arXiv subjects

Di Wu

Publications and source records attributed to Di Wu.

At least 379 records · Page 21Linked to original sources

arXiv2024

Bitcoin ETF: Opportunities and risk

The year 2024 witnessed a major development in the cryptocurrency industry with the long-awaited approval of spot Bitcoin exchange-traded funds (ETFs). This innovation provides investors with a new, regulated path to gain exposure to Bitcoin through a familiar investment vehicle (Kumar et al., 2024). However, unlike traditional ETFs that directly hold underlying assets, Bitcoin ETFs rely on a creation and redemption process managed by authorized participants (APs). This unique structure introduces distinct characteristics in terms of premium/discount behavior compared to traditional ETFs. This paper investigates the premium and discount patterns observed in Bitcoin ETFs during first four-month period (January 11th, 2024, to May 17th, 2024). Our analysis reveals that these patterns differ significantly from those observed in traditional index ETFs, potentially exposing investors to additional risk factors. By identifying and analyzing these risk factors associated with Bitcoin ETF premiums/discounts, this paper aims to achieve two key objectives: Enhance market understanding: Equip and market and investors with a deeper comprehension of the unique liquidity risks inherent in Bitcoin ETFs. Provide a clearer risk management frameworks: Offer a clearer perspective on the risk-return profile of digital asset ETFs, specifically focusing on Bitcoin ETFs. Through a thorough analysis of premium/discount behavior and the underlying factors contributing to it, this paper strives to contribute valuable insights for investors navigating the evolving landscape of digital asset investments

q-fin.TR↗

arXiv2024

Economic Optimal Power Management of Second-Life Battery Energy Storage Systems

Second-life battery energy storage systems (SL-BESS) are an economical means of long-duration grid energy storage. They utilize retired battery packs from electric vehicles to store and provide electrical energy at the utility scale. However, they pose critical challenges in achieving optimal utilization and extending their remaining useful life. These complications primarily result from the constituent battery packs' inherent heterogeneities in terms of their size, chemistry, and degradation. This paper proposes an economic optimal power management approach to ensure the cost-minimized operation of SL-BESS while adhering to safety regulations and maintaining a balance between the power supply and demand. The proposed approach takes into account the costs associated with the degradation, energy loss, and decommissioning of the battery packs. In particular, we capture the degradation costs of the retired battery packs through a weighted average Ah-throughput aging model. The presented model allows us to quantify the capacity fading for second-life battery packs for different operating temperatures and C-rates. To evaluate the performance of the proposed approach, we conduct extensive simulations on a SL-BESS consisting of various heterogeneous retired battery packs in the context of grid operation. The results offer novel insights into SL-BESS operation and highlight the importance of prudent power management to ensure economically optimal utilization.

eess.SY↗

arXiv2024

Convolutional Neural Network Compression Based on Low-Rank Decomposition

Deep neural networks typically impose significant computational loads and memory consumption. Moreover, the large parameters pose constraints on deploying the model on edge devices such as embedded systems. Tensor decomposition offers a clear advantage in compressing large-scale weight tensors. Nevertheless, direct utilization of low-rank decomposition typically leads to significant accuracy loss. This paper proposes a model compression method that integrates Variational Bayesian Matrix Factorization (VBMF) with orthogonal regularization. Initially, the model undergoes over-parameterization and training, with orthogonal regularization applied to enhance its likelihood of achieving the accuracy of the original model. Secondly, VBMF is employed to estimate the rank of the weight tensor at each layer. Our framework is sufficiently general to apply to other convolutional neural networks and easily adaptable to incorporate other tensor decomposition methods. Experimental results show that for both high and low compression ratios, our compression model exhibits advanced performance.

cs.CV↗

arXiv2024

The effects of data preprocessing on probability of default model fairness

In the context of financial credit risk evaluation, the fairness of machine learning models has become a critical concern, especially given the potential for biased predictions that disproportionately affect certain demographic groups. This study investigates the impact of data preprocessing, with a specific focus on Truncated Singular Value Decomposition (SVD), on the fairness and performance of probability of default models. Using a comprehensive dataset sourced from Kaggle, various preprocessing techniques, including SVD, were applied to assess their effect on model accuracy, discriminatory power, and fairness.

