Reviewerly/exHarmony huggingface dataset:
Matrix Normal Cluster-Weighted Models

Abstract Finite mixtures of regressions with fixed covariates are a commonly used model-based clustering methodology to deal with regression data. However, they assume assignment independence, i.e., the allocation of data points to the clusters is made independently of the distribution of the covariates. To take into account the latter aspect, finite mixtures of regressions with random covariates, also known as cluster-weighted models (CWMs), have been proposed in the univariate and multivariate literature. In this paper, the CWM is extended to matrix data, e.g., those data where a set of variables are simultaneously observed at different time points or locations. Specifically, the cluster-specific marginal distribution of the covariates and the cluster-specific conditional distribution of the responses given the covariates are assumed to be matrix normal. Maximum likelihood parameter estimates are derived using an expectation-conditional maximization algorithm. Parameter recovery, classification assessment, and the capability of the Bayesian information criterion to detect the underlying groups are investigated using simulated data. Finally, two real data applications concerning educational indicators and the Italian non-life insurance market are presented.
MI-MOTE: Multiple imputation-based minority oversampling technique for imbalanced and incomplete data classification

Class imbalance and data incompleteness problems occur simultaneously in many real-world classification datasets, which negatively affects the training of classifiers. Given an imbalanced and incomplete training dataset, the conventional approach is to address these two problems sequentially by handling data incompleteness first and then focusing on class imbalance. In this study, we propose a multiple imputation-based minority oversampling technique, named MI-MOTE, to address imbalanced and incomplete data classification simultaneously. Majority instances are imputed once and minority instances are oversampled using multiple different imputations without directly manipulating any of their observed values. Accordingly, minority instances are diversified with less data distortion compared to the conventional approach. The proposed method is applied in the data preprocessing phase, meaning it can be used with any type of classifier. Experimental results for benchmark datasets with various missing rates demonstrate the effectiveness of the proposed method.
Reconstructing random heterogeneous media through differentiable optimization

Microstructure reconstruction is a key enabler of process-structure–property linkages, a central topic in materials engineering. Revisiting classical optimization-based reconstruction techniques, they are recognized as a powerful framework to reconstruct random heterogeneous media, especially due to their generality and controllability. The stochasticity of the available approaches is, however, identified as a performance bottleneck. In this work, reconstruction is approached as a differentiable optimization problem, where the error of a generic prescribed descriptor is minimized under consideration of its derivative. As an exemplary descriptor, a suitable differentiable version of spatial correlations is formulated, along with a multigrid scheme to ensure scalability. The applicability of differentiable optimization realized through this descriptor is demonstrated using a wide variety of heterogeneous media, achieving exact statistical equivalence with errors as low as 0% in a short time. We conclude that, while still in an early stage of development, this approach has the potential to significantly alleviate the computational effort currently associated with reconstructing general random heterogeneous media.
Assessing one-minute diffuse fraction models based on worldwide climate features

Despite the variety of irradiance separation models available in the literature, there is not yet a model that performs equally well for every location worldwide. Nevertheless, separation models still represent an interesting approach when only global irradiance is measured. This work presents further developments of the recently proposed Boland-Ridley-Lauret minute model and proposes 1-min diffuse fraction models for each climate zone in the globe. To accomplish this, 1-min worldwide irradiance data from 51 the Baseline Surface Rresearch Network stations and one from Australian Bureau of Meteorology were used. The Köppen-Geiger classification was used to determine the climate zone of each station: both the simple climate classification (A, B, C, D, E) and the complete classification (Aw, Bsh, etc.). Furthermore, a robust nonlinear regression method was used to build the separation models without removing measurement outliers in advance. The climate-specific models proposed herein present better performance than other models from the literature, such as the BRL, Perez and Skartveit models (developed using hourly irradiance data) and the Engerer model (developed using minute data). Generally, the models proposed for climate zones in the complete Köppen classification presented better similitude with the measured data than the models for the simple classification, especially for stations within climate zones B and E.
Demonstration of a non-Abelian geometric controlled-NOT gate in a superconducting circuit

Holonomies, arising from non-Abelian geometric transformations of quantum states in Hilbert space, offer a promising way for quantum computation. These holonomies are not commutable and thus can be used for the realization of a universal set of quantum logic gates, where the global geometric feature may result in some noise-resilient advantages. Here we report, to our knowledge, the first on-chip realization of a non-Abelian geometric controlled-NOT gate in a superconducting circuit, which is a building block for constructing a holonomic quantum computer. The conditional dynamics is achieved in an all-to-all connected architecture involving multiple frequency-tunable superconducting qubits controllably coupled to a resonator; a holonomic gate between any two qubits can be implemented by tuning their frequencies on-resonance with the resonator and applying a two-tone drive to one of them. This gate represents an important step towards the all-geometric realization of scalable quantum computation on a superconducting platform.
A No-Reference Evaluation Metric for Low-Light Image Enhancement

