Miracle-dz/UniFAR-FTUs huggingface dataset:
Simulated Effects of Transgenic Bt Crops on Specialist Parasitoids of Target Pests

Abstract Large-scale adoption of transgenic crops that produce Bacillus thuringiensis (Bt) toxins could greatly reduce populations of target pests and their specialist parasitoids. We used a spatially explicit model of host-parasitoid population dynamics to examine effects of Bt crops on parasitoid persistence. We modeled a 9,000-ha region with 900 fields of 10 ha, each planted with either a Bt crop or a non-Bt crop refuge. In the 13,524 simulations we conducted, the pest (i.e., host) was never lost from the region before the parasitoid. The parasitoid was lost from the region in 36% of the simulations. The host was lost after the parasitoid in 12% of all simulations. The probability of regional parasitoid loss increased as the percentage of Bt fields and rotation of field types (Bt and non-Bt) increased and as host reproductive rate and parasitoid attack rate decreased. Regional parasitoid loss also increased as the insecticide spray threshold for target pests in non-Bt fields decreased and as parasitoid susceptibility to insecticide increased. With field types fixed, regional parasitoid loss increased as parasitoid movement increased. However, with field types rotated, parasitoid movement did not influence regional parasitoid loss. Host movement did not influence regional parasitoid loss. Factors not modeled that would reduce risk of regional parasitoid loss include use of alternate hosts by the parasitoid, use of alternate host plants by the target pest, and evolution of resistance to the Bt crop by the pest. The results suggest that risk of regional parasitoid loss can be assessed from the parasitoid life history traits and reduced by increasing the percentage of refuge fields, fixing refuge locations, and increasing the insecticide spray threshold for target pests in refuges.
The Pleasures and Lessons of Academic Mythbusting: An Interview with Scott Lilienfeld

Scott O. Lilienfeld is a professor of psychology at Emory University, in Atlanta, Georgia. Dr. Lilienfeld is founder and editor of the journal, Scientific Review of Mental Health Practice, and is past president of the Society for a Science of Clinical Psychology. He has been a member of 11 journal editorial boards, including the Journal of Abnormal Psychology and Clinical Psychology Review. Dr. Lilienfeld has published more than 200 articles, book chapters, and books on personality disorders, personality assessment, anxiety disorders, psychiatric classification and diagnosis, pseudoscience in psychology, and evidence-based practices in clinical psychology. His most recent book, coauthored with Steven Jay Lynn, John Ruscio, and Barry Beyerstein, is 50 Great Myths of Popular Psychology: Shattering Widespread Misconceptions About Human Behavior (Wiley-Blackwell, 2010). Tracy E. Zinn is an associate professor of psychology at James Madison University. She earned a PhD in industrial/organizational psychology from Auburn University in 2002. In 2005, Tracy received the Early Career Award from the Society for the Teaching of Psychology. She has published several articles and book chapters on the topic of effective teaching practices. Tracy currently serves as the program coordinator for the APS-STP Teaching Institute at the Association for Psychological Sciences annual convention.
Enabling Technologies for Ultra-Reliable and Low Latency Communications: From PHY and MAC Layer Perspectives

Future 5th generation networks are expected to enable three key services—enhanced mobile broadband, massive machine type communications and ultra-reliable and low latency communications (URLLC). As per the 3rd generation partnership project URLLC requirements, it is expected that the reliability of one transmission of a 32 byte packet will be at least 99.999% and the latency will be at most 1 ms. This unprecedented level of reliability and latency will yield various new applications, such as smart grids, industrial automation and intelligent transport systems. In this survey we present potential future URLLC applications, and summarize the corresponding reliability and latency requirements. We provide a comprehensive discussion on physical (PHY) and medium access control (MAC) layer techniques that enable URLLC, addressing both licensed and unlicensed bands. This paper evaluates the relevant PHY and MAC techniques for their ability to improve the reliability and reduce the latency. We identify that enabling long-term evolution to coexist in the unlicensed spectrum is also a potential enabler of URLLC in the unlicensed band, and provide numerical evaluations. Lastly, this paper discusses the potential future research directions and challenges in achieving the URLLC requirements.
Improving Care Transitions for Hospitalized Veterans Discharged to Skilled Nursing Facilities: A Focus on Polypharmacy and Geriatric Syndromes

