An Algebraic Approach to Linear-Optical Schemes for Deterministic Quantum Computing

Linear-Optical Passive (LOP) devices and photon counters are sufficient to implement universal quantum computation with single photons, and particular schemes have already been proposed. In this paper we discuss the link between the algebraic structure of LOP transformations and quantum computing. We first show how to decompose the Fock space of N optical modes in finite-dimensional subspaces that are suitable for encoding strings of qubits and invariant under LOP transformations (these subspaces are related to the spaces of irreducible unitary representations of U(N)). Next we show how to design in algorithmic fashion LOP circuits which implement any quantum circuit deterministically. We also present some simple examples, such as the circuits implementing a CNOT gate and a Bell-State Generator/Analyzer.Comment: new version with minor modification

The impact of COVID-19 pandemic on breast surgery in Italy: a multi-centric retrospective observational study

COVID-19 pandemic had an impact on surgical activities. The aim of this multi-centric, retrospective study was to evaluate the impact of the COVID-19 pandemic on breast surgery. The patients who operated during the pre-pandemic year 2019 were compared to those operated in 2020. Fourteen Breast Care Units provided data on breast surgical procedures performed in 2020 and 2019: total number of breast-conserving surgery (BCS), number of 1st level oncoplastic breast surgery (OBS), number of 2nd level OBS; total number of mastectomies, mastectomies without reconstruction, mastectomies with a tissue expander, mastectomies with direct to implant (DTI) reconstruction, mastectomies with immediate flap reconstruction; total number of delayed reconstructions, number of expanders to implant reconstructions, number of delayed flap reconstructions. Overall 20.684 patients were included: 10.850 (52.5%) operated during 2019, and 9.834 (47.5%) during 2020. The overall number of breast oncologic surgical procedures in all centers in 2020 was 8.509, compared to 9.383 in 2019 (- 9%). BCS decreased by 744 cases (- 13%), the overall number of mastectomies decreased by 130 cases (- 3.5%); mastectomy-BCS ratio was 39-61% in 2019, and 42-58% in 2020. Regarding immediate reconstructive procedures mastectomies with DTI reconstruction increased by 166 cases (+ 15%) and mastectomies with immediate expander reconstruction decreased by 297 cases (- 20%). Breast-delayed reconstructive procedures in all centers in 2020 were 142 less than in 2019 (- 10%). The outburst of the COVID-19 pandemic in 2020 determined an implemented number of mastectomies compared to BCS, an implemented number of immediate breast reconstructions, mainly DTI, and a reduction of expander reconstruction

Co-movements of REIT indices with structural changes before and during the subprime mortgage crisis: evidence from Euro-Med markets

This paper examines the long-run relationships between the REIT indices of the UK, Turkey and Israel in the Euro-Med zone with that of MSCI US REIT Index by using weekly data over the period 2003Q3 through 2009Q3, which includes the latest US subprime mortgage crisis and its effects on global stock markets. Although our EG test results do not indicate a long-run relationship, after taking account of the structural changes by applying the GH test, we find a long-run interaction between the REIT indices of UK and Israel with that of the US. However, our results indicate the lack of co-movement between REIT index of Turkey with the US. In addition, our dynamic OLS test results indicate a perfect relationship between the UK and the US indices. Our findings show that international investors who make long-term investments can only gain from diversifying into the real estate market of Turkey among the involved markets in the Euro-Med zone

Deep Neural Network Attribution Methods for Leakage Analysis and Symmetric Key Recovery

Deep Neural Networks (DNNs) have recently received significant attention in the side-channel community due to their state-of-the-art performance in security testing of embedded systems. However, research on the subject mostly focused on techniques to improve the attack efficiency in terms of the number of traces required to extract secret parameters. What has not been investigated in detail is a constructive approach of DNNs as a tool to evaluate and improve the effectiveness of countermeasures against side-channel attacks. In this work, we try to close this gap by applying attribution methods that aim for interpreting DNN decisions, in order to identify leaking operations in cryptographic implementations. In particular, we investigate three different approaches that have been proposed for feature visualization in image classification tasks and compare them regarding their suitability to reveal Points of Interests (POIs) in side-channel traces. We show by experiments with three separate data sets that Layer-wise Relevance Propagation (LRP) proposed by Bach et al. provides the best result in most cases. Finally, we demonstrate that attribution can also serve as a powerful side-channel distinguisher in DNN-based attack setups

