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Measurement of the electric field distribution in streamer discharges
Get PDFUsing electric field induced second harmonic generation (E-FISH), we performed direction-resolved absolute electric field measurements on single-channel streamer discharges in 70 mbar (7 kPa) air with 0.2 mm and 2 ns resolutions. In order to obtain the absolute (local) electric field, we developed a deconvolution method taking into account the phase variations of E-FISH. The acquired field distribution shows good agreement with the simulation results under the same conditions, in direction, magnitude, and shape. This is the first time that E-FISH is applied to streamers of this size (>0.5 cm radius), crossing a large gap. Achieving these high resolution electric field measurements benefits further understanding of streamer discharges and enables future use of E-FISH on cylindrically symmetric (transient) electric field distributions
Future-proof ship pipe routing: Navigating the energy transition
Get PDFShip pipe route design is often overlooked in the context of the energy transition, although it is a crucial
driver for design time and costs. Motivated by this, we propose a mathematical approach for modeling
uncertainty in pipe routing with deterministic optimization, stochastic programming, and robust optimization.
The uncertainty entails not knowing which type of fuel will be used in the ship’s future. All three models
are based on state-of-the-art integer linear programming models for the Stochastic Steiner Forest Problem
and adjusted to the maritime domain using specific constraints for pipe routing. Our results highlight the
importance of accounting for uncertainty in ship pipe routing, demonstrating cost reductions of up to 22%
based on experiments with artificial and realistic data. Our methods enable engineers to explore different levels
of preparedness for the energy transition with minimal effort during the early design phase
Production-inventory control models : 40 years of relevant research : liber amicorum Ton de Kok
Get PDFScalable and fine-tuned privacy pass from group verifiable random functions
Get PDFAnonymous token schemes are cryptographic
protocols for limiting the access to online resources to
credible users. The resource provider issues a set of access
tokens to the credible user that they can later redeem
anonymously, i.e., without the provider being able to link
their redemptions. When combined with credibility tests such
as CAPTCHAs, anonymous token schemes can significantly
increase user experience and provider security, without
exposing user access patterns to providers.
Current anonymous token schemes such as the Privacy
Pass protocol by Davidson et al. rely on oblivious
pseudorandom functions (OPRFs), which let server and user
jointly compute randomly looking access tokens. For those
protocols, token issuing costs are linear in the number of
requested tokens.
In this work, we propose a new approach for building
anonymous token schemes. Instead of relying on two-party
computation to realize a privacy-preserving pseudorandom
function evaluation, we propose to offload token generation
to the user by using group verifiable random functions
(GVRFs). GVRFs are a new cryptographic primitive
that allow users to produce verifiable pseudorandomness.
Opposed to standard VRFs, verification is anonymous within
the group of credible users. We give a construction of group
VRFs from the Dodis-Yampolskiy VRF and Equivalence-
Class Signatures, based on pairings and a new Diffie-
Hellman inversion assumption that we analyze in the Generic
Group Model. Our construction enjoys compact public keys
and proofs, while evaluation and verification costs are only
slightly increased compared to the Dodis-Yampolskiy VRF.
By deploying a group VRF instead of a OPRF, we
obtain an anonymous token scheme where communication
as well as server-side computation during the issuing phase
is constant and independent of the number of tokens a
user requests. Moreover, by means of our new concept of updatable token policies, the number of unspent tokens in
circulation can retrospectively (i.e., even after the credibility
check) be decreased or increased in order to react to
the current or expected network situation. Our tokens are
further countable and publicly verifiable. This comes at the
cost of higher computational efforts for token redemption
and verification as well as somewhat weaker unlinkability
guarantees compared to Privacy Pass
Optimal sizing and control of a grid-connected battery in a stacked revenue model including an energy community
Get PDFRecent years have seen rapid increases in intermittent renewable generation, requiring novel battery energy storage systems (BESS) solutions. One recent trend is the emergence of large grid-connected batteries that can be controlled to provide multiple storage and flexibility services, using a stacked revenue model. Another emerging development is renewable energy communities (REC), in which prosumers invest in their own renewable generation capacity, but also require battery storage for flexibility. In this paper, we study settings in which energy communities rent battery capacity from a battery operator through a battery-as-a-service (BaaS) model. We present a methodology for determining the sizing and pricing of battery capacity that can be rented, such that it provides economic benefits to both the community and the battery operator that participates in the energy market. We examine how sizes and prices vary across a number of different scenarios for different types of tariffs (flat, dynamic) and competing energy market uses. Second, we conduct a systematic study of linear optimization models for battery control when deployed to provide flexibility to energy communities. We show that existing approaches for battery control with daily time windows have a number of important limitations in practical deployments, and we propose a number of regularization functions in the optimization to address them. Finally, we investigate the proposed method using real generation, demand, tariffs, and battery data, based on a practical case study from a large smart battery operator in the Netherlands. For the settings used in our case study, we find that a community of 200 houses equipped with a 330 kW wind turbine can save up to €12,874 per year by renting just 280 kWh of battery capacity (after subtracting the battery rental costs), and the methodology is applicable for a wide variety of other settings and tariff types
