Reviewerly/exHarmony huggingface dataset:
Occurrence and precipitation mechanism of silver in pyrite from chimney fragments in the Edmond hydrothermal field, Central Indian Ridge

The enrichment of precious elements including Au and Ag in submarine hydrothermal sulfide deposits attracts more and more attention. Previous studies indicate that Au and Ag combine distinctly different ligands under the physico-chemical conditions of submarine hydrothermal systems, therefore, the factors controlling their precipitation may be different. In general, silver mineralization has been much less studied than gold mineralization in submarine hydrothermal sulfide deposits. The Ag mineralization process and precipitation mechanism of submarine hydrothermal sulfide deposits formed at Mid-ocean Ridges are still poorly constrained. In this study, we studied the occurrence and precipitation mechanism of Ag in sulfide deposits of Edmond hydrothermal field, located on the intermediate-spreading Central Indian Ridge (CIR). Three ore-forming stages were identified in chimney fragments collected in the Edmond hydrothermal field. The corresponding minerals are (stage I) anhydrite + barite + colloidal/porous pyrite (Py1) + fine-grained sphalerite; (stage II) marcasite; (stage III) euhedral pyrite (Py2) + coarse-grained sphalerite + chalcopyrite + isocubanite. Py1, characterized by colloidal, porous, and micro-sized anhedral morphology, was usually overgrown by marcasite, which is, in turn, surrounded by euhedral-subhedral Py2, usually coexisting with coarse-grained sphalerite, chalcopyrite, and isocubanite. Compared to marcasite and Py2, only Py1 contains abundant native silver nanoparticles. LA-ICP-MS analyses suggest that Py1 contains higher Pb, Cu, Ag, Mn, Tl, Mo, Au, and lower Co as compared to Py2. In-situ LA-ICP-MS and nanoSIMS analyses indicate that Py1 with higher Ag contents (101–586 ppm) has larger variation in sulfur isotopic compositions (0.8 to 6.6‰) than those of Py2 with lower Ag contents (0.22–15.5 ppm; δ34S values: 2.1 to 4.8‰ for Fe-rich samples and 0 to 2.8‰ for Zn-rich samples). Texture, mineral assemblage, pyrite trace element and sulfur isotopic compositions indicate a progressive decrease on the degree of fluid-seawater mixing as the temperature gradually increases during the growth of the chimneys in Edmond hydrothermal field. Phase diagram analysis indicates that the increase in pH and in particular cooling, due to the mixing of the hot hydrothermal fluid with cold seawater, can significantly decrease the solubility of Ag and be the effective mechanisms of silver precipitation. Our results suggest that mixing of hydrothermal fluid with seawater is the main Ag precipitation mechanism for submarine hydrothermal sulfide deposits formed at Mid-ocean Ridges.
Key Student Nodes Mining in the In-Class Social Network Based on Combined Weighted GRA-TOPSIS Method

In this paper, a key node mining algorithm of entropy-CRITIC combined weighted GRA-TOPSIS method is proposed, which is based on the network structure features. First, the method obtained multi-dimensional data of students' identities, seating relationships, social relationships, and so on to build a database. Then, the seating similarity among students was used to construct the in-class social networks and analyze the structural characteristics of them. Finally, the CRITIC and entropy weight method was introduced for obtaining the combined weight values and the GRA-TOPSIS multi-decision fusion algorithm to mine the key student nodes that have negative impact. The experiments showed that the algorithm of this paper can evaluate students objectively based on their classroom social networks, providing technical support for process-oriented comprehensive quality education evaluation.
Incorporating user rating credibility in recommender systems

