Deep Learning-based Face Super-resolution: A Survey

Face super-resolution (FSR), also known as face hallucination, which is aimed at enhancing the resolution of low-resolution (LR) face images to generate high-resolution face images, is a domain-specific image super-resolution problem. Recently, FSR has received considerable attention and witnessed dazzling advances with the development of deep learning techniques. To date, few summaries of the studies on the deep learning-based FSR are available. In this survey, we present a comprehensive review of deep learning-based FSR methods in a systematic manner. First, we summarize the problem formulation of FSR and introduce popular assessment metrics and loss functions. Second, we elaborate on the facial characteristics and popular datasets used in FSR. Third, we roughly categorize existing methods according to the utilization of facial characteristics. In each category, we start with a general description of design principles, present an overview of representative approaches, and then discuss the pros and cons among them. Fourth, we evaluate the performance of some state-of-the-art methods. Fifth, joint FSR and other tasks, and FSR-related applications are roughly introduced. Finally, we envision the prospects of further technological advancement in this field.

Off-The-Grid Variational Sparse Spike Recovery: Methods and Algorithms

Gridless sparse spike reconstruction is a rather new research field with significant results for the super-resolution problem, where we want to retrieve fine-scale details from a noisy and filtered acquisition. To tackle this problem, we are interested in optimisation under some prior, typically the sparsity i.e., the source is composed of spikes. Following the seminal work on the generalised LASSO for measures called the Beurling-Lasso (BLASSO), we will give a review on the chief theoretical and numerical breakthrough of the off-the-grid inverse problem, as we illustrate its usefulness to the super-resolution problem in Single Molecule Localisation Microscopy (SMLM) through new reconstruction metrics and tests on synthetic and real SMLM data we performed for this review.

Optimised multiplex amplicon sequencing for mutation identification using the MinION nanopore sequencer

Objective: Rapid, cost-effective identification of genetic variants in small candiate genomic regions remains a challenge, particularly for less well equipped or lower throughput laboratories. Application of Oxford Nanopore Technologies' MinION sequencer has the potential to fulfil this requirement. We have developed a multiplexing assay which pools PCR amplicons for MinION sequencing to enable sequencing of multiple templates from multiple individuals which could be applied to gene-targeted diagnostics. Methods: A combined strategy of barcoding and sample pooling was developed for simultaneous multiplex MinION sequencing of 100 PCR amplicons, spanning 30 loci in DNA isolated from 82 neurodevelopmental cases and family members. The target regions were chosen for further interegation because a potentially disease-causative variants had been identified in affected individuals by Illumina exome sequencing. The pooled MinION sequences were deconvoluted by aligning to custom references using the guppy aligner software. Results: Our multiplexing approach produced interpretable and expected sequence from 29 of the 30 targeted genetic loci. The sequence variant which was not correctly resolved in the MinION sequence was adjacent to a five nucleotide homopolymer. It is already known that homopolymers present a resolution problem with the MinION approach. Interstingly despite equimolar quantities of PCR amplicon pooled for sequencing, significant variation in the depth of coverage (139x - 21,499x; mean = 9,050, std err = 538.21) was observed. We observed independent relationships between depth of coverage and target length, and depth of coverage and GC content. These relationships demonstrate biases of the MinION sequencer for longer templates and those with lower GC content. Conclusion: We demonstrate an efficient approach for variant discovery or confirmation from short DNA templates using the MinION sequencing device. With less than 140x depth of coverage required for accurate genotyping, the methodology described here allows for rapid highly multiplexed targeted sequencing of large numbers of samples in a minimally equipped laboratory.

Global motion planning and redundancy resolution for large objects manipulation by dual redundant robots with closed kinematics

The multi-arm robotic systems consisting of redundant robots are able to conduct more complex and coordinated tasks, such as manipulating large or heavy objects. The challenges of the motion planning and control for such systems mainly arise from the closed-chain constraint and redundancy resolution problem. The closed-chain constraint reduces the configuration space to lower-dimensional subsets, making it difficult for sampling feasible configurations and planning path connecting them. A global motion planner is proposed in this paper for the closed-chain systems, and motions in different disconnected manifolds are efficiently bridged by two type regrasping moves. The regrasping moves are automatically chosen by the planner based on cost-saving principle, which greatly improve the success rate and efficiency. Furthermore, to obtain the optional inverse kinematic solutions satisfying joint physical limits (e.g., joint position, velocity, acceleration limits) in the planning, the redundancy resolution problem for dual redundant robots is converted into a unified quadratic programming problem based on the combination of two diff erent-level optimizing criteria, i.e. the minimization velocity norm (MVN) and infinity norm torque-minimization (INTM). The Dual-MVN-INTM scheme guarantees smooth velocity, acceleration profiles, and zero final velocity at the end of motion. Finally, the planning results of three complex closed-chain manipulation task using two Franka Emika Panda robots and two Kinova Jaco2 robots in both simulation and experiment demonstrate the effectiveness and efficiency of the proposed method.

