Reviewerly/exHarmony huggingface dataset:
The Role of Eye Tracking Technology in Assessing Older Driver Safety

A growing body of literature is focused on the use of eye tracking (ET) technology to understand the association between objective visual parameters and higher order brain processes such as cognition. One of the settings where this principle has found practical utility is in the area of driving safety. Methods: We reviewed the literature to identify the changes in ET parameters with older adults and neurodegenerative disease. Results: This narrative review provides a brief overview of oculomotor system anatomy and physiology, defines common eye movements and tracking variables that are typically studied, explains the most common methods of eye tracking measurements during driving in simulation and in naturalistic settings, and examines the association of impairment in ET parameters with advanced age and neurodegenerative disease. Conclusion: ET technology is becoming less expensive, more portable, easier to use, and readily applicable in a variety of clinical settings. Older adults and especially those with neurodegenerative disease may have impairments in visual search parameters, placing them at risk for motor vehicle crashes. Advanced driver assessment systems are becoming more ubiquitous in newer cars and may significantly reduce crashes related to impaired visual search, distraction, and/or fatigue.
Quantum neural networks model based on swap test and phase estimation

In this paper, a neural networks model for quantum computer is proposed. The core of this model is quantum neuron. Firstly, the inner product of the input qubits and the weight qubits is mapped to the phase of the control qubits in the neuron by the swap test technology, and then these phases are obtained by the phase estimation method, which are further used as the phase of the output qubit in the neuron. In this way, the mapping of input qubits to output qubit in quantum neuron is completed. The quantum neurons mentioned above can be used to construct quantum neural networks. In this paper, the quantum circuit for each operation step are given. The simulation results on the classic computer verify the effectiveness of the proposed model.
On Global Quantum Communication Networking

Research in quantum communications networks (QCNs), where multiple users desire to generate or transmit common quantum-secured information, is still in its beginning stage. To solve for the problems of both discrete variable- and continuous variable-quantum key distribution (QKD) schemes in a simultaneous manner as well as to enable the next generation of quantum communication networking, in this Special Issue paper we describe a scenario where disconnected terrestrial QCNs are coupled through low Earth orbit (LEO) satellite quantum network forming heterogeneous satellite–terrestrial QCN. The proposed heterogeneous QCN is based on the cluster state approach and can be used for numerous applications, including: (i) to teleport arbitrary quantum states between any two nodes in the QCN; (ii) to enable the next generation of cyber security systems; (iii) to enable distributed quantum computing; and (iv) to enable the next generation of quantum sensing networks. The proposed QCNs will be robust against various channel impairments over heterogeneous links. Moreover, the proposed QCNs will provide an unprecedented security level for 5G+/6G wireless networks, Internet of Things (IoT), optical networks, and autonomous vehicles, to mention a few.
Attribute-based regularization of latent spaces for variational auto-encoders

Selective manipulation of data attributes using deep generative models is an active area of research. In this paper, we present a novel method to structure the latent space of a variational auto-encoder to encode different continuous-valued attributes explicitly. This is accomplished by using an attribute regularization loss which enforces a monotonic relationship between the attribute values and the latent code of the dimension along which the attribute is to be encoded. Consequently, post training, the model can be used to manipulate the attribute by simply changing the latent code of the corresponding regularized dimension. The results obtained from several quantitative and qualitative experiments show that the proposed method leads to disentangled and interpretable latent spaces which can be used to effectively manipulate a wide range of data attributes spanning image and symbolic music domains.
Unsupervised Learning of Category-Specific Symmetric 3D Keypoints from Point Sets

Automatic discovery of category-specific 3D keypoints from a collection of objects of a category is a challenging problem. The difficulty is added when objects are represented by 3D point clouds, with variations in shape and semantic parts and unknown coordinate frames. We define keypoints to be category-specific, if they meaningfully represent objects’ shape and their correspondences can be simply established order-wise across all objects. This paper aims at learning such 3D keypoints, in an unsupervised manner, using a collection of misaligned 3D point clouds of objects from an unknown category. In order to do so, we model shapes defined by the keypoints, within a category, using the symmetric linear basis shapes without assuming the plane of symmetry to be known. The usage of symmetry prior leads us to learn stable keypoints suitable for higher misalignments. To the best of our knowledge, this is the first work on learning such keypoints directly from 3D point clouds for a general category. Using objects from four benchmark datasets, we demonstrate the quality of our learned keypoints by quantitative and qualitative evaluations. Our experiments also show that the keypoints discovered by our method are geometrically and semantically consistent.
On Aggregation of Unsupervised Deep Binary Descriptor With Weak Bits

