Miracle-dz/UniFAR-FTUs huggingface dataset:
Post-weaning growth, ultrasound and skeletal measurements, muscularity scores and carcass traits and composition of progeny of five beef suckler cow genotypes

Post-weaning growth, ultrasound and skeletal measurements, muscularity scores, and carcass traits and composition of the progeny of spring-calving Limousin (L), Charolais (C), Limousin × Holstein-Friesian (LF), Limousin × (Limousin × Holstein- Friesian) (LLF) and Simmental × (Limousin × Holstein-Friesian) (SLF) cow genotypes was determined over 3 years. Bull and heifer progeny were slaughtered at ~460 and ~610 days of age, respectively. Post-weaning growth did not differ significantly between the genotypes. Progeny from LF and SLF cows had the highest (P<0.001) carcass gain per day of age, whereas progeny from L and C cows had the highest (P < 0.01) carcass conformation score and lowest (P < 0.001) fat score. The proportion of meat in the car- cass was higher (P < 0.001) and bone lower (P < 0.001), and meat to bone ratio higher (P < 0.001) for the progeny of L cows than all other genotypes, which were similar. Carcass fat proportion was similar for progeny of L and C cows and lower (P < 0.001) than LLF and SLF, with LF being intermediate. The progeny from L cows tended to have the greatest proportion of hind-quarter in the carcass. Genotype effects were mini- mal when the proportion of high-value cuts was expressed relative to weight of meat in the carcass and hind-quarter. In conclusion, there was no effect of cow genotype on the performance of their progeny from weaning to slaughter. However, crossbred cows with good maternal (milk) traits produced progeny with a higher carcass weight per day of age, whereas the purebred continental cows produced progeny with superior carcass classification traits.
Temperature profiles, plumes, and spectra in the surface layer of convective boundary layers

We survey temperature patterns and heat transport in convective boundary layers (CBLs) from the perspective that these are emergent properties of far-from-equilibrium, complex dynamical systems. We introduce a two-temperature (2T) toy model to define the cross-sectional areas of plumes, and connect the scaling properties of temperature gradients, temperature variance and heat transport to this area. We examine temperature ($T$) probability density functions and $w$-$T$ joint probability density functions, $T$ spectra and $wT$ cospectra observed both within and above the surface friction layer. Here $w$ is vertical velocity. In our discussion of $T$ spectra and $wT$ cospectra we focus on the self-similarity property of the plumes and flux events above the SFL. We interpret the $z^{1/2}$ dependence of the mixed length scale for wavenumbers in the $T$ spectra as reflecting the cross-sectional areas of the plumes, and so with the $z^{-1/2}$ form of the temperature profile, where $z$ is observation height. We introduce new scaling results for $T$ spectra and $wT$ cospectra from within the surface friction layer (SFL), based on a data from the SLTEST experiment. We confirm earlier results showing that the scaling behaviours of $T$ spectra and $wT$ cospectra change for heights below $z/z_s<0.1$, where $z_s$ the height of the SFL, and come to display properties associated with random diffusion. We conclude by contrasting our interpretation of the role of buoyancy as a system-wide action in CBL flows with that of Richardson, whose ideas inform the current interpretation of the statistical fluid mechanics model of boundary-layer flows.
Non-local thermal transport via molecular-dynamics simulation and the Green-Kubo formalism

In this article we develop the theory of nonlocal thermal transport within the context of the fluctuation-dissipation theorem. The theory goes beyond the usual Green-Kubo integrals used to obtain the thermal conductivity by including the response of a system to external heat sources that are nonuniform in space and time. Through the integration of current-current correlation functions, the relevant response functions are determined from equilibrium molecular-dynamics simulations of an Ar crystal modeled using the standard Lennard-Jones potential. It is shown that for low temperatures and short length scales, the approach can be used to elucidate partially-ballistic transport. Transport can be understood to be heat waves in the sense that transport is primarily limited by the sound velocity. By contrast, at longer length scales and higher temperatures, the response functions become more comparable to what is expected from Fourier's law where diffusive heat transport is the dominant mechanism. It is also shown how the effective thermal conductivity can be determined in a partially-ballistic regime. The results demonstrate the known reduction in the effective thermal conductivity seen in the partially-ballistic regime. Finally, we show how determination of the relevant response functions can be used to model heating of a crystal without requiring additional atomic-scale simulations. This approach is used to show how temperature profiles differ from those expected from the diffusive Fourier's law due to nonlocal transport effects.
Do Adversarially Robust ImageNet Models Transfer Better?

