Miracle-dz/UniFAR-FTUs huggingface dataset:
ItemSuggest: A Data Management Platform for Machine Learned Ranking Services

Machine Learning (ML) is a critical component of several novel applications and intelligent features in existing applications. Recent advances in deep learning have fundamentally advanced the state-of-the-art in several areas of research and made it easier to apply ML to a wide variety of problems. However, applied ML projects in industry, where the objective is to build and improve a production feature that uses ML, continues to be complicated and often bottle-necked by data management challenges. ItemSuggest is a platform for building contextual relevance services. In this paper, we describe ItemSuggest with a focus on how we leverage key ideas from data management to make it dramatically easier to build machine-learned ranking services. The platform allows engineers to focus on application-speciﬁc modeling and simpliﬁes key tasks of 1) gathering training data; 2) cleaning, validating, and monitoring data quality; 3) training and evaluating models; 4) managing the feature lifecycle; and 5) running A/B tests. We outline key design choices anchored around the core idea of optimizing for experiment velocity. We describe lessons learned from applications built on this platform that have been in production serving hundreds of millions of users for over a year. Finally, we identify two key components of the platform where data management research can have a major impact—a transformation engine for feature engineering and one for training set representation. We believe such platforms have the potential to accelerate and simplify ML applications the same way data warehouses radically simpli-ﬁed complex reporting applications.
A Ship Rotation Detection Model in Remote Sensing Images Based on Feature Fusion Pyramid Network and Deep Reinforcement Learning

Ship detection plays an important role in automatic remote sensing image interpretation. The scale difference, large aspect ratio of ship, complex remote sensing image background and ship dense parking scene make the detection task difficult. To handle the challenging problems above, we propose a ship rotation detection model based on a Feature Fusion Pyramid Network and deep reinforcement learning (FFPN-RL) in this paper. The detection network can efficiently generate the inclined rectangular box for ship. First, we propose the Feature Fusion Pyramid Network (FFPN) that strengthens the reuse of different scales features, and FFPN can extract the low level location and high level semantic information that has an important impact on multi-scale ship detection and precise location of dense parking ships. Second, in order to get accurate ship angle information, we apply deep reinforcement learning to the inclined ship detection task for the first time. In addition, we put forward prior policy guidance and a long-term training method to train an angle prediction agent constructed through a dueling structure Q network, which is able to iteratively and accurately obtain the ship angle. In addition, we design soft rotation non-maximum suppression to reduce the missed ship detection while suppressing the redundant detection boxes. We carry out detailed experiments on the remote sensing ship image dataset, and the experiments validate that our FFPN-RL ship detection model has efficient detection performance.
Mango Yield Mapping at the Orchard Scale Based on Tree Structure and Land Cover Assessed by UAV

In the value chain, yields are key information for both growers and other stakeholders in market supply and exports. However, orchard yields are often still based on an extrapolation of tree production which is visually assessed on a limited number of trees; a tedious and inaccurate task that gives no yield information at a finer scale than the orchard plot. In this work, we propose a method to accurately map individual tree production at the orchard scale by developing a trade-off methodology between mechanistic yield modelling and extensive fruit counting using machine vision systems. A methodological toolbox was developed and tested to estimate and map tree species, structure, and yields in mango orchards of various cropping systems (from monocultivar to plurispecific orchards) in the Niayes region, West Senegal. Tree structure parameters (height, crown area and volume), species, and mango cultivars were measured using unmanned aerial vehicle (UAV) photogrammetry and geographic, object-based image analysis. This procedure reached an average overall accuracy of 0.89 for classifying tree species and mango cultivars. Tree structure parameters combined with a fruit load index, which takes into account year and management effects, were implemented in predictive production models of three mango cultivars. Models reached satisfying accuracies with R2 greater than 0.77 and RMSE% ranging from 20% to 29% when evaluated with the measured production of 60 validation trees. In 2017, this methodology was applied to 15 orchards overflown by UAV, and estimated yields were compared to those measured by the growers for six of them, showing the proper efficiency of our technology. The proposed method achieved the breakthrough of rapidly and precisely mapping mango yields without detecting fruits from ground imagery, but rather, by linking yields with tree structural parameters. Such a tool will provide growers with accurate yield estimations at the orchard scale, and will permit them to study the parameters that drive yield heterogeneity within and between orchards.
Challenges and Opportunities in DNN-Based Video Analytics: A Demonstration of the BlazeIt Video Query Engine

