Reviewerly/exHarmony huggingface dataset:
Geometric origin of intrinsic dark counts in superconducting nanowire single-photon detectors

The dark count is one of the key physical issues for superconducting nanowire single-photon detectors (SNSPDs) that limits various applications for optical quantum information and classical optics. When the bias current approaches the switching current of SNSPDs, the dark count is actually dominated by the intrinsic dark counts (iDCs). However, the origin of iDCs and its relation to constrictions remains unclear for practical SNSPDs. We herein systematically characterize the iDCs of the SNSPDs with and without artificial geometric constrictions by applying the differential readout method. For these devices with constrictions, we have observed distinct Gaussian distributions in the temporal distribution of iDCs, in which the time difference between the distributions is consistent with the geometric distance between constrictions, and the rates of iDCs produced by each constriction are in good agreement with constrictions' widths. With respect to practical SNSPDs, surprisingly, we also observe several Gaussian distributions in the temporal domain and it shows no significant dependence on the devices’ sizes, demonstrating that the iDCs of SNSPDs are mainly dominated by a few specific constrictions.
Attributed graph clustering with multi-task embedding learning

Attributed graph clustering is challenging as it needs to effectively combine both graph structure and node feature information to accomplish node clustering. Recent studies mostly adopt graph neural networks to learn node embeddings, then apply traditional clustering methods to obtain clusters. However, their node embeddings are not specifically designed for clustering. Moreover, most of their loss functions only rely on either structure or feature information, making both kinds of information not fully retained in node embeddings. In this paper, we propose a multi-task embedding learning method (MTEL) for attributed graph clustering, which constructs two prediction tasks in terms of structure and feature based adjacency matrices respectively. To make the node embeddings helpful for the downstream clustering, in each task, we predict the minimum hop number between each pair of nodes in the adjacency matrix, so that the correlation degrees among nodes can be encoded into node embeddings. To improve the performance of the prediction task, we regularize the model parameters in these two tasks via ℓ2,1 norm, through which the model parameters can be jointly learned. Experiments on real attributed graphs show that MTEL is superior for attributed graph clustering over state-of-the-art methods.
SensiMix: Sensitivity-Aware 8-bit index &amp; 1-bit value mixed precision quantization for BERT compression

Given a pre-trained BERT, how can we compress it to a fast and lightweight one while maintaining its accuracy? Pre-training language model, such as BERT, is effective for improving the performance of natural language processing (NLP) tasks. However, heavy models like BERT have problems of large memory cost and long inference time. In this paper, we propose SensiMix (Sensitivity-Aware Mixed Precision Quantization), a novel quantization-based BERT compression method that considers the sensitivity of different modules of BERT. SensiMix effectively applies 8-bit index quantization and 1-bit value quantization to the sensitive and insensitive parts of BERT, maximizing the compression rate while minimizing the accuracy drop. We also propose three novel 1-bit training methods to minimize the accuracy drop: Absolute Binary Weight Regularization, Prioritized Training, and Inverse Layer-wise Fine-tuning. Moreover, for fast inference, we apply FP16 general matrix multiplication (GEMM) and XNOR-Count GEMM for 8-bit and 1-bit quantization parts of the model, respectively. Experiments on four GLUE downstream tasks show that SensiMix compresses the original BERT model to an equally effective but lightweight one, reducing the model size by a factor of 8× and shrinking the inference time by around 80% without noticeable accuracy drop.
A fractional variational image denoising model with two-component regularization terms

Image denoising is to recover true image from noisy image. Many image deonising models are proposed during the last decades. Some models preserve the margin of tissue, i.e., TV model, while the others, i.e., LLT model, prefer smooth solutions. By decomposing true image into cartoon part and texture part, we propose a fractional image denoising model with two-component regularization terms. Setting some appropriate parameters, the proposed model can deal with both smooth and non-smooth image denosing problems. The existence and uniqueness of solution for the variational model are proved. Moreover, a Split-Bregman(S-B) based numerical algorithm to solve this model is also proposed to validate the theoretical results. Numerical tests show that the proposed model can produce competitive denoising result to the other three published models.
Scalable and Transferable Reinforcement Learning for Multi-Agent Mixed Cooperative–Competitive Environments Based on Hierarchical Graph Attention

