Miracle-dz/UniFAR-FTUs huggingface dataset:
Crop Aggregating for short utterances speaker verification using raw waveforms

Most studies on speaker veriﬁcation systems focus on long-duration utterances, which are composed of sufﬁcient phonetic information. However, the performances of these systems are known to degrade when short-duration utterances are inputted due to the lack of phonetic information as compared to the long utterances. In this paper, we propose a method that compensates for the performance degradation of speaker veriﬁcation for short utterances, referred to as “crop aggregating” . The proposed method adopts an ensemble-based design to improve the stability and accuracy of speaker veriﬁcation systems. The proposed method segments an input utterance into several short utterances and then aggregates the segment embeddings extracted from the segmented inputs to compose a speaker embedding. Then, this method simultaneously trains the segment embeddings and the aggregated speaker embedding. In addition, we also modiﬁed the teacher-student learning method for the proposed method. Experimental results on different input duration using the VoxCeleb1 test set demonstrate that the proposed technique improves speaker veriﬁcation performance by about 45.37% relatively compared to the baseline system with 1-second test utterance condition.
Scale-Equalizing Pyramid Convolution for Object Detection

Feature pyramid has been an efficient method to extract features at different scales. Development over this method mainly focuses on aggregating contextual information at different levels while seldom touching the inter-level correlation in the feature pyramid. Early computer vision methods extracted scale-invariant features by locating the feature extrema in both spatial and scale dimension. Inspired by this, a convolution across the pyramid level is proposed in this study, which is termed pyramid convolution and is a modified 3-D convolution. Stacked pyramid convolutions directly extract 3-D (scale and spatial) features and outperforms other meticulously designed feature fusion modules. Based on the viewpoint of 3-D convolution, an integrated batch normalization that collects statistics from the whole feature pyramid is naturally inserted after the pyramid convolution. Furthermore, we also show that the naive pyramid convolution, together with the design of RetinaNet head, actually best applies for extracting features from a Gaussian pyramid, whose properties can hardly be satisfied by a feature pyramid. In order to alleviate this discrepancy, we build a scale-equalizing pyramid convolution (SEPC) that aligns the shared pyramid convolution kernel only at high-level feature maps. Being computationally efficient and compatible with the head design of most single-stage object detectors, the SEPC module brings significant performance improvement (>4AP increase on MS-COCO2017 dataset) in state-of-the-art one-stage object detectors, and a light version of SEPC also has ~3.5AP gain with only around 7% inference time increase. The pyramid convolution also functions well as a stand-alone module in two-stage object detectors and is able to improve the performance by ~2AP. The source code can be found at https://github.com/jshilong/SEPC.
Sequential aggregation of probabilistic forecasts—Application to wind speed ensemble forecasts

In numerical weather prediction (NWP), the uncertainty about the future state of the atmosphere is described by a set of forecasts (called an ensemble). All ensembles have deficiencies that can be corrected via statistical post‐processing methods. Several ensembles, based on different NWP models, exist and may be corrected using different statistical methods. These raw or post‐processed ensembles can thus be combined. The theory of prediction with expert advice allows us to build combination algorithms with theoretical guarantees on the forecast performance. We adapt this theory to the case of probabilistic forecasts issued as stepwise cumulative distribution functions, computed from raw and post‐processed ensembles. The theory is applied to combine wind speed ensemble forecasts. The second goal of this study is to explore the use of two forecast performance criteria: the continuous ranked probability score (CRPS) and the Jolliffe–Primo test. The usual way to build skilful probabilistic forecasts is to minimize the CRPS. Minimizing the CRPS may not produce reliable forecasts according to the Jolliffe–Primo test. The Jolliffe–Primo test generally selects reliable forecasts, but could lead to issuing suboptimal forecasts in terms of CRPS. We propose to use both criteria to achieve reliable and skilful probabilistic forecasts.
Lorentz estimates for quasilinear elliptic double obstacle problems involving a Schrödinger term

Our goal in this article is to study the global Lorentz estimates for gradient of weak solutions to p‐Laplace double obstacle problems involving the Schrödinger term: −Δpu+𝕍|u|p−2u with bound constraints ψ1 ≤ u ≤ ψ2 in nonsmooth domains. This problem has its own interest in mathematics, engineering, physics, and other branches of science. Our approach makes a novel connection between the study of Calderón‐Zygmund theory for nonlinear Schrödinger type equations and variational inequalities for double obstacle problems.
Optical spectroscopy and X-ray observations of the D-type symbiotic star EF Aql

