Reviewerly/exHarmony huggingface dataset:
Shape-preserving Star Coordinates

Dimensionality reduction is commonly applied to multidimensional data to reduce the complexity of their analysis. In visual analysis systems, projections embed multidimensional data into 2D or 3D spaces for graphical representation. To facilitate a robust and accurate analysis, essential characteristics of the multidimensional data shall be preserved when projecting. Orthographic star coordinates is a state-of-the-art linear projection method that avoids distortion of multidimensional clusters by restricting interactive exploration to orthographic projections. However, existing numerical methods for computing orthographic star coordinates have a number of limitations when putting them into practice. We overcome these limitations by proposing the novel concept of shapepreserving star coordinates where shape preservation is assured using a superset of orthographic projections. Our scheme is explicit, exact, simple, fast, parameter-free, and stable. To maintain a valid shape-preserving star-coordinates configuration during user interaction with one of the star-coordinates axes, we derive an algorithm that only requires us to modify the configuration of one additional compensatory axis. Different design goals can be targeted by using different strategies for selecting the compensatory axis. We propose and discuss four strategies including a strategy that approximates orthographic star coordinates very well and a data-driven strategy. We further present shape-preserving morphing strategies between two shape-preserving configurations, which can be adapted for the generation of data tours. We apply our concept to multiple data analysis scenarios to document its applicability and validate its desired properties.
Characterization of Gram matrices of multimode coherent states

Quantum communication protocols are typically formulated in terms of abstract qudit states and operations, leaving the question of an experimental realization open. Direct translations of these protocols, say, into single photons with some $d$-dimensional degree of freedom, are typically challenging to realize. Multimode coherent states, on the other hand, can be easily generated experimentally. Reformulation of protocols in terms of these states has been a successful strategy for implementation of quantum protocols. Quantum key distribution and the quantum fingerprinting protocol have both followed this route. In this paper, we characterize the Gram matrices of multimode coherent states in an attempt to understand the class of communication protocols which can be implemented using these states. As a side product, we are able to use this characterization to show that the Hadamard exponential of a Euclidean distance matrix is positive semidefinite. We also derive the closure of the Gram matrices, which can be implemented in this way, so that we also characterize those matrices, which can be approximated arbitrarily well using multimode coherent states.
Experimental simulation of shift operators in a quantum processor

The ability to implement quantum operations plays a fundamental role in manipulating quantum systems. Creation and annihilation operators which transform one quantum state into another by adding or subtracting a particle are crucial in constructing the quantum description of many-body quantum theory and quantum field theory. Here we present a quantum algorithm to perform the creation and annihilation operators by the linear combination of unitary operations associated with a two-qubit ancillary system. Our method can realize shift operators akin to creation and annihilation operators simultaneously in the subspace of the whole system. A prototypical experiment was performed with a four-qubit liquid-state nuclear magnetic resonance processor, demonstrating the algorithm via full-state tomography. With a postselected probability of about 50%, the shift operators are realized with a fidelity above 96%. Moreover, our method can be employed to quantum random walk in an arbitrary initial state. With the prosperous development of quantum computing, our work provides a quantum control technology to implement nonunitary evolution in a near-term quantum computer.
Spontaneous emission in quantum walks of a kicked Bose-Einstein condensate

We analytically investigate the recently proposed and implemented discrete-time quantum walk based on a kicked Bose-Einstein condensate. We extend previous work on the effective dynamics by taking into account spontaneous emission due to the kicking light. Spontaneous emission affects both the internal and external degrees of freedom arising from the entanglement between them during the walk dynamics. The result is a measurable degradation of the experimental walk signal that we characterize.
Improving direct state measurements by using rebits in real enlarged Hilbert spaces

We propose a protocol to improve the accuracy of direct complex state measurements (DSM) by using rebits in real Hilbert spaces. We show that to improve the accuracy, the initial complex state should be decomposed into the real and imaginary parts and stored in an extended state (rebit) which can be tracked individually by two bases of an extra qubit. For pure states, the numerical calculations show that the trace distances between the true state and the reconstructed state obtained from the rebit method are more precise than those ones obtained from the usual DSM and quantum state tomography (SQT) because the number of projective measurements is reduced. For mixed states, the rebit method gives the same accuracy in comparison to the usual DSM, while it is less precise than QST. Its precision is also significantly improved when using nearly-pure states. Our proposal holds promises as a reliable tool for quantum computation, testing of quantum circuits by using only real amplitudes.
Interaction Driven Enhancement of Depth Perception in Angiographic Volumes

