases are quite common, deployments are as yet limited, in part due to protocol design immaturity, lack of open source code, and limited experimental evaluation. In this work, we extend recent scavenger advances by providing (1) open-source implementations of two recent scavenger proposals, PCC Proteus (QUIC-based) and LEDBAT++; (2) early benchmarks of the two in a realistic network setup; and (3) a discussion of APIs needed for applications to take advantage of scavenger congestion control. Ultimately, we hope this line of work will lead to further community discussion, open development, and deployment of scavengers yielding better quality of experience for users.

Many Internet applications need high bandwidth but are not time sensitive. This motivates a congestion control "scavenger" that voluntarily yields to higher-priority applications, thus improving overall user experience. However, the existing scavenger protocol, LEDBAT, often fails to yield, has performance shortcomings, and requires a codebase separate from other transport protocols. We present PCC Proteus, a new congestion controller that can behave as an effective scavenger or primary protocol. Proteus incorporates several novel ideas to ensure that it yields to primary flows while still obtaining high performance, including using latency deviation as a signal of competition, and techniques for noise tolerance in dynamic environments. By extending the existing PCC utility framework, Proteus also allows applications to specify a flexible utility function that, in addition to scavenger and primary modes, allows choice of hybrid modes between the two, better capturing application needs. Extensive emulation and real-world evaluation show that Proteus is capable of both being a much more effective scavenger than LEDBAT, and of acting as a high performance primary protocol. Application-level experiments show Proteus significantly improves page load time and DASH video delivery, and its hybrid mode significantly reduces rebuffering in a bandwidth-constrained environment.

Auctions are believed to be effective methods to solve the problem of wireless spectrum allocation. Existing spectrum auction mechanisms are all centralized and suffer from several critical drawbacks of the centralized systems, which motivates the design of distributed spectrum auction mechanisms. However, extending a centralized spectrum auction to a distributed one broadens the strategy space of agents from one dimension (bid) to three dimensions (bid, communication, and computation), and thus cannot be solved by traditional approaches from mechanism design. In this paper, we propose two distributed spectrum auction mechanisms, namely distributed VCG and FAITH. Distributed VCG implements the celebrated Vickrey-Clarke-Groves mechanism in a distributed fashion to achieve optimal social welfare, at the cost of exponential communication overhead. In contrast, FAITH achieves sub-optimal social welfare with tractable computation and communication overhead. We prove that both of the two proposed mechanisms achieve faithfulness, i.e., the agents' individual utilities are maximized, if they follow the intended strategies. Besides, we extend FAITH to adapt to dynamic scenarios where agents can arrive or depart at any time, without violating the property of faithfulness. We implement distributed VCG and FAITH, and evaluate their performance in various setups. Evaluation results show that distributed VCG results in optimal allocation, while FAITH is more efficient in computation and communication.

This paper presents Rate Adaptive Broadcast (RAB), a novel wireless design that enables the rate adaptive broadcast in Internet of things (IoT). Broadcast is common in IoT due to the ubiquitous tree topologies. Channel resource is usually underused in broadcast because there is no rate adaptation in conventional broadcast and the data rate is always set as the lowest one by default. Existing rate adaptation methods work only for unicast or multicast, relying on information interaction between senders and receivers. These methods cannot directly apply in broadcast, which is a one-way transmission without acknowledgement (ACK). It is also impractical to transplant conventional ACK into broadcast, otherwise, massive ACKs will lead to a heavy overhead. To tackle this dilemma, we propose RAB, which allows the sender to broadcast data ceaselessly while adjusting the data rate according to real-time channel states. The core contribution is the subtly designed feedbacks that can be concurrently delivered and do not affect any reception. We implement RAB on USRPs and establish a 20-node IoT testbed. Experiment results demonstrate that the throughput is largely improved. The throughput of RAB is 2.8x of the standard WiFi and 1.3x of MuDRA, the state-of-the-art multicast rate adaptation method.

TCP’s congestion control architecture suffers from notoriously bad performance. Consequently, recent years have witnessed a surge of interest in both academia and industry in novel approaches to congestion control. We show, however, that past approaches fall short of attaining ideal performance. We leverage ideas from the rich literature on online (convex) optimization in machine learning to design Vivace, a novel rate-control protocol, designed within the recently proposed PCC framework. Our theoretical and experimental analyses establish that Vivace significantly outperforms traditional TCP variants, the previous realization of the PCC framework, and BBR in terms of performance (throughput, latency, loss), convergence speed, alleviating bufferbloat, reactivity to changing network conditions, and friendliness towards legacy TCP in a range of scenarios. Vivace requires only sender-side changes and is thus readily deployable.

