Miracle-dz/UniFAR-FTUs huggingface dataset:
Offline Policy Evaluation and Optimization under Confounding

Evaluating and optimizing policies in the presence of unobserved confounders is a problem of growing interest in offline reinforcement learning. Using conventional methods for offline RL in the presence of confounding can not only lead to poor decisions and poor policies, but can also have disastrous effects in critical applications such as healthcare and education. We map out the landscape of offline policy evaluation for confounded MDPs, distinguishing assumptions on confounding based on their time-evolution and effect on the data-collection policies. We determine when consistent value estimates are not achievable, providing and discussing algorithms to estimate lower bounds with guarantees in those cases. When consistent estimates are achievable, we provide sample complexity guarantees. We also present new algorithms for offline policy improvement and prove local convergence guarantees. Finally, we experimentally evaluate our algorithms on gridworld and a simulated healthcare setting of managing sepsis patients. We note that in gridworld, our model-based method provides tighter lower bounds than existing methods, while in the sepsis simulator, our methods significantly outperform confounder-oblivious benchmarks.
Fast Inference from Transformers via Speculative Decoding

Inference from large autoregressive models like Transformers is slow - decoding K tokens takes K serial runs of the model. In this work we introduce speculative decoding - an algorithm to sample from autoregressive models faster without any changes to the outputs, by computing several tokens in parallel. At the heart of our approach lie the observations that (1) hard language-modeling tasks often include easier subtasks that can be approximated well by more efficient models, and (2) using speculative execution and a novel sampling method, we can make exact decoding from the large models faster, by running them in parallel on the outputs of the approximation models, potentially generating several tokens concurrently, and without changing the distribution. Our method can accelerate existing off-the-shelf models without retraining or architecture changes. We demonstrate it on T5-XXL and show a 2X-3X acceleration compared to the standard T5X implementation, with identical outputs.
Lyapunov Design of Multivariable MRAC via Generalized Passivation

A Lyapunov design of multiple input, multiple output (MIMO) model‐reference adaptive control (MRAC) systems is proposed using new generalized passivity concepts based on WSPR and WASPR properties. The new design avoids control overparameterization and leads to the simplest unnormalized gradient algorithm by means of a passivating multiplier. To this end, a necessary and sufficient condition on the high frequency gain matrix to achieve the WASPR property is established. This condition is referred to as Positive Diagonal Jordan (PDJ) form. Based on the robustness WSPR and WASPR properties, a systematic determination of a robust passivating matrix multiplier is then presented. The advantages of the new design are illustrated by numerical simulation including a robotics adaptive visual servoing problem and a 4‐input‐4‐output dynamic system. © 2014 Chinese Automatic Control Society and Wiley Publishing Asia Pty Ltd
Exchange flow through an opening

This paper presents a theoretical and experimental study of the exchange of fluids of different densities through an opening. Three types of openings are examined: a bottom opening (the opening is at the bottom of a gate), a middle opening (in the middle) and a window opening (the opening is in the middle but does not extend across the width). Simultaneous measurements of velocity field and interface position were obtained using flow visualization and image processing techniques. Experimental results confirm the predictions of the internal hydraulic theory that there are two internal hydraulic controls in the flow through bottom openings, but one control in the middle and window opening experiments. The neglect of non-hydrostatic forces and interfacial mixing in the theory, however, results in a significant underestimate of the exchange rate by more than 50 % in the middle and window opening experiments. The fluctuations in the interface position were caused by Kelvin-Helmholtz instabilities as well as basin-scale internal seiche, and the transition of internally supercritical flow to subcritical flow was caused by the mixing generated by these instabilities.
Determining Dust Properties in Protoplanetary Disks: SED-derived Masses and Settling with ALMA

We present spectral energy distribution (SED) modeling of 338 disks around T Tauri stars from 11 star-forming regions, ranging from ∼0.5 to 10 Myr old. The disk masses we infer from our SED models are typically greater than those reported from (sub)millimeter surveys by a factor of 1.5–5, with the discrepancy being generally higher for the more massive disks. Masses derived from (sub)millimeter fluxes rely on the assumption that the disks are optically thin at all millimeter wavelengths, which may cause the disk masses to be underestimated since the observed flux is not sensitive to the whole mass in the disk; SED models do not make this assumption and thus yield higher masses. Disks with more absorbing material should be optically thicker at a given wavelength, which could lead to a larger discrepancy for disks around massive stars when the disk temperature is scaled by the stellar luminosity. We also compare the disk masses and degree of dust settling across the different star-forming regions and find that disks in younger regions are more massive than disks in older regions, but with a similar degree of dust settling. Together, these results offer potential partial solutions to the “missing” mass problem: disks around T Tauri stars may indeed have enough material to form planetary systems, though previous studies have underestimated the mass by assuming the disks to be optically thin; these planetary systems may also form earlier than previously theorized since significant dust evolution (i.e., settling) is already apparent in young disks.
Spin-polarized antichiral exciton-polariton edge states

