Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of Tactics for Management of Sugarcane Aphid (Hemiptera: Aphididae) in Grain Sorghum

Abstract The invasive sugarcane aphid, Melanaphis sacchari (Zehntner), is a devastating new pest of grain sorghum. Studies were conducted utilizing an integrated approach of four management tactics: planting date, insecticidal seed treatment, a foliar-applied insecticide, and plant resistance. Experiments were conducted in 2016 and 2017 at Griffin, Tifton, and Plains Georgia, and in 2016 in Texas, Alabama, and Oklahoma, United States. Early planting was effective in reducing damage and increasing yields when compared to the late planting. Use of a resistant variety reduced cumulative aphid-days, plant injury and usually prevented significant yield loss. Foliar application of flupyradifurone when aphids reached an economic threshold, was an effective management tactic preventing aphid injury and yield loss. Use of clothianidin seed treatment also reduced aphid injury and yield loss of the susceptible hybrid but generally did not prevent injury and yield loss of the resistant hybrid. We conclude that an earlier planting date coupled with a resistant variety and judicious use of an efficacious foliar-applied insecticide can effectively manage sugarcane aphid on grain sorghum. An insecticide seed treatment also may be useful to reduce the risk of sugarcane aphid damage to seedlings of susceptible hybrids.
A simple ex vivo model of human renal allograft preservation using the gonadal vein.

INTRODUCTION Hypothermic machine perfusion, an organ preservation modality, involves flow of chilled preservation fluid through an allograft's vasculature. This study describes a simple, reproducible, human model that allows for interrogation of flow effects during ex vivo organ perfusion. MATERIALS AND METHODS Gonadal veins from deceased human renal allografts were subjected to either static cold storage or hypothermic machine perfusion for up to 24 hours. Caspase-3, Krüppel-like factor 2 expression and electron microscopic analysis were compared between 'flow' and 'no-flow' conditions, with living donor gonadal vein sections serving as negative controls. RESULTS The increase in caspase-3 expression was less pronounced for hypothermic machine-perfused veins compared with static cold storage (median-fold increase 1.2 vs 2.3; P < 0.05). Transmission electron microscopy provided ultrastructural corroboration of endothelial cell apoptosis in static cold storage conditions. For static cold storage preserved veins, Krüppel-like factor 2 expression diminished in a time-dependent manner between baseline and 12 hours (P < 0.05) but was abrogated and reversed by hypothermic machine perfusion (P < 0.05). CONCLUSIONS Our methodology is a simple, reproducible and successful model of ex vivo perfusion in the context of human organ preservation. To demonstrate the model's utility, we establish that two widely used markers of endothelial health (caspase-3 and Krüppel-like factor 2) differ between the flow and no-flow conditions of the two predominant kidney preservation modalities. These findings suggest that ex vivo perfusion may mediate the induction of a biochemically favourable endothelial niche which may contribute tohypothermic machine perfusion's association with improved renal transplantation outcomes.
Older Stroke Survivors in Home Care: Factors Associated with the use of CCAC Rehabilitation Services

Purpose: Despite improvements in the prevention and management of stroke, it continues to have a significant impact on the Canadian population. For individuals who have suffered a stroke, rehabilitation is an important part of post stroke care that can maximize quality of life by reducing the impact of stroke-related impairments and disability. The purpose of this thesis was to describe the profile of older adults with stroke receiving home care services and to examine relationship between characteristics of these individuals and their use of home care rehabilitation services (PT and OT). Information from interRAI home care assessments (RAI-HC and interRAI CA) was used and the Andersen Newman Behavioural model was employed as a conceptual framework. Methods: Two retrospective cohort studies of older stroke survivors receiving services from the Hamilton Niagara Haldimand Brant Community Care Access Centre were conducted. Individuals with acute stroke were identified from hospital discharge records (Discharge Abstract Database) that were linked to interRAI assessments (RAI-HC N=1,181 and interRAI CA N=573), and CCAC service utilization data to identify the use of PT and OT services. Both samples were compared with a non-stroke population in order to describe the characteristics of those with stroke. Logistic regression was used to examine associations between independent variables and the use of services for PT and OT and ordinal logistic regression was used to assess relationships between characteristics and the number of PT/OT visits (0, 1-4, 5+) in both samples. Results: Comparison with the general older population revealed that older stroke survivors had greater difficulty with communication, swallowing, IADLs, ADLs, and a greater prevalence of cognitive impairment. Following RAI-HC assessment those with unsteady gait and difficulty with stairs were more likely to receive PT and OT services. Impairment in ADLs and triggering the Home Environment Optimization CAP were significantly associated with receiving OT. Being married and receiving a greater amount of informal care increased the odds of receiving PT services, while cognitive impairment and having Alzheimer’s disease or dementia decreased the odds of receiving PT. Significant variation by CCAC branch was also found. Male sex, language other than English, location of intake, cognitive decline, and unstable health were significantly associated with the use of OT services among those assessed with the interRAI CA. Referral for rehabilitation, cognitive decline, co-residing caregiver, and difficulty with locomotion were significantly associated with the use of PT services after assessment with the interRAI CA. Comparison of predisposing, enabling, and
A Deep Learning Approach to Grasping the Invisible

