Reviewerly/exHarmony huggingface dataset:
Combining 3D Geological Modeling and 3D Spectral Modeling for Deep Mineral Exploration in the Zhaoxian Gold Deposit, Shandong Province, China

The Jiaodong Peninsula hosts the main large gold deposits and was the first gold production area in China; multisource and multiscale geoscience datasets are available. The area is the biggest drilling mineral-exploration zone in China. This study used three-dimensional (3D) modeling, geology, and ore body and alteration datasets to extract and synthesize mineralization information and analyze the exploration targeting in the Zhaoxian gold deposit in the northwestern Jiaodong Peninsula. The methodology and results are summarized as follows: The regional Jiaojia fault is the key exploration criterion of the gold deposit. The compression torsion characteristics and concave–convex section zones in the 3D deep environment are the main indicators of mineral exploration using 3D geological and ore-body modeling in the Zhaoxian gold deposit. The hyperspectral detailed measurement, interpretation, and data mining used drill-hole data (&gt;1000 m) to analyze the vectors and trends of the ore body and ore-forming fault and the alteration-zone rocks in the Zhaoxian gold deposit. The short-wave infrared Pos2200 values and illite crystallinity in the alteration zone can be used to identify 3D deep gold mineralization and potential targets for mineral exploration. This research methodology can be globally used for other deep mineral explorations.
Wireless Communication Channel Scenarios: Machine-Learning-Based Identification and Performance Enhancement

Wireless communication channel scenario classification is crucial for new modern wireless technologies. Reducing the time consumed by the data preprocessing phase for such identification is also essential, especially for multiple-scenario transitions in 6G. Machine learning (ML) has been used for scenario identification tasks. In this paper, the least absolute shrinkage and selection operator (LASSO) is used instead of ElasticNet in order to reduce the computational time of data preprocessing for ML. Moreover, the computational time and performance of different ML models are evaluated based on a regularization technique. The obtained results reveal that the LASSO operator achieves the same feature selection performance as ElasticNet; however, the LASSO operator consumes less computational time. The achieved run time of LASSO is 0.33 s, while the ElasticNet corresponding value is 0.67 s. The identification for each specific class for K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and k-Means and Gaussian Mixture Model (GMM) is evaluated using Receiver Operating Characteristics (ROC) curves and Area Under the Curve (AUC) scores. The KNN algorithm has the highest class-average AUC score at 0.998, compared to SVM, k-Means, and GMM with values of 0.994, 0.983, and 0.989, respectively. The GMM is the fastest algorithm among others, having the lowest classification time at 0.087 s, compared to SVM, k-Means, and GMM with values of 0.155, 0.26, and 0.087, respectively.
A Comparative Study of Engraved-Digit Data Augmentation by Generative Adversarial Networks

In cases where an efficient information retrieval (IR) system retrieves information from images with engraved digits, as found on medicines, creams, ointments, and gels in squeeze tubes, the system needs to be trained on a large dataset. One of the system applications is to automatically retrieve the expiry date to ascertain the efficacy of the medicine. For expiry dates expressed in engraved digits, it is difficult to collect the digit images. In our study, we evaluated the augmentation performance for a limited, engraved-digit dataset using various generative adversarial networks (GANs). Our study contributes to the choice of an effective GAN for engraved-digit image data augmentation. We conclude that Wasserstein GAN with a gradient norm penalty (WGAN-GP) is a suitable data augmentation technique to address the challenge of producing a large, realistic, but synthetic dataset. Our results show that the stability of WGAN-GP aids in the production of high-quality data with an average Fréchet inception distance (FID) value of 1.5298 across images of 10 digits (0–9) that are nearly indistinguishable from our original dataset.
Calculating concentratable entanglement in graph states

We propose a method to calculate the purity of reduced states of graph states entirely within the stabilizer formalism, using only the stabilizer generators for a given state. We apply this method to find the Concentratable Entanglement of graph states, with examples including a single qubit within any graph state, states proposed for use in quantum repeaters and states that maximise and minimise the overall Concentratable Entanglement.
Sundial-GAN: A Cascade Generative Adversarial Networks Framework for Deciphering Oracle Bone Inscriptions