Transfer learning is a widely-used paradigm in deep learning, where models pre-trained on standard datasets can be efficiently adapted to downstream tasks. Typically, better pre-trained models yield better transfer results, suggesting that initial accuracy is a key aspect of transfer learning performance. In this work, we identify another such aspect: we find that adversarially robust models, while less accurate, often perform better than their standard-trained counterparts when used for transfer learning. Specifically, we focus on adversarially robust ImageNet classifiers, and show that they yield improved accuracy on a standard suite of downstream classification tasks. Further analysis uncovers more differences between robust and standard models in the context of transfer learning. Our results are consistent with (and in fact, add to) recent hypotheses stating that robustness leads to improved feature representations. Our code and models are available at this https URL .
Multi-Wavelength Variability of BL Lacertae Measured with High Time Resolution

In an effort to locate the sites of emission at different frequencies and physical processes causing variability in blazar jets, we have obtained high time-resolution observations of BL Lacertae over a wide wavelength range: with the \emph{Transiting Exoplanet Survey Satellite} (TESS) at 6,000-10,000 A\ with 2-minute cadence; with the Neil Gehrels \emph{Swift} satellite at optical, UV, and X-ray bands; with the Nuclear Spectroscopic Telescope Array at hard X-ray bands; with the \emph{Fermi} Large Area Telescope at $\gamma$-ray energies; and with the Whole Earth Blazar Telescope for measurement of the optical flux density and polarization. All light curves are correlated, with similar structure on timescales from hours to days. The shortest timescale of variability at optical frequencies observed with TESS is $\sim 0.5$ hr. The most common timescale is $13\pm1$~hr, comparable with the minimum timescale of X-ray variability, 14.5 hr. The multi-wavelength variability properties cannot be explained by a change solely in the Doppler factor of the emitting plasma. The polarization behavior implies that there are both ordered and turbulent components to the magnetic field in the jet. Correlation analysis indicates that the X-ray variations lag behind the $\gamma$-ray and optical light curves by up to $\sim 0.4$ days. The timescales of variability, cross-frequency lags, and polarization properties can be explained by turbulent plasma that is energized by a shock in the jet and subsequently loses energy to synchrotron and inverse Compton radiation in a magnetic field of strength $\sim3$ G
Les associations de Gyrins dans les étangs et les lacs du parc du Mont Tremblant

L'étude monographique de H. C. Fall (1922), “The North American species of Gyrinus”, a permis à de nombreux entomologistes de reconnaîrre assez facilement les Gyrins des territoires inventoriés par eux-mêmes et a facilité la mise en ordre des collections existantes. Nous en voyons la preuve dans les quelques additions ou listes qui parurent durant les dix années suivantes. Wallis (1926) décrit le Gyrinus dubius et le G. hatchi; il restaure le G. piceolus Blatchley qui avait été réduit au rang de synonyme par Fall. Chamberlain (1929) décrit le G. gehringi, provenant de l'état du New Hampshire. Brown (1928) donne une liste des espèces canadiennes. Il énumère vingt espèces pour la seule province de Québec, dont la plupart proviennent de la région de Hull ou de Covey Hill. Le Gyrinus opacus n'est cependant mentionné que pour la baie James. En 1931, Fall donne la description de Gyrinus instabilis dont le type et les paratypes proviennent de l'Alaska. Depuis, très peu de travaux semblent avoir été faits sur ce genre d'insectes, soit au Canada, soit aux Etats-Unis.
Where do IceCube neutrinos come from? Hints from the diffuse gamma-ray flux

