Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimizing Kernel Ridge Regression for Remote Sensing Problems

Kernel methods have been very successful in remote sensing problems because of their ability to deal with high dimensional non-linear data. However, they are computationally expensive to train when a large amount of samples are used. In this context, while the amount of available remote sensing data has constantly increased, the size of training sets in kernel methods is usually restricted to few thousand samples. In this work, we modified the kernel ridge regression (KRR) training procedure to deal with large scale datasets. In addition, the basis functions in the reproducing kernel Hilbert space are defined as parameters to be also optimized during the training process. This extends the number of free parameters from two (in the standard KRR with an RBF kernel) to more than fifty thousand in our experiments. The effectiveness of the proposal is illustrated in the problem of surface temperature estimation from MetOp-IASI hyperspectral infrared sounding data. The data set used contains more than one million samples, but the proposed method could potentially be trained with much more data.
Hyperdimensional Computing Exploiting Carbon Nanotube FETs, Resistive RAM, and Their Monolithic 3D Integration

The field of machine learning is witnessing rapid advances along several fronts: new machine learning models, new machine learning algorithms utilizing these models, new hardware architectures for these algorithms, and new technologies for creating energy-efficient implementations of such hardware architectures. Hyperdimensional (HD) computing represents one such model. Emerging nanotechnologies, such as carbon nanotube field-effect transistors (CNFETs), resistive random-access memory (RRAM), and their monolithic 3D integration, enable energy- and area-efficient hardware implementations of HD computing architectures. Such efficient implementations are achieved by exploiting several characteristics of the component nanotechnologies (e.g., energy-efficient logic circuits, dense memory, and incrementers naturally enabled by gradual reset of RRAM cells) and their monolithic 3D integration (enabling tight integration of logic and memory), as well as various characteristics of the HD computing model (e.g., embracing randomness that allows us to utilize rather than avoid inherent variations in RRAM and CNFETs, resilience to errors in the underlying hardware). We experimentally demonstrate and characterize an end-to-end HD computing nanosystem built using monolithic 3D integration of CNFETs and RRAM. Using our nanosystem, we experimentally demonstrate the pairwise classification of 21 languages with measured mean accuracy of up to 98% on >20 000 sentences (6.4 million characters), training using one text sample (~100 000 characters) per language, and resilient operation (98% accuracy) despite 78% of bits in HD representation being stuck at 0 or 1 in hardware. We also show that the monolithic 3D implementations of HD computing can have 35 $\times $ improved energy-execution time product for training and inference of language classification data sets (while using 3 $\times $ less area) compared to silicon CMOS implementations.
Fuzzy Fusion of Change Vector Analysis and Spectral Angle Mapper for Hyperspectral Change Detection

Change Vector Analysis (CVA) is one of the most widely used approaches for change detection in multispectral and hyperspectral images. Although, in CVA, the spectral change vector (CV) comprises the angle as well as the magnitude of the change, typically only the magnitude measure is used as change criterion. On the other hand, the spectral angle mapper (SAM) uses only the angle measure as criterion for change detection. It is envisaged that combining the angle and magnitude for change detection (i.e. combining SAM and magnitude CVA) can improve the change detection performance, yet only a limited number of approaches have been proposed in the literature so far. This paper presents a novel fuzzy inference combination strategy that combines the angle and magnitude distances, referred to as Fuzzy CVA (FuzCVA), and is shown that the proposed approach can provide improved change detection performance by effectively combining magnitude and angle measures.
A decade of vector control activities: Progress and limitations of Chagas disease prevention in a region of Guatemala with persistent Triatoma dimidiata infestation

