Reviewerly/exHarmony huggingface dataset:
Bounds on Instantaneous Nonlocal Quantum Computation

Instantaneous nonlocal quantum computation refers to a process in which spacelike separated parties simulate a nonlocal quantum operation on their joint systems through the consumption of pre-shared entanglement. To prevent a violation of causality, this simulation succeeds up to local errors that can only be corrected after the parties communicate classically with one another. However, this communication is non-interactive, and it involves just the broadcasting of local measurement outcomes. We refer to this operational paradigm as local operations and broadcast communication (LOBC) to distinguish it from the standard local operations and (interactive) classical communication (LOCC). In this paper, we show that an arbitrary two-qubit gate can be implemented by LOBC with $\epsilon$-error using $O(\log(1/\epsilon))$ entangled bits (ebits). This offers an exponential improvement over the best known two-qubit protocols, whose ebit costs behave as $O(1/\epsilon)$. We also consider the family of binary controlled gates on dimensions $d_A\otimes d_B$. We find that any hermitian gate of this form can be implemented by LOBC using a single shared ebit. In sharp contrast, a lower bound of $\log d_B$ ebits is shown in the case of generic (i.e. non-hermitian) gates from this family, even when $d_A=2$. This demonstrates an unbounded gap between the entanglement costs of LOCC and LOBC gate implementation. Whereas previous lower bounds on the entanglement cost for instantaneous nonlocal computation restrict the minimum dimension of the needed entanglement, we bound its entanglement entropy. To our knowledge this is the first such lower bound of its kind.
A Quantum N-Queens Solver

The N-queens problem is to find the position of N queens on an N by N chess board such that no queens attack each other. The excluded diagonals N-queens problem is a variation where queens cannot be placed on some predefined fields along diagonals. This variation is proven NP-complete and the parameter regime to generate hard instances that are intractable with current classical algorithms is known. We propose a special purpose quantum simulator that implements the excluded diagonals N-queens completion problem using atoms in an optical lattice and cavity-mediated long-range interactions. Our implementation has no overhead from the embedding allowing to directly probe for a possible quantum advantage in near term devices for optimization problems.
GAN based image deblurring using dark channel prior

A conditional general adversarial network (GAN) is proposed for image deblurring problem.It is tailored for image deblurring instead of just applying GAN on the deblurring problem.Motivated by that, dark channel prior is carefully picked to be incorporated into the loss function for network training.To make it more compatible with neuron networks, its original indifferentiable form is discarded and L2 norm is adopted instead.On both synthetic datasets and noisy natural images, the proposed network shows improved deblurring performance and robustness to image noise qualitatively and quantitatively.Additionally, compared to the existing end-to-end deblurring networks, our network structure is light-weight, which ensures less training and testing time.
Constraining the parameters of high-dimensional models with active learning

Abstract Constraining the parameters of physical models with $$&gt;5-10$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mo>&gt;</mml:mo><mml:mn>5</mml:mn><mml:mo>-</mml:mo><mml:mn>10</mml:mn></mml:mrow></mml:math> parameters is a widespread problem in fields like particle physics and astronomy. The generation of data to explore this parameter space often requires large amounts of computational resources. The commonly used solution of reducing the number of relevant physical parameters hampers the generality of the results. In this paper we show that this problem can be alleviated by the use of active learning. We illustrate this with examples from high energy physics, a field where simulations are often expensive and parameter spaces are high-dimensional. We show that the active learning techniques query-by-committee and query-by-dropout-committee allow for the identification of model points in interesting regions of high-dimensional parameter spaces (e.g. around decision boundaries). This makes it possible to constrain model parameters more efficiently than is currently done with the most common sampling algorithms and to train better performing machine learning models on the same amount of data. Code implementing the experiments in this paper can be found on GitHub "Image missing"<!-- image only, no MathML or LaTex -->
A machine-learning phase classification scheme for anomaly detection in signals with periodic characteristics

In this paper, we propose a novel machine-learning method for anomaly detection applicable to data with periodic characteristics where randomly varying period lengths are explicitly allowed. A multi-dimensional time series analysis is conducted by training a data-adapted classifier consisting of deep convolutional neural networks performing phase classification. The entire algorithm including data pre-processing, period detection, segmentation, and even dynamic adjustment of the neural networks is implemented for fully automatic execution. The proposed method is evaluated on three example datasets from the areas of cardiology, intrusion detection, and signal processing, presenting reasonable performance.
Facial Expression Restoration Based on Improved Graph Convolutional Networks

