Miracle-dz/UniFAR-FTUs huggingface dataset:
Galaxy Core Formation by Supermassive Black Hole Binaries: The Importance of Realistic Initial Conditions and Galaxy Morphology

The binding energy liberated by the coalescence of supermassive black hole (SMBH) binaries during galaxy mergers is thought to be responsible for the low density cores often found in bright elliptical galaxies. We use high-resolution N-body and Monte Carlo techniques to perform single and multistage galaxy merger simulations and systematically study the dependence of the central galaxy properties on the binary mass ratio, the slope of the initial density cusps, and the number of mergers experienced. We study both the amount of depleted stellar mass (or mass deficit), M def, and the radial extent of the depleted region, r b. We find that r b ≃ r SOI and that M def varies in the range of 0.5–4M •, with r SOI the influence radius of the remnant SMBH and M • its mass. The coefficients in these relations depend weakly on the binary mass ratio and remain remarkably constant through subsequent mergers. We conclude that the core size and mass deficit do not scale linearly with the number of mergers, making it hard to infer merger histories from observations. On the other hand, we show that both M def and r b are sensitive to the morphology of the galaxy merger remnant, and that adopting spherical initial conditions, as done in early work, leads to misleading results. Our models reproduce the range of values for M def found in most observational work, but span nearly an order-of magnitude range around the true ejected stellar mass.
Temporal Graph Functional Dependencies [Extended Version]

Data dependencies have been extended to graphs to characterize topological and value constraints. Existing data dependencies are defined to capture inconsistencies in static graphs. Nevertheless, inconsistencies may occur over evolving graphs and only for certain time periods. The need for capturing such inconsistencies in temporal graphs is evident in anomaly detection and predictive dynamic network analysis. This paper introduces a class of data dependencies called Temporal Graph Functional Dependencies (TGFDs). TGFDs generalize functional dependencies to temporal graphs as a sequence of graph snapshots that are induced by time intervals, and enforce both topological constraints and attribute value dependencies that must be satisfied by these snapshots. (1) We establish the complexity results for the satisfiability and implication problems of TGFDs. (2) We propose a sound and complete axiomatization system for TGFDs. (3) We also present efficient parallel algorithms to detect inconsistencies in temporal graphs as violations of TGFDs. The algorithm exploits data and temporal locality induced by time intervals, and uses incremental pattern matching and load balancing strategies to enable feasible error detection in large temporal graphs. Using real datasets, we experimentally verify that our algorithms achieve lower runtimes compared to existing baselines, while improving the accuracy over error detection using existing graph data constraints, e.g., GFDs and GTARs with 55% and 74% gain in F1-score, respectively.
Improving Human Sequential Decision-Making with Reinforcement Learning

Workers spend a significant amount of time learning how to make good decisions. Evaluating the efficacy of a given decision, however, can be complicated -- e.g., decision outcomes are often long-term and relate to the original decision in complex ways. Surprisingly, even though learning good decision-making strategies is difficult, they can often be expressed in simple and concise forms. Focusing on sequential decision-making, we design a novel machine learning algorithm that is capable of extracting"best practices"from trace data and conveying its insights to humans in the form of interpretable"tips". Our algorithm selects the tip that best bridges the gap between the actions taken by human workers and those taken by the optimal policy in a way that accounts for which actions are consequential for achieving higher performance. We evaluate our approach through a series of randomized controlled experiments where participants manage a virtual kitchen. Our experiments show that the tips generated by our algorithm can significantly improve human performance relative to intuitive baselines. In addition, we discuss a number of empirical insights that can help inform the design of algorithms intended for human-AI interfaces. For instance, we find evidence that participants do not simply blindly follow our tips; instead, they combine them with their own experience to discover additional strategies for improving performance.
Large eddy simulations of an air-helium buoyant jet in a two vented enclosure: influence of the outlet boundary condition

