Miracle-dz/UniFAR-FTUs huggingface dataset:
Successful surf-riding on size spectra: the secret of survival in the sea

All ecosystems require constituent species to survive against a backcloth of biotic and abiotic scenery. How this scenery shapes the life-history strategies of the players and how they in turn shape the scenery are important themes of the play of life. Species surviving in temperate and Arctic shelf seas do so against a scenery dominated by seasonal changes in the size-spectrum of other players. Successful survival in such an environment requires species to ride the big wave of annual productivity as it rolls through the extended size spectrum from phytoplankton to large fish. This wave flattens and broadens as it moves towards higher sizes. We speculate that in a seasonal shelf seas environment the wave shape is such that the Sheldon-Sutcliffe spectrum of equal biomass per log size interval is approximately true as an annual average although it may not be true at any particular moment in the year. Such spectra are structured by biomass being moved up the size spectrum mainly by predation processes, with growth of individuals being a second order process. However, the problem for an individual is to grow up through a size spectrum from its size at birth to its size at reproduction. Hence species need to find survival paths through the fluctuating scenery. This scenery is composed of the biomass of the prey, that of animals of a similar size, and larger predators. The paths followed dictate the life-history strategies of the species. This central problem for sea dwellers in temperate and Arctic shelf seas generates a broad similarity in the choice of life-history strategy for many key players over quite wide geographic areas of the globe. In these seas, strategies of high fecundity, high mortality and high growth rate are particularly common while strategies of low fecundity and parental care are rare for much of the size range. These seas also seem to favour longer trophic chains than terrestrial systems and either several generations per year or multiannual life cycles rather than annual cycles.
NENET: An Edge Learnable Network for Link Prediction in Scene Text

Text detection in scenes based on deep neural networks have shown promising results. Instead of using word bounding box regression, recent state-of-the-art methods have started focusing on character bounding box and pixel-level prediction. This necessitates the need to link adjacent characters, which we propose in this paper using a novel Graph Neural Network (GNN) architecture that allows us to learn both node and edge features as opposed to only the node features under the typical GNN. The main advantage of using GNN for link prediction lies in its ability to connect characters which are spatially separated and have an arbitrary orientation. We show our concept on the well known SynthText dataset, achieving top results as compared to state-of-the-art methods.
Persistence discontinuity in disordered contact processes with long-range interactions

We study the local persistence probability during non-stationary time evolutions in disordered contact processes with long-range interactions by a combination of the strong-disorder renormalization group method, a phenomenological theory of rare regions, and numerical simulations. We find that, for interactions decaying as an inverse power of the distance, the persistence probability tends to a non-zero limit not only in the inactive phase but also in the critical point. Thus, unlike in the contact process with short-range interactions, the persistence in the limit t → ∞ is a discontinuous function of the control parameter. For stretched exponentially decaying interactions, the limiting value of the persistence is found to remain continuous, similar to the model with short-range interactions.
MeshODE: A Robust and Scalable Framework for Mesh Deformation

We present MeshODE, a scalable and robust framework for pairwise CAD model deformation without prespecified correspondences. Given a pair of shapes, our framework provides a novel shape feature-preserving mapping function that continuously deforms one model to the other by minimizing fitting and rigidity losses based on the non-rigid iterative-closest-point (ICP) algorithm. We address two challenges in this problem, namely the design of a powerful deformation function and obtaining a feature-preserving CAD deformation. While traditional deformation directly optimizes for the coordinates of the mesh vertices or the vertices of a control cage, we introduce a deep bijective mapping that utilizes a flow model parameterized as a neural network. Our function has the capacity to handle complex deformations, produces deformations that are guaranteed free of self-intersections, and requires low rigidity constraining for geometry preservation, which leads to a better fitting quality compared with existing methods. It additionally enables continuous deformation between two arbitrary shapes without supervision for intermediate shapes. Furthermore, we propose a robust preprocessing pipeline for raw CAD meshes using feature-aware subdivision and a uniform graph template representation to address artifacts in raw CAD models including self-intersections, irregular triangles, topologically disconnected components, non-manifold edges, and nonuniformly distributed vertices. This facilitates a fast deformation optimization process that preserves global and local details. Our code is publicly available.
Herbage mass, nutritive value and palatability of five grass weeds for cattle in the northern Tohoku region in Japan

