Miracle-dz/UniFAR-FTUs huggingface dataset:
Anthropogenic fire history and red oak forests in south-central Ontario

The regeneration and dominance of northern red oak (Quercus rubra L.) has been associated with fire throughout eastern North Arnerica. Red oak in central Ontario grows near the northern edge of its distribution in mixed hardwood-coniferous forests under mesic conditions where it competes with more shade-tolerant species. We hypothesized that the abundance of red oak in these stands was largely the result of anthropogenic burning and natural fires, which would favor the regeneration and recruitment of northern red oak over such shade-tolerant species as sugar maple (Acer saccharurn Marsh.). Fire histories dating from the mid-1600s were constructed by dendrochronological methods from fire scars on stumps, trees, and natural remnants of red pine (Pinus resinosa Ait.), white pine (Pinus strobus L.), and red oak at six sites in south-central Ontario. Fire histories of the sites are characterized by abrupt changes in fire interval. As much or more variance in fire interval is found within sites as is found among sites. Differences in the mean fue interval among sites are related to the density and migration of historic aboriginal and European populations. The mean fire interval varied from more than 70 years to six years depending on site location and historic period. The occurrence and abundance of red oak is linked to anthropogenic fire regimes.
Association between Prediabetes and Renal Dysfunction from a Community-based Prospective Study

Objective: Our study aimed to evaluate the association between prediabetes and renal dysfunction, and further assess which of glycemic indices of fasting plasma glucose (FPG), postprandial plasma glucose (PPG) and hemoglobin A1c (HbA1c) has a higher risk of renal dysfunction. Methods: This was a community-based prospective cohort study, which included 7015 participants from Beijing and Taian between May and October in 2015. The outcome was the renal dysfunction defined as estimated glomerular filtration rate (eGFR)<60 mL/min/1.73 m2. Univariate and multivariate logistic regression model was performed, and calculated the odds ratio (OR) and 95% confidence interval (95%CI) of renal dysfunction. Receiver operating curve (ROC) analysis was used to predict renal dysfunction for glycemic indices. Results: 121 renal dysfunction cases were identified. We found that the adjusted ORs (95%CI) of renal dysfunction were 1.72 (1.11-2.38), 1.48 (1.09-1.93), 1.97 (1.27-2.89) and 1.35 (1.07-2.13), respectively, for those with prediabetes, impaired fasting glucose (IFG), impaired glucose tolerance (IGT) and elevated HbA1c, compared with individuals with normal glucose tolerance. And IGT presented a higher risk of renal dysfunction than other glycemic indices. The similar results were obtained by performing the subgroup analysis. ROC analysis revealed the PPG had a higher predictive value for renal dysfunction. Conclusion: We found prediabetes was positively associated with the risk of renal dysfunction and PPG had a higher risk and predictive value of renal dysfunction than other glycemic indices of FPG and HbA1c.
Postcopulatory, prezygotic reproductive isolation

Prezygotic reproductive isolation has traditionally been studied from a precopulatory perspective even though postcopulatory events have been known to influence success at fertilization. Postcopulatory, prezygotic reproductive isolation (gametic isolation) has received relatively little attention mainly because the focus of research has been on the events that occur either earlier (pre-mating) or later (postzygotic) in the chain of events that lead to successful reproduction. However, recent evidence from an array of taxa from sea urchins to beetles reveals that postejaculatory, prezygotic events may be an important factor in reproductive isolation. Such a mechanism may be the driving force behind speciation in some taxa, such as free-spawning marine invertebrates, whilst it may complement pre-mating and postzygotic mechanisms in others. In this paper we draw an analogy between sperm–egg interactions and classical sexual selection theory and argue that gametic incompatibility may arise through cryptic female choice.
The effects of climate variability on zooplankton and basking shark (Cetorhinus maximus) relative abundance off southwest Britain

At small spatial scales basking sharks (Cetorhinus maximus) forage selectively on zooplankton along thermal fronts, but the factors influencing broader scale patterns in their abundance and distribution remain largely unknown. Using long-term sightings data collected off southwest Britain between 1988 and 2001, we show that the number of basking sharks recorded was highly correlated with abiotic factors, principally sea surface temperature (SST) and the lagged effect of SST in the previous month, but only very weakly to zooplankton density. This suggests that the changes in number of basking sharks recorded over large spatio-temporal scales are more closely related to the availability of climatedriven thermal resources than prey availability, whereas the converse is supported by previous studies at local scales. Taken together, these results imply scale-dependent behavioural responses in basking sharks, with small-scale foraging movements linked by broad scale responses to temperature variation.
Lookahead Adversarial Semantic Segmentation

