Reviewerly/exHarmony huggingface dataset:
Grating Lobes in Higher-Order Correlation Functions of Arrays of Quantum Emitters: Directional Photon Bunching Versus Correlated Directions

Recent advances in nanofabrication and optical manipulation techniques are making it possible to build arrays of quantum emitters with accurate control over the locations of their individual elements. In analogy with classical antenna arrays, this poses new opportunities for tailoring quantum interference effects by designing the geometry of the array. Here, we investigate the N th -order directional correlation function of the photons emitted by an array of N initially-excited identical quantum emitters, addressing the impact of the appearance of grating lobes. Our analysis reveals that the absence of directivity in the first-order correlation function is contrasted by an enhanced directivity in the N th -order one. This suggests that the emitted light consists of a superposition of directionally entangled photon bunches. Moreover, the photon correlation landscape changes radically with the appearance of grating lobes. In fact, the photons no longer tend to be bunched along the same direction; rather, they are distributed in a set of correlated directions with equal probability. These results clarify basic aspects of light emission from ensembles of quantum emitters. Furthermore, they may find applications in the design of nonclassical light sources.
Research on improved text classification method based on combined weighted model

Summary Text classification is very important in information retrieval, but the traditional text classification model has many problems, such as the feature dimension disaster, the lack of semantic features, etc. Aiming at the problems, this paper proposes an improved TFIDF model combined with the Word2vec model for weighing word vectors. In view of the inability of the Word2vec model to distinguish the importance of words with the text, TFIDF is further introduced to weighing Word2vec word vectors to achieve a weighted Word2vec classification model. For data preprocessing, we optimized the traditional StringToWordVector algorithm. The main improvement of StringToWordVector is the introduction to a new algorithm of stem extraction. First, this paper gives a simple description of the basic steps and algorithms of traditional text classification, and then, the ideas and steps of the improved StringToWordVector algorithm are proposed. Finally, experimental results using our improved algorithm are tested for four different data sets (WEBO_SINA and three standard UCI data sets). The experimental results show that the improved StringToWordVector algorithm combined with the combined weighted model has higher classification accuracy, recall, and F1 values than the traditional text classification model only using the Word2vec model or using TFIDF. The experimental results are satisfactory.
Cross-Modality Feature Learning via Convolutional Autoencoder

Learning robust and representative features across multiple modalities has been a fundamental problem in machine learning and multimedia fields. In this article, we propose a novel MUltimodal Convolutional AutoEncoder (MUCAE) approach to learn representative features from visual and textual modalities. For each modality, we integrate the convolutional operation into an autoencoder framework to learn a joint representation from the original image and text content. We optimize the convolutional autoencoders of different modalities jointly by exploiting the correlation between the hidden representations from the convolutional autoencoders, in particular by minimizing both the reconstructing error of each modality and the correlation divergence between the hidden feature of different modalities. Compared to the conventional solutions relying on hand-crafted features, the proposed MUCAE approach encodes features from image pixels and text characters directly and produces more representative and robust features. We evaluate MUCAE on cross-media retrieval as well as unimodal classification tasks over real-world large-scale multimedia databases. Experimental results have shown that MUCAE performs better than the state-of-the-arts methods.
A <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si11.gif" overflow="scroll"><mml:mrow><mml:mo stretchy="true">(</mml:mo><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>n</mml:mi><mml:mo stretchy="true">)</mml:mo></mml:mrow></mml:math> threshold partial reversible AMBTC-based visual cryptography using one reference image

Recently, Yang et al. introduced a (k,n) approach called Reversible Absolute moment block truncation coding Visual Cryptography Scheme (RAVCS) (Yang et al., 2017) to conceal a secret image into n AMBTC shares. However, a large number of reference images is used in their method. To reduce the number of reference images, a (k,n) PRAVCS using one AMBTC reference image is introduced. In encoding phase, a binary secret image is shared into n AMBTC shadow images according to the base matrices generated by two proposed constructions. In decoding phase, the secret image is recovered by stacking sufficient bitmaps, and the AMBTC reference image is partially recovered as well. When n AMBTC shares are used, losslessly reconstruction for the reference image is obtained. Theoretical analysis and experiments by the proposed method are demonstrated to show the effectiveness. Moreover, Construction 1 has larger contrast, while Construction 2 achieves higher reversibility for more thresholds.
Compressed Domain Moving Object Detection Based on CRF

