Miracle-dz/UniFAR-FTUs huggingface dataset:
Sensitivity analysis of WOFOST for yield simulation of cassava over the major growing areas of India

Crop yield estimations at the field level are laborious and time-consuming, and the crop models are the better choice in such cases to estimate the crop growth and yield parameters. The crop modeling in data-sparse regions is one of the challenges for research workers, and such situations may be overcome by the spatial interpolation of available gridded global weather data. The accuracy and dependency of these data has always been a concern among researchers to model the crop yield in context of food security and climate change. The difference in meteorological input for a specific location can produce very different outputs for that particular crop (Zare et al., 2016; Borkakati et al., 2021; Sreenivas et al., 2021). Therefore, it is necessary to identify the influence of each meteorological input parameters into a crop model to ensure accuracy in the model output. Sensitivity analysis (SA) is widely used to determine the effect of each input parameter on model output, and this identifies the contribution of each parameter to the model’s response (Richtera et al., 2010).
Partial Purification of the Mammalian Alpha- glucosidase Inhibitor from Melothria sp. (Fam. Cucurbitaceae) Leaves and Stem Extract: In Vitro

Diabetes Mellitus Type 2 (DM2) is a chronic disease characterized by insufficient insulin levels, pancreatic beta cells function loss and insulin resistance in peripheral tissue. Voglibose and acarbose are the clinically used alpha-glucosidase inhibitor; however, adverse effects such as nausea, diarrhea, and bloating hinder the utilization of these agents. Hence, there is a need to explore for an alternative drug that has fewer side effects, better activity, and more affordable than the current commercial drugs. In this study, Melothria sp. was purified using normal phase column chromatography. Seven fractions (fraction A-G) were separated and tested for mammalian alpha-glucosidase assay in vitro and phytochemical screening was conducted to determine the metabolites present in each fraction. Fraction F showed a promising activity against the enzyme and comparable to other natural products and commercial alphaglucosidase agent, acarbose. Phenolic compounds such as tannins and flavonoids based on the phytochemical screening were detected present in the sample which can be responsible in the inhibition activity of the semi-purified extracts.
SUBSURFACE AND SURFACE MICROIRRIGATION OF CORN —SOUTHERN HIGH PLAINS

Microirrigation has the potential to minimize application losses to evaporation, field runoff, and deep percolation; improve irrigation control with smaller, frequent applications; supply nutrients to the crop as needed; and improve crop yields. This study was conducted to evaluate subsurface and surface microirrigation (SUB and TOP, respectively) application methods on crop performance. The effects of irrigation frequency, amount, and application method on crop yield, yield components, water use, and water use efficiency of corn (Zea mays L., cv. PIO 3245) were investigated at Bushland, Texas, on a slowly permeable soil [Pullman clay loam (fine, mixed, thermic Torrertic Paleustoll)] in a semi-arid environment in 1993 and 1994. Irrigation frequencies were once a day and once a week; irrigation levels varied from dryland (no post emergence irrigation) to full crop water use replenishment; and application methods were on the soil surface (TOP) and 0.3 m below the surface (SUB) with emitters spaced 0.45 m apart and drip lines spaced 1.5 m apart. Irrigation frequency and application method did not affect crop yields; however, deficit irrigation affected crop yields by reducing the seed mass and the seed number. On the clay loam soil at Bushland, irrigation frequency and application method are less critical than proper irrigation management for microirrigation systems to avoid water deficits that have a larger affect on corn yields.
University of Southern Denmark Ethical decision-making climate in the ICU theoretical framework and validation of a self-assessment tool

Van den Bulcke, Bo; Piers, Ruth; Jensen, Hanne Irene; Malmgren, Johan; Metaxa, Victoria; Reyners, Anna K; Darmon, Michael; Rusinova, Katerina; Talmor, Daniel; Meert, AnnePascale; Cancelliere, Laura; Zubek, Làszló; Maia, Paolo; Michalsen, Andrej; Decruyenaere, Johan; Kompanje, Erwin J O; Azoulay, Elie; Meganck, Reitske; Van de Sompel, Ariëlla; Vansteelandt, Stijn; Vlerick, Peter; Vanheule, Stijn; Benoit, Dominique D
A digital method for the identification of the source of distortion in electric power systems

