The variable-step L1 scheme preserving a compatible energy law for time-fractional Allen-Cahn equation

In this work, we revisit the adaptive L1 time-stepping scheme for solving the time-fractional Allen-Cahn equation in the Caputo's form. The L1 implicit scheme is shown to preserve a variational energy dissipation law on arbitrary nonuniform time meshes by using the recent discrete analysis tools, i.e., the discrete orthogonal convolution kernels and discrete complementary convolution kernels. Then the discrete embedding techniques and the fractional Gr\"onwall inequality were applied to establish an $L^2$ norm error estimate on nonuniform time meshes. An adaptive time-stepping strategy according to the dynamical feature of the system is presented to capture the multi-scale behaviors and to improve the computational performance.Comment: 17 pages, 20 figures, 2 table

Model Debiasing via Gradient-based Explanation on Representation

Machine learning systems produce biased results towards certain demographic groups, known as the fairness problem. Recent approaches to tackle this problem learn a latent code (i.e., representation) through disentangled representation learning and then discard the latent code dimensions correlated with sensitive attributes (e.g., gender). Nevertheless, these approaches may suffer from incomplete disentanglement and overlook proxy attributes (proxies for sensitive attributes) when processing real-world data, especially for unstructured data, causing performance degradation in fairness and loss of useful information for downstream tasks. In this paper, we propose a novel fairness framework that performs debiasing with regard to both sensitive attributes and proxy attributes, which boosts the prediction performance of downstream task models without complete disentanglement. The main idea is to, first, leverage gradient-based explanation to find two model focuses, 1) one focus for predicting sensitive attributes and 2) the other focus for predicting downstream task labels, and second, use them to perturb the latent code that guides the training of downstream task models towards fairness and utility goals. We show empirically that our framework works with both disentangled and non-disentangled representation learning methods and achieves better fairness-accuracy trade-off on unstructured and structured datasets than previous state-of-the-art approaches

Hand gesture recognition for user-defined textual inputs and gestures

Despite recent progress, hand gesture recognition, a highly regarded method of human computer interaction, still faces considerable challenges. In this paper, we address the problem of individual user style variation, which can significantly affect system performance. While previous work only supports the manual inclusion of customized hand gestures in the context of very specific application settings, here, an effective, adaptable graphical interface, supporting user-defined hand gestures is introduced. In our system, hand gestures are personalized by training a camera-based hand gesture recognition model for a particular user, using data just from that user. We employ a lightweight Multilayer Perceptron architecture based on contrastive learning, reducing the size of the data needed and the training timeframes compared to previous recognition models that require massive training datasets. Experimental results demonstrate rapid convergence and satisfactory accuracy of the recognition model, while a user study collects and analyses some initial user feedback on the system in deployment

MLLM-Tool: A Multimodal Large Language Model For Tool Agent Learning

Recently, the astonishing performance of large language models (LLMs) in natural language comprehension and generation tasks triggered lots of exploration of using them as central controllers to build agent systems. Multiple studies focus on bridging the LLMs to external tools to extend the application scenarios. However, the current LLMs' perceiving tool-use ability is limited to a single text query, which may result in ambiguity in understanding the users' real intentions. LLMs are expected to eliminate that by perceiving the visual- or auditory-grounded instructions' information. Therefore, in this paper, we propose MLLM-Tool, a system incorporating open-source LLMs and multi-modal encoders so that the learnt LLMs can be conscious of multi-modal input instruction and then select the function-matched tool correctly. To facilitate the evaluation of the model's capability, we collect a dataset featured by consisting of multi-modal input tools from HuggingFace. Another important feature of our dataset is that our dataset also contains multiple potential choices for the same instruction due to the existence of identical functions and synonymous functions, which provides more potential solutions for the same query. The experiments reveal that our MLLM-Tool is capable of recommending appropriate tools for multi-modal instructions. Codes and data are available at https://github.com/MLLM-Tool/MLLM-Tool.Comment: 21 pages, 9 figures, 10 table

Daily MODIS 500 m Reflectance Anisotropy Direct Broadcast (DB) Products for Monitoring Vegetation Phenology Dynamics

