Data Augmentation for Time-Series Classification: An Extensive Empirical Study and Comprehensive Survey

Data Augmentation (DA) has emerged as an indispensable strategy in Time Series Classification (TSC), primarily due to its capacity to amplify training samples, thereby bolstering model robustness, diversifying datasets, and curtailing overfitting. However, the current landscape of DA in TSC is plagued with fragmented literature reviews, nebulous methodological taxonomies, inadequate evaluative measures, and a dearth of accessible, user-oriented tools. In light of these challenges, this study embarks on an exhaustive dissection of DA methodologies within the TSC realm. Our initial approach involved an extensive literature review spanning a decade, revealing that contemporary surveys scarcely capture the breadth of advancements in DA for TSC, prompting us to meticulously analyze over 100 scholarly articles to distill more than 60 unique DA techniques. This rigorous analysis precipitated the formulation of a novel taxonomy, purpose-built for the intricacies of DA in TSC, categorizing techniques into five principal echelons: Transformation-Based, Pattern-Based, Generative, Decomposition-Based, and Automated Data Augmentation. Our taxonomy promises to serve as a robust navigational aid for scholars, offering clarity and direction in method selection. Addressing the conspicuous absence of holistic evaluations for prevalent DA techniques, we executed an all-encompassing empirical assessment, wherein upwards of 15 DA strategies were subjected to scrutiny across 8 UCR time-series datasets, employing ResNet and a multi-faceted evaluation paradigm encompassing Accuracy, Method Ranking, and Residual Analysis, yielding a benchmark accuracy of 88.94 +- 11.83%. Our investigation underscored the inconsistent efficacies of DA techniques, with..

Polydopamine-based biofunctional substrate coating promotes mesenchymal stem cell migration

Rapid migration of mesenchymal stem cells (MSCs) on device surfaces could support in vivo tissue integration and might facilitate in vitro organoid formation. Here, polydopamine (PDA) is explored as a biofunctional coating to effectively promote MSC motility. It is hypothesized that PDA stimulates fibronectin deposition and in this way enhances integrin-mediated migration capability. The random and directional cell migration was investigated by time-lapse microscopy and gap closure assay respectively, and analysed with softwares as computational tools. A higher amount of deposited fibronectin was observed on PDA substrate, compared to the non-coated substrate. The integrin β1 activation and focal adhesion kinase (FAK) phosphorylation at Y397 were enhanced on PDA substrate, but the F-actin cytoskeleton was not altered, suggesting MSC migration on PDA was regulated by integrin initiated FAK signalling. This study strengthens the biofunctionality of PDA coating for regulating stem cells and offering a way of facilitating tissue integration of devices

Assessing global-scale organic matter reactivity patterns in marine sediments using a lognormal reactive continuum model

Organic matter (OM) degradation in marine sediments is largely controlled by its reactivity and profoundly affects the global carbon cycle. Yet, there is currently no general framework that can constrain OM reactivity on a global scale. In this study, we propose a reactive continuum model based on a lognormal distribution (l-RCM), where OM reactivity is fully described by parameters μ (the mean reactivity of the initial OM bulk mixture) and σ (the variance of OM components around the mean reactivity). We use the l-RCM to inversely determine μ and σ at 123 sites across the global ocean. The results show that the apparent OM reactivity (〈k〉=μ⋅exp⁡(σ2/2)) decreases with decreasing sedimentation rate (ω) and that OM reactivity is more than 3 orders of magnitude higher in shelf than in abyssal regions. Despite the general global trends, higher than expected OM reactivity is observed in certain ocean regions characterized by great water depth or pronounced oxygen minimum zones, such as the eastern–western coastal equatorial Pacific and the Arabian Sea, emphasizing the complex control of the depositional environment (e.g., OM flux, oxygen content in the water column) on benthic OM reactivity. Notably, the l-RCM can also highlight the variability in OM reactivity in these regions. Based on inverse modeling results in our dataset, we establish the significant statistical relationships between 〈k〉 and ω and further map the global OM reactivity distribution. The novelty of this study lies in its unifying view but also in contributing a new framework that allows predicting OM reactivity in data-poor areas based on readily available (or more easily obtainable) information. Such a framework is currently lacking and limits our abilities to constrain OM reactivity in global biogeochemical or Earth system models

The role of anaerobic methane oxidation on the carbonate authigenesis in sediments of the subtropical Beibu Gulf, South China Sea: A reactive–transport modelling approach

