Overcoming Bandwidth Limitations in Wireless Sensor Networks by Exploitation of Cyclic Signal Patterns: An Event-triggered Learning Approach

Wireless sensor networks are used in a wide range of applications, many of which require real-time transmission of the measurements. Bandwidth limitations result in limitations on the sampling frequency and number of sensors. This problem can be addressed by reducing the communication load via data compression and event-based communication approaches. The present paper focuses on the class of applications in which the signals exhibit unknown and potentially time-varying cyclic patterns. We review recently proposed event-triggered learning (ETL) methods that identify and exploit these cyclic patterns, we show how these methods can be applied to the nonlinear multivariable dynamics of three-dimensional orientation data, and we propose a novel approach that uses Gaussian process models. In contrast to other approaches, all three ETL methods work in real time and assure a small upper bound on the reconstruction error. The proposed methods are compared to several conventional approaches in experimental data from human subjects walking with a wearable inertial sensor network. They are found to reduce the communication load by 60–70%, which implies that two to three times more sensor nodes could be used at the same bandwidth

Position Representation of Effective Electron-Electron Interactions in Solids

An essential ingredient in many model Hamiltonians, such as the Hubbard model, is the effective electron-electron interaction $U$, which enters as matrix elements in some localized basis. These matrix elements provide the necessary information in the model, but the localized basis is incomplete for describing $U$. We present a systematic scheme for computing the manifestly basis-independent dynamical interaction in position representation, $U({\bf r},{\bf r}';\omega)$, and its Fourier transform to time domain, $U({\bf r},{\bf r}';\tau)$. These functions can serve as an unbiased tool for the construction of model Hamiltonians. For illustration we apply the scheme within the constrained random-phase approximation to the cuprate parent compounds La$_2$CuO$_4$ and HgBa$_2$CuO$_4$ within the commonly used 1- and 3-band models, and to non-superconducting SrVO$_{3}$ within the $t_{2g}$ model. Our method is used to investigate the shape and strength of screening channels in the compounds. We show that the O 2$p_{x,y}-$Cu 3$d_{x^2-y^2}$ screening gives rise to regions with strong attractive static interaction in the minimal (1-band) model in both cuprates. On the other hand, in the minimal ($t_{2g}$) model of SrVO$_3$ only regions with a minute attractive interaction are found. The temporal interaction exhibits generic damped oscillations in all compounds, and its time-integral is shown to be the potential caused by inserting a frozen point charge at $\tau=0$. When studying the latter within the three-band model for the cuprates, short time intervals are found to produce a negative potential.Comment: 15 pages, 13 figure

The quantum cost function concentration dependency on the parametrization expressivity

Although we are currently in the era of noisy intermediate scale quantum devices, several studies are being conducted with the aim of bringing machine learning to the quantum domain. Currently, quantum variational circuits are one of the main strategies used to build such models. However, despite its widespread use, we still do not know what are the minimum resources needed to create a quantum machine learning model. In this article, we analyze how the expressiveness of the parametrization affects the cost function. We analytically show that the more expressive the parametrization is, the more the cost function will tend to concentrate around a value that depends both on the chosen observable and on the number of qubits used. For this, we initially obtain a relationship between the expressiveness of the parametrization and the mean value of the cost function. Afterwards, we relate the expressivity of the parametrization with the variance of the cost function. Finally, we show some numerical simulation results that confirm our theoretical-analytical predictions. To the best of our knowledge, this is the first time that these two important aspects of quantum neural networks are explicitly connected

Learning to learn with an evolutionary strategy applied to variational quantum algorithms

Variational Quantum Algorithms (VQAs) employ quantum circuits parameterized by $U$, optimized using classical methods to minimize a cost function. While VQAs have found broad applications, certain challenges persist. Notably, a significant computational burden arises during parameter optimization. The prevailing ``parameter shift rule'' mandates a double evaluation of the cost function for each parameter. In this article, we introduce a novel optimization approach named ``Learning to Learn with an Evolutionary Strategy'' (LLES). LLES unifies ``Learning to Learn'' and ``Evolutionary Strategy'' methods. ``Learning to Learn'' treats optimization as a learning problem, utilizing recurrent neural networks to iteratively propose VQA parameters. Conversely, ``Evolutionary Strategy'' employs gradient searches to estimate function gradients. Our optimization method is applied to two distinct tasks: determining the ground state of an Ising Hamiltonian and training a quantum neural network. Results underscore the efficacy of this novel approach. Additionally, we identify a key hyperparameter that significantly influences gradient estimation using the ``Evolutionary Strategy'' method

