Reviewerly/exHarmony huggingface dataset:
WCNN3D: Wavelet Convolutional Neural Network-Based 3D Object Detection for Autonomous Driving

Three-dimensional object detection is crucial for autonomous driving to understand the driving environment. Since the pooling operation causes information loss in the standard CNN, we designed a wavelet-multiresolution-analysis-based 3D object detection network without a pooling operation. Additionally, instead of using a single filter like the standard convolution, we used the lower-frequency and higher-frequency coefficients as a filter. These filters capture more relevant parts than a single filter, enlarging the receptive field. The model comprises a discrete wavelet transform (DWT) and an inverse wavelet transform (IWT) with skip connections to encourage feature reuse for contrasting and expanding layers. The IWT enriches the feature representation by fully recovering the lost details during the downsampling operation. Element-wise summation was used for the skip connections to decrease the computational burden. We trained the model for the Haar and Daubechies (Db4) wavelets. The two-level wavelet decomposition result shows that we can build a lightweight model without losing significant performance. The experimental results on KITTI’s BEV and 3D evaluation benchmark show that our model outperforms the PointPillars-based model by up to 14% while reducing the number of trainable parameters.
TRILLsson: Distilled Universal Paralinguistic Speech Representations

Recent advances in self-supervision have dramatically improved the quality of speech representations.However, deployment of state-of-the-art embedding models on devices has been restricted due to their limited public availability and large resource footprint.Our work addresses these issues by publicly releasing a collection of paralinguistic speech models 1 that are small and near state-of-the-art performance.Our approach is based on knowledge distillation, and our models are distilled on public data only.We explore different architectures and thoroughly evaluate our models on the Non-Semantic Speech (NOSS) benchmark.Our largest distilled model is less than 15% the size of the original model (314MB vs 2.2GB), achieves over 96% the accuracy on 6 of 7 tasks, and is trained on 6.5% the data.The smallest model is 1% in size (22MB) and achieves over 90% the accuracy on 6 of 7 tasks.Our models outperform the open source Wav2Vec 2.0 model on 6 of 7 tasks, and our smallest model outperforms the open source Wav2Vec 2.0 on both emotion recognition tasks despite being 7% the size.
DefectTrack: a deep learning-based multi-object tracking algorithm for quantitative defect analysis of in-situ TEM videos in real-time

Abstract In-situ irradiation transmission electron microscopy (TEM) offers unique insights into the millisecond-timescale post-cascade process, such as the lifetime and thermal stability of defect clusters, vital to the mechanistic understanding of irradiation damage in nuclear materials. Converting in-situ irradiation TEM video data into meaningful information on defect cluster dynamic properties (e.g., lifetime) has become the major technical bottleneck. Here, we present a solution called the DefectTrack , the first dedicated deep learning-based one-shot multi-object tracking (MOT) model capable of tracking cascade-induced defect clusters in in-situ TEM videos in real-time. DefectTrack has achieved a Multi-Object Tracking Accuracy (MOTA) of 66.43% and a Mostly Tracked (MT) of 67.81% on the test set, which are comparable to state-of-the-art MOT algorithms. We discuss the MOT framework, model selection, training, and evaluation strategies for in-situ TEM applications. Further, we compare the DefectTrack with four human experts in quantifying defect cluster lifetime distributions using statistical tests and discuss the relationship between the material science domain metrics and MOT metrics. Our statistical evaluations on the defect lifetime distribution suggest that the DefectTrack outperforms human experts in accuracy and speed.
An extensive review of state-of-the-art transfer learning techniques used in medical imaging: Open issues and challenges

Abstract Deep learning techniques, which use a massive technology known as convolutional neural networks, have shown excellent results in a variety of areas, including image processing and interpretation. However, as the depth of these networks grows, so does the demand for a large amount of labeled data required to train these networks. In particular, the medical field suffers from a lack of images because the procedure for obtaining labeled medical images in the healthcare field is difficult, expensive, and requires specialized expertise to add labels to images. Moreover, the process may be prone to errors and time-consuming. Current research has revealed transfer learning as a viable solution to this problem. Transfer learning allows us to transfer knowledge gained from a previous process to improve and tackle a new problem. This study aims to conduct a comprehensive survey of recent studies that dealt with solving this problem and the most important metrics used to evaluate these methods. In addition, this study identifies problems in transfer learning techniques and highlights the problems of the medical dataset and potential problems that can be addressed in future research. According to our review, many researchers use pre-trained models on the Imagenet dataset (VGG16, ResNet, Inception v3) in many applications such as skin cancer, breast cancer, and diabetic retinopathy classification tasks. These techniques require further investigation of these models, due to training them on natural, non-medical images. In addition, many researchers use data augmentation techniques to expand their dataset and avoid overfitting. However, not enough studies have shown the effect of performance with or without data augmentation. Accuracy, recall, precision, F 1 score, receiver operator characteristic curve, and area under the curve (AUC) were the most widely used measures in these studies. Furthermore, we identified problems in the datasets for melanoma and breast cancer and suggested corresponding solutions.
Quantization Enabled Privacy Protection in Decentralized Stochastic Optimization