Despite the spectacular discovery of an astrophysical neutrino flux by IceCube in 2013, its origin remains a mystery. Whatever its sources, we expect the neutrino flux to be accompanied by a comparable gamma-ray flux. These photons should be degraded in energy by electromagnetic cascades and contribute to the diffuse GeV–TeV flux precisely measured by the Fermi-LAT. Population studies have also permitted to identify the main classes of contributors to this flux, which at the same time have not been associated with major neutrino sources in cross-correlation studies. These considerations allow one to set constraints on the origin and spectrum of the IceCube flux, in particular its low-energy part. We find that, even accounting for known systematic errors, the Fermi-LAT data exclude to at least 95% C.L. any extragalactic transparent source class, irrespective of its redshift evolution, if the neutrino spectrum extends to the TeV scale or below. If the neutrino spectrum has an abrupt cutoff at ∼10 TeV, barely compatible with current observations, the tension can be reduced, but this way out requires a significant modification to the current understanding of the origin of the diffuse extragalactic gamma-ray flux at GeV energies. In contrast, these considerations do not apply if a sizable fraction of IceCube data originates within the Galactic halo (a scenario however typically in tension with other constraints) or from a yet unidentified class of “opaque” extragalactic emitters, which do not let the high-energy gamma rays get out.
Correlation of Structure and Stellar Properties of Galaxies in Stripe 82

Establishing a correlation (or lack thereof) between the bimodal color distribution of galaxies and their structural parameters is crucial to understand the origin of bimodality. To achieve that, we have performed a 2D mass-based structural decomposition (bulge+disk) of all disk galaxies (total = 1263) in the Herschel imaging area of the Stripe 82 region using Ks-band images from the VICS82 survey. The scaling relations thus derived are found to reflect the internal kinematics and are employed in combination to select an indubitable set of classical- and pseudo-bulge-hosting disk galaxies. The rest of the galaxies (<20%) are marked as disks with “ambiguous” bulges. Pseudo- and classical bulge–disk galaxies exhibit clear bimodality in terms of all stellar parameters (M*, specific star formation rate, r − Ks). All pseudo-bulge–disk galaxies are blue and star forming, and all classical bulge–disk galaxies are red and quiescent with less than 5% digressions. Ambiguous bulge–disk galaxies are intermediate to pseudo- and classical bulge–disk galaxies in the distribution of all structural and stellar parameters. —based on the placement of bulges on the Kormendy relation—is found to be the most efficient single structural indicator of both bulge type and stellar activity. The placement of ambiguous bulge–disk galaxies on scaling relations and the fundamental plane, in addition to their peculiar stellar properties, suggests that they are dominantly part of the green valley.
Invasive green crab, Carcinus maenas, on the Atlantic coast and in the Bras d'Or Lakes of Nova Scotia, Canada: larval supply and recruitment

Patterns in larval supply and recruitment of the invasive European green crab, Carcinus maenas, were examined at two sites in Nova Scotia, Canada: a typical Atlantic coastal site with high salinity (∼33) and strong tidal signal (Terence Bay); and a microtidal site in the Bras d'Or Lakes, where surface salinity fluctuated from 0 to 23 (Benacadie Pond). Abundance of all life history stages (zoeae, megalopae, juveniles and ovigerous females) was sampled approximately biweekly at each site from June 2001 to June 2002. The reproductive season, based on the presence of ovigerous females, is ∼5 months (June to October) at both sites. Zoeae-I were collected from June to July 2001 and in June 2002 at Terence Bay, and from June to August 2001 at Benacadie Pond. We did not collect later zoeal stages at either site. Megalopae were collected from September to October 2001 at Terence Bay, and from August to September 2001 at Benacadie Pond. Juveniles 1–6 mm carapace width (CW) were present throughout the summer at Terence Bay, and only in autumn at Benacadie. Juveniles 6–12 and 12–27 mm CW were present throughout the summer and autumn at both sites. At Terence Bay, densities of 1–6 and 12–27 mm CW juveniles did not vary among intertidal zones. In contrast, the 6–12 mm CW size-class appeared to move higher in the intertidal through the season, possibly due to effects of predation.
A metacommunity‐scale comparison of species‐abundance distribution models for plant communities of eastern Australia

