Reviewerly/exHarmony huggingface dataset:
Experimental realization of high-fidelity teleportation via a non-Markovian open quantum system

Open quantum systems and study of decoherence are important for our fundamental understanding of quantum physical phenomena. For practical purposes, there exists a large number of quantum protocols exploiting quantum resources, e.g. entanglement, which allows to go beyond what is possible to achieve by classical means. We combine concepts from open quantum systems and quantum information science, and give a proof-of-principle experimental demonstration -- with teleportation -- that it is possible to implement efficiently a quantum protocol via non-Markovian open system. The results show that, at the time of implementation of the protocol, it is not necessary to have the quantum resource in the degree of freedom used for the basic protocol -- as long as there exists some other degree of freedom, or environment of an open system, which contains useful resources. The experiment is based on a pair of photons, where their polarizations act as open system qubits and frequencies as their environments -- while the path degree of freedom of one of the photons represents the state of Alice's qubit to be teleported to Bob's polarization qubit.
Sample Efficient Reinforcement Learning Method via High Efficient Episodic Memory

Reinforcement Learning (RL), especially Deep Reinforcement Learning (DRL), has made great progress in many areas, such as robots, video games and driving. However, sample inefficiency is a big obstacle to the widespread practical application of DRL. Inspired by the decision making in human brain, this problem can be solved by incorporating instance based learning, i.e. episodic memory. Many episodic memory based RL algorithms have emerged recently. However, these algorithms either only replace parametric DRL algorithm with episodic control or incorporate episodic memory in a single component of DRL. In contrast to preview works, this paper proposes a new sample-efficient reinforcement learning architecture which introduces a new episodic memory module and incorporates episodic thought into some key components of DRL: exploration, experience replay and loss function. Taking Deep Q-Network (DQN) algorithm for example, when combined with DQN, our algorithm is called High Efficient Episodic Memory DQN (HE-EMDQN). In HE-EMDQN, a new non-parametric episodic memory module is introduced to help calculate the loss and modify the predicted value for exploration. For the sake of accelerating the sample learning in experience replay, an auxiliary small buffer called percentile best episode replay memory is designed to compose a mixed mini-batch. We show across the testing environments that our algorithm is significantly more powerful and sample-efficient than DQN and the recent episodic memory deep q-network (EMDQN). This work provides a new perspective for other RL algorithms to improve sample efficiency by utilising episodic memory efficiently.
Cross Domain Mean Approximation for Unsupervised Domain Adaptation

Unsupervised Domain Adaptation (UDA) aims to leverage the knowledge from the labeled source domain to help the task of target domain with the unlabeled data.It is a key step for UDA to minimize the cross-domain distribution divergence.In this paper, we firstly propose a novel discrepancy metric, referred to as Cross Domain Mean Approximation (CDMA) discrepancy, to evaluate the distribution differences between source and target domains, which calculate the sum of the squares of the distances from the source and target domains to the mean of the other domain.Secondly, Joint Distribution Adaptation based on Cross Domain Mean Approximation (JDA-CDMA) is developed on the basis of CDMA to extract shared feature and simultaneously reduce the marginal and conditional distribution discrepancy between domains during the label refinement process.Thirdly, we construct a classifier utilizing CDMA metric and neighbor information.Finally, the proposed feature extraction approach and classifier are combined to realize transfer learning.Results from extensive experiments on five visual benchmarks including object, face, and digit images, show the proposed methods outperform the state-of-the-art unsupervised domain adaptation.
Novel generalized Fourier representations and phase transforms

The Fourier representations (FRs) are indispensable mathematical formulations for modeling and analysis of physical phenomena and engineering systems. This study presents a new set of generalized Fourier representations (GFRs) and phase transforms (PTs). The PTs are special cases of the GFRs and true generalizations of the Hilbert transforms. In particular, the Fourier transform based kernel of the PT is derived and its various properties are discussed. The time derivative and integral, including fractional order, of a signal are obtained using the GFR. It is demonstrated that the general class of time-invariant and time-variant filtering operations, analog and digital modulations can be obtained from the proposed GFR. A narrowband Fourier representation for the time-frequency analysis of a signal is also presented using the GFR. A discrete cosine transform based implementation, to avoid end artifacts due to discontinuities present in the both ends of a signal, is proposed. A fractional-delay in a discrete-time signal using the FR is introduced. The fast Fourier transform implementation of all the proposed representations is developed. Moreover, using the analytic wavelet transform, a wavelet phase transform (WPT) is proposed to obtain a desired phase-shift in a signal under-analysis. A wavelet quadrature transform (WQT) is also presented which is a special case of the WPT with a phase-shift of π/2 radians. Thus, a wavelet analytic signal representation is derived from the WQT. Theoretical analysis and numerical experiments are conducted to evaluate effectiveness of the proposed methods.
Background Noise Filtering and Distribution Dividing for Crowd Counting

