Outstanding hydrogen evolution reaction catalyzed by porous nickel diselenide electrocatalysts

To relieve our strong reliance on fossil fuels and to reduce greenhouse effects, there is an ever-growing interest in using electrocatalytic water splitting to produce green, renewable, and environment-benign hydrogen fuel via the hydrogen evolution reaction. For commercially feasible water electrolysis, it is imperative to develop electrocatalysts that perform as efficiently as Pt but using only earth-abundant commercial materials. However, the highest performance current catalysts consist of nanostructures made by using complex methods. Here we report a porous nickel diselenide (NiSe_2) catalyst that is superior for water electrolysis, exhibiting much better catalytic performance than most first-row transition metal dichalcogenide-based catalysts, well-studied MoS_2, and WS_2-based catalysts. Indeed NiSe2 performs comparably to the state-of-the-art Pt catalysts. We fabricate NiSe_2 directly from commercial nickel foam by acetic acid-assisted surface roughness engineering. To understand the origin of the high performance, we use first-principles calculations to identify the active sites. This work demonstrates the commercial possibility of hydrogen production via water electrolysis using porous bulk NiSe_2 catalysts

Efficient hydrogen evolution by ternary molybdenum sulfoselenide particles on self-standing porous nickel diselenide foam

With the massive consumption of fossil fuels and its detrimental impact on the environment, methods of generating clean power are urgent. Hydrogen is an ideal carrier for renewable energy; however, hydrogen generation is inefficient because of the lack of robust catalysts that are substantially cheaper than platinum. Therefore, robust and durable earth-abundant and cost-effective catalysts are desirable for hydrogen generation from water splitting via hydrogen evolution reaction. Here we report an active and durable earth-abundant transition metal dichalcogenide-based hybrid catalyst that exhibits high hydrogen evolution activity approaching the state-of-the-art platinum catalysts, and superior to those of most transition metal dichalcogenides (molybdenum sulfide, cobalt diselenide and so on). Our material is fabricated by growing ternary molybdenum sulfoselenide particles on self-standing porous nickel diselenide foam. This advance provides a different pathway to design cheap, efficient and sizable hydrogen-evolving electrode by simultaneously tuning the number of catalytic edge sites, porosity, heteroatom doping and electrical conductivity

Infomax Neural Joint Source-Channel Coding via Adversarial Bit Flip

Although Shannon theory states that it is asymptotically optimal to separate the source and channel coding as two independent processes, in many practical communication scenarios this decomposition is limited by the finite bit-length and computational power for decoding. Recently, neural joint source-channel coding (NECST) is proposed to sidestep this problem. While it leverages the advancements of amortized inference and deep learning to improve the encoding and decoding process, it still cannot always achieve compelling results in terms of compression and error correction performance due to the limited robustness of its learned coding networks. In this paper, motivated by the inherent connections between neural joint source-channel coding and discrete representation learning, we propose a novel regularization method called Infomax Adversarial-Bit-Flip (IABF) to improve the stability and robustness of the neural joint source-channel coding scheme. More specifically, on the encoder side, we propose to explicitly maximize the mutual information between the codeword and data; while on the decoder side, the amortized reconstruction is regularized within an adversarial framework. Extensive experiments conducted on various real-world datasets evidence that our IABF can achieve state-of-the-art performances on both compression and error correction benchmarks and outperform the baselines by a significant margin.Comment: AAAI202

Higher-order Structure Based Anomaly Detection on Attributed Networks

Anomaly detection (such as telecom fraud detection and medical image detection) has attracted the increasing attention of people. The complex interaction between multiple entities widely exists in the network, which can reflect specific human behavior patterns. Such patterns can be modeled by higher-order network structures, thus benefiting anomaly detection on attributed networks. However, due to the lack of an effective mechanism in most existing graph learning methods, these complex interaction patterns fail to be applied in detecting anomalies, hindering the progress of anomaly detection to some extent. In order to address the aforementioned issue, we present a higher-order structure based anomaly detection (GUIDE) method. We exploit attribute autoencoder and structure autoencoder to reconstruct node attributes and higher-order structures, respectively. Moreover, we design a graph attention layer to evaluate the significance of neighbors to nodes through their higher-order structure differences. Finally, we leverage node attribute and higher-order structure reconstruction errors to find anomalies. Extensive experiments on five real-world datasets (i.e., ACM, Citation, Cora, DBLP, and Pubmed) are implemented to verify the effectiveness of GUIDE. Experimental results in terms of ROC-AUC, PR-AUC, and Recall@K show that GUIDE significantly outperforms the state-of-art methods