We consider theoretically a system of exciton-polariton micropillars arranged in a honeycomb lattice. The naturally present TE-TM splitting and an alternating Zeeman splitting, where the different sublattices experience opposite Zeeman splitting, shifts the Dirac points in energy, giving rise to antichiral behavior. In a strip geometry having zigzag edges, two pairs of edge states exist and propagate in the same direction (including the states at the opposite edges). The edge modes localized at the opposite edges have opposite spins (circular polarizations), which leads to co-propagating"+-"spin channels. The antichiral edge states are protected by non-zero winding numbers and can propagate around a 60 degree bend without being reflected. We further compare the transport properties of these edge states with chiral edge modes and propose a scheme to realize them experimentally.
SGDraw: Scene Graph Drawing Interface Using Object-Oriented Representation

Scene understanding is an essential and challenging task in computer vision. To provide the visually fundamental graphical structure of an image, the scene graph has received increased attention due to its powerful semantic representation. However, it is difficult to draw a proper scene graph for image retrieval, image generation, and multi-modal applications. The conventional scene graph annotation interface is not easy to use in image annotations, and the automatic scene graph generation approaches using deep neural networks are prone to generate redundant content while disregarding details. In this work, we propose SGDraw, a scene graph drawing interface using object-oriented scene graph representation to help users draw and edit scene graphs interactively. For the proposed object-oriented representation, we consider the objects, attributes, and relationships of objects as a structural unit. SGDraw provides a web-based scene graph annotation and generation tool for scene understanding applications. To verify the effectiveness of the proposed interface, we conducted a comparison study with the conventional tool and the user experience study. The results show that SGDraw can help generate scene graphs with richer details and describe the images more accurately than traditional bounding box annotations. We believe the proposed SGDraw can be useful in various vision tasks, such as image retrieval and generation.
Camelira: An Arabic Multi-Dialect Morphological Disambiguator

We present Camelira, a web-based Arabic multi-dialect morphological disambiguation tool that covers four major variants of Arabic: Modern Standard Arabic, Egyptian, Gulf, and Levantine. Camelira offers a user-friendly web interface that allows researchers and language learners to explore various linguistic information, such as part-of-speech, morphological features, and lemmas. Our system also provides an option to automatically choose an appropriate dialect-specific disambiguator based on the prediction of a dialect identification component. Camelira is publicly accessible at http://camelira.camel-lab.com.
Merger Signatures are Common, but not Universal, in Massive, Recently Quenched Galaxies at z ∼ 0.7

We present visual classifications of merger-induced tidal disturbances in 143 M * ∼ 1011 M ⊙ post-starburst galaxies at z ∼ 0.7 identified in the SQuIGGL⃗E Sample. This sample spectroscopically selects galaxies from the Sloan Digital Sky Survey that have stopped their primary epoch of star formation within the past ∼500 Myr. Visual classifications are performed on Hyper Suprime-Cam imaging. We compare to a control sample of mass- and redshift-matched star-forming and quiescent galaxies from the Large Early Galaxy Census and find that post-starburst galaxies are more likely to be classified as disturbed than either category. This corresponds to a factor of 3.6−1.3+2.9 times the disturbance rate of older quiescent galaxies and 2.1−. 73+1.9 times the disturbance rate of star-forming galaxies. Assuming tidal features persist for ≲500 Myr, this suggests merging is coincident with quenching in a significant fraction of these post-starbursts. Galaxies with tidal disturbances are younger on average than undisturbed post-starburst galaxies in our sample, suggesting tidal features from a major merger may have faded over time. This may be exacerbated by the fact that, on average, the undisturbed subset is fainter, rendering low-surface-brightness tidal features harder to identify. However, the presence of 10 young (≲150 Myr since quenching) undisturbed galaxies suggests that major mergers are not the only fast physical mechanism that shut down the primary epoch of star formation in massive galaxies at intermediate redshift.
Modeling animal contests based on spatio-temporal dynamics

We present a general theoretical model for the spatio-temporal dynamics of animal contests. Inspired by interactions between physical particles, the model is formulated in terms of effective interaction potentials, which map typical elements of contest behaviour into empirically verifiable rules of contestant motion. This allows us to simulate the observable dynamics of contests in various realistic scenarios, notably in dyadic contests over a localized resource. Assessment strategies previously formulated in game-theoretic models, as well as the effects of fighting costs, can be described as variations in our model's parameters. Furthermore, the trends of contest duration associated with these assessment strategies can be derived and understood within the model. Detailed description of the contestants' motion enables the exploration of spatio-temporal properties of asymmetric contests, such as the emergence of chase dynamics. Overall, our framework aims to bridge the growing gap between empirical capabilities and theory in this widespread aspect of animal behaviour.
Swarm-Based Gradient Descent Method for Non-Convex Optimization