Crowd counting is a challenging problem due to the diverse crowd distribution and background interference. In this paper, we propose a new approach for head size estimation to reduce the impact of different crowd scale and background noise. Different from just using local information of distance between human heads, the global information of the people distribution in the whole image is also under consideration. We obey the order of far- to near-region (small to large) to spread head size, and ensure that the propagation is uninterrupted by inserting dummy head points. The estimated head size is further exploited, such as dividing the crowd into parts of different densities and generating a high-fidelity head mask. On the other hand, we design three different head mask usage mechanisms and the corresponding head masks to analyze where and which mask could lead to better background filtering. Based on the learned masks, two competitive models are proposed which can perform robust crowd estimation against background noise and diverse crowd scale. We evaluate the proposed method on three public crowd counting datasets of ShanghaiTech, UCFQNRF and UCFCC_50. Experimental results demonstrate that the proposed algorithm performs favorably against the state-of-the-art crowd counting approaches.
MAD saccade: statistically robust saccade threshold estimation via the median absolute deviation

&#x0D; &#x0D; &#x0D; &#x0D; Saccade detection is a critical step in the analysis of gaze data. A common method for saccade detection is to use a simple threshold for velocity or acceleration values, which can be estimated from the data using the mean and standard deviation. However, this method has the downside of being influenced by the very signal it is trying to detect, the outlying velocities or accelerations that occur during saccades. We propose instead to use the median absolute deviation (MAD), a robust estimator of dispersion that is not influenced by outliers. We modify an algorithm proposed by Nyström and colleagues, and quantify saccade detection performance in both simulated and human data. Our modified algorithm shows a significant and marked improvement in saccade detection - showing both more true positives and less false negatives – especially under higher noise levels. We conclude that robust estimators can be widely adopted in other common, automatic gaze classification algorithms due to their ease of implementation. &#x0D; &#x0D; &#x0D; &#x0D;
Virtual Reality Simulation and Augmented Reality-Guided Surgery for Total Maxillectomy: A Case Report

With the improvement in computer graphics and sensors, technologies like virtual reality (VR) and augmented reality (AR) have created new possibilities for developing diagnostic and surgical techniques in the field of surgery. VR and AR are the latest technological modalities that have been integrated into clinical practice and medical education, and are rapidly emerging as powerful tools in the field of maxillofacial surgery. In this report, we describe a case of total maxillectomy and orbital floor reconstruction in a patient with malignant fibrous histiocytoma of the maxilla, with preoperative planning via VR simulation and AR-guided surgery. Future developments in VR and AR technologies will increase their utility and effectiveness in the field of surgery.
Subvert KEM to Break DEM: Practical Algorithm-Substitution Attacks on Public-Key Encryption

Motivated by the currently widespread concern about mass surveillance of encrypted communications, Bellare et al. introduced at CRYPTO 2014 the notion of Algorithm-Substitution Attack (ASA) where the legitimate encryption algorithm is replaced by a subverted one that aims to undetectably exfiltrate the secret key via ciphertexts. Practically implementable ASAs on various cryptographic primitives (Bellare et al., CRYPTO’14 & ACM CCS’15; Ateniese et al., ACM CCS’15; Berndt and Liśkiewicz, ACM CCS’17) have been constructed and analyzed, leaking the secret key successfully. Nevertheless, in spite of much progress, the practical impact of ASAs (formulated originally for symmetric key cryptography) on public-key (PKE) encryption operations remains unclear, primarily since the encryption operation of PKE does not involve the secret key, and also previously known ASAs become relatively inefficient for leaking the plaintext due to the logarithmic upper bound of exfiltration rate (Berndt and Liśkiewicz, ACM CCS’17). In this work, we formulate a practical ASA on PKE encryption algorithm which, perhaps surprisingly, turns out to be much more efficient and robust than existing ones, showing that ASAs on PKE schemes are far more effective and dangerous than previously believed. We mainly target PKE of hybrid encryption which is the most prevalent way to employ PKE in the literature and in practice. The main strategy of our ASA is to subvert the underlying key encapsulation mechanism (KEM) so that the session key encapsulated could be efficiently extracted, which, in turn, breaks the data encapsulation mechanism (DEM) enabling us to learn the plaintext itself. Concretely, our non-black-box yet quite general attack enables recovering the plaintext from only two successive ciphertexts and minimally depends on a short state of previous internal randomness. A widely used class of KEMs is shown to be subvertible by our powerful attack. Our attack relies on a novel identification and formalization of certain properties that yield practical ASAs on KEMs. More broadly, it points at and may shed some light on exploring structural weaknesses of other “composed cryptographic primitives,” which may make them susceptible to more dangerous ASAs with effectiveness that surpasses the known logarithmic upper bound (i.e., reviewing composition as an attack enabler).
Learning Hybrid Representations for Automatic 3D Vessel Centerline Extraction

