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Single-photon cameras present a promising avenue for high-resolution 3D imaging. They have ultra-high sensitivity -- down to individual photons -- and can record photon arrival times with extremely high (sub-nanosecond) resolution. Single-photon 3D cameras estimate the round-trip time of a laser pulse by forming equi-width (EW) histograms of detected photon timestamps. Acquiring and transferring such EW histograms requires high bandwidth and in-pixel memory, making SPCs less attractive in resource-constrained settings such as mobile devices and AR/VR headsets. In this work we propose a 3D sensing technique based on equi-depth (ED) histograms. ED histograms compress timestamp data more efficiently than EW histograms, reducing the bandwidth requirement. Moreover, to reduce the in-pixel memory requirement, we propose a lightweight algorithm to estimate ED histograms in an online fashion without explicitly storing the photon timestamps. This algorithm is amenable to future in-pixel implementations. We propose algorithms that process ED histograms to perform 3D computer-vision tasks of estimating scene distance maps and performing visual odometry under challenging conditions such as high ambient light. Our work paves the way towards lower bandwidth and reduced in-pixel memory requirements for SPCs, making them attractive for resource-constrained 3D vision applications. Project page: $\href{https://www.computational.camera/pedh}{https://www.computational.camera/pedh}$

Reliable quantum communication over hundreds of kilometers is a daunting yet necessary requirement for a quantum internet. To overcome photon loss, the deployment of quantum repeater stations between distant network nodes is necessary. A plethora of different quantum hardware is being developed for this purpose, each platform with its own opportunities and challenges. Here, we propose to combine two promising hardware platforms in a hybrid quantum repeater architecture to lower the cost and boost the performance of long-distance quantum communication. We outline how ensemble-based quantum memories combined with single-spin photon transducers, which can transfer quantum information between a photon and a single spin, can facilitate massive multiplexing, efficient photon generation, and quantum logic for amplifying communication rates. As a specific example, we describe how a single Rubidium (Rb) atom coupled to nanophotonic resonators can function as a high-rate, telecom-visible entangled photon source with the visible photon being compatible with storage in a Thulium-doped crystal memory (Tm-memory) and the telecom photon being compatible with low loss fiber propagation. We experimentally verify that Tm and Rb transitions are in resonance with each other. Our analysis shows that by employing up to 9 repeater stations, each equipped with two Tm-memories capable of holding up to 625 storage modes, along with four single Rb atoms, one can reach a quantum communication rate of about 10 secret bits per second across distances of up to 1000 km., Comment: 16 pages, 7 figures in total, with 5 appendix sections

Single-photon cameras (SPCs) have emerged as a promising technology for high-resolution 3D imaging. A single-photon 3D camera determines the round-trip time of a laser pulse by capturing the arrival of individual photons at each camera pixel. Constructing photon-timestamp histograms is a fundamental operation for a single-photon 3D camera. However, in-pixel histogram processing is computationally expensive and requires large amount of memory per pixel. Digitizing and transferring photon timestamps to an off-sensor histogramming module is bandwidth and power hungry. Here we present an online approach for distance estimation without explicitly storing photon counts. The two key ingredients of our approach are (a) processing photon streams using race logic, which maintains photon data in the time-delay domain, and (b) constructing count-free equi-depth histograms. Equi-depth histograms are a succinct representation for ``peaky'' distributions, such as those obtained by an SPC pixel from a laser pulse reflected by a surface. Our approach uses a binner element that converges on the median (or, more generally, to another quantile) of a distribution. We cascade multiple binners to form an equi-depth histogrammer that produces multi-bin histograms. Our evaluation shows that this method can provide an order of magnitude reduction in bandwidth and power consumption while maintaining similar distance reconstruction accuracy as conventional processing methods., Comment: Accepted for presentation at the 2023 International Conference on Computational Photography

We numerically study the distribution of entanglement between the Dutch cities of Delft and Eindhoven realized with a processing-node quantum repeater and determine minimal hardware requirements for verifiable blind quantum computation using color centers and trapped ions. Our results are obtained considering restrictions imposed by a real-world fiber grid and using detailed hardware-specific models. By comparing our results to those we would obtain in idealized settings we show that simplifications lead to a distorted picture of hardware demands, particularly on memory coherence and photon collection. We develop general machinery suitable for studying arbitrary processing-node repeater chains using NetSquid, a discrete-event simulator for quantum networks. This enables us to include time-dependent noise models and simulate repeater protocols with cut-offs, including the required classical control communication. We find minimal hardware requirements by solving an optimization problem using genetic algorithms on a high-performance-computing cluster. Our work provides guidance for further experimental progress, and showcases limitations of studying quantum-repeater requirements in idealized situations., Comment: Published version. 13 pages, 5 figures, 1 table + 52 pages, 15 figures, 6 tables appendix

