Reviewerly/exHarmony huggingface dataset:
Invertible residual network with regularization for effective volumetric segmentation

Deep Convolutional Neural Networks (CNNs) i.e. Residual Networks (ResNets) have been used successfully for many computer vision tasks, but are difficult to scale to 3D volumetric medical data. Memory is increasingly often the bottleneck when training 3D Convolutional Neural Networks (CNNs). Recently, invertible neural networks have been applied to significantly reduce activation memory footprint when training neural networks with backpropagation thanks to the invertible functions that allow retrieving input from its output without storing intermediate activations in memory to perform the backpropagation. Among many successful network architectures, 3D Unet1 has been established as a standard architecture for volumetric medical segmentation. Thus, we choose 3D Unet as a baseline for a non-invertible network and we then extend it with the invertible residual network. In this paper, we proposed two versions of invertible Residual Network, namely Partially Invertible Residual Network (Partially-InvRes) and Fully Invertible Residual Network (Fully-InvRes). In Partially-InvRes, the invertible residual layer is defined by a technique called additive coupling2 whereas in Fully-InvRes, both invertible upsampling and downsampling operations are learned based on squeezing (known as pixel shuffle).<sup>3</sup> Furthermore, to avoid the overfitting problem because of less training data, a variational auto-encoder (VAE) branch is added to reconstruct the input volumetric data itself. Our results indicate that by using partially/fully invertible networks as the central workhorse in volumetric segmentation, we not only reduce memory overhead but also achieve compatible segmentation performance compared against the non-invertible 3D Unet. We have demonstrated the proposed networks on various volumetric datasets such as iSeg 2019<sup>4</sup> and BraTS 2020.<sup>5</sup>
A Quantum-Inspired Sperm Motility Algorithm

&lt;abstract&gt; &lt;p&gt;Sperm Motility Algorithm (SMA), inspired by the human fertilization process, was proposed by Abdul-Raof and Hezam &lt;sup&gt;[&lt;xref ref-type="bibr" rid="b1"&gt;1&lt;/xref&gt;]&lt;/sup&gt; to solve global optimization problems. Sperm flow obeys the Stokes equation or the Schrۤinger equation as its derived equivalent. This paper combines a classical SMA with quantum computation features to propose two novel Quantum-Inspired Evolutionary Algorithms: The first is called the Quantum Sperm Motility Algorithm (QSMA), and the second is called the Improved Quantum Sperm Motility Algorithm (IQSMA). The IQSMA is based on the characteristics of QSMA and uses an interpolation operator to generate a new solution vector in the search space. The two proposed algorithms are global convergence guaranteed population-based optimization algorithms, which outperform the original SMA in terms of their search-ability and have fewer parameters to control. The two proposed algorithms are tested using thirty-three standard dissimilarities benchmark functions. Performance and optimization results of the QSMA and IQSMA are compared with corresponding results obtained using the original SMA and those obtained from three state-of-the-art metaheuristics algorithms. The algorithms were tested on a series of numerical optimization problems. The results indicate that the two proposed algorithms significantly outperform the other presented algorithms.&lt;/p&gt; &lt;/abstract&gt;
Zero-Shot Logit Adjustment

Semantic-descriptor-based Generalized Zero-Shot Learning (GZSL) poses challenges in recognizing novel classes in the test phase. The development of generative models enables current GZSL techniques to probe further into the semantic-visual link, culminating in a two-stage form that includes a generator and a classifier. However, existing generation-based methods focus on enhancing the generator's effect while neglecting the improvement of the classifier. In this paper, we first analyze of two properties of the generated pseudo unseen samples: bias and homogeneity. Then, we perform variational Bayesian inference to back-derive the evaluation metrics, which reflects the balance of the seen and unseen classes. As a consequence of our derivation, the aforementioned two properties are incorporated into the classifier training as seen-unseen priors via logit adjustment. The Zero-Shot Logit Adjustment further puts semantic-based classifiers into effect in generation-based GZSL. Our experiments demonstrate that the proposed technique achieves state-of-the-art when combined with the basic generator, and it can improve various generative Zero-Shot Learning frameworks. Our codes are available on https://github.com/cdb342/IJCAI-2022-ZLA.
Graph Normalized-LMP Algorithm for Signal Estimation Under Impulsive Noise