Automatic blood vessel extraction from 3D medical images is crucial for vascular disease diagnoses. Existing methods based on convolutional neural networks (CNNs) may suffer from discontinuities of extracted vessels when segmenting such thin tubular structures from 3D images. We argue that preserving the continuity of extracted vessels requires to take into account the global geometry. However, 3D convolutions are computationally inefficient, which prohibits the 3D CNNs from sufficiently large receptive fields to capture the global cues in the entire image. In this work, we propose a hybrid representation learning approach to address this challenge. The main idea is to use CNNs to learn local appearances of vessels in image crops while using another point-cloud network to learn the global geometry of vessels in the entire image. In inference, the proposed approach extracts local segments of vessels using CNNs, classifies each segment based on global geometry using the point-cloud network, and finally connects all the segments that belong to the same vessel using the shortest-path algorithm. This combination results in an efficient, fully-automatic and template-free approach to centerline extraction from 3D images. We validate the proposed approach on CTA datasets and demonstrate its superior performance compared to both traditional and CNN-based baselines.
3D Geological Image Synthesis From 2D Examples Using Generative Adversarial Networks

Generative Adversarial Networks (GAN) are becoming an alternative to Multiple-point Statistics (MPS) techniques to generate stochastic fields from training images. But a difficulty for all the training image based techniques (including GAN and MPS) is to generate 3D fields when only 2D training data sets are available. In this paper, we introduce a novel approach called Dimension Augmenter GAN (DiAGAN) enabling GANs to generate 3D fields from 2D examples. The method is simple to implement and is based on the introduction of a random cut sampling step between the generator and the discriminator of a standard GAN. Numerical experiments show that the proposed approach provides an efficient solution to this long lasting problem.
Multicore Parallel Tempering Bayeslands for Basin and Landscape Evolution

Abstract The Bayesian paradigm is becoming an increasingly popular framework for estimation and uncertainty quantification of unknown parameters in geophysical inversion problems. Badlands is a landscape evolution model for simulating topography evolution at a broad range of spatial and temporal scales. Our previous work presented Bayeslands that used the Bayesian inference for estimating unknown parameters in the Badlands model using Markov chain Monte Carlo sampling. Bayeslands faced challenges in terms of computational issues and convergence due to multimodal posterior distributions. Parallel tempering is an advanced Markov chain Monte Carlo method suited for irregular and multimodal posterior distributions. In this paper, we extend Bayeslands using parallel tempering with high‐performance computing to address previous limitations in Bayeslands. Our results show that parallel tempering Bayeslands not only reduces the computation time‚ but also provides an improvement in sampling multimodal posterior distributions, which motivates future application to continental scale landscape evolution models.
Discontinuous and Smooth Depth Completion With Binary Anisotropic Diffusion Tensor

We propose an unsupervised real-time dense depth completion from a sparse depth map guided by a single image. Our method generates a smooth depth map while preserving discontinuity between different objects. Our key idea is a Binary Anisotropic Diffusion Tensor (B-ADT) which can completely eliminate smoothness constraint at intended positions and directions by applying it to variational regularization. We also propose an Image-guided Nearest Neighbor Search (IGNNS) to derive a piecewise constant depth map which is used for B-ADT derivation and in the data term of the variational energy. Our experiments show that our method can outperform previous unsupervised and semi-supervised depth completion methods in terms of accuracy. Moreover, since our resulting depth map preserves the discontinuity between objects, the result can be converted to a visually plausible point cloud. This is remarkable since previous methods generate unnatural surface-like artifacts between discontinuous objects.
Robust Clustering With Subpopulation-Specific Deviations

The National Birth Defects Prevention Study (NBDPS) is a case-control study of birth defects conducted across 10 U.S. states. Researchers are interested in characterizing the etiologic role of maternal diet, collected using a food frequency questionnaire. Because diet is multi-dimensional, dimension reduction methods such as cluster analysis are often used to summarize dietary patterns. In a large, heterogeneous population, traditional clustering methods, such as latent class analysis, used to estimate dietary patterns can produce a large number of clusters due to a variety of factors, including study size and regional diversity. These factors result in a loss of interpretability of patterns that may differ due to minor consumption changes. Based on adaptation of the local partition process, we propose a new method, Robust Profile Clustering, to handle these data complexities. Here, participants may be clustered at two levels: (1) globally, where women are assigned to an overall population-level cluster via an overfitted finite mixture model, and (2) locally, where regional variations in diet are accommodated via a beta-Bernoulli process dependent on subpopulation differences. We use our method to analyze the NBDPS data, deriving pre-pregnancy dietary patterns for women in the NBDPS while accounting for regional variability.
Photonic Implementation of Quantum Information Masking