Introduction Chagas disease, a neglected tropical disease that affects millions of Latin Americans, has been effectively controlled in Guatemala after multiple rounds of indoor residual insecticide spraying (IRS). However, a few foci remain with persistent Triatoma dimidiata infestation. One such area is the municipality of Comapa, Department of Jutiapa, in the southeastern region of Guatemala, where control interventions appear less effective. We carried out three cross sectional entomological and serological surveys in Comapa to evaluate a decade of vector control activities. Baseline serological (1999) and entomological (2001–2) surveys were followed by three rounds of insecticide applications (2003–2005) and intermittent focal spraying of infested houses, until approximately 2012. Household inspections to determine entomological indices and construction materials were conducted in 2001, 2007 and 2011. Seroprevalence surveys were conducted in school-age children in 1999, 2007 and 2015, and in women of child bearing age (15–44 years) only in 2015. After multiple rounds of indoor residual sprayings (IRS), the infestation index decreased significantly from 39% (2001–2) to 27% (2011). Household construction materials alone predicted <10% of infested houses. Chagas seroprevalence in Comapa declined in school-aged children by 10–fold, from 10% (1999) to 1% (2015). However, seroprevalence in women of child bearing age remains >10%. Conclusion After a decade of vector control activities in Comapa, there is evidence of significantly reduced transmission. However, the continued risk for vector-borne and congenital transmission pose a threat to the 2022 Chagas disease elimination goal. Systematic integrated vector control and improved Chagas disease screening and treatment programs for congenital and vector-borne disease are needed to reach the elimination goal in regions with persistent vector infestation.
Mitochondrial-Directed Antioxidant Reduces Microglial-Induced Inflammation in Murine In Vitro Model of TC-83 Infection

Venezuelan equine encephalitis virus (VEEV) is an arbovirus that is associated with robust inflammation that contributes to neurodegenerative phenotypes. In addition to triggering central nervous system (CNS) inflammation, VEEV will also induce mitochondrial dysfunction, resulting in increased cellular apoptosis. In this study, we utilize the TC-83 strain of VEEV to determine the role of mitochondrial oxidative stress in mediating inflammation elicited by murine brain microglial cells. Using an in vitro model, we show that murine microglia are susceptible to TC-83 infection, and that these cells undergo mitochondrial stress as the result of infection. We also indicate that bystander microglia contribute more significantly to the overall inflammatory load than directly infected microglia. Use of a mitochondrial targeted antioxidant, mitoquinone mesylate, greatly reduced the pro-inflammatory cytokines released by both direct infected and bystander microglia. Our data suggest that release of interleukin-1β, a key instigator of neuroinflammation during VEEV infection, may be the direct result of accumulating mitochondrial stress. This data improves our understanding inflammation elicited by murine microglia and will aid in the development of more accurate in vitro and in vivo murine model of VEEV-induced neuroinflammation.
Fusion of Multitemporal LiDAR Data for Individual Tree Crown Parameter Estimation on Low Density Point Clouds

The increasingly availability of Light Detection and Ranging (LiDAR) data acquired at different times can be used to analyze the forest dynamics at individual tree level. This often requires to deal with LiDAR point clouds having significantly different point densities. To address this issue, this paper presents a method for the fusion of multitemporal Li-DAR data which aims at using the information provided by high density LiDAR data (higher than 10 pts/m2) to improve the single tree parameter estimation of low density data (up to 5 pts/m2) acquired over the same forest at different times. The method first accurately characterizes the crown shapes on the high density data. Then, it uses the obtained estimates to drive the tree parameter estimation on the low density LiDAR data. The method has been tested on a multitemporal dataset acquired in coniferous forests located in the Italian Alps. Experimental results confirmed the effectiveness of the method.
What is the place of pancreatic endoscopic ultrasound elastography in 2018?

