Reviewerly/exHarmony huggingface dataset:
Coherent generation and manipulation of entangled stationary photons based on a multiple degrees of freedom quantum memory

We propose a quantum memory, each subsystem of which is comprised of two double M-type systems of cold atoms, for the first generation of entangled stationary photons (ESPs). Through the active operation of two pairs of counter-propagating controlling fields in time, the reversible transfer of entanglement between photons and atomic ensembles is realized, and the ESPs can be created due to the tight coupling and balanced competition between the corresponding retrieved signal photons. The reduced density matrix in the photon-polarization basis, which provides the lower bound for any purported entanglement, is constructed for discussing the dynamics evolution of the entanglement in terms of the concurrence. We show that the present scheme can be employed for the entangled photons encoded in degrees of freedom (DOFs) of polarization and orbital angular momentum. Such a multiple DOFs dependent scheme, with many benefits over that in a single one, could pave the way toward quantum nonlinear optics without a cavity and could greatly enhance the tunability and capacity for the quantum information processing.
Echocardiographic Data in Artificial Intelligence Research

Analyses of medical data have been steered to support and justify orthodox systems and graphical models that reproduce the relatively subjective reasoning of experts. Evidence-based decision tree analyses and risk scores are valid tools for clinical decision-making that are widely used in medicine
New Constructions of Hinting PRGs, OWFs with Encryption, and More

Over the last few years, there has been a surge of new cryptographic results, including laconic oblivious transfer [13, 16], (anonymous/ hierarchical) identity-based encryption [9], trapdoor functions [19, 20], chosen-ciphertext security transformations [32, 33], designated-verifier zero-knowledge proofs [30, 34, 37], due to a beautiful framework recently introduced in the works of Cho et al. [13], and Döttling and Garg [14]. The primitive of one-way function with encryption (OWFE) [19, 20] and its relatives (chameleon encryption, one-time signatures with encryption, hinting PRGs, trapdoor hash encryption, batch encryption) [9, 14, 16, 17, 33] have been a centerpiece in all these results. While there exist multiple realizations of OWFE (and its relatives) from a variety of assumptions such as CDH, Factoring, and LWE, all such constructions fall under the same general “missing block” framework [13, 14]. Although this framework has been instrumental in opening up a new pathway towards various cryptographic functionalities via the abstraction of OWFE (and its relatives), it has been accompanied by undesirable inefficiencies that might inhibit a much wider adoption in many practical scenarios. Motivated by the surging importance of the OWFE abstraction (and its relatives), a natural question to ask is whether the existing approaches can be diversified to not only obtain more constructions from different assumptions, but also in developing newer frameworks. We believe answering this question will eventually lead to important and previously unexplored performance trade-offs in the overarching applications of this novel cryptographic paradigm. In this work, we propose a new accumulation-style framework for building a new class of OWFE as well as hinting PRG constructions with a special focus on achieving shorter ciphertext size and shorter public parameter size (respectively). Such performance improvements parlay into shorter parameters in their corresponding applications. Briefly, we explore the following performance trade-offs—(1) for OWFE, our constructions outperform in terms of ciphertext size as well as encryption time, but this comes at the cost of larger evaluation and setup times, (2) for hinting PRGs, our constructions provide a rather dramatic trade-off between evaluation time versus parameter size, with our construction leading to significantly shorter public parameter size. The trade-off enabled by our hinting PRG construction also leads to interesting implications in the CPA-to-CCA transformation provided in [33]. We also provide concrete performance measurements for our constructions and compare them with existing approaches. We believe highlighting such trade-offs will lead to a wider adoption of these abstractions in a practical sense.
Using a Reinforcement Q-Learning-Based Deep Neural Network for Playing Video Games

This study proposed a reinforcement Q-learning-based deep neural network (RQDNN) that combined a deep principal component analysis network (DPCANet) and Q-learning to determine a playing strategy for video games. Video game images were used as the inputs. The proposed DPCANet was used to initialize the parameters of the convolution kernel and capture the image features automatically. It performs as a deep neural network and requires less computational complexity than traditional convolution neural networks. A reinforcement Q-learning method was used to implement a strategy for playing the video game. Both Flappy Bird and Atari Breakout games were implemented to verify the proposed method in this study. Experimental results showed that the scores of our proposed RQDNN were better than those of human players and other methods. In addition, the training time of the proposed RQDNN was also far less than other methods.
A Multi-Heuristic A* Algorithm Based on Stagnation Detection for Path Planning of Manipulators in Cluttered Environments