Despite the thrilling success achieved by existing binary descriptors, most of them are still in the mire of three limitations: 1) vulnerable to the geometric transformations; 2) incapable of preserving the manifold structure when learning binary codes; 3) NO guarantee to find the true match if multiple candidates happen to have the same Hamming distance to a given query. All these together make the binary descriptor less effective, given large-scale visual recognition tasks. In this paper, we propose a novel learning-based feature descriptor, namely Unsupervised Deep Binary Descriptor (UDBD), which learns transformation invariant binary descriptors via projecting the original data and their transformed sets into a joint binary space. Moreover, we involve a ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</sub> -norm loss term in the binary embedding process to gain simultaneously the robustness against data noises and less probability of mistakenly flipping bits of the binary descriptor, on top of it, a graph constraint is used to preserve the original manifold structure in the binary space. Furthermore, a weak bit mechanism is adopted to find the real match from candidates sharing the same minimum Hamming distance, thus enhancing matching performance. Extensive experimental results on public datasets show the superiority of UDBD in terms of matching and retrieval accuracy over state-of-the-arts.
Dynamic Role-Based Access Control for Decentralized Applications

Access control management is an integral part of maintaining the security of an application. For access control management on the Distributed Ledger Technology (DLT), available traditional access control frameworks are inadequate. Existing access control management mechanisms are tightly coupled with the business logic, resulting in an adverse impact on the overall software quality for DLT based Decentralized Applications (dApps). In this paper, we propose a novel framework to implement dynamic role-based access control for dApps. The framework completely decouples the access control logic from the business logic and provides seamless integration with any dApp. The smart contract architecture allows for the independent management of business logic and execution of access control policies. The framework also facilitates secure, low cost, and a high degree of flexibility of access control management. Additionally, it promotes decentralized governance of access control policies and efficient smart contract upgrades. In this paper, we evaluate the framework on relevant various software quality attributes to understand its more profound implications on access control techniques. The framework can be implemented in any smart contract programming language exhibiting Turing completeness. We use the Solidity programming language to implement the framework and discuss the results.
Synthesis of Prostate MR Images for Classification Using Capsule Network-Based GAN Model

Prostate cancer remains a major health concern among elderly men. Deep learning is a state-of-the-art technique for MR image-based prostate cancer diagnosis, but one of major bottlenecks is the severe lack of annotated MR images. The traditional and Generative Adversarial Network (GAN)-based data augmentation methods cannot ensure the quality and the diversity of generated training samples. In this paper, we have proposed a novel GAN model for synthesis of MR images by utilizing its powerful ability in modeling the complex data distributions. The proposed model is designed based on the architecture of deep convolutional GAN. To learn the more equivariant representation of images that is robust to the changes in the pose and spatial relationship of objects in the images, the capsule network is applied to replace CNN used in the discriminator of regular GAN. Meanwhile, the least squares loss has been adopted for both the generator and discriminator in the proposed GAN to address the vanishing gradient problem of sigmoid cross entropy loss function in regular GAN. Extensive experiments are conducted on the simulated and real MR images. The results demonstrate that the proposed capsule network-based GAN model can generate more realistic and higher quality MR images than the compared GANs. The quantitative comparisons show that among all evaluated models, the proposed GAN generally achieves the smallest Kullback–Leibler divergence values for image generation task and provides the best classification performance when it is introduced into the deep learning method for image classification task.
Approximation of quantum control correction scheme using deep neural networks

We study the functional relationship between quantum control pulses in the idealized case and the pulses in the presence of an unwanted drift. We show that a class of artificial neural networks called LSTM is able to model this functional relationship with high efficiency, and hence the correction scheme required to counterbalance the effect of the drift. Our solution allows studying the mapping from quantum control pulses to system dynamics and then analysing the robustness of the latter against local variations in the control profile.
Continuous limits for constrained ensemble Kalman filter

The ensemble Kalman filter method can be used as an iterative particle numerical scheme for state dynamics estimation and control-to-observable identification problems. In applications it may be required to enforce the solution to satisfy equality constraints on the control space. In this work we deal with this problem from a constrained optimization point of view, deriving corresponding optimality conditions. Continuous limits, in time and in the number of particles, allows us to study properties of the method. We illustrate the performance of the method by using test inverse problems from the literature.
Quantum teleportation through atmospheric channels

