Reviewerly/exHarmony huggingface dataset:
Two-Level Wavelet-Based Convolutional Neural Network for Image Deblurring

Image deblurring aims to restore the latent sharp image from the blurred one.In recent years, some learning-based image deblurring methods have achieved significant advances.However, the tradeoff between the texture details and model parameters is still a crucial issue.In this paper, we propose a novel deblurring method based on two-level wavelet-based convolutional neural network (CNN), which embeds discrete wavelet transform (DWT) to separate the image context and texture information and reduces the complexity of calculation.Furthermore, we modify the Inception module by adding pixels-wise attention (PA) mechanism and channel scaling factor to make each convolution kernel have different weights, which increase the receptive field while significantly reduce the parameters of the module.Qualitative and quantitative evaluation on real-word and synthetic datasets shows that the deblurring performance of our method is comparable to the state-of-the-art algorithms.Moreover, compared to the traditional learningbased deblurring method, our model has fewer parameters.
The Notarial Office in E-government: A Blockchain-Based Solution

The Notarial Office(NO), working on providing various essential certificates, still relies on manual handling and requires paper materials from other government departments.That brings lots of inconvenience.The Notarial Office rejects non-local paper materials for their lower credibility in the local place and then cannot provide cross-borders services.It also easily cause sensitive information leakage as copies of paper materials have been stored.In this case, a blockchain-based system is suitable to address challenges in this scenario because of its advantages (e.g, decentralized, immutability, transparency, auditability).We implemented this system on top of the Hyperledger Fabric.Moreover, we replace manual operations with smart contracts, set extra ledgers to off-load different types of transactions and provide encryption for private information when needed.In the end, we get an expected result.That is, the modification outperformed the unmodified network in experiments.
Backward Planning for a Multi-Stage Steerable Needle Lung Robot

Lung cancer is one of the deadliest types of cancer, and early diagnosis is crucial for successful treatment. Definitively diagnosing lung cancer typically requires biopsy, but current approaches either carry a high procedural risk for the patient or are incapable of reaching many sites of clinical interest in the lung. We present a new sampling-based planning method for a steerable needle lung robot that has the potential to accurately reach targets in most regions of the lung. The robot comprises three stages: a transorally deployed bronchoscope, a sharpened piercing tube (to pierce into the lung parenchyma from the airways), and a steerable needle able to navigate to the target. Planning for the sequential deployment of all three stages under health safety concerns is a challenging task, as each stage depends on the previous one. We introduce a new backward planning approach that starts at the target and advances backwards toward the airways with the goal of finding a piercing site reachable by the bronchoscope. This new strategy enables faster performance by iteratively building a single search tree during the entire computation period, whereas previous forward approaches have relied on repeating this expensive tree construction process many times. Additionally, our method further reduces runtime by employing biased sampling and sample rejection based on geometric constraints. We evaluate this approach using simulation-based studies in anatomical lung models. We demonstrate in comparison with existing techniques that the new approach (i) is more likely to find a path to a target, (ii) is more efficient by reaching targets more than 5 times faster on average, and (iii) arrives at lower-risk paths in shorter time.
Benefits of Mobile Contact Tracing on COVID-19: Tracing Capacity Perspectives

OPINION article Front. Public Health, 18 March 2021Sec. Digital Public Health Volume 9 - 2021 | https://doi.org/10.3389/fpubh.2021.586615
DeProCams: Simultaneous Relighting, Compensation and Shape Reconstruction for Projector-Camera Systems

Image-based relighting, projector compensation and depth/normal reconstruction are three important tasks of projector-camera systems (ProCams) and spatial augmented reality (SAR). Although they share a similar pipeline of finding projector-camera image mappings, in tradition, they are addressed independently, sometimes with different prerequisites, devices and sampling images. In practice, this may be cumbersome for SAR applications to address them one-by-one. In this paper, we propose a novel end-to-end trainable model named DeProCams to explicitly learn the photometric and geometric mappings of ProCams, and once trained, DeProCams can be applied simultaneously to the three tasks. DeProCams explicitly decomposes the projector-camera image mappings into three subprocesses: shading attributes estimation, rough direct light estimation and photorealistic neural rendering. A particular challenge addressed by DeProCams is occlusion, for which we exploit epipolar constraint and propose a novel differentiable projector direct light mask. Thus, it can be learned end-to-end along with the other modules. Afterwards, to improve convergence, we apply photometric and geometric constraints such that the intermediate results are plausible. In our experiments, DeProCams shows clear advantages over previous arts with promising quality and meanwhile being fully differentiable. Moreover, by solving the three tasks in a unified model, DeProCams waives the need for additional optical devices, radiometric calibrations and structured light.
Learning From Pixel-Level Label Noise: A New Perspective for Semi-Supervised Semantic Segmentation

