Miracle-dz/UniFAR-FTUs huggingface dataset:
Quaternion Graph Neural Networks

We consider reducing model parameters and moving beyond the Euclidean space to a hyper-complex space in graph neural networks (GNNs). To this end, we utilize the Quaternion space to learn quaternion node and graph embeddings. The Quaternion space, a hyper-complex space, provides highly meaningful computations through Hamilton product compared to the Euclidean and complex spaces. In particular, we propose QGNN -- a new architecture for graph neural networks which is a generalization of GCNs within the Quaternion space. QGNN reduces the model size up to four times and enhances learning graph representations. Experimental results show that our proposed QGNN produces state-of-the-art performances on a range of benchmark datasets for three downstream tasks, including graph classification, semi-supervised node classification, and text classification.
Evaluation of incubated defatted rubber seed meal with sheep rumen liquor for Pangasius diet

ABSTRACT The research evaluated the use of rubber seed meal ( Hevea brasiliensis ; RBS) incubated with sheep rumen liquor as a subtitution of soybean meal in catfish Pangasionodon sp. diet. The fish was cultured for 40 days and fed with the experimental diet containing RBS at five different diet compositions regarding to soybean meal substitution level, i.e. 0% (control), 12%, 23%, 34%, and 44%. Feeding was done three times a day to satiation. No significant different was found on fish-protein retention and survival rate in all treatments. Based on the study result, the use of rubber-seed meal ( Hevea brasiliensis ; RBS) incubated with sheep rumen liquor could substitute soybean meal in catfish Pangasionodon sp. diet. Keywords: Hevea brasiliensis , Pangasionodon sp. , catfish, sheep rumen liquor, rubber seed meal ABSTRAK Penelitian ini mengevaluasi penggunaan tepung bungkil biji karet ( Hevea brasiliensis ; TBBK) yang diinkubasi dengan cairan rumen domba sebagai pengganti tepung bungkil kedelai pada pakan ikan patin Pangasionodon sp. Pemeliharaan ikan dilakukan selama 40 hari dengan pemberian lima komposisi pakan berbeda sesuai tingkat substitusi tepung bungkil kedelai oleh tepung bungkil karet. TBBK yang ditambahkan untuk mengganti bungkil kedelai adalah sebesar 0%, 12%, 23%, 34% dan 44%. Pemberian pakan dilakukan selama tiga kali sehari secara at satiation. Tidak ditemukan perbedaan signifikan (P>0,05) pada nilai retensi protein dalam tubuh dan kelangsungan hidup ikan uji pada semua perlakuan. Berdasarkan hasil penelitian, dapat disimpulkan bahwa tepung bungkil biji karet yang diinkubasi dengan cairan rumen domba dapat digunakan sebagai pengganti bungkil kedelai pada pakan ikan patin Pangasionodon sp. Kata kunci: Hevea brasiliensis , Pangasionodon sp. , patin, rumen domba, tepung biji karet
Scale Groups

Closed subgroups of the group of isometries of the regular tree $\treeq$ that fix an end of the tree and are vertex-transitive are shown to correspond, on one hand, to self-replicating groups acting on rooted trees and, on the other hand, to elements of totally disconnected, locally compact groups having positive scale.
Extensions to models of the galaxy–halo connection

