Entropy最新文献_第3页

Entropy-Assisted Quality Pattern Identification in Finance. 金融中的熵辅助质量模式识别。

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-04-16 DOI: 10.3390/e27040430

Rishabh Gupta, Shivam Gupta, Jaskirat Singh, Sabre Kais

{"title":"Entropy-Assisted Quality Pattern Identification in Finance.","authors":"Rishabh Gupta, Shivam Gupta, Jaskirat Singh, Sabre Kais","doi":"10.3390/e27040430","DOIUrl":"https://doi.org/10.3390/e27040430","url":null,"abstract":"Short-term patterns in financial time series form the cornerstone of many algorithmic trading strategies, yet extracting these patterns reliably from noisy market data remains a formidable challenge. In this paper, we propose an entropy-assisted framework for identifying high-quality, non-overlapping patterns that exhibit consistent behavior over time. We ground our approach in the premise that historical patterns, when accurately clustered and pruned, can yield substantial predictive power for short-term price movements. To achieve this, we incorporate an entropy-based measure as a proxy for information gain: patterns that lead to high one-sided movements in historical data yet retain low local entropy are more \"informative\" in signaling future market direction. Compared to conventional clustering techniques such as K-means and Gaussian Mixture Models (GMMs), which often yield biased or unbalanced groupings, our approach emphasizes balance over a forced visual boundary, ensuring that quality patterns are not lost due to over-segmentation. By emphasizing both predictive purity (low local entropy) and historical profitability, our method achieves a balanced representation of Buy and Sell patterns, making it better suited for short-term algorithmic trading strategies. This paper offers an in-depth illustration of our entropy-assisted framework through two case studies on Gold vs. USD and GBPUSD. While these examples demonstrate the method's potential for extracting high-quality patterns, they do not constitute an exhaustive survey of all possible asset classes.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 4","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12025679/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143990579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Classical Data in Quantum Machine Learning Algorithms: Amplitude Encoding and the Relation Between Entropy and Linguistic Ambiguity. 量子机器学习算法中的经典数据：幅度编码和熵与语言歧义的关系。

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-04-16 DOI: 10.3390/e27040433

Jurek Eisinger, Ward Gauderis, Lin de Huybrecht, Geraint A Wiggins

{"title":"Classical Data in Quantum Machine Learning Algorithms: Amplitude Encoding and the Relation Between Entropy and Linguistic Ambiguity.","authors":"Jurek Eisinger, Ward Gauderis, Lin de Huybrecht, Geraint A Wiggins","doi":"10.3390/e27040433","DOIUrl":"https://doi.org/10.3390/e27040433","url":null,"abstract":"The Categorical Compositional Distributional (DisCoCat) model has been proven to be very successful in modelling sentence meaning as the interaction of word meanings. Words are modelled as quantum states, interacting guided by grammar. This model of language has been extended to density matrices to account for ambiguity in language. Density matrices describe probability distributions over quantum states, and in this work we relate the mixedness of density matrices to ambiguity in the sentences they represent. The von Neumann entropy and the fidelity are used as measures of this mixedness. Via the process of amplitude encoding, we introduce classical data into quantum machine learning algorithms. First, the findings suggest that in quantum natural language processing, amplitude-encoding data onto a quantum computer can be a useful tool to improve the performance of the quantum machine learning models used. Second, the effect that these encoded data have on the above-introduced relation between entropy and ambiguity is investigated. We conclude that amplitude-encoding classical data in quantum machine learning algorithms makes the relation between the entropy of a density matrix and ambiguity in the sentence modelled by this density matrix much more intuitively interpretable.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 4","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12025794/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144003957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spread Spectrum Image Watermarking Through Latent Diffusion Model. 基于潜在扩散模型的扩频图像水印。

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-04-15 DOI: 10.3390/e27040428

Hongfei Wu, Xiaodan Lin, Gewei Tan

{"title":"Spread Spectrum Image Watermarking Through Latent Diffusion Model.","authors":"Hongfei Wu, Xiaodan Lin, Gewei Tan","doi":"10.3390/e27040428","DOIUrl":"https://doi.org/10.3390/e27040428","url":null,"abstract":"The rapid development of diffusion models in image generation and processing has led to significant security concerns. Diffusion models are capable of producing highly realistic images that are indistinguishable from real ones. Although deploying a watermarking system can be a countermeasure to verify the ownership or the origin of images, the regeneration attacks arising from diffusion models can easily remove the embedded watermark from the images, without compromising their perceptual quality. Previous watermarking methods that hide watermark information in the carrier image are vulnerable to these newly emergent attacks. To address these challenges, we propose a robust and traceable watermark framework based on the latent diffusion model, where the spread-spectrum watermark is coupled with the diffusion noise to ensure its security and imperceptibility. Since the diffusion model is trained to reduce information entropy from disordered data to restore its true distribution, the transparency of the hidden watermark is guaranteed. Benefiting from the spread spectrum strategy, the decoder structure is no longer needed for watermark extraction, greatly alleviating the training overhead. Additionally, the robustness and transparency are easily controlled by a strength factor, whose operating range is studied in this work. Experimental results demonstrate that our method performs not only against common attacks, but also against regeneration attacks and semantic-based image editing.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 4","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12025525/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143987696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Semantic Arithmetic Coding Using Synonymous Mappings. 使用同义映射的语义算术编码。