We introduce a new Swarm-Based Gradient Descent (SBGD) method for non-convex optimization. The swarm consists of agents, each is identiﬁed with a position, x , and mass, m . The key to their dynamics is communication: masses are being transferred from agents at high ground to low(-est) ground. At the same time, agents change positions with step size, h = h ( x , m ), adjusted to their relative mass: heavier agents proceed with small time-steps in the direction of local gradient, while lighter agents take larger time-steps based on a backtracking protocol. Accordingly, the crowd of agents is dynamically divided between ‘heavier’ leaders, expected to approach local minima, and ‘lighter’ explorers. With their large-step protocol, explorers are expected to encounter improved position for the swarm; if they do, then they assume the role of ‘heavy’ swarm leaders and so on. Convergence analysis and numerical simulations in one-, two-, and 20-dimensional benchmarks demonstrate the eﬀectiveness of SBGD as a global optimizer.
Instability of ultracompact horizonless spacetimes

Motivated by recent results reporting the instability of horizonless objects with stable light rings, we revisit the linearized stability of such structures. In particular, we consider an exterior Kerr spacetime truncated at a surface where Dirichlet conditions on a massless scalar are imposed.This spacetime has ergoregions and light rings when the surface is placed sufficiently deep in the gravitational potential. We establish that the spacetime is linearly, mode-unstable when it is sufficiently compact, in a mechanism associated with the ergoregion. In particular, such instability has associated zero-modes. At large multipole number the critical surface location for zero modes to exist is precisely the location of the ergosurface along the equator. We show that such modes don't exist when the surface is outside the ergoregion, and that any putative linear instability mechanism acts on timescales $\tau \gtrsim 10^5 M$, where $M$ is the black hole mass. Our results indicate therefore that at least certain classes of objects are linearly stable in the absence of ergoregions, even if rotation and light rings are present.
Probabilistic Symmetry for Multi-Agent Dynamics

Learning multi-agent dynamics is a core AI problem with broad applications in robotics and autonomous driving. While most existing works focus on deterministic prediction, producing probabilistic forecasts to quantify uncertainty and assess risks is critical for downstream decision-making tasks such as motion planning and collision avoidance. Multi-agent dynamics often contains internal symmetry. By leveraging symmetry, specifically rotation equivariance, we can improve not only the prediction accuracy but also uncertainty calibration. We introduce Energy Score, a proper scoring rule, to evaluate probabilistic predictions. We propose a novel deep dynamics model, Probabilistic Equivariant Continuous COnvolution (PECCO) for probabilistic prediction of multi-agent trajectories. PECCO extends equivariant continuous convolution to model the joint velocity distribution of multiple agents. It uses dynamics integration to propagate the uncertainty from velocity to position. On both synthetic and real-world datasets, PECCO shows significant improvements in accuracy and calibration compared to non-equivariant baselines.
Multiplet exchange Auger transitions following resonant Auger decays in Ne 1s photoexcitation

Secondary electron emission with very low kinetic energy (KE) has been measured in the Ne 1s photoexcitation region. A new decay channel for Auger transitions following Ne 1s to 3p excitation has been identified using a two-dimensional mapping technique, in which slow Auger electron signals are displayed as functions of electron kinetic energy and photon energy. Electrons with about 0.68 eV KEs have been ascribed to multiplet exchange Auger electrons from the 2p−2(1S)3d state. This state is formed through the resonant Auger transition from the 1s−13p state, in which the excited 3p electron changes its azimuthal quantum number. Another cascade Auger decay of multiplet exchanging was found as electron emission of about 2.0 eV KEs; 2p−2(1S)4p → 2p−2(3P) + e−. Several cascade decays were found to occur via the photoexcitation into 1s−14p and 1s−15p states.
High-order strongly nonlinear long wave approximation and solitary wave solution. Part 2. Internal waves

Abstract A strongly nonlinear long-wave approximation is adopted to obtain a high-order model for large-amplitude long internal waves in a two-layer system by assuming the water depth is much smaller than the typical wavelength. When truncated at the first order, the model can be reduced to the regularized strongly nonlinear model of Choi et al. (J. Fluid Mech., vol. 629, 2009, pp. 73–85), which lessens the Kelvin–Helmholtz instability excited by the tangential velocity jump across the interface in the inviscid Miyata–Choi–Camassa (MCC) equations. Using the second-order model, the next-order correction to the internal solitary wave solution of the MCC equations is found and its validity is examined with the Euler solution in terms of the wave profile, the effective wavelength and the velocity profile. It is shown that the correction greatly improves the comparison with the Euler solution for the whole range of wave amplitudes and no further correction is necessary for practical applications. Based on a local stability analysis, the region of stability for the second-order long-wave model is identified in the physical parameter space so that the efficient numerical scheme developed for the first-order model can be used for the second-order model.
T-matrix theory of positron annihilation on hydrogen

