Representation In Models Research Articles

The Role of In‐Cloud Wet Removal in Simulating Aerosol Vertical Profiles and Cloud Radiative Forcing

AbstractAmong the physical processes controlling aerosol vertical profiles, in‐cloud wet removal is of utmost importance while its representation in global climate models (GCMs) is crude. In this study, we implement into the Community Atmosphere Model version 6 (CAM6) a physically‐based aerosol wet removal parameterization scheme that explicitly treats aerosol activation, removal and resuspension. Evaluation against in‐situ observations shows that the default scheme substantially overestimates the upper tropospheric black carbon (BC) and sea salt mass. Our physically‐based scheme reduces BC and sea salt mass by a factor of 10 and 1,000, respectively, in better agreement with observations. Also, the new scheme slightly increases number of aerosol particles between 12 nm and 4.8 μm in diameter, thereby mitigating the aerosol number underestimation in the default scheme. Our new scheme reduces the overestimation of coarse‐mode aerosol (0.5–4.8 μm) number. Overall, the aerosol property changes (mass decrease and number increase) reduce the cloud condensation nuclei (CCN) concentration at low supersaturation (i.e., 0.02% and 0.1%), and increase CCN at high supersaturations (i.e., 0.5% and 1%). Consequently, the global annual mean cloud liquid water path increases by 1.89 g m−2 and the ice water path increases by 0.51 g m−2. The global annual mean shortwave, longwave, and net cloud radiative forcing change by −1.06, 0.57, and −0.48 W m−2, respectively. Further improvement is needed to reflect the real physics that the removal efficiencies for aerosol mass and number are disproportionate and to advect cloud‐borne (activated) aerosols for a complete aerosol lifecycle.

SIMULATION MODEL OF A QS WITH HYPEREXPONENTIAL DISTRIBUTION IN THE GPSS WORLD ENVIRONMENT

This article presents results obtained on the development of a pseudo-random sequence generator for simulation modeling of queueing system (QS) with second-order hyperexponential input distribution. In the scientific literature on GPSS WORLD, including web resources, the authors did not find any data in this subject area. The GPSS WORLD library includes many generators of various distribution laws, but there are no generators for such important composite distributions according to the queuing theory. It is known that the hyperexponential distribution concept provides a wide range of changes in the random variable variation coefficient from unity to an arbitrarily large value. Due to the fact that the variation coefficients of arrival and service intervals play an important role in estimating the delay of queue claims in queuing systems, thus this distribution concept plays a special role in modeling of QS. For a hyperexponential distribution of the second order, the authors previously obtained numerical-analytical results based on the Lindley integral equation spectral solution method. The article describes a developed algorythm and a program on GPSS WORLD to simulate the functioning of QS with hyperexponential input distribution incl. description of operation of logical switches. The difference between analytical and simulation modeling of the two-phase representation of a given distribution concept is explained on the example of the developed model. The adequacy of the obtained results was confirmed by comparing the simulation results with the results of numerical simulation in the Mathcad environment.

Physicochemical characterization and source apportionment of Arctic ice-nucleating particles observed in Ny-Ålesund in autumn 2019

Abstract. Ice-nucleating particles (INPs) initiate primary ice formation in Arctic mixed-phase clouds (MPCs), altering cloud radiative properties and modulating precipitation. For atmospheric INPs, the complexity of their spatiotemporal variations, heterogeneous sources, and evolution via intricate atmospheric interactions challenge the understanding of their impact on microphysical processes in Arctic MPCs and induce an uncertain representation in climate models. In this work, we performed a comprehensive analysis of atmospheric aerosols at the Arctic coastal site in Ny-Ålesund (Svalbard, Norway) from October to November 2019, including their ice nucleation ability, physicochemical properties, and potential sources. Overall, INP concentrations (NINP) during the observation season were approximately up to 3 orders of magnitude lower compared to the global average, with several samples showing degradation of NINP after heat treatment, implying the presence of proteinaceous INPs. Particle fluorescence was substantially associated with INP concentrations at warmer ice nucleation temperatures, indicating that in the far-reaching Arctic, aerosols of biogenic origin throughout the snow- and ice-free season may serve as important INP sources. In addition, case studies revealed the links between elevated NINP and heat lability, fluorescence, high wind speeds originating from the ocean, augmented concentration of coarse-mode particles, and abundant organics. Backward trajectory analysis demonstrated a potential connection between high-latitude dust sources and high INP concentrations, while prolonged air mass history over the ice pack was identified for most scant INP cases. The combination of the above analyses demonstrates that the abundance, physicochemical properties, and potential sources of INPs in the Arctic are highly variable despite its remote location.