Various models of the species-abundance distribution (SAD) have been proposed to fit empirically derived data however there is no general consensus as to which model provides the best fit. Further, the zero-sum multinomial SAD model (ZSM) was proposed as a metacommunity model, yet it has not previously been fitted at the metacommunity scale. We note that SAD models based on compound lognormal distributions (such as the Poisson-lognormal, PLN, and the negative binomial-lognormal models, NBLN) can also be thought of as metacommunity models, and we compare these with the ZSM when fitted as metacommunity models to SADs of related communities. We collected five datasets in the Sydney Basin, eastern Australia, representing five different types of subtropical/temperate plant communities ranging from closed warm-temperate rainforest to open wet sclerophyll forest to dry sclerophyll woodland. For each type of plant community, five local communities were identified across the Sydney Basin, and SAD data collected in five randomly placed 0.2-ha quadrats at each local community. Analysis was performed at two levels: all abundance data from each local community were pooled across each metacommunity and analysed as a single pooled community; and a metacommunity model was fitted to all five local communities of a community type, simultaneously. For the pooled data, we considered the negative-binomial (NB) and the log-series (LS) models in addition to ZSM, PLN and NBLN. All five models performed similarly, however the LS had a better fit to three pooled communities and the ZSM and PLN to the remaining two communities. By contrast, the ZSM performed statistically better against the PLN and NBLN when considered as a metacommunity model. We conclude that the ZSM generally provides a more reliable null model for metacommunity abundance data than the lognormal model.
Deep Reinforced Query Reformulation for Information Retrieval

Query reformulations have long been a key mechanism to alleviate the vocabulary-mismatch problem in information retrieval, for example by expanding the queries with related query terms or by generating paraphrases of the queries. In this work, we propose a deep reinforced query reformulation (DRQR) model to automatically generate new reformulations of the query. To encourage the model to generate queries which can achieve high performance when performing the retrieval task, we incorporate query performance prediction into our reward function. In addition, to evaluate the quality of the reformulated query in the context of information retrieval, we first train our DRQR model, then apply the retrieval ranking model on the obtained reformulated query. Experiments are conducted on the TREC 2020 Deep Learning track MSMARCO document ranking dataset. Our results show that our proposed model outperforms several query reformulation model baselines when performing retrieval task. In addition, improvements are also observed when combining with various retrieval models, such as query expansion and BERT.
Response of Seed Carrot to Various Water Regimes: II. Reproductive Development, Seed Yield, and Seed Quality

Seed yield and quality of carrot (Daucus carota var. sativa DC.) were influenced by a wide range of water application regimes and levels. Irrigation treatments were imposed beginning at the time of extension of the primary umbel and extending throughout the reproductive development period. The three application regimes used were: 1) a high-frequency, low water deficit treatment [100% of daily accumulated crop evapotranspiration (ETc)]; 2) a series of five low-frequency (irrigated after 30 mm of accumulated ET,) application treatments with a range of water deficits from moderate to minimal (40% to 120% of ETC applied); and 3) a series of three treatments that had rapidly developing water deficits applied by terminating irrigation at 7, 5, and 2 weeks before harvest after being grown under low-stress conditions. Pure live seed (PLS) yield was optimized by different treatments within each of the three application regimes. Maximum yields were achieved with short-term (2-week) rapidly developing water deficits near harvest, moderate deficit irrigation with 60% to 80% of ETc applied after 30 mm of ETc, or with a low water deficit, high-frequency application. Seed germination percentage decreased as the amount of applied water increased. This effect was more pronounced in the later-developing umbel orders. However, seed quality measured as seedling root length was improved with increasing water application. The seed carrot plant grows in an indeterminate fashion, producing seed in umbels that arise from multi-rank ordered branches. This general growth form results in seed of different maturity stages being present on the plant at the same time. Therefore, when the seed of the primary umbel is mature and ready for harvest, seed from secondary and tertiary umbels may not be mature, and tertiary and quarternary umbels may still be in flower. As a result, there is a great deal of variability in the maturity of the seed at harvest. Since genetic efforts to improve the carrot plant have been directed primarily toward improved root production and quality, carrot reproductive development has remained much the same as that of undomesticated relatives (Braak and Kho, 1958). The effects of water management on carrot seed production and quality are not well-understood, with limited published infor publication 27 Nov. 1989. This research was supported, in part, t from the California Agricultural Technology Institute, California v. , Fresno. Contribution no. 900903 of the California Agricultural y Institute. Use of trade names does not imply endorsement by the S or California State Univ., Fresno. The cost of publishing this paper ed in part by the payment of page charges. Under postal regulations, therefore must be hereby marked advertisement solely to indicate this
RepPoints V2: Verification Meets Regression for Object Detection