We present numerical results from large eddy simulations (LES) of an air-helium buoyant jet in a two vented enclosure. The geometrical configuration mimics the experimental setup studied at CEA Saclay. The dimension of the enclosure was chosen to ensure a laminar/turbulent transition occurring at about the middle height of the cavity. This study focuses on the influence of the boundary conditions on the jet development and its entrainment behavior. We observe that applying constant pressure outlet boundary conditions directly at the vent surfaces underestimates the volumetric flow rate of air entering the enclosure. On the contrary, modeling an exterior region in the computational domain better predicts the air flow-rate entrance. Results are then in good agreement with the experimental PIV data.
Fact-Tree Reasoning for N-ary Question Answering over Knowledge Graphs

Current Question Answering over Knowledge Graphs (KGQA) task mainly focuses on performing answer reasoning upon KGs with binary facts. However, it neglects the n-ary facts, which contain more than two entities. In this work, we highlight a more challenging but under-explored task: n-ary KGQA, i.e., answering n-ary facts questions upon n-ary KGs. Nevertheless, the multi-hop reasoning framework popular in binary KGQA task is not directly applicable on n-ary KGQA. We propose two feasible improvements: 1) upgrade the basic reasoning unit from entity or relation to fact, and 2) upgrade the reasoning structure from chain to tree. Therefore, we propose a novel fact-tree reasoning framework, FacTree, which integrates the above two upgrades. FacTree transforms the question into a fact tree and performs iterative fact reasoning on the fact tree to infer the correct answer. Experimental results on the n-ary KGQA dataset we constructed and two binary KGQA benchmarks demonstrate the effectiveness of FacTree compared with state-of-the-art methods.
Fractons and Exotic Symmetries from Branes

The emerging study of fractons, a new type of quasi‐particle with restricted mobility, has motivated the construction of several classes of interesting continuum quantum field theories with novel properties. One such class consists of foliated field theories which, roughly, are built by coupling together fields supported on the leaves of foliations of spacetime. Another approach, which we refer to as exotic field theory, focuses on constructing Lagrangians consistent with special symmetries (like subsystem symmetries) that are adjacent to fracton physics. A third framework is that of infinite‐component Chern‐Simons theories, which attempts to generalize the role of conventional Chern‐Simons theory in describing (2+1)D Abelian topological order to fractonic order in (3+1)D. The study of these theories is ongoing, and many of their properties remain to be understood. Historically, it has been fruitful to study QFTs by embedding them into string theory. One way this can be done is via D‐branes, extended objects whose dynamics can, at low energies, be described in terms of conventional quantum field theory. QFTs that can be realized in this way can then be analyzed using the rich mathematical and physical structure of string theory. In this paper, we show that foliated field theories, exotic field theories, and infinite‐component Chern‐Simons theories can all be realized on the world‐volumes of branes. We hope that these constructions will ultimately yield valuable insights into the physics of these interesting field theories.
Quad-cone-rotor: a novel tilt quadrotor with severe-fault-tolerant ability

Conventional quadrotors received great attention in trajectory design and fault-tolerant control in these years. The direction of each thrust is perpendicular to the body because of the geometrics in mechanical design. Comparing with the conventional quadrotor, a novel quadrotor named quad-tilt-rotor brings better freedom in manipulating the thrust vector. Quad-tilt-rotor augments the additional degrees of freedom in the thrust, providing the possibility of violating the normal direction of the thrust in the conventional quadrotor. This provides the ability of greater agility in control. This paper presents a novel design of a quad-tilt-rotor (quad-cone-rotor) whose thrust can be assigned along the edge of a cone shape. Besides the inheriting merits in agile from quad-tilt-rotor, the quad-cone-rotor is expected to take fault-tolerant control in the severe dynamic failure (total loss in all thrusts). We simulate the control result in a UAV simulator in SIMULINK, MATLAB.
Provably Efficient Generative Adversarial Imitation Learning for Online and Offline Setting with Linear Function Approximation