The correct identification of the source of distortion in voltages and currents in the electric power systems is an important factor to attain an effective compensation of the non-active components of power. The authors illustrate a method based on the evaluation of the sign of the harmonic active powers that can identify the source of distortion in three-phase systems. The practical application of this method requires the accurate determination of the frequency-domain components of voltages and currents both in amplitude and phase. An instrument is also described based on digital signal processing techniques, specially realized to perform such a frequency-domain analysis in real-time and to indicate whether the current distortion is caused by load nonlinearity or by nonsinusoidal supply voltages. The results of some experimental work are reported and the instrument accuracy is discussed. <<ETX>>
Structural Integrity Monitoring of Bonded Composite Patch Repairs of Aging Airframes Using Wavelet Transforms

In recent years there has been a considerable interest in the design and application of bonded composite repairs to restore cracked or corroded metallic airframes. The long-term durability in terms of disbond of composite patch, fracture at the bond-lines, delaminations in composite patches, and structural crack growth is to be thoroughly investigated before the aerospace industry adopts composite repair technology. Many researchers have investigated the use of structural modal response (natural frequencies and mode shapes) for assessing the integrity of structural systems. Although these methods have been successfully employed in some practical systems, there still exist some problems for applications in bonded repair systems such as the high sensitivity of these systems to model and measurement errors. In this paper wavelet maps and neural networks are used to detect and quantify damage in composite bonded repair systems. Two algorithms are presented, one for the location of damage in both single and multiple locations and the other for quantifying the damage. The developed algorithms have been successfully utilized for detecting and quantifying damage in bonded repair systems using simulation and experimental results. These methods can also be used to detect damage at multiple locations in a bonded repair system. The mode shapes of the test structure are obtained analytically using linear elastic finite element analysis and experimentally with a laser vibrometer. The results obtained are satisfactory.
Context-Dependent Semantic Parsing for Temporal Relation Extraction

Extracting temporal relations among events from unstructured text has extensive applications, such as temporal reasoning and question answering. While it is difficult, recent development of Neural-symbolic methods has shown promising results on solving similar tasks. Current temporal relation extraction methods usually suffer from limited expressivity and inconsistent relation inference. For example, in TimeML annotations, the concept of intersection is absent. Additionally, current methods do not guarantee the consistency among the predicted annotations. In this work, we propose SMARTER, a neural semantic parser, to extract temporal information in text effectively. SMARTER parses natural language to an executable logical form representation, based on a custom typed lambda calculus. In the training phase, dynamic programming on denotations (DPD) technique is used to provide weak supervision on logical forms. In the inference phase, SMARTER generates a temporal relation graph by executing the logical form. As a result, our neural semantic parser produces logical forms capturing the temporal information of text precisely. The accurate logical form representations of an event given the context ensure the correctness of the extracted relations.
Altering Facial Expression Based on Textual Emotion

Faces and their expressions are one of the potent subjects for digital images. Detecting emotions from images is an ancient task in the field of computer vision; however, performing its reverse -- synthesizing facial expressions from images -- is quite new. Such operations of regenerating images with different facial expressions, or altering an existing expression in an image require the Generative Adversarial Network (GAN). In this paper, we aim to change the facial expression in an image using GAN, where the input image with an initial expression (i.e., happy) is altered to a different expression (i.e., disgusted) for the same person. We used StarGAN techniques on a modified version of the MUG dataset to accomplish this objective. Moreover, we extended our work further by remodeling facial expressions in an image indicated by the emotion from a given text. As a result, we applied a Long Short-Term Memory (LSTM) method to extract emotion from the text and forwarded it to our expression-altering module. As a demonstration of our working pipeline, we also create an application prototype of a blog that regenerates the profile picture with different expressions based on the user's textual emotion.
Conversational Agents in Therapeutic Interventions for Neurodevelopmental Disorders: A Survey

