Miracle-dz/UniFAR-FTUs huggingface dataset:
High Plasma Triglyceride Levels in Small Birds during Migratory Flight: A New Pathway for Fuel Supply during Endurance Locomotion at Very High Mass-Specific Metabolic Rates?

Plasma fat metabolites were measured during the nocturnal migratory flight in three species of small passerines. The birds had higher free fatty acid (FFA) and glycerol levels than resting birds that had been fasted overnight. In contrast to exercising mammals and large birds, they also had elevated levels of triglyceride and the very low density lipoprotein (VLDL) fraction. It is hypothesized that FFA reesterification in the liver and delivery of triglyceride-VLDL to the flight muscles helps to circumvent constraints in energy supply during endurance locomotion in animals with very high mass-specific metabolic rates.
Association of Sleep Electroencephalography-Based Brain Age Index With Dementia

Key Points Question Can an electroencephalography-based brain age index, calculated as the difference between chronological and estimated brain age through sleep electroencephalography, serve as a potential biomarker associated with dementia? Findings This cross-sectional study of 9834 polysomnograms using machine learning found that brain age index increased monotonically from nondementia to dementia subpopulations. Meaning These findings suggest that brain age index may be a useful biomarker associated with dementia and could have utility as a screening tool for the presence of underlying neurodegenerative disease and monitoring of disease progression.
Effects of fatty acid profile of supplements on intake, performance, carcass traits, meat characteristics, and meat sensorial analysis of feedlot Bos indicus bulls offered a high-concentrate diet

Abstract This experiment was designed to evaluate the effects of lipid source and fatty acid (FA) profile on intake, performance, carcass characteristics, expression of enzymes, and sensorial analysis of Bos indicus animals offered a high-concentrate diet. On day 0, 96 noncastrated animals were blocked by initial body weight (400 ± 19.3 kg), randomly allocated to 1 of 24 pens (4 animals/pen), and pens were randomly assigned to receive: 1) control: basal diet composed of whole cottonseed and corn germ as lipid substrates (CONT; n = 6), 2) calcium salts of fatty acids (CSFA) of soybean: CSFA of soybean oil as replacement for whole cottonseed and corn germ (calcium salts of soybean oil [CSSO]; n = 6), 3) CSFA-Blend: CSFA of palm, cottonseed, and soybean oil as replacement for whole cottonseed and corn germ (calcium salts of vegetable oils [CSVO]; n = 6), and 4) Mix: basal diet containing whole cottonseed, corn germ, and CSVO (MIXT; n = 6). Experiment lasted 108 d and performance, ultrasound measurements, as well as carcass characteristics were evaluated. Additionally, meat FA profile, expression of enzymes involved in lipid metabolism, and sensorial analysis were evaluated. No treatment effects were observed on performance variables, ultrasound, and carcass traits (P ≥ 0.22), whereas animals receiving CONT had a greater intake of C10:0, C16:0, C16:1 trans-9, C18:1 cis-9, C18:2, C18:3, total FA, monounsatured FA (MUFA), and polyunsaturated FA (PUFA) vs. CSSO and MIXT (P < 0.05). Conversely, intake ratios of saturated FA (SFA):MUFA and SFA:PUFA were all reduced for CONT vs. other treatments. Meat obtained from CONT animals had greater colorimetric (L*, a*, and b*) values vs. MIXT (P < 0.01). On meat FA profile, CONT increased C18:0 vs. supplementation with calcium salts (P < 0.02) and supplementation with CSSO yielded greater meat concentrations of C18:1 trans-10 and C18:2 CLA intermediates (P < 0.01). Expression of SREBP-1, SCD, and LPL was downregulated for CSSO (P < 0.05). For sensorial analysis, regular flavor was greater (P = 0.01) for CSSO vs. other treatments, but also greater aroma (P = 0.05) vs. CONT and CSVO. In summary, addition of different lipid sources with varying FA profiles into high-concentrate diets did not affect performance and carcass characteristics of B. indicus animals, but supplementation with calcium salts of soybean oil inhibited the mRNA expression of enzymes involved in lipid metabolism, whereas flavor and aroma were positively affected by this lipid source.
What is a "large fish"? - taking a species perspective.