This paper addresses semi-supervised semantic segmentation by exploiting a small set of images with pixel-level annotations (strong supervisions) and a large set of images with only image-level annotations (weak supervisions). Most existing approaches aim to generate accurate pixel-level labels from weak supervisions. However, we observe that those generated labels still inevitably contain noisy labels. Motivated by this observation, we present a novel perspective and formulate this task as a problem of learning with pixel-level label noise. Existing noisy label methods, nevertheless, mainly aim at image-level tasks, which can not capture the relationship between neighboring labels in one image. Therefore, we propose a graph-based label noise detection and correction framework to deal with pixel-level noisy labels. In particular, for the generated pixel-level noisy labels from weak supervisions by Class Activation Map (CAM), we train a clean segmentation model with strong supervisions to detect the clean labels from these noisy labels according to the cross-entropy loss. Then, we adopt a superpixel-based graph to represent the relations of spatial adjacency and semantic similarity between pixels in one image. Finally we correct the noisy labels using a Graph Attention Network (GAT) supervised by detected clean labels. We comprehensively conduct experiments on PASCAL VOC 2012, PASCAL-Context, MS-COCO and Cityscapes datasets. The experimental results show that our proposed semi-supervised method achieves the state-of-the-art performances and even outperforms the fully-supervised models on PASCAL VOC 2012 and MS-COCO datasets in some cases.
Unconditional Preparation of Squeezed Vacuum from Rabi Interactions

Squeezed states of harmonic oscillators are a central resource for continuous-variable quantum sensing, computation and communication. Here we propose a method for the generation of very good approximations to highly squeezed vacuum states with low excess anti-squeezing using only a few oscillator-qubit coupling gates through a Rabi-type interaction Hamiltonian. This interaction can be implemented with several different methods, which has previously been demonstrated in superconducting circuit and trapped-ion platforms. The protocol is compatible with other protocols manipulating quantum harmonic oscillators, thus facilitating scalable continuous-variable fault-tolerant quantum computation.
Adaptive Neighborhood Metric Learning

In this paper, we reveal that metric learning would suffer from serious inseparable problem if without informative sample mining. Since the inseparable samples are often mixed with hard samples, current informative sample mining strategies used to deal with inseparable problem may bring up some side-effects, such as instability of objective function, etc. To alleviate this problem, we propose a novel distance metric learning algorithm, named adaptive neighborhood metric learning (ANML). In ANML, we design two thresholds to adaptively identify the inseparable similar and dissimilar samples in the training procedure, thus inseparable sample removing and metric parameter learning are implemented in the same procedure. Due to the non-continuity of the proposed ANML, we develop an ingenious function, named log-exp mean function to construct a continuous formulation to surrogate it, which can be efficiently solved by the gradient descent method. Similar to Triplet loss, ANML can be used to learn both the linear and deep embeddings. By analyzing the proposed method, we find it has some interesting properties. For example, when ANML is used to learn the linear embedding, current famous metric learning algorithms such as the large margin nearest neighbor (LMNN) and neighbourhood components analysis (NCA) are the special cases of the proposed ANML by setting the parameters different values. When it is used to learn deep features, the state-of-the-art deep metric learning algorithms such as Triplet loss, Lifted structure loss, and Multi-similarity loss become the special cases of ANML. Furthermore, the log-exp mean function proposed in our method gives a new perspective to review the deep metric learning methods such as Prox-NCA and N-pairs loss. At last, promising experimental results demonstrate the effectiveness of the proposed method.
An Explainable Multimodal Neural Network Architecture for Predicting Epilepsy Comorbidities Based on Administrative Claims Data

Epilepsy is a complex brain disorder characterized by repetitive seizure events. Epilepsy patients often suffer from various and severe physical and psychological comorbidities (e.g., anxiety, migraine, and stroke). While general comorbidity prevalences and incidences can be estimated from epidemiological data, such an approach does not take into account that actual patient-specific risks can depend on various individual factors, including medication. This motivates to develop a machine learning approach for predicting risks of future comorbidities for individual epilepsy patients. In this work, we use inpatient and outpatient administrative health claims data of around 19,500 U.S. epilepsy patients. We suggest a dedicated multimodal neural network architecture (Deep personalized LOngitudinal convolutional RIsk model-DeepLORI) to predict the time-dependent risk of six common comorbidities of epilepsy patients. We demonstrate superior performance of DeepLORI in a comparison with several existing methods. Moreover, we show that DeepLORI-based predictions can be interpreted on the level of individual patients. Using a game theoretic approach, we identify relevant features in DeepLORI models and demonstrate that model predictions are explainable in light of existing knowledge about the disease. Finally, we validate the model on independent data from around 97,000 patients, showing good generalization and stable prediction performance over time.
Lightweight Compression of Intermediate Neural Network Features for Collaborative Intelligence

