Miracle-dz/UniFAR-FTUs huggingface dataset:
Some theoretical results on the second-order conservative phase field equation

In this paper, a theoretical research on the second-order conservative phase field (SOCPF) equation is presented. The theoretical results include the following three aspects. First, three new derivation methods for the SOCPF equation are given. The SOCPF equation can be viewed as the gradient flow, the special diffusion equation and the diffuse interface form of a sharp interface formulation for the piecewise constant function, respectively. These derivation methods help us to understand the SOCPF equation at different perspectives. Second, the conservation's properties of the solution of SOCPF equation are studied. Compared with the Cahn-Hilliard equation and the Allen-Cahn equation, it is found that the solution of SOCPF equation satisfies more conservation laws. Third, the wetting boundary condition for the SOCPF equation is investigated. We find that the no-flux boundary condition is equivalent to the wetting boundary condition for two-component phase field model. Moreover, applying the no-flux boundary conditions for $N$-component phase field model, we give a set of wetting boundary conditions for $N$ phase field parameters.
Directed Graphical Models and Causal Discovery for Zero-Inflated Data

Modern RNA sequencing technologies provide gene expression measurements from single cells that promise refined insights on regulatory relationships among genes. Directed graphical models are well-suited to explore such (cause-effect) relationships. However, statistical analyses of single cell data are complicated by the fact that the data often show zero-inflated expression patterns. To address this challenge, we propose directed graphical models that are based on Hurdle conditional distributions parametrized in terms of polynomials in parent variables and their 0/1 indicators of being zero or nonzero. While directed graphs for Gaussian models are only identifiable up to an equivalence class in general, we show that, under a natural and weak assumption, the exact directed acyclic graph of our zero-inflated models can be identified. We propose methods for graph recovery, apply our model to real single-cell RNA-seq data on T helper cells, and show simulated experiments that validate the identifiability and graph estimation methods in practice.
Effect of Probiotic Supplementation on Oxidative Stress Enzymes and Mental Health of Athletes

Background: Extreme of professional exercise intensively increase the production of free radicals, damage to body tissues, and productionof stresshormonesandreducestheimmuneactivity. Theuseof probioticsupplementshasattractedsportscommunity attention to promoting the health and performance of athletes. Objectives: In this study, the eﬀect of probiotic supplementation was investigated on psychological conditions and the level of oxidative stress enzymes in athlete soldiers. Methods: This double-blind controlled trial was conducted on 40 swimmers that were randomly divided into two groups: A test group (n = 20 receiving one glass of probiotic juice daily for 12 weeks) and a control group (n = 20 receiving placebo juice for 12 weeks). The probiotic juice contained a probiotic strain of Bacillus coagulans at 1 × 10 9 CFU. The mental health status was evaluated by scoring on the POMS and the concentration of antioxidant enzymes and the total antioxidant capacity in blood samples were determined using the kits at the beginning and the end of the three-month post-test every 15 days. Results: After 12 weeks of intervention, a signiﬁcant increase was observed in the antioxidants levels in the test group compared to thecontrolgroup: SOD(43.10 ± 0.1000vs. 28.25 ± 0.2500u/mL;P< 0.0001),GPX(289.5 ± 1.500 ± vs. 160.0 ± 1.000u/mL;P< 0.0001), totaloxidativestresscapacity(1.975 ± 0.005000vs. 1.365 ± 0.005000u/mL;P< 0.0001). Inaddition,psychologicalparameterswere more favorable in the probiotic group than in the placebo group. Conclusions: Based on the results, the probiotic juice consumption has beneﬁcial eﬀects on the body’s antioxidant system and mental health parameters in athlete soldiers.
Satellite-based Prediction of Forage Conditions for Livestock in Northern Kenya

This paper introduces the first dataset of satellite images labeled with forage quality by on-the-ground experts and provides proof of concept for applying computer vision methods to index-based drought insurance. We also present the results of a collaborative benchmark tool used to crowdsource an accurate machine learning model on the dataset. Our methods significantly outperform the existing technology for an insurance program in Northern Kenya, suggesting that a computer vision-based approach could substantially benefit pastoralists, whose exposure to droughts is severe and worsening with climate change.
DashCam Pay: A System for In-vehicle Payments Using Face and Voice