The objective of this grazing study was to determine the herbage mass, nutritive value and palatability of five grass weed species (Eleusine indica (L.) Gaertn, Paspalum thunbergii Kunth, Digitaria ciliaris (Retz.) Koeler, Setaria glauca (L.) Beauv. and Echinochloa crus-galli (L.) Beauv. var. crus-galli) as feeds compared with six forage cultivars (Lolium perenne L., two Lolium multiflorum cultivars and three Echinochloa crus-galli (L.) Beauv. cultivars). The herbage mass of weed species, with the exception of S. glauca, was most prevalent in August. The total amounts of herbage mass of weed species (except E. indica) were close to those of the Lolium cultivars, but lower than those of the E. crus-galli cultivars. During the summer, weed species (except E. crus-galli var. crus-galli) contained favorable levels of total digestible nutrients (TDN), crude protein (CP), acid detergent fiber (ADF), neutral detergent fiber (NDF) and mineral contents, and exhibited adequate calcium (Ca):phosphorus (P) ratios of 1:2. All weed species had the recommended ratios of potassium (K):(Ca + [magnesium] Mg) < 2.2:1. By autumn, the summertime levels of nutritive values (TDN, CP, ADF and NDF) in the weed species had severely deteriorated, in contrast with those of the forage cultivars. However, they maintained satisfactory mineral content levels and Ca:P and K:(Ca + Mg) ratios (with the exception of E. indica with poor Ca:P ratios and E. crus-galli var. crus-galli with poor P content and Ca:P ratios). All weed species were acceptable to cattle, although there were some differences in palatability among the species.
Vegetation management with glyphosate has little impact on understory species diversity or tree growth in a sub boreal spruce plantation – A case study

Abstract In central British Columbia (BC), forest management practices have altered natural stand development pathways by controlling “non commercial” tree species and other competing species with the objective of maximizing crop tree growth. This potentially decreases overall diversity within the stand. We conducted a retrospective study on a 13-year-old stand to investigate the impacts of vegetation management with glyphosate on tree growth, species diversity and forest health in central BC. Results revealed that spruce was marginally taller than birch in the herbicide treated area and significantly shorter than birch in the untreated area. There was no difference in spruce diameter at breast height (DBH) and mean stem volume by treatment. White pine weevil attack was significantly reduced in the untreated area. In total, 133 plant species were recorded from 2002 to 2006, but 30% of the species present in 2002 were not present in 2006. Species' richness and alpha diversity were similar between treatments but beta diversity was relatively low indicating little treatment effect. There was a greater occurrence and abundance of “weedy” and pioneer species in the herbicide-treated area. Overall, this study suggests that glyphosate application does not remove all birch and showed minimal or no impact on under-story vegetation. Therefore, a move away from broadcast vegetation control to spot control where warranted could result in better tree growth, improved forest health and structural diversity.
Sharper error estimates for Virtual Elements and a bubble-enriched version

In the present contribution we develop a sharper error analysis for the Virtual Element Method, applied to a model elliptic problem, that separates the element boundary and element interior contributions to the error. As a consequence we are able to propose a variant of the scheme that allows to take advantage of polygons with many edges (such as those composing Voronoi meshes or generated by agglomeration procedures) in order to yield a more accurate discrete solution. The theoretical results are supported by numerical experiments.
Joint Item Recommendation and Attribute Inference: An Adaptive Graph Convolutional Network Approach

In many recommender systems, users and items are associated with attributes, and users show preferences to items. The attribute information describes users'(items') characteristics and has a wide range of applications, such as user profiling, item annotation, and feature-enhanced recommendation. As annotating user (item) attributes is a labor intensive task, the attribute values are often incomplete with many missing attribute values. Therefore, item recommendation and attribute inference have become two main tasks in these platforms. Researchers have long converged that user(item) attributes and the preference behavior are highly correlated. Some researchers proposed to leverage one kind of data for the remaining task, and showed to improve performance. Nevertheless, these models either neglected the incompleteness of user~(item) attributes or regarded the correlation of the two tasks with simple models, leading to suboptimal performance of these two tasks. To this end, in this paper, we define these two tasks in an attributed user-item bipartite graph, and propose an Adaptive Graph Convolutional Network(AGCN) approach for joint item recommendation and attribute inference. The key idea of AGCN is to iteratively perform two parts: 1) Learning graph embedding parameters with previously learned approximated attribute values to facilitate two tasks; 2) Sending the approximated updated attribute values back to the attributed graph for better graph embedding learning. Therefore, AGCN could adaptively adjust the graph embedding learning parameters by incorporating both the given attributes and the estimated attribute values, in order to provide weakly supervised information to refine the two tasks. Extensive experimental results on three real-world datasets clearly show the effectiveness of the proposed model.
Hopf link volume simplicity constraints in spin foam models

In this article we consider specific bivector geometries which arise in the large-spin limit of the extension of the Engle–Pereira–Rovelli–Livine spin foam model for quantum gravity by Kaminski, Kisielowski and Lewandowski. We address the implementation of volume simplicity constraints, which are required to ensure that a 4d metric can be reconstructed from the bivector geometry. We find that the necessary conditions are closely related, but not quite equal to the Hopf link volume simplicity constraints introduced in earlier works. We estimate the number of independent geometricity conditions for arbitrary bivector geometries, and find that they always agree with the number of Hopf links on the graph minus one, suggesting that the geometricity conditions can generically be formulated by deformation of the Hopf link volume simplicity constraints.
Effect of Dy substitution in the giant magnetocaloric properties of HoB2