LoMAE: Low-level Vision Masked Autoencoders for Low-dose CT Denoising

Low-dose computed tomography (LDCT) offers reduced X-ray radiation exposure but at the cost of compromised image quality, characterized by increased noise and artifacts. Recently, transformer models emerged as a promising avenue to enhance LDCT image quality. However, the success of such models relies on a large amount of paired noisy and clean images, which are often scarce in clinical settings. In the fields of computer vision and natural language processing, masked autoencoders (MAE) have been recognized as an effective label-free self-pretraining method for transformers, due to their exceptional feature representation ability. However, the original pretraining and fine-tuning design fails to work in low-level vision tasks like denoising. In response to this challenge, we redesign the classical encoder-decoder learning model and facilitate a simple yet effective low-level vision MAE, referred to as LoMAE, tailored to address the LDCT denoising problem. Moreover, we introduce an MAE-GradCAM method to shed light on the latent learning mechanisms of the MAE/LoMAE. Additionally, we explore the LoMAE's robustness and generability across a variety of noise levels. Experiments results show that the proposed LoMAE can enhance the transformer's denoising performance and greatly relieve the dependence on the ground truth clean data. It also demonstrates remarkable robustness and generalizability over a spectrum of noise levels

The Devil Behind the Mirror: Tracking the Campaigns of Cryptocurrency Abuses on the Dark Web

The dark web has emerged as the state-of-the-art solution for enhanced anonymity. Just like a double-edged sword, it also inadvertently becomes the safety net and breeding ground for illicit activities. Among them, cryptocurrencies have been prevalently abused to receive illicit income while evading regulations. Despite the continuing efforts to combat illicit activities, there is still a lack of an in-depth understanding regarding the characteristics and dynamics of cryptocurrency abuses on the dark web. In this work, we conduct a multi-dimensional and systematic study to track cryptocurrency-related illicit activities and campaigns on the dark web. We first harvest a dataset of 4,923 cryptocurrency-related onion sites with over 130K pages. Then, we detect and extract the illicit blockchain transactions to characterize the cryptocurrency abuses, targeting features from single/clustered addresses and illicit campaigns. Throughout our study, we have identified 2,564 illicit sites with 1,189 illicit blockchain addresses, which account for 90.8 BTC in revenue. Based on their inner connections, we further identify 66 campaigns behind them. Our exploration suggests that illicit activities on the dark web have strong correlations, which can guide us to identify new illicit blockchain addresses and onions, and raise alarms at the early stage of their deployment

Personalized Federated Graph Learning On Non-IID Electronic Health Records

Understanding The Latent Disease Patterns Embedded In Electronic Health Records (EHRs) Is Crucial For Making Precise And Proactive Healthcare Decisions. Federated Graph Learning-Based Methods Are Commonly Employed To Extract Complex Disease Patterns From The Distributed EHRs Without Sharing The Client-Side Raw Data. However, The Intrinsic Characteristics Of The Distributed EHRs Are Typically Non-Independent And Identically Distributed (Non-IID), Significantly Bringing Challenges Related To Data Imbalance And Leading To A Notable Decrease In The Effectiveness Of Making Healthcare Decisions Derived From The Global Model. To Address These Challenges, We Introduce A Novel Personalized Federated Learning Framework Named PEARL, Which Is Designed For Disease Prediction On Non-IID EHRs. Specifically, PEARL Incorporates Disease Diagnostic Code Attention And Admission Record Attention To Extract Patient Embeddings From All EHRs. Then, PEARL Integrates Self-Supervised Learning Into A Federated Learning Framework To Train A Global Model For Hierarchical Disease Prediction. To Improve The Performance Of The Client Model, We Further Introduce A Fine-Tuning Scheme To Personalize The Global Model Using Local EHRs. During The Global Model Updating Process, A Differential Privacy (DP) Scheme Is Implemented, Providing A High-Level Privacy Guarantee. Extensive Experiments Conducted On The Real-World MIMIC-III Dataset Validate The Effectiveness Of PEARL, Demonstrating Competitive Results When Compared With Baselines