Reviewerly/exHarmony huggingface dataset:
Blind Super-Resolution of 3D MRI via Unsupervised Domain Transformation

High-resolution medical images can be effectively used for clinical diagnosis. However, the acquisition of high-resolution images is difficult and often limited by medical instruments. Super-resolution (SR) methods provide a solution, where high-resolution (HR) images can be reconstructed from low-resolution (LR) ones. Most of existing deep neural networks for 3D SR medical images trained in a non-blind process, where LR images are directly degraded from HR data via a pre-determined downscale method. Such approaches rely heavily on the assumed degradation model, resulting in inevitable deviations in real clinical practice. Blind super-resolution, as a more attractive research line for this field, aims to generate HR images from LR inputs containing unknown degradation. Towards generalizing SR models for diverse types of degradation, we propose a robust blind SR of 3D medical images in an unsupervised manner with domain correction and upscaling treatment. First, a CycleGAN-based architecture is implemented to generate the LR data from the source domain to the target one for domain correction. Then, an upscaling network is learned via pre-determined HR-LR couples for reconstruction. The proposed framework is able to automatically learn noisy and blurry correction kernels for unpaired 3D SR magnetic resonance images (MRI). Our method achieves better and more robust performances in reconstruction of HR images from LR MRI with multiple unknown degradation processes, and show its superiority to other state-of-the-art supervised models and cycle-consistency based methods, especially in severe distortion cases.
DepFiN: A 12-nm Depth-First, High-Resolution CNN Processor for IO-Efficient Inference

Applying convolutional neural networks (CNNs) on high-resolution images leads to very large intermediate feature maps (FMs), which dominate the memory traffic. Processing in the classical layer-by-layer order creates the requirement to store the complete FMs at once, when moving from one layer to the next. As the size of these FMs only realistically allows this in off-chip memory, this leads to high off-chip bandwidth, which comes at great energy costs. The DepFiN processor chip, presented in this article, overcomes this cost by running CNNs in a deep layer fusion mode, dubbed depth-first execution, made possible by a control flow that supports frequently switching between layers. To furthermore tackle the computational cost as well, the computationally efficient depthwise + pointwise (DW + PW) layer pairs are explicitly supported in DepFiN by a novel accelerator core that can dynamically change its configuration to manage the low computational intensity of the depthwise layers. Benchmarking measurements show the 12-nm DepFiN chip reaching up to 20 TOPS/W peak, 8.2 TOPS/W on the MC-CNN-fast stereo-matching network excluding input-output (IO) power (at 8-bit 0.6 Vdd) and, crucially, 3.95 TOPS/W with the IO power included on the same network and an up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$18\times $ </tex-math></inline-formula> improvement realized by supporting depth-first (MC-CNN-fast at 8-bit, 0.65 V Vdd).
A Novel Paradigm to Artificial Intelligence in Transforming Supply Chain Management in the Agile Business World

Growth and progress of information technology is responsible for bringing inmore and more intensive competition on the global scale for all the business sectors. It is forecasted by number of companies that with the presence of AI, a noticeable change in operation and supply chain management will be seen, and this will be from planning, scheduling, optimization to transportation. As far as “supply chain management” is considered, people will show more interest in machine learning, artificial intelligence, and other intelligent technologies. In last few years it is proved that artificial intelligence has become an extension of human brains that has expanded their cognitive capabilities to such an extent that was never thought to be a reality. It is not true that artificial intelligence will replace the humans, but yes, they will help them to unleash their real strategic and creative potentials. A sample of 209 supply chain managers from different sectors was surveyed to conduct the present study and mean and t-test was applied to analyse the data. Artificial intelligence is playing so many essential roles in modern supply chain management and some of its important roles are explored in present study. The study also concludes that the supply chain management is significantly affected using artificial intelligence.
Visual Computing in 360°: Foundations, Challenges, and Applications

Omnidirectional media are becoming widespread with the increasing popularization of devices for capture and visualization. Unlike traditional pinhole-based images, omnidirectional images are defined on the surface of a sphere, present a full field of view, and store light intensities from a whole scene. In particular, applications exploring immersive augmented, mixed, and virtual reality experiences can strongly benefit from omnidirectional vision. Though omnidirectional images are defined on the spherical domain, they are commonly mapped to one or multiple planes. Those sphere-to-plane mappings generate distorted images, and, if directly applied, most traditional visual computing algorithms tend to present some quality degradation. This tutorial paper revises the spherical imaging model, common capture device types, and prominent representation formats. It also discusses the significant challenges of spherical visual computing and showcases the advances in three selected applications.
Optimal LED Spectral Multiplexing for NIR2RGB Translation