This paper aims to present a novel accurate moving object detection method based on the conditional random field (CRF) for high efficiency video coding/H.265 compressed domain video sequences. For each block, the number of consumed bits, motion vectors (MVs), and partitioning modes for a given block is extracted from the compressed bitstream. After removing outlier MVs, compensating MVs are assigned to the I-blocks based on their neighboring blocks. The information, such as MV, partitioning mode, and bit consumption, is used in the potential functions of a CRF model which is updated for every frame to detect the objects. Then, a number of standard test video sequences are used to verify the performance of the model. The results indicated that the model can offer a precision, that is more than 90% on average for the video sequences. The proposed method offers a 1.8 speedup, compared to the latest works in the compressed domain without losing the objects in the I-frames.
A smart mechatronic base isolation system using earthquake early warning

In earthquake-prone countries such as Japan, effects of earthquakes are one of the major design objectives in buildings and infrastructures. Recent advances in earthquake engineering have improved the safety and reliability of buildings and infrastructures. Better understanding of geomorphology of earthquakes also enables us to better predict and estimate propagation of such natural hazard. For example, the Earthquake Early Warning (EEW) system in Japan provides advanced warnings using the different arrival times of P and S waves. On the other hand, techniques and methods in passive, semi-active and active vibration control have flourished. Base-isolation is a proven technique which enhances earthquake resilience of buildings and bridges. In general, this technique involves decoupling a superstructure from its foundation and lengthens its natural period of vibration. This technique is mature and commercialization has taken place worldwide. However, wind-resistance of base-isolated structures is generally a concern as the lateral stiffness of a base-isolated structure is low and serviceability wind effects may induce unacceptable lateral movement and/or vibration of the structure. This paper presents a new concept which the base-isolation system is activated by EEW system through a mechatronic system. In other times the structure is not base-isolated and provides strong lateral resistance against wind loads. As a backup design, the proposed system also equips with its own network of accelerometers which can trigger base isolation system independent from EEW system. The smart mechatronic base isolation system is fully automated, and resets itself after each ground motion. The paper describes the conceptual framework of proposed system and presents laboratory-scaled proof-of-concept experiments. Four historical earthquake time histories were used as ground excitations. Structural responses are compared between the two triggering mechanisms and results are discussed. Finally, the concept of Internet of Things (IoT) which make use of EEW is discussed, allowing connectivity between a single control unit and a network of infrastructures to achieve economy of scale.
Exact simulation of generalised Vervaat perpetuities

Abstract We consider a generalised Vervaat perpetuity of the form X = Y 1 W 1 + Y 2 W 1 W 2 + · · ·, where $W_i \sim {\cal U}^{1/t}$ and ( Y i ) i ≥0 is an independent and identically distributed sequence of random variables independent from ( W i ) i ≥0 . Based on a distributional decomposition technique, we propose a novel method for exactly simulating the generalised Vervaat perpetuity. The general framework relies on the exact simulation of the truncated gamma process, which we develop using a marked renewal representation for its paths. Furthermore, a special case arises when Y i = 1, and X has the generalised Dickman distribution, for which we present an exact simulation algorithm using the marked renewal approach. In particular, this new algorithm is much faster than existing algorithms illustrated in Chi (2012), Cloud and Huber (2017), Devroye and Fawzi (2010), and Fill and Huber (2010), as well as being applicable to the general payments case. Examples and numerical analysis are provided to demonstrate the accuracy and effectiveness of our method.
Representation learning by hierarchical ELM auto‐encoder with double random hidden layers

Recent research developments of extreme learning machine (ELM) with multilayer network architecture lead to a promising high performance with extremely fast training speed for representation learning. In this work, the authors are dedicated to develop an efficient and expressive representation learning method with hierarchical ELM, and proposing a novel architectural unit named as double random hidden layers ELM auto‐encoder (DELM‐AE). The novel DELM‐AE consists of one input layer, two random hidden mapping layers for encoding feature, and one output layer for decoding feature. When stacking DELM‐AE in the hierarchical structure, they can construct an H‐DELM model, where the input of the current AE is the feature representation learned by the previous one, but the output is identical to the original input information and is not the input. Hence, the H‐DELM can reproduce the original input data as much as possible to learn more expressive and compact feature. They validate their method on various widely public datasets, and the results demonstrate that H‐DELM can bring significant performance improvements in terms of classification accuracy and robustness compared with existing relevant multilayer ELM and other deep learning algorithms at a slight computational cost.
An RNN-Based IMM Filter Surrogate

The problem of varying dynamics of tracked objects, such as pedestrians, is traditionally tackled with approaches like the Interacting Multiple Model (IMM) filter using a Bayesian formulation. By following the current trend towards using deep neural networks, in this paper an RNN-based IMM filter surrogate is presented. Similar to an IMM filter solution, the presented RNN-based model assigns a probability value to a performed dynamic and, based on them, puts out a multi-modal distribution over future pedestrian trajectories. The evaluation is done on synthetic data, reflecting prototypical pedestrian maneuvers.
Texture Relative Superpixel Generation With Adaptive Parameters

