Miracle-dz/UniFAR-FTUs huggingface dataset:
Dilated Neighborhood Attention Transformer

Transformers are quickly becoming one of the most heavily applied deep learning architectures across modalities, domains, and tasks. In vision, on top of ongoing efforts into plain transformers, hierarchical transformers have also gained significant attention, thanks to their performance and easy integration into existing frameworks. These models typically employ localized attention mechanisms, such as the sliding-window Neighborhood Attention (NA) or Swin Transformer's Shifted Window Self Attention. While effective at reducing self attention's quadratic complexity, local attention weakens two of the most desirable properties of self attention: long range inter-dependency modeling, and global receptive field. In this paper, we introduce Dilated Neighborhood Attention (DiNA), a natural, flexible and efficient extension to NA that can capture more global context and expand receptive fields exponentially at no additional cost. NA's local attention and DiNA's sparse global attention complement each other, and therefore we introduce Dilated Neighborhood Attention Transformer (DiNAT), a new hierarchical vision transformer built upon both. DiNAT variants enjoy significant improvements over strong baselines such as NAT, Swin, and ConvNeXt. Our large model is faster and ahead of its Swin counterpart by 1.6% box AP in COCO object detection, 1.4% mask AP in COCO instance segmentation, and 1.4% mIoU in ADE20K semantic segmentation. Paired with new frameworks, our large variant is the new state of the art panoptic segmentation model on COCO (58.5 PQ) and ADE20K (49.4 PQ), and instance segmentation model on Cityscapes (45.1 AP) and ADE20K (35.4 AP) (no extra data). It also matches the state of the art specialized semantic segmentation models on ADE20K (58.1 mIoU), and ranks second on Cityscapes (84.5 mIoU) (no extra data).
Towards Equalised Odds as Fairness Metric in Academic Performance Prediction

The literature for fairness-aware machine learning knows a plethora of different fairness notions. It is however wellknown, that it is impossible to satisfy all of them, as certain notions contradict each other. In this paper, we take a closer look at academic performance prediction (APP) systems and try to distil which fairness notions suit this task most. For this, we scan recent literature proposing guidelines as to which fairness notion to use and apply these guidelines onto APP. Our findings suggest equalised odds as most suitable notion for APP, based on APP's WYSIWYG worldview as well as potential long-term improvements for the population.
Improving alignment of dialogue agents via targeted human judgements

We present Sparrow, an information-seeking dialogue agent trained to be more helpful, correct, and harmless compared to prompted language model baselines. We use reinforcement learning from human feedback to train our models with two new additions to help human raters judge agent behaviour. First, to make our agent more helpful and harmless, we break down the requirements for good dialogue into natural language rules the agent should follow, and ask raters about each rule separately. We demonstrate that this breakdown enables us to collect more targeted human judgements of agent behaviour and allows for more efficient rule-conditional reward models. Second, our agent provides evidence from sources supporting factual claims when collecting preference judgements over model statements. For factual questions, evidence provided by Sparrow supports the sampled response 78% of the time. Sparrow is preferred more often than baselines while being more resilient to adversarial probing by humans, violating our rules only 8% of the time when probed. Finally, we conduct extensive analyses showing that though our model learns to follow our rules it can exhibit distributional biases.
TWO-COMPONENT GALACTIC BULGE PROBED WITH RENEWED GALACTIC CHEMICAL EVOLUTION MODEL

Results of recent observations of the Galactic bulge demand that we discard a simple picture of its formation, suggesting the presence of two stellar populations represented by two peaks of stellar metallicity distribution (MDF) in the bulge. To assess this issue, we construct Galactic chemical evolution models that have been updated in two respects: first, the delay time distribution of Type Ia supernovae (SNe Ia) recently revealed by extensive SN Ia surveys is incorporated into the models. Second, the nucleosynthesis clock, the s-processing in asymptotic giant branch stars, is carefully considered in this study. This novel model first shows that the Galaxy feature tagged by the key elements, Mg, Fe, and Ba, for the bulge as well as thin and thick disks is compatible with a short-delay SN Ia. We present a successful modeling of a two-component bulge including the MDF and the evolutions of [Mg/Fe] and [Ba/Mg], and reveal its origin as follows. A metal-poor component (〈[Fe/H]〉 ∼ −0.5) is formed with a relatively short timescale of ∼1 Gyr. These properties are identical to the thick disk's characteristics in the solar vicinity. Subsequently from its remaining gas mixed with a gas flow from the disk outside the bulge, a metal-rich component (〈[Fe/H]〉 ∼ +0.3) is formed with a longer timescale (∼4 Gyr) together with a top-heavy initial mass function that might be identified with the thin disk component within the bulge.
Continuous PDE Dynamics Forecasting with Implicit Neural Representations

