arXiv subjects

Alexander Hentschel

Publications and source records attributed to Alexander Hentschel.

12 recordsLinked to original sources

arXiv2026

Geometric Guidance for Globally Synchronized Deployment of Elastic Geodesic Grids

Elastic geodesic grids deploy from flat to spatial configurations via complex nonlinear motion that is difficult to represent robustly for simulation. We present a geometric guidance framework that discretizes deployment as synchronized, time-coupled deformation trajectories. Starting from inverse tracing -- collapsing the deployed structure with a lightweight rod model while recording node paths under a shared parameter -- we obtain feasible node paths and formulate a polyline approximation problem that selects {globally synchronized} time steps and minimizes a robust tail-aggregated deviation measure under monotonicity constraints. {We solve the resulting non-smooth optimization problem via global optimization to obtain compact, synchronized displacement sequences for all paths simultaneously}. We evaluate the method using geometry-centric metrics (deviation versus step count, scaling with trajectory count) and demonstrate its utility by driving finite element deployment simulations that avoid intermediate buckling and capture deployment-induced prestress.

cs.GR↗

arXiv2022

A Benchmark Environment Motivated by Industrial Control Problems

In the research area of reinforcement learning (RL), frequently novel and promising methods are developed and introduced to the RL community. However, although many researchers are keen to apply their methods on real-world problems, implementing such methods in real industry environments often is a frustrating and tedious process. Generally, academic research groups have only limited access to real industrial data and applications. For this reason, new methods are usually developed, evaluated and compared by using artificial software benchmarks. On one hand, these benchmarks are designed to provide interpretable RL training scenarios and detailed insight into the learning process of the method on hand. On the other hand, they usually do not share much similarity with industrial real-world applications. For this reason we used our industry experience to design a benchmark which bridges the gap between freely available, documented, and motivated artificial benchmarks and properties of real industrial problems. The resulting industrial benchmark (IB) has been made publicly available to the RL community by publishing its Java and Python code, including an OpenAI Gym wrapper, on Github. In this paper we motivate and describe in detail the IB's dynamics and identify prototypic experimental settings that capture common situations in real-world industry control problems.

cs.AI↗

arXiv2020

Flow: Separating Consensus and Compute -- Block Formation and Execution

Most current blockchains require all full nodes to execute all tasks limits the throughput of existing blockchains, which are well documented and among the most significant hurdles for the widespread adoption of decentralized technology. This paper extends out presentation of Flow, a pipelined blockchain architecture, which separates the process of consensus on the transaction order from transaction computation. As we experimentally showed in our previous white paper, our architecture provides a significant throughput improvement while preserving the security of the system. Flow exploits the heterogeneity offered by the nodes, in terms of bandwidth, storage, and computational capacity, and defines the roles for the nodes based on their tasks in the pipeline, i.e., Collector, Consensus, Execution, and Verification. While transaction collection from the user agents is completed through the bandwidth-optimized Collector Nodes, the execution of them is done by the compute-optimized Execution Nodes. Checking the execution result is then distributed among a more extensive set of Verification Nodes, which confirm the result is correct in a distributed and parallel manner. In contrast to more traditional blockchain architectures, Flow's Consensus Nodes do not execute the transaction. Instead, Verification Nodes report observed faulty executions to the Consensus Nodes, which adjudicate the received challenges and slash malicious actors. In this paper, we detail the lifecycle of the transactions from the submission to the system until they are getting executed. The paper covers the Collector, Consensus, and Execution role. We provide a protocol specification of collecting the transactions, forming a block, and executing the resulting block. Moreover, we elaborate on the safety and liveness of the system concerning these processes.