Low-light images, which are usually taken in dark or back-lighting conditions, are hard to perceive due to the low visibility and low contrast. To improve viewers’ Quality of Experience (QoE) and support the application of vision-based systems, various low-light image enhancement algorithms (LIEAs) have been proposed to lighten low-light images. However, some LIEAs may amplify the hidden distortions in the dark like noise and even further, introduce new distortions such as structural damage, color shift, etc, which severely affect the quality of light-enhanced images and need to be evaluated quantificationally. However, in the literature, few measures are proposed to assess the quality of light-enhanced images. Therefore, in this paper, we develop a no-reference low-light image enhancement evaluation (NLIEE) metric to predict the quality of light-enhanced images. The image quality is mainly assessed from four key aspects: light enhancement, color comparison, noise measurement, and structure evaluation. The experiment results show that NLIEE achieves the best performance among the general no-reference image quality assessment (NR IQA) models and quality descriptors for light enhancement.
The complexity of gradient descent: CLS = PPAD ∩ PLS

We study search problems that can be solved by performing Gradient Descent on a bounded convex polytopal domain and show that this class is equal to the intersection of two well-known classes: PPAD and PLS. As our main underlying technical contribution, we show that computing a Karush-Kuhn-Tucker (KKT) point of a continuously differentiable function over the domain [0,1]2 is PPAD ∩ PLS-complete. This is the first natural problem to be shown complete for this class. Our results also imply that the class CLS (Continuous Local Search) - which was defined by Daskalakis and Papadimitriou as a more "natural" counterpart to PPAD ∩ PLS and contains many interesting problems - is itself equal to PPAD ∩ PLS.
Blind light field image quality assessment by analyzing angular-spatial characteristics

Light Field Image (LFI) can simultaneously record the intensity and direction information of light rays, and provide users with strong immersion experience. However, heterogeneous artifacts may be introduced during LFI processing, which results in degradation of the perceptual quality of LFI. To evaluate the LFI quality effectively, a novel blind light field image quality assessment method by analyzing angular-spatial characteristics is proposed. The main strategy is to quantify the LFI quality degradation by evaluating the angular consistency and spatial quality simultaneously. Firstly, the multi-directional intra-block and inter-block differential operations are employed on macro-pixels to generate Macro-Pixel Residual Blocks (MPRBs) on hue, saturation and luminance of LFI. Secondly, effective perceptual feature extraction schemes based on MPRBs entropy distribution and natural scene statistics of discrete cosine transform coefficients for MPRBs are developed to measure the angular consistency on each color descriptor. Thirdly, Macro-Pixel Variance (MPV) map is defined, and the quality-aware features are extracted from MPV map to measure the occlusion areas of LFI. Fourthly, the perceptual features are extracted from sub-aperture images to comprehensively measure the spatial quality of LFI. Finally, all the extracted features are pooled to predict the objective quality of LFI. Extensive experimental results on four LFI datasets show that the proposed method significantly outperforms the representative 2D, 3D, multi-view image quality assessment methods as well as the state-of-the-art LFI quality assessment methods, and is more in line with the human visual perception.
Application of Deep Learning for Weapons Detection in Surveillance Videos

Weapon detection is a very serious and intense issue as far as the security and safety of the public in general, no doubt it's a hard and difficult task furthermore, its troublesome when you need to do it automatically or with some of the AI model. Different object detection models are available but in case of weapons detection it is difficult to detect the weapons of distinctive size and shapes along with the different colors of the background. Currently, a great deal of Convolutional Neural Network (CNN) based deep learning approaches are proposed for the recognition and classification in real-time. In this paper, we have done the comparative analysis of the two versions which is a state of the art model called YOLOV3 and YOLOV4 for weapons detection. For training purpose, we create weapons dataset and the images are collected from google images along with a portion of different assets. We annotate the images one by one manually in different formats in light of fact that YOLO needs annotation file in text format and some other models need annotation file in XML format. We trained both the versions on a large data set of weapons and afterward tested their results for comparative analysis. We explained in the paper that YOLOV4 performs obviously superior to the YOLOV3 in terms of processing time and sensitivity yet we can compare these two in precision metric. The implementation details and trained models are made public at this link:https://cutt.ly/5kBEPhM.
Interactive Physical ZKP for Connectivity: Applications to Nurikabe and Hitori