Most previous studies on multi-agent systems aim to coordinate agents to achieve a common goal, but the lack of scalability and transferability prevents them from being applied to large-scale multi-agent tasks. To deal with these limitations, we propose a deep reinforcement learning (DRL) based multi-agent coordination control method for mixed cooperative-competitive environments. To improve scalability and transferability when applying in large-scale multi-agent systems, we construct inter-agent communication and use hierarchical graph attention networks (HGAT) to process the local observations of agents and received messages from neighbors. We also adopt the gated recurrent units (GRU) to address the partial observability issue by recording historical information. The simulation results based on a cooperative task and a competitive task not only show the superiority of our method, but also indicate the scalability and transferability of our method in various scale tasks.
Explaining a deep learning based breast ultrasound image classifier with saliency maps

Deep neural networks have achieved good performance in breast mass classification in ultrasound imaging. However, their usage in clinical practice is still limited due to the lack of explainability of decisions conducted by the networks. In this study, to address the explainability problem, we generated saliency maps indicating ultrasound image regions important for the network's classification decisions.Ultrasound images were collected from 272 breast masses, including 123 malignant and 149 benign. Transfer learning was applied to develop a deep network for breast mass classification. Next, the class activation mapping technique was used to generate saliency maps for each image. Breast mass images were divided into three regions: the breast mass region, the peritumoral region surrounding the breast mass, and the region below the breast mass. The pointing game metric was used to quantitatively assess the overlap between the saliency maps and the three selected US image regions.Deep learning classifier achieved the area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity of 0.887, 0.835, 0.801, and 0.868, respectively. In the case of the correctly classified test US images, analysis of the saliency maps revealed that the decisions of the network could be associated with the three selected regions in 71% of cases.Our study is an important step toward better understanding of deep learning models developed for breast mass diagnosis. We demonstrated that the decisions made by the network can be related to the appearance of certain tissue regions in breast mass US images.
Control with Vergence Eye Movement in Augmented Reality See-Through Vision

Augmented Reality (AR) see-through vision has become a recent research focus since it enables the user to see through a wall and see the occluded objects. Most existing works only used common modalities to control the display for see-through vision, e.g., button clicking and speech control. However, we use visual system to observe see-through vision. Using an addition interaction channel will distract the user and degrade the user experience. In this paper, we propose a novel interaction method using vergence eye movement for controlling see-through vision in AR. Specifically, we first customize eye cameras and design gaze depth estimation method for Microsoft HoloLens 2. With our algorithm, fixation depth can be computed from the vergence, and used to manage the see-through vision. We also propose two control techniques of gaze vergence. The experimental results show that the gaze depth estimation method is efficient. The difference cannot be found between these two modalities in terms of completion time and the number of successes.
Scale-aware attention network for weakly supervised semantic segmentation

Weakly supervised semantic segmentation (WSSS) using image-level labels greatly alleviates the burden of obtaining large amounts of pixel-wise annotations. To create pseudo segmentation labels, most WSSS algorithms rely on the local response regions in the class activation maps (CAMs). However, such activation maps only focus on the local discriminative parts of the object, because the classification network does not require the entire object to optimize the objective function. To enhance the network's ability to focus more on non-discriminative parts of the object and generate high-quality pseudo-masks, the Scale-aware Attention Network (SAN) is proposed. Specifically, a pyramidal attention module is introduced to propagate discriminative information to adjacent object regions by adaptively selecting contextual features from the convolutional pyramid with varied filter scales. A multi-scale prediction fusion structure with a joint loss is proposed to make better use of the complementary information of localization maps in different scales. The dense and integral localization maps are obtained in the inference stage by weighted fusion of the multi-scale predictions, which are then used to train segmentation models. This novel SAN has demonstrated its effectiveness in a series of experiments. It has achieved a state-of-the-art result of 71.9% mIoU on the PASCAL VOC 2012 segmentation test set compared with other approaches under the same level of supervision.
Multi‐scale GAN with residual image learning for removing heterogeneous blur

Abstract Processing images with heterogeneous blur remains challenging due to multiple degradation aspects that could affect structural properties. This study proposes a deep learning-based multi-scaled generative adversarial network (GAN) with residual image learning to process variant and in-variant blur. Different scaled images with corresponding gradients are concatenated as a multi-channel single input for the proposed GAN. Residual- and dense-networks are combined to explore salient features in the bottleneck section while addressing the vanishing gradient problem. A hybrid content loss function with a gradient penalty minimises the error between generated and ground truth images. Due to structure sparsity, the generated output may lose some information that leads to artifacts. Residual image learning with dilation and end-to-end training is used to resolve this issue by recovering high-resolution anatomical details. Three different datasets: GoPro, Köhler, and Lai, with variant and in-variant blur, are used to perform qualitative and quantitative analyses. Experiments show the proposed method is effective in reducing blur while preserving structural properties compared to multiple preprocessing techniques for image analysis. Moreover, the consistently improved performance over multiple publicly available datasets validates the merits of the proposed method for large data analysis.
PseudoJumpOn: Jumping onto Steps in Virtual Reality

