Reviewerly/exHarmony huggingface dataset:
Gaussian states of continuous-variable quantum systems provide universal and versatile reservoir computing

Abstract Quantum reservoir computing aims at harnessing the rich dynamics of quantum systems for machine-learning purposes. It can be used for online time series processing while having a remarkably low training cost. Here, we establish the potential of continuous-variable Gaussian states of linear dynamical systems for quantum reservoir computing. We prove that Gaussian resources are enough for universal reservoir computing. We find that encoding the input into Gaussian states is both a source and a means to tune the nonlinearity of the overall input-output map. We further show that the full potential of the proposed model can be reached by encoding to quantum fluctuations, such as squeezed vacuum, instead of classical fields or thermal fluctuations. Our results introduce a research paradigm for reservoir computing harnessing quantum systems and engineered Gaussian quantum states.
Double Similarity Distillation for Semantic Image Segmentation

The balance between high accuracy and high speed has always been a challenging task in semantic image segmentation. Compact segmentation networks are more widely used in the case of limited resources, while their performances are constrained. In this paper, motivated by the residual learning and global aggregation, we propose a simple yet general and effective knowledge distillation framework called double similarity distillation (DSD) to improve the classification accuracy of all existing compact networks by capturing the similarity knowledge in pixel and category dimensions, respectively. Specifically, we propose a pixel-wise similarity distillation (PSD) module that utilizes residual attention maps to capture more detailed spatial dependencies across multiple layers. Compared with exiting methods, the PSD module greatly reduces the amount of calculation and is easy to expand. Furthermore, considering the differences in characteristics between semantic segmentation task and other computer vision tasks, we propose a category-wise similarity distillation (CSD) module, which can help the compact segmentation network strengthen the global category correlation by constructing the correlation matrix. Combining these two modules, DSD framework has no extra parameters and only a minimal increase in FLOPs. Extensive experiments on four challenging datasets, including Cityscapes, CamVid, ADE20K, and Pascal VOC 2012, show that DSD outperforms current state-of-the-art methods, proving its effectiveness and generality. The code and models will be publicly available.
Multitask Learning for Ship Detection From Synthetic Aperture Radar Images

Ship detection from synthetic aperture radar (SAR) images is inherently subject to the special imaging mechanism of SAR. In recent years, deep-learning-based techniques for detecting objects from optical images have rapidly advanced and promoted the development of SAR image detection technology. However, the strong speckle noise in SAR images degrades low-level feature learning in shallow layers, hindering the higher level learning of semantic features for object detection. In view of the problems encountered in direct end-to-end feature learning for object detection and the close relationship between objects and auxiliary cues, a multitask learning-based object detector (MTL-Det) is proposed in this article to distinguish ships in SAR images. The proposed approach models the ship detection problem, not as a single object detection task, but as three cooperative tasks. The model involves two auxiliary subtasks that are focused on learning object-specific cues (e.g., texture and shape) for the ship detection task, which is constrained by the pseudoground truth generated by the main task. Assisted by auxiliary subtasks, the low-level features are robust to speckle noise and reliably support high-level feature learning. Compared with traditional single-task-based object detectors, more discriminative object-specific features are learned by multitask learning without the extra cost of manual labeling. The experiments conducted in this study help demonstrate the advantages of MTL-Det in improving the ship detection performance on two SAR datasets: high-resolution SAR images dataset and large-scale SAR ship detection dataset-v1.0.
Disentangling Long and Short-Term Interests for Recommendation

Modeling user’s long-term and short-term interests is crucial for accurate recommendation. However, since there is no manually annotated label for user interests, existing approaches always follow the paradigm of entangling these two aspects, which may lead to inferior recommendation accuracy and interpretability. In this paper, to address it, we propose a Contrastive learning framework to disentangle Long and Short-term interests for Recommendation (CLSR) with self-supervision. Specifically, we first propose two separate encoders to independently capture user interests of different time scales. We then extract long-term and short-term interests proxies from the interaction sequences, which serve as pseudo labels for user interests. Then pairwise contrastive tasks are designed to supervise the similarity between interest representations and their corresponding interest proxies. Finally, since the importance of long-term and short-term interests is dynamically changing, we propose to adaptively aggregate them through an attention-based network for prediction. We conduct experiments on two large-scale real-world datasets for e-commerce and short-video recommendation. Empirical results show that our CLSR consistently outperforms all state-of-the-art models with significant improvements: GAUC is improved by over 0.01, and NDCG is improved by over 4%. Further counterfactual evaluations demonstrate that stronger disentanglement of long and short-term interests is successfully achieved by CLSR. The code and data are available at https://github.com/tsinghua-fib-lab/CLSR.
Hybrid Solar Forecasting Method Using Satellite Visible Images and Modified Convolutional Neural Networks