We introduce an adaptive graph normalized least mean pth power (GNLMP) algorithm that utilizes graph signal processing (GSP) techniques, including bandlimited filtering and node sampling, to estimate sampled graph signals under impulsive noise. Different from least-squares-based algorithms, such as the adaptive GSP Least Mean Squares (GLMS) algorithm and the normalized GLMS (GNLMS) algorithm, the GNLMP algorithm has the ability to reconstruct a graph signal that is corrupted by non-Gaussian noise with heavy-tailed characteristics. Compared to the recently introduced adaptive GSP least mean pth power (GLMP) algorithm, the GNLMP algorithm reduces the number of iterations to converge to a steady graph signal. The convergence condition of the GNLMP algorithm is derived, and the ability of the GNLMP algorithm to process multidimensional time-varying graph signals with multiple features is demonstrated. Simulations show that the performance of the GNLMP algorithm in estimating steady-state and time-varying graph signals is faster than GLMP and is more robust in comparison to GLMS and GNLMS.
Quantum steering and quantum discord under noisy channels and entanglement swapping

Quantum entanglement, discord, and EPR-steering are properties which are considered as valuable resources for fuelling quantum information-theoretic protocols. EPR-steering is a property that is more general than Bell-nonlocality and yet more restrictive than entanglement. Quantum discord on the other hand, captures non-classical behaviour beyond that of entanglement, and its study has remained of active research interest during the past two decades. Exploring the behaviour of these quantum properties in different physical scenarios, like those simulated by open quantum systems, is therefore of crucial importance for understanding their viability for quantum technologies. In this work, we analyse the behaviour of EPR-steering, entanglement, and quantum discord, for two-qubit states under various quantum processes. First, we consider the three noisy channel scenarios of; phase damping, generalised amplitude damping and stochastic dephasing channel. Second, we explore the behaviour of these quantum properties in an entanglement swapping scenario. We quantify EPR-steering by means of an inequality with three-input two-output measurement settings, and address quantum discord as the interferometric power of quantum states. Our findings are the following. First, we show that some of the relatively straightforward noisy channels here considered, can induce non-trivial dynamics such as sudden death as well as death and revival of EPR-steering and entanglement. Second, we find that although noisy channels in general reduce the amount of correlations present in the system, the swapping protocol on the other hand displays scenarios where these quantum correlations can be enhanced. These results therefore illustrate that quantum processes do not exclusively affect the quantum properties of physical systems in a negative manner, but that they can also have positive effects on such properties.
Quantum walk on the Bloch sphere

A scheme for implementing the discrete-time quantum walk on the Bloch sphere is proposed, which is closely related to the SU(2) group. A spin cluster serves as the walker, whereas its location on the Bloch sphere is described by the spin coherent state. An additional spin that interacts with the spin cluster plays the role of a coin, whose state determines the rotation of the spin cluster. The Wigner function is calculated to visualize the movement of the walker on the Bloch sphere, with which the probability distribution and the standard deviation are also achieved. The quadratic enhancement of variance for the quantum walk on the Bloch sphere is confirmed. Compared to the ideal quantum walk on a circle, the walker's states on the Bloch sphere are generally nonorthogonal, whose drawbacks can be eliminated by increasing the number of spins in the spin cluster.
JIFF: Jointly-aligned Implicit Face Function for High Quality Single View Clothed Human Reconstruction

This paper addresses the problem of single view 3D human reconstruction. Recent implicit function based methods have shown impressive results, but they fail to recover fine face details in their reconstructions. This largely degrades user experience in applications like 3D telepresence. In this paper, we focus on improving the quality of face in the reconstruction and propose a novel Jointly-aligned Implicit Face Function (JIFF) that combines the merits of the implicit function based approach and model based approach. We employ a 3D morphable face model as our shape prior and compute space-aligned 3D features that capture detailed face geometry information. Such space-aligned 3D features are combined with pixel-aligned 2D features to jointly predict an implicit face function for high quality face reconstruction. We further extend our pipeline and introduce a coarse-to-fine architecture to predict high quality texture for our detailed face model. Extensive evaluations have been carried out on public datasets and our proposed JIFF has demonstrates superior performance (both quantitatively and qualitatively) over existing state-of-the-arts.
Optimizing Task Placement and Online Scheduling for Distributed GNN Training Acceleration

Training Graph Neural Networks (GNN) on large graphs is resource-intensive and time-consuming, mainly due to the large graph data that cannot be fit into the memory of a single machine, but have to be fetched from distributed graph storage and processed on the go. Unlike distributed deep neural network (DNN) training, the bottleneck in distributed GNN training lies largely in large graph data transmission for constructing mini-batches of training samples. Existing solutions often advocate data-computation colocation, and do not work well with limited resources where the colocation is infeasible. The potentials of strategical task placement and optimal scheduling of data transmission and task execution have not been well explored. This paper designs an efficient algorithm framework for task placement and execution scheduling of distributed GNN training, to better resource utilization, improve execution pipelining, and expediting training completion. Our framework consists of two modules: (i) an online scheduling algorithm that schedules the execution of training tasks, and the data transmission plan; and (ii) an exploratory task placement scheme that decides the placement of each training task. We conduct thorough theoretical analysis, testbed experiments and simulation studies, and observe up to 67% training speed-up with our algorithm as compared to representative baselines.
Unsupervised Data Selection for Speech Recognition with Contrastive Loss Ratios