There have been many research efforts aimed at improving recommendation accuracy with Collaborative Filtering (CF). Yet there is still a lack of investigation into the integration of CF algorithms with the analysis of users' rating behaviors. In this work, we develop an integrated CF-based recommendation solution by incorporating the credibility of users' ratings, the demographic information of people, and the ontological semantics of items. The users' credibility values are calculated based on their ratings and they are used in finding credible neighbors to improve the accuracy of recommendations. The demographic information and ontological semantics are used in the similarity measurement of users/items to alleviate the issues of sparsity and cold start in CF algorithms. Experiments are conducted on real-world datasets of MovieLens and Yahoo!Movie. Compared with baseline methods, a set of experiments shows that the proposed approach improves the recommendation quality significantly.
A Microgrid Security Defense Method Based on Cooperation in an Edge-Computing Environment

Aiming at the problems of high delay and vulnerable to network attack in the traditional microgrid centralized architecture, a collaborative microgrid security defense method in the edge-computing environment is proposed. First, we build the edge-computing framework for microgrid, deploy the edge-computing server near the equipment terminal to improve the data processing efficiency, and deploy the blockchain in the edge server to ensure the reliability of the system. Then, the fully homomorphic encryption algorithm is used to design the smart contract, and the secure sharing of information is ensured through identity authentication, data encryption call, and so on. Finally, the credibility model is integrated into the election algorithm and is used to build a trusted edge cooperation mechanism to further improve the ability of the system to defend against network attacks. Based on the microgrid model, the experimental demonstration of the proposed method is carried out. The results show that when subjected to a network attack, the current fluctuation range is small and the defense success rate exceeds 95%, which is better than other methods and can better meet the requirements of practical application.
FlowKV: A Semantic-Aware Store for Large-Scale State Management of Stream Processing Engines

We propose FlowKV, a persistent store tailored for large-scale state management of streaming applications. Unlike existing KV stores, FlowKV leverages information from stream processing engines by taking a principled approach toward exploiting information about how and when the applications access data. FlowKV categorizes data access patterns of window operations according to how window boundaries are set and how tuples inside a window are aggregated, and deploys customized in-memory and on-disk data structures optimized for each pattern. In addition, FlowKV takes window metadata as explicit arguments of read and write methods to predict the moment when a window is read, and then loads the tuples of windows in batches from storage ahead of time. Using the NEXMark benchmark as workload, our experiments show that Apache Flink on FlowKV outperforms Flink on RocksDB or Faster with up to 4.12× throughput gain.
Bat: Bi-Alignment Based On Transformation in Multi-Target Domain Adaptation for Semantic Segmentation

While enlightening progress has been made recently in single-target domain adaptive semantic segmentation (ST-DASS), the multi-peak distributed multi-target domain cannot be directly aligned well with the single-peak distributed source domain. As a result, it is impossible for existing methods to handle the more realistic multi-target domain adaptive semantic segmentation (MT-DASS) tasks. To solve this problem, we propose a Bi-Alignment framework based on Transformation (BAT). Specifically, we employ the Fourier style transform to convert the style of the source domain to that of the target domain without training any style transfer networks. In this way, we transform the single-peak distributed source domain into a multi-peak distribution that resembles the multi-target domain. Then, we perform fine-grained global and local dual distribution alignment between the same style of source-target domain pairs to achieve a multi-to-multi distribution alignment. Finally, self-training is utilized to further improve the network’s discriminability. Experimental results show that our approach achieves competitive results over state-of-the-art methods.
A Principled Approach to Model Validation in Domain Generalization

Domain generalization aims to learn a model with good generalization ability, that is, the learned model should not only perform well on several seen domains but also on unseen domains with different data distributions. State-of-the-art domain generalization methods typically train a representation function followed by a classifier jointly to minimize both the classification risk and the domain discrepancy. However, when it comes to model selection, most of these methods rely on traditional validation routines that select models solely based on the lowest classification risk on the validation set. In this paper, we theoretically demonstrate a trade-off between minimizing classification risk and mitigating domain discrepancy, i.e., it is impossible to achieve the minimum of these two objectives simultaneously. Motivated by this theoretical result, we propose a novel model selection method suggesting that the validation process should account for both the classification risk and the domain discrepancy. We validate the effectiveness of the proposed method by numerical results on several domain generalization datasets.
SIGVIC: Spatial Importance Guided Variable-Rate Image Compression