Cortical Surround Interactions and Perceptual Salience via Natural Scene Statistics

Spatial context in images induces perceptual phenomena associated with salience and modulates the responses of neurons in primary visual cortex (V1). However, the computational and ecological principles underlying contextual effects are incompletely understood. We introduce a model of natural images that includes grouping and segmentation of neighboring features based on their joint statistics, and we interpret the firing rates of V1 neurons as performing optimal recognition in this model. We show that this leads to a substantial generalization of divisive normalization, a computation that is ubiquitous in many neural areas and systems. A main novelty in our model is that the influence of the context on a target stimulus is determined by their degree of statistical dependence. We optimized the parameters of the model on natural image patches, and then simulated neural and perceptual responses on stimuli used in classical experiments. The model reproduces some rich and complex response patterns observed in V1, such as the contrast dependence, orientation tuning and spatial asymmetry of surround suppression, while also allowing for surround facilitation under conditions of weak stimulation. It also mimics the perceptual salience produced by simple displays, and leads to readily testable predictions. Our results provide a principled account of orientation-based contextual modulation in early vision and its sensitivity to the homogeneity and spatial arrangement of inputs, and lends statistical support to the theory that V1 computes visual salience

Convolutional Neural Networks with Data Augmentation against Jitter-Based Countermeasures.

International audienceIn the context of the security evaluation of cryptographic implementations, profiling attacks (aka Template Attacks) play a fundamental role. Nowadays the most popular Template Attack strategy consists in approximating the information leakages by Gaussian distributions. Nevertheless this approach suffers from the difficulty to deal with both the traces misalignment and the high dimensionality of the data. This forces the attacker to perform critical preprocessing phases, such as the selection of the points of interest and the realignment of measurements. Some software and hardware countermeasures have been conceived exactly to create such a misalignment. In this paper we propose an end-to-end profiling attack strategy based on the Convolutional Neural Networks: this strategy greatly facilitates the attack roadmap, since it does not require a previous trace realignment nor a precise selection of points of interest. To significantly increase the performances of the CNN, we moreover propose to equip it with the data augmentation technique that is classical in other applications of Machine Learning. As a validation, we present several experiments against traces misaligned by different kinds of countermeasures, including the augmentation of the clock jitter effect in a secure hardware implementation over a modern chip. The excellent results achieved in these experiments prove that Convolutional Neural Networks approach combined with data augmentation gives a very efficient alternative to the state-of-the-art profiling attacks

On the Use of Independent Component Analysis to Denoise Side-Channel Measurements

International audienceIndependent Component Analysis (ICA) is a powerful technique for blind source separation. It has been successfully applied to signal processing problems, such as feature extraction and noise reduction , in many different areas including medical signal processing and telecommunication. In this work, we propose a framework to apply ICA to denoise side-channel measurements and hence to reduce the complexity of key recovery attacks. Based on several case studies, we afterwards demonstrate the overwhelming advantages of ICA with respect to the commonly used preprocessing techniques such as the singular spectrum analysis. Mainly, we target a software masked implementation of an AES and a hardware unprotected one. Our results show a significant Signal-to-Noise Ratio (SNR) gain which translates into a gain in the number of traces needed for a successful side-channel attack. This states the ICA as an important new tool for the security assessment of cryptographic implementations

Correlated topographic analysis: estimating an ordering of correlated components

Abstract This paper describes a novel method, which we call correlated topographic analysis (CTA), to estimate non-Gaussian components and their ordering (topography). The method is inspired by a central motivation of recent variants of independent component analysis (ICA), namely, to make use of the residual statistical dependency which ICA cannot remove. We assume that components nearby on the topographic arrangement have both linear and energy correlations, while far-away components are statistically independent. We use these dependencies to fix the ordering of the components. We start by proposing the generative model for the components. Then, we derive an approximation of the likelihood based on the model. Furthermore, since gradient methods tend to get stuck in local optima, we propose a three-step optimization method which dramatically improves topographic estimation. Using simulated data, we show that CTA estimates an ordering of the components and generalizes a previous method in terms of topography estimation. Finally, to demonstrate that CTA is widely applicable, we learn topographic representations for three kinds of real data: natural images, outputs of simulated complex cells and text data