Reviewerly/exHarmony huggingface dataset:
Novelty competes with saliency for attention

A highly debated question in attention research is to what extent attention is biased by bottom-up factors such as saliency versus top-down factors as governed by the task. Visual search experiments in which participants are briefly familiarized with the task and then see a novel stimulus unannounced and for the first time support yet another factor, showing that novel and surprising features attract attention. In the present study, we tested whether gaze behavior as an indicator for attentional prioritization can be predicted accurately within displays containing both salient and novel stimuli by means of a priority map that assumes novelty as an additional source of activation. To that aim, we conducted a visual search experiment where a color singleton was presented for the first time in the surprise trial and manipulated the color-novelty of the remaining non-singletons between participants. In one group, the singleton was the only novel stimulus (“one-new”), whereas in another group, the non-singleton stimuli were likewise novel (“all-new”). The surprise trial was always target absent and designed such that top-down prioritization of any color was unlikely. The results show that the singleton in the all-new group captured the gaze less strongly, with more early fixations being directed to the novel non-singletons. Overall, the fixation pattern can accurately be explained by noisy priority maps where saliency and novelty compete for gaze control.
Transmission of quantum information through quantum fields

We investigate the quantum channel consisting of two localized quantum systems that communicate through a scalar quantum field. We choose a scalar field rather than a tensor or vector field, such as the electromagnetic field, in order to isolate the situation where the qubits are carried by the field amplitudes themselves rather than, for example, by encoding qubits in the polarization of photons. We find that suitable protocols for this type of quantum channel require the careful navigation of several constraints, such as the no-cloning principle, the strong Huygens principle and the tendency of short field-matter couplings to be entanglement breaking. We nonperturbatively construct a protocol for such a quantum channel that possesses maximal quantum capacity.
Wavelet-Based Enhanced Medical Image Super Resolution

Low-resolution medical images can seriously interfere with the medical diagnosis, and poor image quality can lead to loss of detailed information.Therefore, improving the quality of medical images and accelerating the reconstruction is of particular importance for diagnosis.To solve this problem, we propose a wavelet-based mini-grid network medical image super-resolution (WMSR) method, which is similar to the three-layer hidden-layer-based super-resolution convolutional neural network (SRCNN) method.Due to the amplification characteristics of wavelets, a stationary wavelet transform (SWT) is used instead of a discrete wavelet transform (DWT).Also, due to the nature of redundant (scale-by-scale) wavelets, it is possible to retain additional information about the image and restore high-resolution images in detail.For a large amount of training data, wavelet sub-band images, including approximation and frequency subbands are combined into a predefined full-scale factor.The mapping between the wavelet sub-band image and its approximate image is then determined.In order to ensure the reproducibility of the image, a method of adding a sub-pixel layer is proposed to realize the hidden layer, and replacing the small mini-grid-network on the hidden layer is of considerable significance to speed up the image recovery speed.Experimental results on the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) show that the model has better performance.
Multimodal hand gesture recognition combining temporal and pose information based on CNN descriptors and histogram of cumulative magnitudes

In this paper, we present a new approach for dynamic hand gesture recognition. Our goal is to integrate spatiotemporal features extracted from multimodal data captured by the Kinect sensor. In case the skeleton data is not provided, we apply a novel skeleton estimation method to compute temporal features. Furthermore, we introduce an effective method to extract a fixed number of keyframes to reduce the processing time. To extract pose features from RGB-D data, we take advantage of two different approaches: (1) Convolutional Neural Networks and (2) Histogram of Cumulative Magnitudes. We test different integration methods to fuse the extracted spatiotemporal features to boost recognition performance in a linear SVM classifier. Extensive experiments prove the effectiveness and feasibility of the proposed framework for hand gesture recognition.
Understanding dropout as an optimization trick

As one of standard approaches to train deep neural networks, dropout has been applied to regularize large models to avoid overfitting, and the improvement in performance by dropout has been explained as avoiding co-adaptation between nodes. However, when correlations between nodes are compared after training the networks with or without dropout, one question arises if co-adaptation avoidance explains the dropout effect completely. In this paper, we propose an additional explanation of why dropout works and propose a new technique to design better activation functions. First, we show that dropout can be explained as an optimization technique to push the input towards the saturation area of nonlinear activation function by accelerating gradient information flowing even in the saturation area in backpropagation. Based on this explanation, we propose a new technique for activation functions, gradient acceleration in activation function (GAAF), that accelerates gradients to flow even in the saturation area. Then, input to the activation function can climb onto the saturation area which makes the network more robust because the model converges on a flat region. Experiment results support our explanation of dropout and confirm that the proposed GAAF technique improves image classification performance with expected properties.
Quantum algorithm for the collisionless Boltzmann equation