Land surface vegetation phenology is an efficient bio-indicator for monitoring ecosystem variation in response to changes in climatic factors. The primary objective of the current article is to examine the utility of the daily MODIS 500 m reflectance anisotropy direct broadcast (DB) product for monitoring the evolution of vegetation phenological trends over selected crop, orchard, and forest regions. Although numerous model-fitted satellite data have been widely used to assess the spatio-temporal distribution of land surface phenological patterns to understand phenological process and phenomena, current efforts to investigate the details of phenological trends, especially for natural phenological variations that occur on short time scales, are less well served by remote sensing challenges and lack of anisotropy correction in satellite data sources. The daily MODIS 500 m reflectance anisotropy product is employed to retrieve daily vegetation indices (VI) of a 1 year period for an almond orchard in California and for a winter wheat field in northeast China, as well as a 2 year period for a deciduous forest region in New Hampshire, USA. Compared with the ground records from these regions, the VI trajectories derived from the cloud-free and atmospherically corrected MODIS Nadir BRDF (bidirectional reflectance distribution function) adjusted reflectance (NBAR) capture not only the detailed footprint and principal attributes of the phenological events (such as flowering and blooming) but also the substantial inter-annual variability. This study demonstrates the utility of the daily 500 m MODIS reflectance anisotropy DB product to provide daily VI for monitoring and detecting changes of the natural vegetation phenology as exemplified by study regions comprising winter wheat, almond trees, and deciduous forest

Daily MODIS 500 m Reflectance Anisotropy Direct Broadcast (DB) Products for Monitoring Vegetation Phenology Dynamics

Land surface vegetation phenology is an efficient bio-indicator for monitoring ecosystem variation in response to changes in climatic factors. The primary objective of the current article is to examine the utility of the daily MODIS 500 m reflectance anisotropy direct broadcast (DB) product for monitoring the evolution of vegetation phenological trends over selected crop, orchard, and forest regions. Although numerous model-fitted satellite data have been widely used to assess the spatio-temporal distribution of land surface phenological patterns to understand phenological process and phenomena, current efforts to investigate the details of phenological trends, especially for natural phenological variations that occur on short time scales, are less well served by remote sensing challenges and lack of anisotropy correction in satellite data sources. The daily MODIS 500 m reflectance anisotropy product is employed to retrieve daily vegetation indices (VI) of a 1 year period for an almond orchard in California and for a winter wheat field in northeast China, as well as a 2 year period for a deciduous forest region in New Hampshire, USA. Compared with the ground records from these regions, the VI trajectories derived from the cloud-free and atmospherically corrected MODIS Nadir BRDF (bidirectional reflectance distribution function) adjusted reflectance (NBAR) capture not only the detailed footprint and principal attributes of the phenological events (such as flowering and blooming) but also the substantial inter-annual variability. This study demonstrates the utility of the daily 500 m MODIS reflectance anisotropy DB product to provide daily VI for monitoring and detecting changes of the natural vegetation phenology as exemplified by study regions comprising winter wheat, almond trees, and deciduous forest

Polarographic Analysis of Reductive Fading Reactions of 4-Aza-2\u27-ethyl-4\u27-Diethylaminophenyl-Quinones