The industry practice for night video surveillance is to use auxiliary near-infrared (NIR) LEDs, usually centered at 850nm or 940nm, for scene illumination. NIR LEDs are used to save power consumption while hiding the surveillance coverage area from naked human eyes. The captured images are almost monochromatic, and visual color and texture tend to disappear, which hinders human and machine perception. A few existing studies have tried to convert such NIR images to RGB images through deep learning, which can not provide satisfying results, nor generalize well beyond the training dataset. In this paper, we aim to break the fundamental restrictions on reliable NIR-to-RGB (NIR2RGB) translation by examining the imaging mechanism of single-chip silicon-based RGB cameras under NIR illuminations, and propose to retrieve the optimal LED multiplexing via deep learning. Experimental results show that this translation task can be significantly improved by properly multiplexing NIR LEDs close to the visible spectral range than using 850nm and 940nm LEDs.
Underwater Object Detection Using Restructured SSD

Deep learning has been widely used in computer vision tasks such as image classification, semantic segmentation and object detection, which has achieved many breakthrough results in recent years. Compared with conventional object detection tasks, due to objective factors such as uneven illumination, low contrast, and more impurities in the underwater environment, these is no guarantee of high quality for underwater images, which brings challenges to the underwater object detection task. In this paper, we construct an underwater object detection model based on multi-scale feature fusion (called Multi-scale Feature Fusion Network for Underwater Object Detection, MFFNet). MFFNet uses SSD model as the baseline, then makes an improvement by adding three different modules, which are improved FPN, assisting backbone and CBAM attention module. Based on VGG-16 and ResNet-50 as the backbone network, the composite backbone connection is performed; the attention mechanism CBAM module is involved to make the network pay more attention to the objects; the feature pyramid FPN structure is used for multi-scale feature detection. To verify the effectiveness of the network model proposed in this paper, experiments are carried out on three datasets, i.e., VOC 2007, UPRC and Fish4knowledges. The experimental results show that compared with other main object detection models, the network model proposed in this paper has obvious advantages in underwater object detection, and can obtain higher detection accuracy.
Mapping of Spatiotemporal Scalar Fields by Mobile Robots using Gaussian Process Regression

Spatiotemporal maps are data-driven estimates of time changing phenomena. For environmental science, rather than collect data from an array of static sensors, a mobile sensor platform could reduce setup time and cost, maintain flexibility to be deployed to any area of interest, and provide active feedback during observations. While promising, mapping is challenging with mobile sensors because vehicle constraints limit not only where, but also when observations can be made. By assuming spatial and temporal correlations in the data through kernel functions, this paper uses Gaussian process regression (GPR) to generate a maximum likelihood estimate of the phenomenon while also tracking the estimate uncertainty. Spatiotemporal mapping by GPR is simulated for a single fixed-path mobile robot observing a latent spatiotemporal scalar field. The learned spatiotemporal map captures the structure of the latent scalar field with the largest uncertainties in areas the robot never visited.
Acute Lymphoblastic Leukemia Detection Using Hypercomplex-Valued Convolutional Neural Networks

This paper features convolutional neural networks defined on hypercomplex algebras applied to classify lymphocytes in blood smear digital microscopic images. Such classification is helpful for the diagnosis of acute lymphoblast leukemia (ALL), a type of blood cancer. We perform the classification task using eight hypercomplex-valued convolutional neural networks (HvCNNs) along with real-valued convolutional networks. Our results show that HvCNNs perform better than the real-valued model, showcasing higher accuracy with a much smaller number of parameters. Moreover, we found that HvCNNs based on Clifford algebras processing HSV-encoded images attained the highest observed accuracies. Precisely, our HvCNN yielded an average accuracy rate of 96.6% using the ALL-IDB2 dataset with a 50% train-test split, a value extremely close to the state-of-the-art models but using a much simpler architecture with significantly fewer parameters.
A QUBO Formulation for Minimum Loss Network Reconfiguration

We introduce a novel quadratic unconstrained binary optimization (QUBO) formulation for a classical problem in electrical engineering -- the optimal reconfiguration of distribution grids. For a given graph representing the grid infrastructure and known nodal loads, the problem consists in finding the spanning tree that minimizes the total link ohmic losses. A set of constraints is initially defined to impose topologically valid solutions. These constraints are then converted to a QUBO model as penalty terms. The electrical losses terms are finally added to the model as the objective function to minimize. In order to maximize the performance of solution searching with classical solvers, with hybrid quantum-classical solvers and with quantum annealers, our QUBO formulation has the goal of being very efficient in terms of variables usage. A standard 33-node test network is used as an illustrative example of our general formulation. Model metrics for this example are presented and discussed. Finally, the optimal solution for this example was obtained and validated through comparison with the optimal solution from an independent method.
Structure-Preserving View-Invariant Skeleton Representation for Action Detection

