Reviewerly/exHarmony huggingface dataset:
Experimental Robust Self-Testing of the State Generated by a Quantum Network

Self-testing is a method of quantum state and measurement estimation that does not rely on assumptions about the inner working of the used devices. Its experimental realization has been limited to sources producing single quantum states so far. In this work, we experimentally implement two significant building blocks of a quantum network involving two independent sources, i.e. a parallel configuration in which two parties share two copies of a state, and a tripartite configuration where a central node shares two independent states with peripheral nodes. Then, by extending previous self-testing techniques we provide device-independent lower bounds on the fidelity between the generated states and an ideal state made by the tensor product of two maximally entangled two-qubit states. Given its scalability and versatility, this technique can find application in the certification of larger networks of different topologies, for quantum communication and cryptography tasks and randomness generation protocols.
Detection of replay attacks to GNSS based on partial correlations and authentication data unpredictability

Intentional interference, and in particular GNSS spoofing, is currently one of the most significant concerns of the Positioning, Navigation and Timing (PNT) community. With the adoption of Open Service Navigation Message Authentication (OSNMA) in Galileo, the E1B signal component will continuously broadcast unpredictable cryptographic data. This allows GNSS receivers not only to ensure the authenticity of data origin but also to detect replay spoofing attacks for receivers already tracking real signals with relatively good visibility conditions. Since the spoofer needs to estimate the unpredictable bits introduced by OSNMA with almost zero delay in order to perform a Security Code Estimation and Replay (SCER) attack, the spoofer unavoidably introduces a slight distortion into the signal, which can be the basis of a spoofing detector. In this work, we propose five detectors based on partial correlations of GNSS signals obtained over predictable and unpredictable parts of the signals. We evaluate them in a wide set of test cases, including different types of receiver and spoofing conditions. The results show that one of the detectors is consistently superior to the others, and it is able to detect SCER attacks with a high probability even in favorable conditions for the spoofer. Finally, we discuss some practical considerations for implementing the proposed detector in receivers, in particular when the Galileo OSNMA message structure is used.
Versatile Photonic Entanglement Synthesizer in the Spatial Domain

Multimode entanglement is an essential resource for quantum information in continuous-variable systems. Light-based quantum technologies will arguably not be built upon table-top bulk setups, but will presumably rather resort to integrated optics. Sequential bulk opticslike proposals based on cascaded integrated interferometers are not scalable with the current state-of-the-art low-loss materials used for continuous variables. We analyze the multimode continuous-variable entanglement capabilities of a compact currently available integrated bulk-optics analog: the array of nonlinear waveguides. We theoretically demonstrate that this simple and compact structure, together with a reconfigurable input pump distribution and multimode coherent detection of the output modes, is a versatile entanglement synthesizer in the spatial domain. We exhibit this versatility through analytical and numerically optimized multimode squeezing, entanglement, and cluster-state generation in different encodings. Our results re-establish spatial encoding as a contender in the game of continuous-variable quantum information processing.
Surface Normal Estimation of Tilted Images via Spatial Rectifier

In this paper, we present a spatial rectifier to estimate surface normals of tilted images. Tilted images are of particular interest as more visual data are captured by arbitrarily oriented sensors such as body-/robot-mounted cameras. Existing approaches exhibit bounded performance on predicting surface normals because they were trained using gravity-aligned images. Our two main hypotheses are: (1) visual scene layout is indicative of the gravity direction; and (2) not all surfaces are equally represented by a learned estimator due to the structured distribution of the training data, thus, there exists a transformation for each tilted image that is more responsive to the learned estimator than others. We design a spatial rectifier that is learned to transform the surface normal distribution of a tilted image to the rectified one that matches the gravity-aligned training data distribution. Along with the spatial rectifier, we propose a novel truncated angular loss that offers a stronger gradient at smaller angular errors and robustness to outliers. The resulting estimator outperforms the state-of-the-art methods including data augmentation baselines not only on ScanNet and NYUv2 but also on a new dataset called Tilt-RGBD that includes considerable roll and pitch camera motion.
Optimal Model-Based Trajectory Planning With Static Polygonal Constraints