The transition amplitude for electron-positron annihilation during positron-hydrogen scattering is formulated in terms of the half-shell T-matrix elements obtained from a solution of the momentum space Lippmann-Schwinger equation. The derived expression is well defined at energies below the positronium (Ps) formation threshold. However, singularities in the transition amplitude exist at energies above the Ps-formation threshold. The annihilation parameter for positron-hydrogen scattering is computed in a variety of models at energies below the Ps-formation threshold. Calculations with the largest basis reproduce previous variational calculations of ZJeff for the J = 0 and 1 partial waves. Accurate values of ZJeff were also given for the J = 2, 3 and 4 partial waves to give a summed Zeff that is probably the most accurate so far reported.
Parental Relationships and Family Functioning of Finnish Children Living with LGBTQ+ Parents

ABSTRACT Our knowledge of family relations and well-being in LGBTQ+ families is increasing, yet few studies so far have gathered quantitative data from both children and parents. The Finnish Rainbow Family study conducted surveys of 10–12-year-old children (N = 41), 13–18-year-old adolescents (N = 47), and of parents of 7–18-year-old children (N = 80 parents/103 responses) living in a LGBTQ+ family. Unlike many earlier surveys, we also asked about experiences of transgender adults in the family. Responses were compared with the nationwide School Health Promotion Study in Finland. Results suggest that the parental relationships and family functioning of underaged children in Finnish LGBTQ+ families are good and resemble those of other Finnish children. Children and parents describe their family life rather similarly. Rainbow families had slightly worse parental relations among 10–12-year-old children but overall better family functioning, and parents were very supporting and encouraging. The detected differences may relate to higher levels of both parental separation and parental education in Finnish rainbow families. Additionally, most adolescents with a transgender parent feel proud of their parent, although many have experienced difficulties talking about the transition with other people.
The Impact of Perceived Algorithmic Control on Employee Work Engagement: the mediating role of Burnout

With the advent of the digital age, perceived algorithm control has been more widely used in organizational management, which improves the efficiency of enterprise management and also has an important impact on employees. This paper aims to study the relationship between gig workers' perceived algorithmic control, burnout, and employee work engagement. In the context of the digital economy, this paper constructed the impact mechanism of gig workers' perceived algorithmic control on the employee work engagement, designed questionnaires to collect and analyze the data of digital platform enterprises in F city, explored the mechanism of gig workers' perceived algorithm control on employee work engagement, and tested the mediating effect of burnout and the moderating effect of implicit person theory (entity/ incremental). The results show that: (1) Perceived algorithm control is positively correlated with employee work engagement; (2) Perceived algorithm control is positively correlated with burnout; (3) Implicit person theory (entity/ incremental) plays a moderating role in the relationship between the perceived algorithm control and the burnout. Finally, from the perspective of enterprise sustainable development, theoretical and practical implications are proposed in the conclusion part.
Joint Augmented and Compressed Dictionaries for Robust Image Classification

Dictionary-based Classification (DC) has been a promising learning theory in multimedia computing. Previous studies focused on learning a discriminative dictionary as well as the sparsest representation based on the dictionary, to cope with the complex conditions in real-world applications. However, robustness by learning only one single dictionary is far from the optimal level. What is worse, it cannot take advantage of the available techniques proven in modern machine learning, like data augmentation, to mitigate the same problem. In this work, we propose a novel method that utilizes joint Augmented and Compressed Dictionaries for Robust Dictionary-based Classification (ACD-RDC). For optimization under the noise model introduced by real-world conditions, the objective function of ACD-RDC incorporates only two simple, but well-designed constraints, including one enhanced sparsity constraint by the general data augmentation, which requires less case-by-case and sophisticated tuning, and another discriminative constraint solved by a jointly learned dictionary. The optimization of the objective function is then deduced theoretically to an approximate linear problem. The sparsity and discrimination enhanced by data augmentation guarantees the robustness for image classification under various conditions, which constructs the first positive case using data augmentation to obtain robust dictionary-based classification. Numerous experiments have been conducted on popular facial and object image datasets. The results demonstrate that ACD-RDC obtains more promising classification on diversely collected images than the current dictionary-based classification methods. ACD-RDC is also confirmed to be a state-of-the-art classification method when using deep features as inputs.