Skeleton-based action detection has attracted increasing attention in recent years due to its action-focusing and compactness. To enable the usability of convolutional neural networks, many methods convert a skeleton sequence to a pseudo image by stacking the skeleton joints based on a predefined order. However, this practice ignores the skeletons structure and the influence of viewpoints, thus limiting the performance of learned models. In this paper, we propose a novel representation, which preserves the structure information while being view-invariant. To achieve this, we first generate a structure-preserving chain order by leveraging the depth-first traversal algorithm. Then, to eliminate the influence of viewpoints, we propose a reference joint-based encoding approach. Finally, we improve YOLOv5 by introducing an attention feature learning network, which enables YOLOv5 to automatically select the most informative joints. Comprehensive experiments on the PKU-MMD dataset demonstrate our method achieves state-of-the-art performance while maintaining high efficiency.
Computing Wasserstein-$p$ Distance Between Images with Linear Cost

When the images are formulated as discrete measures, computing Wasserstein-p distance between them is challenging due to the complexity of solving the corresponding Kantorovich's problem. In this paper, we propose a novel algorithm to compute the Wasserstein-p distance between discrete measures by restricting the optimal transport (OT) problem on a subset. First, we define the restricted OT problem and prove the solution of the restricted problem converges to Kantorovich's OT solution. Second, we propose the SparseSinkhorn algorithm for the restricted problem and provide a multi-scale algorithm to estimate the subset. Finally, we implement the proposed algorithm on CUDA and illustrate the linear computational cost in terms of time and memory requirements. We compute Wasserstein-p distance, estimate the transport mapping, and transfer color between color images with size ranges from <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$64\times 64$</tex> to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1920\times 1200$</tex> . (Our code is available at https://github.com/ucascnic/CudaOT)
Construction of Multiple-Rate Quantum LDPC Codes Sharing One Scalable Stabilizer Circuit

Variable-rate coding schemes that support a variety of different rates while maintaining the same fundamental encoder/decoder architectures are of great interest in practical communication systems. Similar error-correcting code schemes are also in demand in quantum settings. However, generally, it is difficult to introduce the variable-rate coding schemes into the quantum coding domain attributed to two challenges: Obtaining new quantum codes from old ones systematically and sharing encoder-decoder components among the newly obtained quantum codes. In this correspondence, a multiple-rate coding scheme is introduced into the quantum coding domain, we achieve this goal by providing a systematic method to construct new non-homogeneous quantum LDPC codes of CSS type from an old one (named as the mother code) whose classical parity-check matrix (PCM) is row-circulant. The basic idea is to split rows of the highest-rate stabilizer elaborately to produce the stabilizer for lower rates, which ensures that the generated code family owns the same code length. More importantly, thanks to the nested stabilizer structures possessed by the resulting quantum codes, our method also enables a scalable stabilizer circuit to be shared among them.
An Endoscopic image Classifier Using Deep Learning Considering Progression of Colorectal Cancer

The purpose of this paper is to develop a CAD (computer-aided diagnosis) system that present quantitative inference results to the clinical doctor for colonoscopy. As a part of this system development, we create a classifier that classifies lesion images into NICE (NBI International Colorectal Endoscopic) classification using deep learning. We developed a Convolutional Neural Network (CNN)-based classifier using a method that consider the progression of colorectal cancer and achieved more than 92% accuracy in NICE classification.
Surface water resources of Santa Catarina state’s southern region - geochemical background of the coal mining territory

ABSTRACT In the environmental reclaiming process, it is essential to distinguish between chemical elements concentrations that occur naturally in water resources (geochemical background), from those originated from anthropogenic activities. Despite the environmental efforts that have been carried out for more than two decades in the southern Brazilian region impacted by coal mining, its geochemical background is still not completely known. Filling this environmental knowledge gap was the objective of this work, which systematically analyzed the results of monitoring efforts carried out by the Geological Survey of Brazil (SGB/CPRM) across three years. Quality parameter distributions of rivers and streams unaffected by anthropogenic impact were analyzed. The database was used to perform principal component analysis (PCA) and cluster analysis, and to define the background values, which was done through the boxplot and ± 2 MAD (Median Absolute Deviation) methods. The results of multivariate statistical analyses clustered the monitoring sites into two groups, one related to the sedimentary rocks of the Paraná Basin and another to the crystalline basement rocks. The sedimentary group had a greater correlation with the parameters Eh, SO42-, Ca, Mg, EC, Mn, Acidity, Zn and Na; while, and the crystalline rocks group were better correlated with pH, Cu, Al, K, and Fe. The results of the geochemical background were similar for both methods, with values of pH, Fe, Al, and Mn being slightly different from those defined in Brazilian legislation (CONAMA 357/05 and MS 888/21).
RFNet-4D: Joint Object Reconstruction and Flow Estimation from 4D Point Clouds

