Reviewerly/exHarmony huggingface dataset:
Detecting telecom single photons with 99.5−2.07+0.5% system detection efficiency and high time resolution

Single photon detectors are indispensable tools in optics, from fundamental measurements to quantum information processing. The ability of superconducting nanowire single photon detectors (SNSPDs) to detect single photons with unprecedented efficiency, short dead time, and high time resolution over a large frequency range enabled major advances in quantum optics. However, combining near-unity system detection efficiency (SDE) with high timing performance remains an outstanding challenge. In this work, we fabricated novel SNSPDs on membranes with 99.5−2.07+0.5% SDE at 1350 nm with 32 ps timing jitter (using a room-temperature amplifier), and other detectors in the same batch showed 94%–98% SDE at 1260–1625 nm with 15–26 ps timing jitter (using cryogenic amplifiers). The SiO2/Au membrane enables broadband absorption in small SNSPDs, offering high detection efficiency in combination with high timing performance. With low-noise cryogenic amplifiers operated in the same cryostat, our efficient detectors reach a timing jitter in the range of 15–26 ps. We discuss the prime challenges in optical design, device fabrication, and accurate and reliable detection efficiency measurements to achieve high performance single photon detection. As a result, the fast developing fields of quantum information science, quantum metrology, infrared imaging, and quantum networks will greatly benefit from this far-reaching quantum detection technology.
Public attitudes towards COVID‐19 contact tracing apps: A UK‐based focus group study

During the 2020 COVID-19 pandemic, one of the key components of many countries' strategies to reduce the spread of the virus is contact tracing.To explore public attitudes to a COVID-19 contact tracing app in the United Kingdom.Online video-conferencing.27 participants, UK residents aged 18 years and older.Qualitative study consisting of six focus groups carried out between 1st-12th May, 2020 (39-50 days into the UK 'lockdown').Participants were divided as to whether or not they felt they would use the app. Analysis revealed five themes: (1) lack of information and misconceptions surrounding COVID-19 contact tracing apps; (2) concerns over privacy; (3) concerns over stigma; (4)concerns over uptake; and (5) contact tracing as the 'greater good'. Concerns over privacy, uptake and stigma were particularly significant amongst those stated they will not be using the app, and the view that the app is for the 'greater good' was particularly significant amongst those who stated they will be using the app. One of the most common misconceptions about the app was that it could allow users to specifically identify and map COVID-19 cases amongst their contacts and in their vicinity.Our participants were torn over whether digital contact tracing is a good idea or not, and views were heavily influenced by moral reasoning.No patients were involved in this study. The public were not involved in the development of the research questions, research design or outcome measures. A pilot focus group with participants not included in the present paper was used to help test and refine the focus group questions. Summary results were disseminated via email to participants prior to publication for feedback and comment.
Efficient facial representations for age, gender and identity recognition in organizing photo albums using multi-output ConvNet

This paper is focused on the automatic extraction of persons and their attributes (gender, year of born) from album of photos and videos. A two-stage approach is proposed in which, firstly, the convolutional neural network simultaneously predicts age/gender from all photos and additionally extracts facial representations suitable for face identification. Here the MobileNet is modified and is preliminarily trained to perform face recognition in order to additionally recognize age and gender. The age is estimated as the expected value of top predictions in the neural network. In the second stage of the proposed approach, extracted faces are grouped using hierarchical agglomerative clustering techniques. The birth year and gender of a person in each cluster are estimated using aggregation of predictions for individual photos. The proposed approach is implemented in an Android mobile application. It is experimentally demonstrated that the quality of facial clustering for the developed network is competitive with the state-of-the-art results achieved by deep neural networks, though implementation of the proposed approach is much computationally cheaper. Moreover, this approach is characterized by more accurate age/gender recognition when compared to the publicly available models.
Improved Monarch Butterfly Optimization Algorithm Based on Opposition-Based Learning and Random Local Perturbation

Many optimization problems have become increasingly complex, which promotes researches on the improvement of different optimization algorithms. The monarch butterfly optimization (MBO) algorithm has proven to be an effective tool to solve various kinds of optimization problems. However, in the basic MBO algorithm, the search strategy easily falls into local optima, causing premature convergence and poor performance on many complex optimization problems. To solve the issues, this paper develops a novel MBO algorithm based on opposition-based learning (OBL) and random local perturbation (RLP). Firstly, the OBL method is introduced to generate the opposition-based population coming from the original population. By comparing the opposition-based population with the original population, the better individuals are selected and pass to the next generation, and then this process can efficiently prevent the MBO from falling into a local optimum. Secondly, a new RLP is defined and introduced to improve the migration operator. This operation shares the information of excellent individuals and is helpful for guiding some poor individuals toward the optimal solution. A greedy strategy is employed to replace the elitist strategy to eliminate setting the elitist parameter in the basic MBO, and it can reduce a sorting operation and enhance the computational efficiency. Finally, an OBL and RLP-based improved MBO (OPMBO) algorithm with its complexity analysis is developed, following on which many experiments on a series of different dimensional benchmark functions are performed and the OPMBO is applied to clustering optimization on several public data sets. Experimental results demonstrate that the proposed algorithm can achieve the great optimization performance compared with a few state-of-the-art algorithms in most of the test cases.
Superfast encodings for fermionic quantum simulation