Effect of Plasma Oxygen Content on the Size and Content of Silicon Nanoclusters in Amorphous SiOx Films Obtained with Plasma-Enhanced Chemical Vapor Deposition

The influence of Ar + SiH4 + O2 plasma formulation on the phase composition and optical properties of amorphous SiOx films with silicon nanoclusters obtained using PECVD with DC discharge modulation was studied. Using a unique technique of ultrasoft X-ray emission spectroscopy, it was found that at a 0.15 mol.% plasma oxygen content, amorphous silicon a-Si films are formed. At a high oxygen content (≥21.5 mol.%), nanocomposite films based on SiOx silicon suboxide containing silicon nanoclusters ncl-Si are formed. It was found that the suboxide matrix consists of a mixture of SiO1.3 and SiO2 phases, and the average oxidation state x in the SiOx suboxide matrix is ~1.5. An increase in the concentration of O2 in the reactor atmosphere from 21.5 to 23 mol.% leads to a decrease in ncl-Si content from 40 to 15% and an increase in the average oxidation state x of SiOx from 1.5 to 1.9. In this case, the suboxide matrix consists of two phases of silicon dioxide SiO2 and non-stoichiometric silicon oxide SiO1.7. Thus, according to the experimental data obtained using USXES, the phase composition of these films in pure form differs in their representation in both random coupling and random mixture models. A decrease in the ncl-Si content of SiOx films is accompanied by a decrease in their sizes from ~3 to ~2 nm and a shift in the photoluminescence band from 1.9 eV to 2.3 eV, respectively.

Learning spatiotemporal lip dynamics in 3D point cloud stream for visual voice activity detection

Visual voice activity detection (V-VAD) aims at identifying speech activities and measuring start/end moments in visual signal streams generated from the speaking-related biological modality. The visual cue is important counterpart of audio modality in noisy scenarios. Most of existing methods rely on 2D sequences, which suffer from limited robustness to variations of illumination and facial pose. This paper proposes a 3D V-VAD method based on lip dynamics modeling and learning spatiotemporal speaking lip representation in point cloud streams. We construct a lightweight temporal speaking dynamics model DualDyn to represent dual dynamics of global lip structure and local lip context in 4D spatiotemporal domain. The activity moment detection is formulated as frame-level state classification and refined by behavior continuity. Our method was verified on a public dataset S3DFM and achieves a state-of-the-art accuracy of 22.6ms and higher efficiency in measuring activity moments. Ablation studies demonstrate the robustness and applicability of our method.

Bioaerosols are the dominant source of warm-temperature immersion-mode INPs and drive uncertainties in INP predictability.

Ice-nucleating particles (INPs) are rare atmospheric aerosols that initiate primary ice formation, but accurately simulating their concentrations and variability in large-scale climate models remains a challenge. Doing so requires both simulating major particle sources and parameterizing their ice nucleation (IN) efficiency. Validating and improving model predictions of INP concentrations requires measuring their concentrations delineated by particle type. We present a method to speciate INP concentrations into contributions from dust, sea spray aerosol (SSA), and bioaerosol. Field campaign data from Bodega Bay, California, showed that bioaerosols were the primary source of INPs between -12° and -20°C, while dust was a minor source and SSA had little impact. We found that recent parameterizations for dust and SSA accurately predicted ambient INP concentrations. However, the model did not skillfully simulate bioaerosol INPs, suggesting a need for further research to identify major factors controlling their emissions and INP efficiency for improved representation in models.