With the increase of solar photovoltaic (PV) permeability in power systems, high-precision PV power forecasting is critical to secure the economic operation of the smart grid. Solar irradiance is the most important factor affecting PV power. Accurately estimating the amount of solar irradiance leads to better production estimates from solar panels. Meanwhile, satellite images provide information on current and future cloud coverage, and are potentially useful in inferring solar irradiance. To accurately capture the effect of cloud on irradiance, this article develops a real-time mapping model between satellite image and solar irradiance. Based on the mapping model, a new ultrashort-term global horizontal irradiance (GHI) forecasting method combining satellite visible images and meteorological information is proposed. First, a modified method is utilized for reducing the influence of the solar zenith angle on pixel intensity and extracting the features in the image processing stage. Then, the cloud cover factors are extracted from satellite visible images by using the modified convolutional neural network. After that, the GHI forecasting model is developed, which is based on the combined use of meteorological information and cloud cover factors. Meanwhile, a cloud motion forecasting method using predicted wind speeds is developed for discussion. The better performance of the proposed method is demonstrated by comparing it with several benchmark models.
Examining the critical factors of computer-assisted audit tools and techniques adoption in the post-COVID-19 period: internal auditors perspective

Purpose In light of the repercussions of the COVID-19 pandemic, electronic auditing otherwise known as computer-assisted audit tools and techniques (CAATTs) has become inevitable to automate the auditing process worldwide. Accordingly, the purpose of this study is to examine the influence of technological, organizational and environmental (TOE) factors on public sector adoption of CAATTs in developing countries such as Jordan under the COVID-19 pandemic conditions. Design/methodology/approach This study used 136 usable responses from the managers of internal audit (IA) of the Jordanian public sector entities. The data collected were analyzed using partial least squares-structural equation modeling. The TOE framework has been used in this study to consider a wide set of TOE factors. Then, this study suggests a CAATTs adoption model that incorporates the related technology factors of the diffusion of innovation theory to environmental and organizational factors. Further, this study contributes to the TOE framework by addressing government regulations, audit bodies’ support and audit task complexity as environmental factors affecting CAATTs adoption in the context of the public sector. Findings The results revealed that for technological factors, only the compatibility affects CAATTs adoption by the IA departments. For organizational factors, organizational readiness, top management support, auditors’ information technology competency and entity size were found to be significant factors. From the environmental factors, both government regulation and audit task complexity influence the CAATTs adoption. Besides, entity size moderates the influence of top management support on the CAATTs adoption in the public sector. Practical implications The findings could highlight the significance of the CAATTs adoption in the public sector institutions (by internal auditors) post-COVID-19, taking into consideration the TOE framework’s factors. Also, the findings are significant for the decision-makers and regulators in declaring new legislation for the electronic IA profession in the Jordanian public sector. Social implications It turns out that the CAATTs adoption in the public sector can definitely enhance their ability to achieve the role of IA in preserving public funds and restricting corrupt practices within the public sector. Originality/value To the best of the authors’ knowledge, this study is one of the first studies that address the professional audit agency support and audit task complexity as environmental factors, as well as the entity size as an organizational factor, that affect CAATTs adoption in the IA department of the public sector.
Estimation of the Volume of the Left Ventricle From MRI Images Using Deep Neural Networks

Segmenting human left ventricle (LV) in magnetic resonance imaging images and calculating its volume are important for diagnosing cardiac diseases. The latter task became the topic of the Second Annual Data Science Bowl organized by Kaggle. The dataset consisted of a large number of cases with only systole and diastole volume labels. We designed a system based on neural networks to solve this problem. It began with a detector to detect the regions of interest (ROI) containing LV chambers. Then a deep neural network named hypercolumns fully convolutional network was used to segment LV in ROI. The 2-D segmentation results were integrated across different images to estimate the volume. With ground-truth volume labels, this model was trained end-to-end. To improve the result, an additional dataset with only segmentation labels was used. The model was trained alternately on these two tasks. We also proposed a variance estimation method for the final prediction. Our algorithm ranked the fourth on the test set in this competition.
Low-Rank Transfer Human Motion Segmentation

