Reviewerly/exHarmony huggingface dataset:
Pose Estimation and Non-Rigid Registration for Augmented Reality During Neurosurgery

A craniotomy is the removal of a part of the skull to allow surgeons to have access to the brain and treat tumors. When accessing the brain, a tissue deformation occurs and can negatively influence the surgical procedure outcome. In this work, we present a novel Augmented Reality neurosurgical system to superimpose pre-operative 3D meshes derived from MRI onto a view of the brain surface acquired during surgery.Our method uses cortical vessels as main features to drive a rigid then non-rigid 3D/2D registration. We first use a feature extractor network to produce probability maps that are fed to a pose estimator network to infer the 6-DoF rigid pose. Then, to account for brain deformation, we add a non-rigid refinement step formulated as a Shape-from-Template problem using physics-based constraints that helps propagate the deformation to sub-cortical level and update tumor location.We tested our method retrospectively on 6 clinical datasets and obtained low pose error, and showed using synthetic dataset that considerable brain shift compensation and low TRE can be achieved at cortical and sub-cortical levels.The results show that our solution achieved accuracy below the actual clinical errors demonstrating the feasibility of practical use of our system.This work shows that we can provide coherent Augmented Reality visualization of 3D cortical vessels observed through the craniotomy using a single camera view and that cortical vessels provide strong features for performing both rigid and non-rigid registration.
Evaluating participant responses to a virtual reality experience using reinforcement learning

Virtual reality applications depend on multiple factors, for example, quality of rendering, responsiveness, and interfaces. In order to evaluate the relative contributions of different factors to quality of experience, post-exposure questionnaires are typically used. Questionnaires are problematic as the questions can frame how participants think about their experience and cannot easily take account of non-additivity among the various factors. Traditional experimental design can incorporate non-additivity but with a large factorial design table beyond two factors. Here, we extend a previous method by introducing a reinforcement learning (RL) agent that proposes possible changes to factor levels during the exposure and requires the participant to either accept these or not. Eventually, the RL converges on a policy where no further proposed changes are accepted. An experiment was carried out with 20 participants where four binary factors were considered. A consistent configuration of factors emerged where participants preferred to use a teleportation technique for navigation (compared to walking-in-place), a full-body representation (rather than hands only), the responsiveness of virtual human characters (compared to being ignored) and realistic compared to cartoon rendering. We propose this new method to evaluate participant choices and discuss various extensions.
SchWARMA: A model-based approach for time-correlated noise in quantum circuits

Temporal noise correlations are ubiquitous in quantum systems, yet often neglected in the analysis of quantum circuits due to the complexity required to accurately characterize and model them. Autoregressive moving average (ARMA) models are a well-known technique from time series analysis that model time correlations in data. By identifying the space of completely positive trace preserving (CPTP) quantum operations with a particular matrix manifold, we generalize ARMA models to the space of CPTP maps to parametrize and simulate temporally correlated noise in quantum circuits. This approach, denoted Schr\"odinger wave ARMA (SchWARMA), provides a natural path for generalization of classic techniques from signal processing, control theory, and system identification for which ARMA models and linear systems are essential. This enables the broad theory of classical signal processing to be applied to quantum system simulation, characterization, and noise mitigation.
SyncUp

The beauty of synchronized dancing lies in the synchronization of body movements among multiple dancers. While dancers utilize camera recordings for their practice, standard video interfaces do not efficiently support their activities of identifying segments where they are not well synchronized. This thus fails to close a tight loop of an iterative practice process (i.e., capturing a practice, reviewing the video, and practicing again). We present SyncUp, a system that provides multiple interactive visualizations to support the practice of synchronized dancing and liberate users from manual inspection of recorded practice videos. By analyzing videos uploaded by users, SyncUp quantifies two aspects of synchronization in dancing: pose similarity among multiple dancers and temporal alignment of their movements. The system then highlights which body parts and which portions of the dance routine require further practice to achieve better synchronization. The results of our system evaluations show that our pose similarity estimation and temporal alignment predictions were correlated well with human ratings. Participants in our qualitative user evaluation expressed the benefits and its potential use of SyncUp, confirming that it would enable quick iterative practice.
StereoARS: Quality Evaluation for Stereoscopic Image Retargeting With Binocular Inconsistency Detection