We consider the problem of planning an obstacle avoidance path for manipulators in cluttered environments especially with narrow passages. Compared to sampling-based planners, heuristic search-based planners are more suitable for such environments due to the consistent heuristic guidance. In order to solve the problem of search stagnation caused by inappropriate heuristic guidance, we use the Shared Multi-Heuristic A* (SMHA*) algorithm and predefine multiple inadmissible heuristics. Meanwhile, when the consistent heuristic guidance is correct and appropriate, in order to avoid the unnecessary inadmissible heuristics to increase the search burden, we improve it by adding heuristic-based stagnation detection for each extended node and the improved algorithm is called SD-SMHA*. Only when the algorithm detects that it ceases to make significant progress towards the goal, the predefined inadmissible heuristics will be introduced. Finally, multiple simulation experiments are carried out and the results show that the improved algorithm effectively improves the planning efficiency and planning success rate in different scenarios.
Active Learning Technique for Multimodal Brain Tumor Segmentation Using Limited Labeled Images

Image segmentation is an essential step in biomedical image analysis. In recent years, deep learning models have achieved significant success in segmentation. However, deep learning requires the availability of large annotated data to train these models, which can be challenging in biomedical imaging domain. In this paper, we aim to accomplish biomedical image segmentation with limited labeled data using active learning. We present a deep active learning framework that selects additional data points to be annotated by combining U-Net with an efficient and effective query strategy to capture the most uncertain and representative points. This algorithm decouples the representative part by first finding the core points in the unlabeled pool and then selecting the most uncertain points from the reduced pool, which are different from the labeled pool. In our experiment, only 13% of the dataset was required with active learning to outperform the model trained on the entire 2018 MICCAI Brain Tumor Segmentation (BraTS) dataset. Thus, active learning reduced the amount of labeled data required for image segmentation without a significant loss in the accuracy.
Construction of Potential Fields for the Local Navigation of Mobile Robots

The issues of local navigation of mobile robots are considered. The method of artificial potential fields is analyzed as a popular method for local navigation of robots. The issue of designing attractive potential fields is investigated. The problem of local minima (traps) is discussed. A new method for avoiding obstacles by a robot moving to a goal is proposed.
Context-based Co-presence detection techniques: A survey

In this paper, we present a systematic survey on the contextual information based proximity detection techniques. These techniques are heavily used for improving security and usability in Zero-Interaction based Co-presence Detection and Authentication (ZICDA) systems. In particular, this survey includes a discussion on the possible adversary and communication models along with the existing security attacks on ZICDA systems. It also reviews the state-of-the-art proximity detection techniques that make use of contextual information. The proximity detection techniques are commonly referred as Contextual Co-presence (COCO) protocols. The COCO protocols dynamically collect and use contextual information to improve the security of ZICDA systems during the proximity verification process. Finally, we summarize the significant challenges and suggest possible innovative and efficient future solutions for securely detecting co-presence between devices in the presence of adversaries. The proximity verification techniques presented in the literature usually involve several trade-offs between metrics such as efficiency, security, deployment cost, and usability. At present, there is no ideal solution which adequately addresses the trade-off between these metrics. Therefore, we trust that this review gives an insight into the strengths and shortcomings of the known research methodologies and pave the way for the design of future practical, secure, and efficient solutions.
Design of FPGA-Based LZ77 Compressor With Runtime Configurable Compression Ratio and Throughput

Data compression reduces the cost of data storage and transmission by decreasing the data size. Previous studies have improved system performance by adaptively choosing the compression ratio (CR) and throughput required for the system by using a trade-off between them in the compression algorithm. Hardware accelerators are widely used to reduce the CPU load caused by compression operations. Several existing compression accelerators have low flexibility in changing the CR and bandwidth. This study proposes a hardware compression accelerator that can adjust the CR and throughput at runtime. The proposed architecture accelerates the LZ77 compression algorithm and supports the throughput-first (TF) and compression ratio-first (CF) modes by changing the degree of parallelism of comparison operations performed during the compression process. In addition, we propose a technique to dynamically change the degree of parallelism of the comparison operation to achieve a better throughput in CF mode and a better CR in TF mode. Experimental results demonstrate that the TF mode provides a throughput higher by 11.39%, and a CR lower by 0.07 than the CF mode. The value 0.07 accounts for 13.21% of the variation in the CR provided by the software implementation of LZ77.
Using Predictive and Differential Methods with K2-Raster Compact Data Structure for Hyperspectral Image Lossless Compression

This paper proposes a lossless coder for real-time processing and compression of hyperspectral images. After applying either a predictor or a differential encoder to reduce the bit rate of an image by exploiting the close similarity in pixels between neighboring bands, it uses a compact data structure called k 2 -raster to further reduce the bit rate. The advantage of using such a data structure is its compactness, with a size that is comparable to that produced by some classical compression algorithms and yet still providing direct access to its content for query without any need for full decompression. Experiments show that using k 2 -raster alone already achieves much lower rates (up to 55% reduction), and with preprocessing, the rates are further reduced up to 64%. Finally, we provide experimental results that show that the predictor is able to produce higher rates reduction than differential encoding.
Participant attack and improving dynamic quantum secret sharing using <i>d</i>-dimensional GHZ state