Human motion segmentation has great potential in real world applications. Conventional segmentation approaches cluster data with no guidance from prior knowledge, which could easily cause unpredictable segmentation output and decrease the performance. To this end, we seek to improve the humanmotion segmentation performance by fully utilizing pre-existing well-labeled source data. Specifically, we design a new transfer subspace clustering method for motion segmentation with a weighted rank constraint. Specifically, our proposed model obtains representations of both source and target sequences by mitigating their distribution divergence, which allows for more effective knowledge transfer to the target. To guide new representation learning, we designed a novel sequential graph to preserve temporal information residing in both the source and target. Furthermore, a weighted low-rank constraint is added to enforce the graph regularizer and uncover clustering structures within data. Experiments are evaluated on four human motion databases, which prove the enhanced performance and increased stability of our model compared to state-of-the-art baselines.
Cost of Quantum Entanglement Simplified

Quantum entanglement is a key physical resource in quantum information processing that allows for performing basic quantum tasks such as teleportation and quantum key distribution, which are impossible in the classical world. Ever since the rise of quantum information theory, it has been an open problem to quantify entanglement in an information-theoretically meaningful way. In particular, every previously defined entanglement measure bearing a precise information-theoretic meaning is not known to be efficiently computable, or if it is efficiently computable, then it is not known to have a precise information-theoretic meaning. In this Letter, we meet this challenge by introducing an entanglement measure that has a precise information-theoretic meaning as the exact cost required to prepare an entangled state when two distant parties are allowed to perform quantum operations that completely preserve the positivity of the partial transpose. Additionally, this entanglement measure is efficiently computable by means of a semidefinite program, and it bears a number of useful properties such as additivity and faithfulness. Our results bring key insights into the fundamental entanglement structure of arbitrary quantum states, and they can be used directly to assess and quantify the entanglement produced in quantum-physical experiments.
Person Re-Identification Between Visible and Thermal Camera Images Based on Deep Residual CNN Using Single Input

In recent years, numerous studies have been undertaken regarding person re-identification (ReID), an important issue for intelligent surveillance systems. Person ReID, however, is an extremely difficult problem because of variables such as different viewpoints and poses, and varying lighting in person regions in images that have been captured from remote distances. A majority of the studies have been performed for visible-light camera-based person ReID, which can be used only in a limited environment owing to the characteristics of a visible-light camera that are considerably dependent on the illumination. To overcome this problem, studies have been conducted for multimodal camera-based person ReID. However, because the previous studies used two or more input images, the computational complexity was high. This paper proposes a novel person ReID method that simplifies the convolutional neural network (CNN) structure by combining visible-light and thermal images as a single input. This method overcomes the limitation of visible-light camera-based person ReID using both a visible-light and thermal camera. To verify the performance of the proposed method, two open databases, the DBPerson-Recog-DB1, and Sun Yat-sen University multiple modality Re-ID (SYSU-MM01) databases were used. The method proposed in this study demonstrated excellent performance compared to the conventional methods.
NeuGraph: Parallel Deep Neural Network Computation on Large Graphs

Recent deep learning models have moved beyond low dimensional regular grids such as image, video, and speech, to high-dimensional graph-structured data, such as social networks, e-commerce user-item graphs, and knowledge graphs. This evolution has led to large graph-based neural network models that go beyond what existing deep learning frameworks or graph computing systems are designed for. We present NeuGraph, a new framework that bridges the graph and dataflow models to support efficient and scalable parallel neural network computation on graphs. NeuGraph introduces graph computation optimizations into the management of data partitioning, scheduling, and parallelism in dataflow-based deep learning frameworks. Our evaluation shows that, on small graphs that can fit in a single GPU, NeuGraph outperforms state-of-the-art implementations by a significant margin, while scaling to large real-world graphs that none of the existing frameworks can handle directly with GPUs. (Please stay tuned for further updates.)
Edge-Semantic Learning Strategy for Layout Estimation in Indoor Environment

