Miracle-dz/UniFAR-FTUs huggingface dataset:
RISE: robust wireless sensing using probabilistic and statistical assessments

Wireless sensing builds upon machine learning shows encouraging results. However, adopting wireless sensing as a large-scale solution remains challenging as experiences from deployments have shown the performance of a machine-learned model to suffer when there are changes in the environment, e.g., when furniture is moved or when other objects are added or removed from the environment. We present Rise, a novel solution for enhancing the robustness and performance of learning-based wireless sensing techniques against such changes during a deployment. Rise combines probability and statistical assessments together with anomaly detection to identify samples that are likely to be misclassified and uses feedback on these samples to update a deployed wireless sensing model. We validate Rise through extensive empirical benchmarks by considering 11 representative sensing methods covering a broad range of wireless sensing tasks. Our results show that Rise can identify 92.3% of misclassifications on average. We showcase how Rise can be combined with incremental learning to help wireless sensing models retain their performance against dynamic changes in the operating environment to reduce the maintenance cost, paving the way for learning-based wireless sensing to become capable of supporting long-term monitoring in complex everyday environments.
Shard Manager: A Generic Shard Management Framework for Geo-distributed Applications

Sharding is widely used to scale an application. Despite a decade of effort to build generic sharding frameworks that can be reused across different applications, the extent of their success remains unclear. We attempt to answer a fundamental question: what barriers prevent a sharding framework from getting adopted by the majority of sharded applications? We analyze hundreds of sharded applications at Facebook and identify two major barriers: 1) lack of support for geo-distributed applications, which account for most of Facebook's applications, and 2) inability to maintain application availability during planned events such as software upgrades, which happen ≈1000 times more frequently than unplanned failures. A sharding framework that does not help applications to address these fundamental challenges is not sufficiently attractive for most applications to adopt it. Other adoption barriers include the burden of supporting many complex applications in a one-size-fit-all sharding framework and the difficulty in supporting sophisticated shard-placement requirements. Theoretically, a constraint solver can handle complex placement requirements, but in practice it is not scalable enough to perform near-realtime shard placement at a global scale. We have overcome these adoption barriers in Facebook's sharding framework called Shard Manager. Currently, Shard Manager is used by hundreds of applications running on over one million machines, which account for about 54% of all sharded applications at Facebook.
Forerunner: Constraint-based Speculative Transaction Execution for Ethereum

Ethereum is an emerging distributed computing platform that supports a decentralized replicated virtual machine at a large scale. Transactions in Ethereum are specified in smart contracts, disseminated through broadcast, accepted into the chain of blocks, and then executed on each node. In this new Dissemination-Consensus-Execution (DiCE) paradigm, the time interval between when a transaction is known (during the dissemination phase) to when the transaction is executed (after the consensus phase) offers a window of opportunity to accelerate transaction processing through speculative execution. However, the traditional speculative execution, which hinges on the ability to predict the future accurately, is inadequate because of DiCE's many-future nature. Forerunner proposes a novel constraint-based approach for speculative execution on Ethereum. In contrast to the traditional approach of predicting a single future and demanding it to be perfectly accurate, Forerunner speculates on multiple futures and can leverage speculative results based on imperfect predictions whenever certain constraints are satisfied. Under these constraints, a transaction execution is substantially accelerated through a novel multi-trace program specialization enhanced by a new form of memoization. The fully implemented Forerunner is evaluated as a node connected to the worldwide Ethereum network. When processing 13 million transactions live in real time, Forerunner achieves an effective average speedup of 8.39x on the transactions that it hears during the dissemination phase, which accounts for 95.71% of all the transactions. The end-to-end speedup over all the transactions is 6.06x. The code and data sets are publicly available.
PACTree: A High Performance Persistent Range Index Using PAC Guidelines

Non-Volatile Memory (NVM), which provides relatively fast and byte-addressable persistence, is now commercially available. However, we cannot equate a real NVM with a slow DRAM, as it is much more complicated than we expect. In this work, we revisit and analyze both NVM and NVM-specific persistent memory indexes. We find that there is still a lot of room for improvement if we consider NVM hardware, its software stack, persistent index design, and concurrency control. Based on our analysis, we propose Packed Asynchronous Concurrency (PAC) guidelines for designing high-performance persistent index structures. The key idea behind the guidelines is to 1) access NVM hardware in a packed manner to minimize its bandwidth utilization and 2) exploit asynchronous concurrency control to decouple the long NVM latency from the critical path of the index. We develop PACTree, a high-performance persistent range index following the PAC guidelines. PACTree is a hybrid index that employs a trie index for its internal nodes and B+-tree-like leaf nodes. The trie index structure packs partial keys in internal nodes. Moreover, we decouple the trie index and B+-tree-like leaf nodes. The decoupling allows us to prevent blocking concurrent accesses by updating internal nodes asynchronously. Our evaluation shows that PACTree outperforms state-of-the-art persistent range indexes by 7x in performance and 20x in 99.99 percentile tail latency.
An aerodynamic, computer vision, and network simulator for networked drone applications

