Reviewerly/exHarmony huggingface dataset:
Genesis of the Jinji gold deposit in the Jiangnan Orogen, South China: Constraints from geology, chlorite geochemistry, age and H-O-S-Pb isotopes

The Jinji gold deposit (northeastern Hunan province) is located in the central Jiangnan Orogenic Belt, South China. The auriferous sulfide-calcite-quartz vein orebodies, controlled by near NNW-trending faults, occur in the Lengjiaxi Group metamorphic rocks spatially associated with the Banshanpu granitic pluton. Metallic minerals include mainly pyrite, arsenopyrite, and pyrrhotite, with minor sphalerite, chalcopyrite, galena, and native gold. The main alteration types include K-feldspar, silicic, sericite, carbonate, and chlorite. The gold mineralization comprises three stages: (1) quartz–K-feldspar–chlorite–carbonate–arsenopyrite–pyrite, (2) quartz–carbonate–chlorite–polymetallic, and (3) quartz–carbonate–fluorite–pyrite. Geothermometry of chlorite yielded formation temperatures of 261–332 °C (stage 1) and 262–290 °C (stage 2), whereas the calculated oxygen fugacity of chlorite is −45 to −40 (stage 1) and −42 to −39 (stage 2). The δ18OH2O and δD values are 8.8–10.5 ‰ and −73.3 to −72.8‰ (stage 1), 3.2–4.8 ‰ and −83.6 to −80.7‰ (stage 2), and −1.4 to −0.4 ‰ and −82.2 to −78.6‰ (stage 3), respectively. These results indicate that the primary ore fluids (stages 1 and 2) were derived from a granitic magma and were mixed with meteoric water in stage 3. The gold-bearing sulfides (pyrite, pyrrhotite and galena) have δ34S values of −4.8 to +5.8‰, whilst their 208Pb/204Pb, 207Pb/204Pb, and 206Pb/204Pb ratios are of 35.975–36.204, 15.160–15.231, and 16.100–16.176, respectively. The sulfide S-Pb isotope compositions indicate that the metals were likely derived from the Banshanpu granitic magma, which was produced by partial melting caused by Early Paleozoic crustal thickening. The Jinji deposit exhibits many features of orogenic gold mineralization, such as the ore-controlling structures and mineralization styles (quartz-calcite-sulfide veins). The Jinji deposit is the first reported gold deposit in the central Jiangnan orogen with a genetic link to early Paleozoic granitic magma.
Conditional invertible image re-scaling

One of the key issue for image re-scaling is modeling the down-sampling loss. The loss can be approached by a prior distribution through an invertible network in recent works. However, this assumption of independence between images and down-sampling losses is not always satisfied in practice. To address the above problem, we design a module to learn a latent variable representing the down-scaling loss conditional on low-resolution(LR) images according to the information entropy theory. Unlike previous methods, the down-scaling loss is recovered through the proposed module whose inputs are both LR images and predefined distributions. The difference between the high-resolution(HR) image and the low-resolution(LR) image is represented with a latent variable through a lossless invertible neural network. In addition, a conditional entropy loss is proposed to train the invertible neural network to suppress the conditional entropy between LR images and latent variables. It helps to decrease the effect of the latent variable to generate the HR image from LR images during the up-scaling procedure. We evaluate the proposed method on widely used data sets, and the experimental results demonstrate that our proposed method performs favorably against SOTA methods in terms of PSNR and SSIM metrics.
Learnable Hierarchical Label Embedding and Grouping for Visual Intention Understanding

Visual intention understanding is to mine the potential and subjective intention behind the images, which includes the user's hidden emotions and perspectives. Due to the label ambiguity, this paper presents a novel learnable Hierarchical Label Embedding and Grouping (HLEG). It is featured in three aspects: 1) For effectively mining the underlying meaning of images, we build a hierarchical transformer structure to model the hierarchy of labels, formulating a multi-level classification scheme. 2) For the label ambiguity issue, we design a novel learnable label embedding with accumulative grouping integrated into the hierarchical structure, which does not require additional annotation. 3) For multi-level classification, we propose a “Hard-First” optimization strategy to adaptively adjust the classification optimization at different levels, avoiding over-classification of the coarse labels. HLEG enhances the F1 score (average +1.24%) and mAP (average +1.48%) on Intentonomy over prominent baseline models. Comprehensive experiments validate the superiority of our proposed method, achieving state-of-the-art performance under various settings. Code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ShiQingHongYa/HLEG</uri> .
Shape-Aware Joint Distribution Alignment for Cross-Domain Image Segmentation