As video volumes grow, analysts are increasingly able to query the real world. Since manually watching these growing volumes of video is infeasible, analysts have increasingly turned to deep learning to perform automatic analyses. However, these meth-ods are: costly (running up to 10x slower than real time, i.e., 3 fps) and cumbersome to deploy, requiring writing complex, imperative code with many low-level libraries (e.g., OpenCV, MXNet). There is an incredible opportunity to leverage techniques from the data management community to automate and optimize these analytics pipelines. In this paper, we describe our ongoing work in the Stanford DAWN lab on BlazeIt , an analytics engine for scalable and usable video analytics that currently contains an optimizing query engine. We propose a demonstration of BlazeIt ’s query language, FrameQL , its use cases, and our preliminary work on debugging machine learning, which will show the feasibility of video analytics at scale. We further describe the challenges that arise from large-scale video, progress we have made in automating and optimizing video analytics pipelines, and our plans to extend BlazeIt .
Thriving in the No Man's Land between Compilers and Databases

When developing new data-intensive applications, one faces a build-or-buy decision: use an existing off-the-shelf data management sys-tem (DMS) or implement a custom solution. While off-the-shelf systems offer quick results, they lack the ﬂexibility to accommodate the changing requirements of long-term projects. Building a solution from scratch in a general-purpose programming language, however, comes with long-term development costs that may not be justiﬁed. What is lacking is a middle ground or, more precisely, a clear migration path from off-the-shelf Data Management Systems to customized applications in general-purpose programming languages. There is, in effect, a no man’s land that neither compiler nor database researchers have claimed. We believe that this problem is an opportunity for the database community to claim a stake. We need to invest effort to transfer the outcomes of data management research into ﬁelds of programming languages and compilers. The common complaint that other ﬁelds are re-inventing database techniques bears witness to the need for that knowledge transfer. In this paper, we motivate the necessity for data management techniques in general-purpose programming languages and outline a number of speciﬁc opportunities for knowledge transfer. This effort will not only cover the no man’s land but also broaden the impact of data management research.
Super-Resolution Reconstruction and Its Application Based on Multilevel Main Structure and Detail Boosting

Vivid main structure and rich texture detail are important factors with which to determine the quality of high-resolution images after super-resolution (SR) reconstruction. Owing to the loss of high-frequency information in the process of SR reconstruction and the limitation of the accurate estimation of the unknown information in the inversion process, a gap still exists between the high-resolution image and the real image. The main structure can better preserve the edge structure of the image, and detail boosting can compensate for the missing high-frequency information in the reconstruction process. Therefore, a novel single remote-sensing image SR reconstruction method based on multilevel main structure and detail boosting (MMSDB-SR) is put forward in this paper. First, the multilevel main structure was obtained based on the decomposition of the remote-sensing image through use of the relative total variation model. Subsequently, multilevel texture detail information was obtained by a difference process. Second, the multilevel main structure and texture detail were reconstructed separately. The detail-boosting function was used to compensate for the missing high-frequency details in the reconstruction process. Finally, the high-resolution remote-sensing image with clear edge and rich texture detail can be obtained by fusing the multilevel main structure and texture-detail information. The experimental results show that the reconstructed high-resolution image has high clarity, high fidelity, and multi-detail visual effects, and the objective evaluation index exhibits significant improvement. Actual results show an average gain in entropy of up to 0.34 dB for an up-scaling of 2. Real results show an average gain in enhancement measure evaluation of up to 2.42 for an up-scaling of 2. The robustness and universality of the proposed SR method are verified.
Improving the Input Information for Medical Software Requirements Specifications using Ontology-Based Intelligent Agent

The conducted analysis of known ontology-based intelligent agents (OBIA) showed that nowadays the task of the research and improvement of characteristics of input information for medical software requirements specifications (M-SRS) was not solved by the OBIAs. Also, they do not provide the quantitative assessment of the level of elaboration of the life cycle initial stages of medical software based on the analysis of specifications. So, the task of this study is the development of the OBIA for improving the input information for the formation of M-SRS. The scheme of the process of improving the input information for the formation of M-SRS using the OBIA is developed, and this intelligent agent (IA) is realized. This IA evaluates and improves the input information for the formation of the requirements specification for medical software. For the experiment, the developed OBIA used the specification of the requirements for real medical software of information and analytical system for accounting of therapeutic and diagnostic activities provided to the wounded during transportation, which was developed by one of the software companies in Khmelnitsky. During this experiment, the developed intelligent agent provided to improve the input information for the formation of the specification of requirements for the specified medical software, namely, IA provided the raising level of sufficiency of input information for determining the software quality in the M-SRS by 24%.
Utilizing Collocated Crop Growth Model Simulations to Train Agronomic Satellite Retrieval Algorithms

