Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Nonlinear Optimal Control via System Level Synthesis

This paper addresses the problem of finite horizon constrained robust optimal control for nonlinear systems subject to norm-bounded disturbances. To this end, the underlying uncertain nonlinear system is decomposed based on a first-order Taylor series expansion into a nominal system and an error (deviation) described as an uncertain linear time-varying system. This decomposition allows us to leverage System Level Synthesis to jointly optimize an affine error feedback, a nominal nonlinear trajectory, and, most importantly, a dynamic linearization error over-bound used to ensure robust constraint satisfaction for the nonlinear system. The proposed approach thereby results in less conservative planning compared with state-of-the-art techniques. We demonstrate the benefits of the proposed approach to control the rotational motion of a rigid body subject to state and input constraints.
A Network Science Perspective of Graph Convolutional Networks: A Survey

The mining and exploitation of graph structural information have been the focal points in the study of complex networks. Traditional structural measures in Network Science focus on the analysis and modelling of complex networks from the perspective of network structure, such as the centrality measures, the clustering coefficient, and motifs and graphlets, and they have become basic tools for studying and understanding graphs. In comparison, graph neural networks, especially graph convolutional networks (GCNs), are particularly effective at integrating node features into graph structures via neighbourhood aggregation and message passing, and have been shown to significantly improve the performances in a variety of learning tasks. These two classes of methods are, however, typically treated separately with limited references to each other. In this work, aiming to establish relationships between them, we provide a network science perspective of GCNs. Our novel taxonomy classifies GCNs from three structural information angles, i.e., the layer-wise message aggregation scope, the message content, and the overall learning scope. Moreover, as a prerequisite for reviewing GCNs via a network science perspective, we also summarise traditional structural measures and propose a new taxonomy for them. Finally and most importantly, we draw connections between traditional structural approaches and graph convolutional networks, and discuss potential directions for future research.
Intensity Interferometry Observations of the Hα Envelope of γCas with MéO and a Portable Telescope

We report on observations of the extended environment of the bright Be star γ-Cas performed using intensity interferometry measurements within its Hα emission line. These observations were performed using a modified version of the I2C intensity interferometry instrument installed onto the 1.54 m MéO optical metrology telescope and a portable 1 m telescope (T1M). In order to better constrain the extent of the H α envelope, observations were performed for two different positions of the T1M telescope, corresponding to an intermediate and long baselines in which the extended region was partially and fully resolved. We find that the observed data are consistent with past interferometric observations of γ-Cas. These observations demonstrate the capability to equip optical telescopes of different optical designs with intensity interferometry capabilities and illustrate the potential to scale a similar system onto many additional telescopes.
Interstellar Heritage and the Birth Environment of the Solar System

In this chapter, we explore the origins of cometary material and discuss the clues cometary composition provides in the context of the origin of our solar system. The review focuses on both cometary refractory and volatile materials, which jointly provide crucial information about the processes that shaped the solar system into what it is today. Both areas have significantly advanced over the past decade. We also view comets more broadly and discuss compositions considering laboratory studies of cometary materials, including interplanetary dust particles and meteoritic material that are potential cometary samples, along with meteorites, and in situ/remote studies of cometary comae. In our review, we focus on key areas from elemental/molecular compositions, isotopic ratios, carbonaceous and silicate refractories, short-lived radionuclides, and solar system dynamics that can be used as probes of the solar birth environment. We synthesize this data that points towards the birth of our solar system in a clustered star-forming environment.
Exit options sustain altruistic punishment and decrease the second-order free-riders, but it is not a panacea

Altruistic punishment, where individuals incur personal costs to punish others who have harmed third parties, presents an evolutionary conundrum as it undermines individual fitness. Resolving this puzzle is crucial for understanding the emergence and maintenance of human cooperation. This study investigates the role of an alternative strategy, the exit option, in explaining altruistic punishment. We analyze a two-stage prisoner's dilemma game in well-mixed and networked populations, considering both finite and infinite scenarios. Our findings reveal that the exit option does not significantly enhance altruistic punishment in well-mixed populations. However, in networked populations, the exit option enables the existence of altruistic punishment and gives rise to complex dynamics, including cyclic dominance and bi-stable states. This research contributes to our understanding of costly punishment and sheds light on the effectiveness of different voluntary participation strategies in addressing the conundrum of punishment.
Nonuniqueness of trajectories on a set of full measure for Sobolev vector fields