In generative adversarial imitation learning (GAIL), the agent aims to learn a policy from an expert demonstration so that its performance cannot be discriminated from the expert policy on a certain predefined reward set. In this paper, we study GAIL in both online and offline settings with linear function approximation, where both the transition and reward function are linear in the feature maps. Besides the expert demonstration, in the online setting the agent can interact with the environment, while in the offline setting the agent only accesses an additional dataset collected by a prior. For online GAIL, we propose an optimistic generative adversarial policy optimization algorithm (OGAP) and prove that OGAP achieves $\widetilde{\mathcal{O}}(H^2 d^{3/2}K^{1/2}+KH^{3/2}dN_1^{-1/2})$ regret. Here $N_1$ represents the number of trajectories of the expert demonstration, $d$ is the feature dimension, and $K$ is the number of episodes. For offline GAIL, we propose a pessimistic generative adversarial policy optimization algorithm (PGAP). For an arbitrary additional dataset, we obtain the optimality gap of PGAP, achieving the minimax lower bound in the utilization of the additional dataset. Assuming sufficient coverage on the additional dataset, we show that PGAP achieves $\widetilde{\mathcal{O}}(H^{2}dK^{-1/2} +H^2d^{3/2}N_2^{-1/2}+H^{3/2}dN_1^{-1/2} \ )$ optimality gap. Here $N_2$ represents the number of trajectories of the additional dataset with sufficient coverage.
Nonsmooth Mechanics Based on Linear Projection Operator

This paper presents a unifying dynamics formulation for nonsmooth multibody systems (MBSs) subject to changing topology and multiple impacts based on linear projection operator. An oblique projection matrix ubiquitously derives all characteristic variables of such systems as follow: i) The constrained acceleration before jump discontinuity from projection of unconstrained acceleration, ii) post-impact velocity from projection of preimpact velocity, iii) impulse during impact from projection of pre-impact momentum, iv) generalized constraint force from projection of generalized input force, and v) post-impact kinetic energy from pre-impact kinetic energy based on projected inertia matrix. All solutions are presented in closed-form with elegant geometrical interpretations. The formulation is general enough to be applicable to MBSs subject to simultaneous multiple impacts with non-identical restitution coefficients, changing topology, i.e., unilateral constraints becomes inactive or vice versa, or even when the overall constraint Jacobian becomes singular. Not only do the solutions always exist regardless the constraint condition, but also the condition number for a generalized constraint inertia matrix is minimized in order to reduce numerical sensitivity in computation of the projection matrix to roundoff errors. The model is proven to be energetically consistent if a global restitution coefficient is assumed. In the case of non-identical restitution coefficients, the set of energetically consistent restitution matrices is characterized by using Linear Matrix Inequality (LMI).
Neutron Star–Neutron Star and Neutron Star–Black Hole Mergers: Multiband Observations and Early Warnings

The detections of gravitational waves (GWs) from binary neutron star systems and neutron star–black hole systems provide new insights into dense matter properties in extreme conditions and associated high-energy astrophysical processes. However, currently, information about the neutron star equation of state (EoS) is extracted with very limited precision. Meanwhile, the fruitful results from the serendipitous discovery of the γ-ray burst alongside GW170817 show the necessity of early warning alerts. Accurate measurements of the matter effects and sky location could be achieved by joint GW detection from space and ground. In our work, based on two example cases, GW170817 and GW200105, we use the Fisher information matrix analysis to investigate the multiband synergy between the space-borne decihertz GW detectors and the ground-based Einstein Telescope (ET). We especially focus on the parameters pertaining to the spin-induced quadrupole moment, tidal deformability, and sky localization. We demonstrate that (i) only with the help of multiband observations we can constrain the quadrupole parameter; and (ii) with the inclusion of decihertz GW detectors, the errors of tidal deformability would be a few times smaller, indicating that many more EoSs could be excluded; (iii) with the inclusion of ET, the sky localization improves by about 1 order of magnitude. Furthermore, we have systematically compared the different limits from four planned decihertz detectors and adopting two widely used waveform models.
The Fractal Nature of Clouds in Global Storm‐Resolving Models

