Miracle-dz/UniFAR-FTUs huggingface dataset:
Field Abundance and Performance of Hoverflies (Diptera: Syrphidae) on Soybean Aphid

ABSTRACT The management of the soybean aphid Aphis glycines Matsumara is a major challenge to soybean production in the north-central United States. The identification and characterization of the insect predator community has informed integrated pest management strategies by providing insight on predators that can suppress soybean aphid populations. Hoverflies (Diptera: Syrphidae) are known predators of A. glycines, but more information is needed on their diversity, abundance, and performance to evaluate their importance as biological control agents of A. glycines. In this study, syrphid abundance was evaluated across two growing seasons in four soybean fields in east-central Minnesota. Six methods were used to quantify syrphid abundance at the larval, pupal, and adult life stages; describe species composition and richness for adults; and directly compare larval abundance to aphid abundance. The syrphid community comprised eight species, dominated by Toxomerus marginatus (Say) and Toxomerus geminatus (Say). Syrphid abundance was relatively low in soybean fields. Feeding trials were conducted to compare the performance of the most common syrphid (T. marginatus) on a diet of A. glycines with two native aphids, Aphis nerii Boyer de Fonscolombe and Aphis monardae Oestlund. Despite their low abundance in soybeans, T. marginatus larvae perform well on A. glycines, A. nerii, and A. monardae in laboratory feeding trials, implying that factors other than host suitability are limiting their potential to exert biological control on soybean aphids.
Computational Approaches to Access Probabilistic Population Codes for Higher Cognition an Decision-Making

In recent years, research unveiled more and more evidence for the so-called Bayesian Brain Paradigm, i.e. the human brain is interpreted as a probabilistic inference machine and Bayesian modelling approaches are hence used successfully. One of the many theories is that of Probabilistic Population Codes (PPC). Although this model has so far only been considered as meaningful and useful for sensory perception as well as motor control, it has always been suggested that this mechanism also underlies higher cognition and decision-making. However, the adequacy of PPC for this regard cannot be confirmed by means of neurological standard measurement procedures. In this article we combine the parallel research branches of recommender systems and predictive data mining with theoretical neuroscience. The nexus of both fields is given by behavioural variability and resulting internal distributions. We adopt latest experimental settings and measurement approaches from predictive data mining to obtain these internal distributions, to inform the theoretical PPC approach and to deduce medical correlates which can indeed be measured in vivo. This is a strong hint for the applicability of the PPC approach and the Bayesian Brain Paradigm for higher cognition and human decision-making.
Entanglement of two interacting ensembles via a Dicke-like quantum phase transition

Two interacting atomic ensembles display a Dicke-like quantum phase transition~(QPT) above a critical coupling strength. We show that an ensemble-ensemble entanglement accompanies the QPT. Inseparability onsets exactly at the critical coupling for the QPT and increases continuously with the coupling strength. Since the nature of entanglement at QPTs is extremely different than the one for spin-squeezed like inseparability, we derive two new criteria for the ensemble-ensemble entanglement. Both criteria witnesses the entanglement successfully. The advantage of this system for generating entanglement, compared to a superradiant QPT, is that it does not include photons which do leak in a cavity. We also observe that a non-trivial Berry phase is present above the critical point.
A 2D dilated residual U-Net for multi-organ segmentation in thoracic CT

Automatic segmentation of organs-at-risk (OAR) in computed tomography (CT) is an essential part of planning effective treatment strategies to combat lung and esophageal cancer. Accurate segmentation of organs surrounding tumours helps account for the variation in position and morphology inherent across patients, thereby facilitating adaptive and computer-assisted radiotherapy. Although manual delineation of OARs is still highly prevalent, it is prone to errors due to complex variations in the shape and position of organs across patients, and low soft tissue contrast between neighbouring organs in CT images. Recently, deep convolutional neural networks (CNNs) have gained tremendous traction and achieved state-of-the-art results in medical image segmentation. In this paper, we propose a deep learning framework to segment OARs in thoracic CT images, specifically for the: heart, esophagus, trachea and aorta. Our approach employs dilated convolutions and aggregated residual connections in the bottleneck of a U-Net styled network, which incorporates global context and dense information. Our method achieved an overall Dice score of 91.57% on 20 unseen test samples from the ISBI 2019 SegTHOR challenge.
Sero-status of preschoolers and disclosure to schools