Geriatric syndromes and polypharmacy are common in older patients discharged to skilled nursing facilities (SNFs) and increase 30-day readmission risk. In a U.S.A. Department of Veterans Affairs (VA)-funded Quality Improvement study to improve care transitions from the VA hospital to area SNFs, Veterans (N = 134) were assessed for geriatric syndromes using standardized instruments as well as polypharmacy, defined as five or more medications. Warm handoffs were used to facilitate the transfer of this information. This paper describes the prevalence of geriatric syndromes, polypharmacy, and readmission rates. Veterans were prescribed an average of 14.7 medications at hospital discharge. Moreover, 75% of Veterans had more than two geriatric syndromes, some of which began during hospitalization. While this effort did not reduce 30-day readmissions, the high prevalence of geriatric syndromes and polypharmacy suggests that future efforts targeting these issues may be necessary to reduce readmissions among Veterans discharged to SNF.
Modeling the effects of temporal context on neural responses across the cortical hierarchy

: The world contains information over multiple timescales. For example, we must combine sequences of syllables to perceive a word, and sequences of words to comprehend a sentence. How does the brain process information over multiple timescales? Previous studies have demonstrated that higher-order brain regions are sensitive to temporal context on longer scales, and that they also express stable activity states over the duration of an event. We set out to model these neural phenomena using a hierarchical temporal auto-encoder (HTA). When augmented with mechanisms for a contextual reset, the HTA successfully reproduces neural phenomena. The HTA also generates a prediction regarding context construction: that low-level regions establish a new context rapidly, while higher order regions establish new context representations more gradually. We confirmed this empirical prediction by applying inter-subject pattern correlation to fMRI responses of sentences heard in different temporal contexts. Overall, we propose that a hierarchy of temporal auto-encoders is a feasible model of temporal information processing in the cortical hierarchy.
TransLink: User Identity Linkage across Heterogeneous Social Networks via Translating Embeddings

Nowadays people tend to create accounts with multiple social networks (SNs) to enjoy a variety of social network services. User identity linkage (UIL) aims to identify those multiple accounts belonging to a same person. UIL is of great importance to user behavior understanding and prediction, information dissemination, viral marketing, wellness diagnosis, etc. Most of existing solutions typically rely on the embedding of either user’s attributes or behaviors into a latent vector space, and establish anchor link based on vector distance. However, these efforts still face challenges originated from the heterogeneity of SNs, incompleteness of user information and lack of enough known anchor links. In this paper, we investigate the UIL problem by presenting a translation-modeling approach-TransLink. It jointly embeds both users and interactive behaviors of various SNs into a unified low-dimensional representation space according to a set of known anchor links. More specifically, we primarily study three typical SNs, e.g, Twitter, Foursquare and Instagram. Before embedding, we abstract schemas of three SNs and extract interaction metapaths for each SN. By doing this we can efficiently address the first two challenges. Furthermore, iterative linkage can ensure linkage performance by using a very small set of known anchor links. Experiment results on two real-world datasets demonstrate the superiority of TransLink over the state-of-the-art approaches.
Food chain structure in Ontario Lakes determines PCB levels in lake trout (Salvelinus namaycush) and other pelagic fish

The trophic structure of pelagic communities in lakes of glaciated regions is highly variable due to restricted dispersal of glacial relict taxa and recent species introductions. Much of the enormous between-lake variability in PCB levels in lake trout flesh (15–10 000 ng/g) from the St. Lawrence system results from differences in the length of pelagic food chains. Ontario Ministry of the Environment data (1978–81) on PCB concentrations in lake trout flesh indicate that PCB concentrations increased with the length of the food chain and tissue lipid content, and decreased with distance north of urban-industrial centres. Each trophic level contributed about a 3.5-fold biomagnification factor to the PCB concentrations in the trout, and the lipid content of the trout flesh increased by a factor of 1.5 for each additional trophic level. An empirical model capable of predicting PCB levels in pelagic salmonids and forage fish (smelt and coregonids) indicated that biomagnification of small atmospheric inputs of p.
Digital Transformation in Asset-intensive Businesses: Lessons Learned from the Metals and Mining Industry

Digital transformation shapes business operations across industries. The present study investigates the challenges of this transformation in asset-intensive businesses. Based on a qualitative case study in the metals and mining industry, the study explores the early attempts to implement digital technologyenabled changes in the inter-organizational exchange as part of firms’ business models in their business ecosystems. In particular, the study identifies the challenges related to the ways firms in the metals and mining industry manage to seize the opportunities that digital technologies provide for their business operations. The challenges include firms’ lack of capabilities to change, goal ambiguity, technological constraints and external constraints. The findings contribute to the discussion of digital transformation as a technology-enabled strategic change and provide suggestions for business practitioners about factors that can impede the realization of this transformation. Keyword: digitalization, dynamic capabilities, organizational change, metals, mining
Comparative effect of various diets on development of Chrysoperla carnea (Neuroptera: Chrysopidae).