Variable-rate mechanism has improved the flexibility and efficiency of learning-based image compression that trains multiple models for different rate-distortion tradeoffs. One of the most common approaches for variable-rate is to channel- wisely or spatial-uniformly scale the internal features. However, the diversity of spatial importance is instructive for bit allocation of image compression. In this paper, we introduce a Spatial Importance Guided Variable-rate Image Compression (SigVIC), in which a spatial gating unit (SGU) is designed for adaptively learning a spatial importance mask. Then, a spatial scaling network (SSN) takes the spatial importance mask to guide the feature scaling and bit allocation for variablerate. Moreover, to improve the quality of decoded image, Top-K shallow features are selected to refine the decoded features through a shallow feature fusion module (SFFM). Experiments show that our method outperforms other learning- based methods (whether variable-rate or not) and traditional codecs, with storage saving and high flexibility.
Grad-StyleSpeech: Any-Speaker Adaptive Text-to-Speech Synthesis with Diffusion Models

There has been a significant progress in Text-To-Speech (TTS) synthesis technology in recent years, thanks to the advancement in neural generative modeling. However, existing methods on any-speaker adaptive TTS have achieved unsatisfactory performance, due to their suboptimal accuracy in mimicking the target speakers’ styles. In this work, we present Grad-StyleSpeech, which is an any-speaker adaptive TTS framework that is based on a diffusion model that can generate highly natural speech with extremely high similarity to target speakers’ voice, given a few seconds of reference speech. Grad-StyleSpeech significantly outperforms recent speaker-adaptive TTS baselines on English benchmarks. Audio samples are available at https://nardien.github.io/grad-stylespeech-demo.
Balanced Mixup Loss for Long-Tailed Visual Recognition

In the real world, the data collected naturally are often long-tailed, which inevitably leads to class-imbalanced prediction and performance degradation. As a simple and effective data augmentation method, Mixup has been proven to be beneficial for the tail class in recent long-tail learning studies. However, samples selected by Mixup are still imbalanced, which could exacerbate the imbalance problem. Existing work always considers adjusting the input distribution to alleviate this problem, while this may lead to over-fitting in the tail class. In this paper, we detail the theoretical analysis of the data imbalance caused by Mixup, and propose a novel Balanced Mixup (BaMix) loss function from the output perspective. By adding a balance term, we theoretically prove the BaMix loss can overcome the imbalance caused by Mixup. In the experiments, our solution achieves the state-of-the-art performance on CIFAR-LT, ImageNet-LT, and iNaturalist 2018.
He-Gan: Differentially Private Gan Using Hamiltonian Monte Carlo Based Exponential Mechanism

Differentially-private (DP) Generative Adversarial Networks (GAN) can be used to protect the privacy of training data and support public downstream learning tasks with synthetic data. However, typical DP mechanisms add noise to the training process and can lead to various convergence problems. We propose HE-GAN, a DP generative framework that eliminates noise addition by using Exponential Mechanism (EM) on the privacy-factor-adjusted posterior predictive distribution of a classifier trained on the private data. EM is more general than many other DP mechanisms including Laplacian and Gaussian mechanisms. EM’s reliance on sampling the output space also prevents the DP noise from corrupting the training process. However, there are two challenges: first, sampling the posterior distribution of the private discriminative classifier may not be able to produce high-quality synthetic samples. Instead, we sample from the latent space of a publicly-trained GAN to optimize the private posterior. Second, we use the highly effective Hamiltonian Monte Carlo (HMC) method for latent space sampling. We perform experiments on MNIST and Fashion-MNIST under public-private splits. Results show that HE-GAN can achieve downstream classification accuracy on par with or better than state-of-the-art scheme over a wide range of privacy budgets.
Cascading and Direct Approaches to Unsupervised Constituency Parsing on Spoken Sentences