ABSTRACT Infants with HIV-infection have longevity due to improved Highly Active Antiretroviral Therapy (HAART), making many realise their developmental progression which includes access to schooling. However, there is scant information that focuses on disclosure of their positive sero-status to schools and how these children understand and communicate their illnesses. This paper reports on a study of experiences of children affected by HIV and AIDS in Kenya. Qualitative phenomenological data from children aged between 5 and 7 years and their teachers in Early Childhood Development and Education (ECDE) centres were collected using multiple methods. Thematic content analysis was utilised with several themes emerging from findings. Obstacles to disclosure, anticipation of exceptional care, accidental disclosures, lack of psychosocial structures, a need for specialised skills training for teachers, and lack of clear guidelines to assist in disclosure processes emerged as some of the challenges faced. The findings also provide insights into teachers' scaffolding efforts to educate children in understanding and communicating their illnesses, and forging support structures through relationships with their siblings and peers for care and management.
A Multicenter Analysis of Elvitegravir Use During Pregnancy on HIV Viral Suppression and Perinatal Outcomes

Abstract Background There is a knowledge gap on the clinical use of elvitegravir (EVG) during pregnancy and maternal viral suppression. Our objective was to evaluate the effects of EVG use in pregnancy on rates of HIV virologic suppression and perinatal outcomes. Methods We conducted a retrospective, multicenter study of pregnant women living with HIV (WLHIV) who used EVG-containing antiretroviral therapy (ART) between January 2014 and March 2017 at 9 tertiary care centers in the United States. WLHIV were included if they took EVG at any time during pregnancy. We described the characteristics of the WLHIV using EVG during the study period and evaluated the rates of HIV suppression and perinatal outcomes. Results Among 134 pregnant WLHIV who received EVG at any time during pregnancy, viral suppression at delivery (HIV-1 RNA < 40 copies/mL) occurred in 81.3%. In WLHIV who initiated EVG before pregnancy and continued through delivery (n = 68), the rate of viral suppression at delivery was 88.2%. The average gestational age at the time of delivery was 37 weeks 6 days, and the overall rate of preterm birth was 20%. No cases of open neural tube defects were noted in women on EVG at the time of conception (n = 82). The perinatal HIV transmission rate was 0.8%. Conclusions EVG use was associated with high sustained levels of HIV suppression during pregnancy and a low rate of perinatal HIV transmission.
Integrated Analysis of Differential miRNA and mRNA Expression Profiles in Human Radioresistant and Radiosensitive Nasopharyngeal Carcinoma Cells

Background The purpose of this study was to identify miRNAs and genes involved in nasopharyngeal carcinoma (NPC) radioresistance, and explore the underlying mechanisms in the development of radioresistance. Methods We used microarrays to compare the differences of both miRNA and mRNA expression profiles in the radioresistant NPC CNE2-IR and radiosensitive NPC CNE2 cells, applied qRT-PCR to confirm the reliability of microarray data, adopted databases prediction and anticorrelated analysis of miRNA and mRNA expression to identify the miRNA target genes, and employed bioinformatics tools to examine the functions and pathways in which miRNA target genes are involved, and construct a miRNA-target gene regulatory network. We further investigated the roles of miRNA-23a and its target gene IL-8 in the NPC radioresistance. Results The main findings were fourfold: (1) fifteen differential miRNAs and 372 differential mRNAs were identified, and the reliability of microarray data was validated for randomly selected eight miRNAs and nine genes; (2) 174 miRNA target were identified, and most of their functions and regulating pathways were related to tumor therapeutic resistance; (3) a posttranscriptional regulatory network including 375 miRNA-target gene pairs was constructed, in which the ten genes were coregulated by the six miRNAs; (4) IL-8 was a direct target of miRNA-23a, the expression levels of IL-8 were elevated in the radioresistant NPC tissues and showed inverse correlation with miRNA-23a expression, and genetic upregulation of miRNA-23a and antibody neutralization of secretory IL-8 could reduce NPC cells radioresistance. Conclusions We identified fifteen differential miRNAs and 372 differential mRNAs in the radioresistant NPC cells, constructed a posttranscriptional regulatory network including 375 miRNA-target gene pairs, discovered the ten target genes coregulated by the six miRNAs, and validated that downregulated miRNA-23a was involved in NPC radioresistance through directly targeting IL-8. Our data form a basis for further investigating the mechanisms of NPC radioresistance.
Optical properties and electronic structure of. beta. '-NiAl

The optical constants and their temperature derivatives have been determined for . beta. '-NiAl from absorption and thermoreflectance measurements in the energy range of 0.2--4.4 eV. The results are interpreted using the self-consistent energy bands of Moruzzi, Williams, and Gelatt. By comparing a calculated joint density of states with epsilon/sub 2/, the imaginary part of the dielectric function, good overall agreement is found between theory and experiment. In contrast to earlier analyses, it is found that the 2.5-eV peak in epsilon/sub 2/ is primarily due to direct interband transitions terminating near the Fermi surface. This new interpretation of the 2.5-eV feature is discussed in relation to previously reported concentration effects and the rigid-band model.
Robust augmented reality tracking based visual pose estimation