This paper proposes an unsupervised data selection method by using a submodular function based on contrastive loss ratios of target and training data sets. A model using a contrastive loss function is trained on both sets. Then the ratio of frame-level losses for each model is used by a submodular function. By using the submodular function, a training set for automatic speech recognition matching the target data set is selected. Experiments show that models trained on the data sets selected by the proposed method outperform the selection method based on log-likelihoods produced by GMM-HMM models, in terms of word error rate (WER). When selecting a fixed amount, e.g. 10 hours of data, the difference between the results of two methods on Tedtalks was 20.23% WER relative. The method can also be used to select data with the aim of minimising negative transfer, while maintaining or improving on performance of models trained on the whole training set. Results show that the WER on the WSJCAM0 data set was reduced by 6.26% relative when selecting 85% from the whole data set.
Salient Object Detection by LTP Texture Characterization on Opposing Color Pairs under SLICO Superpixel Constraint

The effortless detection of salient objects by humans has been the subject of research in several fields, including computer vision, as it has many applications. However, salient object detection remains a challenge for many computer models dealing with color and textured images. Most of them process color and texture separately and therefore implicitly consider them as independent features which is not the case in reality. Herein, we propose a novel and efficient strategy, through a simple model, almost without internal parameters, which generates a robust saliency map for a natural image. This strategy consists of integrating color information into local textural patterns to characterize a color micro-texture. It is the simple, yet powerful LTP (Local Ternary Patterns) texture descriptor applied to opposing color pairs of a color space that allows us to achieve this end. Each color micro-texture is represented by a vector whose components are from a superpixel obtained by the SLICO (Simple Linear Iterative Clustering with zero parameter) algorithm, which is simple, fast and exhibits state-of-the-art boundary adherence. The degree of dissimilarity between each pair of color micro-textures is computed by the FastMap method, a fast version of MDS (Multi-dimensional Scaling) that considers the color micro-textures’ non-linearity while preserving their distances. These degrees of dissimilarity give us an intermediate saliency map for each RGB (Red–Green–Blue), HSL (Hue–Saturation–Luminance), LUV (L for luminance, U and V represent chromaticity values) and CMY (Cyan–Magenta–Yellow) color space. The final saliency map is their combination to take advantage of the strength of each of them. The MAE (Mean Absolute Error), MSE (Mean Squared Error) and Fβ measures of our saliency maps, on the five most used datasets show that our model outperformed several state-of-the-art models. Being simple and efficient, our model could be combined with classic models using color contrast for a better performance.
Evaluation Metrics for Deep Learning Imputation Models

There is growing interest in imputing missing data in tabular datasets using deep learning. A commonly used metric in evaluating the performance of a deep learning-based imputation model is root mean square error (RMSE), which is a prediction evaluation metric. In this paper, we demonstrate the limitations of RMSE for evaluating deep learning-based imputation performance by conducting a comparative analysis between RMSE and alternative metrics in the statistical literature including qualitative, predictive accuracy, and statistical distance. To minimize model and dataset biases, we use two different deep learning imputation models (denoising autoencoders and generative adversarial nets) and a regression imputation model. We also use two tabular datasets with growing amounts of missing data from different industry sectors: healthcare and financial. Our results show that contrary to the commonly used RMSE metric, the statistical metric of Jensen Shannon distance best assessed the imputation models’ performance. The regression model also ranked higher than deep learning when evaluated using the Jensen Shannon metric.
Quantum discords of tripartite quantum systems

The quantum discord of bipartite systems is one of the best-known measures of non-classical correlations and an important quantum resource. In the recent work appeared in [Phys. Rev. Lett 2020, 124:110401], the quantum discord has been generalized to multipartite systems. In this paper, we give analytic solutions of the quantum discord for tripartite states with fourteen parameters.
Research on energy storage capacity configuration for PV power plants using uncertainty analysis and its applications

Compensating for photovoltaic (PV) power forecast errors is an important function of energy storage systems. As PV power outputs have strong random fluctuations and uncertainty, it is difficult to satisfy the grid-connection requirements using fixed energy storage capacity configuration methods. In this paper, a method of configuring energy storage capacity is proposed based on the uncertainty of PV power generation. A k-means clustering algorithm is used to classify weather types based on differences in solar irradiance. The power forecast errors in different weather types are analyzed, and an energy storage system is used to compensate for the errors. The kernel density estimation is used to fit the distributions of the daily maximum power and maximum capacity requirements of the energy storage system; the power and capacity of the energy storage unit are calculated at different confidence levels. The optimized energy storage configuration of a PV plant is presented according to the calculated degrees of power and capacity satisfaction. The proposed method was validated using actual operating data from a PV power station. The results indicated that the required energy storage can be significantly reduced while compensating for power forecast errors.
Pose-Enhanced Relation Feature for Action Recognition in Still Images