We performed high-resolution optical spectroscopy and X-ray observations of the recently identified Mira-type symbiotic star EF Aql. Based on high-resolution optical spectroscopy obtained with the Southern African Large Telescope (SALT), we determine the temperature (∼55 000 K) and the luminosity (∼5.3 L⊙) of the hot component in the system. The heliocentric radial velocities of the emission lines in the spectra reveal possible stratification of the chemical elements. We also estimate the mass-loss rate of the Mira donor star. Our Swift observation did not detect EF Aql in X-rays. The upper limit of the X-ray observations is 10−12 erg cm−2 s−1, which means that EF Aql is consistent with the faintest X-ray systems detected so far. Otherwise we detected it with the UltraViolet and Optical Telescope (UVOT) instrument with an average UVM2 magnitude of 14.05. During the exposure, EF Aql became approximately 0.2 UVM2 magnitudes fainter. The periodogram analysis of the V-band data reveals an improved period of 320.4 ± 0.3 d caused by the pulsations of the Mira-type donor star.
Effect of Addition of Different Acetic Acid Concentrations on the Quality of Marinated Herring

This study investigated the effect of acetic acid concentrations from 3 to 8% on the physicochemical and sensory quality of marinated Atlantic and Baltic herring from fresh and frozen-thawed material. The increased concentration of acetic acid was found to exert a strong impact on decreased sensory scores, weight yield, and pH value as well as on an increased brightness parameter of meat. In turn, moisture, protein hydrolysis products (PHB), total volatile bases (TVB) content, and hardness parameter were affected by the higher addition of acid to a lesser extent. Enhanced protein hydrolysis was observed only at acetic acid concentration of 5% or 5–8%, whereas there was a decrease in the content of TVB only at an acid concentration of up to 4–5%. The marinated frozen-thawed fillets, compared to the fresh ones, required a greater addition of acid in the brine, by 1.5%, to reach a similar pH value. The freezing of fish contributed also to a decrease of PHB and free hydroxyproline, along with an increased concentration of acetic acid. The marinades from frozen-thawed herring contained more TVB, had worse color, and hardness parameters, as well as less yield.
Noisy Differentiable Architecture Search

Simplicity is the ultimate sophistication. Differentiable Architecture Search (DARTS) has now become one of the mainstream paradigms of neural architecture search. However, it largely suffers from several disturbing factors of optimization process whose results are unstable to reproduce. FairDARTS points out that skip connections natively have an unfair advantage in exclusive competition which primarily leads to dramatic performance collapse. While FairDARTS turns the unfair competition into a collaborative one, we instead impede such unfair advantage by injecting unbiased random noise into skip operations' output. In effect, the optimizer should perceive this difficulty at each training step and refrain from overshooting on skip connections, but in a long run it still converges to the right solution area since no bias is added to the gradient. We name this novel approach as NoisyDARTS. Our experiments on CIFAR-10 and ImageNet attest that it can effectively break the skip connection's unfair advantage and yield better performance. It generates a series of models that achieve state-of-the-art results on both datasets. Code will be made available at this https URL.
Deeply Supervised Active Learning for Finger Bones Segmentation

Segmentation is a prerequisite yet challenging task for medical image analysis. In this paper, we introduce a novel deeply supervised active learning approach for finger bones segmentation. The proposed architecture is fine-tuned in an iterative and incremental learning manner. In each step, the deep supervision mechanism guides the learning process of hidden layers and selects samples to be labeled. Extensive experiments demonstrated that our method achieves competitive segmentation results using less labeled samples as compared with full annotation.Clinical relevance— The proposed method only needs a few annotated samples on the finger bones task to achieve comparable results in comparison with full annotation, which can be used to segment finger bones for medical practices, and generalized into other clinical applications.
Fractional ridge regression: a fast, interpretable reparameterization of ridge regression

Abstract Background Ridge regression is a regularization technique that penalizes the L2-norm of the coefficients in linear regression. One of the challenges of using ridge regression is the need to set a hyperparameter (α) that controls the amount of regularization. Cross-validation is typically used to select the best α from a set of candidates. However, efficient and appropriate selection of α can be challenging. This becomes prohibitive when large amounts of data are analyzed. Because the selected α depends on the scale of the data and correlations across predictors, it is also not straightforwardly interpretable. Results The present work addresses these challenges through a novel approach to ridge regression. We propose to reparameterize ridge regression in terms of the ratio γ between the L2-norms of the regularized and unregularized coefficients. We provide an algorithm that efficiently implements this approach, called fractional ridge regression, as well as open-source software implementations in Python and matlab (https://github.com/nrdg/fracridge). We show that the proposed method is fast and scalable for large-scale data problems. In brain imaging data, we demonstrate that this approach delivers results that are straightforward to interpret and compare across models and datasets. Conclusion Fractional ridge regression has several benefits: the solutions obtained for different γ are guaranteed to vary, guarding against wasted calculations; and automatically span the relevant range of regularization, avoiding the need for arduous manual exploration. These properties make fractional ridge regression particularly suitable for analysis of large complex datasets.
Activity of a specialized insectivorous mammal (Myrmecophaga tridactyla) in the Pantanal of Brazil