We study an emerging problem named “grasping the invisible” in robotic manipulation, in which a robot is tasked to grasp an initially invisible target object via a sequence of pushing and grasping actions. In this problem, pushes are needed to search for the target and rearrange cluttered objects around it to enable effective grasps. We propose to solve the problem by formulating a deep learning approach in a critic-policy format. The target-oriented motion critic, which maps both visual observations and target information to the expected future rewards of pushing and grasping motion primitives, is learned via deep Q-learning. We divide the problem into two subtasks, and two policies are proposed to tackle each of them, by combining the critic predictions and relevant domain knowledge. A Bayesian-based policy accounting for past action experience performs pushing to search for the target; once the target is found, a classifier-based policy coordinates target-oriented pushing and grasping to grasp the target in clutter. The motion critic and the classifier are trained in a self-supervised manner through robot-environment interactions. Our system achieves a 93% and 87% task success rate on each of the two subtasks in simulation and an 85% task success rate in real robot experiments on the whole problem, which outperforms several baselines by large margins. Supplementary material is available at https://sites.google. com/umn.edu/grasping-invisible.
Incremental proximal gradient scheme with penalization for constrained composite convex optimization problems

We consider the problem of minimizing a finite sum of convex functions subject to the set of minimizers of a convex differentiable function. In order to solve the problem, an algorithm combining the incremental proximal gradient method with smooth penalization technique is proposed. We show the convergence of the generated sequence of iterates to an optimal solution of the optimization problems, provided that a condition expressed via the Fenchel conjugate of the constraint function is fulfilled. Finally, the functionality of the method is illustrated by some numerical experiments addressing image inpainting problems and generalized Heron problems with least squares constraints.
Probabilistic Model Learning and Long-term Prediction for Contact-rich Manipulation Tasks

Learning dynamics models is an essential component of model-based reinforcement learning. The learned model can be used for multi-step ahead predictions of the state variable, a process referred to as long-term prediction. Due to the recursive nature of the predictions, the accuracy has to be good enough to prevent significant error buildup. Accurate model learning in contact-rich manipulation is challenging due to the presence of varying dynamics regimes and discontinuities. Another challenge is the discontinuity in state evolution caused by impacting conditions. Building on the approach of representing contact dynamics by a system of switching models, we present a solution that also supports discontinuous state evolution. We evaluate our method on a contact-rich motion task, involving a 7-DOF industrial robot, using a trajectory-centric policy and show that it can effectively propagate state distributions through discontinuities.
Reinforcement learning-driven de-novo design of anticancer compounds conditioned on biomolecular profiles

With the advent of deep generative models in computational chemistry, in silico anticancer drug design has undergone an unprecedented transformation. While state-of-the-art deep learning approaches have shown potential in generating compounds with desired chemical properties, they entirely overlook the genetic profile and properties of the target disease. In the case of cancer, this is problematic since it is a highly genetic disease in which the biomolecular profile of target cells determines the response to therapy. Here, we introduce the first deep generative model capable of generating anticancer compounds given a target biomolecular profile. Using a reinforcement learning framework, the transcriptomic profile of cancer cells is used as a context in which anticancer molecules are generated and optimized to obtain effective compounds for the given profile. Our molecule generator combines two pretrained variational autoencoders (VAEs) and a multimodal efficacy predictor - the first VAE generates transcriptomic profiles while the second conditional VAE generates novel molecular structures conditioned on the given transcriptomic profile. The efficacy predictor is used to optimize the generated molecules through a reward determined by the predicted IC50 drug sensitivity for the generated molecule and the target profile. We demonstrate how the molecule generation can be biased towards compounds with high inhibitory effect against individual cell lines or specific cancer sites. We verify our approach by investigating candidate drugs generated against specific cancer types and investigate their structural similarity to existing compounds with known efficacy against these cancer types. We envision our approach to transform in silico anticancer drug design by increasing success rates in lead compound discovery via leveraging the biomolecular characteristics of the disease.
Real Time Dynamics and Confinement in the Zn Schwinger-Weyl lattice model for 1+1 QED