Deep learning in geodesy

Deep learning (DL) can be a way to automate the analysis of predictions. DL algorithms are in the hierarchy of increasing complexity and abstraction while traditional machine learning is linear. In this study, the downscaling of the GRACE-FO satellite was investigated using a convolutional neural network (CNN). Three solutions were used for downscaling with CNN. Down-scaling accuracy is estimated to be 0.1 degree and its absolute error is 1 mm. The results show that this method can improve the GRACE-FO spatial resolution problem with higher efficiency and make it easier to analyze the results. Also, DL can solve many geodesy problems.

XCycles Backprojection Acoustic Super-Resolution

The computer vision community has paid much attention to the development of visible image super-resolution (SR) using deep neural networks (DNNs) and has achieved impressive results. The advancement of non-visible light sensors, such as acoustic imaging sensors, has attracted much attention, as they allow people to visualize the intensity of sound waves beyond the visible spectrum. However, because of the limitations imposed on acquiring acoustic data, new methods for improving the resolution of the acoustic images are necessary. At this time, there is no acoustic imaging dataset designed for the SR problem. This work proposed a novel backprojection model architecture for the acoustic image super-resolution problem, together with Acoustic Map Imaging VUB-ULB Dataset (AMIVU). The dataset provides large simulated and real captured images at different resolutions. The proposed XCycles BackProjection model (XCBP), in contrast to the feedforward model approach, fully uses the iterative correction procedure in each cycle to reconstruct the residual error correction for the encoded features in both low- and high-resolution space. The proposed approach was evaluated on the dataset and showed high outperformance compared to the classical interpolation operators and to the recent feedforward state-of-the-art models. It also contributed to a drastically reduced sub-sampling error produced during the data acquisition.

Deep learning in geodesy

Deep learning (DL) can be a way to automate the analysis of predictions. DL algorithms are in the hierarchy of increasing complexity and abstraction while traditional machine learning is linear. In this study, the downscaling of the GRACE-FO satellite was investigated using a convolutional neural network (CNN). Three solutions were used for downscaling with CNN. Down-scaling accuracy is estimated to be 0.1 degree and its absolute error is 1 mm. The results show that this method can improve the GRACE-FO spatial resolution problem with higher efficiency and make it easier to analyze the results. Also, DL can solve many geodesy problems.

NMN-VD: A Neural Module Network for Visual Dialog

Visual dialog demonstrates several important aspects of multimodal artificial intelligence; however, it is hindered by visual grounding and visual coreference resolution problems. To overcome these problems, we propose the novel neural module network for visual dialog (NMN-VD). NMN-VD is an efficient question-customized modular network model that combines only the modules required for deciding answers after analyzing input questions. In particular, the model includes a Refer module that effectively finds the visual area indicated by a pronoun using a reference pool to solve a visual coreference resolution problem, which is an important challenge in visual dialog. In addition, the proposed NMN-VD model includes a method for distinguishing and handling impersonal pronouns that do not require visual coreference resolution from general pronouns. Furthermore, a new Compare module that effectively handles comparison questions found in visual dialogs is included in the model, as well as a Find module that applies a triple-attention mechanism to solve visual grounding problems between the question and the image. The results of various experiments conducted using a set of large-scale benchmark data verify the efficacy and high performance of our proposed NMN-VD model.

PSOBER: PSO based entity resolution

Entity Resolution is the task of mapping the records within a database to their corresponding entities. The entity resolution problem presents a lot of challenges because of the absence of complete information in records, variant distribution of records for different entities and sometimes overlaps between records of different entities. In this paper, we have proposed an unsupervised method to solve this problem. The previously mentioned problem is set as a partitioning problem. Thereafter, an optimization algorithm-based technique is proposed to solve the entity resolution problem. The presented approach enables the partitioning of records across entities. A comparative analysis with the genetic algorithm over datasets proves the efficiency of the considered approach.