Past work on unsupervised parsing is constrained to written form. In this paper, we present the first study on unsupervised spoken constituency parsing given unlabeled spoken sentences and unpaired textual data. The goal is to determine the spoken sentences’ hierarchical syntactic structure in the form of constituency parse trees, such that each node is a span of audio that corresponds to a constituent. We compare two approaches: (1) cascading an unsupervised automatic speech recognition (ASR) model and an unsupervised parser to obtain parse trees on ASR transcripts, and (2) direct training an unsupervised parser on continuous word-level speech representations. This is done by first splitting utterances into sequences of word-level segments, and aggregating self-supervised speech representations within segments to obtain segment embeddings. We find that separately training a parser on the unpaired text and directly applying it on ASR transcripts for inference produces better results for unsupervised parsing. Additionally, our results suggest that accurate segmentation alone may be sufficient to parse spoken sentences accurately. Finally, we show the direct approach may learn head-directionality correctly for both head-initial and head-final languages without any explicit inductive bias.
Synthetic Pseudo Anomalies for Unsupervised Video Anomaly Detection: A Simple Yet Efficient Framework Based on Masked Autoencoder

Due to the limited availability of anomalous samples for training, video anomaly detection is commonly viewed as a one-class classification problem. Many prevalent methods investigate the reconstruction difference produced by AutoEncoders (AEs) under the assumption that the AEs would reconstruct the normal data well while reconstructing anomalies poorly. However, even with only normal data training, the AEs often reconstruct anomalies well, which depletes their anomaly detection performance. To alleviate this issue, we propose a simple yet efficient framework for video anomaly detection. The pseudo anomaly samples are introduced, which are synthesized from only normal data by embedding random mask tokens without extra data processing. We also propose a normalcy consistency training strategy that encourages the AEs to better learn the regular knowledge from normal and corresponding pseudo anomaly data. This way, the AEs learn more distinct reconstruction boundaries between normal and abnormal data, resulting in superior anomaly discrimination capability. Experimental results demonstrate the effectiveness of the proposed method.
Adaptive Filtering Algorithms For Set-Valued Observations-Symmetric Measurement Approach To Unlabeled And Anonymized Data

Suppose L simultaneous independent stochastic systems generate observations, where the observations from each system depend on the underlying parameter of that system. The observations are unlabeled (anonymized), in the sense that an analyst does not know which observation came from which stochastic system. How can the analyst estimate the underlying parameters of the L systems? Since the anonymized observations at each time are an unordered set of L measurements (rather than a vector), classical stochastic gradient algorithms cannot be directly used. By using symmetric polynomials, we formulate a symmetric measurement equation that maps the observation set to a unique vector. We then construct an adaptive filtering algorithm that yields a statistically consistent estimate of the underlying parameters.
Spatial-Temporal Graph Convolutional Network Boosted Flow-Frame Prediction For Video Anomaly Detection

Video Anomaly Detection (VAD) is a critical technology for intelligent surveillance systems and remains a challenging task in the signal processing community. An intuitive idea for VAD is to use a two-stream network to learn appearance and motion normality, respectively. However, existing approaches usually design a network architecture for the appearance stream with effort, then apply a similar architecture to the motion stream, ignoring the unique appearance and motion characteristics. In this paper, we propose STGCN-FFP, an unsupervised Spatial-Temporal Graph Convolutional Networks (STGCN) boosted Flow-Frame Prediction model. Specifically, we first design an STGCN-based memory module to extract and memorize normal patterns for optical flow, which is more suitable for learning motion normality. Then, we use a memory-augmented auto-encoder to model normal appearance patterns. Finally, the latent representation of two streams is fused to predict future frames, boosting the model to learn spatial-temporal normality. To our knowledge, STGCN-FFP is the first work applying STGCN to uniquely model the motion normality. Our method performs comparably to the state-of-the-art methods on three benchmarks.
Densitytoken: Weakly-Supervised Crowd Counting with Density Classification