Object reconstruction from 3D point clouds has achieved impressive progress in the computer vision and computer graphics research field. However, reconstruction from time-varying point clouds (a.k.a. 4D point clouds) is generally overlooked. In this paper, we propose a new network architecture, namely RFNet-4D, that jointly reconstruct objects and their motion flows from 4D point clouds. The key insight is that simultaneously performing both tasks via learning spatial and temporal features from a sequence of point clouds can leverage individual tasks, leading to improved overall performance. To prove this ability, we design a temporal vector field learning module using unsupervised learning approach for flow estimation, leveraged by supervised learning of spatial structures for object reconstruction. Extensive experiments and analyses on benchmark dataset validated the effectiveness and efficiency of our method. As shown in experimental results, our method achieves state-of-the-art performance on both flow estimation and object reconstruction while performing much faster than existing methods in both training and inference. Our code and data are available at https://github.com/hkust-vgd/RFNet-4D .
Optimal UAVs placement for localization based on artificial hummingbird algorithm

In order to improve the positioning accuracy of the UAVs formation, this paper presents a method to optimize the UAVs formation based on artificial hummingbird algorithm(AHA). Firstly, a target area is delimited according to the target location estimate obtained from the preliminary reconnaissance of the UAVs formation and the radius set in advance, then the objective function of this optimization problem is constructed and the AHA is used to solve the objective function to optimize the formation of UAVs. The simulation result shows that the positioning accuracy of the UAVs formation can be improved effectively by this method, and compared with PSO, GWO and SSA, AHA has better performance in this problem scenario.
Efficient Deep Reinforcement Learning-Enabled Recommendation

Existing recommendations based on machine learning are mainly based on supervised learning. However, these methods affected by historical behavior often bring great difficulties on mining high-quality long-tail items, achieving cold-start recommendations, and causing response inability to real-time environment changes. To this end, this paper proposes a Deep Reinforcement Learning-enabled Recommendation based on Hierarchical attention and Sample-enhanced priority experience replay (HEDRL-Rec). First, we propose a hierarchical attention mechanism to extract more hidden information, including different contributions from single feature and overall feature (comprising combined feature), for enhancing features extraction ability of Actor-Critic architecture. Then, by considering the reusability of historical experiences and differences their contributions, we then propose a sample-enhanced priority experience replay mechanism to alleviate the problems of sample imbalance, sparse data, and excessive action space, where, thereby realizing personalized recommendations in real-time changing environments. Finally, we develop a deep reinforcement learning-enabled recommendation algorithm to solve the problems of non-convergence in the Critic. Extensive experiments demonstrate that, in particular, the recommended Click-Through Rate (CTR) of the HEDRL-Rec is 10.55% higher than the state-of-the-art LIst-wise Recommendation framework based on the Deep Reinforcement learning (ILRD) scheme, while the HEDRL-Rec has better stability and usability in the recommendation scenario, effectively alleviating the cold-start problem of systems lacking manual annotation data.
TDAM: Top-Down Attention Module for Contextually Guided Feature Selection in CNNs

Attention modules for Convolutional Neural Networks (CNNs) are an effective method to enhance performance on multiple computer-vision tasks. While existing methods appropriately model channel-, spatial- and self-attention, they primarily operate in a feedforward bottom-up manner. Consequently, the attention mechanism strongly depends on the local information of a single input feature map and does not incorporate relatively semantically-richer contextual information available at higher layers that can specify “what and where to look” in lower-level feature maps through top-down information flow. Accordingly, in this work, we propose a lightweight top-down attention module (TDAM) that iteratively generates a “visual searchlight” to perform channel and spatial modulation of its inputs and outputs more contextually-relevant feature maps at each computation step. Our experiments indicate that TDAM enhances the performance of CNNs across multiple object-recognition benchmarks and outperforms prominent attention modules while being more parameter and memory efficient. Further, TDAM-based models learn to “shift attention” by localizing individual objects or features at each computation step without any explicit supervision resulting in a 5% improvement for ResNet50 on weakly-supervised object localization. Source code and models are publicly available at: https://github.com/shantanuj/TDAM_Top_down_attention_module .
Combining Theory of Mind and Abduction for Cooperation Under Imperfect Information

In this paper, we formalise and implement an agent model for cooperation under imperfect information. It is based on Theory of Mind (the cognitive ability to understand the mental state of others) and abductive reasoning (the inference paradigm that computes explanations from observations). The combination of these two techniques allows agents to derive the motives behind the actions of their peers, and incorporate this knowledge into their own decision-making. We have implemented this model in a totally domain-independent fashion and successfully tested it for the cooperative card game Hanabi.