Miracle-dz/UniFAR-FTUs huggingface dataset:
Sound Event Detection Using Duration Robust Loss Function

Many methods of sound event detection (SED) based on machine learning regard a segmented time frame as one data sample to model training. However, the sound durations of sound events vary greatly depending on the sound event class, e.g., the sound event ``fan'' has a long time duration, while the sound event ``mouse clicking'' is instantaneous. The difference in the time duration between sound event classes thus causes a serious data imbalance problem in SED. In this paper, we propose a method for SED using a duration robust loss function, which can focus model training on sound events of short duration. In the proposed method, we focus on a relationship between the duration of the sound event and the ease/difficulty of model training. In particular, many sound events of long duration (e.g., sound event ``fan'') are stationary sounds, which have less variation in their acoustic features and their model training is easy. Meanwhile, some sound events of short duration (e.g., sound event ``object impact'') have more than one audio pattern, such as attack, decay, and release parts. We thus apply a class-wise reweighting to the binary-cross entropy loss function depending on the ease/difficulty of model training. Evaluation experiments conducted using TUT Sound Events 2016/2017 and TUT Acoustic Scenes 2016 datasets show that the proposed method respectively improves the detection performance of sound events by 3.15 and 4.37 percentage points in macro- and micro-Fscores compared with a conventional method using the binary-cross entropy loss function.
SimGANs: Simulator-Based Generative Adversarial Networks for ECG Synthesis to Improve Deep ECG Classification

Generating training examples for supervised tasks is a long sought after goal in AI. We study the problem of heart signal electrocardiogram (ECG) synthesis for improved heartbeat classification. ECG synthesis is challenging: the generation of training examples for such biological-physiological systems is not straightforward, due to their dynamic nature in which the various parts of the system interact in complex ways. However, an understanding of these dynamics has been developed for years in the form of mathematical process simulators. We study how to incorporate this knowledge into the generative process by leveraging a biological simulator for the task of ECG classification. Specifically, we use a system of ordinary differential equations representing heart dynamics, and incorporate this ODE system into the optimization process of a generative adversarial network to create biologically plausible ECG training examples. We perform empirical evaluation and show that heart simulation knowledge during the generation process improves ECG classification.
Weakly $\omega$-Categorified Models of Algebraic Theories

We provide the expected constructions of weakly $\omega$-categorified models (in the sense of Bressie) of the theory of groups and quandles which arise by replacing the homotopies used to give equivalence relations in the theory of fundamental groups, fundamental quandles, and knot quandles with homotopies of all orders used as arrows of categorical dimensions one and greater, and discuss other related constructions of weakly $\omega$-categorifed algebras.
Effects of Multiple-Enzyme Mixtures on Growth Performance of Broilers Fed Corn-Soybean Meal Diets

SUMMARY Two experiments were conducted under hot and cool seasons to investigate the effects of multiple-enzyme mixtures on the growth performance of broilers fed reduced energy corn-soybean meal diets. Broilers responded to enzyme supplementation with greater magnitude in the hot season (experiment 1) than in the cool season (experiment 2). Adding adequate activity levels of α-amylase, β-glucanase, and xylanase to broiler starter and grower corn-soybean diets with a 3% reduction in dietary ME allowed full restoration of growth performance of broilers comparable to those fed the adequate energy positive control diet regardless of season.
Dark Side of Weyl Gravity

