Miracle-dz/UniFAR-FTUs huggingface dataset:
Bars and boxy/peanut bulges in thin and thick discs. II. Can bars form in hot thick discs?

The Milky Way as well as a majority of external galaxies possess a thick disc. However, the dynamical role of the (geometrically) thick disc on the bar formation and evolution is not fully understood. Here, we investigate the effect of thick discs in bar formation and evolution by means of a suite of N-body models of (kinematically cold) thin-(kinematically hot) thick discs. We systematically vary the mass fraction of the thick disc, the thin-to-thick disc scale length ratio as well as thick disc's scale height to examine the bar formation under diverse dynamical scenarios. Bars form almost always in our models, even in presence of a massive thick disc. The part of the bar constituted by the thick disc closely follows the overall growth and temporal evolution of the part of the bar constituted by the thin disc, only the part of the bar in the thick disc is weaker than the part of the bar in the thin disc. The formation of stronger bars is associated with a simultaneous larger loss of angular momentum and a larger radial heating. In addition, we demonstrate a preferential loss of angular momentum and a preferential radial heating of disc stars, along the azimuthal direction within the extent of the bar, in both thin and thick disc stars. For purely thick disc models (without any thin disc), the bar formation critically depends on the disc scale length and scale height. A larger scale length and/or a larger vertical scale height delays the bar formation time and/or suppresses the bar formation almost completely in thick-disc-only models. We find that the Ostriker-Peeble criterion predicts the bar instability scenarios in our models better than the Efstathiou-Lake-Negroponte criterion.
Hybrid HMM Decoder for Convolutional Codes by Joint Trellis-Like Structure and Channel Prior

The anti-interference capability of wireless links is a physical layer problem for edge computing. Although convolutional codes have inherent error correction potential due to the redundancy introduced in the data, the performance of the convolutional code is drastically degraded due to multipath effects on the channel. In this paper, we propose the use of a Hidden Markov Model (HMM) for the reconstruction of convolutional codes and decoding by the Viterbi algorithm. Furthermore, to implement soft-decision decoding, the observation of HMM is replaced by Gaussian mixture models (GMM). Our method provides superior error correction potential than the standard method because the model parameters contain channel state information (CSI). We evaluated the performance of the method compared to standard Viterbi decoding by numerical simulation. In the multipath channel, the hybrid HMM decoder can achieve a performance gain of 4.7 dB and 2 dB when using hard-decision and soft-decision decoding, respectively. The HMM decoder also achieves significant performance gains for the RSC code, suggesting that the method could be extended to turbo codes. Code can be available at https://github.com/HaoyyLi/HMM-decoder.
RKKY interactions mediated by topological states in transition metal doped bismuthene

We have investigated magnetic interactions between transition metal ions in bismuthene topological insulators with protected edge states. We find that these topological states have a crucial role in the magnetic interactions in 2D topological insulators. Using first-principles and model Hamiltonian, we make a comparative study of transition metal doped bulk and nanoribbon bismuthene. While a direct overlap between the transition metal prevails in gapped bulk bismuthene, at the borders of nanoribbons, a long-range magnetism is present. The exchange interactions are well described by a Ruderman–Kittel–Kasuya–Yosida-like Hamiltonian mediated by massive and topological states. Our results show a dominance of antiferromagnetism promoted by the topological states, preserving the spin-locked Dirac crossing states due to a global time-reversal symmetry preservation. This extended magnetic interactions mediated mainly by massless electrons can increase the spin diffusion length being promising for fast dissipationless spintronic devices.
Ballot stuffing and participation privacy in pollsite voting

We study the problem of simultaneously addressing both ballot stuffing and participation privacy for pollsite voting systems. Ballot stuffing is the attack where fake ballots (not cast by any eligible voter) are inserted into the system. Participation privacy is about hiding which eligible voters have actually cast their vote. So far, the combination of ballot stuffing and participation privacy has been mostly studied for internet voting, where voters are assumed to own trusted computing devices. Such approaches are inapplicable to pollsite voting where voters typically vote bare handed. We present an eligibility audit protocol to detect ballot stuffing in pollsite voting protocols. This is done while protecting participation privacy from a remote observer - one who does not physically observe voters during voting. Our protocol can be instantiated as an additional layer on top of most existing pollsite E2E-V voting protocols. To achieve our guarantees, we develop an efficient zero-knowledge proof (ZKP), that, given a value $v$ and a set $\Phi$ of commitments, proves $v$ is committed by some commitment in $\Phi$, without revealing which one. We call this a ZKP of reverse set membership because of its relationship to the popular ZKPs of set membership. This ZKP may be of independent interest.
Policy iteration: for want of recursive feasibility, all is not lost