A novel quantum algorithm implementing a discrete-velocity method for the collisionless Boltzmann equation is introduced. The algorithm is designed for application on a quantum computer with a number of quantum bits feasible in the near future (e.g. 40−50). Following the quantum-circuit model of quantum computation, the present works shows the quantum-circuit implementations for the convection or transport part of the kinetic model, inspired by work on quantum algorithms for the Dirac equation. The present work represents the advection step as a quantum walk process, implemented as a series of multiple-input controlled-NOT gates. A detailed discussion on the background to this new method is presented, including how a rarefied-flow problem can be encoded as the quantum state of a qubit register in a quantum computer. A complexity analysis is presented showing potential benefits of the proposed algorithm. Based on the concept of quantum parallelism, the extension to multiple species is demonstrated to not increase the number of required gate operations. A key aspect of the developed algorithm is the implementation of boundary conditions. This work describes how the specular-reflection boundary conditions can be effectively imposed with a quantum circuit implementation. The developed method is then applied to the supersonic flow around a blunt body as well as the free-molecular flow escaping from a rectangular container. As validation, the flow along the stagnation streamline of the blunt-body flow is compared with exact solutions for a piston-driven flow, showing excellent agreement. Finally, directions for future work are discussed in this work.
Rainy Night Scene Understanding With Near Scene Semantic Adaptation

Deep networks have been used for semantic segmentation tasks on scenes of outdoor environments with increasing popularity. However, the majority of existing work centers on daytime scenes with favorable illumination and weather conditions, and relies on supervision with pixel-level annotations. This paper seeks to address the problem of semantic segmentation for rainy, night-time scenes without using pixel-level annotations. We introduce a near scene semantic approach that uses images of daytime scenes as a bridge for transferring knowledge from pre-trained segmentation models to rainy night images. Specifically, we first present near scene oriented Representation Adaptation (RA) to reduce the domain shift on the representation level. Next, we adapt the segmentation model from the daytime scenario, under varying weather conditions, to the rainy night scenario by using near scene oriented Segmentation Space Adaptation (SSA). Consequently, this further reduces the impact of the domain shift on the segmentation space level. For evaluation, we created a new dataset containing 7000 distinct daytime-night-time image pairs of near scenes obtained by a webcam, and 5266 daytime-rainy night image pairs collected by a car-mounted camera. In addition, we carefully annotated 226 rainy night images with classes defined in Cityscapes. The experimental results clearly demonstrate the advantage of the proposed algorithm.
The Transformational Impact of Blockchain Technology on Business Models and Ecosystems: A Symbiosis of Human and Technology Agents

Blockchain technology is an enabler of value transactions on decentralized, secure databases (ledgers). Despite still being in its early stages, we expect blockchain's impact on business and society to be disruptive. We provide a theory-based examination of blockchain technology's transformational impact from a business model and ecosystem perspective to accelerate the debate on its potential effects. By conceptualizing blockchain's key features through the lens of value, actor-network and contract (property rights) theories, we develop a theoretical framework for analyzing the implication of blockchain technology on value creation. We posit that blockchain serves as a resource, a capability, and an agent to its users. Blockchain technology thereby enables a range of efficiency gains, novelties, and lock-in reductions that have sustaining, enabling, and threatening implications for the emergence and attractiveness of business models and business ecosystems. These findings contribute to the discussion on how digital technologies, blockchain in particular, transform business models, and business ecosystems. Additionally, we contribute to the discussion of digital technology's agency by conceptualizing the blockchain as a symbiosis of human/organizational and technological actors that join forces and thereby achieve a new form of agency that is distinct from traditional perspectives on human or machine agency.
An empirical study of interest, task complexity, and search behaviour on user engagement

User engagement has become an important outcome measure in interactive information retrieval (IIR) research, as commercial (e.g., search engines and e-commerce companies) and educational (e.g., libraries) enterprises focus on capturing and retaining customers. User engagement pertains to the kind of investment – emotional, cognitive, behavioural – the user is willing to make in an application. While research has shown how characteristics of users (e.g., individual differences and preferences) and the systems and content with which they interact influence engagement, less is understood about how the tasks people perform using digital applications affect their engagement. Drawing upon a wealth of literature in IIR, this study examined the effects of task on search engagement in a within-subjects Amazon Mechanical Turk (MTurk) experiment. Participants completed six search tasks on different task topics using task versions that included or excluded items and dimensions in the task descriptions. Items refer to things being compared (alternatives) and dimensions correspond to attributes by which items may differ. The task topics were meant to influence user interest in the task, and the versions were intended to manipulate the task doer's degree of certainty as they planned and performed the task, with the expectation that these factors would affect their self-reported engagement. We captured self-reported task perceptions (e.g., complexity, difficulty, interest) and logged search behaviours (e.g., querying, bookmarking) to both validate our manipulations and to understand how these variables related to engagement. Using multi-level modelling (MLM) we discovered that task topic affected user engagement, whereas task version had limited effects. However, participants' perceptions of the tasks as interesting, difficult, and so on affected their engagement. Through the self-report and behavioural data, we observed that effort (more search engine results page exploration, greater perceived task difficulty) had a negative effect on engagement, while bookmarking pages and the ability to understand the task and how to complete it was associated with positive engagement. These results have implications for designing search tasks, deciphering the relationship between user experience and task complexity in IIR experiments, and aligning self-reports and search behaviours in evaluating online search engagement.
Label distribution learning: A local collaborative mechanism