Effective data-driven PDE forecasting methods often rely on fixed spatial and / or temporal discretizations. This raises limitations in real-world applications like weather prediction where flexible extrapolation at arbitrary spatiotemporal locations is required. We address this problem by introducing a new data-driven approach, DINo, that models a PDE's flow with continuous-time dynamics of spatially continuous functions. This is achieved by embedding spatial observations independently of their discretization via Implicit Neural Representations in a small latent space temporally driven by a learned ODE. This separate and flexible treatment of time and space makes DINo the first data-driven model to combine the following advantages. It extrapolates at arbitrary spatial and temporal locations; it can learn from sparse irregular grids or manifolds; at test time, it generalizes to new grids or resolutions. DINo outperforms alternative neural PDE forecasters in a variety of challenging generalization scenarios on representative PDE systems.
Motion and Appearance Adaptation for Cross-Domain Motion Transfer

Motion transfer aims to transfer the motion of a driving video to a source image. When there are considerable differences between object in the driving video and that in the source image, traditional single domain motion transfer approaches often produce notable artifacts; for example, the synthesized image may fail to preserve the human shape of the source image ( cf . Fig. 1 (a)). To address this issue, in this work, we propose a Motion and Appearance Adaptation (MAA) approach for cross-domain motion transfer, in which we regularize the object in the synthesized image to capture the motion of the object in the driving frame, while still preserving the shape and appearance of the object in the source image. On one hand, considering the object shapes of the synthesized image and the driving frame might be different, we design a shape-invariant motion adaptation module that enforces the consistency of the angles of object parts in two images to capture the motion information. On the other hand, we introduce a structure-guided appearance consistency module designed to regularize the similarity between the corresponding patches of the synthesized image and the source image without affecting the learned motion in the synthesized image. Our proposed MAA model can be trained in an end-to-end manner with a cyclic reconstruction loss, and ultimately produces a satisfactory motion transfer result ( cf . Fig. 1 (b)). We conduct extensive experiments on human dancing dataset Mixamo-Video to Fashion-Video and human face dataset Vox-Celeb to Cufs; on both of these, our MAA model outperforms existing methods both quantitatively and qualitatively. (MAA) approach for cross-domain motion transfer. In MAA, we design a shape-invariant motion adaptation module to enforce the consistency of the angles of object parts in two images to capture the motion information. Meanwhile, we introduce a structure-guided appearance consistency module to regularize the similarity between the patches of the synthesized image and the source image. The experimental results demonstrates the effectiveness of our proposed method.
A New Upper Bound for the d-dimensional Algebraic Connectivity of Arbitrary Graphs

In this paper we show that the d -dimensional algebraic connectivity of an arbitrary graph G is bounded above by its 1-dimensional algebraic connectivity, i.e., a d ( G ) ≤ a 1 ( G ) , where a 1 ( G ) corresponds the well-studied second smallest eigenvalue of the graph Laplacian.
CIGALEMC: GALAXY PARAMETER ESTIMATION USING A MARKOV CHAIN MONTE CARLO APPROACH WITH CIGALE