We study the out-of-equilibrium properties of 1+1 dimensional quantum electrodynamics (QED), discretized via the staggered-fermion Schwinger model with an Abelian Zn gauge group. We look at two relevant phenomena: first, we analyze the stability of the Dirac vacuum with respect to particle/antiparticle pair production, both spontaneous and induced by an external electric field; then, we examine the string breaking mechanism. We observe a strong effect of confinement, which acts by suppressing both spontaneous pair production and string breaking into quark/antiquark pairs, indicating that the system dynamics displays a number of out-of-equilibrium features.
Higgs Mechanism and Debye Screening in the Generalized Electrodynamics

In this work we study the Higgs mechanism and the Debye shielding for the Bopp-Podolsky theory of electrodynamics. We find that not only the massless sector of the Podolsky theory acquires a mass in both these phenomena, but also that its massive sector has its mass changed. Furthermore, we find a mathematical analogy in the way these masses change between these two mechanisms. Besides exploring the behaviour of the screened potentials, we find a temperature for which the presence of the generalized gauge field may be experimentally detected.
An Implicit Form of Krasulina's k-PCA Update without the Orthonormality Constraint

We shed new insights on the two commonly used updates for the online k-PCA problem, namely, Krasulina's and Oja's updates. We show that Krasulina's update corresponds to a projected gradient descent step on the Stiefel manifold of orthonormal k-frames, while Oja's update amounts to a gradient descent step using the unprojected gradient. Following these observations, we derive a more implicit form of Krasulina's k-PCA update, i.e. a version that uses the information of the future gradient as much as possible. Most interestingly, our implicit Krasulina update avoids the costly QR-decomposition step by bypassing the orthonormality constraint. A related update, called the Sanger's rule, can be seen as an explicit approximation of our implicit update. We show that the new update in fact corresponds to an online EM step applied to a probabilistic k-PCA model. The probabilistic view of the update allows us to combine multiple models in a distributed setting. We show experimentally that the implicit Krasulina update yields superior convergence while being significantly faster. We also give strong evidence that the new update can benefit from parallelism and is more stable w.r. t. tuning of the learning rate.
The chain rule for VU-decompositions of nonsmooth functions.

In Variational Analysis, VU-theory provides a set of tools that is helpful for understanding and exploiting the structure of nonsmooth functions. The theory takes advantage of the fact that at any point, the space can be separated into two orthogonal subspaces: one that describes the direction of nonsmoothness of the function, and the other on which the function behaves smoothly and has a gradient. For a composite function, this work establishes a chain rule that facilitates the computation of such gradients and characterizes the smooth subspace under reasonable conditions. From the chain rule presented, formulas for the separation, smooth perturbation and sum of functions are provided. Several nonsmooth examples are explored, including norm functions, max-of-quadratic functions and LASSO-type regularizations.
A Survey on Studying the Social Networks of Students

Do studies show that physical and online students' social networks support education? Analyzing interactions between students in schools and universities can provide a wealth of information. Studies on students' social networks can help us understand their behavioral dynamics, the correlation between their friendships and academic performance, community and group formation, information diffusion, and so on. Educational goals and holistic development of students with various academic abilities and backgrounds can be achieved by incorporating the findings attained by the studies in terms of knowledge propagation in classroom and spread of delinquent behaviors. Moreover, we use Social Network Analysis (SNA) to identify isolated students, ascertain the group study culture, analyze the spreading of various habits like smoking, drinking, and so on. In this paper, we present a review of the research showing how analysis of students' social networks can help us identify how improved educational methods can be used to make learning more inclusive at both school and university levels and achieve holistic development of students through expansion of their social networks, as well as control the spread of delinquent behaviors.
Multi-stage domain adversarial style reconstruction for cytopathological image stain normalization

The different stain styles of cytopathological images have a negative effect on the generalization ability of automated image analysis algorithms. This article proposes a new framework that normalizes the stain style for cytopathological images through a stain removal module and a multi-stage domain adversarial style reconstruction module. We convert colorful images into grayscale images with a color-encoding mask. Using the mask, reconstructed images retain their basic color without red and blue mixing, which is important for cytopathological image interpretation. The style reconstruction module consists of per-pixel regression with intradomain adversarial learning, inter-domain adversarial learning, and optional task-based refining. Per-pixel regression with intradomain adversarial learning establishes the generative network from the decolorized input to the reconstructed output. The interdomain adversarial learning further reduces the difference in stain style. The generation network can be optimized by combining it with the task network. Experimental results show that the proposed techniques help to optimize the generation network. The average accuracy increases from 75.41% to 84.79% after the intra-domain adversarial learning, and to 87.00% after interdomain adversarial learning. Under the guidance of the task network, the average accuracy rate reaches 89.58%. The proposed method achieves unsupervised stain normalization of cytopathological images, while preserving the cell structure, texture structure, and cell color properties of the image. This method overcomes the problem of generalizing the task models between different stain styles of cytopathological images.
Noise-robust exploration of quantum matter on near-term quantum devices