We present our ongoing work on developing a system, called DashCam Pay, that enables in-vehicle payments in a seamless and secure manner using face and voice biometrics. A plug-and-play device (dashcam) mounted in the vehicle is used to capture face images and voice commands of passengers. Privacy-preserving biometric comparison techniques are used to compare the biometric data captured by the dashcam with the biometric data enrolled on the users' mobile devices over a wireless interface (e.g., Bluetooth or Wi-Fi Direct) to determine the payer. Once the payer is identified, payment is conducted using the enrolled payment credential on the mobile device of the payer. We conduct preliminary analysis on data collected using a commercially available dashcam to show the feasibility of building the proposed system. A prototype of the proposed system is also developed in Android. DashCam Pay can be integrated as a software solution by dashcam or vehicle manufacturers to enable open loop in-vehicle payments.
Evaluating Machines by their Real-World Language Use

There is a fundamental gap between how humans understand and use language -- in open-ended, real-world situations -- and today's NLP benchmarks for language understanding. To narrow this gap, we propose to evaluate machines by their success at real-world language use -- which greatly expands the scope of language tasks that can be measured and studied. We introduce TuringAdvice, a new challenge for language understanding systems. Given a complex situation faced by a real person, a machine must generate helpful advice. We make our challenge concrete by introducing RedditAdvice, a dataset and leaderboard for measuring progress. Though we release a training set with 600k examples, our evaluation is dynamic, continually evolving with the language people use: models must generate helpful advice for recently-written situations. Empirical results show that today's models struggle at our task, even those with billions of parameters. The best model, a finetuned T5, writes advice that is at least as helpful as human-written advice in only 9% of cases. This low performance reveals language understanding errors that are hard to spot outside of a generative setting, showing much room for progress.
State of the Art on Neural Rendering

Efficient rendering of photo‐realistic virtual worlds is a long standing effort of computer graphics. Modern graphics techniques have succeeded in synthesizing photo‐realistic images from hand‐crafted scene representations. However, the automatic generation of shape, materials, lighting, and other aspects of scenes remains a challenging problem that, if solved, would make photo‐realistic computer graphics more widely accessible. Concurrently, progress in computer vision and machine learning have given rise to a new approach to image synthesis and editing, namely deep generative models. Neural rendering is a new and rapidly emerging field that combines generative machine learning techniques with physical knowledge from computer graphics, e.g., by the integration of differentiable rendering into network training. With a plethora of applications in computer graphics and vision, neural rendering is poised to become a new area in the graphics community, yet no survey of this emerging field exists. This state‐of‐the‐art report summarizes the recent trends and applications of neural rendering. We focus on approaches that combine classic computer graphics techniques with deep generative models to obtain controllable and photorealistic outputs. Starting with an overview of the underlying computer graphics and machine learning concepts, we discuss critical aspects of neural rendering approaches. Specifically, our emphasis is on the type of control, i.e., how the control is provided, which parts of the pipeline are learned, explicit vs. implicit control, generalization, and stochastic vs. deterministic synthesis. The second half of this state‐of‐the‐art report is focused on the many important use cases for the described algorithms such as novel view synthesis, semantic photo manipulation, facial and body reenactment, relighting, free‐viewpoint video, and the creation of photo‐realistic avatars for virtual and augmented reality telepresence. Finally, we conclude with a discussion of the social implications of such technology and investigate open research problems.
Constrained Multi-shape Evolution for Overlapping Cytoplasm Segmentation