We study the Kimble–Braunstein continuous-variable quantum teleportation with the quantum channel physically realized in the turbulent atmosphere. In this context, we examine the applicability of different strategies preserving the Gaussian entanglement (Bohmann et al 2016 Phys. Rev. A 94 010302(R)) for improving the fidelity of the coherent-state teleportation. First, we demonstrate that increasing the squeezing parameter characterizing the entangled state is restricted by its optimal value, which we derive for realistic experimentally-verified examples. Further, we consider the technique of adaptive correlations of losses and show its performance for channels with large squeezing parameters. Finally, we investigate the efficiencies of postselection strategies in dependence on the stochastic properties of the channel transmittance.
Fully Automated Identification of Two-Dimensional Material Samples

Thin nanomaterials are key constituents of modern quantum technologies and materials research. The identification of specimens of these materials with the properties required for the development of state-of-the-art quantum devices is usually a complex and tedious human task. In this work, we provide a neural-network-driven solution that allows for accurate and efficient scanning, data processing, and sample identification of experimentally relevant two-dimensional materials. We show how to approach the classification of imperfect and imbalanced data sets using an iterative application of multiple noisy neural networks. We embed the trained classifier into a comprehensive solution for end-to-end automatized data processing and sample identification.
Quantum Simulations with Complex Geometries and Synthetic Gauge Fields in a Trapped Ion Chain

In recent years, arrays of atomic ions in a linear radio-frequency trap have proven to be a particularly successful platform for quantum simulation. However, a wide range of quantum models and phenomena have, so far, remained beyond the reach of such simulators. In this work we introduce a technique that can substantially extend this reach using an external field gradient along the ion chain and a global, uniform driving field. The technique can be used to generate both static and time-varying synthetic gauge fields in a linear chain of trapped ions, and enables continuous simulation of a variety of coupling geometries and topologies, including periodic boundary conditions and high-dimensional Hamiltonians. We describe the technique, derive the corresponding effective Hamiltonian, propose a number of variations, and discuss the possibility of scaling to quantum-advantage-sized simulators. Additionally, we suggest several possible implementations and briefly examine two: the Aharonov-Bohm ring and the frustrated triangular ladder.Received 6 July 2020Accepted 22 September 2020DOI:https://doi.org/10.1103/PRXQuantum.1.020303Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum information with trapped ionsQuantum simulationQuantum Information
A unifying view of explicit and implicit feature maps of graph kernels

Abstract Non-linear kernel methods can be approximated by fast linear ones using suitable explicit feature maps allowing their application to large scale problems. We investigate how convolution kernels for structured data are composed from base kernels and construct corresponding feature maps. On this basis we propose exact and approximative feature maps for widely used graph kernels based on the kernel trick. We analyze for which kernels and graph properties computation by explicit feature maps is feasible and actually more efficient. In particular, we derive approximative, explicit feature maps for state-of-the-art kernels supporting real-valued attributes including the GraphHopper and graph invariant kernels. In extensive experiments we show that our approaches often achieve a classification accuracy close to the exact methods based on the kernel trick, but require only a fraction of their running time. Moreover, we propose and analyze algorithms for computing random walk, shortest-path and subgraph matching kernels by explicit and implicit feature maps. Our theoretical results are confirmed experimentally by observing a phase transition when comparing running time with respect to label diversity, walk lengths and subgraph size, respectively.
Computer Science and Metaphysics: A Cross-Fertilization

Computational philosophy is the use of mechanized computational techniques to unearth philosophical insights that are either difficult or impossible to find using traditional philosophical methods. Computational metaphysics is computational philosophy with a focus on metaphysics. In this paper, we (a) develop results in modal metaphysics whose discovery was computer assisted, and (b) conclude that these results work not only to the obvious benefit of philosophy but also, less obviously, to the benefit of computer science, since the new computational techniques that led to these results may be more broadly applicable within computer science. The paper includes a description of our background methodology and how it evolved, and a discussion of our new results.
Generation of a frequency-degenerate four-photon entangled state using a silicon nanowire

Abstract Integrated photonics is becoming an ideal platform for generating two-photon entangled states with high brightness, high stability, and scalability. This high brightness and high quality of photon pair sources encourages researchers further to study and manipulate multiphoton entangled states. Here, we experimentally demonstrate frequency-degenerate four-photon entangled state generation based on a single silicon nanowire 1 cm in length. The polarization encoded entangled states are generated with the help of a Sagnac loop using additional optical elements. The states are analyzed using quantum interference and state tomography techniques. As an example, we show that the generated quantum states can be used to achieve phase super-resolution. Our work provides a method for preparing indistinguishable multi-photon entangled states and realizing quantum algorithms in a compact on-chip setting.
Quantum Simulation of Three-Body Interactions in Weakly Driven Quantum Systems