ABSTRACT Recently, a massive magnetocaloric effect near the liquefaction temperature of hydrogen has been reported in the ferromagnetic material HoB2. Here we investigate the effects of Dy substitution in the magnetocaloric properties of Ho1-x Dy x B2 alloys (x = 0, 0.3, 0.5, 0.7, 1.0). We find that the Curie temperature (T C) gradually increases upon Dy substitution, while the magnitude of the magnetic entropy change |ΔS M| and adiabatic temperature change ΔT ad showed a gradual decrease. On the other hand, due to the presence of successive transitions in these alloys, the peak height of the above magnetocaloric properties tends to be kept in a wide temperature range, leading to a relatively robust figure of merit in a wide temperature span. These alloys could be interesting candidates for magnetic refrigeration in the temperature range of 10–60 K. Graphical abstract
Transmission of Tomato Yellow Leaf Curl Geminivirns by Bemisia tabaci (Homoptera: Aleyrodidae)

The sweetpotato whitefly, Bemisia tabaci (Gennadius), is an economically important pest worldwide. A new biotype of sweetpotato whitefly, biotype B, causes extensive damage by direct feeding and by the transmission of plant viruses, such as geminiviruses. Because of the importance of whitefly-transmitted geminiviruses, studies were conducted to quantify the characteristics of tomato yellow leaf curl geminivirus (TYLCV) transmission by the vector B. tabaci. Transmission of an isolate of TYLCV from Egypt was achieved with one adult B. tabaci per plant, but the efficiency of transmission increased fourfold when the number of adults were increased to five per plant. B. tabaci transmitted TYLCV after a minimum acquisition-access period of 15 min, and rate of transmission increased as the acquisition-access period was lengthened and reached a maximum after 24 h. A minimum inoculation-access period of 15 min was observed with the rate of transmission increasing as the inoculation-access period was lengthened, reaching a maximum after a 12-h inoculation-access period. When B. tabaci were transferred serially after acquisition, adults were unable to transmit TILCV until 24 h after the initiation of the acquisition-access period regardless of the length of acquisition provided. The 24-h transmission threshold from initial vector access to a TYLCV-infected plant until transmission of the virus includes both the acquisition-access period and the latent period during which the virus circulates within the vector. Retention of TYLCV from the nymphal to adult stages of B. tabaci was supportive of a circulative mode of transmission. TILCV titer in B. tabaci after an acquisition-access period of 12 h continuously increased on a non-TYLCV host starting at 12 h, reaching a peak at 108 h and remaining stable from 132 to 180 h after the acquisition-access period. These data indicate multiplication of the virus in the vector as the most likely explanation for the continuous increase of TYLCV titer.
Wavelet Based Multivariate Signal Denoising Using Mahalanobis Distance and EDF Statistics

A multivariate signal denoising method is proposed which employs a novel multivariate goodness of fit (GoF) test that is applied at multiple data scales obtained from discrete wavelet transform (DWT). In the proposed multivariate GoF test, we first utilize squared Mahalanobis distance (MD) measure to transform input multivariate data residing in M-dimensional space <inline-formula><tex-math notation="LaTeX">$\mathcal {R}^M$</tex-math></inline-formula> to a single-dimensional space of positive real numbers <inline-formula><tex-math notation="LaTeX">$\mathcal {R}_+$</tex-math></inline-formula>, i.e., <inline-formula><tex-math notation="LaTeX">$\mathcal {R}^M \rightarrow \mathcal {R}_+$</tex-math></inline-formula>, where <inline-formula><tex-math notation="LaTeX">$M > 1$</tex-math></inline-formula>. Owing to the properties of the MD measure, the transformed data in <inline-formula><tex-math notation="LaTeX">$\mathcal {R}_+$</tex-math></inline-formula> follows a distinct distribution. That enables us to apply the GoF test using statistic based on empirical distribution function (EDF) on the resulting data in order to define a test for multivariate normality. We further propose to apply the above test locally on multiple input data scales obtained from discrete wavelet transform, resulting in a multivariate signal denoising framework. Within the proposed method, the reference cumulative distribution function (CDF) is defined as a quadratic transformation of multivariate Gaussian random process. Consequently, the proposed method checks whether a set of DWT coefficients belong to multivariate reference distribution or not; the coefficients belonging to the reference distribution are discarded. The effectiveness of our proposed method is demonstrated by performing extensive simulations on both synthetic and real world datasets.
Joint Deployment and Multiple Access Design for Intelligent Reflecting Surface Assisted Networks