We explore two widely used empirical models for the galaxy-halo connection, subhalo abundance matching (SHAM) and the halo occupation distribution (HOD) and compare their predictions with the hydrodynamical simulation IllustrisTNG (TNG) for a range of statistics that quantify the galaxy distribution at $n_{\rm gal}\approx1.3\times10^{-3}\,[{\rm Mpc}/h]^{-3}$. We observe that in their most straightforward implementations, both models fail to reproduce the two-point clustering measured in TNG. We find that SHAM models constructed using the relaxation velocity, $V_{\rm relax}$, and the peak velocity, $V_{\rm peak}$, perform best, and match the clustering reasonably well, although neither model captures adequately the one-halo clustering. Splitting the total sample into sub-populations, we discover that SHAM overpredicts the clustering of high-mass, blue, star-forming, and late-forming galaxies and uderpredicts that of low-mass, red, quiescent, and early-forming galaxies. We also study various baryonic effects, finding that subhalos in the dark-mater-only simulation have consistently higher values of their SHAM-proxy properties than their full-physics counterparts. We then consider a two-dimensional implementation of the HOD model augmented with a secondary parameter (environment, velocity anisotropy, $\sigma^2R_{\rm halfmass}$, and total potential) and tuned so as to match the two-point clustering of the IllustrisTNG galaxies on large scales. We analyze these galaxy populations adopting alternative statistical tools such as galaxy-galaxy lensing, void-galaxy cross-correlations and cumulants of the smoothed density field, finding that the hydrodynamical galaxy distribution disfavors $\sigma^2 R_{\rm halfmass}$ and the total potential as secondary parameters, while the environment and velocity anisotropy samples are consistent with full-physics across all statistical probes examined.
Diet Composition of Royal Tern (Thalasseus maximus) and Sandwich Tern (Thalasseus sandvicensis) at Isles Dernieres Barrier Island Refuge, Louisiana, USA

Abstract. The Isles Dernieres Barrier Island Refuge in Louisiana, USA, constitutes a major colonial waterbird breeding site, and several restoration projects have been undertaken to sustain waterbird populations on the refuge. However, very little is known about food resources that colonial waterbirds depend on in the Gulf of Mexico. Royal (Thalasseus maximus) and Sandwich (T. sandvicensis) tern diet composition was investigated to determine important food resources during the breeding period. Regurgitated prey items in the 2013 and 2014 breeding seasons were used to compare diet composition, prey item frequency, and prey item mass among four groups: Royal Tern adults, Royal Tern chicks, Sandwich Tern adults, and Sandwich Tern chicks. The two most frequent prey items were Atlantic croaker (Micropogonias undulatus) and bay anchovy (Anchoa mitchilli). Royal Tern adults consumed significantly more Sciaenids than any other tern group, and Sandwich Tern adults consumed more Engraulids than any other tern group. Royal and Sandwich tern adults partitioned food resources by prey species and prey size, while chick diet showed more overlap in prey species composition. Royal and Sandwich tern diet closely reflected the species composition of trawls from nearby waters, suggesting opportunistic prey selection. The diets of both tern species contained a variety of demersal prey items indicating use of discards from local fisheries activities. Interactions between breeding waterbirds and fisheries in coastal Louisiana may be an important conservation consideration with profound implications on breeding abundance and breeding success of waterbirds.
A Zero-Shot Sketch-Based Intermodal Object Retrieval Scheme for Remote Sensing Images

Domain-agnostic data retrieval has lately become essential amidst the availability of large-scale data from different types of sensors. However, the unavailability of a sufficient amount of samples of certain classes during training curtails the utility of existing retrieval models in remote sensing (RS) applications. Here, we propose a novel framework for zero-shot intermodal data retrieval of RS data. Thereupon, we design an encoder–decoder structure that ensures enhanced overlapping among the two data domains utilizing cross-triplet and cross-projection loss functions. Furthermore, we propose a sketch-based representation of the RS database Earth on Canvas with diverse classes. We perform a thorough benchmarking of this data set and demonstrate that the proposed framework outperforms state-of-the-art methods for zero-shot sketch-based retrieval framework for RS data.
DSM-Net: Disentangled Structured Mesh Net for Controllable Generation of Fine Geometry