User interaction has the potential to greatly facilitate the exploration and understanding of 3D medical images for diagnosis and treatment. However, in certain specialized environments such as in an operating room (OR), technical and physical constraints such as the need to enforce strict sterility rules, make interaction challenging. In this paper, we propose to facilitate the intraoperative exploration of angiographic volumes by leveraging the motion of a tracked surgical pointer, a tool that is already manipulated by the surgeon when using a navigation system in the OR. We designed and implemented three interactive rendering techniques based on this principle. The benefit of each of these techniques is compared to its non-interactive counterpart in a psychophysics experiment where 20 medical imaging experts were asked to perform a reaching/targeting task while visualizing a 3D volume of angiographic data. The study showed a significant improvement of the appreciation of local vascular structure when using dynamic techniques, while not having a negative impact on the appreciation of the global structure and only a marginal impact on the execution speed. A qualitative evaluation of the different techniques showed a preference for dynamic chroma-depth in accordance with the objective metrics but a discrepancy between objective and subjective measures for dynamic aerial perspective and shading.
On investigations of refractive indices and birefringence of fiber using duplicate and non‐duplicate micro‐interferograms of double refracting interferometer

Abstract The formula of refractive index of fiber considering the refraction has been reviewed. This formula is used to investigate the effect of refraction on the accuracy of refractive index calculations with different simulated micro‐interferograms of duplicate interference fringes with different fringe shifts order. Also a more accurate formula to calculate the birefringence from the non‐duplicate interference fringes has been derived and tested to calculate the birefringence of simulated and experiment micro‐interferogram. The presented micro‐interferograms were automatically analyzed and the contour lines of the bright fringes were determined using Fourier transform technique. A prepared software program takes the contour line of the fringes and automatically calculate the refractive index, the radius, and the birefringence of fiber. A good result was obtained in case of using immersion liquid matches between the parallel and perpendicular refractive indices of fiber. Formula of refractive index of fiber considering the refraction has been reviewed. The refraction effect on the accuracy of refractive index calculations was presented. More accurate Formula to calculate the birefringence of fiber was derived. Automatic fringe analysis was done for duplicate and non‐duplicate fringes. The contour line of the fringe used directly to calculate the birefringence of fiber.
Constant Time Graph Neural Networks

The recent advancements in graph neural networks (GNNs) have led to state-of-the-art performances in various applications, including chemo-informatics, question-answering systems, and recommender systems. However, scaling up these methods to huge graphs, such as social networks and Web graphs, remains a challenge. In particular, the existing methods for accelerating GNNs either are not theoretically guaranteed in terms of the approximation error or incur at least a linear time computation cost. In this study, we reveal the query complexity of the uniform node sampling scheme for Message Passing Neural Networks, including GraphSAGE, graph attention networks (GATs), and graph convolutional networks (GCNs). Surprisingly, our analysis reveals that the complexity of the node sampling method is completely independent of the number of the nodes, edges, and neighbors of the input and depends only on the error tolerance and confidence probability while providing a theoretical guarantee for the approximation error. To the best of our knowledge, this is the first paper to provide a theoretical guarantee of approximation for GNNs within constant time. Through experiments with synthetic and real-world datasets, we investigated the speed and precision of the node sampling scheme and validated our theoretical results.
Adaptive spatio-temporal context learning for visual tracking

In recent years, a spatio-temporal context (STC) algorithm has attracted the attention of scholars, due to the algorithm makes full use of the information of the target background. Although the STC algorithm achieve tracking at the real-time, but there is still a need to improve the tracking capability when the target is occluded or the size of the target changes. In this paper, we presented an adaptive spatio-temporal context learning for visual tracking (AFSTC). Firstly, in order to accurately describe the appearance of the target, we integrate Histogram of Oriented Gradient (HOG) and Colour-naming (CN) features. And then we use the average difference between two adjacent frames to adjust the learning rate of update model for adaptive tracking. Finally, we adjust parameters of scale update strategy to achieve the competitive results on accuracy and robustness. We perform experiments on the Online Tracking Benchmark (OTB) 2015 dataset. Our tracker achieves a 13% relative gain in distance precision compared to the traditional STC algorithm. Moreover, although the speed of our tracker reduces, but it reaches 129.99 frames per second (FPS) and can still achieve tracking at the real-time.
Tackling Similarity Search for Soccer Match Analysis: Multimodal Distance Measure and Interactive Query Definition