Semantic segmentation is one of the most fundamental problems in computer vision with significant impact on a wide variety of applications. Adversarial learning is shown to be an effective approach for improving semantic segmentation quality by enforcing higher-level pixel correlations and structural information. However, state-of-the-art semantic segmentation models cannot be easily plugged into an adversarial setting because they are not designed to accommodate convergence and stability issues in adversarial networks. We bridge this gap by building a conditional adversarial network with a state-of-the-art segmentation model (DeepLabv3+) at its core. To battle the stability issues, we introduce a novel lookahead adversarial learning (LoAd) approach with an embedded label map aggregation module. We demonstrate that the proposed solution can alleviate divergence issues in an adversarial semantic segmentation setting and results in considerable performance improvements (+5% in some classes) on the baseline for three standard datasets.
Rumor Source Detection With Multiple Observations Under Adaptive Diffusions

Recent work, motivated by anonymous messaging platforms, has introduced adaptive diffusion protocols which can obfuscate the source of a rumor: a “snapshot adversary” with access to the subgraph of “infected” nodes can do no better than randomly guessing the entity of the source node. What happens if the adversary has access to multiple independent snapshots? We study this question when the underlying graph is the infinite $d$-regular tree. We show that (1) a weak form of source obfuscation is still possible in the case of two independent snapshots, but (2) already with three observations there is a simple algorithm that finds the rumor source with constant probability, regardless of the adaptive diffusion protocol. We also characterize the tradeoff between local spreading and source obfuscation for adaptive diffusion protocols (under a single snapshot). These results raise questions about the robustness of anonymity guarantees when spreading information in social networks.
Social Context Modulates Predator Evasion Strategy In Guppies

Social context is a powerful mediator of behavioral decisions across animal taxa, as the presence of conspecifics comes with both costs and benefits. In risky situations, the safety conferred by the presence of conspecifics can outweigh the costs of competition for resources. How the costs and benefits of grouping influence decisions among alternative antipredator behaviors remains largely unexplored. We took advantage of the Trinidadian guppy (Poecilia reticulata) to examine the influence of social context on alternative behavioral responses to threats. We compared the frequency of active (startle) versus passive (freeze) responses to sudden acoustic stimuli in the presence and absence of conspecifics. We found that fish were relatively less likely to startle and more likely to freeze when in a group than when alone, indicating that immediate social context modulates predator evasion strategy in guppies. We suggest that these social context-dependent changes reflect trade-offs between survival and energy expenditure. To our knowledge, an effect of immediate social environment on startle probability has not been previously demonstrated in a teleost.
Comparative cranial morphology of douc langurs

The study describes the cranial morphology of the three species of douc langurs, Pygathrix cinerea, P. nemaeus and P. nigripes. The variability of some features could be analysed on 24 skulls of P. cinerea, 18 skulls of P. nemaeus, and 4 skulls of P. nigripes. 28 linear measurements were taken on all skulls for statistical analyses. As the skulls clearly change with age, correlations between each measurement and age have to be eliminated from statistical analyses. Therefore, age-independent residuals were calculated from all linear measurements in linear regressions against the age class. Morphologically the skulls of the three species of Pygathrix can not be clearly separated from each other by naked eye or simple measurements. Student's t-tests revealed only few statistically significant differences between the species but none in the age-independent residuals between P. cinerea and P. nemaeus. Discriminant analyses of the age-independent residuals indicated the possibility to separate the three species of Pygathrix.
Pedestrian Detection using Linear SVM Classifier with HOG Features

: Detecting pedestrians in an image is a useful technique in the development of the Intelligent Transportation System (ITS). Histogram of Oriented Gradient (HOG) is widely used as a feature algorithm and treated the entire body of the human as a single feature. In this paper, we compared the well-known feature based approaches: Haar features and HOG features in PSU pedestrian dataset under complex backgrounds, wide illuminations, large variation on pose and clothing. In practice, there are many complex movements and backgrounds in the real world environments. The experiment results show that a rich feature set supports the better detection performance. The performance of the detection results show that the accuracy of the HOG training model is 45.93% which is much better than Haar training model. Our proposed method can also apply to detect human in the real-world environment.
Pathogenicity of Mutations Discovered in BRCA1 BRCT Domains is Characterized by Destabilizing the Hydrophobic Interactions

