Reviewerly/exHarmony huggingface dataset:
3D Isotropic Super-resolution Prostate MRI Using Generative Adversarial Networks and Unpaired Multiplane Slices

We developed a deep learning-based super-resolution model for prostate MRI. 2D T2-weighted turbo spin echo (T2w-TSE) images are the core anatomical sequences in a multiparametric MRI (mpMRI) protocol. These images have coarse through-plane resolution, are non-isotropic, and have long acquisition times (approximately 10-15 min). The model we developed aims to preserve high-frequency details that are normally lost after 3D reconstruction. We propose a novel framework for generating isotropic volumes using generative adversarial networks (GAN) from anisotropic 2D T2w-TSE and single-shot fast spin echo (ssFSE) images. The CycleGAN model used in this study allows the unpaired dataset mapping to reconstruct super-resolution (SR) volumes. Fivefold cross-validation was performed. The improvements from patch-to-volume reconstruction (PVR) to SR are 80.17%, 63.77%, and 186% for perceptual index (PI), RMSE, and SSIM, respectively; the improvements from slice-to-volume reconstruction (SVR) to SR are 72.41%, 17.44%, and 7.5% for PI, RMSE, and SSIM, respectively. Five ssFSE cases were used to test for generalizability; the perceptual quality of SR images surpasses the in-plane ssFSE images by 37.5%, with 3.26% improvement in SSIM and a higher RMSE by 7.92%. SR images were quantitatively assessed with radiologist Likert scores. Our isotropic SR volumes are able to reproduce high-frequency detail, maintaining comparable image quality to in-plane TSE images in all planes without sacrificing perceptual accuracy. The SR reconstruction networks were also successfully applied to the ssFSE images, demonstrating that high-quality isotropic volume achieved from ultra-fast acquisition is feasible.
MultiScene: A Large-Scale Dataset and Benchmark for Multiscene Recognition in Single Aerial Images

Aerial scene recognition is a fundamental research problem in interpreting high-resolution aerial imagery. Over the past few years, most studies focus on classifying an image into one scene category, while in real-world scenarios, it is more often that a single image contains multiple scenes. Therefore, in this paper, we investigate a more practical yet underexplored task -- multi-scene recognition in single images. To this end, we create a large-scale dataset, called MultiScene, composed of 100,000 unconstrained high-resolution aerial images. Considering that manually labeling such images is extremely arduous, we resort to low-cost annotations from crowdsourcing platforms, e.g., OpenStreetMap (OSM). However, OSM data might suffer from incompleteness and incorrectness, which introduce noise into image labels. To address this issue, we visually inspect 14,000 images and correct their scene labels, yielding a subset of cleanly-annotated images, named MultiScene-Clean. With it, we can develop and evaluate deep networks for multi-scene recognition using clean data. Moreover, we provide crowdsourced annotations of all images for the purpose of studying network learning with noisy labels. We conduct experiments with extensive baseline models on both MultiScene-Clean and MultiScene to offer benchmarks for multi-scene recognition in single images and learning from noisy labels for this task, respectively. To facilitate progress, we make our dataset and trained models available on https://gitlab.lrz.de/ai4eo/reasoning/multiscene.
Coded Computation Across Shared Heterogeneous Workers With Communication Delay

Distributed computing enables large-scale computation tasks to be processed over multiple workers in parallel. However, the randomness of communication and computation delays across workers causes the straggler effect, which may degrade the performance. Coded computation helps to mitigate the straggler effect, but the amount of redundant load and their assignment to the workers should be carefully optimized. In this work, we consider a multi-master heterogeneous-worker distributed computing scenario, where multiple matrix multiplication tasks are encoded and allocated to workers for parallel computation. The goal is to minimize the communication plus computation delay of the slowest task. We propose worker assignment, resource allocation and load allocation algorithms under both dedicated and fractional worker assignment policies, where each worker can process the encoded tasks of either a single master or multiple masters, respectively. Then, the non-convex delay minimization problem is solved by employing the Markov's inequality-based approximation, Karush-Kuhn-Tucker conditions, and successive convex approximation methods. Through extensive simulations, we show that the proposed algorithms can reduce the task completion delay compared to the benchmarks, and observe that dedicated and fractional worker assignment policies have different scopes of applications.
Classification of Eye Movement and Its Application in Driving Based on a Refined Pre-Processing and Machine Learning Algorithm