Fermionic quantum simulation often require transforming the fermionic Hamiltonian to qubit operators. The authors provide a fermion-to-qubit encoding that simplifies the qubit Hamiltonian by introducing redundant degrees of freedom. This can be used to correct all single qubit errors in the simulation.
A CNN-Based End-to-End Learning Framework Toward Intelligent Communication Systems

Deep learning has been applied in physical-layer communications systems in recent years and has demonstrated fascinating results that were comparable or even better than human expert systems. In this paper, a novel convolutional neural networks (CNNs)-based autoencoder communication system is proposed, which can work intelligently with arbitrary block length, can support different throughput and can operate under AWGN and Rayleigh fading channels as well as deviations from AWGN environments. The proposed generalized communication system is comprised of carefully designed convolutional neural layers and, hence, inherits CNN's breakthrough characteristics, such as generalization, feature learning, classification, and fast training convergence. On the other hand, the end-to-end architecture jointly performs the tasks of encoding/decoding and modulation/demodulation. Finally, we provide the numerous simulation results of the learned system in order to illustrate its generalization capability under various system conditions.
Metric Learning for Image Registration

Image registration is a key technique in medical image analysis to estimate deformations between image pairs. A good deformation model is important for high-quality estimates. However, most existing approaches use ad-hoc deformation models chosen for mathematical convenience rather than to capture observed data variation. Recent deep learning approaches learn deformation models directly from data. However, they provide limited control over the spatial regularity of transformations. Instead of learning the entire registration approach, we learn a spatially-adaptive regularizer within a registration model. This allows controlling the desired level of regularity and preserving structural properties of a registration model. For example, diffeomorphic transformations can be attained. Our approach is a radical departure from existing deep learning approaches to image registration by embedding a deep learning model in an optimization-based registration algorithm to parameterize and data-adapt the registration model itself. Source code is publicly-available at https://github.com/uncbiag/registration.
Improved Success Probability with Greater Circuit Depth for the Quantum Approximate Optimization Algorithm

Present-day, noisy, small or intermediate-scale quantum processors---although far from fault-tolerant---support the execution of heuristic quantum algorithms, which might enable a quantum advantage, for example, when applied to combinatorial optimization problems. On small-scale quantum processors, validations of such algorithms serve as important technology demonstrators. We implement the quantum approximate optimization algorithm (QAOA) on our hardware platform, consisting of two superconducting transmon qubits and one parametrically modulated coupler. We solve small instances of the NP-complete exact-cover problem, with 96.6% success probability, by iterating the algorithm up to level two.
Smooth Function Approximation by Deep Neural Networks with General Activation Functions

There has been a growing interest in expressivity of deep neural networks. However, most of the existing work about this topic focuses only on the specific activation function such as ReLU or sigmoid. In this paper, we investigate the approximation ability of deep neural networks with a broad class of activation functions. This class of activation functions includes most of frequently used activation functions. We derive the required depth, width and sparsity of a deep neural network to approximate any Hölder smooth function upto a given approximation error for the large class of activation functions. Based on our approximation error analysis, we derive the minimax optimality of the deep neural network estimators with the general activation functions in both regression and classification problems.
Aggregated Deep Local Features for Remote Sensing Image Retrieval

Remote Sensing Image Retrieval remains a challenging topic due to the special nature of Remote Sensing imagery. Such images contain various different semantic objects, which clearly complicates the retrieval task. In this paper, we present an image retrieval pipeline that uses attentive, local convolutional features and aggregates them using the Vector of Locally Aggregated Descriptors (VLAD) to produce a global descriptor. We study various system parameters such as the multiplicative and additive attention mechanisms and descriptor dimensionality. We propose a query expansion method that requires no external inputs. Experiments demonstrate that even without training, the local convolutional features and global representation outperform other systems. After system tuning, we can achieve state-of-the-art or competitive results. Furthermore, we observe that our query expansion method increases overall system performance by about 3%, using only the top-three retrieved images. Finally, we show how dimensionality reduction produces compact descriptors with increased retrieval performance and fast retrieval computation times, e.g., 50% faster than the current systems.
Scene Semantic Recognition Based on Modified Fuzzy C-Mean and Maximum Entropy Using Object-to-Object Relations