In collaborative intelligence applications, part of a deep neural network (DNN) is deployed on a lightweight device such as a mobile phone or edge device, and the remaining portion of the DNN is processed where more computing resources are available, such as in the cloud. This paper presents a novel lightweight compression technique designed specifically to quantize and compress the features output by the intermediate layer of a split DNN, without requiring any retraining of the network weights. Mathematical models for estimating the clipping and quantization error of ReLU and leaky-ReLU activations at this intermediate layer are developed and used to compute optimal clipping ranges for coarse quantization. We also present a modified entropy-constrained design algorithm for quantizing clipped activations. When applied to popular object-detection and classification DNNs, we were able to compress the 32-bit floating point intermediate activations down to 0.6 to 0.8 bits, while keeping the loss in accuracy to less than 1%. When compared to HEVC, we found that the lightweight codec consistently provided better inference accuracy, by up to 1.3%. The performance and simplicity of this lightweight compression technique makes it an attractive option for coding an intermediate layer of a split neural network for edge/cloud applications.
A novel hybrid marine predators-Nelder-Mead optimization algorithm for the optimal design of engineering problems

Abstract The marine predators optimization algorithm (MPA) is a recently developed nature-inspired algorithm. In this paper, the Nelder-Mead algorithm is utilized to improve the local exploitation powers of the MPA when described as a hybrid marine predators and Nelder-Mead (HMPANM). Due to the harsh competitive conditions as well as the transition to new vehicles such as hybrid and full-electrical cars, the interest in the design of light and low-cost vehicles is increasing. In this study, a recent metaheuristic addition, a hybrid marine predators optimization algorithm, is used to solve a structural design optimization problem to prove how the HMPANM can be used in solving industrial design problems. The results strongly prove the capability of the HMPANM for the optimum design of components in the automotive industry.
Applying User-Centered Design Methods to Understand Users' Day-to-Day Experiences Can Inform a Mobile Intervention for Binge Eating and Weight Management

Introduction: Weight loss apps to date have not directly addressed binge eating. To inform the design of a new mobile behavioral intervention that addresses binge eating and weight management, we applied user-centered design methods to qualitatively assess how target intervention consumers experience these conditions in their day-to-day lives. Methods: The participants were 22 adults with self-reported obesity (body mass index ≥30) and recurrent binge eating (≥12 episodes in 3 months) who were interested in losing weight and reducing binge eating. The participants completed a digital diary study, which is a user-centered design technique for capturing individuals' day-to-day experiences in relevant contexts. Qualitative data describing the participants' experiences with binge eating and obesity were analyzed using thematic analysis. The results were then used to create personas (i.e., character archetypes of different intervention consumers). Results: The participants described triggers for binge eating and indicated that binge eating and excess weight negatively impact their mental health, physical health, and quality of life. The resulting personas reflected five different struggles individuals with these health problems experience in managing their binge eating and weight. Conclusions: Individuals with binge eating and obesity have varying precipitants of problematic eating as well as varying motivations for and challenges to behavior change. To meet the needs of all who seek intervention, an ideal intervention design will account for variations in these factors and be relevant to diverse experiences. Insights from the diary study and resulting personas will inform the next phases of the user-centered design process of iteratively designing prototypes and testing the intervention in practice.
BARD: A Structured Technique for Group Elicitation of Bayesian Networks to Support Analytic Reasoning

In many complex, real-world situations, problem solving and decision making require effective reasoning about causation and uncertainty. However, human reasoning in these cases is prone to confusion and error. Bayesian networks (BNs) are an artificial intelligence technology that models uncertain situations, supporting probabilistic and causal reasoning and decision making. However, to date, BN methodologies and software require significant upfront training, do not provide much guidance on the model building process, and do not support collaboratively building BNs. BARD (Bayesian ARgumentation via Delphi) is both a methodology and an expert system that utilises (1) BNs as the underlying structured representations for better argument analysis, (2) a multi-user web-based software platform and Delphi-style social processes to assist with collaboration, and (3) short, high-quality e-courses on demand, a highly structured process to guide BN construction, and a variety of helpful tools to assist in building and reasoning with BNs, including an automated explanation tool to assist effective report writing. The result is an end-to-end online platform, with associated online training, for groups without prior BN expertise to understand and analyse a problem, build a model of its underlying probabilistic causal structure, validate and reason with the causal model, and use it to produce a written analytic report. Initial experimental results demonstrate that BARD aids in problem solving, reasoning and collaboration.
Image Stitching Based on Semantic Planar Region Consensus