Facial expression analysis in the wild is challenging when the facial image is with low resolution or partial occlusion. Considering the correlations among different facial local regions under different facial expressions, this paper proposes a novel facial expression restoration method based on generative adversarial network by integrating an improved graph convolutional network (IGCN) and region relation modeling block (RRMB). Unlike conventional graph convolutional networks taking vectors as input features, IGCN can use tensors of face patches as inputs. It is better to retain the structure information of face patches. The proposed RRMB is designed to address facial generative tasks including inpainting and super-resolution with facial action units detection, which aims to restore facial expression as the ground-truth. Extensive experiments conducted on BP4D and DISFA benchmarks demonstrate the effectiveness of our proposed method through quantitative and qualitative evaluations.
A Privacy-Preserved and Cost-Efficient Control Scheme for Coronavirus Outbreak Using Call Data Record and Contact Tracing

Coronavirus or COVID-19, which has been declared pandemic by the World Health Organization, has incurred huge losses to the lives of people throughout the world. Although, the scientists, researchers and doctors are working round the clock to develop a vaccine for COVID-19, it may take a year or two to make a safe and effective vaccine available for the world. In current circumstances, a solution must be developed to control or stop the spread of the virus. For this purpose, a novel technique based on call data record analysis (CDRA)and contact tracing is proposed that can effectively control the coronavirus outbreak. A positive coronavirus patient can be traced through CDRA and contact tracing. The technique can track the path traversed by the patient and collect the cell numbers of all those people who have met with the patient. Keeping in tact the privacy of this group of people, who are contacted through their cell numbers so that they can isolate themselves till the result of their coronavirus test arrives. If a test result of a person comes positive among the group, then he/she must be isolated and same CDRA and contact tracing procedures are adopted for that person. A COVID-19 patient is geo tagged and alerts are sent if any violation of isolation is done by the patient. Moreover, the general public is informed in advance to avoid the path followed by the patients. This cost effective mechanism is not only capable to control the coronavirus outbreak but also helps in isolating the patient in his/her house.
Adversarial Feature Learning and Unsupervised Clustering Based Speech Synthesis for Found Data With Acoustic and Textual Noise

Attention-based sequence-to-sequence (seq2seq) speech synthesis has achieved extraordinary performance. But a studio-quality corpus with manual transcription is necessary to train such seq2seq systems. In this paper, we propose an approach to build high-quality and stable seq2seq based speech synthesis system using challenging found data, where training speech contains noisy interferences (acoustic noise) and texts are imperfect speech recognition transcripts (textual noise). To deal with text-side noise, we propose a VQVAE based heuristic method to compensate erroneous linguistic feature with phonetic information learned directly from speech. As for the speech-side noise, we propose to learn a noise-independent feature in the auto-regressive decoder through adversarial training and data augmentation, which does not need an extra speech enhancement model. Experiments show the effectiveness of the proposed approach in dealing with text-side and speech-side noise. Surpassing the denoising approach based on a state-of-the-art speech enhancement model, our system built on noisy found data can synthesize clean and high-quality speech with MOS close to the system built on the clean counterpart.
The role of entropy in topological quantum error correction

The performance of a quantum error-correction process is determined by the likelihood that a random configuration of errors introduced to the system will lead to the corruption of encoded logical information.In this work we compare two different variants of the surface code with a comparable number of qubits: the surface code defined on a square lattice and the same model on a lattice that is rotated by π/4.This seemingly innocuous change increases the distance of the code by a factor of √ 2. However, as we show, this gain can come at the expense of significantly increasing the number of different failure mechanisms that are likely to occur.We use a number of different methods to explore this tradeoff over a large range of parameter space under an independent and identically distributed noise model.We rigorously analyze the leading order performance for low error rates, where the larger distance code performs best for all system sizes.Using an analytical model and Monte Carlo sampling, we find that this improvement persists for fixed sub-threshold error rates for large system size, but that the improvement vanishes close to threshold.Remarkably, intensive numerics uncover a region of system sizes and sub-threshold error rates where the square lattice surface code marginally outperforms the rotated model.
Implementation and Evaluation of Vision-Based Sensor Image Compression for Close-Range Photogrammetry and Structural Health Monitoring

Much research is still underway to achieve long-term and real-time monitoring using data from vision-based sensors. A major challenge is handling and processing enormous amount of data and images for either image storage, data transfer, or image analysis. To help address this challenge, this study explores and proposes image compression techniques using non-adaptive linear interpolation and wavelet transform algorithms. The effect and implication of image compression are investigated in the close-range photogrammetry as well as in realistic structural health monitoring applications. For this purpose, images and results from three different laboratory experiments and three different structures are utilized. The first experiment uses optical targets attached to a sliding bar that is displaced by a standard one-inch steel block. The effect of image compression in the photogrammetry is discussed and the monitoring accuracy is assessed by comparing the one-inch value with the measurement from the optical targets. The second application is a continuous static test of a small-scale rigid structure, and the last application is from a seismic shake table test of a full-scale 3-story building tested at E-Defense in Japan. These tests aimed at assessing the static and dynamic response measurement accuracy of vision-based sensors when images are highly compressed. The results show successful and promising application of image compression for photogrammetry and structural health monitoring. The study also identifies best methods and algorithms where effective compression ratios up to 20 times, with respect to original data size, can be applied and still maintain displacement measurement accuracy.
Iterative Training of Neural Networks for Intra Prediction