Weakly-supervised crowd counting methods using only the count-level label have achieved great progress recently. However, the count-level label can not provide information about the distribution of the crowd in the scene, which will affect the accuracy of the final count result. Therefore, we design a density token to perceive the crowd distribution in the scenes. Based on this, we propose a Dual Supervision Transformer (DSFormer) to perform weakly-supervised crowd counting in the double supervision of the total count. Specifically, the encoded features in vision transformer are sent to the proposed locality enhanced module (LEM), and one branch of them with density tokens are sent into decoder for interaction. The crowd distribution perception is then realized through cross self-attention, where the other branch of encoded features are used as queries. Finally, the output features of the decoder are respectively fed into a count regression head and a crowd density classification head to obtain the crowd count and the crowd density classification. A series of experiments are conducted on three commonly used crowd counting datasets, both the quantitative and vi-sualization results illustrate the effectiveness of DSFormer. Our model achieve a superior result among weakly supervised crowd counting methods, and code is available at: https://github.com/ZaiyiHu/DSFormer.
Augmenting Transformer-Transducer Based Speaker Change Detection with Token-Level Training Loss

In this work we propose a novel token-based training strategy that improves Transformer-Transducer (T-T) based speaker change detection (SCD) performance. The conventional T-T based SCD model loss optimizes all output tokens equally. Due to the sparsity of the speaker changes in the training data, the conventional T-T based SCD model loss leads to sub-optimal detection accuracy. To mitigate this issue, we use a customized edit-distance algorithm to estimate the token-level SCD false accept (FA) and false reject (FR) rates during training and optimize model parameters to minimize a weighted combination of the FA and FR, focusing the model on accurately predicting speaker changes. We also propose a set of evaluation metrics that align better with commercial use cases. Experiments on a group of challenging real-world datasets show that the proposed training method can significantly improve the overall performance of the SCD model with the same number of parameters.
Syngen: A Syntactic Plug-And-Play Module for Generative Aspect-Based Sentiment Analysis

Aspect-based Sentiment Analysis (ABSA) is a sentiment analysis task at fine-grained level. Recently, generative frameworks have attracted increasing attention in ABSA due to their ability to unify subtasks and their continuity to upstream pre-training tasks. However, these generative models suffer from the neighboring dependency problem that induces neighboring words to get higher attention. In this paper, we propose SynGen, a plug-and-play syntactic information aware module. As a plug-in module, our SynGen can be easily applied to any generative framework backbones. The key insight of our module is to add syntactic inductive bias to attention assignment and thus direct attention to the correct target words. To the best of our knowledge, we are the first ones to introduce syntactic information to generative ABSA frameworks. Our module design is based on two main principles: (1) maintaining the structural integrity of backbone PLMs and (2) disentangling the added syntactic information and original semantic information. Empirical results on four popular ABSA datasets demonstrate that Syn-Gen enhanced model achieves a comparable performance to the state-of-the-art model with relaxed labeling specification and less training consumption.
Deep Low Light Image Enhancement Via Multi-Scale Recursive Feature Enhancement and Curve Adjustment

Photographs taken in low-illumination environment have a low signal-to-noise ratio and impaired visual quality. Enhancing lowlight images tends to amplify noise. To address this problem, we propose a Multi-Scale Recursive Feature Enhancement (MSRFE) network for low light image enhancement. The MSRFE network consists of several Feature Enhancement (FE) blocks which are applied to enhance the multi-scale image feature and remove the noise recursively in each scale residual map between adjacent scale feature. Then, a deep recursive Curve Adjustment (CA) block is proposed further fine-tunes the output of MSRFE netowrk by learning a non-linear curve which can adjust the image luminance and details. We evaluate the proposed method on both real and synthetic datasets. The results show that our proposed method outperforms other state-of-the-art methods on both visual and objective evaluation indicators.