OSPAR has decided to elaborate on the proportion of large fish in survey catches as an Ecological Quality Objective (EcoQO). 'Large fish', as defined from the human perspective, is clearly an elastic concept and a theory that might be used to identify suitable reference levels and to turn the available metrics into a management objective is lacking. I take a species perspective and use a ‘functionally large’ criterion that relates the size of individuals caught to the reported maximum size of the species (Lmax), a metric that can be linked to changes in the reproductive potential of the total fish community. Also, the fraction surviving to a particular size is affected by total mortality. Therefore, at least in principle, reference levels might be set for unexploited conditions. Using North Sea IBTS data, the annual fractions of individuals larger than 50% of their Lmax as well as the fraction larger than length-at-50% maturity were calculated. Although the overall results for these two criteria were similar, responses (levels and slopes) within species were sensitive to the cut-off point selected. The fractions, averaged over all species, declined significantly and consistently (by approximately 15% over a 28-year period). This negative trend significantly affected 24 of the 30 species included in the analysis. Only one species (spurdog) showed a significant positive trend. No progress could be made in defining reference levels, because trends derived from surveys for four commercial roundfish species were not correlated with ratio’s of spawning-stock-biomass/total-biomass based on stock assessment. The evidence for declining fractions of functionally large fish should be viewed in the context of other significant changes observed in the North Sea fish community. The complexity of what is going on should warn against a premature setting of a community EcoQO based on simple-minded metrics that cannot yet be fully understood.
Understanding the Epidemic Course in Order to Improve Epidemic Forecasting

The epidemic course of the severe acute respiratory syndrome (SARS) has been differently divided according to its transmission pattern and the infection and mortality status. Unfortunately, such efforts for the coronavirus disease 2019 (COVID‐19) have been lacking. Does every epidemic have a unique epidemic course? Can we coordinate two arbitrary courses into an integrated course, which could better reflect a common real‐world progression pattern of the epidemics? To what degree can such arbitrary divisions help predict future trends of the COVID‐19 pandemic and future epidemics? Spatial lifecourse epidemiology provides a new perspective to understand the course of epidemics, especially pandemics, and a new toolkit to predict the course of future epidemics on the basis of big data. In the present data‐driven era, data should be integrated to inform us how the epidemic is transmitting at the present moment, how it will transmit at the next moment, and which interventions would be most cost‐effective to curb the epidemic. Both national and international legislations are needed to facilitate the integration of relevant policies of data sharing and confidentiality protection into the current pandemic preparedness guidelines.
Variation in reproductive investment and floret gender ratios in two gynodioecious mat daisies (Raoulia, Asteraceae)

ABSTRACT Widespread species could be expected to demonstrate greater among-population variation in reproductive traits than species with limited distributions. We investigated variability in floret gender and capitula number in populations of two gynomonoecious mat daisies, the widespread Raoulia australis and the alpine specialist Raoulia hectori in Central Otago, lower South Island. Gynomonoecy (female and hermaphrodite florets in a capitulum) is common in the daisy family Asteraceae. Varying gender ratios within capitula could allow for plasticity in reproductive investment in relation to environment and life-history stage. We found that the ratio of female to hermaphrodite florets within capitula did not vary significantly among populations or with plant size for either Raoulia species. Instead, the number of capitula produced varied among populations within species, independent of plant size, and showed different patterns in relationship to plant size between the two species. Smaller plants of the alpine specialist R. hectori consistently showed a proportionally greater allocation to reproduction than larger plants, whereas the relationship between plant size and reproductive allocation varied markedly among populations of R. australis. These results suggest that allocation to capitula may be a key component of reproduction in Raoulia species, and is potentially responsive to environment and life-history stage. However, as our study included few populations and did not follow through to seed production, this hypothesis requires further testing. Moreover, our values for floret gender ratios differed from standard species descriptions, so broader geographic variation in floret gender ratios may still exist.
Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000