Analysts and coaches in soccer sports need to investigate large sets of past matches of opposing teams in a short time to prepare their teams for upcoming matches. Thus, they need appropriate methods and systems supporting them in searching for soccer moves for comparison and explanation. For the search of similar soccer moves, established distance and similarity measures typically only take spatiotemporal features like shape and speed of movement into account. However, movement in invasive team sports such as soccer includes much more than just a sequence of spatial locations. We propose an enhanced similarity measure integrating spatial, player, event as well as high level context such as pressure into the process of similarity search. We present a visual search system supporting analysts in interactively identifying similar contextual enhanced soccer moves in a dataset containing more than 60 soccer matches. Our approach is evaluated by several expert studies. The results of the evaluation reveal the large potential of enhanced similarity measures in the future.
Experimental linear optical computing of the matrix permanent

Linear optical computing (LOC) with thermal light has recently gained attention because the problem is connected to the permanent of a Hermitian positive semidefinite matrix (HPSM), which is of importance in the computational complexity theory. Despite the several theoretical analyses on the computational structure of an HPSM in connection to LOC, the experimental demonstration and the computational complexity analysis via the linear optical system have not been performed yet. We present, herein, experimental LOC for estimating the permanent of an HPSM. From the linear optical experiments and theoretical analysis, we find that the LOC efficiency for a multiplicative error is dependent on the value of the permanent and that the lower bound of the computation time scales exponentially. Furthermore, our results are generalized and applied to LOC of permanents of unitary matrices, which can be implemented with a multiport quantum interferometer involving single photons at the input ports. We find that LOC with single photons, for the permanent estimation, is on average less efficient than the most efficient classical algorithm known to date, even in ideal conditions.
Robust generation of entangled fields against inhomogeneous parameters of coupled superconducting resonators and qubits

We propose an efficient approach for the generation of a stable entangled state of microwave fields in circuit quantum electrodynamics. The system under consideration consists of two linearly coupled superconducting resonators, each of which is coupled to a superconducting flux qubit. By individually driving the qubits to engineer the desired interactions with the resonators, we show that the energy relaxation of qubits can be exploited to steer the two resonators into the two-mode entangled state via a dissipative dynamical process. Since our protocol is based on quantum reservoir engineering, neither specific initialization nor unitary dynamics is required. Moreover, the distinct advantage of our scheme is that the quantum state preparation is robust in relation to inhomogeneous parameters, i.e., there is no need for identical qubit-field couplings, as well as the same resonance frequencies of the resonators. These features make the present scheme pretty feasible for experimental implementation.
Adversarial Learning Semantic Volume for 2D/3D Face Shape Regression in the Wild

Regression based methods have revolutionized 2D landmark localization with the exploitation of deep neural networks and massive annotated datasets in the wild. However, it remains challenging for 3D landmark localization due to the lack of annotated datasets and the ambiguous nature of landmarks under 3D perspective. This paper revisits regression based methods and proposes an adversarial voxel and coordinate regression framework for 2D and 3D facial landmark localization in real-world scenarios. First, a semantic volumetric representation is introduced to encode the per-voxel likelihood of positions being the 3D landmarks. Then, an end-to-end pipeline is designed to jointly regress the proposed volumetric representation and the coordinate vector. Such a pipeline not only enhances the robustness and accuracy of the predictions but also unifies the 2D and 3D landmark localization so that 2D and 3D datasets could be utilized simultaneously. Further, an adversarial learning strategy is exploited to distill 3D structure learned from synthetic datasets to real-world datasets under weakly supervised settings, where an auxiliary regression discriminator is proposed to encourage the network to produce plausible predictions for both synthetic and real-world images. The effectiveness of our method is validated on benchmark datasets 3DFAW and AFLW2000-3D for both 2D and 3D facial landmark localization tasks. Experimental results show that the proposed method achieves significant improvements over previous state-of-the-art methods.
Attention to the Variation of Probabilistic Events: Information Processing with Message Importance Measure

Different probabilities of events attract different attention in many scenarios such as anomaly detection and security systems. To characterize the events' importance from a probabilistic perspective, the message importance measure (MIM) is proposed as a kind of semantics analysis tool. Similar to Shannon entropy, the MIM has its special function in information representation, in which the parameter of MIM plays a vital role. Actually, the parameter dominates the properties of MIM, based on which the MIM has three work regions where this measure can be used flexibly for different goals. When the parameter is positive but not large enough, the MIM not only provides a new viewpoint for information processing but also has some similarities with Shannon entropy in the information compression and transmission. In this regard, this paper first constructs a system model with message importance measure and proposes the message importance loss to enrich the information processing strategies. Moreover, the message importance loss capacity is proposed to measure the information importance harvest in a transmission. Furthermore, the message importance distortion function is discussed to give an upper bound of information compression based on the MIM. Additionally, the bitrate transmission constrained by the message importance loss is investigated to broaden the scope for Shannon information theory.
Interactive semi-automated specification mining for debugging: An experience report