In this paper, we resolve an important long-standing question of Alberti \cite{alberti2012generalized} that asks if there is a continuous vector field with bounded divergence and of class $W^{1, p}$ for some $p \geq 1$ such that the ODE with this vector field has nonunique trajectories on a set of initial conditions with positive Lebesgue measure? This question belongs to the realm of well-known DiPerna--Lions theory for Sobolev vector fields $W^{1, p}$. In this work, we design a divergence-free vector field in $W^{1, p}$ with $p<d$ such that the set of initial conditions for which trajectories are not unique is a set of full measure. The construction in this paper is quite explicit; we can write down the expression of the vector field at any point in time and space. Moreover, our vector field construction is novel. We build a vector field $\boldsymbol{u}$ and a corresponding flow map $X^{\boldsymbol{u}}$ such that after finite time $T>0$, the flow map takes the whole domain $\mathbb{T}^d$ to a Cantor set $\mathcal{C}_\Phi$, i.e., $X^{\boldsymbol{u}}(T, \mathbb{T}^d) = \mathcal{C}_\Phi$ and the Hausdorff dimension of this Cantor set is strictly less than $d$. The flow map $X^{\boldsymbol{u}}$ constructed as such is not a regular Lagrangian flow. The nonuniqueness of trajectories on a full measure set is then deduced from the existence of the regular Lagrangian flow in the DiPerna--Lions theory.
Local consistency as a reduction between constraint satisfaction problems

We study the use of local consistency methods as reductions between constraint satisfaction problems (CSPs), and promise version thereof, with the aim to classify these reductions in a similar way as the algebraic approach classifies gadget reductions between CSPs. This research is motivated by the requirement of more expressive reductions in the scope of promise CSPs. While gadget reductions are enough to provide all necessary hardness in the scope of (finite domain) non-promise CSP, in promise CSPs a wider class of reductions needs to be used. We provide a general framework of reductions, which we call consistency reductions, that covers most (if not all) reductions recently used for proving NP-hardness of promise CSPs. We prove some basic properties of these reductions, and provide the first steps towards understanding the power of consistency reductions by characterizing a fragment associated to arc-consistency in terms of polymorphisms of the template. In addition to showing hardness, consistency reductions can also be used to provide feasible algorithms by reducing to a fixed tractable (promise) CSP, for example, to solving systems of affine equations. In this direction, among other results, we describe the well-known Sherali-Adams hierarchy for CSP in terms of a consistency reduction to linear programming.
Causal Triplet: An Open Challenge for Intervention-centric Causal Representation Learning

Recent years have seen a surge of interest in learning high-level causal representations from low-level image pairs under interventions. Yet, existing efforts are largely limited to simple synthetic settings that are far away from real-world problems. In this paper, we present Causal Triplet, a causal representation learning benchmark featuring not only visually more complex scenes, but also two crucial desiderata commonly overlooked in previous works: (i) an actionable counterfactual setting, where only certain object-level variables allow for counterfactual observations whereas others do not; (ii) an interventional downstream task with an emphasis on out-of-distribution robustness from the independent causal mechanisms principle. Through extensive experiments, we find that models built with the knowledge of disentangled or object-centric representations significantly outperform their distributed counterparts. However, recent causal representation learning methods still struggle to identify such latent structures, indicating substantial challenges and opportunities for future work. Our code and datasets will be available at https://sites.google. com/view/causaltriplet.
Parallel Interior-Point Solver for Block-Structured Nonlinear Programs on SIMD/GPU Architectures

We investigate how to port the standard interior-point method to new exascale architectures for block-structured nonlinear programs with state equations. Computationally, we decompose the interior-point algorithm into two successive operations: the evaluation of the derivatives and the solution of the associated Karush-Kuhn-Tucker (KKT) linear system. Our method accelerates both operations using two levels of parallelism. First, we distribute the computations on multiple processes using coarse parallelism. Second, each process uses a SIMD/GPU accelerator locally to accelerate the operations using fine-grained parallelism. The KKT system is reduced by eliminating the inequalities and the state variables from the corresponding equations, to a dense matrix encoding the sensitivities of the problem's degrees of freedom, drastically minimizing the memory exchange. We demonstrate the method's capability on the supercomputer Polaris, a testbed for the future exascale Aurora system. Each node is equipped with four GPUs, a setup amenable to our two-level approach. Our experiments on the stochastic optimal power flow problem show that the method can achieve a 50x speed-up compared to the state-of-the-art method.
Exchange Bias Demonstrated in Bulk Nanocomposites Processed by High-Pressure Torsion