An effective aggregation heuristic for Capacitated Facility Location Problems with many demand points
Get PDFIn location analysis, the effects of demand aggregation have been the subject of many studies. This body of literature is mainly focused on -median and -center problems. Relatively few papers in the literature on aggregation explicitly concern the Capacitated Facility Location Problem (CFLP). Our work examines the beneficial use of aggregation in the context of the CFLP. We focus on problems where there are significantly more demand points than potential facility locations, since this is where aggregation is most applicable in reducing complexity. We examine ways to obtain an aggregation at a fixed resolution, that is likely to perform well for a given instance of the problem. These aggregation techniques will form the core of a broader algorithmic framework, which contributes to the literature concerning heuristics for CFLPs. Our core aggregation method is based on applying -means clustering in , where is the number of potential facilities. The space in which we apply the clustering is constructed by applying a transformation to the normalized distance matrix corresponding to the original CFLP problem. The aim of applying the transformation is to magnify differences in distance where relevant, and to compress irrelevant differences in distance. We evaluate our heuristic method on larger instances based on a real-world problem in reverse logistics. The results are encouraging and indicate that our method is capable of outperforming an intuitive benchmark aggregation method. We find that choosing the right hyperparameters and starting with a good initialization help our method perform better
G-ALP: Rethinking Light-weight Encodings for GPUs
Get PDFThis paper introduces G-ALP, a GPU-optimized version of ALP, which is a recent and state-of-the-art compression scheme for floating-point. This GPU-optimization is based on two core ideas. First, all parts of the decoding process must be fully data-parallelized. In this paper, we fully data-parallelize exception patching, which typically applies to only 1% of the data. While patching has negligible performance cost on CPUs, it can become the main bottleneck on GPUs if it is not data-parallel. Second, the decoding API must minimize its register footprint, a highly scarce resource on GPUs, and hence deliver just one value-at-a-time. Our unique aim is to integrate G-ALP decoding into GPU kernels that consume data from global memory, rather than let decompression be a separate kernel. We consider these two ideas general guidelines for future GPU-optimized lightweight encodings, and a significant evolution of our new FastLanes file format, making it GPU-friendly. We extensively test G-ALP in a series of microbenchmarks and evaluate its performance on an NVIDIA V100 GPU and an NVIDIA RTX4070 Super Ti GPU, demonstrating superior performance compared to NVIDIA nvCOMP and ndzip in both decoding and filtering queries
The foliage partition: An easy-to-compute lc-invariant for graph states
Get PDFThis paper introduces the foliage partition, an easy-to-compute LC-invariant for graph states, of computational complexity O(n3) in the number of qubits. Inspired by the foliage of a graph, our invariant has a natural graphical representation in terms of leaves, axils, and twins. It captures both, the connection structure of a graph and the 2-body marginal properties of the associated graph state. We relate the foliage partition to the size of LC-orbits and use it to bound the number of LC-automorphisms of graphs. We also show the invariance of the foliage partition when generalized to weighted graphs and qudit graph states
Hypercontractivity for quantum erasure channels via variable multipartite log-Sobolev inequality
No full textWe prove an almost optimal hypercontractive inequality for products of quantum erasure channels, generalizing the hypercontractivity for classical binary erasure channels. To our knowledge, this is the first tensorization-type hypercontractivity bound for quantum channels with no fixed states. The traditional inductive arguments for classical hypercontractivity cannot be generalized to the quantum setting due to the nature of the non-commutativity of matrices. To overcome the difficulty, we establish a novel quantum log-Sobolev inequality for Bernoulli entropy, which includes the classical log-Sobolev inequality and the quantum log-Sobolev inequality as one-partite cases. To our knowledge, its classical counterpart is also unknown prior to this work. We establish a connection between our quantum log-Sobolev inequality and the hypercontractivity bound for quantum erasure channels via a refined quantum Gross’ lemma, extending the analogous connection between the quantum log- Sobolev inequality and the hypercontractivity for qubit unital channels. As an application, we prove an almost tight bound (up to a constant factor) on the classical communication complexity of two-party common randomness generation assisted with erasednoisy EPR states, generalizing the tight bound on the same task assisted with erased-noisy random strings due to Guruswami and Radhakrishnan
A nearly optimal randomized algorithm for explorable heap selection
Get PDFExplorable heap selection is the problem of selecting the nth smallest value in a binary heap. The key values can only be accessed by traversing through the underlying infinite binary tree, and the complexity of the algorithm is measured by the total distance traveled in the tree (each edge has unit cost). This problem was originally proposed as a model to study search strategies for the branch-and-bound algorithm with storage restrictions by Karp, Saks and Widgerson (FOCS ’86), who gave deterministic and randomized n·exp(O(logn)) time algorithms using O(log(n)2.5) and O(logn) space respectively. We present a new randomized algorithm with running time O(nlog(n)3) against an oblivious adversary using O(logn) space, substantially improving the previous best randomized running time at the expense of slightly increased space usage. We also show an Ω(log(n)n/log(log(n))) lower bound for any algorithm that solves the problem in the same amount of space, indicating that our algorithm is nearly optimal