In this paper, we present a robust fiducials tracking method for real time Augmented Reality systems. Our approach identifies the target object with an internal barecode of the fiducial and extracts its 2D features points. Given the 2D feature points and a 3D object model, object pose consists in recovering the position and the orientation of the object with respect to the camera. For pose estimation, we presented two methods for recovering pose using the Extended Kalman Filter and the Orthogonal Iteration algorithm. The first algorithm is a sequential estimator that predicts and corrects the state vector. While the later uses the object space collinearity error and derives an iterative algorithm to compute orthogonal rotation matrices. Due to lighting or contrast conditions or occlusion of the target object by an other object, the tracking may fail. Therefore, we extend our tracking method using a RANSAC algorithm to deal with occlusions. The algorithm is tested with different camera viewpoints under various image conditions and shows to be accurate and robust.
Compact Selective Bandpass Filter With Wide Stopband for TETRA Band Applications

A selective miniaturized bandpass filter with wide stopband for TETRA band applications is presented in this paper. Basic resonator used in the filter construction is an open stub-loaded-short-circuited stepped-impedance resonator, which has the inherent property of having harmonics far from the fundamental mode. The high selectivity of the filter is obtained by virtue of the transmission zeros around the passband. The transmission zeros are generated because of the mixed electric and magnetic coupling between the resonators and the feedline structures. The selective filter has a miniaturized size of $0.078\lambda _{g}\times 0.082\lambda _{g}$ with a wide stopband extended up to 4.91 times the operating frequency.
Hyaluronan concentration and molecular mass in psoriatic arthritis: biomarkers of disease severity, resistance to treatment, and outcome

Objective: Low molecular mass hyaluronan causes inflammatory processes and can act as a pro-inflammatory cytokine in skin and other sites of activity in psoriatic arthritis (PsA). This study investigated whether the molecular mass distribution of hyaluronan (HA) in skin and the quantity of circulating HA are related to the clinical inflammatory picture in PsA with active disease and to the effect of treatment with anti-tumour necrosis factor-α (anti-TNF-α) adalimumab. Methods: Twenty patients with TNF-α-naïve active polyarticular PsA were included in this prospective clinical trial of treatment with 40 mg s.c. adalimumab according to standard procedure. Clinical activity, patients’ assessments, and skin biopsies were captured at inclusion and at the 12 week follow-up. Ten healthy individuals were recruited for comparison of HA analyses. Histochemistry of skin inflammation, serum HA, and molecular mass of HA were determined. Results: Overall improvements in clinical parameters were observed. Eight of 18 patients reached minimum disease activity after 12 weeks and disease activity was significantly reduced (p < 0.0001). Patients with elevated serum HA values were significantly older, had later onset of arthritis and more deformed joints, still had swollen joints after treatment, and had more circulating inflammatory biomarkers. More severe disease pathology showed a wide spectrum of high-molecular-mass HA accompanied by low mass HA. The treatment appears partly to normalize the HA mass distribution. Conclusion: HA concentration and mass seem to be two possible factors in the inflammatory pathology of PsA acting as biomarkers for disease severity, resistance to treatment, and worse outcome.
HIGAN: Cosmic Neutral Hydrogen with Generative Adversarial Networks

One of the most promising ways to observe the Universe is by detecting the 21cm emission from cosmic neutral hydrogen (HI) through radio-telescopes. Those observations can shed light on fundamental astrophysical questions only if accurate theoretical predictions are available. In order to maximize the scientific return of these surveys, those predictions need to include different observables and be precise on non-linear scales. Currently, one of the best ways to achieve this is via cosmological hydrodynamic simulations; however, the computational cost of these simulations is high -- tens of millions of CPU hours. In this work, we use Wasserstein Generative Adversarial Networks (WGANs) to generate new high-resolution ($35~h^{-1}{\rm kpc}$) 3D realizations of cosmic HI at $z=5$. We do so by sampling from a 100-dimension manifold, learned by the generator, that characterizes the fully non-linear abundance and clustering of cosmic HI from the state-of-the-art simulation IllustrisTNG. We show that different statistical properties of the produced samples -- 1D PDF, power spectrum, bispectrum, and void size function -- match very well those of IllustrisTNG, and outperform state-of-the-art models such as Halo Occupation Distributions (HODs). Our WGAN samples reproduce the abundance of HI across 9 orders of magnitude, from the Ly$\alpha$ forest to Damped Lyman Absorbers. WGAN can produce new samples orders of magnitude faster than hydrodynamic simulations.
An introductory view into Simultaneous Localization And Mapping