Masking of quantum information spreads it over nonlocal correlations and hides it from the subsystems. It is known that no operation can simultaneously mask all pure states [Phys. Rev. Lett. 120, 230501 (2018)], so in what sense is quantum information masking useful? Here, we extend the definition of quantum information masking to general mixed states, and show that the resource of maskable quantum states are far more abundant than the no-go theorem seemingly suggests. Geometrically, the simultaneously maskable states lays on hyperdisks in the state hypersphere, and strictly contain the broadcastable states. We devise a photonic quantum information masking machine using time-correlated photons to experimentally investigate the properties of qubit masking, and demonstrate the transfer of quantum information into bipartite correlations and its faithful retrieval. The versatile masking machine has decent extensibility, and may be applicable to quantum secret sharing and fault-tolerant quantum communication. Our results provide some insights on the comprehension and potential application of quantum information masking.
Polarization-based truncated SU(1,1) interferometer based on four-wave mixing in Rb vapor

We propose and demonstrate a polarization-based truncated SU(1,1) interferometer that outputs the desired optical joint-quadrature of a two-mode squeezed vacuum field and allows its measurements using a single balanced homodyne detector. Using such a setup, we demonstrate up to ≈2dB of quantum noise suppression below the shot-noise limit in intensity-difference and phase-sum joint-quadratures, and confirm entanglement between the two quantum fields. Our proposed technique results in a better balance between the two ports of the detector and, consequently, in better common noise suppression for differential measurements. As a result, we are able to observe flat joint-quadrature squeezing and entanglement at a wide range of detection frequencies: from several MHz (limited by the photodiode gain bandwidth) down to a few hundred Hz (limited by electronic noises).
Enhanced energy-constrained quantum communication over bosonic Gaussian channels

Abstract Quantum communication is an important branch of quantum information science, promising unconditional security to classical communication and providing the building block of a future large-scale quantum network. Noise in realistic quantum communication channels imposes fundamental limits on the communication rates of various quantum communication tasks. It is therefore crucial to identify or bound the quantum capacities of a quantum channel. Here, we consider Gaussian channels that model energy loss and thermal noise errors in realistic optical and microwave communication channels and study their various quantum capacities in the energy-constrained scenario. We provide improved lower bounds to various energy-constrained quantum capacities of these fundamental channels and show that higher communication rates can be attained than previously believed. Specifically, we show that one can boost the transmission rates of quantum information and private classical information by using a correlated multi-mode thermal state instead of the single-mode thermal state of the same energy.
Linear programs for entanglement and key distribution in the quantum internet

Quantum networks will allow to implement communication tasks beyond the reach of their classical counterparts. A pressing and necessary issue for the design of quantum network protocols is the quantification of the rates at which these tasks can be performed. Here, we propose a simple recipe that yields efficiently computable lower and upper bounds on the maximum achievable rates. For this we make use of the max-flow min-cut theorem and its generalization to multi-commodity flows to obtain linear programs. We exemplify our recipe deriving the linear programs for bipartite settings, settings where multiple pairs of users obtain entanglement in parallel as well as multipartite settings, covering almost all known situations. We also make use of a generalization of the concept of paths between user pairs in a network to Steiner trees spanning a group of users wishing to establish Greenberger-Horne-Zeilinger states.
A Unified View of Label Shift Estimation

Under label shift, the label distribution p(y) might change but the class-conditional distributions p(x|y) do not. There are two dominant approaches for estimating the label marginal. BBSE, a moment-matching approach based on confusion matrices, is provably consistent and provides interpretable error bounds. However, a maximum likelihood estimation approach, which we call MLLS, dominates empirically. In this paper, we present a unified view of the two methods and the first theoretical characterization of MLLS. Our contributions include (i) consistency conditions for MLLS, which include calibration of the classifier and a confusion matrix invertibility condition that BBSE also requires; (ii) a unified framework, casting BBSE as roughly equivalent to MLLS for a particular choice of calibration method; and (iii) a decomposition of MLLS's finite-sample error into terms reflecting miscalibration and estimation error. Our analysis attributes BBSE's statistical inefficiency to a loss of information due to coarse calibration. Experiments on synthetic data, MNIST, and CIFAR10 support our findings.
Quantum correlations in the Kerr Ising model

Abstract In this article we present a full description of the quantum Kerr Ising model—a linear optical network of parametrically pumped Kerr nonlinearities. We consider the non-dissipative Kerr Ising model and, using variational techniques, show that the energy spectrum is primarily determined by the adjacency matrix in the Ising model and exhibits highly non-classical cat like eigenstates. We then introduce dissipation to give a quantum mechanical treatment of the measurement process based on homodyne detection via the conditional stochastic Schrodinger equation. Finally, we identify a quantum advantage in comparison to the classical analogue for the example of two anti-ferromagnetic cavities.