cs.DC↗

arXiv2019

Flow: Separating Consensus and Compute

Throughput limitations of existing blockchain architectures are one of the most significant hurdles for their wide-spread adoption. Attempts to address this challenge include layer-2 solutions, such as Bitcoin's Lightning or Ethereum's Plasma network, that move work off the main chain. Another prominent technique is sharding, i.e., breaking the network into many interconnected networks. However, these scaling approaches significantly increase the complexity of the programming model by breaking ACID guarantees increasing the cost and time for application development. In this paper, we describe a novel approach where we split the work traditionally assigned to cryptocurrency miners into two different node roles. Specifically, the selection and ordering of transactions are performed independently from their execution. The focus of this paper is to formalize the split of consensus and computation, and prove that this approach increases throughput without compromising security. In contrast to most existing proposals, our approach achieves scaling via separation of concerns, i.e., better utilization of network resources, rather than sharding. This approach allows established programming paradigms for smart contracts (which generally assume transactional atomicity) to persist without introducing additional complexity. We present simulations on a proof-of-concept network of 32 globally distributed nodes. While the consensus algorithm was identical in all simulations (a 2-step-commit protocol with rotating block proposer), block computation was either included in a consensus nodes' regular operations (conventional architecture) or delegated to specialized execution nodes (separation of concerns). Separation of concerns enables our system to achieve a throughput increase by a factor of 56 compared to conventional architectures without loss of safety or decentralization.

cs.DC↗

arXiv2019

Flow: Separating Consensus and Compute -- Execution Verification

Throughput limitations of existing blockchain architectures are well documented and are one of the most significant hurdles for their wide-spread adoption. In our previous proof-of-concept work, we have shown that separating computation from consensus can provide a significant throughput increase without compromising security. In our architecture, Consensus Nodes only define the transaction order but do not execute transactions. Instead, computing the block result is delegated to compute-optimized Execution Nodes, and dedicated Verification Nodes check the computation result. During normal operation, Consensus Nodes do not inspect the computation but oversee that participating nodes execute their tasks with due diligence and adjudicate potential result challenges. While the architecture can significantly increase throughput, Verification Nodes still have to duplicate the computation fully. In this paper, we refine the architecture such that result verification is distributed and parallelized across many Verification Nodes. The full architecture significantly increases throughput and delegates the computation work to the specialized Execution Nodes and the onus of checking it to a variety of less powerful Verification Nodes. We provide a full protocol specification of the verification process, including challenges to faulty computation results and the resulting adjudication process. Furthermore, we formally prove liveness and safety of the system.

cs.DC↗

arXiv2017

Introduction to the "Industrial Benchmark"

A novel reinforcement learning benchmark, called Industrial Benchmark, is introduced. The Industrial Benchmark aims at being be realistic in the sense, that it includes a variety of aspects that we found to be vital in industrial applications. It is not designed to be an approximation of any real system, but to pose the same hardness and complexity.

cs.LG↗

arXiv2017

Particle Swarm Optimization for Generating Interpretable Fuzzy Reinforcement Learning Policies

Fuzzy controllers are efficient and interpretable system controllers for continuous state and action spaces. To date, such controllers have been constructed manually or trained automatically either using expert-generated problem-specific cost functions or incorporating detailed knowledge about the optimal control strategy. Both requirements for automatic training processes are not found in most real-world reinforcement learning (RL) problems. In such applications, online learning is often prohibited for safety reasons because online learning requires exploration of the problem's dynamics during policy training. We introduce a fuzzy particle swarm reinforcement learning (FPSRL) approach that can construct fuzzy RL policies solely by training parameters on world models that simulate real system dynamics. These world models are created by employing an autonomous machine learning technique that uses previously generated transition samples of a real system. To the best of our knowledge, this approach is the first to relate self-organizing fuzzy controllers to model-based batch RL. Therefore, FPSRL is intended to solve problems in domains where online learning is prohibited, system dynamics are relatively easy to model from previously generated default policy transition samples, and it is expected that a relatively easily interpretable control policy exists. The efficiency of the proposed approach with problems from such domains is demonstrated using three standard RL benchmarks, i.e., mountain car, cart-pole balancing, and cart-pole swing-up. Our experimental results demonstrate high-performing, interpretable fuzzy policies.