Segmenting overlapping cytoplasm of cells in cervical smear images is a clinically essential task, for quantitatively measuring cell-level features in order to diagnose cervical cancer. This task, however, remains rather challenging, mainly due to the deficiency of intensity (or color) information in the overlapping region. Although shape prior-based models that compensate intensity deficiency by introducing prior shape information (shape priors) about cytoplasm are firmly established, they often yield visually implausible results, mainly because they model shape priors only by limited shape hypotheses about cytoplasm, exploit cytoplasm-level shape priors alone, and impose no shape constraint on the resulting shape of the cytoplasm. In this paper, we present a novel and effective shape prior-based approach, called constrained multi-shape evolution, that segments all overlapping cytoplasms in the clump simultaneously by jointly evolving each cytoplasm's shape guided by the modeled shape priors. We model local shape priors (cytoplasm--level) by an infinitely large shape hypothesis set which contains all possible shapes of the cytoplasm. In the shape evolution, we compensate intensity deficiency for the segmentation by introducing not only the modeled local shape priors but also global shape priors (clump--level) modeled by considering mutual shape constraints of cytoplasms in the clump. We also constrain the resulting shape in each evolution to be in the built shape hypothesis set, for further reducing implausible segmentation results. We evaluated the proposed method in two typical cervical smear datasets, and the extensive experimental results show that the proposed method is effective to segment overlapping cytoplasm, consistently outperforming the state-of-the-art methods.
Re-translation versus Streaming for Simultaneous Translation

There has been great progress in improving streaming machine translation, a simultaneous paradigm where the system appends to a growing hypothesis as more source content becomes available. We study a related problem in which revisions to the hypothesis beyond strictly appending words are permitted. This is suitable for applications such as live captioning an audio feed. In this setting, we compare custom streaming approaches to re-translation, a straightforward strategy where each new source token triggers a distinct translation from scratch. We find re-translation to be as good or better than state-of-the-art streaming systems, even when operating under constraints that allow very few revisions. We attribute much of this success to a previously proposed data-augmentation technique that adds prefix-pairs to the training data, which alongside wait-k inference forms a strong baseline for streaming translation. We also highlight re-translation’s ability to wrap arbitrarily powerful MT systems with an experiment showing large improvements from an upgrade to its base model.
Energy resource centres (ERCs) as vehicles for extracting higher benefits from solar power in rural and suburban areas of developing countries (the case of Uganda)

Energy resource centres (ERCs) as vehicles for extracting higher benefits from solar power in rural and suburban areas of developing countries (the case of Uganda) Conference or Workshop Item How to cite: Mbalyohere, Charles; Aguti, Jessica and Nabushawo, Harriet (2019). Energy resource centres (ERCs) as vehicles for extracting higher benefits from solar power in rural and suburban areas of developing countries (the case of Uganda). In: Open Impact Conference, 13-14 Dec 2019, Uganda.
Measuring the impact of retailers’ marketing mix on food price inflation using scanner data

We implement a three-way panel data model to test the effect of retailers’ marketing mix on food price inflation. Using high frequency scanner data for different dairy products in Italy we compute a weekly drift-free price index, specific for product category, chain and type of store. Exploiting the panel data structure to control for unobservable marketing mix by chain, type of store and time, we test if unobservables are statistically significant in influencing the food inflation rate on each of the products covered by our analysis. In general chain and type-of-store specific unobservables have a significant role in controlling the rise of prices. Moreover, we identify the role of some observable marketing mix variables on controlling or on facilitating price inflation rates. Results show that while higher PL shares help on limiting an upward inflation rate, reversely a higher PL line extension tends to accelerate it. Sales, as expected, alleviate the burden of a general increase in prices. However, PL sales have an effect on reducing the price inflation rate which is proportionally smaller than the overall average, indicating that proportionally sales on PL contribute less intensively to reduce a generalized upward price trend. Finally, assortment has a mixed effect depending on the competition environment of the market we refer to.
Developing bait and lure-based integrated pest management module for mango fruit fly (Bactrocera dorsalis) management in Orissa