We present an unsupervised domain adaptation method for image segmentation which aligns high-order statistics, computed for the source and target domains, encoding domain-invariant spatial relationships between segmentation classes. Our method first estimates the joint distribution of predictions for pairs of pixels whose relative position corresponds to a given spatial displacement. Domain adaptation is then achieved by aligning the joint distributions of source and target images, computed for a set of displacements. Two enhancements of this method are proposed. The first one uses an efficient multi-scale strategy that enables capturing long-range relationships in the statistics. The second one extends the joint distribution alignment loss to features in intermediate layers of the network by computing their cross-correlation. We test our method on the task of unpaired multi-modal cardiac segmentation using the Multi-Modality Whole Heart Segmentation Challenge dataset and prostate segmentation task where images from two datasets are taken as data in different domains. Our results show the advantages of our method compared to recent approaches for cross-domain image segmentation. Code is available at https://github.com/WangPing521/Domain_adaptation_shape_prior.
Compressed Collective Sparse-Sketch for Distributed Data-Parallel Training of Deep Learning Models

Distributed data-parallel training (DDP) is prevalent in large-scale deep learning. To increase the training throughput and scalability, high-performance collective communication methods such as AllReduce have recently proliferated for DDP use. However, these approaches require long communication periods with increasing model sizes. Collective communication transmits many sparse gradient values that can be efficiently compressed to reduce the required training time. State-of-the-art compression approaches do not provide mergeable compression for AllReduce and lack convergence bounds. We present a sparse sketch reducer (S2Reducer), a sparsity-preserving sketch-based collective communication method. S2Reducer preserves gradient sparsity and reduces communication costs via a bitmap informed count sketch structure and adapts to efficient AllReduce operators. We tune the count sketch organization to minimize the hash conflicts in a fixed-size budget. We prove that our method has the same convergence rate as vanilla data-parallel training and a much smaller communication overhead than those of state-of-the-art methods. We implement a GPU-accelerated S2Reducer for the Ring AllReduce-based DDP system. We perform extensive evaluations against four state-of-the-art methods across seven deep learning models. Our results show that S2Reducer converges to the same accuracy as that of state-of-the-art approaches while reducing the sparse communication overhead by up to 86% and achieving a speedup of up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.5\times $ </tex-math></inline-formula> in distributed training.
Shadowless Projection Mapping using Retrotransmissive Optics

This paper presents a shadowless projection mapping system for interactive applications in which a target surface is frequently occluded from a projector with a user's body. We propose a delay-free optical solution for this critical problem. Specifically, as the primary technical contribution, we apply a large format retrotransmissive plate to project images onto the target surface from wide viewing angles. We also tackle technical issues unique to the proposed shadowless principle. First, the retrotransmissive optics inevitably suffer from stray light, which leads to significant contrast degradation of the projected result. We propose to block the stray light by covering the retrotransmissive plate with a spatial mask. Because the mask reduces not only the stray light but the achievable luminance of the projected result, we develop a computational algorithm that determines the shape of the mask to balance the image quality. Second, we propose a touch sensing technique by leveraging the optically bidirectional property of the retrotransmissive plate to support interaction between the user and the projected contents on the target object. We implement a proof-of-concept prototype and validate the above-mentioned techniques through experiments.
Gaining the High Ground: Teleportation to Mid-Air Targets in Immersive Virtual Environments

Most prior teleportation techniques in virtual reality are bound to target positions in the vicinity of selectable scene objects. In this paper, we present three adaptations of the classic teleportation metaphor that enable the user to travel to mid-air targets as well. Inspired by related work on the combination of teleports with virtual rotations, our three techniques differ in the extent to which elevation changes are integrated into the conventional target selection process. Elevation can be specified either simultaneously, as a connected second step, or separately from horizontal movements. A user study with 30 participants indicated a trade-off between the simultaneous method leading to the highest accuracy and the two-step method inducing the lowest task load as well as receiving the highest usability ratings. The separate method was least suitable on its own but could serve as a complement to one of the other approaches. Based on these findings and previous research, we define initial design guidelines for mid-air navigation techniques.
Effects of the Visual Fidelity of Virtual Environments on Presence, Context-dependent Forgetting, and Source-monitoring Error

Advances in virtual reality technology have enabled the creation of virtual environments (VEs) with significantly high visual fidelity when compared to real environments (REs). In this study, we use a high-fidelity VE to examine two effects caused by alternating VE and RE experiences: "context-dependent forgetting" and "source-monitoring errors." The former effect is that memories learned in VEs are more easily recalled in VEs than in REs, whereas memories learned in REs are more easily recalled in REs than in VEs. The source-monitoring error is that memories learned in VEs are easily confused with those learned in REs, making discriminating the source of the memory difficult. We hypothesized that the visual fidelity of VEs is responsible for these effects and conducted an experiment using two types of VEs: a high-fidelity VE created using photogrammetry techniques and low-fidelity VE created with primitive shapes and materials. The results show that the high-fidelity VE significantly improved the sense of presence. However, the level of the visual fidelity of the VEs did not show any effect on context-dependent forgetting and source-monitoring errors. Notably, the null results of the context-dependent forgetting between the VE and RE were strongly supported by Bayesian analysis. Thus, we indicate that context-dependent forgetting does not necessarily occur, which will be helpful for VR-based education and training.
Assisted walking-in-place: Introducing assisted motion to walking-by-cycling in embodied virtual reality