Many stereoscopic image retargeting (SIR) methods have been developed for automatically and intelligently resizing stereoscopic images and we cannot always rely on time-consuming subjective user studies to validate the performance of different SIR methods. It is therefore required to design reliable objective metrics for SIR quality evaluation. This paper extends our previous 2D aspect ratio similarity (ARS) metric to a stereo 3D version termed as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">StereoARS</i> where the key idea is to investigate into retargeting inconsistency between the original stereo correspondences. Our proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">StereoARS</i> operates via two stages: monocular quality estimation and binocular inconsistency detection. In the first stage, monocular quality estimation is performed by applying a modified ARS measure on the left and right views separately to quantify the quality degradation within each monocular view. In the second stage, binocular inconsistency detection is performed in both pixel-level and grid-level to characterize the influence of binocular rivalry and stereo visual discomfort on SIR quality. In addition, we also measure to what extent the original pixel visibility relation is preserved after SIR as another binocular quality factor. Finally, these monocular and binocular quality estimates are fused to produce an overall SIR quality score. Extensive experiments have demonstrated that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">StereoARS</i> achieves better alignment with human subjective ratings than the existing metrics by a large margin.
Deepnoise: Learning sensor and process noise to detect data integrity attacks in CPS

Cyber-physical systems (CPS) have been widely deployed in critical infrastructures and are vulnerable to various attacks. Data integrity attacks manipulate sensor measurements and cause control systems to fail, which are one of the prominent threats to CPS. Anomaly detection methods are proposed to secure CPS. However, existing anomaly detection studies usually require expert knowledge (e.g., system model-based) or are lack of interpretability (e.g., deep learning-based). In this paper, we present Deepnoise, a deep learning-based anomaly detection method for CPS with interpretability. Specifically, we utilize the sensor and process noise to detect data integrity attacks. Such noise represents the intrinsic characteristics of physical devices and the production process in CPS. One key enabler is that we use a robust deep autoencoder to automatically extract the noise from measurement data. Further, an LSTM-based detector is designed to inspect the obtained noise and detect anomalies. Data integrity attacks change noise patterns and thus are identified as the root cause of anomalies by Deepnoise. Evaluated on the SWaT testbed, Deep-noise achieves higher accuracy and recall compared with state-of-the-art model-based and deep learning-based methods. On average, when detecting direct attacks, the precision is 95.47%, the recall is 96.58%, and F is 95.98%. When detecting stealthy attacks, precision, recall, and F scores are between 96% and 99.5%.
Violation of Bell's inequality with quantum-dot single-photon sources

We investigate the possibility of realizing a loophole-free violation of Bell's inequality using deterministic single-photon sources. We provide a detailed analysis of a scheme to achieve such violations over long distances with immediate extensions to device-independent quantum key distribution. We investigate the effect of key experimental imperfections that are unavoidable in real-world single-photon sources including the finite degree of photon indistinguishability, single-photon purity, and the overall source efficiency. We benchmark the performance requirements to state-of-the-art deterministic single-photon sources based on quantum dots in photonic nanostructures and find that experimental realizations appear to be within reach. We also evaluate the requirements for a post-selected version of the protocol, which relaxes the demanding requirements with respect to the source efficiency.
Evolutionary Mahalanobis Distance-Based Oversampling for Multi-Class Imbalanced Data Classification

The number of sensing data are often imbalanced across data classes, for which oversampling on the minority class is an effective remedy. In this paper, an effective oversampling method called evolutionary Mahalanobis distance oversampling (EMDO) is proposed for multi-class imbalanced data classification. EMDO utilizes a set of ellipsoids to approximate the decision regions of the minority class. Furthermore, multi-objective particle swarm optimization (MOPSO) is integrated with the Gustafson-Kessel algorithm in EMDO to learn the size, center, and orientation of every ellipsoid. Synthetic minority samples are generated based on Mahalanobis distance within every ellipsoid. The number of synthetic minority samples generated by EMDO in every ellipsoid is determined based on the density of minority samples in every ellipsoid. The results of computer simulations conducted herein indicate that EMDO outperforms most of the widely used oversampling schemes.
Frequency-multiplexed entanglement for continuous-variable quantum key distribution

Quantum key distribution with continuous variables already uses advantageous high-speed single-mode homodyne detection with low electronic noise at room temperature. Together with continuous-variable information encoding to nonclassical states, the distance for secure key transmission through lossy channels can approach 300 km in current optical fibers. Such protocols tolerate higher channel noise and also limited data processing efficiency compared to coherent-state protocols. The secret key rate can be further increased by increasing the system clock rates, and, further, by a suitable frequency-mode-multiplexing of optical transmission channels. However, the multiplexed modes couple together in the source or any other part of the protocol. Therefore, multiplexed communication will experience cross talk and the gain can be minuscule. Advantageously, homodyne detectors allow solving this cross-talk problem by proper data processing. It is a potential advantage over protocols with single-photon detectors, which do not enable similar data processing techniques. We demonstrate the positive outcome of this methodology on the experimentally characterized frequency-multiplexed entangled source of femtosecond optical pulses with natural cross talk between eight entangled pairs of modes. As the main result, we predict the almost 15-fold higher secret key rate. This experimental test and analysis of frequency-multiplexed entanglement source open the way for the field implementation of high-capacity quantum key distribution with continuous variables.
Overview of the Priority 2030 Strategic Academic Leadership Program