The fundamental intelligent reflecting surface (IRS) deployment problem is investigated for IRS-assisted networks, where one IRS is arranged to be deployed in a specific region for assisting the communication between an access point (AP) and multiple users. Specifically, three multiple access schemes are considered, namely non-orthogonal multiple access (NOMA), frequency division multiple access (FDMA), and time division multiple access (TDMA). The weighted sum rate maximization problem for joint optimization of the deployment location and the reflection coefficients of the IRS as well as the power allocation at the AP is formulated. The non-convex optimization problems obtained for NOMA and FDMA are solved by employing monotonic optimization and semidefinite relaxation to find a performance upper bound. The problem obtained for TDMA is optimally solved by leveraging the time-selective nature of the IRS. Furthermore, for all three multiple access schemes, low-complexity suboptimal algorithms are developed by exploiting alternating optimization and successive convex approximation techniques, where a local region optimization method is applied for optimizing the IRS deployment location. Numerical results are provided to show that: 1) near-optimal performance can be achieved by the proposed suboptimal algorithms; 2) asymmetric and symmetric IRS deployment strategies are preferable for NOMA and FDMA/TDMA, respectively; 3) the performance gain achieved with IRS can be significantly improved by optimizing the deployment location.
Cosmological Information from the Small-scale Redshift-space Distortion

The redshift-space distortion (RSD) in the observed distribution of galaxies is known as a powerful probe of cosmology. Observations of large-scale RSD, caused by the coherent gravitational infall of galaxies, have given tight constraints on the linear growth rate of the large-scale structures in the universe. On the other hand, the small-scale RSD, caused by galaxy–random motions inside clusters, has not been much used in cosmology, but it also has cosmological information because universes with different cosmological parameters have different halo mass functions and virialized velocities. We focus on the projected correlation function w(rp) and the multipole moments ξl on small scales (1.4–30 h−1 Mpc). Using simulated galaxy samples generated from a physically motivated most bound particle (MBP)–galaxy correspondence scheme in the Multiverse Simulation, we examine the dependence of the small-scale RSD on the cosmological matter density parameter Ωm; the satellite velocity bias with respect to MBPs, ; and the merger timescale parameter α. We find that α = 1.5 gives an excellent fit to the w(rp) and ξl measured from the Sloan Digital Sky Survey–Korea Institute for Advanced Study value-added galaxy catalog. We also define the “strength” of the Fingers of God as the ratio of the parallel and perpendicular size of the contour in the two-point correlation function set by a specific threshold value and show that the strength parameter helps constrain by breaking the degeneracy among them. The resulting parameter values from all measurements are , indicating a slight reduction of satellite galaxy velocity relative to the MBP. However, considering that the average MBP speed inside halos is 0.94 times the dark matter velocity dispersion, the main drivers behind the galaxy velocity bias are gravitational interactions, rather than baryonic effects.
PHANGS CO Kinematics: Disk Orientations and Rotation Curves at 150 pc Resolution

We present kinematic orientations and high-resolution (150 pc) rotation curves for 67 main-sequence star-forming galaxies surveyed in CO (2–1) emission by PHANGS–ALMA. Our measurements are based on the application of a new fitting method tailored to CO velocity fields. Our approach identifies an optimal global orientation as a way to reduce the impact of nonaxisymmetric (bar and spiral) features and the uneven spatial sampling characteristic of CO emission in the inner regions of nearby galaxies. The method performs especially well when applied to the large number of independent lines of sight contained in the PHANGS CO velocity fields mapped at 1″ resolution. The high-resolution rotation curves fitted to these data are sensitive probes of mass distribution in the inner regions of these galaxies. We use the inner slope as well as the amplitude of our fitted rotation curves to demonstrate that CO is a reliable global dynamical mass tracer. From the consistency between photometric orientations from the literature and kinematic orientations determined with our method, we infer that the shapes of stellar disks in the mass range of log( ) = 9.0–10.9 probed by our sample are very close to circular and have uniform thickness.
Learning Joint Nonlinear Effects from Single-variable Interventions in the Presence of Hidden Confounders

We propose an approach to estimate the effect of multiple simultaneous interventions in the presence of hidden confounders. To overcome the problem of hidden confounding, we consider the setting where we have access to not only the observational data but also sets of single-variable interventions in which each of the treatment variables is intervened on separately. We prove identifiability under the assumption that the data is generated from a nonlinear continuous structural causal model with additive Gaussian noise. In addition, we propose a simple parameter estimation method by pooling all the data from different regimes and jointly maximizing the combined likelihood. We also conduct comprehensive experiments to verify the identifiability result as well as to compare the performance of our approach against a baseline on both synthetic and real-world data.