econ.EM↗

arXiv2024

PPVF: An Efficient Privacy-Preserving Online Video Fetching Framework with Correlated Differential Privacy

Online video streaming has evolved into an integral component of the contemporary Internet landscape. Yet, the disclosure of user requests presents formidable privacy challenges. As users stream their preferred online videos, their requests are automatically seized by video content providers, potentially leaking users' privacy. Unfortunately, current protection methods are not well-suited to preserving user request privacy from content providers while maintaining high-quality online video services. To tackle this challenge, we introduce a novel Privacy-Preserving Video Fetching (PPVF) framework, which utilizes trusted edge devices to pre-fetch and cache videos, ensuring the privacy of users' requests while optimizing the efficiency of edge caching. More specifically, we design PPVF with three core components: (1) \textit{Online privacy budget scheduler}, which employs a theoretically guaranteed online algorithm to select non-requested videos as candidates with assigned privacy budgets. Alternative videos are chosen by an online algorithm that is theoretically guaranteed to consider both video utilities and available privacy budgets. (2) \textit{Noisy video request generator}, which generates redundant video requests (in addition to original ones) utilizing correlated differential privacy to obfuscate request privacy. (3) \textit{Online video utility predictor}, which leverages federated learning to collaboratively evaluate video utility in an online fashion, aiding in video selection in (1) and noise generation in (2). Finally, we conduct extensive experiments using real-world video request traces from Tencent Video. The results demonstrate that PPVF effectively safeguards user request privacy while upholding high video caching performance.

cs.MM↗

arXiv2024

Static neutral black holes in Kalb-Ramond gravity

The Kalb-Ramond (KR) gravity theory, a modified gravity theory that nonminimally couples a KR field with a nonzero vacuum expectation value for the gravitational field, can spontaneously break the Lorentz symmetry of gravity. In a recent work, Yang et al. [Phys. Rev. D 108, 124004 (2023)] successfully derived Schwarzschild-like black hole solutions both with and without a nonzero cosmological constant within the framework of KR gravity. However, their analysis did not address the more general case of static, neutral, spherically symmetric black holes. In this paper, we fill this gap by resolving the field equations to construct more general static, neutral, spherically symmetric black hole solutions both with and without a nonzero cosmological constant. Our black hole solutions are shown to obey the first law and the Bekenstein-Smarr mass formulas of black hole thermodynamics. Moreover, we demonstrate that our static neutral spherically symmetric AdS black hole does not always satisfy the reverse isoperimetric inequality (RII), as the isoperimetric ratio can be larger or smaller than unity depending on the placement of the solution parameters within the parameter space. This behavior contrasts with the above-mentioned Schwarzschild-like AdS black hole in the KR gravity theory, which always obeys the RII. Significantly, the present more general static, neutral, spherically symmetric AdS black hole is the first example of a static AdS black hole that can violate the RII.

hep-th↗

arXiv2024

Fairness-Aware Streaming Feature Selection with Causal Graphs

Its crux lies in the optimization of a tradeoff between accuracy and fairness of resultant models on the selected feature subset. The technical challenge of our setting is twofold: 1) streaming feature inputs, such that an informative feature may become obsolete or redundant for prediction if its information has been covered by other similar features that arrived prior to it, and 2) non-associational feature correlation, such that bias may be leaked from those seemingly admissible, non-protected features. To overcome this, we propose Streaming Feature Selection with Causal Fairness (SFCF) that builds two causal graphs egocentric to prediction label and protected feature, respectively, striving to model the complex correlation structure among streaming features, labels, and protected information. As such, bias can be eradicated from predictive modeling by removing those features being causally correlated with the protected feature yet independent to the labels. We theorize that the originally redundant features for prediction can later become admissible, when the learning accuracy is compromised by the large number of removed features (non-protected but can be used to reconstruct bias information). We benchmark SFCF\ on five datasets widely used in streaming feature research, and the results substantiate its performance superiority over six rival models in terms of efficiency and sparsity of feature selection and equalized odds of the resultant predictive models.