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-04-15 DOI: 10.3390/e27040429

Zijian Liang, Kai Niu, Jin Xu, Ping Zhang

引用次数: 0

Unsupervised Domain Adaptation Method Based on Relative Entropy Regularization and Measure Propagation. 基于相对熵正则化和度量传播的无监督域自适应方法。

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-04-14 DOI: 10.3390/e27040426

Lianghao Tan, Zhuo Peng, Yongjia Song, Xiaoyi Liu, Huangqi Jiang, Shubing Liu, Weixi Wu, Zhiyuan Xiang

{"title":"Unsupervised Domain Adaptation Method Based on Relative Entropy Regularization and Measure Propagation.","authors":"Lianghao Tan, Zhuo Peng, Yongjia Song, Xiaoyi Liu, Huangqi Jiang, Shubing Liu, Weixi Wu, Zhiyuan Xiang","doi":"10.3390/e27040426","DOIUrl":"https://doi.org/10.3390/e27040426","url":null,"abstract":"This paper presents a novel unsupervised domain adaptation (UDA) framework that integrates information-theoretic principles to mitigate distributional discrepancies between source and target domains. The proposed method incorporates two key components: (1) relative entropy regularization, which leverages Kullback-Leibler (KL) divergence to align the predicted label distribution of the target domain with a reference distribution derived from the source domain, thereby reducing prediction uncertainty; and (2) measure propagation, a technique that transfers probability mass from the source domain to generate pseudo-measures-estimated probabilistic representations-for the unlabeled target domain. This dual mechanism enhances both global feature alignment and semantic consistency across domains. Extensive experiments on benchmark datasets (OfficeHome and DomainNet) demonstrate that the proposed approach consistently outperforms State-of-the-Art methods, particularly in scenarios with significant domain shifts. These results confirm the robustness, scalability, and theoretical grounding of our framework, offering a new perspective on the fusion of information theory and domain adaptation.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 4","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12025361/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143986106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Significance of the Entropic Measure of Time in Natural Sciences. 时间熵在自然科学中的意义。

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-04-14 DOI: 10.3390/e27040425

Leonid M Martyushev

引用次数: 0

Quasi-Optimal Path Convergence-Aided Automorphism Ensemble Decoding of Reed-Muller Codes. Reed-Muller码的拟最优路径收敛辅助自同构集成译码。

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-04-14 DOI: 10.3390/e27040424

Kairui Tian, He Sun, Yukai Liu, Rongke Liu

{"title":"Quasi-Optimal Path Convergence-Aided Automorphism Ensemble Decoding of Reed-Muller Codes.","authors":"Kairui Tian, He Sun, Yukai Liu, Rongke Liu","doi":"10.3390/e27040424","DOIUrl":"https://doi.org/10.3390/e27040424","url":null,"abstract":"By exploiting the rich automorphisms of Reed-Muller (RM) codes, the recently developed automorphism ensemble (AE) successive cancellation (SC) decoder achieves a near-maximum-likelihood (ML) performance for short block lengths. However, the appealing performance of AE-SC decoding arises from the diversity gain that requires a list of SC decoding attempts, which results in a high decoding complexity. To address this issue, this paper proposes a novel quasi-optimal path convergence (QOPC)-aided early termination (ET) technique for AE-SC decoding. This technique detects strong convergence between the partial path metrics (PPMs) of SC constituent decoders to reliably identify the optimal decoding path at runtime. When the QOPC-based ET criterion is satisfied during the AE-SC decoding, only the identified path is allowed to proceed for a complete codeword estimate, while the remaining paths are terminated early. The numerical results demonstrated that for medium-to-high-rate RM codes in the short-length regime, the proposed QOPC-aided ET method incurred negligible performance loss when applied to fully parallel AE-SC decoding. Meanwhile, it achieved a complexity reduction that ranged from 35.9% to 47.4% at a target block error rate (BLER) of 10-3, where it consistently outperformed a state-of-the-art path metric threshold (PMT)-aided ET method. Additionally, under a partially parallel framework of AE-SC decoding, the proposed QOPC-aided ET method achieved a greater complexity reduction that ranged from 81.3% to 86.7% at a low BLER that approached 10-5 while maintaining a near-ML decoding performance.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 4","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12026239/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143974164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Preventing Posterior Collapse with DVAE for Text Modeling. 基于DVAE的文本建模预防后塌陷。