Superpixel generation, which is an essential step in many image processing applications, has attracted increasing attention from researchers. In this paper, we present an efficient flooding-based superpixel generation algorithm that generates compact and highly boundary adherent superpixels. In particular, by considering various superpixel properties, we measure the similarities between image pixels by proposing a new distance metric that combines various image features (e.g., colors, spatial locations, neighbor information, and texture features). To control the relative significance of these image features, we acquire the weights of image features (e.g., colors, texture features, and neighbor information of pixels) through a neural network. Then, the final superpixels are obtained through a greedy optimization that considers both the current superpixel and its neighboring superpixels. We perform extensive experiments on two datasets to verify the efficacy of our algorithm. The results show that our algorithm has considerable advantages over existing state-of-the-art methods, particularly regarding the compactness of the resulting superpixels.
SSVD: Structural SVD-based image quality assessment

In the last decade, some impressive image quality metrics have been proposed; however, designing an image quality metric which predicts human judgments is still a challenging issue. It is due to the complexity of the human visual system. Singular value decomposition (SVD), as a useful tool, has been employed for evaluating the perceptual quality of visual information. The efficiency of the SVD-based image quality assessment (IQA) methods is related to its ability to extract the structural information of the viewing scene. In this paper, a new SVD-based IQA method is presented in which the structural information of the distorted image is evaluated based on its reflection on the original singular vector matrices. The experimental results show that the proposed algorithm can effectively evaluate the natural image quality in a consistent manner with the human visual perception.
A Two-Step Cooperative Energy Detection Algorithm Robust to Noise Uncertainty

In order to achieve accurate interference detection in complex electromagnetic environments, a two-step cooperative stochastic resonance energy detection (TCSRED) algorithm is proposed to address the problem, where the traditional energy detection (ED) performance is susceptible to noise uncertainty. By combining two thresholds and two-step cooperation, the generalized stochastic resonance is applied to the energy detection, which effectively reduces the complexity and detection time. In particular, when a certain decision result is obtained in the first step of detection, the decision is finished and the second step of detection is unnecessary. Otherwise, the second step of detection is performed to obtain the final decision result. Simulation results show that the proposed algorithm is robust to the noise uncertainty. Even in the case of a low signal-to-noise ratio (SNR), it also performs better than existing methods without significant increment of the complexity.
A two-stage route optimization algorithm for light aircraft transport systems

This paper presents a route optimization algorithm for light aircraft operating under visual flight rules. The problem aims at finding a minimum-duration, collision-free route in three spatial dimensions with possible aircraft maneuvers. The optimal route takes into account the aircraft kinodynamic characteristics and its interaction with external wind. A data processing approach is presented to recast the flying environment as a series of polyhedrons based on which a mixed-integer linear model is formulated. A two-stage route optimization model is then introduced to solve real-life instances. Computational experiments depict the efficiency of this approach.
The Evaluation of DCNN on Vector-SIMD DSP

In current computer vision tasks, deep convolutional neural networks (DCNN) achieve state-of-the-art results. At present, the DCNN has been widely used in many usage scenarios. It is also important to develop DCNN on supercomputers in the age of artificial intelligence. As a general-purpose accelerator deployed in a supercomputer which ranks the fourth in the latest Top500 list, the Matrix2000 is a vector single instruction multiple data (vector-SIMD) digital signal processor (DSP). The research of DCNN implementation based on DSP is important and meaningful. Till now, there are few systematic studies published on DCNN implementation based on DSP. Qualcomm, CEVA, and Cadence have said that they have implemented DCNN on their own DSP. However, Qualcomm and CEVA have not published how they implemented DCNN based on their own DSP and Cadence has only implemented a convolution layer based on Vision P6. In this paper, we proposed a vectorization mapping method and a high efficient partition analysis model for implementing DCNN on vector-SIMD DSP. Based on the vectorization mapping method and analysis model, we implemented all layers of typical DCNN models on Matrix2000 and tested the computation and energy efficiency. The experiments demonstrate that the average computation efficiency of this paper based on Matrix2000 is 20~35% higher than GPU, 35~45% higher than Xeon Phi, about 8% higher than Vision P6 DSP, about 62~75% higher than an existing evaluation of DCNN based on Matrix2000, and the average energy efficiency is about 9~30% higher than GPU, and about 56% higher than the existing evaluation of DCNN based on Matrix2000. The results show that the vector-SIMD DSP with a suitable programming mapping method is also a suitable platform in the age of artificial intelligence.
Optimal SUAS Path Planning in Three-Dimensional Constrained Environments