The purpose of this study was to correlate the activity and habitat use of a large and ecologically specialized mammal (Myrmecophaga tridactyla) with the time of day and minimum daily temperature in Brazil's Pantanal wetland. Seven giant anteaters were fitted with very high frequency (VHF) radio transmitters and monitored from March to December 2001. The animal's state of activity or rest, time and ambient temperature were recorded in different patches of the study site, which were covered by a mosaic of vegetation types and several ponds. Whenever a giant anteater was inside a forest patch, the temperature inside and outside of the patch was also recorded. The temperatures throughout the day were recorded by the meteorological station at the study site. The period of greatest activity of giant anteaters started at 18:00 h on hot days, but whenever the daily minimum temperature declined, the species' activity began earlier. Giant anteaters use predominantly forest habitats for rest and open habitats for activity. The forest patches work as a temperature buffer, because they are cooler than the matrix of open habitats during the hot hours of the day and warmer than the matrix during the cold hours. Although the giant anteater is known to be associated with open habitats, this study indicates the need of preserving forest patches for the conservation of this species.
Effect of Binarity in Star Cluster Dynamical Mass Determination

In this paper we explore the effects that the presence of a fraction of binary stars has in the determination of a star cluster mass via the virial theorem. To reach this aim in an accurate and consistent way, we run a set of simulations using the direct summation, high precision, code NBODY7. By means of this suite of simulations we are able to quantify the overestimate of open-star-cluster-like models’ dynamical masses when making a straight application of the virial theorem using available position and radial velocity measurements. The mass inflation caused by the binary “heating” contribution to the measured velocity dispersion depends, of course, on the initial binary fraction, fb0 and its following dynamical evolution. For an fb (evolved up to 1.5 Gyr) in the range 8%–42% the overestimate of the mass done using experimentally sounding estimates for the velocity dispersion can be up to a factor of 45. We provide a useful fitting formula to correct the dynamical mass determination for the presence of binaries, and underline how neglecting the role of binaries in stellar systems might lead to erroneous conclusions about their total mass budget. If this trend remains valid for larger systems like dwarf spheroidal galaxies, which are still far out of reach for high-precision dynamical simulations taking their binaries into account, it would imply an incorrect overestimation of their dark matter content, as inferred by means of available velocity dispersion measurements.
Fine-Grained Analysis of Cross-Linguistic Syntactic Divergences

The patterns in which the syntax of different languages converges and diverges are often used to inform work on cross-lingual transfer. Nevertheless, little empirical work has been done on quantifying the prevalence of different syntactic divergences across language pairs. We propose a framework for extracting divergence patterns for any language pair from a parallel corpus, building on Universal Dependencies. We show that our framework provides a detailed picture of cross-language divergences, generalizes previous approaches, and lends itself to full automation. We further present a novel dataset, a manually word-aligned subset of the Parallel UD corpus in five languages, and use it to perform a detailed corpus study. We demonstrate the usefulness of the resulting analysis by showing that it can help account for performance patterns of a cross-lingual parser.
Safe Data-Driven Distributed Coordination of Intersection Traffic

This work addresses the problem of traffic management at and near an isolated un-signalized intersection for autonomous and networked vehicles through coordinated optimization of their trajectories. We decompose the trajectory of each vehicle into two phases: the provisional phase and the coordinated phase. A vehicle, upon entering the region of interest, initially operates in the provisional phase, in which the vehicle is allowed to optimize its trajectory but is constrained to guarantee in-lane safety and to not enter the intersection. Periodically, all the vehicles in their provisional phase switch to their coordinated phase, which is obtained by coordinated optimization of the schedule of the vehicles' intersection usage as well as their trajectories. For the coordinated phase, we propose a data-driven solution, in which the intersection usage order is obtained through a data-driven online "classification" and the trajectories are computed sequentially. This approach is computationally very efficient and does not compromise much on optimality. Moreover, it also allows for incorporation of "macro" information such as traffic arrival rates into the solution. We also discuss a distributed implementation of this proposed data-driven sequential algorithm. Finally, we compare the proposed algorithm and its two variants against traditional methods of intersection management and against some existing results in the literature by micro-simulations.
Linear Independence of Generalized Poincaré Series for Anti-de Sitter 3-Manifolds

