Land Surface Models Evaluation for Two Different Land-Cover Types: Cropland and Forest

Land Surface Model (LSM) is an important tool used to understand the complicated hydro-meteorological flux interaction systems between the land surface and atmosphere in hydrological cycles. Over the past few decades, LSMs have further developed to more accurately estimate weather and climate hydrological processes. Common Land Model (CLM) and Noah Land Surface Model (Noah LSM) are used in this paper to estimate the hydro-meteorological fluxes for model applicability assessment at two different flux tower sites in Korea during the summer monsoon season. The estimated fluxes such as net radiation (RN), sensible heat flux (H), latent heat flux (LE), ground heat flux (G), and soil temperature (Ts) were compared with the observed data from flux towers. The simulated RN from both models corresponded well with the in situ data. The root-mean-square error (RMSE) values were 39 - 44 W m-2 for the CLM and 45 - 50 W m-2 for the Noah LSM while the H and LE showed relatively larger discrepancies with each observation. The estimated Ts from the CLM corresponded comparatively well with the observed soil temperature. The CLM estimations generally showed better statistical results than those from the Noah LSM, even though the estimated hydro-meteorological fluxes from both models corresponded reasonably with the observations. A sensitivity test indicated that differences according to different locations between the estimations from models and observations were caused by field conditions including the land-cover type and soil texture. In addition the estimated RN, H, LE, and G were more sensitive than the estimated Ts in both models

Intercomparison of Downscaling Techniques for Satellite Soil Moisture Products

During recent decades, various downscaling methods of satellite soil moisture (SM) products, which incorporate geophysical variables such as land surface temperature and vegetation, have been studied for improving their spatial resolution. Most of these studies have used least squares regression models built from those variables and have demonstrated partial improvement in the downscaled SM. This study introduces a new downscaling method based on support vector regression (SVR) that includes the geophysical variables with locational weighting. Regarding the in situ SM, the SVR downscaling method exhibited a smaller root mean square error, from 0.09 to 0.07m(3).m(-3), and a larger average correlation coefficient increased, from 0.62 to 0.68, compared to the conventional method. In addition, the SM downscaled using the SVR method had a greater statistical resemblance to that of the original advanced scatterometer SM. A residual magnitude analysis for each model with two independent variables was performed, which indicated that only the residuals from the SVR model were not well correlated, suggesting a more effective performance than regression models with a significant contribution of independent variables to residual magnitude. The spatial variations of the downscaled SM products were affected by the seasonal patterns in temperature-vegetation relationships, and the SVR downscaling method showed more consistent performance in terms of seasonal effect. Based on these results, the suggested SVR downscaling method is an effective approach to improve the spatial resolution of satellite SM measurement

GenQuery: Supporting Expressive Visual Search with Generative Models

Designers rely on visual search to explore and develop ideas in early design stages. However, designers can struggle to identify suitable text queries to initiate a search or to discover images for similarity-based search that can adequately express their intent. We propose GenQuery, a novel system that integrates generative models into the visual search process. GenQuery can automatically elaborate on users' queries and surface concrete search directions when users only have abstract ideas. To support precise expression of search intents, the system enables users to generatively modify images and use these in similarity-based search. In a comparative user study (N=16), designers felt that they could more accurately express their intents and find more satisfactory outcomes with GenQuery compared to a tool without generative features. Furthermore, the unpredictability of generations allowed participants to uncover more diverse outcomes. By supporting both convergence and divergence, GenQuery led to a more creative experience.Comment: 18 pages and 12 figure

Unmanned aerial vehicle based tree canopy characteristics measurement for precision spray applications

The critical components for applying the correct amount of agrochemicals are fruit tree characteristics such as canopy height, canopy volume, and canopy coverage. An unmanned aerial vehicle (UAV)-based tree canopy characteristics measurement system was developed using image processing approaches. The UAV captured images using a high-resolution red-green-blue (RGB) camera. A digital surface model (DSM) and a digital terrain model (DTM) were generated from the captured images. A tree canopy height map was generated from the subtraction of DSM and DTM. A total of 24 apple trees were randomly targeted to measure the canopy characteristics. Region of interest (ROI) was generated across the boundary of each targeted tree. The height of all pixels within each ROI was computed separately. The pixel with maximum height was considered as the height of the respective tree. For computing canopy volume, the sum of all pixel heights from individual ROI was multiplied by the square of ground sample distance (GSD) of 5.69 mm·pixel−1. A segmentation method was employed to calculate the canopy coverage of the individual trees. The segmented canopy pixel area was divided by the total pixel area within the ROI. The results showed an average relative error of 0.2 m(6.64%) while comparing automatically measured tree height with ground measurements. For tree canopy volume, a mean absolute error of 0.25 m3 and a root mean square error of 0.33 m3 were achieved. The study estimated the possible agrochemical requirement for spraying the fruit trees, ranging from 0.1 to 0.32 l based on tree canopy volumes. The overall investigations suggest that the UAV-based tree canopy characteristics measurements could be a potential tool to calculate the pesticide requirement for precision spraying applications in tree fruit orchards

A Unified Compression Framework for Efficient Speech-Driven Talking-Face Generation