Experiments were conducted during 2003-06 in high density mango (Mangifera indica L.) orchards in Orissa to develop integrated pest management (IPM) module for fruit fly (Bactrocera dorsalis Hendel ) in mango. Three food baits and 5 substrates as carrier of methyl eugenol were evaluated to find out the most effective food bait and the best carrier to be used in BAT (bait application technique) and MAT (male annihilation technique), respectively. Jaggery was found better food bait, whereas plywood block was observed to be better carrier of methyl eugenol. The control and cost-effectiveness of male annihilation technique (50 mm x 50 mm x 12 mm) plywood blocks soaked in ethyl alcohol: methyl eugenol: malathion in the ratio of 6:4:1 for 48 hr and hung in branches @ 10/ha in bottle traps in 2 rounds) together with bait application technique (2 g carbaryl and 100 g jaggery in 1 litre water), supplemented with field sanitation was compared with the insecticide spray in the orchard. Male annihiliation technique and bait application technique together utilized 11.11 times less insecticide and 1.32 times less cost but resulted in nearly double protection (93-97%), as well as double cost: benefit ratio (1 : 21.76) than that of cover spray (53-56% and 1 : 10.9, respectively). Pesticide : produce ratio was 19.02 kg fruit/ml of insecticide, whereas only 1.14 in cover sprays. As such, the IPM module was field tested at constant level of male annihilation technique (2 rounds) and variable level of bait application technique (8, 6 and 4 rounds at 6, 8 and 12 days interval, respectively) in large area of mango plantation. Two rounds of male annihilation technique in conjunction with 6-8 rounds of bait application technique at 6-8 days interval supplemented with field sanitation can reduce fruit fly damage up to 87-95% in mango.
Physical Literacy for the Older Adult

THIS ARTICLE INTRODUCES THE CONSTRUCT OF PHYSICAL LITERACY PARTICULARLY AS IT RELATES TO THE OLDER ADULT. MOST ARTICLES PERTAINING TO PHYSICAL LITERACY IN THE SCIENTIFIC LITERATURE HAVE SHARED INFORMATION RELATIVE TO THE SECTORS OF YOUTH SPORTS AND CHILDHOOD EDUCATION. HOWEVER, THE DEFINITION OF PHYSICAL LITERACY HIGHLIGHTS THE IMPORTANCEOF TAKING RESPONSIBILITY FOR ENGAGEMENT IN PHYSICAL ACTIVITIES FOR THE FULL LIFE SPAN. THEREFORE, THE FOCUS OF THIS ARTICLE IS ON THE OLDER ADULT, AN OFTEN OVERLOOKED AUDIENCE, AND ADDRESSES SPECIFIC SAMPLE EXERCISES THAT CAN HELP START THAT AUDIENCE ON THE PATH OF PHYSICAL LITERACY. GUIDELINES FOR THE STRENGTH AND CONDITIONING PRACTITIONER ARE PROVIDED.
The role of protein tyrosine phosphatase 1b in the central regulation of energy homeostasis