By enabling multiple agents to cooperatively solve a global optimization problem in the absence of a central coordinator, decentralized stochastic optimization is gaining increasing attention in areas as diverse as machine learning, control, and sensor networks. Since the associated data usually contain sensitive information, such as user locations and personal identities, privacy protection has emerged as a crucial need in the implementation of decentralized stochastic optimization. In this paper, we propose a decentralized stochastic optimization algorithm that is able to guarantee provable convergence accuracy even in the presence of aggressive quantization errors that are proportional to the amplitude of quantization inputs. The result applies to both convex and non-convex objective functions, and enables us to exploit aggressive quantization schemes to obfuscate shared information, and hence enables privacy protection without losing provable optimization accuracy. In fact, by using a stochastic ternary quantization scheme, which quantizes any value to three numerical levels, we achieve quantization-based rigorous differential privacy in decentralized stochastic optimization, which has not been reported before. In combination with the presented quantization scheme, the proposed algorithm ensures, for the first time, rigorous differential privacy in decentralized stochastic optimization without losing provable convergence accuracy. Simulation results for a distributed estimation problem as well as numerical experiments for decentralized learning on a benchmark machine learning dataset confirm the effectiveness of the proposed approach.
Dynamic linkage of the bitcoin market and energy consumption:An analysis across time

With the control of the cryptocurrency market in environmental protection, investors pay attention to the risk conduction mechanism between energy consumption and the Bitcoin market. This paper applies quantile connectedness to analyse the overall situation and dynamic evolution of information spillover in the system of the Bitcoin market. The results show that the hashrate and electricity demand are the primary sources of risk in the information network, and their fluctuations have intensified the risk spillover effects in the system. In addition, the spillover level is more prominent in extreme cases, which means the information linkage in the system is integrated. The spillover effect of each variable fluctuates and is uncertain with time. This helps in the sustainable development of Bitcoin and guides the government's policy development and supervision of cryptocurrencies. The risk infection path helps prevent the risk of infection in the Bitcoin market and improves the sustainability of the encrypted market.
Model-free prediction of multistability using echo state network

In the field of complex dynamics, multistable attractors have been gaining significant attention due to their unpredictability in occurrence and extreme sensitivity to initial conditions. Co-existing attractors are abundant in diverse systems ranging from climate to finance and ecological to social systems. In this article, we investigate a data-driven approach to infer different dynamics of a multistable system using an echo state network. We start with a parameter-aware reservoir and predict diverse dynamics for different parameter values. Interestingly, a machine is able to reproduce the dynamics almost perfectly even at distant parameters, which lie considerably far from the parameter values related to the training dynamics. In continuation, we can predict whole bifurcation diagram significant accuracy as well. We extend this study for exploring various dynamics of multistable attractors at an unknown parameter value. While we train the machine with the dynamics of only one attractor at parameter p, it can capture the dynamics of a co-existing attractor at a new parameter value p+Δp. Continuing the simulation for a multiple set of initial conditions, we can identify the basins for different attractors. We generalize the results by applying the scheme on two distinct multistable systems.
Object Detection Using Sim2Real Domain Randomization for Robotic Applications

Robots working in unstructured environments must be capable of sensing and interpreting their surroundings. One of the main obstacles of deep-learning-based models in the field of robotics is the lack of domain-specific labeled data for different industrial applications. In this article, we propose a sim2real transfer learning method based on domain randomization for object detection with which labeled synthetic datasets of arbitrary size and object types can be automatically generated. Subsequently, a state-of-the-art convolutional neural network, YOLOv4, is trained to detect the different types of industrial objects. With the proposed domain randomization method, we could shrink the reality gap to a satisfactory level, achieving 86.32% and 97.38% mAP50 scores, respectively, in the case of zero-shot and one-shot transfers, on our manually annotated dataset containing 190 real images. Our solution fits for industrial use as the data generation process takes less than 0.5 s per image and the training lasts only around 12 h, on a GeForce RTX 2080 Ti GPU. Furthermore, it can reliably differentiate similar classes of objects by having access to only one real image for training. To our best knowledge, this is the only work thus far satisfying these constraints.
When Age-Invariant Face Recognition Meets Face Age Synthesis: A Multi-Task Learning Framework and a New Benchmark