We address the issue of a dynamical breakdown of scale invariance in quantum Weyl gravity together with related cosmological implications. In the first part, we build on our previous work [Phys. Rev. D2020, 101, 044050], where we found a non-trivial renormalization group fixed point in the infrared sector of quantum Weyl gravity. Here, we prove that the ensuing non-Gaussian IR fixed point is renormalization scheme independent. This confirms the feasibility of the analog of asymptotic safety scenario for quantum Weyl gravity in the IR. Some features, including non-analyticity and a lack of autonomy, of the system of β-functions near a turning point of the renormalization group at intermediate energies are also described. We further discuss an extension of the renormalization group analysis to the two-loop level. In particular, we show universal properties of the system of β-functions related to three couplings associated with C2 (Weyl square), G (Gauss–Bonnet), and R2 (Ricci curvature square) terms. Finally, we discuss various technical and conceptual issues associated with the conformal (trace) anomaly and propose possible remedies. In the second part, we analyze physics in the broken phase. In particular, we show that, in the low-energy sector of the broken phase, the theory looks like Starobinsky f(R) gravity with a gravi-cosmological constant that has a negative sign in comparison to the usual matter-induced cosmological constant. We discuss implications for cosmic inflation and highlight a non-trivial relation between Starobinsky’s parameter and the gravi-cosmological constant. Salient issues, including possible UV completions of quantum Weyl gravity and the role of the trace anomaly matching, are also discussed.
Dynamic Difficulty Adjustment via Fast User Adaptation

Dynamic difficulty adjustment (DDA) is a technology that adapts a game's challenge to match the player's skill. It is a key element in game development that provides continuous motivation and immersion to the player. However, conventional DDA methods require tuning in-game parameters to generate the levels for various players. Recent DDA approaches based on deep learning can shorten the time-consuming tuning process, but require sufficient user demo data for adaptation. In this paper, we present a fast user adaptation method that can adjust the difficulty of the game for various players using only a small amount of demo data by applying a meta-learning algorithm. In the video game environment user test (n=9), our proposed DDA method outperformed a typical deep learning-based baseline method.
Adai: Separating the Effects of Adaptive Learning Rate and Momentum Inertia

Adaptive Momentum Estimation (Adam), which combines Adaptive Learning Rate and Momentum, is the most popular stochastic optimizer for accelerating training of deep neural networks. But Adam often generalizes significantly worse than Stochastic Gradient Descent (SGD). It is still mathematically unclear how Adaptive Learning Rate and Momentum affect saddle-point escaping and minima selection. Based on the diffusion theoretical framework, we separate the effects of Adaptive Learning Rate and Momentum on saddle-point escaping and minima selection. We find that SGD escapes saddle points very slowly along the directions of small-magnitude eigenvalues of the Hessian. We prove that Adaptive Learning Rate can make learning dynamics near saddle points approximately Hessian-independent, but cannot select flat minima as SGD does. In contrast, Momentum provides a momentum drift effect to help passing through saddle points, and almost does not affect flat minima selection. This mathematically explains why SGD (with Momentum) generalizes better, while Adam generalizes worse but converges faster. Motivated by the diffusion theoretical analysis, we design a novel adaptive optimizer named Adaptive Inertia Estimation (Adai), which uses parameter-wise adaptive inertia to accelerate training and provably favors flat minima as much as SGD. Our real-world experiments demonstrate that Adai can converge similarly fast to Adam, but generalize significantly better. Adai even generalizes better than SGD, when converging fast to Adam is not required. The source is available to the public: \url{this https URL}.
Estimating the impact of fungal parasites on phytoplankton populations