We introduce a fast Markov Chain Monte Carlo (MCMC) exploration of the astrophysical parameter space using a modified version of the publicly available code Code Investigating GALaxy Emission (CIGALE). The original CIGALE builds a grid of theoretical spectral energy distribution (SED) models and fits to photometric fluxes from ultraviolet to infrared to put constraints on parameters related to both formation and evolution of galaxies. Such a grid-based method can lead to a long and challenging parameter extraction since the computation time increases exponentially with the number of parameters considered and results can be dependent on the density of sampling points, which must be chosen in advance for each parameter. MCMC methods, on the other hand, scale approximately linearly with the number of parameters, allowing a faster and more accurate exploration of the parameter space by using a smaller number of efficiently chosen samples. We test our MCMC version of the code CIGALE (called CIGALEMC) with simulated data. After checking the ability of the code to retrieve the input parameters used to build the mock sample, we fit theoretical SEDs to real data from the well-known and -studied Spitzer Infrared Nearby Galaxy Survey sample. We discuss constraints on the parameters and show the advantages of our MCMC sampling method in terms of accuracy of the results and optimization of CPU time.
Does Zero-Shot Reinforcement Learning Exist?

A zero-shot RL agent is an agent that can solve any RL task in a given environment, instantly with no additional planning or learning, after an initial reward-free learning phase. This marks a shift from the reward-centric RL paradigm towards"controllable"agents that can follow arbitrary instructions in an environment. Current RL agents can solve families of related tasks at best, or require planning anew for each task. Strategies for approximate zero-shot RL ave been suggested using successor features (SFs) [BBQ+ 18] or forward-backward (FB) representations [TO21], but testing has been limited. After clarifying the relationships between these schemes, we introduce improved losses and new SF models, and test the viability of zero-shot RL schemes systematically on tasks from the Unsupervised RL benchmark [LYL+21]. To disentangle universal representation learning from exploration, we work in an offline setting and repeat the tests on several existing replay buffers. SFs appear to suffer from the choice of the elementary state features. SFs with Laplacian eigenfunctions do well, while SFs based on auto-encoders, inverse curiosity, transition models, low-rank transition matrix, contrastive learning, or diversity (APS), perform unconsistently. In contrast, FB representations jointly learn the elementary and successor features from a single, principled criterion. They perform best and consistently across the board, reaching 85% of supervised RL performance with a good replay buffer, in a zero-shot manner.
A boundary element regularized Stokeslet method applied to cilia- and flagella-driven flow

A boundary element implementation of the regularized Stokeslet method of Cortez is applied to cilia and flagella-driven flows in biology. Previously published approaches implicitly combine the force discretization and the numerical quadrature used to evaluate boundary integrals. By contrast, a boundary element method can be implemented by discretizing the force using basis functions, and calculating integrals using accurate numerical or analytic integration. This substantially weakens the coupling of the mesh size for the force and the regularization parameter, and greatly reduces the number of degrees of freedom required. When modelling a cilium or flagellum as a one-dimensional filament, the regularization parameter can be considered a proxy for the body radius, as opposed to being a parameter used to minimize numerical errors. Modelling a patch of cilia, it is found that: (i) for a fixed number of cilia, reducing cilia spacing reduces transport, (ii) for fixed patch dimension, increasing cilia number increases the transport, up to a plateau at 9×9 cilia. Modelling a choanoflagellate cell, it is found that the presence of a lorica structure significantly affects transport and flow outside the lorica, but does not significantly alter the force experienced by the flagellum.
A systematic look at the effects of radiative feedback on disc galaxy formation

Galaxy formation models and simulations rely on various feedback mechanisms to reproduce the observed baryonic scaling relations and galaxy morphologies. Although dwarf galaxy and giant elliptical properties can be explained using feedback from supernova and active galactic nuclei, Milky Way-sized galaxies still represent a challenge to current theories of galaxy formation. In this paper, we explore the possible role of feedback from stellar radiation in regulating the main properties of disc galaxies such as our own Milky Way. We have performed a suite of cosmological simulations of the same ~1012M{N-ary circled dot operator} halo selected based on its rather typical mass accretion history. We have implemented radiative feedback from young stars using a crude model of radiative transfer for ultraviolet and infrared radiation. However, the model is realistic enough such that the dust opacity plays a direct role in regulating the efficiency of our feedback mechanism. We have explored various models for the dust opacity, assuming different constant dust temperatures, as well as a varying dust temperature model. We find that while strong radiative feedback appears as a viable mechanism to regulate the stellarmass fraction inmassive galaxies, it also prevents the formation of discs with reasonable morphologies. In models with strong stellar radiation feedback, stellar discs are systematically too thick while the gas disc morphology is completely destroyed due to the efficient mixing between the feedback-affected gas and its surroundings. At the resolution of our simulation suite, we find it impossible to preserve spiral discmorphology while at the same time expelling enough baryons to satisfy the abundance matching constraints. (Less)
Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees

We consider reinforcement learning in an environment modeled by an episodic, finite, stage-dependent Markov decision process of horizon $H$ with $S$ states, and $A$ actions. The performance of an agent is measured by the regret after interacting with the environment for $T$ episodes. We propose an optimistic posterior sampling algorithm for reinforcement learning (OPSRL), a simple variant of posterior sampling that only needs a number of posterior samples logarithmic in $H$, $S$, $A$, and $T$ per state-action pair. For OPSRL we guarantee a high-probability regret bound of order at most $\widetilde{\mathcal{O}}(\sqrt{H^3SAT})$ ignoring $\text{poly}\log(HSAT)$ terms. The key novel technical ingredient is a new sharp anti-concentration inequality for linear forms which may be of independent interest. Specifically, we extend the normal approximation-based lower bound for Beta distributions by Alfers and Dinges [1984] to Dirichlet distributions. Our bound matches the lower bound of order $\Omega(\sqrt{H^3SAT})$, thereby answering the open problems raised by Agrawal and Jia [2017b] for the episodic setting.
The evolution of Lyman-break galaxies in CDM

We make a detailed investigation of the properties of Lyman-break galaxies (LBGs) in the LambdaCDM model. We present predictions for two published variants of the GALFORM semi-analytical model: the Baugh et al. (2005) model, which has star formation at high redshifts dominated by merger-driven starbursts with a top-heavy IMF, and the Bower et al. (2006) model, which has AGN feedback and a standard Solar neighbourhood IMF throughout. We show predictions for the evolution of the rest-frame far-UV luminosity function in the redshift range z=3-20, and compare with the observed luminosity functions of LBGs at z=3-10. We find that the Baugh et al. model is in excellent agreement with these observations, while the Bower et al. model predicts too many high-luminosity LBGs. Dust extinction, which is predicted self-consistently based on galaxy gas contents, metallicities and sizes, is found to have a large effect on LBG luminosities. We compare predictions for the size evolution of LBGs at different luminosities with observational data for 2<z<7, and find the Baugh et al. model to be in good agreement. We present predictions for stellar, halo and gas masses, star formation rates, circular velocities, bulge-to-disk ratios, gas and stellar metallicities and clustering bias, as functions of far-UV luminosity and redshift. We find broad consistency with current observational constraints. We then present predictions for the abundance and angular sizes of LBGs out to very high redshift (z<20), finding that planned deep surveys with JWST should detect objects out to z<15. The typical UV luminosities of galaxies are predicted to be very low at high redshifts, which has implications for detecting the galaxies responsible for reionizing the IGM; for example, at z=10, 50% of the ionizing photons are expected to be produced by galaxies fainter than M_AB(1500A)-5logh ~ -15.
LARGE-SCALE AZIMUTHAL STRUCTURES OF TURBULENCE IN ACCRETION DISKS: DYNAMO TRIGGERED VARIABILITY OF ACCRETION

We investigate the significance of large-scale azimuthal, magnetic, and velocity modes for the magnetorotational instability (MRI) turbulence in accretion disks. We perform three-dimensional global ideal MHD simulations of global stratified protoplanetary disk models. Our domains span azimuthal angles of π/4, π/2, π, and 2π. We observe up to 100% stronger magnetic fields and stronger turbulence for the restricted azimuthal domain models π/2 and π/4 compared to the full 2π model. We show that for those models the Maxwell stress is larger due to strong axisymmetric magnetic fields generated by the αΩ dynamo. Large radial extended axisymmetric toroidal fields trigger temporal magnification of accretion stress. All models display a positive dynamo-α in the northern hemisphere (upper disk). The parity is distinct in each model and changes on timescales of 40 local orbits. In model 2π, the toroidal field is mostly antisymmetric with respect to the midplane. The eddies of the MRI turbulence are highly anisotropic. The major wavelengths of the turbulent velocity and magnetic fields are between one and two disk scale heights. At the midplane, we find magnetic tilt angles around 8°–9° increasing up to 12°–13° in the corona. We conclude that an azimuthal extent of π is sufficient to reproduce most turbulent properties in three-dimensional global stratified simulations of magnetized accretion disks.
Extracción y cuantificación indirecta de las saponinasde agave lechuguilla Torrey