Ferromagnetic (Fe or Fe20Ni80) and antiferromagnetic (NiO) phases were deformed by high-pressure torsion, a severe plastic deformation technique, to manufacture bulk-sized nanocomposites and demonstrate an exchange bias, which has been reported predominantly for bilayer thin films. High-pressure torsion deformation at elevated temperatures proved to be the key to obtaining homogeneous bulk nanocomposites. X-ray diffraction investigations detected nanocrystallinity of the ferromagnetic and antiferromagnetic phases. Furthermore, an additional phase was identified by X-ray diffraction, which formed during deformation at elevated temperatures through the reduction of NiO by Fe. Depending on the initial powder composition of Fe50NiO50 or Fe10Ni40NiO50 the new phase was magnetite or maghemite, respectively. Magnetometry measurements demonstrated an exchange bias in high-pressure torsion-processed bulk nanocomposites. Additionally, the tailoring of magnetic parameters was demonstrated by the application of different strains or post-process annealing. A correlation between the amount of applied strain and exchange bias was found. The increase of exchange bias through applied strain was related to the microstructural refinement of the nanocomposite. The nanocrystalline maghemite was considered to have a crucial impact on the observed changes of exchange bias through applied strain.
Bandgaps of bent and buckled carbon nanotubes

Carbon nanotube’s large electro-mechanical coupling and robustness makes them attractive for applications where bending and buckling is present. But the nature of this coupling is not well understood. Existing theory treats only weak and homogeneous deformations. We generalize it and derive close-form expressions for bandgaps under non-homogeneous deformation. The theory is ﬁrst compared with a number of published DFT simulations, and then applied to the speciﬁc case of bending and buckling – where a kink is present. In the pre-buckling regime, bandgaps change ∝ ± κ 4 where κ is the bending curvature; inside the kink at post-buckling, while near criticality, the kink is shallow and the gap is ∝ ± κ 1 / 2 , where the sign depends on the chiral integers. For a deeper kink but still with an open cross-section, both the bandgap and local Fermi energy strongly downshift.
Construction of Wannier functions from the spectral moments of correlated electron systems

When the first four spectral moments are considered, spectral features missing in standard Kohn-Sham (KS) density-functional theory (DFT), such as upper and lower Hubbard bands, as well as spectral satellite peaks, can be described, and the bandwidths can be corrected. Therefore, we have devised a \textit{moment-functional based spectral density functional theory} (MFbSDFT) recently. However, many computational tools in theoretical solid state physics, such as the construction of maximally localized Wannier functions (MLWFs), have been developed for KS-DFT and require modifications if they are supposed to be used in MFbSDFT. Here, we show how generalized Wannier functions may be constructed from the first four spectral moment matrices. We call these functions \textit{maximally localized spectral moment Wannier functions} (MLSMWFs). We demonstrate how MLSMWFs may be used to compute the anomalous Hall effect (AHE) in fcc Ni by Wannier interpolation. More generally, MLSMWFs may be computed from the first $2P$ moments ($P=1,2,3,\dots$). Using more than 4 moments opens the perspective of reproducing all spectral features accurately in MFbSDFT.
Fourier coefficients of the net-baryon number density

We calculate Fourier coeﬃcients of the net-baryon number as a function of a purely imaginary chemical potential. The asymptotic behavior of these coeﬃcients is governed by the singularity structure of the QCD partition function and thus encodes information on phase transitions. For the calculation of the Fourier coeﬃcients from lattice data of the Bielefeld-Parma collaboration we use a novel Filon-type quadrature, designed for highly oscillatory integrals. We ﬁnd sensitivity to chiral scaling in a narrow temperature interval below the Roberge-Weiss transition temperature. Scaling ﬁts yield reasonable values for the position of the Lee-Yang edge singularity in the complex chemical potential plane. Our lattice data has been obtained from simulations with (2+1)-ﬂavors of highly improved staggered quarks (HISQ) at imaginary chemical potential on 𝑁 𝜏 = 4 , 6 and 8 lattices at physical quark masses.
Multiple imputation of incomplete multilevel data using Heckman selection models

Missing data is a common problem in medical research, and is commonly addressed using multiple imputation. Although traditional imputation methods allow for valid statistical inference when data are missing at random (MAR), their implementation is problematic when the presence of missingness depends on unobserved variables, that is, the data are missing not at random (MNAR). Unfortunately, this MNAR situation is rather common, in observational studies, registries and other sources of real‐world data. While several imputation methods have been proposed for addressing individual studies when data are MNAR, their application and validity in large datasets with multilevel structure remains unclear. We therefore explored the consequence of MNAR data in hierarchical data in‐depth, and proposed a novel multilevel imputation method for common missing patterns in clustered datasets. This method is based on the principles of Heckman selection models and adopts a two‐stage meta‐analysis approach to impute binary and continuous variables that may be outcomes or predictors and that are systematically or sporadically missing. After evaluating the proposed imputation model in simulated scenarios, we illustrate it use in a cross‐sectional community survey to estimate the prevalence of malaria parasitemia in children aged 2‐10 years in five regions in Uganda.
Shadows of Kerr–Vaidya-like black holes