3D shape generation is a fundamental operation in computer graphics. While significant progress has been made, especially with recent deep generative models, it remains a challenge to synthesize high-quality geometric shapes with rich detail and complex structure, in a controllable manner. To tackle this, we introduce DSM-Net, a deep neural network that learns a disentangled structured mesh representation for 3D shapes, where two key aspects of shapes, geometry and structure, are encoded in a synergistic manner to ensure plausibility of the generated shapes, while also being disentangled as much as possible. This supports a range of novel shape generation applications with intuitive control, such as interpolation of structure (geometry) while keeping geometry (structure) unchanged. To achieve this, we simultaneously learn structure and geometry through variational autoencoders (VAEs) in a hierarchical manner for both, with bijective mappings at each level. In this manner we effectively encode geometry and structure in separate latent spaces, while ensuring their compatibility: the structure is used to guide the geometry and vice versa. At the leaf level, the part geometry is represented using a conditional part VAE, to encode high-quality geometric details, guided by the structure context as the condition. Our method not only supports controllable generation applications, but also produces high-quality synthesized shapes, outperforming state-of-the-art methods.
Biological Assay for Compost Quality

A plant biological assay or bioassay for determining compost quality and/or maturity has received attention over the past two decades. However, no universal acceptance for compost quality is evident and cress, which was first reported to be used as a plant bioassay, is still the most commonly used. Furthermore, there is evidence indicating that cress is not sensitive enough to distinguish between mature and immature composts. Fourteen seed propagated species were surveyed to see if one or more would be useful as a bioassay for compost quality. The study confirmed that cress is a less sensitive indicator than several species, for example, lettuce, carrot or Chinese cabbage. Amaranthus tricolor was identified as a potential sensitive indicator species since it did not germinate in an immature compost extract. When the compost extract was diluted, the germination index was linear with extract concentration. While cress responded by differences in root growth, amaranthus responded by reduced germination and root growth which gave it a more definitive response. The study concluded that most of the species, including the commonly used cress, are not sensitive enough to detect differences between mature and immature composts. However, Chinese cabbage appears to be the best of the commonly used assay plants. Amaranthus' potential as a sensitive compost maturity indicator was discovered and more studies are needed to confirm this finding.
Enhanced steady-state coherence via repeated system-bath interactions

The appearance of steady-state coherence (SSC) from system-bath interaction proves that quantum effects can appear without an external drive. Such SSC could become a resource to demonstrate quantum advantage in the applications. We predict the generation of SSC if the target system repeatedly interacts with independent and non-correlated bath elements. To describe their behavior, we use the collision model approach of system-bath interaction, where the system interacts with one bath element (initially in an incoherent state) at a time, asymptotically (in the fast-collision regime) mimicking a macroscopic Markovian bath coupled to the target system. Therefore, the SSC qualitatively appears to be the same as if the continuous Markovian bath would be used. We confirm that the presence of composite system-bath interactions under the rotating-wave approximation (RWA) is the necessary condition for the generation of SSC using thermal resources in collision models. Remarkably, we show that SSC substantially increases if the target system interacts collectively with more than one bath element at a time. Already few bath elements collectively interacting with the target system are sufficient to increase SSC at non-zero temperatures at the cost of tolerable lowering the final state purity. From the thermodynamic perspective, the SSC generation in our collision models is inevitably linked to a non zero power input (and thus heat dissipated to the bath) necessary to reach the steady-state, although such energetic cost can be lower compared to cases relying on SSC non generating interactions.
Anomaly localization by modeling perceptual features

Although unsupervised generative modeling of an image dataset using a Variational AutoEncoder (VAE) has been used to detect anomalous images, or anomalous regions in images, recent works have shown that this method often identifies images or regions that do not concur with human perception, even questioning the usability of generative models for robust anomaly detection. Here, we argue that those issues can emerge from having a simplistic model of the anomaly distribution and we propose a new VAE-based model expressing a more complex anomaly model that is also closer to human perception. This Feature-Augmented VAE is trained by not only reconstructing the input image in pixel space, but also in several different feature spaces, which are computed by a convolutional neural network trained beforehand on a large image dataset. It achieves clear improvement over state-of-the-art methods on the MVTec anomaly detection and localization datasets.
Darwinian evolution as Brownian motion on the simplex: A geometric perspective on stochastic replicator dynamics