We develop, implement, and demonstrate an open-source simulator, called AirSimN, for evaluating drone-based wireless networks in this extended abstract. AirSimN is different from all prior attempts in the literature because it concurrently supports aerodynamic, computer vision, and network simulations. We carefully design it to minimize the effort of realizing virtually arbitrary drone applications, thanks to the active and popular AirSim and NS-3 projects. Many mobile computing and wireless networking projects on, e.g., drone feedback controllers, drone vision algorithms, and 5G/6G cellular network planning, can leverage AirSimN for large-scale evaluations.
Experience: developing a usable battery drain testing and diagnostic tool for the mobile industry

In this paper, we report on our 6-year experience developing Eagle Tester (eTester for short) - a mobile battery drain testing and diagnostic tool. We show how eTester evolved from an "academic" prototype to a fully automated tool usable by the mobile industry. We first present the design of our initial research prototype and discuss 8 key requirements for a usable battery drain testing and diagnostic tool gathered from some of the most popular software vendors in the Android ecosystem. These requirements posed interesting scientific and engineering challenges such as how to accurately estimate battery drain without requiring a priori power modeling, work on unmodified devices, and automatically monitor code evolution to generate high-fidelity battery spike alerts with actionable insights. These requirements motivated a complete overhaul of the eTester design and led to the creation of a novel battery drain testing methodology. We show how the redesigned eTester was used to effortlessly find battery bugs in some of the most popular Android apps with hundreds of millions of users, such as Netflix and CNN. We are open-sourcing eTester to encourage further research in battery diagnosis and to empower developers to write battery-efficient mobile software.
Understanding and Detecting Software Upgrade Failures in Distributed Systems

Upgrade is one of the most disruptive yet unavoidable maintenance tasks that undermine the availability of distributed systems. Any failure during an upgrade is catastrophic, as it further extends the service disruption caused by the upgrade. The increasing adoption of continuous deployment further increases the frequency and burden of the upgrade task. In practice, upgrade failures have caused many of today's high-profile cloud outages. Unfortunately, there has been little understanding of their characteristics. This paper presents an in-depth study of 123 real-world upgrade failures that were previously reported by users in 8 widely used distributed systems, shedding lights on the severity, root causes, exposing conditions, and fix strategies of upgrade failures. Guided by our study, we have designed a testing framework DUPTester that revealed 20 previously unknown upgrade failures in 4 distributed systems, and applied a series of static checkers DUPChecker that discovered over 800 cross-version data-format incompatibilities that can lead to upgrade failures. DUPChecker has been requested by HBase developers to be integrated into their toolchain.
MagX: wearable, untethered hands tracking with passive magnets

Accurate tracking of the hands and fingers allows users to employ natural gestures in various interactive applications. Hand tracking also supports health applications, such as monitoring face-touching, a common vector for infectious disease. However, for both types of applications, the utility of hand tracking is often limited by the impracticality of bulky tethered systems (e.g., instrumented gloves) or inherent limitations (e.g., Line of Sight or privacy concerns with vision-based systems). These limitations have severely restricted the adoption of hand tracking in real-world applications. We present MagX, a fully untethered on-body hand tracking system utilizing passive magnets and a novel magnetic sensing platform. Since passive magnets require no maintenance, they can be worn on the hands indefinitely, and only the sensor board needs recharging, akin to a smartwatch. We used MagX to conduct a series of experiments, finding a wearable sensing array can achieve millimeter-accurate 5 DoF tracking of two magnets independently. For example, at 11 cm distance, a 6cm × 6cm sensing array can achieve positional and orientational errors of 0.76 cm and 0.11 rad. At 21 cm distance, the tracking errors are 2.65 cm and 0.41 rad. MagX can leverage larger sensor arrays for improved long-distance tracking, e.g., a 9.8cm × 9.8cm array can achieve 2.62 cm and 0.55 rad tracking performance on two magnets at 27 cm distance. The robust performance can facilitate ubiquitous adoption of magnetic tracking in various applications. Finally, MagX can perform all compute locally and only requires 0.38W total (220mW on the sensor platform plus 159mW on the computing unit) to perform real-time tracking, offering "all day" fully untethered operation on a typical smartwatch-sized battery.
Kauri: Scalable BFT Consensus with Pipelined Tree-Based Dissemination and Aggregation