In this work, we study the shadow boundary curves of rotating time-dependent black hole solutions which have well-defined Kerr and Vaidya limits. These solutions are constructed by applying the Newman–Janis algorithm to a spherically symmetric seed metric conformal to the Vaidya solution with a mass function that is linear in Eddington–Finkelstein coordinates. Equipped with a conformal Killing vector field, this class of solution exhibits separability of null geodesics, thus allowing one to develop an analytic formula for the boundary curve of its shadow. We find a simple power law describing the dependence of the mean radius and asymmetry factor of the shadow on the accretion rate. Applicability of our model to recent Event Horizon Telescope observations of M87 ∗ and Sgr A ∗ is also discussed.
Numerical Analysis of the ONERA-M6 Wing with Wind Tunnel Wall Interference

The flow around an ONERA-M6 wing, including the effect of wind tunnel wall interference, is computed using CFD analysis with a porous wall model. The computational domain sets porous walls at the top and bottom of the wing section similar to actual wind tunnel experiments. The computational result captures almost the same shock wave shape as the wind tunnel. This could not be computed exactly in previous works that did not include wall effects. The interference of the porous walls reduces the Mach number and attack angle of the flow, and these effects alter the swept angle of front shock wave and the location of rear shock wave. The aerodynamics coefficients are also affected by the wall interference. The lift coefficient becomes smaller due to the reduction in attack angle. The lower Mach number decreases the drag coefficient, while the reduction in attack angle causes additional drag by a mechanism similar to induced drag.
A Synoptic Evaluation of the NCEP Ensemble

Abstract Ensemble forecasting has been operational at NCEP (formerly the National Meteorological Center) since December 1992. In March 1994, more ensemble forecast members were added. In the new configuration, 17 forecasts with the NCEP global model are run every day, out to 16-day lead time. Beyond the 3 control forecasts (a T126 and a T62 resolution control at 0000 UTC and a T126 control at 1200 UTC), 14 perturbed forecasts are made at the reduced T62 resolution. Global products from the ensemble forecasts are available from NCEP via anonymous FTP. The initial perturbation vectors are derived from seven independent breeding cycles, where the fast-growing nonlinear perturbations grow freely, apart from the periodic rescaling that keeps their magnitude compatible with the estimated uncertainty within the control analysis. The breeding process is an integral part of the extended-range forecasts, and the generation of the initial perturbations for the ensemble is done at no computational cost beyond that of.
Effects of Body Size and Condition on Antipredator Behavior Related to Nuchal Glands in Rhabdophis subminiatus

Abstract. Snakes have a wide range of antipredator behaviors that are often associated with unique morphological modification. Rhabdophis subminiatus (Red-Neck Keelback) is a snake with nuchal glands where toxins sequestered from prey animals are stored that work in conjunction with a set of antipredator behaviors. In this study, we investigated antipredator behavior in this species, particularly behavior that is coordinated with the presence of the nuchal glands. We tested the hypothesis that the nuchal gland-related behavior is exhibited more frequently as snake size increases because larger snakes have had more opportunities to consume toads and acquire toxins. We also examined the effects of sex and body condition on antipredator behavior. All snakes performed body flattening and neck flattening in response to a standardized stimulus, which suggests that these responses are their major antipredator behaviors. However, the results did not support our hypothesis. Snake body size was negatively correlated with the frequency of neck butt, neck flatten, and neck arch. This indicates that the tendency to perform the nuchal gland-related behavior declines as body size increases. There was no significant sexual difference except that females tended to exhibit a higher frequency of flight than males. Furthermore, we found a negative correlation between the frequency of neck butt and body condition. Overall, our study suggests that smaller snakes, particularly those in below-average body condition, depend more on nuchal glands to deter predators.
Biology of Andrena (Callandrena Sensu Lato) Asteris Robertson (Hymenoptera: Andrenidae), an Eastern Aster Specialist that Makes a Very Deep Nest

Abstract - Here we present the first description of nest architecture, immature stages, and brood-parasitism of Andrena (Callandrena s. l.) asteris (Aster Miner Bee) and the first description of the nesting biology of any Callandrena in eastern North America. Brood cells varied from 50 to 91 cm in depth, making this the deepest solitary bee nest recorded in northeastern North America. Additionally, we assembled data on soil texture, phenology, geographic distribution, and host-plant preferences. By modeling publicly available observation data, we find that areas of peak habitat suitability for A. asteris are in proximity to coastal and inland shorelines and major water courses. Our results corroborate a recent assessment of the conservation status of New York pollinators, which ranked A. asteris as “vulnerable”.