To minimize the impact of age variation on face recognition, age-invariant face recognition (AIFR) extracts identity-related discriminative features by minimizing the correlation between identity- and age-related features while face age synthesis (FAS) eliminates age variation by converting the faces in different age groups to the same group. However, AIFR lacks visual results for model interpretation and FAS compromises downstream recognition due to artifacts. Therefore, we propose a unified, multi-task framework to jointly handle these two tasks, termed MTLFace, which can learn the age-invariant identity-related representation for face recognition while achieving pleasing face synthesis for model interpretation. Specifically, we propose an attention-based feature decomposition to decompose the mixed face features into two uncorrelated components-identity- and age-related features-in a spatially constrained way. Unlike the conventional one-hot encoding that achieves group-level FAS, we propose a novel identity conditional module to achieve identity-level FAS, which can improve the age smoothness of synthesized faces through a weight-sharing strategy. Benefiting from the proposed multi-task framework, we then leverage those high-quality synthesized faces from FAS to further boost AIFR via a novel selective fine-tuning strategy. Furthermore, to advance both AIFR and FAS, we collect and release a large cross-age face dataset with age and gender annotations, and a new benchmark specifically designed for tracing long-missing children. Extensive experimental results on five benchmark cross-age datasets demonstrate that MTLFace yields superior performance than state-of-the-art methods for both AIFR and FAS. We further validate MTLFace on two popular general face recognition datasets, obtaining competitive performance on face recognition in the wild. The source code and datasets are available at http://hzzone.github.io/MTLFace.
histolab: A Python library for reproducible Digital Pathology preprocessing with automated testing

<h2>Abstract</h2> Deep Learning (DL) is rapidly permeating the field of Digital Pathology with algorithms successfully applied to ease daily clinical practice and to discover novel associations. However, most DL workflows for Digital Pathology include custom code for data preprocessing, usually tailored to data and tasks of interest, resulting in software that is error-prone and hard to understand, peer-review, and test. In this work, we introduce histolab, a Python package designed to standardize the preprocessing of Whole Slide Images in a reproducible environment, supported by automated testing. In addition, the package provides functions for building datasets of WSI tiles, including augmentation and morphological operators, a tile scoring framework, and stain normalization methods. histolab is modular, extensible, and easily integrable into DL pipelines, with support of the OpenSlide and large_image backends. To guarantee robustness, histolab embraces software engineering best practices such as multiplatform automated testing and Continuous Integration.
Enhanced Ali Baba and the forty thieves algorithm for feature selection

Feature Selection (FS) aims to ameliorate the classification rate of dataset models by selecting only a small set of appropriate features from the initial range of features. In consequence, a reliable optimization method is needed to deal with the matters involved in this problem. Often, traditional methods fail to optimally reduce the high dimensionality of the feature space of complex datasets, which lead to the elicitation of weak classification models. Meta-heuristics can offer a favorable classification rate for high-dimensional datasets. Here, a binary version of a new human-based algorithm named Ali Baba and the Forty Thieves (AFT) was applied to tackle a pool of FS problems. Although AFT is an efficient meta-heuristic for optimizing many problems, it sometimes exhibits premature convergence and low search performance. These issues were mitigated by proposing three enhanced versions of AFT, namely: (1) A Binary Multi-layered AFT called BMAFT which uses hierarchical and distributed frameworks, (2) Binary Elitist AFT (BEAFT) which uses an elitist learning strategy, and, (3) Binary Self-adaptive AFT (BSAFT) which uses an adapted tracking distance parameter. These versions along with the basic Binary AFT (BAFT) were expansively assessed on twenty-four problems gathered from different repositories. The results showed that the proposed algorithms substantially enhance the performance of BAFT in terms of convergence speed and solution accuracy. On top of that, the overall results showed that BMAFT is the most competitive, which provided the best results with excellent performance scores compared to other competing algorithms.
Metaverse and Banking Industry – 2023 The Year of Metaverse Adoption