The eyes are the first channel used by humans to obtain various types of visual information from the outside world, especially for driver, 80-90% of information is received through eyes in driving. The eye movement behaviors are generally divided into six types, but attention is often paid to fixation, saccade, and smooth pursuit. Therefore, it is essential to classify eye movement behaviors accurately using an excellent classification algorithm. The classification of eye movement should be a complete process, including three steps of pre-processing, classification and post-processing. However, it is very uncommon that all these steps are included in eye tracking literature where eye movement classification is discussed. Therefore, first, this paper proposes a refined eye movement data pre-processing framework and the improved method of three steps are emphatically introduced. Second, an eye movement classification algorithm based on improved decision tree that is independent of the threshold setting and application environment is proposed, and a post-processing of merges adjacent fixations and discards short fixations is described. Finally, the application of the classified eye movement behavior in the driving field is described, including the estimation of preview time using fixation and the estimation of time-to-collision using smooth pursuit. Two important results are obtained in this paper. One is about the classification accuracy of eye movement behavior, the F1-score of fixation, saccade and smooth pursuit are respectively 92.63%, 93.46% and 65.29%, which is higher than that of the other algorithms. The other is about the application in driving, on the one hand, the preview time calculated by fixation is mostly distributed around 1-6s, which is more real compared with the traditional setting of 1s, at the same time, the regression relationship between preview time and road turning radius is also quantitatively analyzed and their regression function is obtained. On the other hand, the average estimated error of time-to-collision used by smooth pursuit is 7.37%. These results play an important role in the development of ADAS and the improvement of traffic safety.
Simple sufficient condition for subspace to be completely or genuinely entangled

Abstract We introduce a simple sufficient criterion, which allows one to tell whether a subspace of a bipartite or multipartite Hilbert space is entangled. The main ingredient of our criterion is a bound on the minimal entanglement of a subspace in terms of entanglement of vectors spanning that subspace expressed for geometrical measures of entanglement. The criterion is applicable to both completely and genuinely entangled subspaces. We explore its usefulness in several important scenarios. Further, an entanglement criterion for mixed states following directly from the condition is stated. As an auxiliary result we provide a formula for the generalized geometric measure of entanglement of the d -level Dicke states.
Characterization of Unlinked Cases of COVID-19 and Implications for Contact Tracing Measures: Retrospective Analysis of Surveillance Data

Background Contact tracing and intensive testing programs are essential for controlling the spread of COVID-19. However, conventional contact tracing is resource intensive and may not result in the tracing of all cases due to recall bias and cases not knowing the identity of some close contacts. Few studies have reported the epidemiological features of cases not identified by contact tracing (“unlinked cases”) or described their potential roles in seeding community outbreaks. Objective For this study, we characterized the role of unlinked cases in the epidemic by comparing their epidemiological profile with the linked cases; we also estimated their transmission potential across different settings. Methods We obtained rapid surveillance data from the government, which contained the line listing of COVID-19 confirmed cases during the first three waves in Hong Kong. We compared the demographics, history of chronic illnesses, epidemiological characteristics, clinical characteristics, and outcomes of linked and unlinked cases. Transmission potentials in different settings were assessed by fitting a negative binomial distribution to the observed offspring distribution. Results Time interval from illness onset to hospital admission was longer among unlinked cases than linked cases (median 5.00 days versus 3.78 days; P&lt;.001), with a higher proportion of cases whose condition was critical or serious (13.0% versus 8.2%; P&lt;.001). The proportion of unlinked cases was associated with an increase in the weekly number of local cases (P=.049). Cluster transmissions from the unlinked cases were most frequently identified in household settings, followed by eateries and workplaces, with the estimated probability of cluster transmissions being around 0.4 for households and 0.1-0.3 for the latter two settings. Conclusions The unlinked cases were positively associated with time to hospital admission, severity of infection, and epidemic size—implying a need to design and implement digital tracing methods to complement current conventional testing and tracing. To minimize the risk of cluster transmissions from unlinked cases, digital tracing approaches should be effectively applied in high-risk socioeconomic settings, and risk assessments should be conducted to review and adjust the policies.
Tighter Monogamy and Polygamy Relations of Quantum Entanglement in Multi-qubit Systems

We investigate the monogamy relations related to the concurrence, the entanglement of formation, convex-roof extended negativity, Tsallis-q entanglement and Rényi-α entanglement, the polygamy relations related to the entanglement of formation, Tsallis-q entanglement and Rényi-α entanglement. Monogamy and polygamy inequalities are obtained for arbitrary multipartite qubit systems, which are proved to be tighter than the existing ones. Detailed examples are presented.
Computationally Efficient Quantum Expectation with Extended Bell Measurements