During the last years, many Physical Zero-knowledge Proof (ZKP) protocols for Nikoli’s puzzles have been designed. In this paper, we propose two ZKP protocols for the two Nikoli’s puzzles called Nurikabe and Hitori. These two puzzles have some similarities, since in their rules at least one condition requires that some cells are connected to each other, horizontally or vertically. The novelty in this paper is to propose two techniques that allow us to prove such connectivity without leaking any information about a solution.
Glove Word Embedding and DBSCAN algorithms for Semantic Document Clustering

In the recently developed document clustering, word embedding has the primary role in constructing semantics, considering and measuring the times a specific word appears in its context. Word2vect and Glove word embedding are the two most used word embeddings in document clustering. Previous works do not consider the use of glove word embedding with DBSCAN clustering algorithm in document clustering. In this work, a preprocessing with and without stemming of Wikipedia and IMDB datasets applied to glove word embedding algorithm, then word vectors as a result are applied to the DBSCAN clustering algorithm. For the evaluation of experiments, seven metrics have been used: Silhouette average, purity, accuracy, F1, completeness, homogeneity, and NMI score. The experimental results are compared with the results of TFIDF and K-means algorithms on six datasets. The results of this work outperform the results of the TFIDF and K-means approach using the four main evaluation metrics and CPU time consuming.
Medical image denoising system based on stacked convolutional autoencoder for enhancing 2-dimensional gel electrophoresis noise reduction

Image denoising is the technique of removing noise or distortions from an image. During medical image acquisition, random noise is added, which results in a lower contrast in those images. For that, image denoising is a crucial task for medical imaging analysis. In this study, a denoising system using three heterogeneous medical datasets is proposed based on stacked convolutional autoencoder (SCAE) technique. To validate its efficiency, different evaluation metrics are used, such as mean squared error (MSE), Peak signal-to-noise ratio (PSNR), contrast-to-noise ratio (CNR), structural similarity index measure (SSIM) and cross correlation (CC). The proposed denoising system gives good results among the medical and microscopic datasets that are used for training. The best average results obtained are 0.0039 for MSE, 24.07 for PSNR, 0.1220 for CNR, 0.85 for SSIM, and 0.6358 for CC. Then, the proposed SCAE denoising system was applied to the LECB 2-D PAGE database for denoising real 2-DGE images. The results of denoising 2-DGE images are evaluated by MSE, spot efficiency, false discovery rate (FDR), and signal-to-noise ratio (SNR). The best average results for 2-DGE images are 0.014 for MSE, 75 spot efficiency, 36.3 for FDR and 18.41 for SNR. The proposed system has enhanced the denoising of 2-DGE images by 0.9% to 17.6% when compared to other techniques.
Slashing Communication Traffic in Federated Learning by Transmitting Clustered Model Updates

Federated Learning (FL) is an emerging decentralized learning framework through which multiple clients can collaboratively train a learning model. However, a major obstacle that impedes the wide deployment of FL lies in massive communication traffic. To train high dimensional machine learning models (such as CNN models), heavy communication traffic can be incurred by exchanging model updates via the Internet between clients and the parameter server (PS), implying that the network resource can be easily exhausted. Compressing model updates is an effective way to reduce the traffic amount. However, a flexible unbiased compression algorithm applicable for both uplink and downlink compression in FL is still absent from existing works. In this work, we devise the Model Update Compression by Soft Clustering (MUCSC) algorithm to compress model updates transmitted between clients and the PS. In MUCSC, it is only necessary to transmit cluster centroids and the cluster ID of each model update. Moreover, we prove that: 1) The compressed model updates are unbiased estimation of their original values so that the convergence rate by transmitting compressed model updates is unchanged; 2) MUCSC can guarantee that the influence of the compression error on the model accuracy is minimized. Then, we further propose the boosted MUCSC (B-MUCSC) algorithm, a biased compression algorithm that can achieve an extremely high compression rate by grouping insignificant model updates into a super cluster. B-MUCSC is suitable for scenarios with very scarce network resource. Ultimately, we conduct extensive experiments with the CIFAR-10 and FEMNIST datasets to demonstrate that our algorithms can not only substantially reduce the volume of communication traffic in FL, but also improve the training efficiency in practical networks.
Soft-IntroVAE: Analyzing and Improving the Introspective Variational Autoencoder