cs.LG↗

arXiv2024

Thermodynamic topology of Phantom AdS Black Holes in Massive Gravity

In this work, we explore the thermodynamic topology of phantom AdS black holes in the context of massive gravity. To this end, we evaluate these black holes in two distinct ensembles: the canonical and grand canonical ensembles (GCE). We begin by examining the topological charge linked to the critical point and confirming the existence of a conventional critical point $(CP_{1})$ in the canonical ensemble (CE), this critical point has a topological charge of $-1$ and acts as a point of phase annihilation, this situation can only be considered within the context of the classical Einstein-Maxwell (CEM) theory $(η=1)$, while no critical point is identified in the GCE. Furthermore, we consider black holes as a topological defect within the thermodynamic space. To gain an understanding of the local and global topological configuration of this defect, we will analyze its winding numbers, and observe that the total topological charge in the CE consistently remains at $1$. When the system experiences a pressure below the critical threshold, it gives rise to the occurrence of annihilation and generation points. The value of electric potential determines whether the total topological charge in the GCE is zero or one. As a result, we detect a point of generation point or absence of generation/annihilation point. Based on our analysis, it can be inferred that ensembles significantly impact the topological class of phantom AdS black holes in massive gravity.

gr-qc↗

arXiv2024

Long-Tailed Classification Based on Coarse-Grained Leading Forest and Multi-Center Loss

Long-tailed (LT) classification is an unavoidable and challenging problem in the real world. Most existing long-tailed classification methods focus only on solving the class-wise imbalance while ignoring the attribute-wise imbalance. The deviation of a classification model is caused by both class-wise and attribute-wise imbalance. Due to the fact that attributes are implicit in most datasets and the combination of attributes is complex, attribute-wise imbalance is more difficult to handle. For this purpose, we proposed a novel long-tailed classification framework, aiming to build a multi-granularity classification model by means of invariant feature learning. This method first unsupervisedly constructs Coarse-Grained forest (CLF) to better characterize the distribution of attributes within a class. Depending on the distribution of attributes, one can customize suitable sampling strategies to construct different imbalanced datasets. We then introduce multi-center loss (MCL) that aims to gradually eliminate confusing attributes during feature learning process. The proposed framework does not necessarily couple to a specific LT classification model structure and can be integrated with any existing LT method as an independent component. Extensive experiments show that our approach achieves state-of-the-art performance on both existing benchmarks ImageNet-GLT and MSCOCO-GLT and can improve the performance of existing LT methods. Our codes are available on GitHub: \url{https://github.com/jinyery/cognisance}

cs.CV↗

arXiv2024

HAIFIT: Human-to-AI Fashion Image Translation

In the realm of fashion design, sketches serve as the canvas for expressing an artist's distinctive drawing style and creative vision, capturing intricate details like stroke variations and texture nuances. The advent of sketch-to-image cross-modal translation technology has notably aided designers. However, existing methods often compromise these sketch details during image generation, resulting in images that deviate from the designer's intended concept. This limitation hampers the ability to offer designers a precise preview of the final output. To overcome this challenge, we introduce HAIFIT, a novel approach that transforms sketches into high-fidelity, lifelike clothing images by integrating multi-scale features and capturing extensive feature map dependencies from diverse perspectives. Through extensive qualitative and quantitative evaluations conducted on our self-collected dataset, our method demonstrates superior performance compared to existing methods in generating photorealistic clothing images. Our method excels in preserving the distinctive style and intricate details essential for fashion design applications. In addition, our method also has obvious advantages in model training and inference speed, contributing to reducing designers' time costs and improving design efficiency.

cs.CV↗

arXiv2024

Infra-YOLO: Efficient Neural Network Structure with Model Compression for Real-Time Infrared Small Object Detection