eases, such as cancer, lead to a change in tissue hardness. Elastography examines the elastic properties of tissues by applying a slight compression to the tissue and comparing an image obtained before and after this compression. Endoscopic ultrasound (EUS) is the best technique available today to diagnose small pancreatic masses and to obtain histology from such lesions. However, in around 10%–15% of cases, the EUS-guided fine needle aspiration (EUS-FNA) is non-contributory; since 2006 (date of the first publication on EUS elastography), the majority of published studies have tried to prove that EUS elastography should replace EUS-FNA. The main problem with EUS elastography (both strain ratio and semiquantitative) has been low specificity and also the reproducibility of the image during the procedure. The elasticity of soft tissues depends to a large extent on their molecular building blocks (fat, collagen, etc.), and on the microscopic and macroscopic structural organization of these blocks. In the normal pancreas, for example, the glandular structure may be firmer than the surrounding connective tissue, which in turn is firmer than the subcutaneous fat. EUS elastography allows the hardness or stiffness of biological tissues to be estimated and imaged using conventional ultrasound instruments with modified software. It is known that certain pathologic conditions, such as malignant tumors, often manifest changes in the mechanical properties of their tissues. The belief is that the elastic properties of benign lesions are fairly uniform, i. e. similar throughout a benign tumor, while cancerous tumors, in contrast, grow in a very disorganized way. Therefore, within a given malignant tumor, the elastic properties of one area of the tumor may be significantly different to those in another area. The concept relating to the measurement of these tissue changes is an extension of the basic principles associated with traditional medical ultrasound imaging. The principle is based on the fact that tissues are deformed slightly when a small displacement is externally applied [1–3].
AIS Electronic Library (AISeL)

The disruptive nature of digital innovation has led incumbent organizations to face enormous challenges and pressure. To address the fundamentally new nature of digital innovations, incumbents have established new managerial roles, such as Chief Digital Officers (CDOs), to champion innovation. We explore the role these innovation champions play for digital innovation success and argue that internal reorganization and the sourcing of external knowledge constitute important mechanisms through which CDOs might contribute to organizations’ digital innovation success. We will empirically test our proposed research model using longitudinal data on the world’s largest companies. Our study is expected to contribute to literature on knowledge recombination and innovation management by examining how innovation champions at the C-level use internal reorganization and sourcing of external knowledge to enhance digital innovation success.
The High Temporal Detection of Land Surface Freeze and Thaw States via a Combination of Passive Microwave Estimates

The states of the Earth surface in terms of high-latitude freeze and thaw (FT) cycles significantly impact many physical applications that include biogeochemical transitions, hydrological phenomena, and ecosystem evolution. We have shown that land surface emissivity estimates have great potential for use in the detection of FT states since that parameter primarily depends on surface characteristics instead of on direct use of brightness temperatures. This study aims to investigate the potential of merging passive microwave sensors and their land surface emissivity estimates from Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E), Special Sensor Microwave Imager (SSM/I), AMSR2, and the Global Precipitation Measurement (GPM) Microwave Imager (GMI) to provide high temporal resolution (sub-daily) FT states. This factor is of critical importance and usage, primarily during the transitions between freeze and thaw that frequently occur at sub-daily time-frames in spring seasons. Data fusion techinques were used to construct diurnal estimates in order to accurately predicting the exact time of the freeze-thaw transition for a variety of land cover types and geographical regions. The results revealed that emissivity difference values between low and high frequencies (such as 10.7 GHz and 89GHz) at horizontal polarization from multiple platforms have a strong correlation with ground-based soil temperature diurnal values at 5-cm depth. Evaluation of the proposed approach with independent ground observations from year 2015 to 2017 showed that the data fusion of land surface emissivities in high-latitudes was able to notably capture the frequent FT transitions.
Introducing a Framework of Self-Organizing Maps for Regression of Soil Moisture with Hyperspectral Data

In this paper, we introduce a framework to solve regression problems based on high-dimensional and small datasets. This framework involves two self-organizing maps (SOM) and combines unsupervised with supervised learning. We investigate the impacts of SOM hyperparameters on the regression performance and compare the results of the SOM framework with two established regressors on a measured dataset. The derived results reveal the potential of the SOM framework. Finally, we propose further research aspects for the SOM framework to analyze its capabilities and limitations. We have published our dataset in [1] to ensure the reproducibility of the results.
schwimmbad: A uniform interface to parallel processing pools in Python