Applying Occam's Razor to Transformer-Based Dependency Parsing: What Works, What Doesn't, and What is Really Necessary

The introduction of pre-trained transformer-based contextualized word embeddings has led to considerable improvements in the accuracy of graph-based parsers for frameworks such as Universal Dependencies (UD). However, previous works differ in various dimensions, including their choice of pre-trained language models and whether they use LSTM layers. With the aims of disentangling the effects of these choices and identifying a simple yet widely applicable architecture, we introduce STEPS, a new modular graph-based dependency parser. Using STEPS, we perform a series of analyses on the UD corpora of a diverse set of languages. We find that the choice of pre-trained embeddings has by far the greatest impact on parser performance and identify XLM-R as a robust choice across the languages in our study. Adding LSTM layers provides no benefits when using transformer-based embeddings. A multi-task training setup outputting additional UD features may contort results. Taking these insights together, we propose a simple but widely applicable parser architecture and configuration, achieving new state-of-the-art results (in terms of LAS) for 10 out of 12 diverse languages.Comment: 14 pages, 1 figure; camera-ready version for IWPT 202

Die normativen Grundbegriffe der Alterssicherung in Deutschland : eine diskursanalytische Rekonstruktion der Reformdebatten 2001, 2004 und 2007

During the first decade of this century, Germany saw a radical transformation of its old age security system. The coalition of social democrats and the green party transformed the traditional German pension system into a three-pillar-system and reduced the generosity of the pay-as-you-go-pillar. Subsequently, a Grand Coalition of social democrats and Christian democrats raised the retirement age to 67. This study examines the debates of this decade by using a discourse analytical approach in combination with the veto-term-approach. It reconstructs the public and political discourses of the reform laws of 2001, 2004, and 2007. Research interest of this study is if the normative concepts of the German old age security system have transformed in accordance with the institutional transformations. Did the popular terms of that time individual responsibility, sustainability and flexibility become part of the normative basic vocabulary or did the traditional value terms remain dominant? Outcome of the study is a reconstruction of the main political arguments of the transformation era and a dictionary of the main normative concepts of German old age security

Startups versus incumbents in ‘green’ industry transformations : A comparative study of business model archetypes in the electrical power sector

Scholars have recently argued that startups and incumbents play differential roles in the disruptive transformations of industries toward sustainability and that the transformations are only likely to succeed if both startups and incumbents contribute. To understand their respective contributions and, thus, to understand how industries make the transition toward sustainability, comparative studies of incumbents versus startups during this transformation have been identified as a central pursuit, but yet they are mostly lacking. Since business models have become a principal way of characterizing firms, the present study takes a business model perspective and derives business model archetypes in the electrical power sector from an analysis of 280 startups and incumbents in three different countries. The selected countries (USA, UK, and India) represent three different energy profiles and leading instances of disruption in the energy sector. The article, then, undertakes a comparative analysis of startups and incumbents based on the empirically distilled business model archetypes and develops propositions on startups, incumbents, and business models in industry transformations. This analysis produces several important insights. First, incumbents do not seem to engage in less business model experimentation than startups. Second, incumbents have adopted several new business models that are not pursued by startups. Third, startups have espoused some business models that are not pursued by incumbents. Fourth, foreign firms can also affect the ‘green’ transformation of an industry in a focal country. Finally, the identified business model archetypes are likely to be of interest to scholars and practitioners who are seeking an improved understanding of business models in the electrical power industry and the industry's competitive landscape.© 2021 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)This research project is financially supported by the Swiss Innovation Agency Innosuisse and is part of the Swiss Competence Center for Energy Research SCCER CREST. Innosuisse had no influence on study design, the collection, analysis, and interpretation of data, the writing of the manuscript, and on the decision to submit the manuscript for publication. We thank the editors, three anonymous reviewers, as well as Rolf Wüstenhagen for their helpful comments.fi=vertaisarvioitu|en=peerReviewed