With advances in machine vision systems (e.g., artificial eye, unmanned aerial vehicles, surveillance monitoring) scene semantic recognition (SSR) technology has attracted much attention due to its related applications such as autonomous driving, tourist navigation, intelligent traffic and remote aerial sensing.Although tremendous progress has been made in visual interpretation, several challenges remain (i.e., dynamic backgrounds, occlusion, lack of labeled data, changes in illumination, direction, and size).Therefore, we have proposed a novel SSR framework that intelligently segments the locations of objects, generates a novel Bag of Features, and recognizes scenes via Maximum Entropy.First, denoising and smoothing are applied on scene data.Second, modified Fuzzy C-Means integrates with super-pixels and Random Forest for the segmentation of objects.Third, these segmented objects are used to extract a novel Bag of Features that concatenate different blobs, multiple orientations, Fourier transform and geometrical points over the objects.An Artificial Neural Network recognizes the multiple objects using the different patterns of objects.Finally, labels are estimated via Maximum Entropy model.During experimental evaluation, our proposed system illustrated a remarkable mean accuracy rate of 90.07%over the MSRC dataset and 89.26% over the Caltech 101 for object recognition, and 93.53% over the Pascal-VOC12 dataset for scene recognition, respectively.The proposed system should be applicable to various emerging technologies, such as augmented reality, to represent the real-world environment for military training and engineering design, as well as for entertainment, artificial eyes for visually impaired people and traffic monitoring to avoid congestion or road accidents.
Segmenting Transparent Objects in the Wild with Transformer

This work presents a new fine-grained transparent object segmentation dataset, termed Trans10K-v2, extending Trans10K-v1, the first large-scale transparent object segmentation dataset. Unlike Trans10K-v1 that only has two limited categories, our new dataset has several appealing benefits. (1) It has 11 fine-grained categories of transparent objects, commonly occurring in the human domestic environment, making it more practical for real-world application. (2) Trans10K-v2 brings more challenges for the current advanced segmentation methods than its former version. Furthermore, a novel Transformer-based segmentation pipeline termed Trans2Seg is proposed. Firstly, the Transformer encoder of Trans2Seg provides the global receptive field in contrast to CNN's local receptive field, which shows excellent advantages over pure CNN architectures. Secondly, by formulating semantic segmentation as a problem of dictionary look-up, we design a set of learnable prototypes as the query of Trans2Seg's Transformer decoder, where each prototype learns the statistics of one category in the whole dataset. We benchmark more than 20 recent semantic segmentation methods, demonstrating that Trans2Seg significantly outperforms all the CNN-based methods, showing the proposed algorithm's potential ability to solve transparent object segmentation.Code is available in https://github.com/xieenze/Trans2Seg.
Provable Robustness of ReLU networks via Maximization of Linear Regions

It has been shown that neural network classifiersare not robust. This raises concernsabout their usage in safety-critical systems.We propose in this paper a regularizationscheme for ReLU networks which provablyimproves the robustness of the classifier bymaximizing the linear regions of the classifieras well as the distance to the decision boundary.Using our regularization we can even findthe minimal adversarial perturbation for a certainfraction of test points for large networks.In the experiments we show that our approachimproves upon pure adversarial training bothin terms of lower and upper bounds on therobustness and is comparable or better thanthe state of the art in terms of test error androbustness.
Comparison of Diverse Decoding Methods from Conditional Language Models

While conditional language models have greatly improved in their ability to output high quality natural language, many NLP applications benefit from being able to generate a diverse set of candidate sequences. Diverse decoding strategies aim to, within a given-sized candidate list, cover as much of the space of high-quality outputs as possible, leading to improvements for tasks that rerank and combine candidate outputs. Standard decoding methods, such as beam search, optimize for generating high likelihood sequences rather than diverse ones, though recent work has focused on increasing diversity in these methods. In this work, we perform an extensive survey of decoding-time strategies for generating diverse outputs from a conditional language model. In addition, we present a novel method where we over-sample candidates, then use clustering to remove similar sequences, thus achieving high diversity without sacrificing quality.
Chaotic Image Encryption Using Hopfield and Hindmarsh–Rose Neurons Implemented on FPGA