Jumping onto steps is a promising action for creating an instant height movement in VR, but installing physical steps is impractical. We propose PseudoJumpOn, a novel locomotion technique using a common VR setup that allows the user to experience virtual step-up jumping motion by applying two types of viewpoint-manipulation methods to a physical jump on a flat floor. The core idea is to replicate the physical characteristics of ascending jumps, and thus we designed two viewpoint-manipulation methods: gain manipulation, which differentiates the ascent and descent height, and peak shifting, which delays the peak timing. We conducted a user study asking participants (N = 20) to experience two-legged step-up jumps onto 0.2&#x2013;0.8-m heights in VR as the two methods were applied in combination (gain manipulation: five conditions where the ascending gain was 1.0&#x2013;5.0; peak shifting: four conditions where the peak timing in VR was delayed by 0&#x2013;1.0 ratios of the original timing). The results showed that the participants in most conditions felt positively in terms of reality and naturalness of actually jumping onto steps, even though knowing no physical steps existed. In addition, subsequent analyses also derived practical guidelines for determining the appropriate gains and the potential use of peak shifting to achieve a natural step-up jumping experience.
Content-based Medical Image Retrieval based on Deep Features Expansion

The collections of various digital image databases have significantly grown and many users have recognized that finding and recovering important images from large collections is a difficult task. Where the success of any image retrieval system is heavily dependent on the feature extraction capacity of the feature descriptor, therefore successful and effective retrieval method has been developed to provide an effective and rapid search and retrieval process. We present a unique deep learning-based approach for extracting high-level and compact features from medical images in this paper. To capture the discriminative features of medical images, we use Residual Networks (ResNets), a popular multi-layered deep neural network. The query is then broadened by reformulating the query image using the mean values for deep features from each database class&#x0027;s top-ranking images. Two publicly available databases in various forms were used to evaluate the performance of our technique. These studies demonstrated the benefits of our proposed strategy, with retrieval accuracy greatly improved.
Forward privacy multikeyword ranked search over encrypted database

Dynamic searchable encryption (SE) aims at achieving varied search function over encrypted database in dynamic setting, which is a trade-off in efficiency, security, and functionality. Recent work proposes a file-injection attack which can successfully attack by utilizing some information leaked in the update process. To mitigate this attack, some SE schemes with forward privacy are proposed. However, these schemes are designed to achieve single keyword or conjunctive keyword search, which cannot support multikeyword search. Moreover, these schemes do not consider the function of results ranking. In this paper, we propose a forward privacy multikeyword ranked search scheme over encrypted database. We design a forward privacy multikeyword search scheme based on the classic MRSE scheme. Our scheme makes the cloud cannot obtain the actual match results of the past query with the newly updated files by adding the well-chosen dummy elements to the original index and query vectors. We rank the search results based on the matched keyword number and the T F Ã— I D F $TF\times IDF$ rule in the dynamic setting. Our scheme uses only the symmetric encryption primitive. We implement our scheme for COVID-19 data set and the experimental evaluation results show that the proposed scheme is secure and efficient.
School students creating a virtual reality learning resource for children

There is increasing interest in shifting the focus of virtual reality (VR) in education from consumption activities to content creation where students can produce and share their own virtual environments. This paper reports on a pilot study where junior secondary science students created a 360° VR learning resource for primary (elementary) school students. The study used a mixed methodology, participatory research approach to explore learning outcomes for students. While technical set-up and time constraints affected the research, secondary students generally enjoyed the experience with the teacher observing good levels of engagement. The virtual environments produced by female students were graded higher than their male classmates. There was no increase in the content knowledge of primary school students after viewing the VR learning resource but most reported good systems usability. The pilot study indicated that having students create learning content for an authentic audience, such as younger students, is feasible with the potential to generate positive learning outcomes if organizational and time constraints can be addressed.
Symbolic identification of shared memory based bank conflicts for GPUs