Neurodevelopmental Disorders (NDD) are a group of conditions with onset in the developmental period characterized by deficits in the cognitive and social areas. Conversational agents have been increasingly explored to support therapeutic interventions for people with NDD. This survey provides a structured view of the crucial design features of these systems, the types of therapeutic goals they address, and the empirical methods adopted for their evaluation. From this analysis, we elaborate a set of recommendations and highlight the gaps left unsolved in the state of the art, upon which we ground a research agenda on conversational agents for NDD.
Hierarchical Neural Implicit Pose Network for Animation and Motion Retargeting

We present HIPNet, a neural implicit pose network trained on multiple subjects across many poses. HIPNet can disentangle subject-specific details from pose-specific details, effectively enabling us to retarget motion from one subject to another or to animate between keyframes through latent space interpolation. To this end, we employ a hierarchical skeleton-based representation to learn a signed distance function on a canonical unposed space. This joint-based decomposition enables us to represent subtle details that are local to the space around the body joint. Unlike previous neural implicit method that requires ground-truth SDF for training, our model we only need a posed skeleton and the point cloud for training, and we have no dependency on a traditional parametric model or traditional skinning approaches. We achieve state-of-the-art results on various single-subject and multi-subject benchmarks.
ASK ME!—Routine measurement of patient experience with patient safety in ambulatory care: A mixed-mode survey

Objective Routine measurement of patient safety from the patients’ perspective receives increasing attention as an important component of safety measurement systems. The aim of this study was to examine patients’ experience with patient safety in ambulatory care and the results’ implications for routine patient safety measurement in ambulatory care. Design Cross-sectional mixed-mode survey. Setting General practitioner and specialist practices. Participants Patients aged >18 years seeking care in ambulatory care practices between February and June 2020. Methods A 22-item-questionnaire was completed in the practice or at home either on paper or online. Multivariate logistic regression was used to analyse the influence of survey mode and patient characteristics on patient experience with patient safety. Results The overall response rate was 71.1%. Most patients completed the questionnaire on site (76.6%) and on paper (96.1%). Between 30.1% to 68.5% of the respondents report the most positive option for patient experience with the main domains of patient safety. A total of 2.9% of patients reported having experienced a patient-safety event (PSE) during the last 12 months. Patients who filled in the questionnaire off site were more likely to report negative experiences for the scales communication & information (OR 1.2, 95% CI 1.0–1.5), rapport & participation (OR 1.4, 95% CI 1.1–1.7) and access (OR 1.3, 95% CI 0.9–1.4) than those who completed it on site. Those who chose a paper questionnaire were more likely to report negative experiences for all five scales compared to web responders. Conclusion Routine measurement of patient experience with factors contributing to the occurrence of PSEs can achieve high response rates by offering flexible participation options. Results gained from mixed-mode surveys need to take mode-effects into account when interpreting and using the results. Further research is needed in how to adequately assess number and type of experienced events in routine measurements.
Different bisphenols induce non-monotonous changes in miRNA expression and LINE-1 methylation in two cell lines