Although convolutional neural networks have made outstanding achievements in visible light target detection, there are still many challenges in infrared small object detection because of the low signal-to-noise ratio, incomplete object structure, and a lack of reliable infrared small object dataset. To resolve limitations of the infrared small object dataset, a new dataset named InfraTiny was constructed, and more than 85% bounding box is less than 32x32 pixels (3218 images and a total of 20,893 bounding boxes). A multi-scale attention mechanism module (MSAM) and a Feature Fusion Augmentation Pyramid Module (FFAFPM) were proposed and deployed onto embedded devices. The MSAM enables the network to obtain scale perception information by acquiring different receptive fields, while the background noise information is suppressed to enhance feature extraction ability. The proposed FFAFPM can enrich semantic information, and enhance the fusion of shallow feature and deep feature, thus false positive results have been significantly reduced. By integrating the proposed methods into the YOLO model, which is named Infra-YOLO, infrared small object detection performance has been improved. Compared to yolov3, mAP@0.5 has been improved by 2.7%; and compared to yolov4, that by 2.5% on the InfraTiny dataset. The proposed Infra-YOLO was also transferred onto the embedded device in the unmanned aerial vehicle (UAV) for real application scenarios, where the channel pruning method is adopted to reduce FLOPs and to achieve a tradeoff between speed and accuracy. Even if the parameters of Infra-YOLO are reduced by 88% with the pruning method, a gain of 0.7% is still achieved on mAP@0.5 compared to yolov3, and a gain of 0.5% compared to yolov4. Experimental results show that the proposed MSAM and FFAFPM method can improve infrared small object detection performance compared with the previous benchmark method.

cs.CV↗

arXiv2024

U2++ MoE: Scaling 4.7x parameters with minimal impact on RTF

Scale has opened new frontiers in natural language processing, but at a high cost. In response, by learning to only activate a subset of parameters in training and inference, Mixture-of-Experts (MoE) have been proposed as an energy efficient path to even larger and more capable language models and this shift towards a new generation of foundation models is gaining momentum, particularly within the field of Automatic Speech Recognition (ASR). Recent works that incorporating MoE into ASR models have complex designs such as routing frames via supplementary embedding network, improving multilingual ability for the experts, and utilizing dedicated auxiliary losses for either expert load balancing or specific language handling. We found that delicate designs are not necessary, while an embarrassingly simple substitution of MoE layers for all Feed-Forward Network (FFN) layers is competent for the ASR task. To be more specific, we benchmark our proposed model on a large scale inner-source dataset (160k hours), the results show that we can scale our baseline Conformer (Dense-225M) to its MoE counterparts (MoE-1B) and achieve Dense-1B level Word Error Rate (WER) while maintaining a Dense-225M level Real Time Factor (RTF). Furthermore, by applying Unified 2-pass framework with bidirectional attention decoders (U2++), we achieve the streaming and non-streaming decoding modes in a single MoE based model, which we call U2++ MoE. We hope that our study can facilitate the research on scaling speech foundation models without sacrificing deployment efficiency.

cs.CL↗

arXiv2024

HydraFormer: One Encoder For All Subsampling Rates

In automatic speech recognition, subsampling is essential for tackling diverse scenarios. However, the inadequacy of a single subsampling rate to address various real-world situations often necessitates training and deploying multiple models, consequently increasing associated costs. To address this issue, we propose HydraFormer, comprising HydraSub, a Conformer-based encoder, and a BiTransformer-based decoder. HydraSub encompasses multiple branches, each representing a distinct subsampling rate, allowing for the flexible selection of any branch during inference based on the specific use case. HydraFormer can efficiently manage different subsampling rates, significantly reducing training and deployment expenses. Experiments on AISHELL-1 and LibriSpeech datasets reveal that HydraFormer effectively adapts to various subsampling rates and languages while maintaining high recognition performance. Additionally, HydraFormer showcases exceptional stability, sustaining consistent performance under various initialization conditions, and exhibits robust transferability by learning from pretrained single subsampling rate automatic speech recognition models\footnote{Model code and scripts: https://github.com/HydraFormer/hydraformer}.

eess.AS↗

arXiv2024

Robust Load Prediction of Power Network Clusters Based on Cloud-Model-Improved Transformer