Background: Breast and ovarian cancer are the most common cancers among hetrogenetically diversified women. It is quite difficult to categorize the population at a high risk of breast cancer using peer genetic information because one particular mutation can be found in the same or in different families. Several mutations have been discovered across the full length of BRCA1 gene, and categorizing their pathogenicity is a major challenge. Carriers of BRCA1 mutations have an increased risk of developing cancer. In the breast cancer database BIC, approximately 1,500 genetic variants have been reported. It is very difficult to characterize each of the reported mutations. Given the complexities in characterizing the mutations, we decided to investigate functional basis associated with the mutations, rather than looking at each mutation. Materials and methods: BRCA1 BRCT domains were cloned, expressed, and purified using e-coli bacterial expression system. Mutations were generated using site-directed mutagenesis techniques, and all the mutations were sequence verified. The secondary structure of the mutant was characterized by Circular dichroism (CD) and Fluorescence spectroscopy. Molecular dynamics simulations were performed using Desmond software. Hydrophobic interactions and hydrogen bonding of docked molecules were compared using the LigPlot program. Results: Genetic mutations were discovered throughout BRCA1, and most of the pathogenic mutations were buried in the hydrophobic core and destabilized the BRCA1 BRCT domain. This unstable BRCT domain destabilized the full-length BRCA1, resulting in a loss of function. We conclude that the pathogenicity of each of the mutations in the BRCT domain can be categorized on the basis of its ability to destabilize the hydrophobic interactions. Although such instability is not sufficient to predispose someone to cancer, it provides a basis for formulating a concept for genetic counseling and targeted therapy.
Melting Snow from Passive Microwave Observations for a Microwave/Visible Blended Product: First Results

Snow cover is a key component of the Earth’s energy balance and a key storage mechanism for water. In many areas of the world, people rely on snowmelt runoff for their water resources. It is essential that the extent and water content of seasonal snow cover be determined to a degree of accuracy concomitant with the instrumentation available. The ability to characterize snow storage more accurately at the drainage basin scale is crucial for improved water resource management. Global patterns of snow storage are also key components to the understanding of global change. A project aimed at providing snow cover fraction and area, snow water equivalent (SWE), snowmelt onset and snow albedo for the first time into a single, user-friendly product is currently being carried out by the Goddard Snow Team (GoST) at the Goddard Space Flight Center, in Greenbelt, MD, USA, and funded by the Air Force Weather Agency and NASA. In this study, we show first results of an algorithm blending existing snow products to map snow extent and SWE, and using passive-microwave data to map areas of actively-melting snow as derived from the Advanced Microwave Scanning Radiometer (AMSR-E) brightness temperatures at 0.25 degree resolution. Melting snow is detected by means of threshold values on both brightness temperature values and relative differences between daytime and nighttime values (diurnal amplitude variations, DAV). Microwave and visible data are re-projected into the polar stereographic projection with a resolution of 0.25 degrees and co-registered for the generation of the blended product. First results obtained over a test area in the northern United States, and Canada are reported for the period February – April, 2003. We found that the number of melting days between February and April, 2003 ranged between 2 and 16 days. Also, we identified two distinct melting events on February 22 and February 26, 2003 over a test area located nearby the Great Lakes.
Gradient Descent in RKHS with Importance Labeling

Labeling cost is often expensive and is a fundamental limitation of supervised learning. In this paper, we study importance labeling problem, in which we are given many unlabeled data and select a limited number of data to be labeled from the unlabeled data, and then a learning algorithm is executed on the selected one. We propose a new importance labeling scheme and analyse the generalization error of gradient descent combined with our labeling scheme in least squares regression in Reproducing Kernel Hilbert Spaces (RKHS). We show that the proposed importance labeling leads to much better generalization ability than uniform one under near interpolation settings. Numerical experiments verify our theoretical findings.
An analytic theory of shallow networks dynamics for hinge loss classification

Neural networks have been shown to perform incredibly well in classification tasks over structured high-dimensional datasets. However, the learning dynamics of such networks is still poorly understood. In this paper we study in detail the training dynamics of a simple type of neural network: a single hidden layer trained to perform a classification task. We show that in a suitable mean-field limit this case maps to a single-node learning problem with a time-dependent dataset determined self-consistently from the average nodes population. We specialize our theory to the prototypical case of a linearly separable data and a linear hinge loss, for which the dynamics can be explicitly solved in the infinite dataset limit. This allows us to address in a simple setting several phenomena appearing in modern networks such as slowing down of training dynamics, crossover between rich and lazy learning, and overfitting. Finally, we assess the limitations of mean-field theory by studying the case of large but finite number of nodes and of training samples.
A Computational Multicriteria Optimization Approach to Controller Design for Physical Human-Robot Interaction

Physical human-robot interaction (pHRI) integrates the benefits of human operator and a collaborative robot in tasks involving physical interaction, with the aim of increasing the task performance. However, the design of interaction controllers that achieve safe and transparent operations is challenging, mainly due to the contradicting nature of these objectives. Knowing that attaining perfect transparency is practically unachievable, controllers that allow better compromise between these objectives are desirable. In this article, we propose a multicriteria optimization framework, which jointly optimizes the stability robustness and transparency of a closed-loop pHRI system for a given interaction controller. In particular, we propose a Pareto optimization framework that allows the designer to make informed decisions by thoroughly studying the tradeoff between stability robustness and transparency. The proposed framework involves a search over the discretized controller parameter space to compute the Pareto front curve and a selection of controller parameters that yield maximum attainable transparency and stability robustness by studying this tradeoff curve. The proposed framework not only leads to the design of an optimal controller, but also enables a fair comparison among different interaction controllers. In order to demonstrate the practical use of the proposed approach, integer and fractional order admittance controllers are studied as a case study and compared both analytically and experimentally. The experimental results validate the proposed design framework and show that the achievable transparency under fractional order admittance controller is higher than that of integer order one, when both controllers are designed to ensure the same level of stability robustness.
Certified Randomness from Bell's Theorem and Remote State Preparation Dimension Witness