This whitepaper discusses the potential of Metaverse to revolutionize the banking industry. It highlights the various ways in which banks can leverage Metaverse to improve their operations, including through the use of NFTs, blockchain technology, and smart contracts. The paper is aimed to explore the possibilities of Metaverse in the Banking Industry and understand its practical implementation radius. This paper navigates to various topics from the definition of Metaverse to business aspects in banking and financial services. Explores the various business opportunities in the customer communication domain, cross-border transactions, mortgages, digital assets, green loans, and achieving carbon net zero through Metaverse. The role of Innovation Leaders is discussed in detail assisting readers to understand and appreciate the value of innovation in today’s rapidly changing technology times. The paper argues that banks must take a proactive role in driving the adoption of Metaverse, support circular economy, and reduce waste by enabling web3.0 technology in the banking sector.
HTMatch: An efficient hybrid transformer based graph neural network for local feature matching

Local feature matching plays a vital role in various computer vision tasks. In this work, we present a novel network that combines feature matching and outlier rejection for finding reliable correspondences between image pairs. The proposed method is a hybrid transformer-based graph neural network (GNN), termed HTMatch, which aims to achieve high accuracy and efficient feature matching. Specifically, we first propose a hybrid transformer that integrates self- and cross-attention together to condition the feature descriptors between image pairs. By doing so, the intra/inter-graph attentional aggregation can be realized by a single transformer layer, which achieves more efficient message passing. Then, we introduce a new spatial embedding module to enhance the spatial constraints across images. The spatial information from one image is embedded into another, which can significantly improve matching performance. Finally, we adopt a seeded GNN architecture for establishing a sparse graph, which improves both efficiency and effectiveness. Experiments show that HTMatch reaches state-of-the-art results on several public benchmarks.
Daylight entanglement-based quantum key distribution with a quantum dot source

Abstract Entanglement-based quantum key distribution can enable secure communication in trusted node-free networks and over long distances. Although implementations exist both in fiber and in free space, the latter approach is often considered challenging due to environmental factors. Here, we implement a quantum communication protocol during daytime for the first time using a quantum dot source. This technology presents advantages in terms of narrower spectral bandwidth—beneficial for filtering out sunlight—and negligible multiphoton emission at peak brightness. We demonstrate continuous operation over the course of three days, across an urban 270 m-long free-space optical link, under different light and weather conditions.
Towards automated video game testing

As the complexity and scope of game development increases, playtesting remains an essential activity to ensure the quality of video games. Yet, the manual, ad-hoc nature of playtesting gives space to improvements in the process. In this study, we investigate gaps between academic solutions in the literature for automated video game testing and the needs of video game developers in the industry. We performed a literature review on video game automated testing and applied an online survey with video game developers. The literature results show a rise in research topics related to automated video game testing. The survey results show that game developers are skeptical about using automated agents to test games. We conclude that there is a need for new testing approaches that did not disrupt the developer workflow. As for the researchers, the focus should be on the testing goal and testing oracle.
Inertia-Guided Flow Completion and Style Fusion for Video Inpainting

Physical objects have inertia, which resists changes in the velocity and motion direction. Inspired by this, we introduce inertia prior that optical flow, which reflects object motion in a local temporal window, keeps unchanged in the adjacent preceding or subsequent frame. We propose a flow completion network to align and aggregate flow features from the consecutive flow sequences based on the inertia prior. The corrupted flows are completed under the supervision of customized losses on reconstruction, flow smoothness, and consistent ternary census transform. The completed flows with high fidelity give rise to significant improvement on the video inpainting quality. Nevertheless, the existing flow-guided cross-frame warping methods fail to consider the lightening and sharpness variation across video frames, which leads to spatial incoherence after warping from other frames. To alleviate such problem, we propose the Adaptive Style Fusion Network (ASFN), which utilizes the style information extracted from the valid regions to guide the gradient refinement in the warped regions. Moreover, we design a data simulation pipeline to reduce the training difficulty of ASFN. Extensive experiments show the superiority of our method against the state-of-the-art methods quantitatively and qualitatively. The project page is at https://github.com/hitachinsk/ISVI.
UAVS FLIGHT ROUTES OPTIMIZATION IN CHANGING WEATHER CONDITIONS – CONSTRAINT PROGRAMMING APPROACH

The problem of delivering goods in a distribution network is considered in which a fleet of Unmanned Aerial Vehicles (UAV) carries out transport operations. The changing weather conditions in which the transport operations take place and the UAVs energy capacity levels influenced by the weather conditions are taken into account as factors that affect the determination of a collision-free route. The goods must be delivered to the customers in a given time window. Establishing the routes are the focus of this study. Solutions maximizing the level of customer satisfaction are focused and the computational experiments presented in the study show the impact of weather conditions on route determination.