Alkalinity biases in CMIP6 Earth system models and implications for simulated CO2 drawdown via artificial alkalinity enhancement

Abstract. The partitioning of CO2 between atmosphere and ocean depends to a large degree not only on the amount of dissolved inorganic carbon (DIC) but also on alkalinity in the surface ocean. That is also why ocean alkalinity enhancement (OAE) is discussed as one potential approach in the context of negative emission technologies. Although alkalinity is thus an important variable of the marine carbonate system, little knowledge exists on how its representation in models compares with measurements. We evaluated the large-scale alkalinity distribution in 14 CMIP6 Earth system models (ESMs) against the observational data set GLODAPv2 and show that most models, as well as the multi-model mean, underestimate alkalinity at the surface and in the upper ocean and overestimate it in the deeper ocean. The decomposition of the global mean alkalinity biases into contributions from (i) physical processes (preformed alkalinity), which include the physical redistribution of biased alkalinity originating from the soft tissue and carbonates pumps; (ii) remineralization; and (iii) carbonate formation and dissolution showed that the bias stemming from the physical redistribution of alkalinity is dominant. However, below the upper few hundred meters the bias from carbonate dissolution can gain similar importance to physical biases, while the contribution from remineralization processes is negligible. This highlights the critical need for better understanding and quantification of processes driving calcium carbonate dissolution in microenvironments above the saturation horizons and implementation of these processes into biogeochemical models. For the application of the models to assess the potential of OAE to increase ocean carbon uptake, a back-of-the-envelope calculation was conducted with each model's global mean surface alkalinity, DIC, and partial pressure of CO2 in seawater (pCO2) as input parameters. We evaluate the following two metrics: (1) the initial pCO2 reduction at the surface ocean after alkalinity addition and (2) the uptake efficiency (ηCO2) after air–sea equilibration is reached. The relative biases of alkalinity versus DIC at the surface affect the Revelle factor and therefore the initial pCO2 reduction after alkalinity addition. The global mean surface alkalinity bias relative to GLODAPv2 in the different models ranges from −85 mmol m−3 (−3.6 %) to +50 mmol m−3 (+2.1 %) (mean: −25 mmol m−3 or −1.1 %). For DIC the relative bias ranges from −55 mmol m−3 (−2.6 %) to 53 mmol m−3 (+2.5 %) (mean: −13 mmol m−3 or −0.6 %). All but two of the CMIP6 models evaluated here overestimate the Revelle factor at the surface by up to 3.4 % and thus overestimate the initial pCO2 reduction after alkalinity addition by up to 13 %. The uptake efficiency, ηCO2, then takes into account that a higher Revelle factor and a higher initial pCO2 reduction after alkalinity addition and equilibration mostly compensate for each other, meaning that resulting DIC differences in the models are small (−0.1 % to 1.1 %). The overestimation of the initial pCO2 reduction has to be taken into account when reporting on efficiencies of ocean alkalinity enhancement experiments using CMIP6 models, especially as long as the CO2 equilibrium is not reached.

High-resolution modeling of meander neck cutoffs: laboratory and field scales

Meandering rivers are distinguished by their characteristic sinuosity, which is subject to modulation through channel cutoff, resulting in the formation of oxbow lakes within the abandoned meander loops. Throughout the evolutionary course of a river, these cutoffs establish a connection between the channel and floodplain systems, both crucial to maintaining the dynamic equilibrium of the river system. Nonetheless, the interactive dynamic between the channel and floodplain and its influence on the transient behavior of the channel’s morphodynamics during a cutoff event are frequently reduced to simplistic representations in computational models. This study introduces a comprehensive numerical model that elucidates the adaptive processes of bed and planform during and subsequent to the inception of cutoff and oxbow lakes. The model is assessed through its application to a laboratory scale cutoff, before being employed to a real-world meandering river, specifically the Ucayali River in Peru, in order to gain understanding into channel development and the intricate patterns of planform dynamics following cutoff events. The model is able to capture the main modes of planform migration, translation and expansion for the case of the bend in the Ucayali River. During the neck cutoff, the model simulates the progression of erosional and depositional waves traveling in upstream and downstream directions respectively, underscoring the importance of incorporating both hydrodynamic and morphodynamic factors in characterizing the river dynamics associated with meander cutoffs.