Due to the lack of motion information, action recognition in still images is considered a challenging task. Previous works focused on contextual information in the image, including human pose, surrounding objects, etc. But they rarely consider the relation between the local pose and the entire human body, so that poses related to the action are not fully utilized. In this paper, we propose a solution for action recognition in still images, which makes complete and effective use of pose information. The multi-key points calculation method is carefully designed for generating pose regions that explicitly includes possible actions. The extensible Pose-Enhanced Relation Module extracts the implicit relation between pose and human body, and outputs the Pose-Enhanced Relation Feature which owns powerful representation capabilities. Surrounding objects information is also applied to strengthen the solution. Through experiments, it can be found that the proposed solution exceed the state-of-the-art performance on two commonly used datasets, PASCAL VOC 2012 Action and Stanford 40 Actions. Visualization shows that the proposed solution enables the network to pay more attention to the pose regions related to the action.
An Overview of Contextual Topic Modeling Using Bidirectional Encoder Representations from Transformers

Topic modeling refers to a range of algorithms in natural language processing that gives us an insight into the ‘latent’ semantic topics or patterns in a collection of documents. These patterns of word co-occurrence are used to determine the hidden ‘topics’ which are present in the corpus. Topic modeling has been used successfully for information retrieval, classifying documents, summarizing them and for exploratory analysis of large corpora of texts. This survey studies various algorithms that have been used for topic modeling over time including TF-IDF, latent Dirichlet algorithm (LDA), clustering on sentence-level BERT embeddings and a newer hybrid approach of generating contextual topics using a combination of LDA and BERT vectors. This survey will analyze the advantages and limitations of these algorithms.
A Little Bit More: Bitplane-Wise Bit-Depth Recovery

Imaging sensors digitize incoming scene light at a dynamic range of 10–12 bits (i.e., 1024–4096 tonal values). The sensor image is then processed onboard the camera and finally quantized to only 8 bits (i.e., 256 tonal values) to conform to prevailing encoding standards. There are a number of important applications, such as high-bit-depth displays and photo editing, where it is beneficial to recover the lost bit depth. Deep neural networks are effective at this bit-depth reconstruction task. Given the quantized low-bit-depth image as input, existing deep learning methods employ a single-shot approach that attempts to either (1) directly estimate the high-bit-depth image, or (2) directly estimate the residual between the high- and low-bit-depth images. In contrast, we propose a training and inference strategy that recovers the residual image bitplane-by-bitplane. Our bitplane-wise learning framework has the advantage of allowing for multiple levels of supervision during training and is able to obtain state-of-the-art results using a simple network architecture. We test our proposed method extensively on several image datasets and demonstrate an improvement from 0.5dB to 2.3dB PSNR over prior methods depending on the quantization level.
Improved Soft-Aided Decoding of Product Codes With Dynamic Reliability Scores

Products codes (PCs) are conventionally decoded with efficient iterative bounded-distance decoding (iBDD) based on hard-decision channel outputs which entails a performance loss compared to a soft-decision decoder.Recently, several hybrid algorithms have been proposed aimed to improve the performance of iBDD decoders via the aid of a certain amount of soft information while keeping the decoding complexity similarly low as in iBDD.We propose a novel hybrid low-complexity decoder for PCs based on error-and-erasure (EaE) decoding and dynamic reliability scores (DRSs).This decoder is based on a novel EaE component code decoder, which is able to decode beyond the designed distance of the component code but suffers from an increased miscorrection probability.The DRSs, reflecting the reliability of a codeword bit, are used to detect and avoid miscorrections.Simulation results show that this policy can reduce the miscorrection rate significantly and improves the decoding performance.The decoder requires only ternary message passing and a slight increase of computational complexity compared to iBDD, which makes it suitable for high-speed communication systems.Coding gains of up to 1.2 dB compared to the conventional iBDD decoder are observed.
Digital forensics and cyber forensics investigation: security challenges, limitations, open issues, and future direction

Digital forensics (DF) is the scientific investigation of digital criminal activities, illegal attempts, and cyber-attacks through computer systems. It is becoming a crucial aspect of law enforcement agencies, court law, and business farms to identify, preserve, examine, and analyse digital evidence using proof approve and efficient techniques for eventual demonstration of evidence that help to take further actions. This review paper explores the different methodologies and frameworks related to digital forensics investigation and incident response, and explain the impact of forgery and tampering in the evidence chain-of-custody. Moreover, we highlight a list of popular investigation tools along with the features in terms of distinct prospects. Finally, we have evaluated and examined various applications, implementation research challenges, and limitations. The open research areas and future directions also address which need concern for a better and more efficient digital investigation.