Specification mining techniques are typically used to extract the specification of a software in the absence of (up-to-date) specification documents. This is useful for program comprehension, testing, and anomaly detection. However, specification mining can also potentially be used for debugging, where a faulty behavior is abstracted to give developers a context about the bug and help them locating it. In this project, we investigate this idea in an industrial setting. We propose a very basic semi-automated specification mining approach for debugging and apply that on real reported issues from an AutoPilot software system from our industry partner, MicroPilot Inc. The objective is to assess the feasibility and usefulness of the approach in a real-world setting. The approach is developed as a prototype tool, working on C code, which accept a set of relevant state fields and functions, per issue, and generates an extended finite state machine that represents the faulty behavior, abstracted with respect to the relevant context (the selected fields and functions). We qualitatively evaluate the approach by a set of interviews (including observational studies) with the company’s developers on their real-world reported bugs. The results show that (a) our approach is feasible, (b) it can be automated to some extent, and (c) brings advantages over only using their code-level debugging tools. We also compared this approach with traditional fully automated state-merging algorithms and reported several issues when applying those techniques on a real-world debugging context. The main conclusion of this study is that the idea of an “interactive” specification mining rather than a fully automated mining tool is NOT impractical and indeed is useful for the debugging use case.
Polynomial time algorithms for estimating spectra of adiabatic Hamiltonians

Addressing the computational power of adiabatic quantum algorithms is a big challenge and simplified symmetric systems are often used to advance in this arena. Here, a more realistic and less symmetrical system is analyzed using a tight-binding approach, and a toy model is constructed to provide insight into two traditional problems: adiabatic search in an unstructured environment and adiabatic simulation of ground states.
Promotion of Answer Value Measurement With Domain Effects in Community Question Answering Systems

In the area of community question answering (CQA), answer selection and answer ranking are two tasks which are applied to help users quickly access valuable answers.Existing solutions mainly exploit the syntactic or semantic correlation between a question and its related answers (Q&A), where the multifacet domain effects in CQA are still underexplored.In this paper, we propose a unified model, enhanced attentive recurrent neural network (EARNN), for both answer selection and answer ranking tasks by taking full advantages of both Q&A semantics and multifacet domain effects (i.e., topic effects and timeliness).Specifically, we develop a serialized long short-term memory to learn the unified representations of Q&A, where two attention mechanisms at either sentence level or word level are designed for capturing the deep effects of topics.Meanwhile, the emphasis of Q&A can be automatically distinguished.Furthermore, we design a time-sensitive ranking function to model the timeliness in CQA.To effectively train EARNN, a question-dependent pairwise learning strategy is also developed.Finally, we conduct extensive experiments on a realworld dataset from Quora.Experimental results validate the effectiveness and interpretability of our proposed EARNN model.
Flexibility Optimized Control for Robot Efficient Moving in Corridors Based on Viability Theory

The speed of mobile robots moving in corridors is important for the efficiency of material transportation. This paper proposes a novel control method based on the viability theory for optimizing the flexibility of robots passing through narrow corridors. The viability theory can take both environmental constraints and robot dynamical limitation into account simultaneously. The viability of a straight corridor is first analyzed. Then, this analysis is extended to the corner. With these two typical environments, a general corridor working scenario for a mobile robot can be formulated. Through the viability analyses, the largest safety area of the corridor is provided with respect to the robot traveling at a high speed. Furthermore, with the facility of viability, a flexibility optimized control is derived by modifying a general model predictive control framework. The simulation results validate that the proposed flexible control method is secure, accurate, efficient, and lower computational burden. With these properties, this method can also be applied in the area of autonomous vehicle control.
Semi-Fragile Watermarking for JPEG Image Authentication: A Comparative Study

One of the ways to protect images from tampering is embedding digital watermarks destroyed by unauthorized changes. The development of digital watermarking methods that are robust against allowable transformations, whose most typical example is lossy compression, presents a challenging task. Such methods and watermarks embedded by them are called semi-fragile. The paper considers different semi-fragile watermarking techniques that resisting JPEG. We focus on the analysis of the effect of watermark capacity, positions of spectral coefficients selected for embedding, and specific algorithms of coefficients modification on the quality of the resulting (secure) image and the authentication accuracy. In the paper, we also provide recommendations on the choice of the best combinations of these parameters depending on the terms of use of a protected image.