—This paper investigates recursive feasibility, recursive robust stability and near-optimality properties of policy iteration (PI). For this purpose, we consider de- terministic nonlinear discrete-time systems whose inputs are generated by PI for undiscounted cost functions. We ﬁrst assume that PI is recursively feasible, in the sense that the optimization problems solved at each iteration admit a solution. In this case, we provide novel conditions to establish recursive robust stability properties for a general attractor, meaning that the policies generated at each iteration ensure a robust KL -stability property with respect to a general state measure. We then derive novel explicit bounds on the mismatch between the (suboptimal) value function returned by PI at each iteration and the optimal one. Afterwards, motivated by a counter-example that shows that PI may fail to be recursively feasible, we modify PI so that recursive feasibility is guaranteed a priori under mild conditions. This modiﬁed algorithm, called PI + , is shown to preserve the recursive robust stability when the attractor is compact. Additionally, PI + enjoys the same near-optimality properties as its PI counterpart under the same assumptions. Therefore, PI + is an attractive tool for generating near-optimal stabilizing control of deterministic discrete-time nonlinear systems.
Bayesian estimation of the 
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 factor and thermonuclear reaction rate for 
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The $^{16}$O(p,$\gamma$)$^{17}$F reaction is the slowest hydrogen-burning process in the CNO mass region. Its thermonuclear rate sensitively impacts predictions of oxygen isotopic ratios in a number of astrophysical sites, including AGB stars. The reaction has been measured several times at low bombarding energies using a variety of techniques. The most recent evaluated experimental rates have a reported uncertainty of about 7.5\% below $1$~GK. However, the previous rate estimate represents a best guess only and was not based on rigorous statistical methods. We apply a Bayesian model to fit all reliable $^{16}$O(p,$\gamma$)$^{17}$F cross section data, and take into account independent contributions of statistical and systematic uncertainties. The nuclear reaction model employed is a single-particle potential model involving a Woods-Saxon potential for generating the radial bound state wave function. The model has three physical parameters, the radius and diffuseness of the Woods-Saxon potential, and the asymptotic normalization coefficients (ANCs) of the final bound state in $^{17}$F. We find that performing the Bayesian $S$ factor fit using ANCs as scaling parameters has a distinct advantage over adopting spectroscopic factors instead. Based on these results, we present the first statistically rigorous estimation of experimental $^{16}$O(p,$\gamma$)$^{17}$F reaction rates, with uncertainties ($\pm 4.2$\%) of about half the previously reported values.
Ab Initio Prediction of Ultra‐Wide Band Gap BxAl1−xN Materials

Ultra‐wide bandgap (UWBG) materials are poised to play an important role in the future of power electronics. Devices made from UWBG materials are expected to operate at higher voltages, frequencies, and temperatures than current silicon and silicon‐carbide‐based devices and can even lead to significant miniaturization of such devices. In the UWBG field, aluminum nitride and boron nitride have attracted great interest; however, the BxAl1−xN alloys are much less studied. In this work, using first‐principles simulations combining density‐functional theory and the cluster expansion method, the crystal structure of BxAl1−xN alloys is predicted. Seventeen ground state structures of BxAl1−xN with formation energies between 0.11 and 0.25 eV atom‐1 are found. All of these structures are found to be dynamically stable. The BxAl1−xN structures are found to have predominantly a tetrahedral bonding environment; however, some structures exhibit sp2 bonds similar to hexagonal BN. This work expands knowledge of the structures, energies, and bonding in BxAl1−xN which aids their synthesis, the innovation of lateral or vertical devices, and discovery of compatible dielectric and Ohmic contact materials.
History-Based, Bayesian, Closure for Stochastic Parameterization: Application to Lorenz '96

Physical parameterizations are used as representations of unresolved subgrid processes within weather and global climate models or coarse-scale turbulent models, whose resolutions are too coarse to resolve small-scale processes. These parameterizations are typically grounded on physically-based, yet empirical, representations of the underlying small-scale processes. Machine learning-based parameterizations have recently been proposed as an alternative and have shown great promises to reduce uncertainties associated with small-scale processes. Yet, those approaches still show some important mismatches that are often attributed to stochasticity in the considered process. This stochasticity can be due to noisy data, unresolved variables or simply to the inherent chaotic nature of the process. To address these issues, we develop a new type of parameterization (closure) which is based on a Bayesian formalism for neural networks, to account for uncertainty quantification, and includes memory, to account for the non-instantaneous response of the closure. To overcome the curse of dimensionality of Bayesian techniques in high-dimensional spaces, the Bayesian strategy is based on a Hamiltonian Monte Carlo Markov Chain sampling strategy that takes advantage of the likelihood function and kinetic energy's gradients with respect to the parameters to accelerate the sampling process. We apply the proposed Bayesian history-based parameterization to the Lorenz '96 model in the presence of noisy and sparse data, similar to satellite observations, and show its capacity to predict skillful forecasts of the resolved variables while returning trustworthy uncertainty quantifications for different sources of error. This approach paves the way for the use of Bayesian approaches for closure problems.
Adaptive scaling of the learning rate by second order automatic differentiation

In the context of the optimization of Deep Neural Networks, we propose to rescale the learning rate using a new technique of automatic diﬀerentiation. This technique relies on the computation of the curvature , a second order information whose computational complexity is in between the computation of the gradient and the one of the Hessian-vector product. If (1 C, 1 M ) represents respectively the computational time and memory footprint of the gradient method, the new technique increase the overall cost to either (1 . 5 C, 2 M ) or (2 C, 1 M ) . This rescaling has the appealing characteristic of having a natural interpretation, it allows the practitioner to choose between exploration of the parameters set and convergence of the algorithm. The rescaling is adaptive, it depends on the data and on the direction of descent. The numerical experiments highlight the diﬀerent exploration/convergence regimes.
Investigating the Role of Centering Theory in the Context of Neural Coreference Resolution Systems