Oracle Bone Inscription (OBI) is an early hieroglyph in China, which is the most famous ancient writing system in the world. However, only a small number of OBI characters have been fully deciphered today. Chinese characters have different forms in different historical stages; therefore, it is very difficult to directly translate OBI characters to modern Chinese characters due to the long historic evolutionary process. In this paper, we propose a cascade generative adversarial networks (GAN) framework for deciphering OBI characters, named "Sundial-GAN'', which is a cascaded structure to simulate Chinese characters' evolutionary process from an OBI character to its potential modern Chinese character. We select four representative stages in the evolutionary process of OBI, each of which is implemented by an individual GAN structure based on the characteristics of each evolutionary stage. These structures are cascaded in sequence to accurately simulate the Chinese characters' evolutionary process. For each input OBI character, Sundial-GAN can successfully generate the input's different forms at the four historical stages. Extensive experiments and comparisons demonstrate that generated characters at each stage have high similarities with real existing characters; therefore, the proposed method can significantly improve the efficiency and accuracy of OBI deciphering for archaeological researchers. Compared to direct image-to-image translation methods, our approach allows for a smoother translation process, a better grasp of details, and more effective avoiding random mappings in GANs.
Semi-supervised Semantic Segmentation via Prototypical Contrastive Learning

The key idea of semi-supervised semantic segmentation is to leverage both labeled and unlabeled data. To achieve the goal, most existing methods resort to pseudo-labels for training. However, the dispersed feature distribution and biased category centroids could inevitably lead to the calculation deviation of feature distances and noisy pseudo labels. In this paper, we propose to denoise pseudo labels with representative prototypes. Specifically, to mitigate the effects of outliers, we first employ automatic clustering to model multiple prototypes with which the distribution of outliers can be better characterized. Then, a compact structure and clear decision boundary can be obtained by using contrastive learning. It is worth noting that our prototype-wise pseudo segmentation strategy can also be applied in most existing semantic segmentation networks. Experimental results show that our method outperforms other state-of-the-art approaches on both Cityscapes and Pascal VOC semantic segmentation datasets under various data partition protocols.
An Efficient Multi-View Multimodal Data Processing Framework for Social Media Popularity Prediction

Popularity of social media is an important symbol of its communication power. Predictions of social media popularity have tremendous business and social value. In this paper, we propose an efficient multimodal data processing framework, which can comprehensively extract the multi-view features from multimodal social media data and achieve accurate popularity prediction. We utilize Transformer and sliding window average to extract time series features of posts, utilize CatBoost to calculate the importance of different features, and integrate important features extracted from multiple views for accurate prediction of social media popularity. We evaluate our proposed approach with the Social Media Prediction Dataset. Experimental results show that our approach achieves excellent performance in the social media popularity prediction task.
Wavelet-enhanced Weakly Supervised Local Feature Learning for Face Forgery Detection

Face forgery detection is getting increasing attention due to the security threats caused by forged faces. Recently, local patch-based approaches have achieved sound achievements due to effective attention to local details. However, there are still unignorable problems: a) local feature learning requires patch-level labels to circumvent label noise, which is not practical in real-world scenarios; b) the commonly used DCT (FFT) transform loses all spatial information, which brings difficulty in handling local details. To compensate for such limitations, a novel wavelet-enhanced weakly supervised local feature learning framework is proposed in this paper. Specifically, to supervise the learning of local features with only image-level labels, two modules are devised based on the idea of multi-instance learning: local relation constraint module (LRCM) and category knowledge-guided local feature aggregation module (CKLFA). LRCM constrains the maximum distance between local features of forged face images greater than that of real face images. CKLFA adaptively aggregates local features based on their correlation to global embedding containing global category information. Combining these two modules, the network is encouraged to learn discriminative local features supervised only by image-level labels. Besides, a multi-level wavelet-powered feature enhancement module is developed to promote the network mining local forgery artifacts from spatio-frequency domain, which is beneficial to learning discriminative local features. Extensive experiments show that our approach outperforms previous state-of-the-art methods when only image-level labels are available and achieves comparable or even better performance than counterparts using patch-level labels.
Free-Lunch for Cross-Domain Few-Shot Learning: Style-Aware Episodic Training with Robust Contrastive Learning