Clouds in observations are fractals: they show self‐similarity across scales ranging from 1 to 1,000 km. This includes individual storms and large‐scale cloud structures typical of organized convection. It is not known whether global storm‐resolving models reproduce the observed fractal scaling laws for clouds and organized convection. We compute the fractal dimension of clouds using Himawari satellite data and compare this to global storm‐resolving model simulations completed as part of the DYAMOND intercomparison project. We find cloud fields in these simulations are indeed fractal, and reproduce the observed fractal dimension to within 10%. We find the fractal dimension is sensitive to the choice of boundary layer parametrization scheme used in each model simulation, and not to the convection parametrization as might have been expected.
A signal of competitive dominance in mid-latitude herbaceous plant communities

Understanding the main determinants of species coexistence across space and time is a central question in ecology. However, ecologists still know little about the scales and conditions at which biotic interactions matter and how these interact with the environment to structure species assemblages. Here we use recent theory developments to analyze plant distribution and trait data across Europe and find that plant height clustering is related to both evapotranspiration and gross primary productivity. This clustering is a signal of interspecies competition between plants, which is most evident in mid-latitude ecoregions, where conditions for growth (reflected in actual evapotranspiration rates and gross primary productivities) are optimal. Away from this optimum, climate severity likely overrides the effect of competition, or other interactions become increasingly important. Our approach bridges the gap between species-rich competition theories and large-scale species distribution data analysis.
Learning Multiscale Convolutional Dictionaries for Image Reconstruction

Convolutional neural networks (CNNs) have been tremendously successful in solving imaging inverse problems. To understand their success, an effective strategy is to construct simpler and mathematically more tractable convolutional sparse coding (CSC) models that share essential ingredients with CNNs. Existing CSC methods, however, underperform leading CNNs in challenging inverse problems. We hypothesize that the performance gap may be attributed in part to how they process images at different spatial scales: While many CNNs use multiscale feature representations, existing CSC models mostly rely on single-scale dictionaries. To close the performance gap, we thus propose a multiscale convolutional dictionary structure. The proposed dictionary structure is derived from the U-Net, arguably the most versatile and widely used CNN for image-to-image learning problems. We show that incorporating the proposed multiscale dictionary in an otherwise standard CSC framework yields performance competitive with state-of-the-art CNNs across a range of challenging inverse problems including CT and MRI reconstruction. Our work thus demonstrates the effectiveness and scalability of the multiscale CSC approach in solving challenging inverse problems.
Framework elucidating the supersaturation dynamics of nanocrystal growth

Supersaturation is the fundamental parameter driving crystal formation, yet its dynamics during the growth of colloidal nanocrystals (NCs) are poorly understood. Experimental characterization of supersaturation in colloidal syntheses has been difficult, limiting insight into the phenomena underlying NC growth. Hence, despite significant interest in the topic, how many types of NCs grow remain unclear. Here, we develop a framework to quantitatively characterize supersaturation in situ throughout NC growth. Using this approach, we investigate the seed-mediated synthesis of colloidal Au nanocubes, revealing a triphasic sequence for the supersaturation dynamics: rapid monomer consumption, sustained supersaturation, and then gradual monomer depletion. These NCs undergo different shape evolutions in different phases of the supersaturation dynamics. As shown with the Au nanocubes, we can use the supersaturation profile to theoretically predict the growth profile of NCs. We then apply these insights to rationally modulate shape evolutions, decreasing the yield of impurity NCs. Our findings demonstrate that the supersaturation dynamics of NC growth can be more complex than previously understood. While this study focuses experimentally on Au NCs, our framework is facile and applicable to a broad range of NCs undergoing classical growth. Thus, our methodology facilitates deeper understanding of the phenomena governing nanoscale crystal growth and provides insight towards the rational design of NCs.
Refractive Outcomes of Retropupillary Fixated Iris-Claw Lens for Exchange of the Dislocated Intraocular Lens Using Modified Scleral Incisions