The main contribution of this article is a novel method for planning globally optimal trajectories for dynamical systems subject to polygonal constraints. The proposed method is a hybrid trajectory planning approach, which combines graph search, i.e., a discrete roadmap method, with convex optimization, i.e., a complete path method. Contrary to past approaches, which have focused on using simple obstacle approximations, or suboptimal spatial discretizations, our approach is able to use the exact geometry of polygonal constraints in order to plan optimal trajectories. The performance and flexibility of the proposed method are evaluated via simulations by planning distance-optimal trajectories for a Dubins car model, as well as time-, distance-, and energy-optimal trajectories for a marine vehicle.
Poribohon-BD: Bangladeshi local vehicle image dataset with annotation for classification

Vehicle Classification has become tremendously important due to various applications such as traffic video surveillance, accident avoidance, traffic congestion prevention, bringing intelligent transportation systems. This article presents 'Poribohon-BD' dataset for vehicle classification purposes in Bangladesh. The vehicle images are collected from two sources: i) smartphone camera, ii) social media. The dataset contains 9058 labeled and annotated images of 15 native Bangladeshi vehicles such as bus, motorbike, three-wheeler rickshaw, truck, wheelbarrow. Data augmentation techniques have been applied to keep the number of images comparable to each type of vehicle. For labeling the images, LabelImg tool by Tzuta Lin has been used. Human faces have also been blurred to maintain privacy and confidentiality. The dataset is compatible with various CNN architectures such as YOLO, VGG-16, R-CNN, DPM. It is available for research purposes at https://data.mendeley.com/datasets/pwyyg8zmk5/2.
A double-slit proposal for quantum annealing

We formulate and analyze a double-slit proposal for quantum annealing, which involves observing the probability of finding a two-level system (TLS) undergoing evolution from a transverse to a longitudinal field in the ground state at the final time $t_f$. We demonstrate that for annealing schedules involving two consecutive diabatic transitions, an interference effect is generated akin to a double-slit experiment. The observation of oscillations in the ground state probability as a function of $t_f$ (before the adiabatic limit sets in) then constitutes a sensitive test of coherence between energy eigenstates. This is further illustrated by analyzing the effect of coupling the TLS to a thermal bath: increasing either the bath temperature or the coupling strength results in a damping of these oscillations. The theoretical tools we introduce significantly simplify the analysis of the generalized Landau-Zener problem. Furthermore, our analysis connects quantum annealing algorithms exhibiting speedups via the mechanism of coherent diabatic transitions to near-term experiments with quantum annealing hardware.
Generating Fault-Tolerant Cluster States from Crystal Structures

Measurement-based quantum computing (MBQC) is a promising alternative to traditional circuit-based quantum computing predicated on the construction and measurement of cluster states. Recent work has demonstrated that MBQC provides a more general framework for fault-tolerance that extends beyond foliated quantum error-correcting codes. We systematically expand on that paradigm, and use combinatorial tiling theory to study and construct new examples of fault-tolerant cluster states derived from crystal structures. Included among these is a robust self-dual cluster state requiring only degree-3 connectivity. We benchmark several of these cluster states in the presence of circuit-level noise, and find a variety of promising candidates whose performance depends on the specifics of the noise model. By eschewing the distinction between data and ancilla, this malleable framework lays a foundation for the development of creative and competitive fault-tolerance schemes beyond conventional error-correcting codes.
Secret-Key Distillation across a Quantum Wiretap Channel under Restricted Eavesdropping