Let $\Gamma$ be a discrete group acting properly discontinuously and isometrically on the three-dimensional anti-de Sitter space $\mathrm{AdS}^{3}$, and $\square$ the Laplacian which is a second-order hyperbolic differential operator. We study linear independence of a family of generalized Poincare series introduced by Kassel-Kobayashi [Adv. Math. 2016], which are defined by the $\Gamma$-average of certain eigenfunctions on $\mathrm{AdS}^{3}$. We prove that the multiplicities of $L^{2}$-eigenvalues of the hyperbolic Laplacian $\square$ on $\Gamma\backslash\mathrm{AdS}^{3}$ are unbounded when $\Gamma$ is finitely generated. Moreover, we prove that the multiplicities of \textit{stable $L^{2}$-eigenvalues} for compact anti-de Sitter $3$-manifolds are unbounded.
Seismic Shot Gather Noise Localization Using a Multi-Scale Feature-Fusion-Based Neural Network

Deep learning-based models, such as convolutional neural networks, have advanced various segments of computer vision. However, this technology is rarely applied to seismic shot gather noise localization problem. This letter presents an investigation on the effectiveness of a multi-scale feature-fusion-based network for seismic shot-gather noise localization. Herein, we describe the following: (1) the construction of a real-world dataset of seismic noise localization based on 6,500 seismograms; (2) a multi-scale feature-fusion-based detector that uses the MobileNet combined with the Feature Pyramid Net as the backbone; and (3) the Single Shot multi-box detector for box classification/regression. Additionally, we propose the use of the Focal Loss function that improves the detector's prediction accuracy. The proposed detector achieves an AP@0.5 of 78.67\% in our empirical evaluation.
Elk habitat selection on the clearwater national forest, Idaho

Habitat management for bull and cow elk (Cervus elaphus nelsoni) may require different forest management standards because of likely sexual differences in distribution and habitat selection patterns. Current standards are based on the habitat use patterns of cow elk. Thus, we located 121 radiocollared elk (101 bulls, 20 cows) 4,527 times in the forested habitats of northcentral Idaho during 1986-90 to determine patterns of habitat selection. During winter, habitat selection patterns of ≥2-year-old and yearling bull elk were similar. but cow elk used more shrub habitats and less-open timber types. Cows typically used moderately steep areas on south-facing to west-facing aspects on the middle to lower elevational portions of the winter range. Bulls were more often found using small benches or ridgetop areas near the upper portion of hillsides. From spring through fall, elk shifted from using a high proportion of shrub and open timber habitats to use of timber habitats. In general, elk in areas with roads used habitats with greater canopy cover. This pattern was most pronounced for cow and ≥2-year-old bull elk. Yearling bulls tended to select habitats in proportion to availability, whereas cow and ≥2-year-old bull elk showed preference for open timber habitats during fall in non-roaded habitats and for timber habitat in areas with roads during summer and fall. Bulls tended to use higher proportions of lower slopes and stream bottoms than did cows during summer, and somewhat steeper areas during fall. Concern over forage production on summer range should be secondary to reducing disturbance and providing secure habitat during fall hunting seasons.
The sensitivity of presupernova neutrinos to stellar evolution models

We examine the sensitivity of neutrino emissions to stellar evolution models for a 15$M_\odot$ progenitor, paying particular attention to a phase prior to the collapse. We demonstrate that the number luminosities in both electron-type neutrinos ($\nu_e$) and their anti-partners ($\bar{\nu}_e$) differ by more than an order of magnitude by changing spatial resolutions and nuclear network sizes on stellar evolution models. We also develop a phenomenological model to capture the essential trend of the diversity, in which neutrino luminosities are expressed as a function of central density, temperature and electron fraction. In the analysis, we show that neutrino luminosity can be well characterized by these central quantities. This analysis also reveals that the most influential quantity to the time evolution of $\nu_e$ luminosity is matter density, while it is temperature for $\bar{\nu}_e$. These qualitative trends will be useful and applicable to constrain the physical state of progenitors at the final stages of stellar evolution from future neutrino observations, although more detailed systematic studies including various mass progenitors are required to assess the applicability.
Genetic parameters for birth weight, growth, and litter size in Nordic sheep breeds

Abstract In this study, genetic parameters were estimated for the Danish populations of Danish Marsk, Finnish Landrace, Gotland Pelt and Spel for birth weight (BW), average daily gain until two months (DG2) and litter size (LS). A multivariate animal model was used for estimation of genetic parameters, including fixed effects, both direct and maternal additive genetic effects, common litter effects and permanent environmental effects. Mean birth weight and DG2 ranged from, respectively, 3.39 kg and 262 g to 4.61kg and 286 g. Litter size ranged from 1.60 to 2.07. Direct heritability for BW ranged from 0.12 to 0.24, and maternal heritability for BW was about 0.23 for all breeds. Direct heritability of DG2 ranged from 0.19 to 0.33. The heritability for LS was between 0.08 and 0.13. The significant genetic correlations between the direct and maternal effect on both BW and DG2 were negative. The genetic correlations between the growth traits and LS were not uniform.