Assessing Feature Importance for Short-Term Prediction of Electricity Demand in Medium-Voltage Loads

The design of new monitoring systems for intelligent distribution networks often requires both real-time measurements and pseudomeasurements to be processed. The former are obtained from smart meters, phasor measurement units and smart electronic devices, whereas the latter are predicted using appropriate algorithms—with the typical objective of forecasting the behaviour of power loads and generators. However, depending on the technique used for data encoding, the attempt at making predictions over a period of several days may trigger problems related to the high number of features. To contrast this issue, feature importance analysis becomes a tool of primary importance. This article is aimed at illustrating a technique devised to investigate the importance of features on data deemed relevant for predicting the next hour demand of aggregated, medium-voltage electrical loads. The same technique allows us to inspect the hidden layers of multilayer perceptrons entrusted with making the predictions, since, ultimately, the content of any hidden layer can be seen as an alternative encoding of the input data. The possibility of inspecting hidden layers can give wide support to researchers in a number of relevant tasks, including the appraisal of the generalisation capability reached by a multilayer perceptron and the identification of neurons not relevant for the prediction task.

Evalutation of Turbo Decoder Performance Through Software Reference Model

Turbo encoder and decoder are two important blocks of long-term evolution (LTE) systems, as they address the data encoding and decoding in a communication system. In recent years, the wireless communication has advanced to suit the user needs. The power optimization can be achieved by proposing early termination of decoding iteration where the number of iterations is made adjustable which stops the decoding as it finishes the process. Clock gating technique is used at the RTL level to avoid the unnecessary clock given to sequential circuits; here clock supplies are a major source of power dissipation. The performance of a system is affected due to the numbers of parameters, including channel noise, type of decoding and encoding techniques, type of interleaver, number of iterations, and frame length on the Matlab Simulink platform. A software reference model for turbo encoder and decoder are modeled using MATLAB Simulink. Performance of the proposed model is estimated and analyzed on various parameters like frame length, number of iterations, and channel noise.

Bubble-Proof Algorithm for Wave Union TDCs

Nowadays state-of-the-art time-to-digital converters (TDCs) are commonly implemented in field-programmable gate array (FPGA) devices using different variations of the wave union method. To take full advantage of this method many design challenges need to be overcome, one of which is an efficient data encoding. In this work, we describe in detail an effective algorithm to decode raw output data from a newly designed multisampling wave union TDC. The algorithm is able to correct bubble errors and detect any number of transitions, which occur in the wave union TDC output code. This allows us to reach a mean resolution as high as 0.39 ps and a single shot precision of 2.33 ps in the Xilinx Kintex-7 FPGA chip. The presented algorithm can be used for any kind of wave union TDCs and is intended for partial hardware implementation.

APAIC Report on the Holocode Crisis

The APAIC Report on the Holocode Crisis is a short story that imagines the future of machine-readable data encodings. In this story, I speculate about the next stage in the development of data encoding patterns: after barcodes and QR codes, the invention of “holocodes” will make it possible to store unprecedented amounts of data in a minuscule physical surface. As a collage of nested fictional materials (including ethnographic fieldnotes, interview transcripts, OCR scans, and intelligence reports) this story builds on the historical role of barcodes in supporting consumer logistics and the ongoing deployment of QR codes as anchors for the platform economy, concluding that the geopolitical future of optical governance is tied to unassuming technical standards such as those formalizing machine-readable representations of data.

Encoding-dependent generalization bounds for parametrized quantum circuits

A large body of recent work has begun to explore the potential of parametrized quantum circuits (PQCs) as machine learning models, within the framework of hybrid quantum-classical optimization. In particular, theoretical guarantees on the out-of-sample performance of such models, in terms of generalization bounds, have emerged. However, none of these generalization bounds depend explicitly on how the classical input data is encoded into the PQC. We derive generalization bounds for PQC-based models that depend explicitly on the strategy used for data-encoding. These imply bounds on the performance of trained PQC-based models on unseen data. Moreover, our results facilitate the selection of optimal data-encoding strategies via structural risk minimization, a mathematically rigorous framework for model selection. We obtain our generalization bounds by bounding the complexity of PQC-based models as measured by the Rademacher complexity and the metric entropy, two complexity measures from statistical learning theory. To achieve this, we rely on a representation of PQC-based models via trigonometric functions. Our generalization bounds emphasize the importance of well-considered data-encoding strategies for PQC-based models.

Analysis of Decoherence in Linear and Cyclic Quantum Walks

We theoretically analyze the case of noisy Quantum walks (QWs) by introducing four qubit decoherence models into the coin degree of freedom of linear and cyclic QWs. These models include flipping channels (bit flip, phase flip and bit-phase flip), depolarizing channel, phase damping channel and generalized amplitude damping channel. Explicit expressions for the probability distribution of QWs on a line and on a cyclic path are derived under localized and delocalized initial states. We show that QWs which begin from a delocalized state generate mixture probability distributions, which could give rise to useful algorithmic applications related to data encoding schemes. Specifically, we show how the combination of delocalzed initial states and decoherence can be used for computing the binomial transform of a given set of numbers. However, the sensitivity of QWs to noisy environments may negatively affect various other applications based on QWs.