This paper presents an iterative training of neural networks for intra prediction in a block-based image and video codec. First, the neural networks are trained on blocks arising from the codec partitioning of images, each paired with its context. Then, iteratively, blocks are collected from the partitioning of images via the codec including the neural networks trained at the previous iteration, each paired with its context, and the neural networks are retrained on the new pairs. Thanks to this training, the neural networks can learn intra prediction functions that both stand out from those already in the initial codec and boost the codec in terms of rate-distortion. Moreover, the iterative process allows the design of training data cleansings essential for the neural network training. When the iteratively trained neural networks are put into H.265 (HM-16.15), -4.2% of mean dB-rate reduction is obtained. By moving them into H.266 (VTM-5.0), the mean dB-rate reduction reaches -1.9%.
Side Channel Information Set Decoding Using Iterative Chunking

This paper presents an attack based on side-channel information and (ISD) on the code-based Niederreiter cryptosystem and an evaluation of the practicality of the attack using an electromagnetic side channel. We start by directly adapting the timing side-channel plaintext-recovery attack by Shoufan et al. from 2010 to the constant-time implementation of the Niederreiter cryptosystem as used in the official FPGA-implementation of the NIST finalist “Classic McEliece”. We then enhance our attack using ISD and a new technique that we call iterative chunking to further significantly reduce the number of required side-channel measurements. We theoretically show that our attack improvements have a significant impact on reducing the number of required side-channel measurements. For example, for the 256-bit security parameter set kem/mceliece6960119 of “Classic McEliece”, we improve the basic attack that requires 5415 measurements to less than 562 measurements on average to mount a successful plaintext-recovery attack. Further reductions can be achieved at the price of increasing the cost of the ISD computations. We confirm our findings by practically mounting the attack on the official FPGA-implementation of “Classic McEliece” for all proposed parameter sets.
Certified Quantum Computation in Isabelle/HOL

In this article we present an ongoing effort to formalise quantum algorithms and results in quantum information theory using the proof assistant Isabelle/HOL. Formal methods being critical for the safety and security of algorithms and protocols, we foresee their widespread use for quantum computing in the future. We have developed a large library for quantum computing in Isabelle based on a matrix representation for quantum circuits, successfully formalising the no-cloning theorem, quantum teleportation, Deutsch's algorithm, the Deutsch-Jozsa algorithm and the quantum Prisoner's Dilemma. We discuss the design choices made and report on an outcome of our work in the field of quantum game theory.
Personalizing Diversity Versus Accuracy in Session-Based Recommender Systems

One of the most important concerns about recommender systems is the filter bubble phenomenon. While recommender systems try to personalize information, they tighten the filter bubble around the users and deprive them of a wide range of content. To overcome this problem, one can diversify the personalized recommendation list. A diversified list usually presents a broader content to the user. Session-based recommender systems are types of recommenders in which only the current session of the user is available, and therefore, they should recommend the next item given the items in the current session. While diversifying conventional recommender systems has been well assessed in the literature, it has gained less attention in session-based recommenders. Diversity and accuracy usually have a negative correlation, i.e., by improving one the other one will be declined. In this study, we propose diversity and accuracy enhancing approaches based on sequential rule mining and session-based k-nearest neighbor methods. Finally, we propose a performance balancing approach that improves both the diversity and accuracy of these session-based recommender systems. We demonstrate the performance of the proposed methods on four music recommender datasets.
All-Optical Optimal <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>N</mml:mi></mml:math> -to- <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>M</mml:mi></mml:math> Quantum Cloning of Coherent States