Graphic processing units (GPUs) are routinely used for general purpose computations to improve performance. To achieve the sought performance gains, care must be invested in fine tuning the way GPU programs interact with the underlying architecture, accounting for the shared memory bank conflicts and the entailed shared memory transactions. Uncovering inputs leading to particular bank conflicts can turn out to be quite hard given the intricacy of the access patterns and their dependence on the inputs. We propose a symbolic execution based framework to systematically uncover shared memory bank conflicts, to propose inputs to realize a given number of shared memory transactions, and to refute the existence of such inputs if the number of shared memory transactions is impossible to achieve during the execution. This allows programmers to more formally reason about the shared memory conflicts and to validate their impact on performance and security. We have implemented our approach and report on our experiments to explore its usefulness towards performance enhancement and quantifying shared memory side-channel leakage in security applications.
Face recognition under mask-wearing based on residual inception networks

Purpose This paper proposes a solution for recognizing human faces under mask-wearing. The lower part of human face is occluded and could not be used in the learning process of face recognition. So, the proposed solution is developed to recognize human faces on any available facial components which could be varied depending on wearing or not wearing a mask. Design/methodology/approach The proposed solution is developed based on the FaceNet framework, aiming to modify the existing facial recognition model to improve the performance of both scenarios of mask-wearing and without mask-wearing. Then, simulated masked-face images are computed on top of the original face images, to be used in the learning process of face recognition. In addition, feature heatmaps are also drawn out to visualize majority of parts of facial images that are significant in recognizing faces under mask-wearing. Findings The proposed method is validated using several scenarios of experiments. The result shows an outstanding accuracy of 99.2% on a scenario of mask-wearing faces. The feature heatmaps also show that non-occluded components including eyes and nose become more significant for recognizing human faces, when compared with the lower part of human faces which could be occluded under masks. Originality/value The convolutional neural network based solution is tuned up for recognizing human faces under a scenario of mask-wearing. The simulated masks on original face images are augmented for training the face recognition model. The heatmaps are then computed to prove that features generated from the top half of face images are correctly chosen for the face recognition.
Structure-conditioned adversarial learning for unsupervised domain adaptation

Unsupervised domain adaptation (UDA) typically carries out knowledge transfer from a label-rich source domain to an unlabeled target domain by adversarial learning. In principle, existing UDA approaches mainly focus on the global distribution alignment between domains while ignoring the intrinsic local distribution properties. Motivated by this observation, we propose an end-to-end structure-conditioned adversarial learning scheme (SCAL) that is able to preserve the intra-class compactness during domain distribution alignment. By using local structures as structure-aware conditions, the proposed scheme is implemented in a structure-conditioned adversarial learning pipeline. The above learning procedure is iteratively performed by alternating between local structures establishment and structure-conditioned adversarial learning. Experimental results demonstrate the effectiveness of the proposed scheme in UDA scenarios.
Image Desaturation for SDO/AIA Using Mixed Convolution Network

Abstract The Atmospheric Imaging Assembly (AIA) onboard the Solar Dynamics Observatory (SDO) provides full-disk solar images with high temporal cadence and spatial resolution over seven extreme ultraviolet (EUV) wave bands. However, as violent solar flares happen, images captured in EUV wave bands may have saturation in active regions, resulting in signal loss. In this paper, we propose a deep learning model to restore the lost signal in saturated regions by referring to both unsaturated/normal regions within a solar image and statistical probability model of massive normal solar images. The proposed model, namely mixed convolution network (MCNet), is established over conditional generative adversarial network (GAN) and the combination of partial convolution (PC) and validness migratable convolution (VMC). These two convolutions were originally proposed for image inpainting. In addition, they are implemented only on unsaturated/valid pixels, followed by certain compensation to compensate the deviation of PC/VMC relative to normal convolution. Experimental results demonstrate that the proposed MCNet achieves favorable desaturated results for solar images and outperforms the state-of-the-art methods both quantitatively and qualitatively.
Shifting towards non-anthropocentrism: In dialogue with speculative design futures

We are living in an earth crisis and the world at large is demanding profound change. Some researchers claim that one necessary shift to accomplish this change is that from anthropocentrism to non-anthropocentrism. This shift has, e.g., been explored in design research. This paper aims to give voice to the design processes and experiences along The Age of Entanglement project, a project in which designers collaborated with professionals from diverse fields ranging from philosophy and biology to space engineering, to further their understanding of what a shift to non-anthropocentrism might entail. As we interviewed and observed these designers in their discussions a number of thematic challenges were touched upon: How to engage with temporalities, how to collaborate productively across diverse disciplines, how to find appropriately inclusive language for expressing ideas, how to express entanglement through concrete and clear examples as well as how to maintain a productive humble and open-minded overall disposition in the designers' sense.