The theory of quantum cryptography aims to guarantee unconditional information-theoretic security against an omnipotent eavesdropper. In many practical scenarios, however, the assumption of an all-powerful adversary is excessive and can be relaxed considerably. In this paper we study secret-key distillation across a lossy and noisy quantum wiretap channel between Alice and Bob, with a separately parameterized realistically lossy quantum channel to the eavesdropper Eve. We show that under such restricted eavesdropping, the key rates achievable can exceed the secret-key-distillation capacity against an unrestricted eavesdropper in the quantum wiretap channel. Furthermore, we show upper bounds on the key rates based on the relative entropy of entanglement. This simple restricted eavesdropping model is widely applicable, for example, to free-space quantum optical communication, where realistic collection of light by Eve is limited by the finite size of her optical aperture. Future work will include calculating bounds on the amount of light Eve can collect under various realistic scenarios.
The Detached Self: Investigating the Effect of Depersonalisation on Self-Bias in the Visual Remapping of Touch

Abstract There is a growing consensus that our most fundamental sense of self is structured by the ongoing integration of sensory and motor information related to our own body. Depersonalisation (DP) is an intriguing form of altered subjective experience in which people report feelings of unreality and detachment from their sense of self. The current study used the visual remapping of touch (VRT) paradigm to explore self-bias in visual–tactile integration in non-clinical participants reporting high and low levels of depersonalisation experiences. We found that the high-DP group showed an increased overall VRT effect but a no-self-face bias, instead showing a greater VRT effect when observing the face of another person. In addition, across all participants, self-bias was negatively predicted by the occurrence of anomalous body experiences. These results indicate disrupted integration of tactile and visual representations of the bodily self in those experiencing high levels of DP and provide greater understanding of how disruptions in multisensory perception of the self may underlie the phenomenology of depersonalisation.
Quantum dense metrology by an SU(2)-in-SU(1,1) nested interferometer

With the help of quantum entanglement, quantum dense metrology (QDM) is a technique that can make joint estimates of two conjugate quantities such as phase and amplitude modulations of an optical field, with an accuracy beating the standard quantum limit simultaneously. SU(1,1) interferometers (SUIs) can realize QDM with detection loss tolerance but is limited in absolute sensitivity. Here, we present a QDM scheme with a linear or SU(2) interferometer nested inside an SUI. By using a degenerate SUI and controlling the phase angle of the phase-sensitive amplifiers in the SUI, we can achieve the optimum quantum enhancement in the measurement precision of an arbitrary mixture of phase and amplitude modulation.
On the Mass of Static Metrics with Positive Cosmological Constant: II

In this paper we develop the theory of quantum reverse hypercontractivity inequalities and show how they can be derived from log-Sobolev inequalities. Next we prove a generalization of the Stroock-Varopoulos inequality in the non-commutative setting which allows us to derive quantum hypercontractivity and reverse hypercontractivity inequalities solely from $2$-log-Sobolev and $1$-log-Sobolev inequalities respectively. We then prove some tensorization-type results providing us with tools to prove hypercontractivity and reverse hypercontractivity not only for certain quantum superoperators but also for their tensor powers. Finally as an application of these results, we generalize a recent technique for proving strong converse bounds in information theory via reverse hypercontractivity inequalities to the quantum setting. We prove strong converse bounds for the problems of quantum hypothesis testing and classical-quantum channel coding based on the quantum reverse hypercontractivity inequalities that we derive.
Efficient Generation of Low Autocorrelation Binary Sequences

Simple and efficient algorithm based on heuristic search by shotgun hill climbing to construct binary sequences with small peak sidelobe levels (PSL) is suggested. The algorithm is applied for generation of binary sequences of lengths between 106 and 300. Improvements are obtained in almost half of the considered lengths while for the rest of the lengths, binary sequences with the same PSL values as reported in the state-of-the-art publications are found.
Action principles for quantum automata and Lorentz invariance of discrete time quantum walks