The introduction of a high performance packet scheduler to the Linux kernel and modular congestion control system from BBR makes it possible to draw research congestion control algorithms into the Linux kernel. In this paper, we discuss the introduction of the PCC family of congestion control algorithms into the Linux kernel. We implement both lossand latencybased congestion control using the rate-based PCC architecture and discuss possible interfaces for choosing congestion

Enterprise networks today have highly diverse correctness requirements and relatively common performance objectives. As a result, preferred abstractions for enterprise networks are those which allow matching correctness specification, while transparently managing performance. Existing SDN network management architectures, however, bundle correctness and performance as a single abstraction. We argue that this creates an SDN ecosystem that is unnecessarily hard to build, maintain and evolve. We advocate a separation of the diverse correctness abstractions from generic performance optimization, to enable easier evolution of SDN controllers and platforms. We propose Oreo, a first step towards a common and relatively transparent performance optimization layer for SDN. Oreo performs the optimization by first building a model that describes every flow in the network, and then performing network-wide, multi-objective optimization based on this model without disrupting higher level correctness.

In mobile wireless networks, the emerging proximity-based applications have led to the need for highly effective and energy-efficient neighbor discovery protocols. However, existing works cannot realize the optimal worst-case latency in the symmetric case, and their performances with asymmetric duty cycles can still be improved. In this paper, we investigate asynchronous neighbor discovery through a code-based approach, including the symmetric and asymmetric cases. We derive the tight worst-case latency bound in the case of symmetric duty cycle. We design a novel class of symmetric patterns called Diff-Codes, which is optimal when the Diff-Code can be extended from a perfect difference set. We further consider the asymmetric case and design ADiff-Codes. To evaluate (A)Diff-Codes, we conduct both simulations and testbed experiments. Both simulation and experiment results show that (A)Diff-Codes significantly outperform existing neighbor discovery protocols in both the median case and worst case. Specifically, in the symmetric case, the maximum worst-case improvement is up to 50%; in both symmetric and asymmetric cases, the median case gain is as high as 30%.

In this paper, we argue that by carefully considering spatial reusability of the wireless communication media, we can tremendously improve the end-to-end throughput in multi-hop wireless networks. To support our argument, we propose spatial reusability-aware single-path routing (SASR) and anypath routing (SAAR) protocols, and compare them with existing single-path routing and anypath routing protocols, respectively. Our evaluation results show that our protocols significantly improve the end-to-end throughput compared with existing protocols. Specifically, for single-path routing, the throughput gain is up to 2.9× for anypath routing, the throughput gain is up to 62.7%.

In mobile wireless networks, the emerging proximity-based applications have led to needs for highly effective and energy-efficient neighbor discovery protocols. However, existing works cannot realize the optimal worst-case latency in symmetric case, and their performances with asymmetric duty cycles can still be improved. In this work, we investigate asynchronous neighbor discovery through a code-based approach, including the symmetric and asymmetric cases. We derive the tight worst-case latency bound in the case of symmetric duty cycle. We design a novel class of symmetric patterns called Diff-Codes, which is optimal when the Diff-Code can be extended from a perfect difference set. We further consider the asymmetric case, and design ADiff-Codes. To evaluate (A)Diff-Codes, we conduct both simulations and testbed experiments. Both simulation and experiment results show that (A)Diff-Codes significantly outperform existing neighbor discovery protocols in both the median case and worst-case. Specifically, in symmetric case, the maximum worst-case improvement is up to 50%; in both symmetric and asymmetric cases, the median case gain is as high as 30%.

Routing metrics are essential to achieve a good end-to-end throughput on a min-cost path in multi-hop wireless networks. Originally, most routing algorithms were based on min-hop count metric. Then link-quality aware metrics, ETX and ETT, are proposed. After that, the emergence of opportunistic and any-path routing, leads to metrics such as EOTX and EAX. But almost all the previous routing metrics take the sum of all the link weights when choosing a min-cost path. They ignore that a pair of links on a path might be able to transmit at the same time without interference and hence it is inappropriate to include both of their weights in the whole path. In this work, we suggest exploiting such spatial reusability when designing routing metric to improve end-to-end throughput. We propose Spatial Reusability Aware Routing Metric (SAR) with the corresponding Spatial Reusability Aware Routing Algorithm. Our simulation results have shown good improvement in throughput.