Many scientific and computing problems require doing some calculation on all elements of some data set. If the calculations can be executed in parallel (i.e. without any communication between calculations), these problems are said to be perfectly parallel. On computers with multiple processing cores, these tasks can be distributed and executed in parallel to greatly improve performance. A common paradigm for handling these distributed computing problems is to use a processing “pool”: the “tasks” (the data) are passed in bulk to the pool, and the pool handles distributing the tasks to a number of worker processes when available.
Khmer Release V2.1: Software for Biological Sequence Analysis

1Lab for Data Intensive Biology; School of Veterinary Medicine; University of California, Davis 2Integrative Genetics and Genomics Graduate Group; University of California, Davis 3Molecular, Cellular, and Integrative Physiology Graduate Group; University of California, Davis 4Common Workflow Language Project 5Wild Tree Tech 6Computer Science Graduate Group; University of California, Davis 7ARC Centre of Excellence in Plant Energy Biology; Australian National University 8Microbiology Graduate Group; Univerity of California, Davis 9University of Washington 10Debian Project 11Google, Inc. 12Department of Population Health and Reproduction; School of Veterinary Medicine; University of California, Davis
High Spatial Soil Moisture Mapping Using Small Unmanned Aerial System

Soil moisture is of fundamental importance to many hydrological, biological and biogeochemical processes, plays an important role in the development and evolution of convective weather and precipitation, and impacts precision agriculture, water resource management, and flood runoff prediction. The launch of NASA's Soil Moisture Active/Passive (SMAP) mission in 2015 provide new passive global measurements of soil moisture and surface freeze/thaw state at fixed crossing times and spatial resolutions of 36 km. There exists a need for measurements of soil moisture on much smaller spatial scales and arbitrary diurnal times for SMAP validation, precision agriculture, flood runoff prediction, evaporation and transpiration studies of boundary layer heat transport, and tundra thaw studies. The Lobe Differencing Correlation Radiometer (LDCR) provides a means of mapping soil moisture on spatial scales as small as several meters (i.e., the height of the platform). Compared with various other proposed methods of validation based on either in situ measurements or existing airborne sensors suitable for manned aircraft deployment, the integrated design of the LDCR on a lightweight small unmanned aerial system (sUAS) can provide sub-watershed (~km scale) coverage at very high spatial resolution (~15 m) suitable for scaling scale studies. The Tempest sUAS, flies at very low operator cost compared to manned aircraft. To demonstrate the LDCR Rev A and Rev B, several flights had been performed during field experiments at the Canton Oklahoma Soilscape site on September 8th and 9th, 2015 and Yuma Colorado Irrigation Research Foundation (IRF) site from June to August 2016, and October 2017. The LDCR antenna temperature and soil moisture maps will be presented, and scientific intercomparisons between LDCR soil moisture data and in-situ measurements will be presented.
Classification of PolSAR Images Based on SVM with Self-Paced Learning Optimization

A novel classification method for polarimetric synthetic aperture radar (PolSAR) images using support vector machine (SVM) with self-paced learning (SPL) optimization is proposed in the paper. In our method, Cloude-Pottier polarimetric decomposition components and the eigenvalues of coherency matrix are used as features. Classification is carried out using SVM, and SPL is used to improve the classifier and achieve a stronger generalization capacity. Under SPL paradigm, the classifier learns the easier samples first and gradually involves more difficult samples into the training process. The proposed method achieves the overall classification accuracies of 89.79% on the Flevoland dataset. Such results are comparable with the compared algorithms.
Information visualisation and machine learning: characteristics, convergence and perspective

This paper discusses how information visualisation and machine learning can cross-fertilise. On the one hand, the user-centric ﬁeld of information visualisation can help machine learning to better integrate users in the learning, assessment and interpretation processes. On the other hand, machine learning can provide powerful algorithms for clustering, dimensionality reduction, data cleansing, outlier detection, etc. Such inference tools are required to create eﬃcient visualisations. This paper highlight opportunities to collaborate for experts in both ﬁelds.
Transition Education for Young Adults With Type 1 Diabetes: Pilot Feasibility Study for a Group Telehealth Intervention