Verification and regression are two general methodologies for prediction in neural networks. Each has its own strengths: verification can be easier to infer accurately, and regression is more efficient and applicable to continuous target variables. Hence, it is often beneficial to carefully combine them to take advantage of their benefits. In this paper, we take this philosophy to improve state-of-the-art object detection, specifically by RepPoints. Though RepPoints provides high performance, we find that its heavy reliance on regression for object localization leaves room for improvement. We introduce verification tasks into the localization prediction of RepPoints, producing RepPoints v2, which provides consistent improvements of about 2.0 mAP over the original RepPoints on the COCO object detection benchmark using different backbones and training methods. RepPoints v2 also achieves 52.1 mAP on COCO \texttt{test-dev} by a single model. Moreover, we show that the proposed approach can more generally elevate other object detection frameworks as well as applications such as instance segmentation. The code is available at this https URL.
Dual Graph Embedding for Object-Tag Link Prediction on the Knowledge Graph

Knowledge graphs (KGs) composed of users, objects, and tags are widely used in web applications ranging from E-commerce, social media sites to news portals. This paper concentrates on an attractive application which aims to predict the object-tag links in the KG for better tag recommendation and object explanation. When predicting the object-tag links, both the first-order and high-order proximities between entities in the KG propagate essential similarity information for better prediction. Most existing methods focus on preserving the first-order proximity between entities in the KG. However, they cannot capture the high-order proximities in an explicit way, and the adopted margin-based criterion cannot measure the first-order proximity on the global structure accurately. In this paper, we propose a novel approach named Dual Graph Embedding (DGE) that models both the first-order and high-order proximities in the KG via an auto-encoding architecture to facilitate better object-tag relation inference. Here the dual graphs contain an object graph and a tag graph that explicitly depict the high-order object-object and tag-tag proximities in the KG. The dual graph encoder in DGE then encodes these high-order proximities in the dual graphs into entity embeddings. The decoder formulates a skipgram objective that maximizes the first-order proximity between observed object-tag pairs over the global proximity structure. With the supervision of the decoder, the embeddings derived by the encoder will be refined to capture both the first-order and high-order proximities in the KG for better link prediction. Extensive experiments on three real-world datasets demonstrate that DGE outperforms the state-of-the-art methods.
UvA-DARE (Digital Academic Repository) Natural Graph Networks

A key requirement for graph neural networks is that they must process a graph in a way that does not depend on how the graph is described. Traditionally this has been taken to mean that a graph network must be equivariant to node permutations. Here we show that instead of equivariance, the more general concept of naturality is sufﬁcient for a graph network to be well-deﬁned, opening up a larger class of graph networks. We deﬁne global and local natural graph networks, the latter of which are as scalable as conventional message passing graph neural networks while being more ﬂexible. We give one practical instantiation of a natural network on graphs which uses an equivariant message network parameterization, yielding good performance on several benchmarks.