With the growing commercial interest in blockchains, permissioned implementations have received increasing attention. Unfortunately, the BFT consensus algorithms that are the backbone of most of these blockchains scale poorly and offer limited throughput. Many state-of-the-art algorithms require a single leader process to receive and validate votes from a quorum of processes and then broadcast the result, which is inherently non-scalable. Recent approaches avoid this bottleneck by using dissemination/aggregation trees to propagate values and collect and validate votes. However, the use of trees increases the round latency, which ultimately limits the throughput for deeper trees. In this paper we propose Kauri, a BFT communication abstraction that can sustain high throughput as the system size grows, leveraging a novel pipelining technique to perform scalable dissemination and aggregation on trees. Our evaluation shows that Kauri outperforms the throughput of state-of-the-art permissioned blockchain protocols, such as HotStuff, by up to 28x. Interestingly, in many scenarios, the parallelization provided by Kauri can also decrease the latency.
SiWa: see into walls via deep UWB radar

Being able to see into walls is crucial for diagnostics of building health; it enables inspections of wall structure without undermining the structural integrity. However, existing sensing devices do not seem to offer a full capability in mapping the in-wall structure while identifying their status (e.g., seepage and corrosion). In this paper, we design and implement SiWa as a low-cost and portable system for wall inspections. Built upon a customized IR-UWB radar, SiWa scans a wall as a user swipes its probe along the wall surface; it then analyzes the reflected signals to synthesize an image and also to identify the material status. Although conventional schemes exist to handle these problems individually, they require troublesome calibrations that largely prevent them from practical adoptions. To this end, we equip SiWa with a deep learning pipeline to parse the rich sensory data. With innovative construction and training, the deep learning modules perform structural imaging and the subsequent analysis on material status, without the need for repetitive parameter tuning and calibrations. We build SiWa as a prototype and evaluate its performance via extensive experiments and field studies; results evidently confirm that SiWa accurately maps in-wall structures, identifies their materials, and detects possible defects, suggesting a promising solution for diagnosing building health with minimal effort and cost.
Crisp-BP: continuous wrist PPG-based blood pressure measurement

Arterial blood pressure (ABP) monitoring using wearables has emerged as a promising approach to empower users with self-monitoring for effective diagnosis and control of hypertension. However, existing schemes mainly monitor ABP at discrete time intervals, involve some form of user effort, have insufficient accuracy, and require collecting sufficient training data for model development. To tackle these problems, we propose Crisp-BP, a novel ABP monitoring system leveraging the PPG sensor available in commercial wrist-worn devices (e.g., smartwatches or fitness trackers). It enables continuous, accurate, user-independent ABP monitoring and requires no behavior changes during collecting PPG data. The basic idea is to illuminate a skin/tissue, measure the light absorption, and characterize ABP-related blood volume change in the artery. To obtain accurate measurements and relieve the pain of training data collection, we use an arterial pulse extraction method that removes interference caused by capillary pulses. Moreover, we design a contact pressure estimation method to combat the deficiency of PPG waveform being sensitive to the contact pressure between the sensor and the skin. In addition, we leverage the great power of Bidirectional Long Short Term Memory and design a hybrid neural network model to enable user-independent ABP monitoring, so that users do not have to provide training data for model development. Furthermore, we propose a transfer learning method that first extracts general knowledge from online PPG data, then use it to improve the learning of a new model on our target problem. Extensive experiments with 35 participants demonstrate that Crisp-BP obtains the average estimation error of 0.86 mmHg and 1.67 mmHg and the standard deviation error of 6.55 mmHg and 7.31 mmHg for diastolic pressure and systolic pressure, respectively. These errors are within the acceptable range regulated by the FDA's AAMI protocol, which allows average errors of up to 5 mmHg and a standard deviation of up to 8 mmHg. Our results demonstrate that Crisp-BP is promising for improving the diagnosis and control of hypertension as it provides continuousness, comfort, convenience, and accuracy.
LegoDNN: block-grained scaling of deep neural networks for mobile vision

Deep neural networks (DNNs) have become ubiquitous techniques in mobile and embedded systems for applications such as image/object recognition and classification. The trend of executing multiple DNNs simultaneously exacerbate the existing limitations of meeting stringent latency/accuracy requirements on resource constrained mobile devices. The prior art sheds light on exploring the accuracy-resource tradeoff by scaling the model sizes in accordance to resource dynamics. However, such model scaling approaches face to imminent challenges: (i) large space exploration of model sizes, and (ii) prohibitively long training time for different model combinations. In this paper, we present LegoDNN, a lightweight, block-grained scaling solution for running multi-DNN workloads in mobile vision systems. LegoDNN guarantees short model training times by only extracting and training a small number of common blocks (e.g. 5 in VGG and 8 in ResNet) in a DNN. At run-time, LegoDNN optimally combines the descendant models of these blocks to maximize accuracy under specific resources and latency constraints, while reducing switching overhead via smart block-level scaling of the DNN. We implement LegoDNN in TensorFlow Lite and extensively evaluate it against state-of-the-art techniques (FLOP scaling, knowledge distillation and model compression) using a set of 12 popular DNN models. Evaluation results show that LegoDNN provides 1,296x to 279,936x more options in model sizes without increasing training time, thus achieving as much as 31.74% improvement in inference accuracy and 71.07% reduction in scaling energy consumptions.
Caracal: Contention Management with Deterministic Concurrency Control