Purpose To evaluate refractive outcomes after retropupillary iris-claw intraocular lens (IOL) implantation and to compare postoperative astigmatism from (1) a conventional 5.5-mm sclerocorneal incision, (2) an L-shaped scleral tunnel incision, and (3) a frown scleral tunnel incision. Methods This was a retrospective study of eyes undergoing dislocated IOL exchange for a retropupillary iris-claw IOL. Uncorrected and corrected distance visual acuity (UDVA and CDVA) and postoperative complications were evaluated until postoperative months 24. The refractive outcomes and the surgically induced astigmatism (SIA) vector were compared between groups. Results The medical records of 107 eyes from 107 patients with mean age of 65.31 ± 12.15 years were reviewed. Eyes with a frown incision showed the best UDVA, followed by those with L-shaped and conventional incisions (P = 0.003). Eyes with an L-shaped incision or frown incision had a lower SIA than that of the conventional incision group at postoperative 6 months (mean ± standard deviation [SD] SIA, 0.86 ± 0.85 D, 0.63 ± 0.37 D, and 1.70 ± 1.27 D for frown incision, L-shaped incision, and conventional incisions, respectively; P = 0.004). Conclusion The frown incision and L-shaped incision induced a significantly smaller SIA than the conventional incisions, which was associated with better UDVA postoperatively.
A Novel Tension Machine Promotes Bone Marrow Mesenchymal Stem Cell Osteoblastic and Fibroblastic Differentiation by Applying Cyclic Tension

Bone marrow mesenchymal stem cells (BMSCs) are intraosseous stem cells, and the effects of tensile strain on BMSC differentiation mediate several bone-related treatments. To study the response of BMSCs under tension, we designed and developed a small cellular tension instrument, iStrain. When iStrain applied tension on BMSCs, these cells exhibited convergence in the alignment direction and lengthening of the cell processes and cell body. Real-time quantitative polymerase chain reaction (RT-qPCR) and western blotting demonstrated that iStrain-mediated cyclic tension promotes the differentiation of BMSCs toward osteogenesis and fibrogenesis. And the mRNA and protein expression of differentiation-related genes changes with the extension of tension time.
Pheno4D: A spatio-temporal dataset of maize and tomato plant point clouds for phenotyping and advanced plant analysis

Understanding the growth and development of individual plants is of central importance in modern agriculture, crop breeding, and crop science. To this end, using 3D data for plant analysis has gained attention over the last years. High-resolution point clouds offer the potential to derive a variety of plant traits, such as plant height, biomass, as well as the number and size of relevant plant organs. Periodically scanning the plants even allows for performing spatio-temporal growth analysis. However, highly accurate 3D point clouds from plants recorded at different growth stages are rare, and acquiring this kind of data is costly. Besides, advanced plant analysis methods from machine learning require annotated training data and thus generate intense manual labor before being able to perform an analysis. To address these issues, we present with this dataset paper a multi-temporal dataset featuring high-resolution registered point clouds of maize and tomato plants, which we manually labeled for computer vision tasks, such as for instance segmentation and 3D reconstruction, providing approximately 260 million labeled 3D points. To highlight the usability of the data and to provide baselines for other researchers, we show a variety of applications ranging from point cloud segmentation to non-rigid registration and surface reconstruction. We believe that our dataset will help to develop new algorithms to advance the research for plant phenotyping, 3D reconstruction, non-rigid registration, and deep learning on raw point clouds. The dataset is freely accessible at https://www.ipb. uni-bonn.de/data/pheno4d/.