Satellite Evidence of HONO/NO2 Increase With Fire Radiative Power

AbstractWildfires are important sources of atmospheric reactive nitrogen. The reactive nitrogen species partitioning generally depends on fire characteristics. One reactive nitrogen compound, nitrous acid (HONO), is a source of hydroxyl radicals and nitric oxide, which can impact the oxidizing capacity of the atmosphere and fire plume chemistry and composition. We study the Australian wildfire season of 2019–2020, known as Black Summer, where numerous large and intense wildfires burned throughout the continent. We use HONO and nitrogen dioxide (NO2) from the TROPOspheric Monitoring Instrument (TROPOMI) and fire radiative power (FRP) from the Visible Infrared Imaging Radiometer Suite to investigate HONO and NO2 relationships with fire characteristics. The ratio of HONO to NO2 increases linearly with FRP both in Australia and globally. Both Australian and global fire relationships depend strongly on land cover type. These relationships can be applied to emission inventories to improve wildfire emission representation in models.

The development of a modular design workflow for 3D printable bioresorbable patient-specific bone scaffolds to facilitate clinical translation

ABSTRACT A streamlined design workflow that facilitates the efficient design and manufacture of patient-specific scaffolds independently applied by the surgical team has been recognised as a key step in a holistic approach towards the envisioned routine clinical translation of scaffold-guided bone regeneration (SGBR). A modular design workflow was developed to semi-automatically fill defect cavities, ensure patient specificity and ideal surgical scaffold insertion for a given surgical approach, add fixation points to secure the scaffolds to the host bone and generate scaffold based on Voronoi, periodic lattice and triply periodic minimal surface pore architectures. The adopted functional representation modelling technique produces models free from 3D printing mesh errors. It was applied to a clinical case of a complicated femoral bone defect. All models were free from mesh errors and the patient-specific fit and unobstructive insertion were validated via digital inspection and physical investigation by way of 3D printed prototypes. The real-time responsiveness of the workflow to user input allows the designer to receive real-time feedback from the surgeon, which is associated with reducing the time to finalise a patient-specific scaffold design. In summary, an efficient workflow was developed that substantially facilitates routine clinical implementation of SGBR through its ability to streamline the design of 3D printed scaffolds.

Remarks on the quasi-position representation in models of generalized uncertainty principle

This note aims to elucidate certain aspects of the quasi-position representation frequently used in the investigation of one-dimensional models based on the generalized uncertainty principle (GUP). We specifically focus on two key points: (i) contrary to recent claims, the quasi-position operator can possess physical significance even though it is non-Hermitian, and (ii) in the quasi-position representation, operators associated with the position, such as the potential energy, also behave as a derivative operator on the quasi-position coordinate, unless the method of computing expectation values is modified. The development of both points revolves around the observation that the position and quasi-position operators share the same set of eigenvalues and are connected through a non-unitary canonical transformation. This outcome may have implications for widely referenced constraints on GUP parameters.

The Sensitivity of Southern Ocean Air‐Sea Carbon Fluxes to Background Turbulent Diapycnal Mixing Variability

AbstractThe Southern Ocean (SO) connects major ocean basins and hosts large air‐sea carbon fluxes due to the resurfacing of deep nutrient and carbon‐rich waters. While wind‐induced turbulent mixing in the SO mixed layer is significant for air‐sea fluxes, the importance of the orders‐of‐magnitude weaker background mixing below is less well understood. The direct impact of altering background mixing on tracers, as opposed to the response due to a longer‐term change in large‐scale ocean circulation, is also poorly studied. Topographically induced upward propagating lee waves, wind‐induced downward propagating waves generated at the base of the mixed layer, shoaling of southward propagating internal tides, and turbulence under sea ice are among the processes known to induce upper ocean background turbulence but typically are not represented in models. Here, we show that abruptly altering the background mixing in the SO over a range of values typically used in climate models ( m2 s−1– m2 s−1) can lead to a ∼70% change in annual SO air‐sea CO2 fluxes in the first year of perturbations, and around a ∼40% change in annual SO air‐sea CO2 fluxes over the 6‐year duration of the experiment, with even greater changes on a seasonal timescale. This is primarily through altering the temperature and the dissolved inorganic carbon and alkalinity distribution in the surface water. Given the high spatiotemporal variability of processes that induce small‐scale background mixing, this work demonstrates the importance of their representation in climate models for accurate simulation of global biogeochemical cycles.