Due to its worldwide coverage and high revisit time, satellite-based remote sensing provides the ability to monitor in-season crop state variables and yields globally. In this study, we presented a novel approach to training agronomic satellite retrieval algorithms by utilizing collocated crop growth model simulations and solar-reflective satellite measurements. Specifically, we showed that bidirectional long short-term memory networks (BLSTMs) can be trained to predict the in-season state variables and yields of Agricultural Production Systems sIMulator (APSIM) maize crop growth model simulations from collocated Moderate Resolution Imaging Spectroradiometer (MODIS) 500-m satellite measurements over the United States Corn Belt at a regional scale. We evaluated the performance of the BLSTMs through both k-fold cross validation and comparison to regional scale ground-truth yields and phenology. Using k-fold cross validation, we showed that three distinct in-season maize state variables (leaf area index, aboveground biomass, and specific leaf area) can be retrieved with cross-validated R2 values ranging from 0.4 to 0.8 for significant portions of the season. Several other plant, soil, and phenological in-season state variables were also evaluated in the study for their retrievability via k-fold cross validation. In addition, by comparing to survey-based United State Department of Agriculture (USDA) ground truth data, we showed that the BLSTMs are able to predict actual county-level yields with R2 values between 0.45 and 0.6 and actual state-level phenological dates (emergence, silking, and maturity) with R2 values between 0.75 and 0.85. We believe that a potential application of this methodology is to develop satellite products to monitor in-season field-scale crop growth on a global scale by reproducing the methodology with field-scale crop growth model simulations (utilizing farmer-recorded field-scale agromanagement data) and collocated high-resolution satellite data (fused with moderate-resolution satellite data).
Fuzzy Logic Type 1 and 2 for Social Robots and Apps for Children with Autism

Technology is a valuable tool for the treatment of autism in children. It can enhance the therapy, providing a new social channel of interaction in a friendly and controlled environment. This paper proposes an integral technological platform based on two main elements. The first one is a robot, specifically designed to collaborate in the intervention process eliciting pro-social behavior, and a tablet application program, explicitly intended to complement the intervention activities with the robot, giving at the same time administration support to the therapist offering information about the statistical performance of the child during the intervention. On the other hand, fuzzy logic systems are implemented in the technological platform to increase the performance in the robot and the tablet. Fuzzy logic includes an inference system based on linguistic rules that help therapist to interact with the children when they are taking the integral therapy because the inference system can cover different social situations in automatic way. This paper shows the complete design of the robot and tablet application as the main supported tools. The social skill therapy is designed and implemented to different children with autism. Moreover, fuzzy logic demonstrated that is a robust system for dealing with uncertainties so that the robot can generate a correct response according with the input signal from the child. Finally, the preliminary results give a good insight about the potential of this proposed integral therapy. In addition, fuzzy logic is a principal component for designing the therapy.
Satellite Retrieval of Surface Water Nutrients in the Coastal Regions of the East China Sea

Due to the tremendous flux of terrestrial nutrients from the Changjiang River, the waters in the coastal regions of the East China Sea (ECS) are exposed to heavy eutrophication. Satellite remote sensing was proven to be an ideal way of monitoring the spatiotemporal variability of these nutrients. In this study, satellite retrieval models for nitrate and phosphate concentrations in the coastal regions of the ECS are proposed using the back-propagation neural network (BP-NN). Both the satellite-retrieved sea surface salinity (SSS) and remote-sensing reflectance (Rrs) were used as inputs in our model. Compared with models that only use Rrs or SSS, the newly proposed model performs much better in the study area, with determination coefficients (R2) of 0.98 and 0.83, and mean relative error (MRE) values of 18.2% and 17.2% for nitrate and phosphate concentrations, respectively. Based on the proposed model and satellite-retrieved Rrs and SSS datasets, monthly time-series maps of nitrate and phosphate concentrations in the coastal regions of the ECS for 2015–2017 were retrieved for the first time. The results show that the distribution of nutrients had a significant seasonal variation. Phosphate concentrations in the ECS were lower in spring and summer than those in autumn and winter, which was mainly due to phytoplankton uptake and utilization. However, nitrate still spread far out into the ocean in summer because the diluted Changjiang River water remained rich in nitrogen.
Polarization of Rheumatoid Macrophages by TNF Targeting Through an IL-10/STAT3 Mechanism

