Reviewerly/exHarmony huggingface dataset:
A memristor-based analogue reservoir computing system for real-time and power-efficient signal processing

Reservoir computing offers a powerful neuromorphic computing architecture for spatiotemporal signal processing. To boost the power efficiency of the hardware implementations of reservoir computing systems, analogue devices and components—including spintronic oscillators, photonic modules, nanowire networks and memristors—have been used to partially replace the elements of fully digital systems. However, the development of fully analogue reservoir computing systems remains limited. Here we report a fully analogue reservoir computing system that uses dynamic memristors for the reservoir layer and non-volatile memristors for the readout layer. The system can efficiently process spatiotemporal signals in real time with three orders of magnitude lower power consumption than digital hardware. We illustrate the capabilities of the system using temporal arrhythmia detection and spatiotemporal dynamic gesture recognition tasks, achieving accuracies of 96.6% and 97.9%, respectively. Our memristor-based fully analogue reservoir computing system could be of use in edge computing applications that require extremely low power and hardware cost. Dynamic and non-volatile memristors can be used to create hardware-based reservoir and readout layers in artificial neural networks, providing a fully analogue signal processing chain for efficient data classification.
Experimental measurement-device-independent type quantum key distribution with flawed and correlated sources

The security of quantum key distribution (QKD) is severely threatened by discrepancies between realistic devices and theoretical assumptions. Recently, a significant framework called the reference technique was proposed to provide security against arbitrary source flaws under current technology such as state preparation flaws, side channels caused by mode dependencies, the Trojan horse atttacks and pulse correlations. Here, we adopt the reference technique to prove security of an efficient four-phase measurement-device-independent QKD using laser pulses against potential source imperfections. We present a characterization of source flaws and connect them to experiments, together with a finite-key analysis against coherent attacks. In addition, we demonstrate the feasibility of our protocol through a proof-of-principle experimental implementation and achieve a secure key rate of 253 bps with a 20 dB channel loss. Compared with previous QKD protocols with imperfect devices, our study considerably improves both the secure key rate and the transmission distance, and shows application potential in the practical deployment of secure QKD with device imperfections.
Hyper-sausage coverage function neuron model and learning algorithm for image classification

Recently, deep neural networks (DNNs) promote mainly by network architectures and loss functions; however, the development of neuron models has been quite limited. In this study, inspired by the mechanism of human cognition, a hyper-sausage coverage function (HSCF) neuron model possessing a high flexible plasticity. Then, a novel cross-entropy and volume-coverage (CE_VC) loss is defined, which compresses the volume of the hyper-sausage to the hilt, and helps alleviate confusion among different classes, thus ensuring the intra-class compactness of the samples. Finally, a divisive iteration method is introduced, which considers each neuron model as a weak classifier, and iteratively increases the number of weak classifiers. Thus, the optimal number of the HSCF neuron is adaptively determined and an end-to-end learning framework is constructed. In particular, to improve the classification performance, the HSCF neuron can be applied to classical DNNs. Comprehensive experiments on eight datasets in several domains demonstrate the effectiveness of the proposed method. The proposed method exhibits the feasibility of boosting DNNs with neuron plasticity and provides a novel perspective for further developments in DNNs. The source code is available at https://github.com/Tough2011/HSCFNet.git .
Dense Depth Priors for Neural Radiance Fields from Sparse Input Views

Neural radiance fields (NeRF) encode a scene into a neural representation that enables photo-realistic rendering of novel views. However, a successful reconstruction from RGB images requires a large number of input views taken under static conditions — typically up to a few hundred images for room-size scenes. Our method aims to synthesize novel views of whole rooms from an order of magnitude fewer images. To this end, we leverage dense depth priors in order to constrain the NeRF optimization. First, we take advantage of the sparse depth data that is freely available from the structure from motion (SfM) preprocessing step used to estimate camera poses. Second, we use depth completion to convert these sparse points into dense depth maps and uncertainty estimates, which are used to guide NeRF optimization. Our method enables data-efficient novel view synthesis on challenging indoor scenes, using as few as 18 images for an entire scene.
Overview of the 8th Workshop on Asian Translation

This paper presents the results of the shared tasks from the 8th workshop on Asian translation (WAT2021).For the WAT2021, 28 teams participated in the shared tasks and 24 teams submitted their translation results for the human evaluation.We also accepted 5 research papers.About 2,100 translation results were submitted to the automatic evaluation server, and selected submissions were manually evaluated.
Affect-Driven Dialog Generation

Pierre Colombo, Wojciech Witon, Ashutosh Modi, James Kennedy, Mubbasir Kapadia. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
IDTxl: The Information Dynamics Toolkit xl: a Python package for the efficient analysis of multivariate information dynamics in networks

github.com/pwollstadt/IDTxl).
Computer-aided diagnosis for breast cancer classification using deep neural networks and transfer learning