Although online education has become a viable and major component of higher education in many fields, its employment in engineering disciplines has been limited. COVID-19 pandemic compelled the global and abrupt conversion of conventional face-to-face instruction to the online format. The negative impact of such sudden change is undeniable. Urgent and careful planning is needed to mitigate pandemic negative effects on engineering education, especially for vulnerable, disadvantaged, and underrepresented students who have to deal with additional challenges (e.g. digital equity gap). To enhance engineering online instruction during the pandemic era, we conducted an observational study at California State University, Long Beach (a minority-serving institution). 110 faculty and 627 students from six engineering departments participated in our surveys and answered quantitative and qualitative questions to highlight the challenges they experienced during the online instruction in Spring 2020. In this work, we present the results of these surveys in detail and propose solutions to address the identified issues including logistical, technical, learning/teaching challenges, assessment methods, and hands-on training. As the pandemic continues, sharing these results with other educators can help with more effective planning and choice of best practices to improve the online engineering education during COVID-19 and beyond. INDEX TERMS Engineering Education, COVID-19, Online Learning, Observational Study, Education Enhancement
Neighborhood Matters: Influence Maximization in Social Networks With Limited Access

Influence maximization (IM) aims at maximizing the spread of influence by offering discounts to influential users (called seeding). In many applications, due to user’s privacy concern, overwhelming network scale etc., it is hard to target any user in the network as one wishes. Instead, only a small subset of users is initially accessible. Such access limitation would significantly impair the influence spread, since IM often relies on seeding high degree users, which are particularly rare in such a small subset due to the power-law structure of social networks. In this paper, we attempt to solve the limited IM in real-world scenarios by the adaptive approach with seeding and diffusion uncertainty considered. Specifically, we consider fine-grained discounts and assume users accept the discount probabilistically. The diffusion process is depicted by the independent cascade model. To overcome the access limitation, we prove the set-wise friendship paradox (FP) phenomenon that neighbors have higher degree in expectation, and propose a two-stage seeding model with the FP embedded, where neighbors are seeded. On this basis, for comparison we formulate the non-adaptive case and adaptive case, both proven to be NP-hard. In the non-adaptive case, discounts are allocated to users all at once. We show the monotonicity of influence spread w.r. t. discount allocation and design a two-stage coordinate descent framework to decide the discount allocation. In the adaptive case, users are sequentially seeded based on observations of existing seeding and diffusion results. We prove the adaptive submodularity and submodularity of the influence spread function in two stages. Then, a series of adaptive greedy algorithms are proposed with constant approximation ratio. Extensive experiments on real-world datasets show that our adaptive algorithms achieve larger influence spread than non-adaptive and other adaptive algorithms (up to a maximum of 116 percent).
Assessing the Helpfulness of Learning Materials with Inference-Based Learner-Like Agent

Many English-as-a-second language learners have trouble using near-synonym words (e.g., small vs.little; briefly vs.shortly) correctly, and often look for example sentences to learn how two nearly synonymous terms differ. Prior work uses hand-crafted scores to recommend sentences but has difficulty in adopting such scores to all the near-synonyms as near-synonyms differ in various ways. We notice that the helpfulness of the learning material would reflect on the learners' performance. Thus, we propose the inference-based learner-like agent to mimic learner behavior and identify good learning materials by examining the agent's performance. To enable the agent to behave like a learner, we leverage entailment modeling's capability of inferring answers from the provided materials. Experimental results show that the proposed agent is equipped with good learner-like behavior to achieve the best performance in both fill-in-the-blank (FITB) and good example sentence selection tasks. We further conduct a classroom user study with college ESL learners. The results of the user study show that the proposed agent can find out example sentences that help students learn more easily and efficiently. Compared to other models, the proposed agent improves the score of more than 17% of students after learning.
Empirical Likelihood Inference in Randomized Controlled Trials with High-Dimensional Covariates