Image stitching for two images without a global transformation between them is notoriously difficult. In this paper, noticing the importance of semantic planar structures under perspective geometry, we propose a new image stitching method which stitches images by allowing for the alignment of a set of matched dominant semantic planar regions. Clearly different from previous methods resorting to plane segmentation, the key to our approach is to utilize rich semantic information directly from RGB images to extract semantic planar image regions with a deep Convolutional Neural Network (CNN). We specifically design a module implementing our newly proposed clustering loss to make full use of existing semantic segmentation networks to accommodate region segmentation. To train the network, a dataset for semantic planar region segmentation is constructed. With the prior of semantic planar region, a set of local transformation models can be obtained by constraining matched regions, enabling more precise alignment in the overlapping area. We also use this prior to estimate a transformation field over the whole image. The final mosaic is obtained by mesh-based optimization which maintains high alignment accuracy and relaxes similarity transformation at the same time. Extensive experiments with both qualitative and quantitative comparisons show that our method can deal with different situations and outperforms the state-of-the-arts on challenging scenes.
Effects of stereopsis on vection, presence and cybersickness in head-mounted display (HMD) virtual reality

Abstract Stereopsis provides critical information for the spatial visual perception of object form and motion. We used virtual reality as a tool to understand the role of global stereopsis in the visual perception of self-motion and spatial presence using virtual environments experienced through head-mounted displays (HMDs). Participants viewed radially expanding optic flow simulating different speeds of self-motion in depth, which generated the illusion of self-motion in depth (i.e., linear vection). Displays were viewed with the head either stationary (passive radial flow) or laterally swaying to the beat of a metronome (active conditions). Multisensory conflict was imposed in active conditions by presenting displays that either: (i) compensated for head movement (active compensation condition), or (ii) presented pure radial flow with no compensation during head movement (active no compensation condition). In Experiment 1, impairing stereopsis by anisometropic suppression in healthy participants generated declines in reported vection strength, spatial presence and severity of cybersickness. In Experiment 2, vection and presence ratings were compared between participants with and without clinically-defined global stereopsis. Participants without global stereopsis generated impaired vection and presence similarly to those found in Experiment 1 by subjects with induced stereopsis impairment. We find that reducing global stereopsis can have benefits of reducing cybersickness, but has adverse effects on aspects of self-motion perception in HMD VR.
Multiedge-type low-density parity-check codes for continuous-variable quantum key distribution

Continuous-variable quantum key distribution (QKD) utilizes an ensemble of coherent states of light to distribute secret encryption keys between two parties. An essential ingredient of the QKD protocol is highly efficient information reconciliation. To achieve highly efficient reconciliation, error-correcting codes with a low channel coding rate are inevitable in the most common schemes of multilevel coding and multistage decoding (MLC-MSD) and multidimensional reconciliation. Multiedge-type (MET) low-density parity-check (LDPC) codes are well suited for highly efficient reconciliation at low rates. Here, we calculate the optimal channel coding rates in the MLC-MSD scheme for reverse reconciliation, introduce the concept of generalized extrinsic information transfer charts for MET-LDPC codes, which constitute a simple and fast asymptotic analysis tool, and present a set of MET-LDPC codes with asymptotic efficiency $>97%$ for channel coding rates 0.1, 0.05, 0.02, and 0.01. We believe that our codes will find wide application in implementations of continuous-variable quantum key distribution based on Gaussian modulation.
Deep Learning-Based Phase Noise Compensation in Multicarrier Systems

This letter presents a deep learning (DL) algorithm for compensating the impacts of phase noise in channel estimation and data detection for multicarrier systems. The proposed DL algorithm replaces conventional estimators’ iterative process and therefore reduces complexity. The input to this algorithm is the first iteration of the conventional least squares (LS) estimator, which requires no knowledge of channel and phase-noise statistics. The proposed algorithm is trained offline using simulation data and then exploited for online phase noise compensation. The simulation results show that the proposed DL algorithm outperforms the conventional estimators and achieves a 6 dB reduction in the mean-squared error (MSE) of channel estimation and a 45% improvement in the bit-error rate (BER).
Experimental measurement-device-independent quantum key distribution with the double-scanning method

The measurement-device-independent quantum key distribution (MDI-QKD) can be immune to all detector side-channel attacks. Moreover, it can be easily implemented combining with the matured decoy-state methods under current technology. It thus seems a very promising candidate in practical implementation of quantum communications. However, it suffers from severe finite-data-size effect in most existing MDI-QKD protocols, resulting in relatively low key rates. Recently, Jiang et al. [Phys. Rev. A 103, 012402 (2021)] proposed a double-scanning method to drastically increase the key rate of MDI-QKD. Based on Jiang et al.'s theoretical work, here we for the first time implement the double-scanning method into MDI-QKD and carry out corresponding experimental demonstration. With a moderate number of pulses of 10^10, we can achieve 150 km secure transmission distance which is impossible with all former methods. Therefore, our present work paves the way towards practical implementation of MDI-QKD.