Abstract 4,4ʹ-Isopropylidenediphenol (bisphenol A, BPA), a chemical substance that is widely used mainly as a monomer in the production of polycarbonates, in epoxy resins, and in thermal papers, is suspected to cause epigenetic modifications with potentially toxic consequences. Due to its negative health effects, BPA is banned in several products and is replaced by other bisphenols such as bisphenol S and bisphenol F. The present study examined the effects of BPA, bisphenol S, bisphenol F, p,pʹ-oxybisphenol, and the BPA metabolite BPA β-d-glucuronide on the expression of a set of microRNAs (miRNAs) as well as long interspersed nuclear element-1 methylation in human lung fibroblast and Caco-2 cells. The results demonstrated a significant modulation of the expression of different miRNAs in both cell lines including miR-24, miR-155, miR-21, and miR-146a, known for their regulatory functions of cell cycle, metabolism, and inflammation. At concentrations between 0.001 and 10 µg/ml, especially the data of miR-155 and miR-24 displayed non-monotonous and often significant dose–response curves that were U- or bell-shaped for different substances. Additionally, BPA β-d-glucuronide also exerted significant changes in the miRNA expression. miRNA prediction analysis indicated effects on multiple molecular pathways with relevance for toxicity. Besides, long interspersed nuclear element-1 methylation, a marker for the global DNA methylation status, was significantly modulated by two concentrations of BPA and p,pʹ-oxybisphenol. This pilot study suggests that various bisphenols, including BPA β-d-glucuronide, affect epigenetic mechanisms, especially miRNAs. These results should stimulate extended toxicological studies of multiple bisphenols and a potential use of miRNAs as markers.
Bony Characteristics Determine the Behaviour of the Interosseous Membrane (IOM) During Supination, Neutral Position, and Pronation of the Forearm

To analyse the behaviour of the forearm interosseous membrane (IOM) in vitro, forearm anatomical specimens supple enough to simulate in vivo movements, were observed in supination, neutral position, and pronation. The bony, so-called interosseous borders of radius and ulna lie in frontal plane in supination. Their distal halves partly follow each other’s curvatures, leaving the IOM in-between undulated and rather lax. The neutral or “handshake” position, ulna unchanged while the radius’ arched curvature lies in sagittal plane, creates largest distances between the bones, causing the IOM to be maximally taut. In full pronation (radius and ulna crossed) the interosseous borders re-approach each other, causing the IOM to become undulated and lax again. These bony characteristics, directed differently dependent of forearm position, causing the IOM to be whether lax or taut, may generate different proprioceptive signals in vivo, e.g. in handling vertical PC mice.
Target Propagation via Regularized Inversion

Target Propagation (TP) algorithms compute targets instead of gradients along neural networks and propagate them backward in a way that is similar yet different than gradient back-propagation (BP). The idea was first presented as a perturbative alternative to back-propagation that may achieve greater accuracy in gradient evaluation when training multi-layer neural networks (LeCun et al., 1989). However, TP has remained more of a template algorithm with many variations than a well-identified algorithm. Revisiting insights of LeCun et al., (1989) and more recently of Lee et al. (2015), we present a simple version of target propagation based on regularized inversion of network layers, easily implementable in a differentiable programming framework. We compare its computational complexity to the one of BP and delineate the regimes in which TP can be attractive compared to BP. We show how our TP can be used to train recurrent neural networks with long sequences on various sequence modeling problems. The experimental results underscore the importance of regularization in TP in practice.
The Smoothing Proximal Gradient Algorithm with Extrapolation for the Relaxation of $l_0$ Regularization Problem

In this paper, we consider the exact continuous relaxation model of $l_0$ regularization problem which was given by Bian and Chen (SIAM J. Numer. Anal 58(1): 858-883, 2020) and propose a smoothing proximal gradient algorithm with extrapolation (SPGE) for this kind of problem. We show that any accumulation point of the sequence generated by SPGE algorithm is a lifted stationary point of the relaxation model for a fairly general choice of extrapolation parameter. Moreover, it is shown that the whole sequence generated by SPGE algorithm converges to a lifted stationary point of the relaxation model. The convergence rate of $o(\frac{1}{k^{1-\sigma}})(\sigma\in(0,1))$ with respect to squared proximal residual is established. Finally, we conduct three important numerical experiments to illustrate the faster efficiency of the SPGE algorithm compared with the smoothing proximal gradient(SPG) algorithm proposed by Bian and Chen (SIAM J. Numer. Anal 58(1):858-883, 2020).
High efficiency broadband power amplifier MMIC