Long-distance quantum communication via entanglement distribution is of great importance for the quantum internet. However, scaling up to such long distances has proved challenging due to the loss of photons, which grows exponentially with the distance covered. Quantum repeaters could in theory be used to extend the distances over which entanglement can be distributed, but in practice hardware quality is still lacking. Furthermore, it is generally not clear how an improvement in a certain repeater parameter, such as memory quality or attempt rate, impacts the overall network performance, rendering the path towards scalable quantum repeaters unclear. In this work we propose a methodology based on genetic algorithms and simulations of quantum repeater chains for optimization of entanglement generation and distribution. By applying it to simulations of several different repeater chains, including real-world fiber topology, we demonstrate that it can be used to answer questions such as what are the minimum viable quantum repeaters satisfying given network performance benchmarks. This methodology constitutes an invaluable tool for the development of a blueprint for a pan-European quantum internet. We have made our code, in the form of NetSquid simulations and the smart-stopos optimization tool, freely available for use either locally or on high-performance computing centers., Comment: Fixed typos

In order to bring quantum networks into the real world, we would like to determine the requirements of quantum network protocols including the underlying quantum hardware. Because detailed architecture proposals are generally too complex for mathematical analysis, it is natural to employ numerical simulation. Here we introduce NetSquid, the NETwork Simulator for QUantum Information using Discrete events, a discrete-event based platform for simulating all aspects of quantum networks and modular quantum computing systems, ranging from the physical layer and its control plane up to the application level. We study several use cases to showcase NetSquid's power, including detailed physical layer simulations of repeater chains based on nitrogen vacancy centres in diamond as well as atomic ensembles. We also study the control plane of a quantum switch beyond its analytically known regime, and showcase NetSquid's ability to investigate large networks by simulating entanglement distribution over a chain of up to one thousand nodes., Comment: NetSquid is freely available at https://netsquid.org; refined main text sections

Streaming applications from algorithmic trading to traffic management deploy Kleene patterns to detect and aggregate arbitrarily-long event sequences, called event trends. State-of-the-art systems process such queries in two steps. Namely, they first construct all trends and then aggregate them. Due to the exponential costs of trend construction, this two-step approach suffers from both a long delays and high memory costs. To overcome these limitations, we propose the Graph-based Real-time Event Trend Aggregation (Greta) approach that dynamically computes event trend aggregation without first constructing these trends. We define the Greta graph to compactly encode all trends. Our Greta runtime incrementally maintains the graph, while dynamically propagating aggregates along its edges. Based on the graph, the final aggregate is incrementally updated and instantaneously returned at the end of each query window. Our Greta runtime represents a win-win solution, reducing both the time complexity from exponential to quadratic and the space complexity from exponential to linear in the number of events. Our experiments demonstrate that Greta achieves up to four orders of magnitude speed-up and up to 50--fold memory reduction compared to the state-of-the-art two-step approaches., Comment: Technical report for the paper in VLDB 2017

Streaming systems evaluate massive workloads of event sequence aggregation queries. State-of-the-art approaches suffer from long delays caused by not sharing intermediate results of similar queries and by constructing event sequences prior to their aggregation. To overcome these limitations, our Shared Online Event Sequence Aggregation (Sharon) approach shares intermediate aggregates among multiple queries while avoiding the expensive construction of event sequences. Our Sharon optimizer faces two challenges. One, a sharing decision is not always beneficial. Two, a sharing decision may exclude other sharing opportunities. To guide our Sharon optimizer, we compactly encode sharing candidates, their benefits, and conflicts among candidates into the Sharon graph. Based on the graph, we map our problem of finding an optimal sharing plan to the Maximum Weight Independent Set (MWIS) problem. We then use the guaranteed weight of a greedy algorithm for the MWIS problem to prune the search of our sharing plan finder without sacrificing its optimality. The Sharon optimizer is shown to produce sharing plans that achieve up to an 18-fold speed-up compared to state-of-the-art approaches., Comment: Technical report for the paper in ICDE 2018