A discrete action principle for general quantum automata is proposed. This action principle is particularized to Discrete Time Quantum Walks (DTQWs) and then extended into an energy and momentum preserving, manifestly covariant formulation. Space-time coordinates are introduced as new variables of the action and their equations of motion enforce energy and momentum conservation. This guarantees that the proposed action can be used to build future, DTQW-based self-consistent models of spinors interacting with gauge fields. A discrete stress-energy tensor for the DTQW is also obtained by functional differentiation of the action with respect to the gradients of the coordinates viewed as functions of the discrete grid points. The manifest covariance of the formulation highlights the special role played by the grid reference frame in the DTQW dynamics. The main discussion is complemented by three appendices.
L1-2D2PCANet: a deep learning network for face recognition

We propose a deep learning network L1-2D2PCANet for face recognition, which is based on L1-norm-based two-dimensional principal component analysis (L1-2DPCA). In our network, the role of L1-2DPCA is to learn the filters of multiple convolution layers. After the convolution layers, we deploy binary hashing and blockwise histogram for pooling. We test our network on some benchmark facial datasets, including Yale, AR face database, extended Yale B, labeled faces in the wild-aligned, and Face Recognition Technology database with the convolution neural network, PCANet, 2DPCANet, and L1-PCANet as comparison. The results show that the recognition performance of L1-2D2PCANet in all tests is better than baseline networks, especially when there are outliers in the test data. Owing to the L1-norm, L1-2D2PCANet is robust to outliers and changes of the training images.
Recent Advances and Trends in Lightweight Cryptography for IoT Security

Lightweight cryptography is a novel diversion from conventional cryptography to minimise its high level of resource requirements, thus it would impeccably fit in the internet-of-things (IoT) environment. The IoT platform is constrained in terms of physical size, internal capacity, other storage allocations like RAM/ROM and data rates. The devices are often battery powered, hence maintenance of the charged energy at least for a few years is essential. However, provision of sufficient security is challenging because the existing cryptographic methods are too heavy to adopt in the IoT. Consequently, an interest arose in the recent past to construct new cryptographic algorithms in a lightweight scale, but the attempts are still struggling to gain robustness against improved IoT threats and hazards.There exists a lack of literature studies to offer overall and up-to-date knowledge on lightweight cryptography. Therefore, this effort is to bridge the areas in the subject by summarising the content we explored during our complete survey recently. This work contains the development of lightweight cryptographic algorithms, its current advancements and futuristic enhancements. In contrast, this covers the history, parametric limitations of the invented methods, research progresses of cryptology as well as cryptanalysis.
Model architectures to extrapolate emotional expressions in DNN-based text-to-speech

This paper proposes architectures that facilitate the extrapolation of emotional expressions in deep neural network (DNN)-based text-to-speech (TTS). In this study, the meaning of “extrapolate emotional expressions” is to borrow emotional expressions from others, and the collection of emotional speech uttered by target speakers is unnecessary. Although a DNN has potential power to construct DNN-based TTS with emotional expressions and some DNN-based TTS systems have demonstrated satisfactory performances in the expression of the diversity of human speech, it is necessary and troublesome to collect emotional speech uttered by target speakers. To solve this issue, we propose architectures to separately train the speaker feature and the emotional feature and to synthesize speech with any combined quality of speakers and emotions. The architectures are parallel model (PM), serial model (SM), auxiliary input model (AIM), and hybrid models (PM&AIM and SM&AIM). These models are trained through emotional speech uttered by few speakers and neutral speech uttered by many speakers. Objective evaluations demonstrate that the performances in the open-emotion test provide insufficient information. They make a comparison with those in the closed-emotion test, but each speaker has their own manner of expressing emotion. However, subjective evaluation results indicate that the proposed models could convey emotional information to some extent. Notably, the PM can correctly convey sad and joyful emotions at a rate of >60%.
Local Water-Filling Algorithm for Shadow Detection and Removal of Document Images