Evaluating an expectation value of an arbitrary observable $A\in{\mathbb C}^{2^n\times 2^n}$ through na\"ive Pauli measurements requires a large number of terms to be evaluated. We approach this issue using a method based on Bell measurement, which we refer to as the extended Bell measurement method. This analytical method quickly assembles the $4^n$ matrix elements into at most $2^{n+1}$ groups for simultaneous measurements in $O(nd)$ time, where $d$ is the number of non-zero elements of $A$. The number of groups is particularly small when $A$ is a band matrix. When the bandwidth of $A$ is $k=O(n^c)$, the number of groups for simultaneous measurement reduces to $O(n^{c+1})$. In addition, when non-zero elements densely fill the band, the variance is $O((n^{c+1}/2^n)\,{\rm tr}(A^2))$, which is small compared with the variances of existing methods. The proposed method requires a few additional gates for each measurement, namely one Hadamard gate, one phase gate and at most $n-1$ CNOT gates. Experimental results on an IBM-Q system show the computational efficiency and scalability of the proposed scheme, compared with existing state-of-the-art approaches. Code is available at https://github.com/ToyotaCRDL/extended-bell-measurements.
Quantum eraser from duality-entanglement perspective

Wave-particle duality is a bizarre feature at the heart of quantum mechanics which refers to the mutually exclusive dual attributes of quantum objects as the wave and the particle. Quantum eraser presents a counterintuitive aspect of the wave-particle duality. In this paper, we show that quantum eraser can be quantitatively understood in terms of the recently developed duality-entanglement relation. In other words, we show that wave-particle-entanglement triality captures all the physics of the quantum erasure. We find that a controllable partial erasure of the which-path information is attainable, enabling the partial recovery of the quantum interference and extending the scope of the conventional quantum eraser protocols.
Fast, high precision dynamics in quantum optimal control theory

Quantum optimal control theory is becoming increasingly crucial as quantum devices become more precise, but the need to quickly optimize these systems classically remains a significant bottleneck in their operation. Here we present a new theoretical quantum control framework for much faster optimization than the state of the art by replacing standard time propagation with a product of short-time propagators, each calculated using the Magnus expansion. The derived formulas for exact series terms and their gradients, based on earlier approximate integrals in a simulation setting, allow us to subsume the high cost of calculating commutators and integrals as an initial overhead. This provides an order of magnitude speedup for quantum control optimization.
Non-Homogeneous Haze Removal via Artificial Scene Prior and Bidimensional Graph Reasoning

Due to the lack of natural scene and haze prior information, it is greatly challenging to completely remove the haze from a single image without distorting its visual content. Fortunately, the real-world haze usually presents non-homogeneous distribution, which provides us with many valuable clues in partial well-preserved regions. In this paper, we propose a Non-Homogeneous Haze Removal Network (NHRN) via artificial scene prior and bidimensional graph reasoning. Firstly, we employ the gamma correction iteratively to simulate artificial multiple shots under different exposure conditions, whose haze degrees are different and enrich the underlying scene prior. Secondly, beyond utilizing the local neighboring relationship, we build a bidimensional graph reasoning module to conduct non-local filtering in the spatial and channel dimensions of feature maps, which models their long-range dependency and propagates the natural scene prior between the well-preserved nodes and the nodes contaminated by haze. To the best of our knowledge, this is the first exploration to remove non-homogeneous haze via the graph reasoning based framework. We evaluate our method on different benchmark datasets. The results demonstrate that our method achieves superior performance over many state-of-the-art algorithms for both the single image dehazing and hazy image understanding tasks. The source code of the proposed NHRN is available on https://github.com/whrws/NHRNet.
Autoencoder-Based Semantic Novelty Detection: Towards Dependable AI-Based Systems

Many autonomous systems, such as driverless taxis, perform safety-critical functions. Autonomous systems employ artificial intelligence (AI) techniques, specifically for environmental perception. Engineers cannot completely test or formally verify AI-based autonomous systems. The accuracy of AI-based systems depends on the quality of training data. Thus, novelty detection, that is, identifying data that differ in some respect from the data used for training, becomes a safety measure for system development and operation. In this study, we propose a new architecture for autoencoder-based semantic novelty detection with two innovations: architectural guidelines for a semantic autoencoder topology and a semantic error calculation as novelty criteria. We demonstrate that such a semantic novelty detection outperforms autoencoder-based novelty detection approaches known from the literature by minimizing false negatives.
Data-Driven Representations for Testing Independence: Modeling, Analysis and Connection With Mutual Information Estimation