Agave lechuguilla Torrey es una planta que pertenece a la familia de las Agavaceae, crece en zonas aridas, en terrenos calizos y rocosos. Se explota para la obtencion de la fibra, conocida en Mexico como ixtle, la cual se utiliza ampliamente en las industrias de la fabricacion de cepillos y de la construccion debido a sus caracteristicas abrasivas y alto indice de retencion de agua (65 por ciento). La fibra se obtiene por tallado de la hoja constituida por un 15 por ciento de fibra y un 85 por ciento de pulpa. La pulpa contiene compuestos bioactivos entre los que destacan las saponinas con varias propiedades farmacologicas. Para determinar las condiciones de extraccion de saponinas se aplico un diseno experimental factorial, en donde se evaluo el efecto de tipo de solvente, relacion: solido/liquido, temperatura y numero de lote, determinando indirectamente el contenido de saponinas como variable de respuesta. De acuerdo al analisis de varianza efectuado, los factores que influyen en el rendimiento de obtencion de saponinas son temperatura y numero de lote. Para la cuantificacion de saponinas se utilizo como base el metodo de Hiai, en donde se vario el tipo de acido y los tiempos de reaccion. La variable de respuesta fueron los azucares generados por la hidrolisis de la molecula. Estas modificaciones resultan en un metodo menos oxidante y con mayor repetibilidad, asi como un alto indice de correlacion entre el contenido de azucares y la cantidad de saponinas presentes.
Long-term general relativistic simulation of binary neutron stars collapsing to a black hole

General relativistic simulations for the merger of binary neutron stars are performed as an extension of a previous work [M. Shibata and K. Taniguchi, Phys. Rev. D 73, 064027 (2006). ]. We prepare binary neutron stars with a large initial orbital separation and employ the moving-puncture formulation, which enables one to follow merger and ringdown phases for a long time, even after black hole formation. For modeling inspiraling neutron stars, which should be composed of cold neutron stars, the Akmal-Pandharipande-Ravenhall (APR) equation of state (EOS) is adopted. After the onset of merger, the hybrid-type EOS is used; i.e., the cold and thermal parts are given by the APR and {gamma}-law EOSs, respectively. Three equal-mass binaries, each with mass 1.4M{sub {center_dot}}, 1.45M{sub {center_dot}}, and 1.5M{sub {center_dot}}, and two unequal-mass binaries with mass, 1.3 vs 1.6M{sub {center_dot}} and 1.35 vs 1.65M{sub {center_dot}}, are prepared. We focus primarily on the black hole formation case, and explore mass and spin of the black hole, mass of disks which surround the black hole, and gravitational waves emitted during the black hole formation. We find that (i) the black hole is promptly formed if total mass of the system initially satisfies m{sub 0} > or approx. 2.9M{submore » {center_dot}}; (ii) for the systems of m{sub 0}=2.9-3.0M{sub {center_dot}} and of mass ratio {approx_equal}0.8, the mass of disks which surround the formed black hole is 0.006-0.02M{sub {center_dot}}; (iii) the spin of the formed black hole is 0.78{+-}0.02 when a black hole is formed after the merger in the dynamical time scale. This value depends weakly on the total mass and mass ratio, and is about 0.1 larger than that of a black hole formed from nonspinning binary black holes; (iv) for the black hole formation case, Fourier spectrum shape of gravitational waves emitted in the merger and ringdown phases has a universal qualitative feature irrespective of the total mass and mass ratio, but quantitatively, the spectrum reflects the parameters of the binary neutron stars. « less