This paper concerns the effect of substituent groups of phenolic couplers on dark stability of cyan quinonimine dyes deduced from the polarographic analysis of the solutions of two structure types of this class of dyes. These dyes were prepared by oxidative coupling of CD-2 developing agent (2-amino-5-diethylaminotoluene) with m-cresol or a 2-, 5-diacylamino-Pheno1. Polarographic measurements of these dyes were carried out in a mixture of ethanol and Britton-Robinson (borate-acetate-phosphate) buffer solution (volume ratio, 1.5 : 1) as a supporting electrolyte at 20±O.2℃,bubbled with nitrogen gas. Plots of E versus log {i/(i_d-i)} reveal that the leuco dye formations of these dyes are two-electron processes. In the case of the m-cresol dye, the proton number calculated from a plot of pH versus E_ was 2.8 in the range of pH 6.1～6.9,and 2.0, pH 7.1～7.8. Consequently, the fading reaction of the dye can be written as follows : Dye+2e+3H^+ → Leuco Dye・H^+ (Protonated Leuco Dye), in the lower pH region, and Dye+2e+2H^+ → Leuco Dye, in the higher pH region. The half-wave potential of the acylaminophenol dye was much more negative than that of the m-cresol dye. Thus, the fading reaction of the dye does not proceed easily in the presence of a reducing agent. The proton number that the dye required was approximately 2.0 in the pH range 5.9～7.6. The chemical equation for the redox reaction of the dye can be expressed as follows : Dye+2e+2H^+ → Leuco Dye. The m-cresol dye could be easily reduced and thus fade in the presence of thiosulfate or EDTA-Fe (II), because there was a big difference between the half-wave potential of the dye and these reducing agents. For example, in the faded solution with thiosulfate polarographic waves of the leuco dye and tetrathionate ions were observed separately. Therefore, the leuco dye formation of m-cresol dye in the fading reaction with reducing agent can be written as follows : Dye+2Red.+3H^+ → Leuco Dye・H^++2O_x. in the lower pH region, Dye+2Red.+2H^+ → Leuco Dye+2Ox. in the higher pH region. On the other hand, the half-wave potential of the acylamino-Phenol dye is close to those of the reducing agents. As 21 result, the polarographic waves of the leuco dye and the oxides of the reeucing agents were not observed separately. It is suggested that the cyan dye image formed with an acylaminophenol dye would be much more stable than a m-cresol dye under the reductive conditions. Consequently, some basic information to predict dad stability of cyan quinonimine dye images under reductive conditions can be obtained by measurement of the half-wave potential of dye solutions

Estimation of surface albedo and directional reflectance from Moderate Resolution Imaging Spectroradiometer (MODIS) observations

Land surface albedo is one of the key geophysical variables controlling the surface radiation budget. In recent years, land surface albedo products have been generated using data from various satellites. However, some problems exist in those products due to either the failure of the current retrieving procedures resulting from persistent clouds and/or abrupt surface changes, or the reduced temporal or spatial coverage, which may limit their applications. Rapidly generated albedo products that help reduce the impacts of cloud contamination and improve the capture of events such as ephemeral snow and vegetation growth are in demand. In this study, we propose a method for estimating the land surface albedo from Moderate Resolution Imaging Spectroradiometer (MODIS) data using a short temporal window. Instead of executing the atmospheric correction first and then fitting the surface reflectance in the current MODIS albedo procedure, the atmospheric properties (e.g., aerosol optical depth) and surface properties (e.g., surface bidirectional reflectance) were estimated simultaneously. Validations were carried out using various data sources including ground measurements (e.g., from the Surface Radiation (SURFRAD) Network and Greenland Climate Network (GC-Net)) and MODIS AERONET-based Surface Reflectance Validation Network (MODASRVN) data. The results showed comparable albedo estimates with both MODIS data and ground measurements, and the MODASRVN instantaneous surface reflectance was in good agreement with the reflectance estimation from our method. Aerosol optical depth (AOD) retrievals over SURFRAD and MODASRVN sites were also compared with ground measurements. Validation results showed estimation accuracies similar to those of MODIS aerosol products

Modifying Geometric-Optical Bidirectional Reflectance Model for Direct Inversion of Forest Canopy Leaf Area Index

Forest canopy leaf area index (LAI) inversion based on remote sensing data is an important method to obtain LAI. Currently, the most widely-used model to achieve forest canopy structure parameters is the Li-Strahler geometric-optical bidirectional reflectance model, by considering the effect of crown shape and mutual shadowing, which is referred to as the GOMS model. However, it is difficult to retrieve LAI through the GOMS model directly because LAI is not a fundamental parameter of the model. In this study, a gap probability model was used to obtain the relationship between the canopy structure parameter nR2 and LAI. Thus, LAI was introduced into the GOMS model as an independent variable by replacing nR2 The modified GOMS (MGOMS) model was validated by application to Dayekou in the Heihe River Basin of China. The LAI retrieved using the MGOMS model with optical multi-angle remote sensing data, high spatial resolution images and field-measured data was in good agreement with the field-measured LAI, with an R-square (R2) of 0.64, and an RMSE of 0.67. The results demonstrate that the MGOMS model obtained by replacing the canopy structure parameter nR2 of the GOMS model with LAI can be used to invert LAI directly and precisely