Macrophages contribute to the pathogenesis of rheumatoid arthritis (RA). They can display different states of activation or “polarization,” notably the so-called inflammatory “M1” and the various alternative “M2” polarizations, characterized by distinct functions. Data regarding the effects of RA anti-cytokine biological disease-modifying anti-rheumatic drugs (bDMARDs) on macrophage polarization are scarce. We aimed to assess in vitro modulation of macrophage polarization by bDMARDs targeting pro-inflammatory cytokines in RA. We generated monocyte derived macrophages using blood samples from 20 RA patients with active RA and 30 healthy controls. We evaluated in vitro the impact on M1 inflammatory macrophages of: etanercept (ETA), adalimumab (ADA), certolizumab (CZP), tocilizumab (TCZ), and rituximab (RTX). We assessed the impact on macrophage polarization using flow cytometry and RTqPCR to study the expression of surface markers and perform functional studies of cytokine production, phagocytosis, and negative feedback control of inflammation. Among evaluated bDMARDs, anti-TNF agents modulated the polarization of inflammatory macrophages by decreasing inflammatory surface markers (CD40, CD80) and favoring alternative markers (CD16, CD163, MerTK). Anti-TNF agents also induced alternative functions in macrophages activated in inflammatory condition with (i) the inhibition of inflammatory cytokines (TNF, IL-6, IL-12), (ii) an increase in phagocytosis. These findings were mechanistically related to an increase in early IL-10 production, responsible for higher negative feedback control of inflammation involving SOCS3 and Gas6. This IL-10 effect was STAT3-dependent. Anti-TNF agents not only inhibit in vitro inflammatory functions of macrophages, but also favor resolution of inflammation through polarization toward alternative features specifically involving the IL-10/STAT3 axis.
Effort and Fatigue-Related Functional Connectivity in Mild Traumatic Brain Injury

Mental fatigue in healthy individuals is typically observed under conditions of high cognitive demand, particularly when effort is required to perform a task for a long period of time—thus the concepts of fatigue and effort are closely related. In brain injured individuals, mental fatigue can be a persistent and debilitating symptom. Presence of fatigue after brain injury is prognostic for return to work/school and engagement in activities of daily life. As such, it should be a high priority for treatment in this population, but because there is little understanding of its behavioral and neural underpinnings, the target for such treatment is unknown. Here, the neural underpinnings of fatigue and effort are investigated in active duty military service members with mild traumatic brain injury (mTBI) and demographically-matched orthopedic controls. Participants performed a Constant Effort task for which they were to hold a pre-defined effort level constant for long durations during fMRI scanning. The task allowed for investigation of the neural systems underlying fatigue and their relationship with sense of effort. While brain activation associated with effort and fatigue did not differentiate the mTBI and controls, functional connectivity amongst active brain regions did. The mTBI group demonstrated immediate hyper-connectivity that increased with effort level but diminished quickly when there was a need to maintain effort. Controls, in contrast, demonstrated a similar pattern of hyper-connectivity, but only when maintaining effort over time. Connectivity, particularly between the left anterior insula, rostral anterior cingulate cortex, and right-sided inferior frontal regions, correlated with effort-level and state fatigue in mTBI participants. These connections also correlated with effort level in the Control group, but only the connection between the left insula and superior medial frontal gyrus correlated with fatigue, suggesting a differing pattern of connectivity. These findings align, in part, with the dopamine imbalance, and neural efficiency hypotheses that pose key roles for medial frontal connections with insular or striatal regions in motivating or optimizing performance. Sense of effort and fatigue are closely related. As people fatigue, sense of effort increases systematically. The data propose a complex link between sense of effort, fatigue, and mTBI that is centered in what may be an inefficient neural system due to brain trauma that warrants further investigation.
Brain Vital Signs: Expanding From the Auditory to Visual Modality

The critical need for rapid objective, physiological evaluation of brain function at point-of-care has led to the emergence of brain vital signs—a framework encompassing a portable electroencephalography (EEG) and an automated, quick test protocol. This framework enables access to well-established event-related potential (ERP) markers, which are specific to sensory, attention, and cognitive functions in both healthy and patient populations. However, all our applications to-date have used auditory stimulation, which have highlighted application challenges in persons with hearing impairments (e.g., aging, seniors, dementia). Consequently, it has become important to translate brain vital signs into a visual sensory modality. Therefore, the objectives of this study were to: 1) demonstrate the feasibility of visual brain vital signs; and 2) compare and normalize results from visual and auditory brain vital signs. Data were collected from 34 healthy adults (33 ± 13 years) using a 64-channel EEG system. Visual and auditory sequences were kept as comparable as possible to elicit the N100, P300, and N400 responses. Visual brain vital signs were elicited successfully for all three responses across the group (N100: F = 29.8380, p < 0.001; P300: F = 138.8442, p < 0.0001; N400: F = 6.8476, p = 0.01). Initial auditory-visual comparisons across the three components showed attention processing (P300) was found to be the most transferrable across modalities, with no group-level differences and correlated peak amplitudes (rho = 0.7, p = 0.0001) across individuals. Auditory P300 latencies were shorter than visual (p < 0.0001) but normalization and correlation (r = 0.5, p = 0.0033) implied a potential systematic difference across modalities. Reduced auditory N400 amplitudes compared to visual (p = 0.0061) paired with normalization and correlation across individuals (r = 0.6, p = 0.0012), also revealed potential systematic modality differences between reading and listening language comprehension. This study provides an initial understanding of the relationship between the visual and auditory sequences, while importantly establishing a visual sequence within the brain vital signs framework. With both auditory and visual stimulation capabilities available, it is possible to broaden applications across the lifespan.