Chaotic systems implemented by artificial neural networks are good candidates for data encryption. In this manner, this paper introduces the cryptographic application of the Hopfield and the Hindmarsh–Rose neurons. The contribution is focused on finding suitable coefficient values of the neurons to generate robust random binary sequences that can be used in image encryption. This task is performed by evaluating the bifurcation diagrams from which one chooses appropriate coefficient values of the mathematical models that produce high positive Lyapunov exponent and Kaplan–Yorke dimension values, which are computed using TISEAN. The randomness of both the Hopfield and the Hindmarsh–Rose neurons is evaluated from chaotic time series data by performing National Institute of Standard and Technology (NIST) tests. The implementation of both neurons is done using field-programmable gate arrays whose architectures are used to develop an encryption system for RGB images. The success of the encryption system is confirmed by performing correlation, histogram, variance, entropy, and Number of Pixel Change Rate (NPCR) tests.
Mitigating Depolarizing Noise on Quantum Computers with Noise-Estimation Circuits

A significant problem for current quantum computers is noise. While there are many distinct noise channels, the depolarizing noise model often appropriately describes average noise for large circuits involving many qubits and gates. We present a method to mitigate the depolarizing noise by first estimating its rate with a noise-estimation circuit and then correcting the output of the target circuit using the estimated rate. The method is experimentally validated on the simulation of the Heisenberg model. We find that our approach in combination with readout-error correction, randomized compiling, and zero-noise extrapolation produces results close to exact results even for circuits containing hundreds of CNOT gates.
CVM-Cervix: A hybrid cervical Pap-smear image classification framework using CNN, visual transformer and multilayer perceptron

Cervical cancer is the seventh most common cancer among all the cancers worldwide and the fourth most common cancer among women. Cervical cytopathology image classification is an important method to diagnose cervical cancer. However, manual inspection is very troublesome, and experts are prone to make mistakes. The emergence of the automatic computer-aided diagnosis system solves this problem. This paper proposes a framework called CVM-Cervix based on deep learning to perform cervical cell classification tasks. It can analyze pap slides quickly and accurately. CVM-Cervix first proposes a Convolutional Neural Network module and a Visual Transformer module for local and global feature extraction respectively, then a Multilayer Perceptron module is designed to fuse the local and global features for the final classification. Experimental results show the effectiveness and potential of the proposed CVM-Cervix in the field of cervical Pap smear image classification. In addition, according to the practical needs of clinical work, we perform a lightweight post-processing to compress the model.
Self-testing mutually unbiased bases in the prepare-and-measure scenario

Mutually unbiased bases (MUBs) constitute the canonical example of incompatible quantum measurements. One standard application of MUBs is the task known as quantum random access code (QRAC), in which classical information is encoded in a quantum system, and later part of it is recovered by performing a quantum measurement. We analyse a specific class of QRACs, known as the $2^{d} \to 1$ QRAC, in which two classical dits are encoded in a $d$-dimensional quantum system. It is known that among rank-1 projective measurements MUBs give the best performance. We show (for every $d$) that this cannot be improved by employing non-projective measurements. Moreover, we show that the optimal performance can only be achieved by measurements which are rank-1 projective and mutually unbiased. In other words, the $2^{d} \to 1$ QRAC is a self-test for a pair of MUBs in the prepare-and-measure scenario. To make the self-testing statement robust we propose measures which characterise how well a pair of (not necessarily projective) measurements satisfies the MUB conditions and show how to estimate these measures from the observed performance. Similarly, we derive explicit bounds on operational quantities like the incompatibility robustness or the amount of uncertainty generated by the uncharacterised measurements. For low dimensions the robustness of our bounds is comparable to that of currently available technology, which makes them relevant for existing experiments. Lastly, our results provide essential support for a recently proposed method for solving the long-standing existence problem of MUBs.
Double-image encryption based on interference and logistic map under the framework of double random phase encoding

A novel double-image encryption approach has been proposed based on optical interference and logistic map. Initially, original images are rearranged into two scrambled components with the use of the random sequence engendered based on the logistic map with the given conditions such as initial value and bifurcate parameter. One component is used as the input image of the framework of double random phase encoding and encrypted into the ciphertext. Another is decomposed into two phase masks, which are placed into the input plane and transform plane of double random phase encoding, respectively. To strength the nonlinearity of the cryptosystem, these phase masks considered as the main secret keys are further encoded by the aid of two random sequences. Because these keys are produced in the encryption process and directly related to original images, the proposed scheme has high resistance against the potential attacks such as chosen plaintext attack. Meanwhile, the parameters such as wavelength, axial distance, and conditions of the logistic map are considered as additional keys, which can enhance the security level further. Most importantly, the annoying silhouette problem existed in the interference-based encryption methods can be avoided effectively. A set of numerical simulations are presented to demonstrate the validity and feasibility of the proposed technique.