In 2017, Qin and Dai [Quantum Inf. Process. 16, 64 (2017). https://doi.org/10.1007/s11128-017-1525-y ], proposed a dynamic quantum secret sharing (DQSS) scheme based on the d-dimensional state. However, as shown in this study, a malicious participant can reveal the secret key of other participants without being detected. Furthermore, this study identifies a security issue in Qin and Dai’s DQSS protocol pertaining to the honesty of a revoked participant. Without considering these security issues, the DQSS protocol could fail at providing secret-sharing function. Therefore, two improvements are proposed to circumvent these problems.
A Non-parametric Approach Toward Structural Health Monitoring for Processing Big Data Collected from the Sensor Network

Advanced sensor network techniques recently allow collecting large amounts of data for structural health monitoring and damage detection, while how to effectively interpret these complex sensor data to technical information poses many challenges. This paper presents a machine learning algorithm for processing of big data collected from the sensor networks of civil structure. The proposed approach consists of training and monitoring phases. The training phase was focused on the extracting statistical features and conducting Moving Kernel Principal Component Analysis (MKPCA) in order to derive the damage sensitive indices. The monitoring phase included tracking of errors associated with the derived models. The main goal was to analyze the efficiency of the developed system for health monitoring of the benchmark experimental data with the 17 different damage scenarios. In this paper KPCA has been implemented in a new form as Moving KPCA (MKPCA) for effectively segmenting large data and for determining the changes, as data are continuously collected. Numerical results revealed that, the proposed health monitoring system has a satisfactory performance for the detection of the damage scenarios in three-story frame aluminum structure. Furthermore, enhanced version of KPCA methods exhibited significantly improvement in sensitivity, accuracy and effectiveness over conventional methods.
A C-GAN denoising algorithm in Projection Domain for Micro-CT

Micro-CT provides a high-resolution 3D imaging of micro-architecture in a non-invasive way, which becomes a significant tool in biomedical research and preclinical applications. Due to the limited power of micro-focus X-ray tube, photon starving occurs and noise is inevitable for the projection images, resulting in the degradation of spatial resolution, contrast and image details. In this paper, we propose a C-GAN (Conditional Generative Adversarial Nets) denoising algorithm in projection domain for Micro-CT imaging. The noise statistic property is utilized directly and a novel variance loss is developed to suppress the blurry effects during denoising procedure. Conditional Generative Adversarial Networks (C-GAN) is employed as a framework to implement the denoising task. To guarantee the pixelwised accuracy, fully convolutional network is served as the generator structure. During the alternative training of the generator and the discriminator, the network is able to learn noise distribution automatically. Moreover, residual learning and skip connection architecture are applied for faster network training and further feature fusion. To evaluate the denoising performance, mouse lung, milkvetch root and bamboo stick are imaged by micro-CT in the experiments. Compared with BM3D, CNN-MSE and CNN-VGG, the proposed method can suppress noise effectively and recover image details without introducing any artifacts or blurry effect. The result proves that our method is feasible, efficient and practical.
Controlling Japanese Honorifics in English-to-Japanese Neural Machine Translation

In the Japanese language different levels of honorific speech are used to convey respect, deference, humility, formality and social distance.In this paper, we present a method for controlling the level of formality of Japanese output in English-to-Japanese neural machine translation (NMT).By using heuristics to identify honorific verb forms, we classify Japanese sentences as being one of three levels of informal, polite, or formal speech in parallel text.The English source side is marked with a feature that identifies the level of honorific speech present in the Japanese target side.We use this parallel text to train an English-Japanese NMT model capable of producing Japanese translations in different honorific speech styles for the same English input sentence.
DINRec: Deep Interest Network Based API Recommendation Approach for Mashup Creation

Recommending appropriate APIs for Mashup creation has become a challenge as the number of APIs from different sources grows fast. In order to understand the relationships among multiple ecosystem APIs, most existing API recommendation methods focus on semantic similarity relationships but underutilize the composition and cooperation relationships between APIs, which may lead to low recommendation precision. In view of this problem, a Deep Interest Network based API Recommendation approach (DINRec) for Mashup development is proposed in this paper. In this approach, APIs are chosen incrementally for compositing into a Mashup and in that process the embedding vector of the Mashup’s existing composition features will be updated adaptively by using Deep Interest Network. Moreover, a Doc2simu model is used to help training industrial deep networks with relatively small amounts of dataset. Finally, some experiments on real-world dataset are implemented to verify the efficiency of our proposed approach.
A Convolutional Neural Network-Based Method for Human Movement Patterns Classification in Alzheimer’s Disease

