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

A Comparison of Weight Initializers in Deep Learning-based Side-channel Analysis

The usage of deep learning in profiled side-channel analysis requires a careful selection of neural network hyperparameters. In recent publications, different network architectures have been presented as efficient profiled methods against protected AES implementations. Indeed, completely different convolutional neural network models have presented similar performance against public side-channel traces databases. In this work, we analyze how weight initializers\u27 choice influences deep neural networks\u27 performance in the profiled side-channel analysis. Our results show that different weight initializers provide radically different behavior. We observe that even high-performing initializers can reach significantly different performance when conducting multiple training phases. Finally, we found that this hyperparameter is more dependent on the choice of dataset than other, commonly examined, hyperparameters. When evaluating the connections with other hyperparameters, the biggest connection is observed with activation functions

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

DLDDO: Deep Learning to Detect Dummy Operations

Recently, research on deep learning based side-channel analysis (DLSCA) has received a lot of attention. Deep learning-based profiling methods similar to template attacks as well as non-profiling-based methods similar to differential power analysis have been proposed. DLSCA methods have been proposed for targets to which masking schemes or jitter-based hiding schemes are applied. However, most of them are methods for finding the secret key, except for methods for preprocessing, and there are no studies on the target to which the dummy-based hiding schemes or shuffling schemes are applied. In this paper, we propose a DLSCA for detecting dummy operations. In the previous study, dummy operations were detected using the method called BCDC, but there is a disadvantage in that it is impossible to detect dummy operations for commercial devices such as an IC card. We consider the detection of dummy operations as a multi-label classification problem and propose a deep learning method based on CNN to solve it. As a result, it is possible to successfully perform detection of dummy operations on an IC card, which was not possible in the previous study

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

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

One trace is all it takes: Machine Learning-based Side-channel Attack on EdDSA

Profiling attacks, especially those based on machine learning proved as very successful techniques in recent years when considering side-channel analysis of block ciphers implementations. At the same time, the results for implementations public-key cryptosystems are very sparse. In this paper, we consider several machine learning techniques in order to mount a power analysis attack on EdDSA using the curve Curve25519 as implemented in WolfSSL. The results show all considered techniques to be viable and powerful options. The results with convolutional neural networks (CNNs) are especially impressive as we are able to break the implementation with only a single measurement in the attack phase while requiring less than 500 measurements in the training phase. Interestingly, that same convolutional neural network was recently shown to perform extremely well for attacking the AES cipher. Our results show that some common grounds can be established when using deep learning for profiling attacks on distinct cryptographic algorithms and their corresponding implementations

Intravenous magnesium prevents atrial fibrillation after coronary artery bypass grafting: a meta-analysis of 7 double-blind, placebo-controlled, randomized clinical trials

<p>Abstract</p> <p>Background</p> <p>Postoperative atrial fibrillation (POAF) is the most common complication after coronary artery bypass grafting (CABG). The preventive effect of magnesium on POAF is not well known. This meta-analysis was undertaken to assess the efficacy of intravenous magnesium on the prevention of POAF after CABG.</p> <p>Methods</p> <p>Eligible studies were identified from electronic databases (Medline, Embase, and the Cochrane Library). The primary outcome measure was the incidence of POAF. The meta-analysis was performed with the fixed-effect model or random-effect model according to heterogeneity.</p> <p>Results</p> <p>Seven double-blind, placebo-controlled, randomized clinical trials met the inclusion criteria including 1,028 participants. The pooled results showed that intravenous magnesium reduced the incidence of POAF by 36% (RR 0.64; 95% confidence interval (CI) 0.50-0.83; <it>P </it>= 0.001; with no heterogeneity between trials (heterogeneity <it>P </it>= 0.8, <it>I</it>²= 0%)).</p> <p>Conclusions</p> <p>This meta-analysis indicates that intravenous magnesium significantly reduces the incidence of POAF after CABG. This finding encourages the use of intravenous magnesium as an alternative to prevent POAF after CABG. But more high quality randomized clinical trials are still need to confirm the safety.</p

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