A population-dynamic model is formulated to estimale the impact of chytrid and related fungal parasites on phytoplankton populations. The specific loss rate of uninfected host cells due to infection is used as a measure of the impact. Calculating this loss rate requires information about four parameters: prevalence of infection (i.e. the proportion of the host cells infected), development time of the sporangia of the parasite, specific growth rate of the uninfected host, and difference between loss rates of infected and uninfected host cells due to loss factors other than parasitism. When only cell counts of the host population are available instead of growth and loss rate values, the model can be used to calculate a minimum estimate of the impact of the parasite. The impact of the parasite at a given prevalence of infection can vary considerably as a consequence of environmental effects on the latter three parameters. Therefore, prevalence is not a good parameter to judge the severity of fungal epidemics. A phytoplankton population starts to decline when prevalence exceeds a certain threshold value. This critical prevalence of infection can be calculated with the model and is also highly dependent on external conditions. The model is applied to epidemics of the chytrid Rhizophydium planktonicum Canter emend. in ,a population of the diatom Asterionella formosa Hass. in Lake Maarsseveen 1. The end of both the spring and summer 1984 bloom of the diatom was caused by epidemics of tlhe chytrid. Model results suggested also that higher sinking rates, caused by an increasing proportion of dead cells in the colonies, resulted in an additional loss factor due to parasitism for this colcnrial diatom. Infection of planktonic algae with fungi, mostly (chytrids, is a common phenomenon in freshwaters (Sparrow 1960), and several studies have suggested that these parasites can have a considerable impact on the population dynamics of phytoplankton. Diatom and desmid populations in lakes of the English Lake District for instance may be largely controlled by fungal epidemics (Canter 1979). In Lake Constance, fungal parasitism was the ,major source of diatom mortality according to Sommer (1987), who suggested that fungal parasitism in general may be a particularly important factor for those algae that are fairly resistant to grazing by zooplankton, like the larger diatoms. Because most fungal phytoplankton parasites are specialists that infect only one or a few related host species, they may play an important role in the seasonal periodicity of
Number of leaves needed to model leaf area in jack bean plants using leaf dimensions = Número de folhas para modelar a área foliar de feijão de porco por dimensões foliares

Leaf area estimation models based on linear leaf dimensions are an important method because their application is not destructive to the leaves. For these models to be reliable, it is important that the estimation of model parameters is accurate, and for that to occur, the models must be generated using an adequate sample size (number of leaves). The objective of this study was to determine the number of leaves necessary to accurately model the leaf area of jack beans (Y), determined by digital photos, according to the width of the central leaflet (x), by a power model (Y = ax b ) generated through an iterative process. Accordingly, an experiment was performed in a 256 m 2 area. A total of 745 leaves were randomly collected at six different crop development stages (29, 43, 57, 73, 87 and 101 days after emergence). Each leaf was comprised of a left, central and right leaflet. The width of the central leaflet (x) was measured on the 745 leaves. Leaf area (sum of the area of the left, central and right leaflets; Y) was then determined using a digital photo method. The number of leaves necessary for the estimation of the parameters a and b and the coefficient of determination (R 2 ) of the power model were determined through resampling with replacement. The power model (Ŷ = 4.2049x 1.8215 , R 2 = 0.9701), based on the width of the central leaflet was determined to be adequate for estimating jack bean leaf area. Data collected from a sample of 200 leaves were determined to be sufficient for constructing an accurate power model for the leaf area of jack beans (Y) as a function of the width of the central leaflet (x), based on determinations of leaf area using digital photos.
The Heterogeneity Hypothesis: Finding Layer-Wise Dissimilated Network Architecture

In this paper, we tackle the problem of convolutional neural network design. Instead of focusing on the overall architecture design, we investigate a design space that is usually overlooked, \ie adjusting the channel configurations of predefined networks. We find that this adjustment can be achieved by pruning widened baseline networks and leads to superior performance. Base on that, we articulate the ``heterogeneity hypothesis'': with the same training protocol, there exists a layer-wise dissimilated network architecture (LW-DNA) that can outperform the original network with regular channel configurations under lower level of model complexity. The LW-DNA models are identified without added computational cost and training time compared with the original network. This constraint leads to controlled experiment which directs the focus to the importance of layer-wise specific channel configurations. Multiple sources of hints relate the benefits of LW-DNA models to overfitting, \ie the relative relationship between model complexity and dataset size. Experiments are conducted on various networks and datasets for image classification, visual tracking and image restoration. The resultant LW-DNA models consistently outperform the compared baseline models.
SMOS brightness temperature forward modelling, bias correction and long term monitoring at ECMWF