Cross-Domain Few-Shot Learning (CDFSL) aims for training an adaptable model that can learn out-of-domain classes with a handful of samples. Compared to the well-studied few-shot learning problem, the difficulty for CDFSL lies in that the available training data from test tasks is not only extremely limited but also presents severe class differences from training tasks. To tackle this challenge, we propose Style-aware Episodic Training with Robust Contrastive Learning (SET-RCL), which is motivated by the key observation that a remarkable style-shift between tasks from source and target domains plays a negative role in cross-domain generalization. SET-RCL addresses the style-shift from two perspectives: 1) simulating the style distributions of unknown target domains (data perspective); and 2) learning a style-invariant representation (model perspective). Specifically, Style-aware Episodic Training (SET) focuses on manipulating the styl distribution of training tasks in the source domain, such that the learned model can achieve better adaption on test tasks with domain-specific styles. To further improve cross-domain generalization under style-shift, we develop Robust Contrastive Learning (RCL) to capture style-invariant and discriminative representations from the manipulated tasks. Notably,our SET-RCL is orthogonal to existing FSL approaches, thus can be adopted as a "free-lunch" for boosting their CDFSL performance. Extensive experiments on nine benchmark datasets and six baseline methods demonstrate the effectiveness of our method.
PRO-Face: A Generic Framework for Privacy-preserving Recognizable Obfuscation of Face Images

A number of applications (e.g., video surveillance and authentication) rely on automated face recognition to guarantee functioning of secure services, and meanwhile, have to take into account the privacy of individuals exposed under camera systems. This is the so-called Privacy-Utility trade-off. However, most existing approaches to facial privacy protection focus on removing identifiable visual information from images, leaving protected face unrecognizable to machine, which sacrifice utility for privacy. To tackle the privacy-utility challenge, we propose a novel, generic, effective, yet lightweight framework for Privacy-preserving Recognizable Obfuscation of Face images (named as PRO-Face). The framework allows one to first process a face image using any preferred obfuscation, such as image blur, pixelate and face morphing. It then leverages a Siamese network to fuse the original image with its obfuscated form, generating the final protected image visually similar to the obfuscated one from human perception (for privacy) but still recognized as the original identity by machine (for utility). The framework supports various obfuscations for facial anonymization. The face recognition can be performed accurately not only across anonymized images but also between plain and anonymized ones, based on only pre-trained recognizers. Those feature the "generic" merit of the proposed framework. In-depth objective and subjective evaluations demonstrate the effectiveness of the proposed framework in both privacy protection and utility preservation under distinct scenarios. Our source code, models and any supplementary materials are made publicly available.
Content and Gradient Model-driven Deep Network for Single Image Reflection Removal

Single image reflection removal (SIRR) is an extremely challenging, ill-posed problem with many application scenarios. In recent years, massive deep learning-based methods have been proposed to remove undesirable reflections from a single input image. However, these methods lack interpretability and do not fully utilize the intrinsic physical structure of reflection images. In this paper, we propose a content and gradient-guided deep network (CGDNet) for single image reflection removal, which is a full-interpretable and model-driven network. Firstly, using the multi-scale convolutional dictionary, we design a novel single image reflection removal model, which combines the image content prior and gradient prior information. Then, the model is optimized using an optimization algorithm based on the proximal gradient technique and unfolded into a neural network, i.e., CGDNet. All the parameters of CGDNet can be automatically learned by end-to-end training. Besides, we introduce a reflection detection module into CGDNet to obtain a probabilistic confidence map and ensure that the network pays attention to reflection regions. Extensive experiments on four benchmark datasets demonstrate that CGDNet is more efficient than state-of-the-art methods in terms of both subjective and objective evaluations. Code is available at https://github.com/zynwl/CGDNet.
Multi-Granular Semantic Mining for Weakly Supervised Semantic Segmentation

This paper solves the problem of learning image semantic segmentation using image-level supervision. The task is promising in terms of reducing annotation efforts, yet extremely challenging due to the difficulty to directly associate high-level concepts with low-level appearance. While current efforts handle each concept independently, we take a broader perspective to harvest implicit, holistic structures of semantic concepts, which express valuable prior knowledge for accurate concept grounding. This raises multi-granular semantic mining, a new formalism allowing flexible specification of complex relations in the label space. In particular, we propose a heterogeneous graph neural network (Hgnn) to model the heterogeneity of multi-granular semantics within a set of input images. The Hgnn consists of two types of sub-graphs: 1) an external graph characterizes the relations across different images to mine inter-image contexts; and for each image, 2) an internal graph is constructed to mine inter-class semantic dependencies within each individual image. Through heterogeneous graph learning, our Hgnn is able to land a comprehensive understanding of object patterns, leading to more accurate semantic concept grounding. Extensive experimental results show that Hgnn outperforms the current state-of-the-art approaches on the popular PASCAL VOC 2012 and COCO 2014 benchmarks. Our code is available at: https://github.com/maeve07/HGNN.git.
Self-Supervised Representation Learning for Skeleton-Based Group Activity Recognition