The development of academic leadership is an important task that has been set for the domestic scientific community. One of the first projects aimed at solving it was the Russian Academic Excellence Project. Although this project did not fully achieve its goals, nevertheless, it introduced Russian universities to world standards and made it possible to create some groundwork for future programs of this kind. The conceptual continuation of the Russian Academic Excellence Project is the new Priority-2030 program. The overview provides basic information about the new program of strategic academic leadership “Priority 2030”. Its goalsand main features are highlighted. The normative documents governing the procedure for the implementation of the Priority-2030 program are presented. Some controversial aspects that could potentially have a negative impact and reduce the positive effects from the implementation of the Priority 2030 program are noted.
Lithological information extraction and classification in hyperspectral remote sensing data using Backpropagation Neural Network

The purposes are to solve the isomorphism encountered while processing hyperspectral remote sensing data and improve the accuracy of hyperspectral remote sensing data in extracting and classifying lithological information. Taking rocks as the research object, Backpropagation Neural Network (BPNN) is introduced. After the hyperspectral image data are normalized, the lithological spectrum and spatial information are the feature extraction targets to construct a deep learning-based lithological information extraction model. The performance of the model is analyzed using specific instance data. Results demonstrate that the overall accuracy and the Kappa coefficient of the lithological information extraction and classification model based on deep learning were 90.58% and 0.8676, respectively. This model can precisely distinguish the properties of rock masses and provide better performance compared with the state of other analysis models. After introducing deep learning, the recognition accuracy and the Kappa coefficient of the proposed BPNN model increased by 8.5% and 0.12, respectively, compared with the traditional BPNN. The proposed extraction and classification model can provide some research values and practical significances for the hyperspectral rock and mineral classification.
Virtual Mirror and Beyond: The Psychological Basis for Avatar Embodiment via a Mirror

In virtual reality (VR), a virtual mirror is often used to display the VR avatar to the user for enhancing the embodiment. The reflected image of the synchronization of the virtual body with the user’s movement is expected to be recognized as the user’s own reflection. In addition to the visuo-motor synchrony, there are some mirror reflection factors that are probably involved in avatar embodiment. This paper reviews literature on the psychological studies that involve mirror-specific self-identification and embodied perception to clarify how the reflected image of the virtual body is embodied. Furthermore, subjective misconceptions about mirror reflections reported in naïve optics have also been reviewed to discuss the potential of virtual mirror displays to modulate avatar embodiment.
Coupling rare event algorithms with data-based learned committor functions using the analogue Markov chain

Rare events play a crucial role in many physics, chemistry, and biology phenomena, when they change the structure of the system, for instance in the case of multistability, or when they have a huge impact. Rare event algorithms have been devised to simulate them efficiently, avoiding the computation of long periods of typical fluctuations. We consider here the family of splitting or cloning algorithms, which are versatile and specifically suited for far-from-equilibrium dynamics. To be efficient, these algorithms need to use a smart score function during the selection stage. Committor functions are the optimal score functions. In this work we propose a new approach, based on the analogue Markov chain, for a data-based learning of approximate committor functions. We demonstrate that such learned committor functions are extremely efficient score functions when used with the Adaptive Multilevel Splitting algorithm. We illustrate our approach for a gradient dynamics in a three-well potential, and for the Charney-DeVore model, which is a paradigmatic toy model of multistability for atmospheric dynamics. For these two dynamics, we show that having observed a few transitions is enough to have a very efficient data-based score function for the rare event algorithm. This new approach is promising for use for complex dynamics: the rare events can be simulated with a minimal prior knowledge and the results are much more precise than those obtained with a user-designed score function.
Dressed emitters as impurities

Abstract Dressed states forming when quantum emitters or atoms couple to a photonic bath underpin a number of phenomena and applications, in particular nonradiating effective interactions occurring within photonic bandgaps. Here, we present a compact formulation of the resolvent-based theory for calculating atom-photon dressed states built on the idea that the atom behaves as an effective impurity. This establishes an explicit connection with the standard impurity problem in condensed matter. Moreover, it allows us to formulate and settle – independently of the bath Hamiltonian – a number of properties previously known only for specific models or not entirely formalized. The framework is next extended to the case of more than one emitter, which is used to derive a general expression of dissipationless effective Hamiltonians explicitly featuring the overlap of single-emitter dressed bound states.
In-depth research on the interpretable disruption predictor in HL-2A