This report presents the European Centre for Medium-Range Weather Forecasts (ECMWF) radiative transfer modelling activities conducted to use Soil Moisture and Ocean Salinity (SMOS) brightness temperature observations for Numerical Weather Forecast (NWP) applications. The Community Microwave Emission Modelling Platform (CMEM) is used as the ECMWF SMOS forward operator to simulate L-band brightness temperatures (TBs). In a first part, simulated brightness temperature are compared to the observed SMOS near real time reprocessed brightness temperature product for 2010-2011 for several configurations of CMEM using different set of parameterisations. We show that simulated brightness temperatures are more sensitive to the choice of vegetation opacity and soil roughness models than to the dielectric model. Best configurations of CMEM are shown to be those using the so-called Wigneron vegetation opacity model with the simple empirical Wigneron soil roughness model. The Wang and Schmugge and the Mironov soil dielectric models perform similarly and lead to better agreement with SMOS observations than the Dobson dielectric model. Based on this intercomparison the configuration of CMEM retained for ECMWF SMOS forward modelling activities is the one based the Wang and Schmugge dielectric model, the Wigneron simple roughness model and the Wigneron vegetation model. In a second part, this paper presents the SMOS brightness temperature bias correction developed and used at ECMWF. It is a monthly Cumulative Distribution Function bias correction based on SMOS and ECMWF re-analysis-based brightness temperatures for the period from 1 January 2010 to 31 December 2013. Results show that it efficiently corrects for systematic differences between observations and model, with global root mean square differences (RMSD) and global mean bias for 2010-2013 for 30, ◦40 ◦, 50 ◦incidence angles decreasing from 16.7 K and -2.1 K before bias correction to 7.91K and 0.0016 K after bias correction, respectively. The monthly approach allows to correct for seasonal cycles systematic differences, with correlation values improved from 0.56 before bias correction and 0.62 after bias correction. Residual differences remaining after bias correction correspond to random differences between the model and observations which provide relevant information for monitoring and data assimilation purposes. Finally, in a third part long term monitoring of SMOS brightness temperature monitoring is presented covering a 7-year period 2010-2016 at both polarisations, at 40 degrees incidence angle. RMSD, correlation and anomaly correlation statistics show that SMOS and ECMWF reanalysis-based brightness temperature agreement steadily improves between 2010 and 2016, indicating improvement of SMOS products quality through the SMOS lifetime.
Squeezing metrology: a unified framework

Quantum metrology theory has up to now focused on the resolution gains obtainable thanks to the entanglement among N probes. Typically, a quadratic gain in resolution is achievable, going from the 1/sqrt(N) of the central limit theorem to the 1/N of the Heisenberg bound. Here we focus instead on quantum squeezing and provide a unified framework for metrology with squeezing, showing that, similarly, one can generally attain a quadratic gain when comparing the resolution achievable by a squeezed probe to the best N-probe classical strategy achievable with the same energy. Namely, here we give a quantification of the Heisenberg squeezing bound for arbitrary estimation strategies that employ squeezing. Our theory recovers known results (e.g.~in quantum optics and spin squeezing), but it uses the general theory of squeezing and holds for arbitrary quantum systems.
Extrapolation of compactness on weighted spaces II: Off-diagonal and limited range estimates.

In a previous paper by one of us, a ``compact version'' of Rubio de Francia's weighted extrapolation theorem was proved, which allows one to extrapolate the compactness of an operator from just one space to the full range of weighted spaces, where this operator is bounded. In this paper, we obtain generalizations of this extrapolation of compactness for operators that are bounded from one space to a different one (``off-diagonal estimates'') or only in a limited range of the $L^p$ scale. As applications, we easily recover recent results on the weighted compactness of commutators of fractional integrals and pseudo-differential operators, and obtain new results about the weighted compactness of Bochner--Riesz multipliers.
Spring wheat seed size and seeding rate effects on yield loss due to wild oat (Avena fatua) interference