Deterministic databases offer several benefits: they ensure serializable execution while avoiding concurrency-control related aborts, and they scale well in distributed environments. Today, most deterministic database designs use partitioning to scale up and avoid contention. However, partitioning requires significant programmer effort, leads to poor performance under skewed workloads, and incurs unnecessary overheads in certain uncontended workloads. We present the design of Caracal, a novel shared-memory, deterministic database that performs well under both skew and contention. Our deterministic scheme batches transactions in epochs and executes the transactions in an epoch in a predetermined order. Our scheme enables reducing contention by batching concurrency control operations. It also allows analyzing the transactions in the epoch to determine contended keys accurately. Certain transactions can then be split into independent contended and uncontended pieces and run deterministically and in parallel, further reducing contention. Based on these ideas, we present two novel optimizations, batch append and split-on-demand, for managing contention. With these optimizations, Caracal scales well and outperforms existing deterministic schemes in most workloads by 1.9x to 9.7x.
Exploiting Nil-Externality for Fast Replicated Storage

Do some storage interfaces enable higher performance than others? Can one identify and exploit such interfaces to realize high performance in storage systems? This paper answers these questions in the affirmative by identifying nil-externality, a property of storage interfaces. A nil-externalizing (nilext) interface may modify state within a storage system but does not externalize its effects or system state immediately to the outside world. As a result, a storage system can apply nilext operations lazily, improving performance. In this paper, we take advantage of nilext interfaces to build high-performance replicated storage. We implement Skyros, a nilext-aware replication protocol that offers high performance by deferring ordering and executing operations until their effects are externalized. We show that exploiting nil-externality offers significant benefit: for many workloads, Skyros provides higher performance than standard consensus-based replication. For example, Skyros offers 3x lower latency while providing the same high throughput offered by throughput-optimized Paxos.
Reconfigurable nonlinear response of dielectric and semiconductor metasurfaces

Abstract Optically resonant dielectric and semiconductor metasurfaces are an emerging and promising area of nanophotonics and light–matter interaction at the nanoscale. Recently, active tuning of the linear response and nonlinear effects of these components has received an increasing amount of interest. However, so far these research directions have remained separated with only few sporadic works that study their combination beginning to appear in the literature. The evolution of nonlinear metasurfaces based on dielectric and semiconductor materials toward reconfigurable and dynamic components could potentially answer the demand of integrated on-chip components that realize essential functionalities such as frequency conversion, active switching, optical isolation, and all-optical routing. This review provides an overview of recent investigations in this field, reviews the main physical phenomena enabling the dynamic control of the nonlinear response and compares the temporal dynamics of the diverse approaches that have been explored so far. Finally, future directions of dynamic nonlinear metasurfaces are outlined.
Scandinavian Journal of Pain

Objectives: The aim of the current study was to examine the convergent validity of the Central Sensitization Inventory by quantifying the correlation with experimental measures of pain sensitivity and self-reported psycho-social questionnaires, in a low back pain population. Methods: All participants were recruited from an outpatient hospital spine care clinic (Spine Centre of Southern Denmark). Participants underwent a standardized experimental pain test protocol and completed the Central Sensitization Inventory (CSI) along with additional self-reported questionnaires to assess psycho-social constructs across different domains. The association between the CSI, experimental pain measures and other self-reported psycho-social questionnaires were analyzed using correlation and contingency tests. ROC-curve analysis was used to determine sensitivity and specificity for CSI. Results: One hundred sixty-eight (168) participants were included. The CSI was weakly correlated with nine out of 20 variables in the experimental pain test protocol (rho range −0.37 to 0.22). The CSI was more closely correlated with psycho-social factors such as work ability, disability, and symptoms of exhaustion disorder. ROC-analysis identified an optimal cut-point of 44 on CSI (Sn=39.1% Sp=87.4%). The CSI had an area under the ROC curve of 0.656. Fisher’s exact test demonstrated a statistically significant association between participants scoring ≥40 on CSI and participants categorized as sensitized by experimental pain tests (p-value=0.03). Conclusions: Our findings are consistent with previous studies, indicating that the CSI is related to psycho-social constructs. However, the convergent validity with experimental pain measures is small and probably not clinically meaningful.