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-04-14 DOI: 10.3390/e27040423

Tianbao Song, Zongyi Huang, Xin Liu, Jingbo Sun

{"title":"Preventing Posterior Collapse with DVAE for Text Modeling.","authors":"Tianbao Song, Zongyi Huang, Xin Liu, Jingbo Sun","doi":"10.3390/e27040423","DOIUrl":"https://doi.org/10.3390/e27040423","url":null,"abstract":"This paper introduces a novel variational autoencoder model termed DVAE to prevent posterior collapse in text modeling. DVAE employs a dual-path architecture within its decoder: path A and path B. Path A makes the direct input of text instances into the decoder, whereas path B replaces a subset of word tokens in the text instances with a generic unknown token before their input into the decoder. A stopping strategy is implemented, wherein both paths are concurrently active during the early phases of training. As the model progresses towards convergence, path B is removed. To further refine the performance, a KL weight dropout method is employed, which randomly sets certain dimensions of the KL weight to zero during the annealing process. DVAE compels the latent variables to encode more information about the input texts through path B and fully utilize the expressiveness of the decoder, as well as avoiding the local optimum when path B is active through path A and the stopping strategy. Furthermore, the KL weight dropout method augments the number of active units within the latent variables. Experimental results show the excellent performance of DVAE in density estimation, representation learning, and text generation.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 4","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12026048/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143968496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Quantum Error Mitigation in Optimized Circuits for Particle-Density Correlations in Real-Time Dynamics of the Schwinger Model. Schwinger模型实时动力学中粒子密度相关优化电路中的量子误差缓解。

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-04-14 DOI: 10.3390/e27040427

Domenico Pomarico, Mahul Pandey, Riccardo Cioli, Federico Dell'Anna, Saverio Pascazio, Francesco V Pepe, Paolo Facchi, Elisa Ercolessi

{"title":"Quantum Error Mitigation in Optimized Circuits for Particle-Density Correlations in Real-Time Dynamics of the Schwinger Model.","authors":"Domenico Pomarico, Mahul Pandey, Riccardo Cioli, Federico Dell'Anna, Saverio Pascazio, Francesco V Pepe, Paolo Facchi, Elisa Ercolessi","doi":"10.3390/e27040427","DOIUrl":"https://doi.org/10.3390/e27040427","url":null,"abstract":"Quantum computing gives direct access to the study of the real-time dynamics of quantum many-body systems. In principle, it is possible to directly calculate non-equal-time correlation functions, from which one can detect interesting phenomena, such as the presence of quantum scars or dynamical quantum phase transitions. In practice, these calculations are strongly affected by noise, due to the complexity of the required quantum circuits. As a testbed for the evaluation of the real-time evolution of observables and correlations, the dynamics of the Zn Schwinger model in a one-dimensional lattice is considered. To control the computational cost, we adopt a quantum-classical strategy that reduces the dimensionality of the system by restricting the dynamics to the Dirac vacuum sector and optimizes the embedding into a qubit model by minimizing the number of three-qubit gates. The time evolution of particle-density operators in a non-equilibrium quench protocol is both simulated in a bare noisy condition and implemented on a physical IBM quantum device. In either case, the convergence towards a maximally mixed state is targeted by means of different error mitigation techniques. The evaluation of the particle-density correlation shows a well-performing post-processing error mitigation for properly chosen coupling regimes.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 4","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12026438/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143985622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient Integer Quantization for Compressed DETR Models. 压缩DETR模型的有效整数量化。

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-04-13 DOI: 10.3390/e27040422

Peng Liu, Congduan Li, Nanfeng Zhang, Jingfeng Yang, Li Wang

{"title":"Efficient Integer Quantization for Compressed DETR Models.","authors":"Peng Liu, Congduan Li, Nanfeng Zhang, Jingfeng Yang, Li Wang","doi":"10.3390/e27040422","DOIUrl":"https://doi.org/10.3390/e27040422","url":null,"abstract":"The Transformer-based target detection model, DETR, has powerful feature extraction and recognition capabilities, but its high computational and storage requirements limit its deployment on resource-constrained devices. To solve this problem, we first replace the ResNet-50 backbone network in DETR with Swin-T, which realizes the unification of the backbone network with the Transformer encoder and decoder under the same Transformer processing paradigm. On this basis, we propose a quantized inference scheme based entirely on integers, which effectively serves as a data compression method for reducing memory occupation and computational complexity. Unlike previous approaches that only quantize the linear layer of DETR, we further apply integer approximation to all non-linear operational layers (e.g., Sigmoid, Softmax, LayerNorm, GELU), thus realizing the execution of the entire inference process in the integer domain. Experimental results show that our method reduces the computation and storage to 6.3% and 25% of the original model, respectively, while the average accuracy decreases by only 1.1%, which validates the effectiveness of the method as an efficient and hardware-friendly solution for target detection.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 4","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12025429/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143976574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0