Abstract The development of competitive cropping systems could minimize the negative effects of wild oat competition on cereal grain yield, and in the process, help augment herbicide use. A 3-yr field experiment was conducted at Kalispell, MT, to investigate the effects of spring wheat seed size and seeding rate on wheat spike production, biomass, and grain yield under a range of wild oat densities. Wheat plant density, spikes, biomass, and yield all increased as seed size and seeding rates increased. Averaged across all other factors, the use of higher seeding rates and larger seed sizes improved yields by 12 and 18%, respectively. Accordingly, grain yield was more highly correlated with seed size than with seeding rate effects. However, the combined use of both tactics resulted in a more competitive cropping system, improving grain yields by 30%. Seeding rate effects were related to spike production, whereas seed size effects were related to biomass production. As such, plants derived from large seed appear to have greater vigor and are able to acquire a larger share of plant growth factors relative to plants derived from small seed. Nomenclature: Wild oat, Avena fatua L. AVEFA; wheat, Triticum aestivum L. ‘McNeal’.
Molecular Characterization of Exotic and Indigenous Maize Inbreds for Biofortification with Kernel Carotenoids

Maize, consumed as food and feed, possesses a number of carotenoids for which ample genetic variability exists. Two of them, lutein and zeaxanthin, function as antioxidants while β-cryptoxanthin and β-carotene serve as precursors to vitamin A in human body. We report here genetic diversity in 48 diverse maize inbreds for their utilization in the biofortification program. While there was wide genetic variation for lutein (0.36–15.75 μg/g), zeaxanthin (0.25–22.76 μg/g), and β-carotene (0.07–17.41 μg/g), low variation was observed for β-cryptoxanthin (0.06–4.37 μg/g). Fifty-eight microsatellite markers having genome-wide distribution generated a total of 225 polymorphic alleles among the inbreds. Two to seven alleles with a mean of 3.88 alleles per marker locus were detected. A total of 35 rare alleles and 11 unique alleles were obtained. The polymorphism information content ranged from 0.14 to 0.75 with an average of 0.52. Mean pair-wise genetic dissimilarity of 0.74 revealed the diverse nature of the inbreds. Neighbor-joining based clustering pattern grouped the inbreds into six major clusters. Principal coordinate analysis also depicted the diverse origin of the genotypes consistent with their pedigree. The CIMMYT-HarvestPlus bred inbreds with high kernel β-carotene concentration were genetically distant from the inbreds of Indian origin. Based on genetic distance and carotenoid concentration, potential heterotic combinations have been identified for biofortification of lutein and zeaxanthin. The CIMMYT-HarvestPlus inbreds with high β-carotene can serve as potential donors of β-carotene hydroxylase (crtRB1) favorable allele for introgression into Indian maize inbreds through marker-assisted selection.
The Heterogeneous Helmholtz Problem with Spherical Symmetry: Green's Operator and Stability Estimates

We study wave propagation phenomena modelled in the frequency domain by the Helmholtz equation in heterogeneous media with focus on media with discontinuous, highly oscillating wave speed. We restrict to problems with spherical symmetry and will derive explicit representations of the Green’s operator and stability estimates which are explicit in the frequency and the wave speed.
Decay of two-dimensional quantum turbulence in binary Bose-Einstein condensates

We study two-dimensional quantum turbulence in miscible binary Bose-Einstein condensates in either a harmonic trap or a steep-wall trap through the numerical simulations of the Gross-Pitaevskii equations. The turbulence is generated through a Gaussian stirring potential. When the condensates have unequal intracomponent coupling strengths or asymmetric trap frequencies, the turbulent condensates undergo a dramatic decay dynamics to an interlaced array of vortex-antidark structures, a quasiequilibrium state, of like-signed vortices with an extended size of the vortex core. The time of formation of this state is shortened when the parameter asymmetry of the intracomponent couplings or the trap frequencies is enhanced. The corresponding spectrum of the incompressible kinetic energy exhibits two noteworthy features: (i) a ${k}^{\ensuremath{-}3}$ power law around the range of the wave number determined by the spin healing length (the size of the extended vortex core) and (ii) a flat region around the range of the wave number determined by the density healing length. The latter is associated with the small scale phase fluctuation relegated outside the Thomas-Fermi radius and is more prominent as the strength of intercomponent interaction approaches the strength of intracomponent interaction. We also study the impact of the intercomponent interaction to the cluster formation of like-signed vortices in an elliptical steep-wall trap, finding that the intercomponent coupling gives rise to the decay of the clustered configuration.