The realization of effective Hamiltonians featuring many-body interactions beyond pairwise coupling would enable the quantum simulation of central models underpinning topological physics and quantum computation. We overcome crucial limitations of perturbative Floquet engineering and discuss the highly accurate realization of a purely three-body Hamiltonian in superconducting circuits and molecular nanomagnets.
One-View Occlusion Detection for Stereo Matching With a Fully Connected CRF Model

In this paper, we extend the standard belief propagation (BP) sequential technique proposed in the tree-reweighted sequential method to the fully connected CRF models with the geodesic distance affinity. The proposed method has been applied to the stereo matching problem. Also a new approach to the BP marginal solution is proposed that we call one-view occlusion detection (OVOD). In contrast to the standard winner takes all (WTA) estimation, the proposed OVOD solution allows to find occluded regions in the disparity map and simultaneously improve the matching result. As a result we can perform only one energy minimization process and avoid the cost calculation for the second view and the left-right check procedure. We show that the OVOD approach considerably improves results for cost augmentation and energy minimization techniques in comparison with the standard one-view affinity space implementation. We apply our method to the Middlebury data set and reach state-of-the-art especially for median, average and mean squared error metrics.
On-Line Learning of Linear Dynamical Systems: Exponential Forgetting in Kalman Filters

Kalman filter is a key tool for time-series forecasting and analysis. We show that the dependence of a prediction of Kalman filter on the past is decaying exponentially, whenever the process noise is non-degenerate. Therefore, Kalman filter may be approximated by regression on a few recent observations. Surprisingly, we also show that having some process noise is essential for the exponential decay. With no process noise, it may happen that the forecast depends on all of the past uniformly, which makes forecasting more difficult. Based on this insight, we devise an on-line algorithm for improper learning of a linear dynamical system (LDS), which considers only a few most recent observations. We use our decay results to provide the first regret bounds w.r.t. to Kalman filters within learning an LDS. That is, we compare the results of our algorithm to the best, in hindsight, Kalman filter for a given signal. Also, the algorithm is practical: its per-update run-time is linear in the regression depth.
Makeup Presentation Attacks: Review and Detection Performance Benchmark

The application of facial cosmetics may cause substantial alterations in the facial appearance, which can degrade the performance of facial biometrics systems.Additionally, it was recently demonstrated that makeup can be abused to launch so-called makeup presentation attacks.More precisely, an attacker might apply heavy makeup to obtain the facial appearance of a target subject with the aim of impersonation or to conceal their own identity.We provide a comprehensive survey of works related to the topic of makeup presentation attack detection, along with a critical discussion.Subsequently, we assess the vulnerability of a commercial off-the-shelf and an open-source face recognition system against makeup presentation attacks.Specifically, we focus on makeup presentation attacks with the aim of impersonation employing the publicly available Makeup Induced Face Spoofing (MIFS) and Disguised Faces in the Wild (DFW) databases.It is shown that makeup presentation attacks might seriously impact the security of face recognition systems.Further, we propose different image pair-based, i.e. differential, attack detection schemes which analyse differences in feature representations obtained from potential makeup presentation attacks and corresponding target face images.The proposed detection systems employ various types of feature extractors including texture descriptors, facial landmarks, and deep (face) representations.To distinguish makeup presentation attacks from genuine, i.e. bona fide presentations, machine learning-based classifiers are used.The classifiers are trained with a large number of synthetically generated makeup presentation attacks utilising a generative adversarial network for facial makeup transfer in conjunction with image warping.Experimental evaluations conducted using the MIFS database and a subset of the DFW database reveal that deep face representations achieve competitive detection equal error rates of 0.7% and 1.8%, respectively.
Virtual Teaching as the 'New Norm’: Analyzing Science Teachers’ Attitude toward Online Teaching, Technological Competence and Access

The demand for virtual teaching is increasingly being embraced by the educational system in the Philippines due to the COVID-19 pandemic which made the conduct of the traditional classroom instruction an implausible means for the continuous delivery of education. Thus, it becomes a pressing need to determine teachers’ attitude toward the virtual teaching of Science, technological competence and access. The study enlisted a total of 256 purposively selected teachers assigned to teach Science subjects. Moreover, the investigation intended to deterime whether there is a gender divide among variables of the study, and that whether a significant relationship exist among the respondents’ attitude toward online teaching, technological competence and access. The study disclosed interesting results.