In this paper, we investigate the use of a motorized bike to support the walk of a self-avatar in virtual reality (VR). While existing walking-in-place (WIP) techniques render compelling walking experiences, they can be judged repetitive and strenuous. Our approach consists in assisting a WIP technique so that the user does not have to actively move in order to reduce effort and fatigue. We chose to assist a technique called walking-by-cycling, which consists in mapping the cycling motion of a bike onto the walking of the user's self-avatar, by using a motorized bike. We expected that our approach could provide participants with a compelling walking experience while reducing the effort required to navigate. We conducted a within-subjects study where we compared "assisted walking-by-cycling" to a traditional active walking-by-cycling implementation, and to a standard condition where the user is static. In the study, we measured embodiment, including ownership and agency, walking sensation, perceived effort and fatigue. Results showed that assisted walking-by-cycling induced more ownership, agency, and walking sensation than the static simulation. Additionally, assisted walking-by-cycling induced levels of ownership and walking sensation similar to that of active walking-by-cycling, but it induced less perceived effort. Taken together, this work promotes the use of assisted walking-by-cycling in situations where users cannot or do not want to exert much effort while walking in embodied VR such as for injured or disabled users, for prolonged uses, medical rehabilitation, or virtual visits.
How to Maximise Spatial Presence: Design Guidelines for a Virtual Learning Environment for School Use

Research on learning with and in immersive virtual reality (VR) continues to grow, yielding more insights into how immersive learning works. However, the actual use of VR learning environments in schools is still in its infancy. A major hurdle that hinders the use of immersive digital media in schools is the lack of guidelines for designing VR learning environments for practical use in schools. Such guidelines need to consider how students interact and learn in VR learning environments and how teachers can use such environments on a day-to-day basis. Using a design-based research approach, we explored the guidelines for creating VR learning content for tenth-grade students in a German secondary school and recreated a real-world, out-of-school VR learning space which can be used for hands-on instruction. This paper investigated how to maximise the experience of spatial presence by creating a VR learning environment in several microcycles. Furthermore, it took a closer look at the influence of the spatial situation model and cognitive involvement on this process. The results were evaluated with ANOVAs and path analyses, showing, for example, that involvement does not influence spatial presence in highly immersive and realistic VR learning environments.
Causal Language Model Aided Sequential Decoding With Natural Redundancy

A high-performance communication receiver desires a sufficient and accurate recognition of transmitted sources. In this paper, we propose a sequential decoding algorithm for the robust reception of sources with natural redundancy (NR) over the AWGN channel. To fully exploit the abundant NR in the exemplified English text sources, a causal language modeling (CLM) with a powerful Transformer decoder neural network (NN) structure is adopted for modeling the joint probability distribution. For versatility of byte-level tokenization, the UTF-8 encoding scheme is considered for the Wikipedia corpus, namely the English edition of the Wiki-40B dataset. The tree search-based M-algorithm (MA) is integrated with CLM, denoted as CLM-MA algorithm, to synthesize the a priori probability of information sequence and likelihood values of polluted symbols. Both simulation experiments and hardware platform evaluations are conducted to analyze the performance of the CLM-MA algorithm. With an adequately large M value, tremendous performance profit is achievable, especially for high-efficiency modulation levels and extremely noisy conditions. This mechanism of adopting the pre-trained NN model merely desires self-supervised learning architecture and eliminates the requirement of high-cost and accurate labels, which paves a new way for communication receiver design.
Node-Centric Random Walk for Fast Index-Free Personalized PageRank

Personalized PageRank (PPR) is a popular graph computation in various real-world applications. Since massive real-world graphs are evolving rapidly, PPR computation methods require index-free and fast. In general, index-free methods go through Forward Push phase and random walk Monte-Carlo simulation phase respectively. While existing methods have succeeded in accelerating the Forward Push phase, there is a space for running-time improvements in the second phase that performs a large number of sequential random walks. Through this sequential process, each random walk needs to obtain neighbor nodes for every single step, which causes redundant operation at each node as a result. Our proposal is a node-centric random walk that aggregates random walks at each node and minimizes the total number of obtaining neighbor nodes in the second phase. Most of the random walks can be aggregated while maintaining theoretical guarantees because they do not need to memorize the starting node. In addition, we review the expected running time of random walk Monte-Carlo simulation focusing on the total number of obtaining neighbor nodes. We conducted extensive experiments using four real-world graphs. Experimental results showed that our proposed method is up to 3.3x faster than the existing methods.
CardioGenesis4D: Interactive Morphological Transitions of Embryonic Heart Development in a Virtual Learning Environment