Nine diets were offered to Chrysoperla carnea larvae; out of those six diets were comprised of minerals and vitamins and remaining three were natural. Developmental time, mortality percentages, pupal weight and laid eggs were determined. Except for eggs of Sitotroga cerealella and chicken liver, all other diets were presented in the form of semi-solid bolls. Minimum total duration (13.9 d), total mortality (15%) and pre-oviposition period (3.4 d) along with maximum cocoon weight (0.828 g) and eggs laid (717 eggs) were recorded when eggs o f S. cerealella (diet 9). Diet 4 showed a maximum total duration (27.65 d) and diet3 resulted in maximum (65%) total mortality.
Learning Structured Latent Factors from Dependent Data: A Generative Model Framework from Information-Theoretic Perspective

Learning controllable and generalizable representation of multivariate data with desired structural properties remains a fundamental problem in machine learning. In this paper, we present a novel framework for learning generative models with various underlying structures in the latent space. We represent the inductive bias in the form of mask variables to model the dependency structure in the graphical model and extend the theory of multivariate information bottleneck to enforce it. Our model provides a principled approach to learn a set of semantically meaningful latent factors that reflect various types of desired structures like capturing correlation or encoding invariance, while also offering the flexibility to automatically estimate the dependency structure from data. We show that our framework unifies many existing generative models and can be applied to a variety of tasks including multi-modal data modeling, algorithmic fairness, and invariant risk minimization.
A morphometric analysis of Daniellia (Fabaceae – Caesalpinioideae)

A multivariate morphometric study of Daniellia, an endemic genus of tropical and subtropical Africa, indicates that nine species may be recognized: D. alsteeniana, D. klainei, D. oblonga, D. ogea, D. oliveri, D. pilosa, D. pynaertii, D. soyauxii and D. thurifera. In our study we found that some characters, not previously studied in detail, were significant in species delimitation: petiole indumentum, petiole width, number and position of glands on the lower surface of the leaflets and presence or absence of glands at the insertion of each pair of leaflets. The rare and scattered material of D. pilosa and D. soyauxii made their classification uncertain, although some qualitative characters support their differentiation. © 2009 The Linnean Society of London, Botanical Journal of the Linnean Society, 2009, 159, 268–279.
Rationality of Prescription Writing

Prescription writing is one of the crucial tasks performed by the health professionals. A prescription is an instruction from prescriber to a patient as well as dispenser. Use of proper format for prescription writing ensures appropriate use of drug and helps in minimizing errors. Health professionals must take account of appropriateness, effectiveness, side effects, contraindications and cost when prescribing any medicine. Special care and precautions are needed in prescribing the drugs with abuse potentials to avoid their misuse. Irrational prescribing may account to professional and legal threat to a prescriber if done injudiciously as it indicates the prescriber’s responsibility towards clinical care and safety of patient. Every country has its own standards and regulations for prescription writing. There is no global standard for prescription writing which may suggest a need for introduction of universal guidelines. This article presents a comprehensive and simplified review on prescription writing. It may benefit the readers, especially the budding students to ensure the correct format in prescription writing and other relevant aspects.
Analysing Trends And Correlations From Internet Searches: Case Study Of Romania

In the recent years, the scientific community explored the possibility to use thepublicly available web searches volume histories from search engines together with time-seriesanalysis to forecast and analyse, among other things, travel decisions in specific countries. In thisstudy, we use their approach in a specific case study for Romania, by using query-specific searchdata to analyse the correlations with other keywords and the seasonality and geographicdistribution of the historical online searches. We analyse a data set consisting of searchesperformed from 2014-2017 using specific keywords. Based on this approach, interestingcorrelations with different keywords are revealed by those tools. Moreover, we can observe ahistorical change in the queries performed, which reveal interesting facts about a change ofbehaviour.
Lesbian, Gay, Bisexual, and Transgender (LGBT) Older Adults

It is generally stated that the prevalence of substance abuse and dependence among people who are lesbian, gay, bisexual or transexual is substantially higher than in the general population (1, 2, 3). Some early studies said the misuse rate was 30% and higher, but these samples probably oversampled heavy users. More recent research points out that younger lesbian women (particularly those aged 20-34) report using alcohol more frequently. They also drank in greater quantities than heterosexuals. That pattern of drinking in turn conceivably places some of them at greater risk for alcohol-related problems (4, 5). The women were also more likely to smoke, with the potential for tobacco-related health problems in later life. (6)
The linguistic environment