cs.NE↗

arXiv2017

Batch Reinforcement Learning on the Industrial Benchmark: First Experiences

The Particle Swarm Optimization Policy (PSO-P) has been recently introduced and proven to produce remarkable results on interacting with academic reinforcement learning benchmarks in an off-policy, batch-based setting. To further investigate the properties and feasibility on real-world applications, this paper investigates PSO-P on the so-called Industrial Benchmark (IB), a novel reinforcement learning (RL) benchmark that aims at being realistic by including a variety of aspects found in industrial applications, like continuous state and action spaces, a high dimensional, partially observable state space, delayed effects, and complex stochasticity. The experimental results of PSO-P on IB are compared to results of closed-form control policies derived from the model-based Recurrent Control Neural Network (RCNN) and the model-free Neural Fitted Q-Iteration (NFQ). Experiments show that PSO-P is not only of interest for academic benchmarks, but also for real-world industrial applications, since it also yielded the best performing policy in our IB setting. Compared to other well established RL techniques, PSO-P produced outstanding results in performance and robustness, requiring only a relatively low amount of effort in finding adequate parameters or making complex design decisions.

cs.LG↗

arXiv2011

An Efficient Algorithm for Optimizing Adaptive Quantum Metrology Processes

Quantum-enhanced metrology infers an unknown quantity with accuracy beyond the standard quantum limit (SQL). Feedback-based metrological techniques are promising for beating the SQL but devising the feedback procedures is difficult and inefficient. Here we introduce an efficient self-learning swarm-intelligence algorithm for devising feedback-based quantum metrological procedures. Our algorithm can be trained with simulated or real-world trials and accommodates experimental imperfections, losses, and decoherence.

quant-ph↗

arXiv2011

Ordered Measurements of Permutationally-Symmetric Qubit Strings

We show that any sequence of measurements on a permutationally-symmetric (pure or mixed) multi-qubit string leaves the unmeasured qubit substring also permutationally-symmetric. In addition, we show that the measurement probabilities for an arbitrary sequence of single-qubit measurements are independent of how many unmeasured qubits have been lost prior to the measurement. Our results are valuable for quantum information processing of indistinguishable particles by post-selection, e.g. in cases where the results of an experiment are discarded conditioned upon the occurrence of a given event such as particle loss. Furthermore, our results are important for the design of adaptive-measurement strategies, e.g. a series of measurements where for each measurement instance, the measurement basis is chosen depending on prior measurement results.

math-ph↗

arXiv2010

Machine Learning for Precise Quantum Measurement

Adaptive feedback schemes are promising for quantum-enhanced measurements yet are complicated to design. Machine learning can autonomously generate algorithms in a classical setting. Here we adapt machine learning for quantum information and use our framework to generate autonomous adaptive feedback schemes for quantum measurement. In particular our approach replaces guesswork in quantum measurement by a logical, fully-automatic, programmable routine. We show that our method yields schemes that outperform the best known adaptive scheme for interferometric phase estimation.

quant-ph↗

arXiv2007

Testing the nested light-cone Bethe equations of the AdS_5 x S^5 superstring

We perform a detailed test of the quantum integrability of the AdS_5 x S^5 superstring in uniform light-cone gauge in its near plane-wave limit. For this we establish the form of the general nested light-cone Bethe equations for the quantum string from the long range psu(2,2|4) Bethe equations of Beisert and Staudacher. Moreover the scheme for translating excited string states into Bethe root excitations is given. We then confront the direct perturbative diagonalization of the light-cone string Hamiltonian in the near plane-wave limit with the energy spectrum obtained from the general nested light-cone Bethe equations in various higher rank sectors. The analysis is performed both analytically and numerically up to the level of six impurity states and subsectors of maximal rank four. We find perfect agreement in all cases lending strong support to the quantum integrability of the AdS_5 x S^5 superstring.

hep-th↗