Many developed and non-developed countries worldwide suffer from cancer-related fatal diseases. In particular, the rate of breast cancer in females increases daily, partially due to unawareness and undiagnosed at the early stages. A proper first breast cancer treatment can only be provided by adequately detecting and classifying cancer during the very early stages of its development. The use of medical image analysis techniques and computer-aided diagnosis may help the acceleration and the automation of both cancer detection and classification by also training and aiding less experienced physicians. For large datasets of medical images, convolutional neural networks play a significant role in detecting and classifying cancer effectively. This article presents a novel computer-aided diagnosis method for breast cancer classification (both binary and multi-class), using a combination of deep neural networks (ResNet 18, ShuffleNet, and Inception-V3Net) and transfer learning on the BreakHis publicly available dataset. Our proposed method provides the best average accuracy for binary classification of benign or malignant cancer cases of 99.7%, 97.66%, and 96.94% for ResNet, InceptionV3Net, and ShuffleNet, respectively. Average accuracies for multi-class classification were 97.81%, 96.07%, and 95.79% for ResNet, Inception-V3Net, and ShuffleNet, respectively.
HCFNN: High-order coverage function neural network for image classification

Recent advances in deep neural networks (DNNs) have mainly focused on innovations in network architecture and loss function. In this paper, we introduce a flexible high-order coverage function (HCF) neuron model to replace the fully-connected (FC) layers. The approximation theorem and proof for the HCF are also presented to demonstrate its fitting ability. Unlike the FC layers, which cannot handle high-dimensional data well, the HCF utilizes weight coefficients and hyper-parameters to mine underlying geometries with arbitrary shapes in an n-dimensional space. To explore the power and potential of our HCF neuron model, a high-order coverage function neural network (HCFNN) is proposed, which incorporates the HCF neuron as the building block. Moreover, a novel adaptive optimization method for weights and hyper-parameters is designed to achieve effective network learning. Comprehensive experiments on nine datasets in several domains validate the effectiveness and generalizability of the HCF and HCFNN. The proposed method provides a new perspective for further developments in DNNs and ensures wide application in the field of image classification. The source code is available at https://github.com/Tough2011/HCFNet.git
The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink

Machine learning (ML) workloads have rapidly grown, raising concerns about their carbon footprint. We show four best practices to reduce ML training energy and carbon dioxide emissions. If the whole ML field adopts best practices, we predict that by 2030, total carbon emissions from training will decline.
Challenges and opportunities on AR/VR technologies for manufacturing systems in the context of industry 4.0: A state of the art review

Manufacturing industries are currently experiencing the fourth revolution with penetration of new immersive technologies extremely focused on the flexible manufacturing and human machine interaction. The immersive technologies such as Augmented Reality (AR) and Virtual Reality (VR) have received much attention among the manufacturing industries to achieve mass production and mass customization for different product variants. This paper demonstrates the research progress and developments in the AR/VR technologies for product design and evaluation, Repair & Maintenance, Assembly, Warehouse management, Quality control, Plant layout and CNC simulation. The critical challenges in AR/VR technology implementation at hardware and software level and opportunities for the further developments are discussed.
HumanNeRF: Free-viewpoint Rendering of Moving People from Monocular Video

We introduce a free-viewpoint rendering method - HumanNeRF - that works on a given monocular video of a human performing complex body motions, e.g. a video from YouTube. Our method enables pausing the video at any frame and rendering the subject from arbitrary new camera viewpoints or even a full 360-degree camera path for that particular frame and body pose. This task is particularly challenging, as it requires synthesizing photorealistic details of the body, as seen from various camera angles that may not exist in the input video, as well as synthesizing fine details such as cloth folds and facial appearance. Our method optimizes for a volumetric representation of the person in a canonical T-pose, in concert with a motion field that maps the estimated canonical representation to every frame of the video via backward warps. The motion field is decomposed into skeletal rigid and non-rigid motions, produced by deep networks. We show significant performance improvements over prior work, and compelling examples of free-viewpoint renderings from monocular video of moving humans in challenging uncontrolled capture scenarios.
InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions

Compared to the great progress of large-scale vision transformers (ViTs) in recent years, large-scale models based on convolutional neural networks (CNNs) are still in an early state. This work presents a new large-scale CNN-based foundation model, termed InternImage, which can obtain the gain from increasing parameters and training data like ViTs. Different from the recent CNNs that focus on large dense kernels, InternImage takes deformable convolution as the core operator, so that our model not only has the large effective receptive field required for downstream tasks such as detection and segmentation, but also has the adaptive spatial aggregation conditioned by input and task information. As a result, the proposed InternImage reduces the strict inductive bias of traditional CNNs and makes it possible to learn stronger and more robust patterns with large-scale parameters from massive data like ViTs. The effectiveness of our model is proven on challenging benchmarks including ImageNet, COCO, andADE20K. It is worth mentioning that InternImage-H achieved a new record 65.4 mAP on COCO test-dev and 62.9 mIoU on ADE20K, outperforming current leading CNNs and ViTs.
Deep Learning for Channel Estimation: Interpretation, Performance, and Comparison

Deep learning (DL) has emerged as an effective tool for channel estimation in wireless communication systems, especially under some imperfect environments. However, even with such unprecedented success, DL methods are often regarded as black boxes and are lack of explanations on their internal mechanisms, which severely limits their further improvement and extension. In this paper, we present preliminary theoretical analysis on DL based channel estimation for single-input multiple-output (SIMO) systems to understand and interpret its internal mechanisms. As deep neural network (DNN) with rectified linear unit (ReLU) activation function is mathematically equivalent to a piecewise linear function, the corresponding DL estimator can achieve universal approximation to a large family of functions by making efficient use of piecewise linearity. We demonstrate that DL based channel estimation does not restrict to any specific signal model and asymptotically approaches to the minimum mean-squared error (MMSE) estimation in various scenarios without requiring any prior knowledge of channel statistics. Therefore, DL based channel estimation outperforms or is at least comparable with traditional channel estimation, depending on the types of channels. Simulation results confirm the accuracy of the proposed interpretation and demonstrate the effectiveness of DL based channel estimation under both linear and nonlinear signal models.
Human Short Long-Term Cognitive Memory Mechanism for Visual Monitoring in IoT-Assisted Smart Cities

In the industry 4.0 era, the visualization and real-time automatic monitoring of smart cities supported by the Internet of Things is becoming increasingly important. The use of filtering algorithms in smart city monitoring is a feasible method for this purpose. However, maintaining fast and accurate monitoring in complex surveillance environments with restricted resources remains a major challenge. Since the cognitive theory in visual monitoring is difficult to realize in practice, efficient monitoring of complex environments is accordingly hard to be achieved. Moreover, current monitoring methods do not consider the particularities of the human cognitive system, so the remonitoring ability of the process/target is weak in case of monitoring failure by the monitoring system. To overcome these issues, this article proposes a novel human short-long cognitive memory mechanism for video surveillance in smart cities. In this mechanism, a memory with a high reliability target is used as a “long-term memory,” whereas a memory with a low reliability target is used as a “short-term memory.” During the monitoring process, the “short-term memory” and “long-term memory” alternation strategy is combined with the stored target appearance characteristics, ensuring that the original model in the memory will not be contaminated or mislaid by changes in the external environment (occlusion, fast motion, motion blur, and background clutter). Extensive simulations showcase that the algorithm proposed in this article not only improves the monitoring speed without hindering its real-time operation but also monitors and traces the monitored target accurately, ultimately improving the robustness of the detection in complex scenery, and enabling its application to IoT-assisted smart cities.
How digitalised are vocational teachers? Assessing digital competence in vocational education and looking at its underlying factors

Teachers' digital competence (TDC) is an important condition for the effective integration of technologies in education, and it depends on personal and context-related factors. Different frameworks and instruments have been developed to measure TDC. However, research on digital competence in vocational education and training (VET) is still scarce. Therefore, this study aims: (a) to provide a snapshot of the status quo of digital competence on a sample of 1692 Swiss VET teachers, both using an assessment instrument that relies on previous validated questionnaires, and at the same time accounting for VET-related specificities; (b) to examine possible differences in digital competence depending on the VET teachers' profiles (i.e. professional baccalaureate, professional subject and general education teachers); and (c) to investigate whether personal and context-related factors play a similar role in VET like in other educational settings. The results confirm the validity of the 10-dimensional structure of the TDC assessment instrument. Compared with previous results in other educational domains, our findings indicate that VET teachers place themselves at a similar degree of digital competence, with a few differences across profiles in some of the sub-competences. Moreover, multiple regression analysis highlights the main role of attitude towards technology and digital tool use frequency among the personal factors that contribute to TDC development. The teachers’ workload, rarely considered in previous studies, is also a relevant factor. For the context-related factors, curriculum support is the element with the largest effect on TDC, although it has a smaller impact than the personal factors.
Floquet metamaterials