We prove that stochastic replicator dynamics can be interpreted as intrinsic Brownian motion on the simplex equipped the Aitchison geometry. As an immediate consequence we derive three approximation results in the spirit of Wong-Zakai approximation, Donsker's invariance principle and a JKO-scheme. Finally, using the Fokker-Planck equation and Wasserstein-contraction estimates, we study the long time behavior of the stochastic replicator equation, as an example of a non-gradient drift diffusion on the Aitchison simplex.
Monochromatic triangle packings in red-blue graphs

We prove that in every $2$-edge-colouring of $K_n$ there is a collection of $n^2/12 + o(n^2)$ edge-disjoint monochromatic triangles, thus confirming a conjecture of Erdős. We also prove a corresponding stability result, showing that $2$-colourings that are close to attaining the aforementioned bound have a colour class which is close to bipartite. As part of our proof, we confirm a recent conjecture of Tyomkyn about the fractional version of this problem.
Constraining the depth of the winds on Uranus and Neptune via Ohmic dissipation

Determining the depth of atmospheric winds in the outer planets of the Solar system is a key topic in planetary science. We provide constraints on these depths in Uranus and Neptune via the total induced Ohmic dissipation, due to the interaction of the zonal flows and the planetary magnetic fields. An upper bound can be placed on the induced dissipation via energy and entropy flux throughout the interior. The induced Ohmic dissipation is directly linked to the electrical conductivity profile of the materials involved in the flow. We present a method for calculating electrical conductivity profiles of ionically conducting hydrogen–helium–water mixtures under planetary conditions, using results from ab initio simulations. We apply this prescription on several ice giant interior structure models available in the literature, where all the heavy elements are represented by water. According to the energy (entropy) flux budget, the maximum penetration depth for Uranus lies above 0.93 RU (0.90 RU) and for Neptune above 0.95 RN (0.92 RN). These results for the penetration depths are upper bounds and are consistent with previous estimates based on the contribution of the zonal winds to the gravity field. As expected, interior structure models with higher water abundance in the outer regions also have a higher electrical conductivity and therefore reach the Ohmic limit at shallower regions. Thus, our study shows that the likelihood of deep-seated winds on Uranus and Neptune drops significantly with the presence of water in the outer layers.
Competitive Demand Learning: a Data-Driven Pricing Algorithm

Dynamic pricing is used to maximize the revenue of a firm over a finite-period planning horizon, given that the firm may not know the underlying demand curve "a priori". In emerging markets, in particular, firms constantly adjust pricing strategies to collect adequate demand information, which is a process known as price experimentation. To date, few papers have investigated the pricing decision process in a competitive environment with unknown demand curves, conditions that make analysis more complex. Asynchronous price updating can render the demand information gathered by price experimentation less informative or inaccurate, as it is nearly impossible for firms to remain informed about the latest prices set by competitors. Hence, firms may set prices using available, yet out-of-date, price information of competitors. In this paper, we design an algorithm to facilitate synchronized dynamic pricing, in which competitive firms estimate their demand functions based on observations and adjust their pricing strategies in a prescribed manner. The process is called "learning and earning" elsewhere in the literature. The goal is for the pricing decisions, determined by estimated demand functions, to converge to underlying equilibrium decisions. The main question that we answer is whether such a mechanism of periodically synchronized price updates is optimal for all firms. Furthermore, we ask whether prices converge to a stable state and how much regret firms incur by employing such a data-driven pricing algorithm.
Text Classification based on Multi-granularity Attention Hybrid Neural Network

Neural network-based approaches have become the driven forces for Natural Language Processing (NLP) tasks. Conventionally, there are two mainstream neural architectures for NLP tasks: the recurrent neural network (RNN) and the convolution neural network (ConvNet). RNNs are good at modeling long-term dependencies over input texts, but preclude parallel computation. ConvNets do not have memory capability and it has to model sequential data as un-ordered features. Therefore, ConvNets fail to learn sequential dependencies over the input texts, but it is able to carry out high-efficient parallel computation. As each neural architecture, such as RNN and ConvNets, has its own pro and con, integration of different architectures is assumed to be able to enrich the semantic representation of texts, thus enhance the performance of NLP tasks. However, few investigation explores the reconciliation of these seemingly incompatible architectures. To address this issue, we propose a hybrid architecture based on a novel hierarchical multi-granularity attention mechanism, named Multi-granularity Attention-based Hybrid Neural Network (MahNN). The attention mechanism is to assign different weights to different parts of the input sequence to increase the computation efficiency and performance of neural models. In MahNN, two types of attentions are introduced: the syntactical attention and the semantical attention. The syntactical attention computes the importance of the syntactic elements (such as words or sentence) at the lower symbolic level and the semantical attention is used to compute the importance of the embedded space dimension corresponding to the upper latent semantics. We adopt the text classification as an exemplifying way to illustrate the ability of MahNN to understand texts.
Mean-field spin-oscillation dynamics beyond the single-mode approximation for a harmonically trapped spin-1 Bose-Einstein condensate