Label distribution learning (LDL) is a generalized machine learning framework for dealing with label ambiguity, as it can explore the relative importance levels of different labels in the description of each sample. Although several algorithms have been proposed to solve LDL problems, they usually destroy the consistency of geometric structures between feature space and label space to a certain extent, which frequently plays a significant role in learning tasks. Meanwhile, most existing LDL algorithms only take predictive performances into consideration, while ignoring the computational cost and robustness to noises. To remedy above deficiencies, we propose a novel algorithm, i.e., Local Collaborative Representation based Label Distribution Learning, shortly LCR-LDL. In LCR-LDL, an unlabeled sample is treated as the collaborative representation of the local dictionary constructed by the neighborhood of the unlabeled sample, and the discriminatory information of representation coefficients is used to reconstruct the label distribution of the unlabeled sample. Experimental results on 20 real-world LDL data sets compared with results produced by 11 state-of-the-art algorithms show that, the proposed LCR-LDL algorithm can not only effectively improve the predictive performances for LDL tasks, but also exhibit higher robustness and a lightweight computational overhead. This study suggests new trends for considering the computational cost and robustness issues in the LDL community.
DIC: Deep Image Clustering for Unsupervised Image Segmentation

Unsupervised segmentation is an essential pre-processing technique in many computer vision tasks. However, current unsupervised segmentation techniques are sensitive to the parameters such as the segmentation numbers or of high training and inference complexity. Encouraged by neural networks' flexibility and their ability for modelling intricate patterns, an unsupervised segmentation framework based on a novel deep image clustering (DIC) model is proposed. The DIC consists of a feature transformation subnetwork (FTS) and a trainable deep clustering subnetwork (DCS) for unsupervised image clustering. FTS is built on a simple and capable network architecture. DCS can assign pixels with different cluster numbers by updating cluster associations and cluster centers iteratively. Moreover, a superpixel guided iterative refinement loss is designed to optimize the DIC parameters in an overfitting manner. Extensive experiments have been conducted on the Berkley Segmentation Database. The experimental results show that DCS is more effective in aggregating features during the clustering procedure. DIC has also proven to be less sensitive to varying segmentation parameters and of lower computation costs, and DIC can achieve significantly better segmentation performance compared to the state-of-the-art techniques. The source code is available on https://github.com/zmbhou/DIC.
Autoencoder-Based Error Correction Coding for One-Bit Quantization

This paper proposes a novel deep learning-based error correction coding scheme for AWGN channels under the constraint of one-bit quantization in receivers. Specifically, it is first shown that the optimum error correction code that minimizes the probability of bit error can be obtained by perfectly training a special autoencoder, in which “perfectly” refers to converging the global minima. However, perfect training is not possible in most cases. To approach the performance of a perfectly trained autoencoder with a suboptimum training, we propose utilizing turbo codes as an implicit regularization, i.e., using a concatenation of a turbo code and an autoencoder. It is empirically shown that this design gives nearly the same performance as to the hypothetically perfectly trained autoencoder, and we also provide a theoretical proof of why that is so. The proposed coding method is as bandwidth efficient as the integrated (outer) turbo code, since the autoencoder exploits the excess bandwidth from pulse shaping and packs signals more intelligently thanks to sparsity in neural networks. Our results show that the proposed coding scheme at finite block lengths outperforms conventional turbo codes even for QPSK modulation. Furthermore, the proposed coding method can make one-bit quantization operational even for 16-QAM.
Pilot-Assisted Channel Estimation and Signal Detection in Uplink Multi-User MIMO Systems With Deep Learning