Protein tyrosine phosphatase 1B (PTP1B) is a ubiquitously expressed tyrosine phosphatase implicated in the central control of energy homeostasis via negative regulation of leptin signaling. Mice with central nervous system (CNS)-specific PTP1B deficiency demonstrate clear metabolic improvements, including decreased body weight and adiposity presumably due to enhanced leptin sensitivity. Interestingly, compound PTP1B:leptin double mutants show attenuated weight gain compared to leptin single mutants, suggesting that the metabolic effects of PTP1B deficiency may also involve non-leptin signaling pathways. Indeed, insulin and other non-leptin cytokines, such as interleukin-6, have been implicated in the CNS control of energy balance, and these pathways are also candidates of PTP1B regulation. Thus, whether or not the metabolic effects of PTP1B deficiency are due exclusively to enhanced leptin signaling remains unclear. This dissertation examines the role of central PTP1B in the control of leptin receptor-dependent energy homeostasis. A leptin receptorexpressing cell specific PTP1B-deficient (LepRb PTP1B / ) mouse model was generated and its metabolic phenotype was analyzed in comparison to wildtype controls and to whole body PTP1B knockouts. Though subtle phenotypic differences between LepRb-PTP1B-/and PTP1B-/mice exist, LepRb-PTP1B-/mice demonstrate a majority of the phenotype observed in whole body PTP1B knockouts, including decreased body weight and adiposity and improved leptin sensitivity compared to wildtype controls. To further elucidate whether or not central PTP1B regulates non-leptin signaling pathways, a compound hypothalamic PTP1B:LepRb double mutant (Nkx2.1-PTP1B-/-:LepRb-/-) was generated and metabolically phenotyped in comparison to hypothalamic leptin receptor (Nkx2.1-LepRb-/-) and PTP1B (Nkx2.1-PTP1B-/-) mutant mice. While Nkx2.1-PTP1B-/mice show decreased body weight and adiposity compared to wildtypes, both Nkx2.1-PTP1B-/-:LepRb /and Nkx2.1-LepRb-/show a severe obese phenotype marked by similarly increased weight gain, total fat mass, food intake, and glucose intolerance, indicating that the metabolic benefits of hypothalamic PTP1B deficiency are dependent upon functional leptin receptor signaling. Finally, whether PTP1B can regulate interleukin-6 signaling was explored using an immortalized mouse hypothalamic cell line. In summary, these data show that PTP1B is a critical regulator of energy balance within leptin receptor-expressing cells and within the hypothalamus specifically, and further begin to unravel the signaling pathways mediating the beneficial metabolic effects of central PTP1B deficiency. Degree Type Dissertation Degree Name Doctor of Philosophy (PhD) Graduate Group Neuroscience First Advisor Kendra K. Bence This dissertation is available at ScholarlyCommons: http://repository.upenn. edu/edissertations/1477
Morphological and molecular identification of Colletotrichum gloeosporioides from Citrus reticulata.

INTRODUCTION Colletotrichum spp. causes anthracnose disease and postharvest decay on many tropical, subtropical, and temperate fruits. On citrus, postbloom fruit drop is caused by Colletotrichum acutatum J.H. Simmonds. Infection results in necrotic brown lesions on petals and premature fruit drop. Postharvest anthracnose is caused by C. gloeosporioides (Penz.) Penz. & Sacc. , and is a problem especially for citrus fruits harvested early that must be degreened with ethylene. In Serbia, the occurrence of anthracnose on mandarin fruit (import from Greece), has been found during 2010. Typical symptoms include dark, sunken, and circular lesion that produce mucilaginous, orange conidial masses (Figure 1a). Economic losses caused by the disease are mainly attributed to lower fruit quality and marketability. Therefore, the objective of the present study was to identify the causing agent of mandarin anthracnose using morphological and molecular analysis.
Influence of assimilate supply on leaf formation in sweet pepper and tomato

SummaryThe influence of assimilate supply on leaf formation in sweet pepper and tomato was investigated. Assimilate supply for vegetative growth was varied by light intensity, by plant density and by fruit, truss and leaf pruning. In both species, a higher assimilate supply led to an increasing dry weight of the vegetative parts of up to 82%, whereas leaf appearance rate was hardly affected. Area of individual leaves was hardly influenced by fruit or leaf pruning, but increased with increasing light intensity, decreasing plant density or the removal of every other truss. Increased assimilate supply resulted in an increase in dry weight of individual leaves and a decrease in specific leaf area. In addition, for sweet pepper grown under daylight conditions at a constant 24-hour temperature, the rate of dry weight increase of total plant and vegetative parts changed considerably (coefficient of variation (CV) 0.52 and 0.44, respectively), but leaf appearance rate was rather constant during a growing season (.
The anti-tumour activity and mode of action of artesunate and dihydroartemisinin (alone and in combination with aspirin) against human leukaemia HL-60 and colorectal HT-29-AK cancer cells

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Optimization of an FaeG DNA vaccine in pigs

We have used FaeG DNA to immunize piglets by the intradermal (ID) and the intramuscular (IM) route in a heterologous prime/boost model. ID immunization with DNA resulted in a better induction of cellular immunity, whereas only the IM immunization could prime an F4-specific serum IgA response. However, ID administration of plasmid-encoded GM-CSF one week before the ID immunization enhanced the F4-specific humoral and cellular immune response and even primed the F4-specific IgA response more efficiently than the IM immunization did.