The laws of quantum mechanics forbid the perfect copying of an unknown quantum state, known as the no-cloning theorem. In spite of this, approximate cloning with imperfect fidelity is possible, which opens up the field of quantum cloning. In general, quantum cloning can be divided into discrete variable and continuous variable (CV) categories. In the CV regime, all-optical implementation of the optimal $N\ensuremath{\rightarrow}M$ quantum cloning has been proposed in two original parallel works, which involves a parametric amplifier and a set of beam splitters and thus avoids the optic-electro and electro-optic conversions in the current CV quantum cloning technologies. However, such original proposal of all-optical CV optimal $N\ensuremath{\rightarrow}M$ quantum cloning scheme has never been experimentally implemented. Here, we show that optimal $N\ensuremath{\rightarrow}M$ quantum cloning of coherent states can be realized by utilizing a parametric amplifier based on four-wave mixing process in a hot atomic vapor and a set of beam splitters. In particular, we realize $1\ensuremath{\rightarrow}M$, $2\ensuremath{\rightarrow}M$, and $4\ensuremath{\rightarrow}M$ quantum cloning. We find that the fidelity of $N\ensuremath{\rightarrow}M$ quantum cloning increases with the decrease of clone number $M$ and the increase of original replica number $N$. The best cloning fidelity achieved in our experiment is about 93.3% $\ifmmode\pm\else\textpm\fi{}1.0%$ in the $4\ensuremath{\rightarrow}5$ case. Our results may find potential applications in realizing all-optical high-fidelity quantum state transfer and all-optical high-compatibility eavesdropping attack in quantum communication networks.
Efficient sampling of ground and low-energy Ising spin configurations with a coherent Ising machine

We show that the nonlinear stochastic dynamics of a measurement-feedback-based coherent Ising machine (MFB-CIM) in the presence of quantum noise can be exploited to sample degenerate ground and low-energy spin configurations of the Ising model. We formulate a general discrete-time Gaussian-state model of the MFB-CIM which faithfully captures the nonlinear dynamics present at and above system threshold. This model overcomes the limitations of both mean-field models, which neglect quantum noise, and continuous-time models, which assume long photon lifetimes. Numerical simulations of our model show that when the MFB-CIM is operated in a quantum-noise-dominated regime with short photon lifetimes (i.e., low cavity finesse), homodyne monitoring of the system can efficiently produce samples of low-energy Ising spin configurations, requiring many fewer roundtrips to sample than suggested by established high-finesse, continuous-time models. We find that sampling performance is robust to, or even improved by, turning off or altogether reversing the sign of the parametric drive, but performance is critically reduced in the absence of optical nonlinearity. For the class of MAX-CUT problems with binary-signed edge weights, the number of roundtrips sufficient to fully sample all spin configurations up to the first-excited Ising energy, including all degeneracies, scales as $1.08^N$. At a problem size of $N = 100$ with a few dozen (median of 20) such desired configurations per instance, we have found median sufficient sampling times of $6\times10^6$ roundtrips; in an experimental implementation of an MFB-CIM with a 10 GHz repetition rate, this corresponds to a wall-clock sampling time of 60 ms.
Simulating extremal temporal correlations

The correlations arising from sequential measurements on a single quantum system form a polytope. This is defined by the arrow-of-time (AoT) constraints, meaning that future choices of measurement settings cannot influence past outcomes. We discuss the resources needed to simulate the extreme points of the AoT polytope, where resources are quantified in terms of the minimal dimension, or "internal memory" of the physical system. First, we analyze the equivalence classes of the extreme points under symmetries. Second, we characterize the minimal dimension necessary to obtain a given extreme point of the AoT polytope, including a lower scaling bound in the asymptotic limit of long sequences. Finally, we present a general method to derive dimension-sensitive temporal inequalities for longer sequences, based on inequalities for shorter ones, and investigate their robustness to imperfections.
ReportAGE: Automatically extracting the exact age of Twitter users based on self-reports in tweets

Advancing the utility of social media data for research applications requires methods for automatically detecting demographic information about social media study populations, including users’ age. The objective of this study was to develop and evaluate a method that automatically identifies the exact age of users based on self-reports in their tweets. Our end-to-end automatic natural language processing (NLP) pipeline, ReportAGE, includes query patterns to retrieve tweets that potentially mention an age, a classifier to distinguish retrieved tweets that self-report the user’s exact age (“age” tweets) and those that do not (“no age” tweets), and rule-based extraction to identify the age. To develop and evaluate ReportAGE, we manually annotated 11,000 tweets that matched the query patterns. Based on 1000 tweets that were annotated by all five annotators, inter-annotator agreement (Fleiss’ kappa) was 0.80 for distinguishing “age” and “no age” tweets, and 0.95 for identifying the exact age among the “age” tweets on which the annotators agreed. A deep neural network classifier, based on a RoBERTa-Large pretrained transformer model, achieved the highest F 1 -score of 0.914 (precision = 0.905, recall = 0.942) for the “age” class. When the age extraction was evaluated using the classifier’s predictions, it achieved an F 1 -score of 0.855 (precision = 0.805, recall = 0.914) for the “age” class. When it was evaluated directly on the held-out test set, it achieved an F 1 -score of 0.931 (precision = 0.873, recall = 0.998) for the “age” class. We deployed ReportAGE on a collection of more than 1.2 billion tweets, posted by 245,927 users, and predicted ages for 132,637 (54%) of them. Scaling the detection of exact age to this large number of users can advance the utility of social media data for research applications that do not align with the predefined age groupings of extant binary or multi-class classification approaches.