In recent years autonomous vehicles have gained attention as being the potential future for the automotive industry. A central part of an autonomous vehicle is the way that the vehicle locates itself relative to the surrounding environment. This localization can be efficiently performed simultaneously as building a map of the environment, a process called Simultaneous Localization And Mapping or SLAM. In this thesis, the basic algorithms that lay the foundation for the current state of the art of SLAM, and their refinements are studied with the goal of procuring a comprehensive list. The secondary goal is to create a simulation platform for assessing the performance of different algorithms. The thesis is commissioned by automotive consultancy AVL that will use the results to build their knowledge base within the autonomous field. Each algorithm that is mentioned has been looked at from a historical, mathematical and application perspective. This has resulted in a literature study containing thirteen algorithms and a table containing a categorization for easy and quick comparison. Categories of SLAM algorithms are identified to be map type, complexity, hypothesis and basic algorithm. Among the basic algorithms, the classic Kalman Filter and Particle filters have been found, along with modern Maximum a Posteriori algorithms. Furthermore, EKF and fastSLAM have been simulated in a Matlab environment for comparison. A development and simulation platform has also been developed in software frameworks Gazebo and ROS. This platform ensures modularity where algorithms can be exchanged and simulations can be run simultaneously as a real life implementation. The results from the simulations show that the simulation in Gazebo can produce similar results as the traditional simulations in Matlab. It is difficult to know exactly which algorithms that are used in commercial applications. Using the information from the literature review, a discussion is made regarding which algorithms may be suitable for real life usage. The conclusion is that most likely, combinations of several algorithms are used in order to leverage the benefits from each, as well as mitigate weaknesses.
Self Training Autonomous Driving Agent

Intrinsically, driving is a Markov Decision Process which suits well the reinforcement learning paradigm. In this paper, we propose a novel agent which learns to drive a vehicle without any human assistance. We use the concept of reinforcement learning and evolutionary strategies to train our agent in a 2D simulation environment. Our model's architecture goes beyond the World Model's by introducing difference images in the autoencoder. This novel involvement of difference images in the auto-encoder gives a better representation of the latent space concerning the motion of the vehicle and helps an autonomous agent to learn more efficiently how to drive a vehicle. Results show that our method requires fewer (96% less) total agents, (87.5% less) agents per generations, (70% less) generations and (90% less) rollouts than the original architecture while achieving the same accuracy of the original.
Leveraging Fine-Grained Wikipedia Categories for Entity Search

Ad-hoc entity search, which is to retrieve a ranked list of relevant entities in response to a query of natural language question, has been widely studied. It has been shown that category matching of entities, especially when matching to fine-grained entity types/categories, is critical to the performance of entity search. However, the potentials of the fine-grained Wikipedia entity categories, has not been well exploited by existing studies. Based on the observation of how people describe entities of a specific type, we propose a headword-and-modifier model to deeply interpret both queries and fine-grained entity types/categories. Probabilistic generative models are designed to effectively estimate the relevance of headwords and modifiers as a pattern-based matching problem, taking the Wikipedia type taxonomy as an important input to address the ad-hoc representations of concepts/entities in queries. Extensive experimental results on three widely-used test sets: INEX-XER 2009, SemSearch-LS and TREC-Entity, show that our method achieves a significant improvement of the entity search performance over the state-of-the-art methods.
A Legal and Cinematic Diagnosis

The term “insanity” has been retired from medical nomenclature for about 100 years. Formerly interchangeable with the legal term, implying unsoundness of mind, it persists as a legal determination, mainly in criminal matters. However, the most prevalent uses of “insanity” are in colloquial speech and media. We track “insanity” in medical and legal parlance, reasons for its disappearance from psychiatry, and its persistence in popular culture. During the 19th century, specific types of legal insanity fell out of favor, especially “moral insanity,” referring to irresistible impulses. The term persisted, for example, in some civil cases and in criminal cases, both denoting lack of capacity. In America, early 20th century focus on disease classification and nomenclature shifted from catchall terms (such as insanity, dementia, mania, and idiocy) to medical labels (psychosis and neurosis). Psychiatrist William Alanson White led the movement to change nomenclature. In 1921, the American Journal of Insanity became the American Journal of Psychiatry. By the time White was the American Psychiatric Association president in 1925, the medical use of “insanity” had been replaced in textbooks by progressive terminology. However, variations on “insane,” suggesting loss of reason without diagnostic specificity, have become a staple among film tropes.
Cortical object segregation and categorization by multi-scale line and edge coding

In this paper we present an improved scheme for line and edge detection in cortical area V1, based on responses of simple and complex cells, truly multi-scale with no free parameters. We illustrate the multi-scale representation for visual reconstruction, and show how object segregation can be achieved with coarse-to-finescale groupings. A two-level object categorization scenario is tested in which pre-categorization is based on coarse scales only, and final categorization on coarse plus fine scales. Processing schemes are discussed in the framework of a complete cortical architecture.