A GaAs broadband, dual-channel, high-efficiency power amplifier monolithic microwave integrated circuit (MMIC) is presented. The average performance for a single channel of the power amplifier was 18.0-dB small-signal gain, 16% power-added efficiency, and 2-dB compressed output power of 29.4 dBm from 6 to 18 GHz at 25 degrees C. The two channels combined off chip achieved 32-dBm average output power. This 0.5- mu m ion-implanted MESFET amplifier MMIC has been demonstrated in volume production with 154 wafer starts over three months, resulting in a 30% total yield through fixtured RF test. <<ETX>>
Leveraging Human Selective Attention for Medical Image Analysis with Limited Training Data

The human gaze is a cost-efficient physiological data that reveals human underlying attentional patterns. The selective attention mechanism helps the cognition system focus on task-relevant visual clues by ignoring the presence of distractors. Thanks to this ability, human beings can efficiently learn from a very limited number of training samples. Inspired by this mechanism, we aim to leverage gaze for medical image analysis tasks with small training data. Our proposed framework includes a backbone encoder and a Selective Attention Network (SAN) that simulates the underlying attention. The SAN implicitly encodes information such as suspicious regions that is relevant to the medical diagnose tasks by estimating the actual human gaze. Then we design a novel Auxiliary Attention Block (AAB) to allow information from SAN to be utilized by the backbone encoder to focus on selective areas. Specifically, this block uses a modified version of a multi-head attention layer to simulate the human visual search procedure. Note that the SAN and AAB can be plugged into different backbones, and the framework can be used for multiple medical image analysis tasks when equipped with task-specific heads. Our method is demonstrated to achieve superior performance on both 3D tumor segmentation and 2D chest X-ray classification tasks. We also show that the estimated gaze probability map of the SAN is consistent with an actual gaze fixation map obtained by board-certified doctors.
A More Scalable Mixed-Integer Encoding for Metric Temporal Logic

The state-of-the-art in optimal control from timed temporal logic specifications, including Metric Temporal Logic (MTL) and Signal Temporal Logic (STL), is based on Mixed-Integer Convex Programming (MICP). The standard MICP approach is sound and complete, but struggles to scale to long and complex specifications. Drawing on recent advances in trajectory optimization for piecewise-affine systems, we propose a new MICP encoding for finite transition systems that significantly improves scalability to long and complex MTL specifications. Rather than seeking to reduce the number of variables in the MICP, we focus instead on designing an encoding with a tight convex relaxation. This leads to a larger optimization problem, but significantly improves branch-and-bound solver performance. In simulation experiments involving a mobile robot in a grid-world, the proposed encoding can reduce computation times by several orders of magnitude.
A novel stroke lesion network mapping approach: improved accuracy yet still low deficit prediction

Abstract Lesion network mapping estimates functional network abnormalities caused by a focal brain lesion. The method requires embedding the volume of the lesion into a normative functional connectome and using the average functional magnetic resonance imaging signal from that volume to compute the temporal correlation with all other brain locations. Lesion network mapping yields a map of potentially functionally disconnected regions. Although promising, this approach does not predict behavioural deficits well. We modified lesion network mapping by using the first principal component of the functional magnetic resonance imaging signal computed from the voxels within the lesioned area for temporal correlation. We measured potential improvements in connectivity strength, anatomical specificity of the lesioned network and behavioural prediction in a large cohort of first-time stroke patients at 2-weeks post-injury (n = 123). This principal component functional disconnection approach localized mainly cortical voxels of high signal-to-noise; and it yielded networks with higher anatomical specificity, and stronger behavioural correlation than the standard method. However, when examined with a rigorous leave-one-out machine learning approach, principal component functional disconnection approach did not perform better than the standard lesion network mapping in predicting neurological deficits. In summary, even though our novel method improves the specificity of disconnected networks and correlates with behavioural deficits post-stroke, it does not improve clinical prediction. Further work is needed to capture the complex adjustment of functional networks produced by focal damage in relation to behaviour.