The recently introduced introspective variational autoencoder (IntroVAE) exhibits outstanding image generations, and allows for amortized inference using an image encoder. The main idea in IntroVAE is to train a VAE adversarially, using the VAE encoder to discriminate between generated and real data samples. However, the original IntroVAE loss function relied on a particular hinge-loss formulation that is very hard to stabilize in practice, and its theoretical convergence analysis ignored important terms in the loss. In this work, we take a step towards better under-standing of the IntroVAE model, its practical implementation, and its applications. We propose the Soft-IntroVAE, a modified IntroVAE that replaces the hinge-loss terms with a smooth exponential loss on generated samples. This change significantly improves training stability, and also enables theoretical analysis of the complete algorithm. Interestingly, we show that the IntroVAE converges to a distribution that minimizes a sum of KL distance from the data distribution and an entropy term. We discuss the implications of this result, and demonstrate that it induces competitive image generation and reconstruction. Finally, we describe an application of Soft-IntroVAE to unsupervised image translation, and demonstrate compelling results. Code and additional information is available on the project website -taldatech.github.io/soft-intro-vae-web.
Unsupervised Human Pose Estimation through Transforming Shape Templates

Human pose estimation is a major computer vision problem with applications ranging from augmented reality and video capture to surveillance and movement tracking. In the medical context, the latter may be an important biomarker for neurological impairments in infants. Whilst many methods exist, their application has been limited by the need for well annotated large datasets and the inability to gen-eralize to humans of different shapes and body compositions, e.g. children and infants. In this paper we present a novel method for learning pose estimators for human adults and infants in an unsupervised fashion. We approach this as a learnable template matching problem facilitated by deep feature extractors. Human-interpretable landmarks are estimated by transforming a template consisting of predefined body parts that are characterized by 2D Gaussian distributions. Enforcing a connectivity prior guides our model to meaningful human shape representations. We demonstrate the effectiveness of our approach on two different datasets including adults and infants. Project page: infantmotion.github.io
Exploiting Edge-Oriented Reasoning for 3D Point-based Scene Graph Analysis

Scene understanding is a critical problem in computer vision. In this paper, we propose a 3D point-based scene graph generation (SGG <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">point</inf> ) framework to effectively bridge perception and reasoning to achieve scene under-standing via three sequential stages, namely scene graph construction, reasoning, and inference. Within the reasoning stage, an EDGE-oriented Graph Convolutional Network (EdgeGCN) is created to exploit multi-dimensional edge features for explicit relationship modeling, together with the exploration of two associated twinning interaction mechanisms between nodes and edges for the independent evolution of scene graph representations. Overall, our integrated SGG <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">point</inf> framework is established to seek and infer scene structures of interest from both real-world and synthetic 3D point-based scenes. Our experimental results show promising edge-oriented reasoning effects on scene graph generation studies. We also demonstrate our method advantage on several traditional graph representation learning benchmark datasets, including the node-wise classification on citation networks and whole-graph recognition problems for molecular analysis.
DeFLOCNet: Deep Image Editing via Flexible Low-level Controls

User-intended visual content fills the hole regions of an input image in the image editing scenario. The coarse low- level inputs, which typically consist of sparse sketch lines and color dots, convey user intentions for content creation (i.e., free-form editing). While existing methods combine an input image and these low-level controls for CNN inputs, the corresponding feature representations are not sufficient to convey user intentions, leading to unfaithfully generated content. In this paper, we propose DeFLOCNet which relies on a deep encoder-decoder CNN to retain the guidance of these controls in the deep feature representations. In each skip-connection layer, we design a structure generation block. Instead of attaching low-level controls to an input image, we inject these controls directly into each structure generation block for sketch line refinement and color propagation in the CNN feature space. We then concatenate the modulated features with the original decoder features for structure generation. Meanwhile, DeFLOCNet involves another decoder branch for texture generation and detail enhancement. Both structures and textures are rendered in the decoder, leading to user-intended editing results. Experiments on benchmarks demonstrate that DeFLOCNet effectively transforms different user intentions to create visually pleasing content.
LAU-Net: Latitude Adaptive Upscaling Network for Omnidirectional Image Super-resolution

The omnidirectional images (ODIs) are usually at low-resolution, due to the constraints of collection, storage and transmission. The traditional two-dimensional (2D) image super-resolution methods are not effective for spherical ODIs, because ODIs tend to have non-uniformly distributed pixel density and varying texture complexity across latitudes. In this work, we propose a novel latitude adaptive upscaling network (LAU-Net) for ODI super-resolution, which allows pixels at different latitudes to adopt distinct upscaling factors. Specifically, we introduce a Laplacian multi-level separation architecture to split an ODI into different latitude bands, and hierarchically upscale them with different factors. In addition, we propose a deep reinforcement learning scheme with a latitude adaptive reward, in order to automatically select optimal upscaling factors for different latitude bands. To the best of our knowledge, LAU-Net is the first attempt to consider the latitude difference for ODI super-resolution. Extensive results demonstrate that our LAU-Net significantly advances the super-resolution performance for ODIs. Codes are available at https://github.com/wangh-allen/LAU-Net.