Visual cognition of the indoor environment can benefit from the spatial layout estimation, which is to represent an indoor scene with a 2-D box on a monocular image. In this paper, we propose to fully exploit the edge and semantic information of a room image for layout estimation. More specifically, we present an encoder–decoder network with shared encoder and two separate decoders, which are composed of multiple deconvolution (transposed convolution) layers, to jointly learn the edge maps and semantic labels of a room image. We combine these two network predictions in a scoring function to evaluate the quality of the layouts, which are generated by ray sampling and from a predefined layout pool. Guided by the scoring function, we apply a novel refinement strategy to further optimize the layout hypotheses. Experimental results show that the proposed network can yield accurate estimates of edge maps and semantic labels. By fully utilizing the two different types of labels, the proposed method achieves the state-of-the-art layout estimation performance on the benchmark datasets.
Tracing physical behavior in virtual reality: A narrative review of applications to social psychology

Virtual reality (VR) offers unique benefits to social psychological research, including a high degree of experimental control alongside strong ecological validity, a capacity to manipulate any variable of interest, and an ability to trace the physical, nonverbal behavior of the user in a very fine-grained and automated manner. VR improves upon traditional behavioral measurement techniques (e.g., observation and coding) on several fronts as data collection is covert, continuous, passive, and occurs within a controlled context. The current review synthesizes extant methods for tracing physical behavior in VR, such as gaze tracking and interpersonal distance measurement, and describes how researchers have applied these methods to understand important phenomena within the context of social psychology. To date, primary areas of application have included the assessment of social approach and avoidance, social evaluation and bias, and engagement. The limitations of behavioral tracing methods in VR, as well as future directions for their continued application and extension, are discussed. This narrative review equips readers with a thorough understanding of behavioral tracing methods that can be implemented in VR, their benefits and drawbacks, the insight they may offer into social processes, and future avenues of work for applying emergent technologies to research questions in social psychology.
Securing Land Registration using Blockchain

The current land registration process involves a lot of vulnerabilities and people uses it to cheat the common people and the government. This paper discusses about a secure land registry implemented using blockchain which works on the basis of majority consensus. By implementing the land registry in blockchain, the security issue is resolved to a great extent. The hash value calculated for each block will be unique as it is linked to the hash of the previous block. The algorithm that is used for hashing is SHA256. Along with SHA256, Proof Of Work(PoW) algorithm is also used which makes the information related to each transaction more secure. Message digest that is generated for each block is of fixed size and each hash represents a complete set of transaction within a given block. The proposed land registry blockchain network consists of 12 nodes which calculates the proof of work. Nodes are responsible for verifying a transaction, mining a new block and adding the new block to the blockchain. A total of 200 land transactions are recorded using the blockchain methodology which offers a tamper proof and updated version of land registry. Elliptic curve cryptographic algorithm is used for signature generation which is used for verifying whether the transaction is signed by the owner or not. Merkle tree is used for linking the transactions using hash and in turn reduces the disk usage. The proposed implementation of land registry using blockchain thus offers a 99% reduction in manual effort spent in record keeping.
Domain Adaptation by Joint Distribution Invariant Projections

Domain adaptation addresses the learning problem where the training data are sampled from a source joint distribution (source domain), while the test data are sampled from a different target joint distribution (target domain). Because of this joint distribution mismatch, a discriminative classifier naively trained on the source domain often generalizes poorly to the target domain. In this article, we therefore present a Joint Distribution Invariant Projections (JDIP) approach to solve this problem. The proposed approach exploits linear projections to directly match the source and target joint distributions under the L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -distance. Since the traditional kernel density estimators for distribution estimation tend to be less reliable as the dimensionality increases, we propose a least square method to estimate the L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -distance without the need to estimate the two joint distributions, leading to a quadratic problem with analytic solution. Furthermore, we introduce a kernel version of JDIP to account for inherent nonlinearity in the data. We show that the proposed learning problems can be naturally cast as optimization problems defined on the product of Riemannian manifolds. To be comprehensive, we also establish an error bound, theoretically explaining how our method works and contributes to reducing the target domain generalization error. Extensive empirical evidence demonstrates the benefits of our approach over state-of-the-art domain adaptation methods on several visual data sets.