In this paper, we propose two deep learning (DL) based receiver schemes in uplink multiple-input multiple-output (MIMO) systems. In the first scheme, we design a pilot-assisted MIMO receiver using a data-driven full connected neural network. This data-driven receiver can recover transmitted signal directly in an end-to-end manner without explicitly estimating channel. In the second scheme, we adopt a model-driven network which combines communication knowledge with DL. The model-driven scheme divides the MIMO receiver into channel estimation subnet and signal detection subnet, and each subnet is composed of a traditional solution as initialization and a DL network to further improve the accurate. The simulation results show that both of the two schemes achieve better bit error ratio (BER) performance than traditional methods. In particular, the data-driven scheme can achieve optimal BER performance in low-dimensional MIMO systems, while the model-driven scheme can be trained with fewer trainable parameters and outperforms the data-driven scheme in high-dimension MIMO systems.
Radio Frequency Fingerprint Extraction Based on Multi-Dimension Approximate Entropy

With the advance in wireless network technique, its security becomes of paramount importance. Radio Frequency Fingerprint (RFF) is the underlying characteristic of hardware chains in transmitters, which can be used as a unique ID for specific emitter identification (SEI) and a non-cryptographic access authentication technology in physical layer to enhance wireless network security. To date, few studies have extracted the inevitable non-linearity in the transmitter as RFF features. Hence, this letter provides a novel nonlinear dynamics approach based on Multi-dimension Approximate Entropy (MApEn) for SEI. Specifically, this method utilizes the steady-state portion of the preamble structure of Standard IEEE802.11b/g to obtain the nonlinear properties of wireless network cards. The experimental results demonstrate that the proposed identification algorithm outperforms the existing steady-state methods in terms of the identification accuracy.
360-Indoor: Towards Learning Real-World Objects in 360° Indoor Equirectangular Images

While there are several widely used object detection datasets, current computer vision algorithms are still limited in conventional images. Such images narrow our vision in a restricted region. On the other hand, 360° images provide a thorough sight. In this paper, our goal is to provide a standard dataset to facilitate the vision and machine learning communities in 360° domain. To facilitate the research, we present a real-world 360° panoramic object detection dataset, 360-Indoor, which is a new benchmark for visual object detection and class recognition in 360° indoor images. It is achieved by gathering images of complex indoor scenes containing common objects and the intensive annotated bounding field-of-view. In addition, 360-Indoor has several distinct properties: (1) the largest category number (37 labels in total). (2) the most complete annotations on average (27 bounding boxes per image). The selected 37 objects are all common in indoor scene. With around 3k images and 90k labels in total, 360-Indoor achieves the largest dataset for detection in 360° images. In the end, extensive experiments on the state-of-the-art methods for both classification and detection are provided. We will release this dataset in the near future.
Multistage pyrites in the Nibao disseminated gold deposit, southwestern Guizhou Province, China: Insights into the origin of Au from textures, in situ trace elements, and sulfur isotope analyses

Nibao is a unique thrust fault-controlled and strata-bound disseminated gold deposit in southwestern Guizhou Province, China. In Nibao, pyrite is the major sulfide mineral and Au is structurally bound (Au+) within the pyrite lattice. In this study, we conducted detailed analyses of the pyrite chemistry and S isotope composition in Nibao using laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) and laser ablation multi-collector inductively coupled plasma mass spectrometry (LA-MC-ICP-MS), respectively. Through petrographic and pyrite chemical studies, four pyrite generations (Py1–Py4) were distinguished: framboidal pyrite (Py1, pre-ore sedimentary stage), clean pyrite overgrowing framboidal pyrite (Py2, pre-ore diagenetic stage), “spongy” pyrite (Py3, early ore stage), and overgrowth of narrow pyrite rims surrounding Py2/Py3 and disseminated pyrite associated with arsenopyrite (Py4, main ore stage). Among these, Py2 and Py4 are the most abundant. The trace element content in Py2 is characterized by a wide range of As, Cu, Sb, and Pb concentrations (~2480–58100 ppm, ~55.4–1610 ppm, ~29.1–232 ppm, and ~24.1–376 ppm, respectively), while Py4 has the highest Au, As, Cu, and Se contents (~70 ppm, ~4200, ~1630 ppm, and ~38.3 ppm, respectively). The δ34S values of pre-ore pyrites measured by LA-MC-ICP-MS in this study and the available data in the literature range broadly from −53.3 to 114.8‰, indicating that they were most likely generated by bacterial reduction from marine sulfate during sedimentation/diagenesis. Meanwhile, the δ34S values of ore pyrites have relative narrow δ34S values, mostly varying from −5 to 5‰, and indicating that the S was derived either from the average of sedimentary rocks or from a magmatic source. Since igneous rocks are scarce in the region and the exposed (~77–99 Ma) are clearly younger than the mineralization age of the Nibao gold deposit (~141 Ma), a magmatic source is unlikely in Nibao. All pyrites in this study show a positive correlation (R2 = 0.71) between Co and Ni, and the Co/Ni and Zn/Ni ratios of main stage Py4 are close to or within the range defined for a sedimentary–diagenetic origin, suggesting a sedimentary source is more likely in Nibao.