Streaming applications from health care analytics to algorithmic trading deploy Kleene queries to detect and aggregate event trends. Rich event matching semantics determine how to compose events into trends. The expressive power of state-of-the-art systems remains limited in that they do not support the rich variety of these semantics. Worse yet, they suffer from long delays and high memory costs because they opt to maintain aggregates at a fine granularity. To overcome these limitations, our Coarse-Grained Event Trend Aggregation (Cogra) approach supports this rich diversity of event matching semantics within one system. Better yet, Cogra incrementally maintains aggregates at the coarsest granularity possible for each of these semantics. In this way, Cogra minimizes the number of aggregates -- reducing both time and space complexity. Our experiments demonstrate that Cogra achieves up to four orders of magnitude speed-up and up to eight orders of magnitude memory reduction compared to state-of-the-art approaches., Comment: Technical report for the paper in SIGMOD 2019

Users often have to integrate information about entities from multiple data sources. This task is challenging as each data source may represent information about the same entity in a distinct form, e.g., each data source may use a different name for the same person. Currently, data from different representations are translated into a unified one via lengthy and costly expert attention and tuning. Such methods cannot scale to the rapidly increasing number and variety of available data sources. We demonstrate ProgMap, a entity-matching framework in which data sources learn to collaborate and integrate information about entities on-demand and with minimal expert intervention. The data sources leverage user feedback to improve the accuracy of their collaboration and results. ProgMap also has techniques to reduce the amount of required user feedback to achieve effective matchings., Comment: demonstration

Intelligent Transportation Systems (ITS) use data and information technology to improve the operation of our transportation network. ITS contributes to sustainable development by using technology to make the transportation system more efficient; improving our environment by reducing emissions, reducing the need for new construction and improving our daily lives through reduced congestion. A key component of ITS is traveler information. The Oregon Department of Transportation (ODOT) recently implemented a new traveler information system on selected freeways to provide drivers with travel time estimates that allow them to make more informed decisions about routing to their destinations. The ODOT project aims to improve traffic flow and promote efficient traffic movement, which can reduce emissions rates and improve air quality. The new ODOT system is based on travel data collected from a recently-increased set of sensors installed on its freeways. Our current project investigates novel data cleaning methodologies and the integration of those methodologies into the prediction of travel times. We use machine learning techniques on our archive to identify suspect data, and calculate revised travel times excluding this suspect data. We compare the resulting travel time predictions to ground-truth data, and to predictions based on simple, rule-based data cleaning. We report on the results of our study using qualitative and quantitative methods.

Stream processing addresses the needs of real-time applications. Transaction processing addresses the coordination and safety of short atomic computations. Heretofore, these two modes of operation existed in separate, stove-piped systems. In this work, we attempt to fuse the two computational paradigms in a single system called S-Store. In this way, S-Store can simultaneously accommodate OLTP and streaming applications. We present a simple transaction model for streams that integrates seamlessly with a traditional OLTP system. We chose to build S-Store as an extension of H-Store, an open-source, in-memory, distributed OLTP database system. By implementing S-Store in this way, we can make use of the transaction processing facilities that H-Store already supports, and we can concentrate on the additional implementation features that are needed to support streaming. Similar implementations could be done using other main-memory OLTP platforms. We show that we can actually achieve higher throughput for streaming workloads in S-Store than an equivalent deployment in H-Store alone. We also show how this can be achieved within H-Store with the addition of a modest amount of new functionality. Furthermore, we compare S-Store to two state-of-the-art streaming systems, Spark Streaming and Storm, and show how S-Store matches and sometimes exceeds their performance while providing stronger transactional guarantees.

Distributed consistency is perhaps the most discussed topic in distributed systems today. Coordination protocols can ensure consistency, but in practice they cause undesirable performance unless used judiciously. Scalable distributed architectures avoid coordination whenever possible, but under-coordinated systems can exhibit behavioral anomalies under fault, which are often extremely difficult to debug. This raises significant challenges for distributed system architects and developers. In this paper we present Blazes, a cross-platform program analysis framework that (a) identifies program locations that require coordination to ensure consistent executions, and (b) automatically synthesizes application-specific coordination code that can significantly outperform general-purpose techniques. We present two case studies, one using annotated programs in the Twitter Storm system, and another using the Bloom declarative language., Comment: Updated to include additional materials from the original technical report: derivation rules, output stream labels

A group of senior database researchers gathers every few years to assess the state of database research and to point out problem areas that deserve additional focus. This report summarizes the discussion and conclusions of the sixth ad-hoc meeting held May 4-6, 2003 in Lowell, Mass. It observes that information management continues to be a critical component of most complex software systems. It recommends that database researchers increase focus on: integration of text, data, code, and streams; fusion of information from heterogeneous data sources; reasoning about uncertain data; unsupervised data mining for interesting correlations; information privacy; and self-adaptation and repair., Comment: Details of this workshop (presentations and notes) are at http://research.microsoft.com/~gray/lowell/