Small Unmanned Aircraft Systems have grown in autonomy and capability and continue to complement Department of Defense mission objectives. Teaming unmanned aircraft with manned vehicles can expand mission profiles and reduce risk to human life. To fully leverage unmanned systems, vehicles must be efficient and autonomous in path planning development. The work herein explores direct orthogonal collocation optimal control techniques combined with fast geometric path planning algorithms to reduce computation time and increase solution accuracy for small unmanned aircraft systems path planning missions. Previous work in the two-dimensional plane demonstrated a methodology to provide optimal flight paths through defined simplex corridors and simplified the optimal control parameter bounds by formulating the problem in the barycentric coordinate system. These methodologies are extended in this paper for three-dimensional flight and are solved with two different formulations for flight in an urban environment. The first formulation solves the constrained optimal control problem using a single phase while modeling the building constraints with superquadric functions. The second formulation implements the simplex methodology, eliminating polygonal constraints from the search domain, and solving the optimal path in a multiple phase approach. Results illustrate the benefits gained in computation time and accuracy when implementing simplex methods into the optimal control design and provide a foundation for closing the gap to real-time, onboard operations for unmanned vehicle path planning.
Multi-Information Flow CNN and Attribute-Aided Reranking for Person Reidentification

This paper presents a multi-information flow convolutional neural network (MiF-CNN) model for person reidentification (re-id). It contains several specific multilayer convolutional structures, where the input and output of a convolutional layer are concatenated together on channel dimension. With this idea, layers of model can go deeper and feature maps can be reused by each subsequent layer. Inspired by an image caption, a person attribute recognition network is proposed based on long-short-term memory network and attention mechanism. By fusing identification results of MiF-CNN and attribute recognition, this paper introduces the attribute-aided reranking algorithm to improve the accuracy of person re-id further. Experiments on VIPeR, CUHK01, and Market1501 datasets verify the proposed MiF-CNN can be trained sufficiently with small-scale datasets and obtain outstanding accuracy of person re-id. Contrast experiments also confirm the availability of the attribute-assisted reranking algorithm.
A low complexity wavelet-based blind image quality evaluator

The aim of blind image quality assessment (BIQA) methods is to evaluate the perceptual quality of a distorted image without any prior information regarding its reference image. Although some impressive image quality metrics have been proposed, due to the complexity of the human visual system and the lack of a reference image, designing an image quality metric which accurately predicts human judgments is still a challenging issue. In this paper, a low complexity wavelet-based image quality assessment is proposed. Firstly, the interaction of fine and coarse details of the image, which is extracted by Haar wavelet, is analyzed. In the proposed approach, the joint statistics of two normalized high frequency subbands which indicate coarse and fine structures is utilized for extracting features. Actually, analyzing the relation between image details of different granularities is the main idea of the proposed method. After feature extraction phase, support vector regression (SVR) is adopted in order to provide a quality score. Experimental results show the effectiveness of the proposed low complexity approach.
Online Learning of Weighted Relational Rules for Complex Event Recognition

Systems for symbolic complex event recognition detect occurrences of events in time using a set of event definitions in the form of logical rules. The Event Calculus is a temporal logic that has been used as a basis in event recognition applications, providing among others, connections to techniques for learning such rules from data. We advance the state-of-the-art by combining an existing online algorithm for learning crisp relational structure with an online method for weight learning in Markov Logic Networks (MLN). The result is an algorithm that learns complex event patterns in the form of Event Calculus theories in the MLN semantics. We evaluate our approach on a challenging real-world application for activity recognition and show that it outperforms both its crisp predecessor and competing online MLN learners in terms of predictive performance, at the price of a small increase in training time. Code related to this paper is available at: https://github.com/nkatzz/OLED .
Emergent Policy Discovery for Visual Reinforcement Learning Through Tangled Program Graphs: A Tutorial

Tangled Program Graphs (TPG) represents a framework by which multiple programs can be organized to cooperate and decompose a task with minimal a priori information. TPG agents begin with least complexity and incrementally coevolve to discover a complexity befitting the nature of the task. Previous research has demonstrated the TPG framework under visual reinforcement learning tasks from the Arcade Learning Environment and VizDoom first person shooter game that are competitive with those from Deep Learning. However, unlike Deep Learning the emergent constructive properties of TPG results in solutions that are orders of magnitude simpler, thus execution never needs hardware support. In this work, our goal is to provide a tutorial overview demonstrating how the emergent properties of TPG have been achieved as well as providing specific examples of decompositions discovered under the VizDoom task.