Abstract Recent progress in nanophotonics and material science has inspired a strong interest in optically-induced material dynamics, opening new research directions in the distinct fields of Floquet matter and time metamaterials. Floquet phenomena are historically rooted in the condensed matter community, as they exploit periodic temporal drives to unveil novel phases of matter, unavailable in systems at equilibrium. In parallel, the field of metamaterials has been offering a platform for exotic wave phenomena based on tailored materials at the nanoscale, recently enhanced by incorporating time variations and switching as new degrees of freedom. In this Perspective, we connect these research areas and describe the exciting opportunities emerging from their synergy, hinging on giant wave-matter interactions enabled by metamaterials and on the exotic wave dynamics enabled by Floquet and parametric phenomena. We envision Floquet metamaterials in which nontrivial modulation dynamics, and their interplay with tailored material dispersion and nontrivial material properties such as anisotropy, non-Hermiticity and nonreciprocity, introduce a plethora of novel opportunities for wave manipulation and control.
Radar sensor network resource allocation for fused target tracking: A brief review

Traditional networked radar fusion technology adopts an open-loop signal processing manner, in which multiple nodes independently detect or track target first, and then send their raw data or processed measurements to the fusion center for target state estimation. In such a working manner, each radar sensor operates within predetermined rules, adopts fixed transmit and receiving mode for changeable target tracking environment, and thus will definitely cause an insufficient use of its limited resource. Resource allocation (RA) has emerged as a promising technology to exploit the limited radar resource in a more efficient way by allocating the available resource parameters w.r.t. multiple nodes collaboratively in view of the fused target tracking performance. The goal of this paper is to provide a brief overview of the RA methods for fused target tracking in radar sensor network. We mainly investigate the existing RA works and divide them into two types, namely tracking quality constrained RA type and performance driven RA type. Solution techniques for both types appeared in the literatures are also summarized. Finally, some potential future research directions in this field are discussed.
Image Reconstruction With Deep CNN for Mirrored Aperture Synthesis

In mirrored aperture synthesis (MAS), the existing brightness temperature image reconstruction methods include inverse cosine transform and impulse matrix reconstruction methods. However, the quality of the MAS brightness temperature images reconstructed by the existing methods is still poor and needs to be improved. This article proposes a method of MAS brightness temperature image reconstruction with deep convolutional neural network (CNN). The network includes two fully connected (FC) layers, multiple convolutional layers, and deconvolutional layers, which realize the image reconstruction for MAS. This method uses deep CNN to learn the MAS image reconstruction mapping and system errors, so as to improve the performance of the brightness temperature image reconstruction. Both simulation and experimental results verify that the performance of the proposed MAS-CNN method is better than the existing MAS image reconstruction methods.
Vision Transformers for Classification of Breast Ultrasound Images

Medical ultrasound (US) imaging has become a prominent modality for breast cancer imaging due to its ease of use, low cost, and safety. In the past decade, convolutional neural networks (CNNs) have emerged as the method of choice in vision applications and have shown excellent potential in the automatic classification of US images. Despite their success, their restricted local receptive field limits their ability to learn global context information. Recently, Vision Transformer (ViT) designs, based on self-attention between image patches, have shown great potential to be an alternative to CNNs. In this study, for the first time, we utilize ViT to classify breast US images using different augmentation strategies. We also adopted a weighted cross-entropy loss function since breast ultrasound datasets are often imbalanced. The results are provided as classification accuracy and Area Under the Curve (AUC) metrics, and the performance is compared with the SOTA CNNs. The results indicate that the ViT models have comparable efficiency with or even better than the CNNs in the classification of US breast images. Clinical relevance- This work shows the potential of Vision Transformers in the automatic classification of masses in breast ultrasound, which helps clinicians diagnose and make treatment decisions more precisely.
An efficient image segmentation method for skin cancer imaging using improved golden jackal optimization algorithm

Skin cancer is one of the worst cancers nowadays that poses a severe threat to the health and safety of individuals. Therefore, skin cancer classification and early diagnosis are recommended to preserve human life. Multilevel thresholding image segmentation is well-known and influential technique for extracting regions of interest from skin cancer images to improve the classification process. Therefore, this paper proposes an efficient version of the recently developed golden jackal optimization (GJO) algorithm, the opposition-based golden jackal optimizer (IGJO). The IGJO algorithm is used to solve the multilevel thresholding problem using Otsu's method as an objective function. The proposed algorithm is compared with seven other meta-heuristic algorithms: whale optimization algorithm, seagull optimization algorithm, salp swarm algorithm, Harris hawks optimization, artificial gorilla troops optimizer, marine predators' algorithms, and original GJO algorithm. The performance of the proposed algorithm is evaluated using four popular performance measures: peak signal-to-noise ratio, structure similarity index, feature similarity index, and mean square error. Experimental results show that the proposed algorithm outperforms other alternative algorithms in terms of PSNR, SSIM, FSIM, and MSE segmentation metrics and effectively resolves the segmentation problem.