Shadow detection and removal is an important task for digitized document applications. It is hard for many methods to distinguish shadow from printed text due to the high darkness similarity. In this paper, we propose a local water-filling method to remove shadows by mapping a document image into a structure of topographic surface. Firstly, we design a local water-filling approach including a flooding and effusing process to estimate the shading map, which can be used to detect umbra and penumbra. Then, the umbra is enhanced using Retinex Theory. For penumbra, we propose a binarized water-filling strategy to correct illumination distortions. Moreover, we build up a dataset called optical shadow removal (OSR dataset), which includes hundreds of shadow images. Experiments performed on OSR dataset show that our method achieves an average ErrorRatio of 0.685 with a computation time of 0.265 s to process an image size of 960×544 pixels on a desktop. The proposed method can remove the shading artifacts and outperform some state-of-the-art methods, especially for the removal of shadow boundaries.
Deterministically fabricated strain-tunable quantum dot single-photon sources emitting in the telecom O-band

Most quantum communication schemes aim at the long-distance transmission of quantum information. In the quantum repeater concept, the transmission line is subdivided into shorter links interconnected by entanglement distribution via Bell-state measurements to overcome inherent channel losses. This concept requires on-demand single-photon sources with a high degree of multi-photon suppression and high indistinguishability within each repeater node. For a successful operation of the repeater, a spectral matching of remote quantum light sources is essential. We present a spectrally tunable single-photon source emitting in the telecom O-band with the potential to function as a building block of a quantum communication network based on optical fibers. A thin membrane of GaAs embedding InGaAs quantum dots (QDs) is attached onto a piezoelectric actuator via gold thermocompression bonding. Here the thin gold layer acts simultaneously as an electrical contact, strain transmission medium and broadband backside mirror for the QD-micromesa. The nanofabrication of the QD-micromesa is based on in-situ electron-beam lithography, which makes it possible to integrate pre-selected single QDs deterministically into the center of monolithic micromesa structures. The QD pre-selection is based on distinct single-QD properties, signal intensity and emission energy. In combination with strain-induced fine tuning this offers a robust method to achieve spectral resonance in the emission of remote QDs. We show that the spectral tuning has no detectable influence on the multi-photon suppression with $g^{(2)}(0)$ as low as 2-4% and that the emission can be stabilized to an accuracy of 4 $\mu$eV using a closed-loop optical feedback.
Real-Time Monitoring for Hydraulic States Based on Convolutional Bidirectional LSTM with Attention Mechanism

By monitoring a hydraulic system using artificial intelligence, we can detect anomalous data in a manufacturing workshop. In addition, by analyzing the anomalous data, we can diagnose faults and prevent failures. However, artificial intelligence, especially deep learning, needs to learn much data, and it is often difficult to get enough data at the real manufacturing site. In this paper, we apply augmentation to increase the amount of data. In addition, we propose real-time monitoring based on a deep-learning model that uses convergence of a convolutional neural network (CNN), a bidirectional long short-term memory network (BiLSTM), and an attention mechanism. CNN extracts features from input data, and BiLSTM learns feature information. The learned information is then fed to the sigmoid classifier to find out if it is normal or abnormal. Experimental results show that the proposed model works better than other deep-learning models, such as CNN or long short-term memory (LSTM).
Increasing Precision of Division Property

In this paper we propose new techniques related to division property. We describe for the first time a practical algorithm for computing the propagation tables of 16-bit Super-Sboxes, increasing the precision of the division property by removing a lot of false division trails. We also improve the complexity of the procedure introduced by Lambin et al. (Design, Codes and Cryptography, 2020) to extend a cipher with linear mappings and show how to decrease the number of transitions to look for. While search procedures for integral distinguishers most often rely on MILP or SAT solvers for their ease of programming the propagation constraints, such generic solvers can only handle small 4/8-bit Sboxes. Thus we developed an ad-hoc tool handling larger Sboxes and all the improvements described in the paper. As a result, we found new integral distinguishers on SKINNY-64, HIGHT and Midori-64.