In the embryonic human heart, complex dynamic shape changes take place in a short period of time on a microscopic scale, making this development difficult to visualize. However, spatial understanding of these processes is essential for students and future cardiologists to properly diagnose and treat congenital heart defects. Following a user centered approach, the most crucial embryological stages were identified and translated into a virtual reality learning environment (VRLE) to enable the understanding of the morphological transitions of these stages through advanced interactions. To address individual learning types, we implemented different features and evaluated the application regarding usability, perceived task load, and sense of presence in a user study. We also assessed spatial awareness and knowledge gain, and finally obtained feedback from domain experts. Overall, students and professionals rated the application positively. To minimize distraction from interactive learning content, such VRLEs should consider features for different learning types, allow for gradual habituation, and at the same time provide enough playful stimuli. Our work previews how VR can be integrated into a cardiac embryology education curriculum.
ParaNet: A Single Blocked Network for Mobile Edge Computing Devices

Nowadays, deep learning-based approaches have achieved significant performances in diverse applications. However, due to having millions of parameters and higher complexity, these high-performing architectures are not suitable to be deployed in edge devices, the Internet of Things (IoT), Vehicular edge computing, and microservices-based real-time applications. Though numerous approaches have proposed lightweight architectures to reduce required computational resources, there are still some concerns about latency, execution, and response time. To the best of our knowledge, no prior works have considered reorganizing the sequential blocks into parallel forward propagation i.e, converting sequential forward propagation into parallel forward propagation. In this paper, instead of reducing the time required by the network for end-to-end sequential execution, we propose a novel technique to obtain a parallel network called ParaNet to minimize the execution time by paralleling the network. Firstly, we dissect a CNN block-wise where all the blocks are deployed parallelly to construct ParaNet. Each block is treated as an individual network and can be deployed into different low computational edge devices for parallel processing. To further improve the performances we deploy the knowledge distillation technique into each ParaNet version. Our proposed method offers state-of-the-art results using low computational resources with very low execution delay compared to the corresponding baseline architectures. Our extensive analysis and results express the superiority of the ParaNet regarding both accuracy and execution time.
Credit Card Fraud Detection: A Hybrid of PSO and K-Means Clustering Unsupervised Approach

With the rise of the internet and online shopping, the use of credit cards for online purchases skyrocketed and so did the incidents of online financial frauds. In the year 2018 alone, 24.26 Billion USD was lost worldwide due to credit card frauds. As a result of these frauds, many innocent people have lost a lot of their earnings which discouraged them from ever taking part in online payment activities. The most common victims of such frauds are people in old age as they are less tech-savvy. Traditionally machine learning and deep learning approaches to credit card fraud detection have been around supervised learning methods but supervised learning methods always have to rely on labelled datasets which means that they need to get prior ideas about what type of transaction is considered fraud before giving predictions on other transactions. Some recent work has been done on fraud detection using unsupervised learning. In this work, we analyze the performance of unsupervised learning techniques such as k-means clustering on a credit card fraud detection dataset. We also try to further the research in this area by proposing a Particle swarm optimization and k-means clustering hybrid model for the same. The model based on our proposed approach has improved the performance of k-means clustering approach. Our Hybrid approach has shown better accuracy, precision, and recall than the k-means clustering approach.
Application of Supply Chain Management in Blockchain and IoT - A Generic Use Case

To prove a technology worthy of something, a use case is required. Blockchain and the Internet of Things (IoT) are the two technologies, that have emerged as the most talkative technologies in the past decade. Blockchain was not started in 2008 but it was started in 1982 by David Chaum. Similarly, IoT was not started in 1990 rather the beginning of IoT started with the invention of the electromagnetic telegraph in 1832. This Paper focuses on all the introductory aspects of Blockchain and IoT. Blockchain and IoT have a generic use case i.e., Supply Chain Management (SCM) that has gained popularity from the time, this concept was invented. SCM is the management of the flow of the products/goods/services and will include all the processes as well that will participate in converting the raw materials into the final and furnished products/goods. This paper addresses the flow of transferring digital assets with complete SCM implemented with Blockchain and involves IoT.
Thresholding based Smart Home Automation System using K-means

Home automation is a technology that is often viewed as a luxury and not a necessity. The means of achieving home automation is always considered expensive and inconvenient in the long run. Due to this there is no success in achieving this technology. Our aim is to make life much easier, more comfortable, and much more convenient and reduce our daily workload. To achieve this, our approach was to make a highly compact and simplified product that would be affordable to middle-class families by using familiar applications like blynk and machine learning - for making the home automation personalized to every specific user. Not only that but also to take utmost care of the privacy of our users.