We describe a resource-efficient approach to studying many-body quantum states on noisy, intermediate-scale quantum devices. We employ a sequential generation model that allows to bound the range of correlations in the resulting many-body quantum states. From this, we characterize situations where the estimation of local observables does not require the preparation of the entire state. Instead smaller patches of the state can be generated from which the observables can be estimated. This reduces the required circuit size and number of qubits for the computation of physical properties of quantum matter. Moreover, we show that the effect of noise decreases along the computation. Our results apply to a broad class of widely studied tensor network states and can be directly applied to near-term implementations of variational quantum algorithms.
Visible Spectroscopy from the Mission Accessible Near-Earth Object Survey (MANOS): Taxonomic Dependence on Asteroid Size

The Mission Accessible Near-Earth Object Survey (MANOS) aims to observe and characterize small (mean absolute magnitude H ∼ 25 mag) Near-Earth Objects (NEOs) that are accessible by spacecraft (mean Δv ∼ 5.7 km s−1) and that make close approaches with the Earth (mean Minimum Orbital Intersection Distance MOID ∼ 0.03 au). We present here the first results of the MANOS visible spectroscopic survey. The spectra were obtained from August 2013 to March 2018 at Lowell Observatory’s Discovery Channel 4.3 m telescope, and both Gemini North and South facilities. In total, 210 NEOs have been observed and taxonomically classified. Our taxonomic distribution shows significant variations with respect to surveys of larger objects. We suspect these to be due to a dependence of Main Belt source regions on object size. Compared to previous surveys of larger objects, we report a lower fraction of S+Q-complex asteroids of 43.8 ± 4.6%. We associate this decrease with a lack of Phocaea family members at very small size. We also report higher fractions of X-complex and A-type asteroids of 23.8 ± 3.3% and 3.8 ± 1.3% respectively due to an increase of Hungaria family objects at small size. We find a strong correlation between the Q/S ratio and perihelion distance. We suggest this correlation is due to planetary close encounters with Venus playing a major role in turning asteroids from S to Q-type. This hypothesis is supported by a similar correlation between the Q/S ratio and Venus MOID.
The Contribution of Dwarf Planets to the Origin of Jupiter Family Comets

We explore the long-term evolution of a bias-free orbital representation of the cometary nuclei (with diameters above 2 km) of the Kuiper Belt, using the so-called L7 synthetic model from CFEPS, which consists of three dynamical subpopulations: the Classical, the Resonant, and the Scattering. The dynamical evolution of belt particles is studied under the gravitational influence of the Sun and the four giant planets, as well as of the 34 largest known trans-Neptunian objects (i.e., those with HV < 4). Here, we indistinctly call Dwarf Planets (DPs) to the full sample of 34 large TNOs. Over a 1 Gyr timescale, we analyze the secular influence of the DPs over Kuiper Belt disk particles and their contribution to the injection rate of new visible Jupiter Family Comets (JFCs). We find that DPs globally increase the number of JFCs by 12.6%, when compared with the comets produced by the giant planets alone. When considering each population separately, we find the increment produced by DPs to be 17%, 12%, and 3% for the Classical, Resonant, and Scattering populations, respectively. Given the rate of escapes from the Kuiper Belt, we find upper limits to the number of objects in each population required to maintain the JFCs in steady state; the results are 55.9 × 106, 78.5 × 106, and 274.3 × 106 for the Scattering, Resonant, and Classical populations, respectively. Finally, we find that the Plutinos are the most important source of comets that were originally in a resonant configuration, where the presence of Pluto alone enhances by 10% the number of JFCs.
Gyrokinetic theory with polynomial transforms: a model for ions and electrons in maximal ordering

We propose a novel derivation of the gyrokinetic field-particle Lagrangian for non-collisional ion-electron plasmas in a magnetic background with strong variations (maximal ordering). Our approach follows the two-step reduction process, where the guiding-center coordinate transformation is followed by the gyrocenter coordinate transformation in the single-particle phase space. For the first time both steps are addressed within a unique methodology, based on near-identity coordinate transformations constructed as polynomial transforms. These are well-defined transformations composed of a finite number of terms that are linear and algebraic with respect to the generating functions. The derivation is carried out in a fully non-dimensional framework, based on parameters governing the magnetic fusion experiments ASDEX Upgrade and ITER. Our method leads to a gyrokinetic Vlasov-Maxwell model for ions and electrons, derived without the use of Lie perturbation methods. It is found that, based on the employed ordering, curvature terms such as the gyro-gauge term and the Banos drift appear at first order in the ion Hamiltonian, whereas ion polarization terms appear only at second order. By contrast, curvature terms are absent from the first-order electron Hamiltonian, where instead magnetic flutter plays a role.