Compared to single-component Bose-Einstein condensates, spinor Bose-Einstein condensates display much richer dynamics. In addition to density oscillations, spinor Bose-Einstein condensates exhibit intriguing spin dynamics that is associated with population transfer between different hyperfine components. This work analyzes the validity of the widely employed single-mode approximation when describing the spin dynamics in response to a quench of the system Hamiltonian. The single-mode approximation assumes that the different hyperfine states all share the same time-independent spatial mode. This implies that the resulting spin Hamiltonian only depends on the spin interaction strength and not on the density interaction strength. Taking the spinor sodium Bose-Einstein condensate in the $f=1$ hyperfine manifold as an example and working within the mean-field theory framework, it is found numerically that the single-mode approximation misses, in some parameter regimes, intricate details of the spin and spatial dynamics. We develop a physical picture that explains the observed phenomenon. Moreover, using that the population oscillations described by the single-mode approximation enter into the effective potential felt by the mean-field spinor, we derive a semi-quantitative condition for when dynamical mean-field induced corrections to the single-mode approximation are relevant. Our mean-field results have implications for a variety of published and planned experimental studies.
Prismatic cohomology and de Rham-Witt forms

For any prism $(A, I)$ we construct a canonical map $W_r(A/I)\to A/I\phi(I)\ldots\phi^{r-1}(I)$. This map is necessary for existence of a canonical base change comparison between prismatic cohomology and de Rham-Witt forms. We construct a canonical map from prismatic cohomology to de Rham-Witt forms and prove that it is an isomorphism in the perfect case. Using this we get an explicit description of the prismatic cohomology for a polynomial algebra over $A/d$.
The stellar mass assembly of low-redshift, massive, central galaxies in SDSS and the TNG300 simulation

The stellar mass assembly of galaxies can be affected by both secular and environmental processes. In this study, for the first time, we investigate the stellar mass assembly of $\sim90,000$ low redshift, central galaxies selected from SDSS group catalogues (M$_{\rm Stellar}\gtrsim10^{9.5}$M$_{\odot}$, M$_{\rm Halo}\gtrsim10^{12}$M$_{\odot}$) as a function of both stellar and halo mass. We use estimates of the times at which 10, 50 and 90 per cent of the stellar mass was assembled from photometric spectral energy distribution fitting, allowing a more complete investigation than single stellar ages alone. We consider trends in both stellar and halo mass simultaneously, finding dependencies of all assembly times on both. We find that galaxies with higher stellar masses (at constant halo mass) have on average older lookback times, similar to previous studies of galaxy assembly. We also find that galaxies at higher halo mass (at constant stellar mass) have younger lookback times, possibly due to a larger reservoir of gas for star formation. An exception to this is a sub sample with high stellar-to-halo mass ratios, which are likely massive, field spirals. We compare these observed trends to those predicted by the TNG300 simulation, finding good agreement overall as a function of either stellar or halo mass. However, some differences in the assembly times (of up to $\sim 3$ Gyr) appear when considering both stellar and halo mass simultaneously, noticeably at intermediate stellar masses (M$_{\rm Stellar} \sim 10^{11}$ M$_{\odot}$). These discrepancies are possibly linked to the quenched fraction of galaxies and the kinetic mode AGN feedback implemented in TNG300.