Alzheimer&rsquo;s disease (AD) constitutes a neurodegenerative pathology that presents mobility disorders as one of its earliest symptoms. Current smartphones integrate accelerometers that can be used to collect mobility data of Alzheimer&rsquo;s patients. This paper describes a method that processes these accelerometer data and a convolutional neural network (CNN) that classifies the stage of the disease according to the mobility patterns of the patient. The method is applied in a case study with 35 Alzheimer&rsquo;s patients, in which a classification success rate of 91% was obtained.
Bayesian Deep Learning for Deformable Medical Image Registration

Deformable image registration is one of the challenging inverse problems in medical images due to inevitable geometric distortions during the imaging. Conventional convolutional neural networks (CNNs) are limited to the fixed weights, and require substantial examples for supervised training. In this paper, we employ Bayesian deep learning in order to register the medical images that are corrupted by nonlinear geometric distortions. Given a pair of reference and moving image, our objective is to register the moving image onto the reference image coordinates while maintaining the pixel distribution of moving image. To this end, we propose a novel Bayesian deep architecture that contains 5 downsampling and 4 upsampling layers with kernel size of 3 $$\times $$ 3 interleaved by pooling layer with tanh and rectified linear units (ReLU) as activation functions. The architecture estimates the displacement vector field (DVF) of the moving image. Finally, it undergoes the grid resampling to warp the moving image using estimated DVF. We demonstrate that the proposed Bayesian deep architecture learns the probability distribution over the weights and produce accurate registration. Experiments are performed on the publicly available Cardiac MRI and Lungs CT scan datasets. The proposed model achieves better performance when compared to state-of-the-art approaches.
High-dimensional entanglement between a photon and a multiplexed atomic quantum memory

Multiplexed quantum memories and high-dimensional entanglement can improve the performance of quantum repeaters by promoting the entanglement generation rate and the quantum communication channel capacity. Here, we experimentally generate a high-dimensional entangled state between a photon and a collective spin-wave excitation stored in the multiplexed atomic quantum memory. We verify the entanglement dimension by the quantum witness and the entanglement of formation. Then we use the high-dimensional entangled state to test the violation of the Bell-type inequality. Our paper provides an effective method to generate multidimensional entanglement between the flying photonic pulses and the atomic quantum interface.
Learning to Find Common Objects Across Few Image Collections

Given a collection of bags where each bag is a set of images, our goal is to select one image from each bag such that the selected images are from the same object class. We model the selection as an energy minimization problem with unary and pairwise potential functions. Inspired by recent few-shot learning algorithms, we propose an approach to learn the potential functions directly from the data. Furthermore, we propose a fast greedy inference algorithm for energy minimization. We evaluate our approach on few-shot common object recognition as well as object co-localization tasks. Our experiments show that learning the pairwise and unary terms greatly improves the performance of the model over several well-known methods for these tasks. The proposed greedy optimization algorithm achieves performance comparable to state-of-the-art structured inference algorithms while being ~10 times faster.
Domes to Drones: Self-Supervised Active Triangulation for 3D Human Pose Reconstruction

Existing state-of-the-art estimation systems can detect 2d poses of multiple people in images quite reliably. In contrast, 3d pose estimation from a single image is ill-posed due to occlusion and depth ambiguities. Assuming access to multiple cameras, or given an active system able to position itself to observe the scene from multiple viewpoints, reconstructing 3d pose from 2d measurements becomes well-posed within the framework of standard multi-view geometry. Less clear is what is an informative set of viewpoints for accurate 3d reconstruction, particularly in complex scenes, where people are occluded by others or by scene objects. In order to address the view selection problem in a principled way, we here introduce ACTOR, an active triangulation agent for 3d human pose reconstruction. Our fully trainable agent consists of a 2d pose estimation network (any of which would work) and a deep reinforcement learning-based policy for camera viewpoint selection. The policy predicts observation viewpoints, the number of which varies adaptively depending on scene content, and the associated images are fed to an underlying pose estimator. Importantly, training the policy requires no annotations - given a 2d pose estimator, ACTOR is trained in a self-supervised manner. In extensive evaluations on complex multi-people scenes filmed in a Panoptic dome, under multiple viewpoints, we compare our active triangulation agent to strong multi-view baselines, and show that ACTOR produces significantly more accurate 3d pose reconstructions. We also provide a proof-of-concept experiment indicating the potential of connecting our view selection policy to a physical drone observer.