This work addresses testing the independence of two continuous and finite-dimensional random variables from the design of a data-driven partition. The empirical log-likelihood statistic is adopted to approximate the sufficient statistics of an oracle test against independence (that knows the two hypotheses). It is shown that approximating the sufficient statistics of the oracle test offers a learning criterion for designing a data-driven partition that connects with the problem of mutual information estimation. Applying these ideas in the context of a data-dependent tree-structured partition (TSP), we derive conditions on the TSP’s parameters to achieve a strongly consistent distribution-free test of independence over the family of probabilities equipped with a density. Complementing this result, we present finite-length results that show our TSP scheme’s capacity to detect the scenario of independence structurally with the data-driven partition as well as new sampling complexity bounds for this detection. Finally, some experimental analyses provide evidence regarding our scheme’s advantage for testing independence compared with some strategies that do not use data-driven representations.
On-chip scalable highly pure and indistinguishable single-photon sources in ordered arrays: Path to quantum optical circuits

Realization of quantum optical circuits is at the heart of quantum photonic information processing. A long-standing obstacle, however, has been the absence of a suitable platform of single photon sources (SPSs). Such SPSs need to be in spatially ordered arrays and produce, on-demand, highly pure, and indistinguishable single photons with sufficiently uniform emission characteristics to enable controlled interference between photons from distinct sources underpinning functional quantum optical networks. We report on such a platform of SPSs based on a unique class of epitaxial quantum dots dubbed mesa-top single quantum dot. Under resonant excitation, the spatially ordered SPSs (without Purcell enhancement) show single photon purity of >99% [g(2)(0) ~ 0.015], high two-photon Hong-Ou-Mandel interference visibilities of 0.82 ± 0.03 (at 11.5 kelvin, without cavity), and spectral nonuniformity of <3 nanometers, within established locally tunable technology. Our platform of SPSs paves the path to creating on-chip scalable quantum photonic networks for communication, computation, simulation, sensing and imaging.
Reasoning on Multirelational Contextual Hierarchies via Answer Set Programming with Algebraic Measures

Abstract Dealing with context-dependent knowledge has led to different formalizations of the notion of context. Among them is the Contextualized Knowledge Repository (CKR) framework, which is rooted in description logics but links on the reasoning side strongly to logic programs and Answer Set Programming (ASP) in particular. The CKR framework caters for reasoning with defeasible axioms and exceptions in contexts, which was extended to knowledge inheritance across contexts in a coverage (specificity) hierarchy. However, the approach supports only this single type of contextual relation and the reasoning procedures work only for restricted hierarchies, due to nontrivial issues with model preference under exceptions. In this paper, we overcome these limitations and present a generalization of CKR hierarchies to multiple contextual relations, along with their interpretation of defeasible axioms and preference. To support reasoning, we use ASP with algebraic measures, which is a recent extension of ASP with weighted formulas over semirings that allows one to associate quantities with interpretations depending on the truth values of propositional atoms. Notably, we show that for a relevant fragment of CKR hierarchies with multiple contextual relations, query answering can be realized with the popular asprin framework. The algebraic measures approach is more powerful and enables, for example, reasoning with epistemic queries over CKRs, which opens interesting perspectives for the use of quantitative ASP extensions in other applications.
Fine-grained spatial information extraction in radiology as two-turn question answering

Radiology reports contain important clinical information that can be used to automatically construct fine-grained labels for applications requiring deep phenotyping. We propose a two-turn question answering (QA) method based on a transformer language model, BERT, for extracting detailed spatial information from radiology reports. We aim to demonstrate the advantage that a multi-turn QA framework provides over sequence-based methods for extracting fine-grained information. Our proposed method identifies spatial and descriptor information by answering queries given a radiology report text. We frame the extraction problem such that all the main radiology entities (e.g., finding, device, anatomy) and the spatial trigger terms (denoting the presence of a spatial relation between finding/device and anatomical location) are identified in the first turn. In the subsequent turn, various other contextual information that acts as important spatial roles with respect to a spatial trigger term are extracted along with identifying the spatial and other descriptor terms qualifying a radiological entity. The queries are constructed using separate templates for the two turns and we employ two query variations in the second turn. When compared to the best-reported work on this task using a traditional sequence tagging method, the two-turn QA model exceeds its performance on every component. This includes promising improvements of 12, 13, and 12 points in the average F1 scores for identifying the spatial triggers, Figure, and Ground frame elements, respectively. Our experiments suggest that incorporating domain knowledge in the query (a general description about a frame element) helps in obtaining better results for some of the spatial and descriptive frame elements, especially in the case of the clinical pre-trained BERT model. We further highlight that the two-turn QA approach fits well for extracting information for complex schema where the objective is to identify all the frame elements linked to each spatial trigger and finding/device/anatomy entity, thereby enabling the extraction of more comprehensive information in the radiology domain. Extracting fine-grained spatial information from text in the form of answering natural language queries holds potential in achieving better results when compared to more standard sequence labeling-based approaches.