Virtual humans have gained considerable attention in numerous industries, e.g., entertainment and e-commerce. As a core technology, synthesizing photorealistic face frames from target speech and facial identity has been actively studied with generative adversarial networks. Despite remarkable results of modern talking-face generation models, they often entail high computational burdens, which limit their efficient deployment. This study aims to develop a lightweight model for speech-driven talking-face synthesis. We build a compact generator by removing the residual blocks and reducing the channel width from Wav2Lip, a popular talking-face generator. We also present a knowledge distillation scheme to stably yet effectively train the small-capacity generator without adversarial learning. We reduce the number of parameters and MACs by 28$\times$ while retaining the performance of the original model. Moreover, to alleviate a severe performance drop when converting the whole generator to INT8 precision, we adopt a selective quantization method that uses FP16 for the quantization-sensitive layers and INT8 for the other layers. Using this mixed precision, we achieve up to a 19$\times$ speedup on edge GPUs without noticeably compromising the generation quality.Comment: MLSys Workshop on On-Device Intelligence, 2023; Demo: https://huggingface.co/spaces/nota-ai/compressed_wav2li

Efficacy of smartphone application-based multi-domain cognitive training in older adults without dementia

BackgroundAs the population ages and the prevalence of dementia increases, there is a growing emphasis on the importance of cognitive training to prevent dementia. A smartphone application-based cognitive training software program, BeauBrain Trainer (BBT), has been developed to provide better access to cognitive training for older adults. Numerous studies have revealed the effectiveness of cognitive training using a cognitive assessment tool. However, relatively few studies have evaluated brain activation using brain imaging as a result of improved cognitive function.MethodsAll participants were required to download the BBT, an Android-based application for cognitive training, onto their own smartphone or tablet computer and to engage in cognitive training at home. Older adults without dementia were enrolled in this study, including 51 participants in the intervention group and 50 participants in the control group. The BBT comprised a set of 12 cognitive tasks, including two tasks in each of the following six cognitive domains: attention, language, calculation, visuospatial function, memory, and frontal/executive function. Each cognitive task was divided into four blocks based on its level of difficulty. A 16-week cognitive training was designed to carry out cognitive tasks using a total of 48 blocks (12 tasks × 4 levels) for at least 1.5 h per day, 5 days per week. All participants in the intervention group were given BBT tasks that gradually increased in difficulty level, which they submitted through a smartphone application daily for 16 weeks. The researchers monitored the participants’ task performance records on the website and encouraged participants to engage in cognitive training through regular contact. This study was conducted to investigate the improvement in cognitive function and the activation pattern of the frontal cortex in older adults participating in smartphone application-based cognitive training. The cognitive assessment tool was the BeauBrain cognitive screening test (CST), a tablet-based computerized cognitive screening test. The activation pattern of the frontal cortex was measured using functional near-infrared spectroscopy (fNIRS). Additionally, this study aimed to determine the positive effects of cognitive training on everyday functioning and psychological states using a questionnaire.ResultsOf 101 participants, 85 older adults without dementia (84.1%) who completed the study protocol were included in the statistical analysis. There were 41 participants (80.3%) in the intervention group and 44 participants (88.0%) in the control group. A two-way repeated-measures analysis of variance (ANOVA) was used to compare the cognitive scores over a 16-week period between the intervention and control groups. According to the CST results, the intervention group exhibited a statistically significant increase in the language subtest scores, specifically the phonemic word fluency test, compared to those of the control group. The fNIRS results revealed greater activation in the dorsolateral prefrontal cortex during the STROOP incongruent task in the intervention group than did the control group. However, the effectiveness of cognitive training was not observed across a variety of rating scales, including everyday functioning, depression, self-efficacy, attention, and subjective memory complaints.ConclusionThis study revealed that a smartphone-based cognitive training application led to improvements in phonemic generative naming ability and activation of the prefrontal cortex in older adults without dementia. This study is meaningful because it confirmed that cognitive training is partially effective in enhancing frontal lobe function. It also provided information on the brain mechanisms related to the effects of cognitive training using fNIRS

Prevalence and detection of low-allele-fraction variants in clinical cancer samples

Accurate detection of genomic alterations using high-throughput sequencing is an essential component of precision cancer medicine. We characterize the variant allele fractions (VAFs) of somatic single nucleotide variants and indels across 5095 clinical samples profiled using a custom panel, CancerSCAN. Our results demonstrate that a significant fraction of clinically actionable variants have low VAFs, often due to low tumor purity and treatment-induced mutations. The percentages of mutations under 5% VAF across hotspots in EGFR, KRAS, PIK3CA, and BRAF are 16%, 11%, 12%, and 10%, respectively, with 24% for EGFR T790M and 17% for PIK3CA E545. For clinical relevance, we describe two patients for whom targeted therapy achieved remission despite low VAF mutations. We also characterize the read depths necessary to achieve sensitivity and specificity comparable to current laboratory assays. These results show that capturing low VAF mutations at hotspots by sufficient sequencing coverage and carefully tuned algorithms is imperative for a clinical assay

Doctoral thesis recital (piano) chamber

Piano quartet in G minor, K. 478 / Wolfgang Amadeus Mozart -- Piano quartet in C minor, op. 60 / Johannes Brahms.Musi

Doctoral thesis recital (piano (lecture))

[Lecture]: Birds, water, and bells : representation of sound in Ravel's Miroirs -- [Performance]: Miroirs. Oiseaux tristes ; Une barque sur l'océan ; La vallé des cloches / Maurice Ravel.Musi

Doctoral thesis recital (piano)

Piano sonata in A-flat major, op. 110 / Ludwig van Beethoven -- Danse macabre : op. 40 / Camille Saint-Saens -- Pictures at an exhibition / Modest Mussorgsky.Musi