Group activity recognition (GAR) is a challenging task for discerning the behavior of a group of actors. This paper aims at learning discriminative representation for GAR in a self-supervised manner based on human skeletons. As modeling relations between actors lie at the center of GAR, we propose a valid self-supervised learning pretext task with a matching framework, where a representation model is driven to identify subgroups in a synthetic group based on actors' skeleton sequences. For backbone networks, while spatial-temporal graph convolution networks have dominated the skeleton-based action recognition, they under-explore the group relevant interactions among actors. To address this issue, we come up with a novel plug-in Actor-Association Graph Convolution Module (AAGCM) based on inductive graph convolution, which can be integrated into many common backbones. It can not only model the interactions at different levels but also adapt to variable group sizes. The effectiveness of our approaches is demonstrated by extensive experiments on three benchmark datasets: Volleyball, Collective Activity, and Mutual NTU.
A Probabilistic Model for Controlling Diversity and Accuracy of Ambiguous Medical Image Segmentation

Medical image segmentation tasks often have more than one plausible annotation for a given input image due to its inherent ambiguity. Generating multiple plausible predictions for a single image is of interest for medical critical applications. Many methods estimate the distribution of the annotation space by developing probabilistic models to generate multiple hypotheses. However, these methods aim to improve the diversity of predictions at the expense of the more important accuracy. In this paper, we propose a novel probabilistic segmentation model, called Joint Probabilistic U-net, which successfully achieves flexible control over the two abstract conceptions of diversity and accuracy. Specifically, we (i) model the joint distribution of images and annotations to learn a latent space, which is used to decouple diversity and accuracy, and (ii) transform the Gaussian distribution in the latent space to a complex distribution to improve model's expressiveness. In addition, we explore two strategies for preventing the latent space collapse, which are effective in improving the model's performance on datasets with limited annotation. We demonstrate the effectiveness of the proposed model on two medical image datasets, i.e. LIDC-IDRI and ISBI 2016, and achieved state-of-the-art results on several metrics.
Progressive Attribute Embedding for Accurate Cross-modality Person Re-ID

Attributes are important information to bridge the appearance gap across modalities, but have not been well explored in cross-modality person ReID. This paper proposes a progressive attribute embedding module (PAE) to effectively fuse the fine-grained semantic attribute information and the global structural visual information. Through a novel cascade way, we use attribute information to learn the relationship between the person images in different modalities, which significantly relieves the modality heterogeneity. Meanwhile, by embedding attribute information to guide more discriminative image feature generation, it simultaneously reduces the inter-class similarity and the intra-class discrepancy. In addition, we propose an attribute-based auxiliary learning strategy (AAL) to supervise the network to learn modality-invariant and identity-specific local features by joint attribute and identity classification losses. The PAE and AAL are jointly optimized in an end-to-end framework, namely, progressive attribute embedding network (PAENet). One can plug PAE and AAL into current mainstream models, as we implement them in five cross-modality person ReID frameworks to further boost the performance. Extensive experiments on public datasets demonstrate the effectiveness of the proposed method against the state-of-the-art cross-modality person ReID methods.
Action-conditioned On-demand Motion Generation

We propose a novel framework, On-Demand MOtion Generation (ODMO), for generating realistic and diverse long-term 3D human motion sequences conditioned only on action types with an additional capability of customization. ODMO shows improvements over SOTA approaches on all traditional motion evaluation metrics when evaluated on three public datasets (HumanAct12, UESTC, and MoCap). Furthermore, we provide both qualitative evaluations and quantitative metrics demonstrating several first-known customization capabilities afforded by our framework, including mode discovery, interpolation, and trajectory customization. These capabilities significantly widen the spectrum of potential applications of such motion generation models. The novel on-demand generative capabilities are enabled by innovations in both the encoder and decoder architectures: (i) Encoder: Utilizing contrastive learning in low-dimensional latent space to create a hierarchical embedding of motion sequences, where not only the codes of different action types form different groups, but within an action type, codes of similar inherent patterns (motion styles) cluster together, making them readily discoverable; (ii) Decoder: Using a hierarchical decoding strategy where the motion trajectory is reconstructed first and then used to reconstruct the whole motion sequence. Such an architecture enables effective trajectory control. Our code is released on the Github page: https://github.com/roychowdhuryresearch/ODMO https://github.com/roychowdhuryresearch/ODMO