Abstract In-depth research is implemented on the disruption predictor in HL-2A to improve the accuracy and interpretability of the model. For higher accuracy, four adjustments are tried to solve four corresponding problems in a baseline model. Reductive comparison experiments are designed to evaluate their contribution to performance. The result shows that these adjustments together can improve the AUC (area under receiver operating characteristic curve) of the baseline model by 0.039. For interpretability of model, an interpretation method is proposed to evaluate the real-time importance of each input signal. The result of single shot interpretation shows good coherence with the causes of disruptions. To further validate the reasonability of this interpretation method, disruption causes of shot nos. 20000–36000 are analysed to make a disruption cause dataset. Statistical analysis of the output of the interpretation algorithm on this dataset also shows a good coherence with the disruption causes. Then a Bayes classifier is developed to recognize the cause of disruption based on the interpretation algorithm’s output. This classifier has an accuracy of 71.2% on the labelled dataset, which contains 605 disruptive shots categorized into five disruption causes.
Clifford Circuit Optimization with Templates and Symbolic Pauli Gates

The Clifford group is a finite subgroup of the unitary group generated by the Hadamard, the CNOT, and the Phase gates. This group plays a prominent role in quantum error correction, randomized benchmarking protocols, and the study of entanglement. Here we consider the problem of finding a short quantum circuit implementing a given Clifford group element. Our methods aim to minimize the entangling gate count assuming all-to-all qubit connectivity. First, we consider circuit optimization based on template matching and design Clifford-specific templates that leverage the ability to factor out Pauli and SWAP gates. Second, we introduce a symbolic peephole optimization method. It works by projecting the full circuit onto a small subset of qubits and optimally recompiling the projected subcircuit via dynamic programming. CNOT gates coupling the chosen subset of qubits with the remaining qubits are expressed using symbolic Pauli gates. Software implementation of these methods finds circuits that are only 0.2% away from optimal for 6 qubits and reduces the two-qubit gate count in circuits with up to 64 qubits by 64.7% on average, compared with the Aaronson-Gottesman canonical form.
Online algorithms for estimating change rates of web pages

A search engine maintains local copies of different web pages to provide quick search results. This local cache is kept up-to-date by a web crawler that frequently visits these different pages to track changes in them. Ideally, the local copy should be updated as soon as a page changes on the web. However, finite bandwidth availability and server restrictions limit how frequently different pages can be crawled. This brings forth the following optimization problem: maximize the freshness of the local cache subject to the crawling frequencies being within prescribed bounds. While tractable algorithms do exist to solve this problem, these either assume the knowledge of exact page change rates or use inefficient methods such as MLE for estimating the same. We address this issue here. We provide three novel schemes for online estimation of page change rates, all of which have extremely low running times per iteration. The first is based on the law of large numbers and the second on stochastic approximation. The third is an extension of the second and includes a heavy-ball momentum term. All these schemes only need partial information about the page change process, i.e., they only need to know if the page has changed or not since the last crawled instance. Our main theoretical results concern asymptotic convergence and convergence rates of these three schemes. In fact, our work is the first to show convergence of the original stochastic heavy-ball method when neither the gradient nor the noise variance is uniformly bounded. We also provide some numerical experiments (based on real and synthetic data) to demonstrate the superiority of our proposed estimators over existing ones such as MLE. Our algorithms are readily applicable to the synchronization of databases and network inventory management.
Inclusion of domain-knowledge into GNNs using mode-directed inverse entailment

We present a general technique for constructing Graph Neural Networks (GNNs) capable of using multi-relational domain knowledge. The technique is based on mode-directed inverse entailment (MDIE) developed in Inductive Logic Programming (ILP). Given a data instance e and background knowledge B, MDIE identifies a most-specific logical formula $$\bot _B(e)$$ that contains all the relational information in B that is related to e. We represent $$\bot _B(e)$$ by a "bottom-graph" that can be converted into a form suitable for GNN implementations. This transformation allows a principled way of incorporating generic background knowledge into GNNs: we use the term 'BotGNN' for this form of graph neural networks. For several GNN variants, using real-world datasets with substantial background knowledge, we show that BotGNNs perform significantly better than both GNNs without background knowledge and a recently proposed simplified technique for including domain knowledge into GNNs. We also provide experimental evidence comparing BotGNNs favourably to multi-layer perceptrons that use features representing a "propositionalised" form of the background knowledge; and BotGNNs to a standard ILP based on the use of most-specific clauses. Taken together, these results point to BotGNNs as capable of combining the computational efficacy of GNNs with the representational versatility of ILP.