Robust intra-model teleconnection patterns for extreme heatwaves

Introduction: The goal of this study is to provide analysis of statistics and dynamics of extreme heatwaves over two areas of Europe, France and Scandinavia, while comparing and contrasting the representation in climate models and reanalysis.Methods: The 1000 year long datasets are generated using respectively two climate models of different complexity. The composite maps of the heatwaves found in these datasets are compared to the ones observed in reanalysis by estimating significance of such patterns. We also employ time series analysis, in particular compare return time plots and use Gaussian stochastic processes to model the temporal correlations for rare events.Results: Our investigation reveals that recurrent wavenumber three teleconnection patterns distinctly underpin heatwaves in France and Scandinavia. These patterns manifest in both surface temperature and mid-tropospheric geopotential height. For heatwaves of return times of 4 years such patterns are robustly represented across diverse models of complexity and reanalysis data. For longer return times, reanalysis records are too short to give statistically significant results, while models confirm the relevance of these large scale patterns for the most extreme heatwaves. Moreover, A time series analysis shows that heatwave indices defined at synoptic scale are fairly well described by Gaussian stochastic processes, and that these Gaussian processes reproduce well return time plots even for very rare events.Discussion: These results suggest that extreme heatwaves over different areas of Europe show recurrent typical behaviours in terms of long-range spatial correlations and subseasonal-scale temporal correlations. These properties are consistently represented among models of different complexity and observations, thus suggesting their relevance for a better understanding of the drivers and causes of the occurrence of extreme midlatitude heatwaves and their predictability.

The Database of European Forest Insect and Disease Disturbances: DEFID2.

Insect and disease outbreaks in forests are biotic disturbances that can profoundly alter ecosystem dynamics. In many parts of the world, these disturbance regimes are intensifying as the climate changes and shifts the distribution of species and biomes. As a result, key forest ecosystem services, such as carbon sequestration, regulation of water flows, wood production, protection of soils, and the conservation of biodiversity, could be increasingly compromised. Despite the relevance of these detrimental effects, there are currently no spatially detailed databases that record insect and disease disturbances on forests at the pan-European scale. Here, we present the new Database of European Forest Insect and Disease Disturbances (DEFID2). It comprises over 650,000 harmonized georeferenced records, mapped as polygons or points, of insects and disease disturbances that occurred between 1963 and 2021 in European forests. The records currently span eight different countries and were acquired through diverse methods (e.g., ground surveys, remote sensing techniques). The records in DEFID2 are described by a set of qualitative attributes, including severity and patterns of damage symptoms, agents, host tree species, climate-driven trigger factors, silvicultural practices, and eventual sanitary interventions. They are further complemented with a satellite-based quantitative characterization of the affected forest areas based on Landsat Normalized Burn Ratio time series, and damage metrics derived from them using the LandTrendr spectral-temporal segmentation algorithm (including onset, duration, magnitude, and rate of the disturbance), and possible interactions with windthrow and wildfire events. The DEFID2 database is a novel resource for many large-scale applications dealing with biotic disturbances. It offers a unique contribution to design networks of experiments, improve our understanding of ecological processes underlying biotic forest disturbances, monitor their dynamics, and enhance their representation in land-climate models. Further data sharing is encouraged to extend and improve the DEFID2 database continuously. The database is freely available at https://jeodpp.jrc.ec.europa.eu/ftp/jrc-opendata/FOREST/DISTURBANCES/DEFID2/.