In this paper, we propose a data-adaptive empirical likelihood approach for treatment effect inference, which overcomes the obstacle of the traditional empirical likelihood approaches in the high-dimensional setting by adopting penalized regression and machine learning methods to model the covariate-outcome relationship. In particular, we show that our procedure successfully recovers the true variance of Zhang's treatment effect estimator \citep{Zhang2018} by utilizing a data-splitting technique. Our proposed estimator is proved to be asymptotically normal and semiparametric efficient under mild regularity conditions. Simulation studies indicate that our estimator is more efficient than the estimator proposed by \citet{Wager2016} when random forest is employed to model the covariate-outcome relationship. Moreover, when multiple machine learning models are imposed, our estimator is at least as efficient as any regular estimator with a single machine learning model. We compare our method to existing ones using the ACTG175 data and the GSE118657 data, and confirm the outstanding performance of our approach.
Possible multiorbital ground state in 
CeCu2Si2

The crystal-field ground state wave function of CeCu$_2$Si$_2$ has been investigated with linear polarized $M$-edge x-ray absorption spectroscopy from 250mK to 250K, thus covering the superconducting ($T_{\text{c}}$=0.6K), the Kondo ($T_{\text{K}}$$\approx$20K) as well as the Curie-Weiss regime. The comparison with full-multiplet calculations shows that the temperature dependence of the experimental linear dichroism is well explained with a $\Gamma_7^{(1)}$ crystal-field ground-state and the thermal population of excited states at around 30meV. The crystal-field scheme does not change throughout the entire temperature range thus making the scenario of orbital switching unlikely. Spectroscopic evidence for the presence of the Ce 4$f^0$ configuration in the ground state is consistent with the possibility for a multi-orbital character of the ground state. We estimate from the Kondo temperature and crystal-field splitting energies that several percents of the higher lying $\Gamma_6$ state and $\Gamma_7^{(2)}$ crystal-field states are mixed into the primarily $\Gamma_7^{(1)}$ ground state. This estimate is also supported by re-normalized band-structure calculations that uses the experimentally determined crystal-field scheme.
Unsupervised Cross-lingual Image Captioning

Most recent image captioning works are conducted in English as the majority of image-caption datasets are in English. However, there are a large amount of non-native English speakers worldwide. Generating image captions in different languages is worth exploring. In this paper, we present a novel unsupervised method to generate image captions without using any caption corpus. Our method relies on 1) a cross-lingual auto-encoding, which learns the scene graph mapping function along with the scene graph encoders and sentence decoders on machine translation parallel corpora, and 2) an unsupervised feature mapping, which seeks to map the encoded scene graph features from image modality to sentence modality. By leveraging cross-lingual auto-encoding, cross-modal feature mapping, and adversarial learning, our method can learn an image captioner to generate captions in different languages. We verify the effectiveness of our proposed method on the Chinese image caption generation. The comparisons against several baseline methods demonstrate the effectiveness of our approach.
Towards ML Engineering: A Brief History Of TensorFlow Extended (TFX)