Load data from power network clusters indicates economic development in each area, crucial for predicting regional trends and guiding power enterprise decisions. The Transformer model, a leading method for load prediction, faces challenges modeling historical data due to variables like weather, events, festivals, and data volatility. To tackle this, the cloud model's fuzzy feature is utilized to manage uncertainties effectively. Presenting an innovative approach, the Cloud Model Improved Transformer (CMIT) method integrates the Transformer model with the cloud model utilizing the particle swarm optimization algorithm, with the aim of achieving robust and precise power load predictions. Through comparative experiments conducted on 31 real datasets within a power network cluster, it is demonstrated that CMIT significantly surpasses the Transformer model in terms of prediction accuracy, thereby highlighting its effectiveness in enhancing forecasting capabilities within the power network cluster sector.

cs.LG↗

arXiv2024

Federated Learning based Latent Factorization of Tensors for Privacy-Preserving QoS Prediction

In applications related to big data and service computing, dynamic connections tend to be encountered, especially the dynamic data of user-perspective quality of service (QoS) in Web services. They are transformed into high-dimensional and incomplete (HDI) tensors which include abundant temporal pattern information. Latent factorization of tensors (LFT) is an extremely efficient and typical approach for extracting such patterns from an HDI tensor. However, current LFT models require the QoS data to be maintained in a central place (e.g., a central server), which is impossible for increasingly privacy-sensitive users. To address this problem, this article creatively designs a federated learning based on latent factorization of tensors (FL-LFT). It builds a data-density -oriented federated learning model to enable isolated users to collaboratively train a global LFT model while protecting user's privacy. Extensive experiments on a QoS dataset collected from the real world verify that FL-LFT shows a remarkable increase in prediction accuracy when compared to state-of-the-art federated learning (FL) approaches.

cs.LG↗

arXiv2024

Propagation of Uncertainty with the Koopman Operator

This paper proposes a new method to propagate uncertainties undergoing nonlinear dynamics using the Koopman Operator (KO). Probability density functions are propagated directly using the Koopman approximation of the solution flow of the system, where the dynamics have been projected on a well-defined set of basis functions. The prediction technique is derived following both the analytical (Galerkin) and numerical (EDMD) derivation of the KO, and a least square reduction algorithm assures the recursivity of the proposed methodology.

cs.IT↗

arXiv2024

Appformer: A Novel Framework for Mobile App Usage Prediction Leveraging Progressive Multi-Modal Data Fusion and Feature Extraction

This article presents Appformer, a novel mobile application prediction framework inspired by the efficiency of Transformer-like architectures in processing sequential data through self-attention mechanisms. Combining a Multi-Modal Data Progressive Fusion Module with a sophisticated Feature Extraction Module, Appformer leverages the synergies of multi-modal data fusion and data mining techniques while maintaining user privacy. The framework employs Points of Interest (POIs) associated with base stations, optimizing them through comprehensive comparative experiments to identify the most effective clustering method. These refined inputs are seamlessly integrated into the initial phases of cross-modal data fusion, where temporal units are encoded via word embeddings and subsequently merged in later stages. The Feature Extraction Module, employing Transformer-like architectures specialized for time series analysis, adeptly distils comprehensive features. It meticulously fine-tunes the outputs from the fusion module, facilitating the extraction of high-calibre, multi-modal features, thus guaranteeing a robust and efficient extraction process. Extensive experimental validation confirms Appformer's effectiveness, attaining state-of-the-art (SOTA) metrics in mobile app usage prediction, thereby signifying a notable progression in this field.

cs.AI↗

arXiv2024

Topological classes of thermodynamics of the rotating charged AdS black holes in gauged supergravities

In this paper, we investigate the topological numbers of rotating charged AdS black holes in both four- and five-dimensional gauged supergravity theories. Our analysis is conducted within the framework of the thermodynamical topological approach to black holes, utilizing the generalized off-shell Helmholtz free energy. We demonstrate that the number of rotation parameters plays a significant role in determining the topological numbers of five-dimensional rotating AdS black holes. Moreover, our findings indicate that the topological numbers of both four- and five-dimensional rotating AdS black holes are not influenced by the number of electric charge parameters. This highlights a distinct difference in how rotation and electric charge parameters impact the thermodynamic topological properties of these black holes.

hep-th↗