Background Young adults with type 1 diabetes (T1D) experience a decline in glycemic outcomes and gaps in clinical care. A diabetes education and support program designed for young adults was delivered through group videoconference and mobile Web. Objective The objective of our study was to assess the feasibility, acceptability, and preliminary efficacy of the program as measured by attendance and webpage views, satisfaction, and pre- and postintervention psychosocial outcomes, respectively. Methods Young adults aged 18-25 years were recruited to attend five 30-minute group diabetes education videoconferences during an 8-week period. Videoconferences included an expert presentation followed by a moderated group discussion. Within 48 hours of each videoconference, participants were sent a link to more information on the study website. Feasibility was assessed using data on videoconference attendance and webpage views. Acceptability was assessed via a Satisfaction Survey completed at the conclusion of the study. Descriptive statistics were generated. Preliminary efficacy was assessed via a survey to measure changes in diabetes-specific self-efficacy and diabetes distress. Pre- and postintervention data were compared using paired samples t tests. Results In this study, 20 young adults (mean age 19.2 [SD 1.1] years) attended an average of 5.1 (SD 1.0) videoconferences equivalent to 153 (SD 30.6) minutes of diabetes education per participant during an 8-week period. Average participant satisfaction scores were 62.2 (SD 2.6) out of a possible 65 points. A total of 102 links sent via text message (short message service) or email resulted in 504 webpage views. There was no statistically significant difference between pre- and postintervention diabetes-specific self-efficacy or diabetes-related distress. Conclusions Delivery of diabetes education via group videoconference using mobile Web follow-up is feasible and acceptable to young adults with T1D. This model of care delivery has the potential to improve attendance, social support, and patient-reported satisfaction. Nevertheless, further research is required to establish the effect on long-term psychosocial and glycemic outcomes.
Identifying and Understanding Communities Using Twitter to Connect About Depression: Cross-Sectional Study

Background Depression is the leading cause of diseases globally and is often characterized by a lack of social connection. With the rise of social media, it is seen that Twitter users are seeking Web-based connections for depression. Objective This study aimed to identify communities where Twitter users tweeted using the hashtag #MyDepressionLooksLike to connect about depression. Once identified, we wanted to understand which community characteristics correlated to Twitter users turning to a Web-based community to connect about depression. Methods Tweets were collected using NCapture software from May 25 to June 1, 2016 during the Mental Health Month (n=104) in the northeastern United States and Washington DC. After mapping tweets, we used a Poisson multilevel regression model to predict tweets per community (county) offset by the population and adjusted for percent female, percent population aged 15-44 years, percent white, percent below poverty, and percent single-person households. We then compared predicted versus observed counts and calculated tweeting index values (TIVs) to represent undertweeting and overtweeting. Last, we examined trends in community characteristics by TIV using Pearson correlation. Results We found significant associations between tweet counts and area-level proportions of females, single-person households, and population aged 15-44 years. TIVs were lower than expected (TIV 1) in eastern, seaboard areas of the study region. There were communities tweeting as expected in the western, inland areas (TIV 2). Counties tweeting more than expected were generally scattered throughout the study region with a small cluster at the base of Maine. When examining community characteristics and overtweeting and undertweeting by county, we observed a clear upward gradient in several types of nonprofits and TIV values. However, we also observed U-shaped relationships for many community factors, suggesting that the same characteristics were correlated with both overtweeting and undertweeting. Conclusions Our findings suggest that Web-based communities, rather than replacing physical connection, may complement or serve as proxies for offline social communities, as seen through the consistent correlations between higher levels of tweeting and abundant nonprofits. Future research could expand the spatiotemporal scope to confirm these findings.