The EarthCARE mission – science and system overview

Abstract. The Earth Cloud, Aerosol and Radiation Explorer (EarthCARE) is a satellite mission implemented by the European Space Agency (ESA), in cooperation with the Japan Aerospace Exploration Agency (JAXA), to measure global profiles of aerosols, clouds and precipitation properties together with radiative fluxes and derived heating rates. The simultaneous measurements of the vertical structure and horizontal distribution of cloud and aerosol fields, together with outgoing radiation, will be used in particular to evaluate their representation in weather forecasting and climate models and to improve our understanding of cloud and aerosol radiative impact and feedback mechanisms. To achieve the objective, the goal is that a retrieved scene with footprint size of 10 km × 10 km is measured with sufficiently high resolution that the atmospheric vertical profile of short-wave (solar) and long-wave (thermal) flux can be reconstructed with an accuracy of 10 W m−2 at the top of the atmosphere. To optimise the performance of the two active instruments, the platform will fly at a relatively low altitude of 393 km, with an equatorial revisit time of 25 d. The scientific payload consists of four instruments: an atmospheric lidar, a cloud-profiling radar with Doppler capability, a multi-spectral imager and a broadband radiometer. Co-located measurements from these instruments are processed in the ground segment, which produces and distributes a wide range of science data products. As well as the Level 1 (L1) product of each instrument, a large number of multiple-instrument L2 products have been developed, in both Europe and Japan, benefiting from the data synergy. An end-to-end simulator and several test scenes have been developed that simulate EarthCARE observations and provide a development and test environment for L1 and L2 processors. Within this paper the EarthCARE observational requirements are addressed. An overview is given of the space segment with a detailed description of the four science instruments, demonstrating how the observational requirements will be met. Furthermore, the elements of the space segment and ground segment that are relevant for science data users are described and the data products are introduced.

Contrail radiative dependence on ice particle number concentration

Recent studies on low aromatic fuels have shown that lower soot number emissions may reduce contrail ice particle number concentrations (N ice). Here we implemented, in a sophisticated radiative transfer model, two ice particle size distribution schemes in order to estimate the contrail radiative forcing’s (RFs) dependence on these prospective N ice reductions resulting from the introduction of sustainable aviation fuels. The results show that an 85% contrail N ice reduction produces a 35% smaller contrail RF, while neglecting all non-radiative effects. This estimate of an RF reduction only considers the effects of the N ice change assumed here, and neglects other potentially important microphysical mechanisms that may change the relationship between soot number emissions and N ice. A comparison of our results with previous published estimates from full climate model simulations, shows similar RF reductions to those which also take into account non-radiative mechanisms, evidencing the need for more studies in order to allocate the contribution from radiative and non-radiative changes, as this would guide possible mitigation implementations. Despite these modeled contrail RF reductions being largely independent of the assumed ice water content (IWC), it is only through simultaneous improvement of the IWC and N ice representation in models that contrail RF estimates can be better constrained. This is because our calculated RF varied by a factor of 3 when assuming a ±30% IWC range; and by a factor of 5 if a, still conservative, ±60% IWC range was prescribed, suggesting that the differences in the prescribed IWC and N ice values in different models may explain the large discrepancies amongst published RF estimates. Recent estimates of higher N ice values, and lower IWCs found in contrails even after several hours, compared to surrounding cirrus under similar atmospheric conditions, were assessed to conclude that it is mainly the differences in IWC that make young contrails have a smaller RF, and to reduce our previous estimate for linear contrail RF for year 2006 by 65%.

ContextLoc++: A Unified Context Model for Temporal Action Localization.

Effectively tackling the problem of temporal action localization (TAL) necessitates a visual representation that jointly pursues two confounding goals, i.e., fine-grained discrimination for temporal localization and sufficient visual invariance for action classification. We address this challenge by enriching the local, global and multi-scale contexts in the popular two-stage temporal localization framework. Our proposed model, dubbed ContextLoc++, can be divided into three sub-networks: L-Net, G-Net, and M-Net. L-Net enriches the local context via fine-grained modeling of snippet-level features, which is formulated as a query-and-retrieval process. Furthermore, the spatial and temporal snippet-level features, functioning as keys and values, are fused by temporal gating. G-Net enriches the global context via higher-level modeling of the video-level representation. In addition, we introduce a novel context adaptation module to adapt the global context to different proposals. M-Net further fuses the local and global contexts with multi-scale proposal features. Specially, proposal-level features from multi-scale video snippets can focus on different action characteristics. Short-term snippets with fewer frames pay attention to the action details while long-term snippets with more frames focus on the action variations. Experiments on the THUMOS14 and ActivityNet v1.3 datasets validate the efficacy of our method against existing state-of-the-art TAL algorithms.