Software Engineering, as a discipline, has matured over the past 5+ decades. The modern world heavily depends on it, so the increased maturity of Software Engineering was an eventuality. Practices like testing and reliable technologies help make Software Engineering reliable enough to build industries upon. Meanwhile, Machine Learning (ML) has also grown over the past 2+ decades. ML is used more and more for research, experimentation and production workloads. ML now commonly powers widely-used products integral to our lives. But ML Engineering, as a discipline, has not widely matured as much as its Software Engineering ancestor. Can we take what we have learned and help the nascent field of applied ML evolve into ML Engineering the way Programming evolved into Software Engineering [1]? In this article we will give a whirlwind tour of Sibyl [2] and TensorFlow Extended (TFX) [3], two successive end-to-end (E2E) ML platforms at Alphabet. We will share the lessons learned from over a decade of applied ML built on these platforms, explain both their similarities and their differences, and expand on the shifts (both mental and technical) that helped us on our journey. In addition, we will highlight some of the capabilities of TFX that help realize several aspects of ML Engineering. We argue that in order to unlock the gains ML can bring, organizations should advance the maturity of their ML teams by investing in robust ML infrastructure and promoting ML Engineering education. We also recommend that before focusing on cutting-edge ML modeling techniques, product leaders should invest more time in adopting interoperable ML platforms for their organizations. In closing, we will also share a glimpse into the future of TFX.
Painting Outside as Inside: Edge Guided Image Outpainting via Bidirectional Rearrangement with Step-By-Step Learning

Image outpainting is a very intriguing problem as the outside of a given image can be continuously filled by considering as the context of the image. This task has two main challenges. The first is to maintain the spatial consistency in contents of generated regions and the original input. The second is to generate a high-quality large image with a small amount of adjacent information. Conventional image outpainting methods generate inconsistent, blurry, and repeated pixels. To alleviate the difficulty of an outpainting problem, we propose a novel image outpainting method using bidirectional boundary region rearrangement. We rearrange the image to benefit from the image inpainting task by reflecting more directional information. The bidirectional boundary region rearrangement enables the generation of the missing region using bidirectional information similar to that of the image inpainting task, thereby generating the higher quality than the conventional methods using unidirectional information. Moreover, we use the edge map generator that considers images as original input with structural information and hallucinates the edges of unknown regions to generate the image. Our proposed method is compared with other state-of-the-art outpainting and inpainting methods both qualitatively and quantitatively. We further compared and evaluated them using BRISQUE, one of the No-Reference image quality assessment (IQA) metrics, to evaluate the naturalness of the output. The experimental results demonstrate that our method outperforms other methods and generates new images with 360{\deg}panoramic characteristics.
Direct Expected Quadratic Utility Maximization for Mean-Variance Controlled Reinforcement Learning

In real-world decision-making problems, risk management is critical. Among various risk management approaches, the mean-variance criterion is one of the most widely used in practice. In this paper, we suggest expected quadratic utility maximization (EQUM) as a new framework for policy gradient style reinforcement learning (RL) algorithms with mean-variance control. The quadratic utility function is a common objective of risk management in finance and economics. The proposed EQUM framework has several interpretations, such as reward-constrained variance minimization and regularization, as well as agent utility maximization. In addition, the computation of the EQUM framework is easier than that of existing mean-variance RL methods, which require double sampling. In experiments, we demonstrate the effectiveness of the proposed framework in the benchmarks of RL and financial data.
Adaptive Neural Layer for Globally Filtered Segmentation

This study is motivated by typical images taken during ultrasonic examinations in the clinic. Their grainy appearance, low resolution, and poor contrast demand an eye of a very qualified expert to discern targets and to spot pathologies. Training a segmentation model on such data is frequently accompanied by excessive pre-processing and image adjustments, with an accumulation of the localization error emerging due to the digital post-filtering artifacts and due to the annotation uncertainty. Each patient case generally requires an individually tuned frequency filter to obtain optimal image contrast and to optimize the segmentation quality. Thus, we aspired to invent an adaptive global frequency-filtering neural layer to "learn" optimal frequency filter for each image together with the weights of the segmentation network itself. Specifically, our model receives the source image in the spatial domain, automatically selects the necessary frequencies from the frequency domain, and transmits the inverse-transform image to the convolutional neural network for concurrent segmentation. In our experiments, such "learnable" filters boosted typical U-Net segmentation performance by 10% and made the training of other popular models (DenseNet and ResNet) almost twice faster. In our experiments, this trait holds both for two public datasets with ultrasonic images (breast and nerves), and for natural images (Caltech birds).