The formation and burial of authigenic carbonate in marine sediment significantly affect the sedimentary carbon cycle and its isotopic mass balance in geological history. Anaerobic oxidation of methane (AOM) is the primary driver of authigenic carbonate precipitation within the sulfate-methane transition zone (SMTZ). Quantitative estimations of the role of AOM on the authigenic carbonate precipitation and its carbon isotope under non-steady-state processes (e.g., changes in methane fluxes at the bottom sediment, sedimentation rates or organic fluxes in the surface sediment), however, are still limited. In this study, we use geochemical data from porewater (e.g., the concentration of sulfate, calcium, magnesium, strontium, dissolved inorganic carbon, total alkalinity) and solid sediment (e.g., organic matter content, and carbonate content) in different depositional environments of the subtropical Beibu Gulf, South China Sea, combined with a diagenetic reactive-transport modelling approach, to estimate the mineralogy of authigenic carbonate, the relationship between AOM and authigenic carbonate precipitation, and the impact of AOM rate on carbon isotope of sediment carbonate (δ13CCar). The results show that high-Mg carbonates (high-Mg calcite and dolomite) are the main type of authigenic carbonate (∼80%) formed in the methane-bearing sediments, leading to higher porewater Sr2+/Ca2+ (>0.02) and Mg2+/Ca2+ (>20) within the SMTZ. Our modelling analysis highlights that the non-steady-state induced by increased methane flux from the underlying sediments can significantly accelerate the authigenic carbonates formation within the SMTZ. Using parametric sensitivity analysis, we observed that even a 1% increase in the authigenic carbonate fraction of sediment carbonates results in significant changes in δ13CCar within the SMTZ (from −1‰ to −2‰), mainly due to lighter carbon isotopes produced by more intensive AOM processes. Noteworthily, the terrestrial-to-marine transition was identified by the sediment and porewater geochemical profiles at site SO-8. Although lower authigenic carbonate precipitation occurs in terrestrial sedimentary environments, the proportion of authigenic carbonate in terrestrial environments (11%) is much higher than that in marine environments (1%), resulting in carbon isotopes of carbonate in terrestrial sediments becoming more negative (−5‰)

Acetoin Catabolism and Acetylbutanediol Formation by Bacillus pumilus in a Chemically Defined Medium

BACKGROUND: Most low molecular diols are highly water-soluble, hygroscopic, and reactive with many organic compounds. In the past decades, microbial research to produce diols, e.g. 1,3-propanediol and 2,3-butanediol, were considerably expanded due to their versatile usages especially in polymer synthesis and as possible alternatives to fossil based feedstocks from the bioconversion of renewable natural resources. This study aimed to provide a new way for bacterial production of an acetylated diol, i.e. acetylbutanediol (ABD, 3,4-dihydroxy-3-methylpentan-2-one), by acetoin metabolism. METHODOLOGY/PRINCIPAL FINDINGS: When Bacillus pumilus ATCC 14884 was aerobically cultured in a chemically defined medium with acetoin as the sole carbon and energy source, ABD was produced and identified by gas chromatography--chemical ionization mass spectrometry and NMR spectroscopy. CONCLUSIONS/SIGNIFICANCE: Although the key enzyme leading to ABD from acetoin has not been identified yet at this stage, this study proposed a new metabolic pathawy to produce ABD in vivo from using renewable resources--in this case acetoin, which could be reproduced from glucose in this study--making it the first facility in the world to prepare this new bio-based diol product

Probing New Physics via pp-> W+W- -> lvjj at the CERN LHC

TeV scale new Physics, e.g., Large Extra Dimensions or Models with anomalous triple vector boson couplings, can lead to excesses in various kinematic regions on the semi-leptonic productions of pp -> WW -> lvjj at the CERN LHC, which, although suffers from large QCD background compared with the pure leptonic channel, can benefit from larger production rates and the reconstructable 4-body mass Mlvjj. We study the search sensitivity through the lvjj channel at the 7TeV LHC on relevant new physics, via probing the hard tails on the reconstructed Mlvjj and the transverse momentum of W-boson (PTW), taking into account main backgrounds and including the parton shower and detector simulation effects. Our results show that with integrated luminosity of 5fb-1, the LHC can already discovery or exclude a large parameter region of the new physics, e.g., 95% CL. limit can be set on the Large Extra Dimensions with a cut-off scale up to 1.5 TeV, and the WWZ anomalous coupling down to, e.g. |\lambda_Z|~0.1. Brief results are also given for the 8TeV LHC.Comment: 13 pages, 6 figures, 2 table

Quantitative Implementation of Artificial Intelligence Based on Task Completion Analysis

With the further development of the new generation of artificial intelligence science and technology, the new generation of artificial intelligence science and technology has been applied in many fields. AlphaGo program uses high technology of quantitative analysis to realize qualitative research and development of artificial intelligence, which has important reference significance for the research and development of a new generation of artificial intelligence in the future. From the perspective of task accessibility, this paper analyzes the defects of the disturbance, so as to achieve the quantitative implementation of the new generation of artificial intelligence task accessibility analysis method