A Spatial–Temporal Bayesian Deep Image Prior Model for Moderate Resolution Imaging Spectroradiometer Temporal Mixture Analysis

Time-series remote sensing images are important in agricultural monitoring and investigation. However, most time-series data with high temporal resolution have the problem of insufficient spatial resolution which cannot meet the requirement of precision agriculture. The unmixing technique can obtain the object abundances with richer spatial information from the coarse-resolution images. Although the unmixing technique is widely used in hyperspectral data, it is insufficiently researched for time-series data. Temporal unmixing extends spectral unmixing to the time domain from the spectral domain, and describes the temporal characteristics rather than the spectral characteristics of different ground objects. Deep learning (DL) techniques have achieved promising performance for the unmixing problem in recent years, but there are still few studies on temporal mixture analysis (TMA), especially in the application of crop phenological monitoring. This paper presents a novel spatial–temporal deep image prior method based on a Bayesian framework (ST-Bdip), which innovatively combines the knowledge-driven TMA model and the DL-driven model. The normalized difference vegetation index (NDVI) time series of moderate resolution imaging spectroradiometer (MODIS) data is used as the object for TMA, while the extracted seasonal crop signatures and the fractional coverages are perceived as the temporal endmembers (tEMs) and corresponding abundances. The proposed ST-Bdip method mainly includes the following contributions. First, a deep image prior model based on U-Net architecture is designed to efficiently learn the spatial context information, which enhances the representation of abundance modeling compared to the traditional non-negative least squares algorithm. Second, The TMA model is incorporated into the U-Net training process to exploit the knowledge in the forward temporal model effectively. Third, the temporal noise heterogeneity in time-series images is considered in the model optimization process. Specifically, the anisotropic covariance matrix of observations from different time dimensions is modeled as a multivariate Gaussian distribution and incorporated into the calculation of the loss function. Fourth, the "purified means" approach is used to further optimize crop tEMs and the corresponding abundances. Finally, the expectation–maximization (EM) algorithm is designed to solve the maximum a posterior (MAP) problem of the model in the Bayesian framework. Experimental results on three synthetic datasets with different noise levels and two real MODIS datasets demonstrate the superiority of the proposed approach in comparison with seven traditional and advanced unmixing algorithms.

A contrastive learning framework for Event Detection via semantic type prototype representation modelling

The diversity of natural language expressions for describing events poses a challenge for the task of Event Detection (ED) with machine learning methods. To detect and classify event mentions, ED models essentially need to construct a semantic linkage between representations of the mentions and a set of target types. Unfortunately, most existing models use meaningless homogeneous one-hot vectors to represent the event type classes in ED, ignoring the fact that the event type labels also consist of meaningful words and can provide important clues for type representation learning. In this paper, we propose a Contrastive Semantic Prototype Representation Learning Framework for Event Detection (SemPRE), which exploits the pre-defined event type label words to inject the semantic information of the types and guide event detection. Specifically, we utilize pre-trained BERT to fuse text and event type into a joint representation space, and employ a contrastive-regularized module to enhance cross-type interaction. We conduct extensive experiments on the ACE 2005 and MAVEN benchmark datasets. The performance results show that our proposed SemPRE model achieves state-of-the-art performance on the datasets and outperforms existing baselines on limited annotated data and without using any external resources. Further analysis shows that our model is also effective in detecting multiple events and ambiguous trigger words.

Machiavelli’s Ambush: perspectives in an age of conspiracy

ABSTRACT In this essay I revisit The Prince and the Discourses and argue that across the design of these two texts on the theme of conspiracy Machiavelli constructs an ambush on Medici princes. I reconsider Mary Dietz's (1986), and Langton's and Dietz's (1987) suggestion that Machiavelli's The Prince was a deceptive political act through an exploration of the link Dietz and Sheldon Wolin (2004) draw between Machiavelli's method and Renaissance artistry. I suggest that Machiavelli applied a one-point linear perspective – a scientific and visual method of pictorial representation and geometrical modelling that emerged for the first time in the Renaissance – to the political field. I test this hypothesis on the theme of conspiracy in Machiavelli’s work by arguing that The Prince ultimately presents one vantage point – that of the prince – while the Discourses offers another – that of the conspirators. I argue that a blind spot is created by these two texts when they are jointly considered: conspirators seduced, recruited, and trained by the Discourses eliminate a prince caught off guard for having followed the advice on conspiracy in The Prince.