Randomness can be device-independently certified from a set of experimental data by Bell's theorem without placing assumptions about the experimental devices. The certification procedure in previous studies underestimated the generated randomness, due to a non-optimal lower bound of the entropy. We solve this by developing an analytic upper bound for the joint outcome probability p(ab|xy), and show that the lower the Bell inequality violation value is, the more advantageous the use of the joint outcome probability becomes. In the same experimental data, when a few general assumptions are introduced to characterize the experimental setups, substantially more semi-device-independent randomness can be certified by a witness we call remote state preparation dimension witness without using Bell's theorem. This is one important step towards practical use.
Effects of synthetic plant growth retardants and abscisic acid on root functions of Brassica rapa plants exposed to low root-zone temperature

SUMMARY Possible interactions of two synthetic plant-growth retardants during the short-term response of Brassica rapa L. ssp. oleifera (DC.) Metzger plants to low root-zone temperature were investigated by pretreating with mefluidide or paclobutrazol. Water and solute transfers were studied by measuring xylem sap volume flow (under root pressure exudation) and ion flow from the roots. Relations with nitrate uptake rate were also considered. Root pretreatment with paclobutrazol strongly restricted the cold-inducible processes which normally restore water and solute flow from the root xylem. Paclobutrazol decreased the rates of nitrate uptake and exudation flow from the root xylem (principally by reducing root hydraulic conductivity) with dramatic consequences for ion flow, especially that of nitrate. The effects of root ABA pretreatment on plant response to root cooling were then studied separately or in association with a pretreatment with paclobutrazol. Despite a slight decrease in nitrate uptake rate, ABA pretreatment of the roots enabled the plant to develop rapid mechanisms for adaptation to cold constraint at the root level. Moreover, this action of exogenous ABA greatly reduced the effect of a simultaneous paclobutrazol pretreatment and partly restored water and solute flows. Thus, the improvement of plant resistance to cold conditions brought about by treatments with mefluidide and paclobutrazol (previously shown in long-term experiments) cannot simply be explained by their short-term effects.
High Accuracy SGD Using Low-Precision Arithmetic and Variance Reduction (for Linear Models)

Stochastic gradient descent (SGD) is a popular algorithm for solving optimization problems, many of which originate from the training of linear models. The performance of SGD is dependent upon its ability to efficiently process large-scale data sets. With this dependency in mind, we consider a new SGD-style algorithm, referred to as HALP, which utilizes low-precision arithmetic and variance reduction techniques. We are interested in this algorithm because of its potential to unlock the benefits of low-precision arithmetic, such as increased computational throughput and a reduced memory foot-print, without being limited by quantization error or a constrained representational domain. To begin to benchmark these benefits on CPUs, we implement an 8-bit version of this algorithm, and run preliminary experiments measuring its convergence per iteration and wall-clock time.
UK COVID-19 Lockdown: What are the impacts on air pollution

A country-wide `lock-down' imposed on the 23red March 2020 in the UK had a significant impact on the UK's anthropogenic movements. The closure of work-places and restrictions imposed on visiting friends and family has radically reduced the amount of traffic on the roads. In this short communication, we use data from UK air-quality sensors to quantify air pollution trends pre- and post-lock-down. While we detect large falls in nitrogen dioxide at levels not seen over the last decade, trends in other pollutants were mixed especially when compared to historical data. It suggests that the implication that lock-down was beneficial for the environment was not so obvious.
Minimax rates without the fixed sample size assumption.

We generalize the notion of minimax convergence rate. In contrast to the standard definition, we do not assume that the sample size is fixed in advance. Allowing for varying sample size results in time-robust minimax rates and estimators. These can be either strongly adversarial, based on the worst-case over all sample sizes, or weakly adversarial, based on the worst-case over all stopping times. We show that standard and time-robust rates usually differ by at most a logarithmic factor, and that for some (and we conjecture for all) exponential families, they differ by exactly an iterated logarithmic factor. In many situations, time-robust rates are arguably more natural to consider. For example, they allow us to simultaneously obtain strong model selection consistency and optimal estimation rates, thus avoiding the "AIC-BIC dilemma".