The context of language Topics  Language input (baby talk)  Culture; parental speech styles  Development of Conversation  Methods to Study Input  Research Findings Cultural Attitudes Piaget: " …very rarely have I been able, in America, to expound any aspect of my stage theory without being asked 'How can I speed up development? " K'iche' (Pye)-children are at risk-protection of the 'soul'-parents direct little speech to children-parents speak for their children-no language games or songs Sample Transcript Adult to Child: " Do you know what you are holding? " Mother to Adult: " he says 'girl' " Mother to Child: " You say little horsie " (2x) Mother to Adult: " Play. He says little horsie play " Cultural Attitudes Towards Infants Mohave: the fetus is born capable of understanding the adult language Samoan: rules of etiquette restrict parents from speaking to young infants Javanese: infants are not yet human Baby Talk The language used by anyone in the linguistic community when addressing a child Baby Talk Features 1. Prosodic: higher pitch, more varied intonation 2.
Noise aware depth denoising for a time-of-flight camera

A time-of-ﬂight camera provides depth maps of the scene at video frame rate. However, their depth measurements are severely inﬂuenced by random noise and systematic bias. Previous approaches on depth denoising are usually variants of adaptive joint bilateral ﬁltering with the help of a color image of the same scene. In this paper, we access to the raw range measurements of the ToF sensor instead of the transformed depth values, and we acquire range error proﬁle for each pixel along the range measurement by capturing a planar scene at diﬀerent distances. We correct the range bias using plane ﬁtting and then the remaining noise can be assumed to follow a zero-mean Gaussian distribution with variance according to the pixel location and the range measurement. Since the whole process is done beforehand leaving variance information, any kind of depth denoising algorithm assuming zero-mean Gaussian noise can perform well with our noise estimation.
Recognition of Species of Insectivorous Bats by Their Echolocation Calls

The use of frequency, duration, and patterns of frequency change over time to identify 39 species of North American and African bats by their search phase echolocation calls is described. The data were obtained from an oscilloscope display of the cells presented by a zero-crossing period meter when the calls had been detected by a broadband microphone. Species using high intensity calls are more easily sampled than those using calls of intermediate intensity; bats using low intensity calls are not readily sampled by this system. Identification of bats in the field by their calls requires observation of known individuals under field conditions. The echolocation calls included frequency components from 8 to 210 kHz. At all sites, species using frequency modulated (FM) calls with frequencies from 25 to 65 kHz predominated; larger faunas exhibited greater variability in types of calls. The identification of echolocating bats by their calls depends upon the size of the bat fauna, the levels of bat activity, and inherent characteristics of the calls.
Focus Measure for Synthetic Aperture Imaging Using a Deep Convolutional Network

Synthetic aperture imaging is a technique that mimics a camera with a large virtual convex lens with a camera array. Objects on the focal plane will be sharp and off the focal plane blurry in the synthesized image, which is the most important effect that can be achieved with synthetic aperture imaging. The property of focusing makes synthetic aperture imaging an ideal tool to handle the occlusion problem. Unfortunately, to automatically measure the focusness of a single synthetic aperture image is still a challenging problem and commonly employed pixel-based methods include using variance or using a ”manual focus” interface. In this paper, a novel method is proposed to automatically determine whether or not a synthetic aperture image is in focus. Unlike conventional focus estimation methods which pick the focal plane with the minimum variance computed by the variance of corresponding pixels captured by different views in a camera array, our method automatically determines if the synthetic aperture image is focused or not from one single image of a scene without other views using a deep neural network. In particular, our method can be applied to automatically select the focal plane for synthetic aperture images. The experimental results show that the proposed method outperforms the traditional automatic focusing methods in synthetic aperture imaging as well as other focus estimation methods. In addition, our method is more than five times faster than the state-of-the-art methods. By combining with object detection or tracking algorithms, our proposed method can also be used to automatically select the focal plane that keeps the moving objects in focus. To the authors’ best knowledge, it is the first time that such a method of using a deep neural network has been proposed for estimating whether or not a single synthetic aperture image is in focus.
Why Do Bullies Bully?: An Examination of the Role of Intrinsic and Extrinsic Factors in Motivating and Enabling Bullying Behavior

This study examined the relationships between a number of intrinsic and extrinsic factors and two necessary precedents of bullying behavior in 191 students in grades 9-12. A survey was administered to examine these factors on scales developed by the researcher, one of which measuring the moral attitudes of students toward various bullying behaviors, and the other measuring the likelihood of bullying to occur in a variety of situations. These factors were grouped into categories of motivating and enabling factors . Motivating factors make people