Centering theory (CT; Grosz et al., 1995) provides a linguistic analysis of the structure of discourse. According to the theory, local coherence of discourse arises from the manner and extent to which successive utterances make reference to the same entities. In this paper, we investigate the connection between centering theory and modern coreference resolution systems. We provide an operationalization of centering and systematically investigate if neural coreference resolvers adhere to the rules of centering theory by deﬁning various discourse metrics and de-veloping a search-based methodology. Our information-theoretic analysis reveals a positive dependence between coreference and centering; but also shows that high-quality neural coreference resolvers may not bene-ﬁt much from explicitly modeling centering ideas. Our analysis further shows that contextualized embeddings contain much of the coherence information, which helps explain why CT can only provide little gains to modern neural coreference resolvers which make use of pretrained representations. Finally, we discuss factors that contribute to coreference which are not modeled by CT such as world knowledge and recency bias. We formulate a version of CT that also models recency and show that it captures coreference information better compared to vanilla CT.
Bures-Wasserstein Barycenters and Low-Rank Matrix Recovery

We revisit the problem of recovering a low-rank positive semidefinite matrix from rank-one projections using tools from optimal transport. More specifically, we show that a variational formulation of this problem is equivalent to computing a Wasserstein barycenter. In turn, this new perspective enables the development of new geometric first-order methods with strong convergence guarantees in Bures-Wasserstein distance. Experiments on simulated data demonstrate the advantages of our new methodology over existing methods.
A bibliometric analysis and review on reinforcement learning for transportation applications

Transportation is the backbone of the economy and urban development. Improving the efficiency, sustainability, resilience, and intelligence of transportation systems is critical and also challenging. The constantly changing traffic conditions, the uncertain influence of external factors (e.g. weather, accidents), and the interactions among multiple travel modes and multi-type flows result in the dynamic and stochastic natures of transportation systems. The planning, operation, and control of transportation systems require flexible and adaptable strategies in order to deal with uncertainty, non-linearity, variability, and high complexity. In this context, Reinforcement Learning (RL) that enables autonomous decision-makers to interact with the complex environment, learn from the experiences, and select optimal actions has been rapidly emerging as one of the most useful approaches for smart transportation applications. This paper conducts a bibliometric analysis to identify the development of RL-based methods for transportation applications, representative journals/conferences, and leading topics in recent 10 years. Then, this paper presents a comprehensive literature review on applications of RL in transportation based on specific topics. The potential future research directions of RL applications and developments are also discussed.
Network Functional Compression for Control Applications

The trend of future communication systems is to aim for the steering and control of cyber-physical systems. These systems can quickly become congested in environments like those presented in Industry 4.0. In these scenarios, a plethora of sensor data is transmitted wirelessly to multiple in-network controllers that compute the control functions of the cyber-physical systems. In this paper, we show an implementation of network Functional Compression (FC) as a proof of concept to drastically reduce the data traffic in these scenarios. FC is a form of goal-oriented communication scheme in which the objective of the sender-receiver pair is to transmit the minimum amount of information to compute a function at the receiver end. In our scenario, the senders transmit an encoded and compressed version of the sensor data to a destination, an in-network controller interested in computing as its target function, a proportional-integral-derivative (PID) controller. We show that it is possible to achieve compression rates of over 50% in some cases by employing FC. We also show that using FC in a distributed cascade fashion can achieve more significant compression rates while reducing computational costs.
Towards automatically extracting UML class diagrams from natural language specifications

In model-driven engineering (MDE), UML class diagrams serve as a way to plan and communicate between developers. However, it is complex and resource-consuming. We propose an automated approach for the extraction of UML class diagrams from natural language software specifications. To develop our approach, we create a dataset of UML class diagrams and their English specifications with the help of volunteers. Our approach is a pipeline of steps consisting of the segmentation of the input into sentences, the classification of the sentences, the generation of UML class diagram fragments from sentences, and the composition of these fragments into one UML class diagram. We develop a quantitative testing framework specific to UML class diagram extraction. Our approach yields low precision and recall but serves as a benchmark for future research.
Galois closures and elementary components of Hilbert schemes of points

Bhargava and the first-named author of this paper introduced a functorial Galois closure operation for finite-rank ring extensions, generalizing constructions of Grothendieck and Katz--Mazur. In this paper, we generalize Galois closures and apply them to construct a new infinite family of irreducible components of Hilbert schemes of points. We show that these components are elementary, in the sense that they parametrize algebras supported at a point. Furthermore, we produce secondary families of elementary components obtained from Galois closures by modding out by suitable socle elements.
Old Stellar Populations. VI. Absorption-Line Spectra of Galaxy Nuclei and Globular Clusters

We present absorption-line strengths on the Lick/IDS line-strength system of 381 galaxies and 38 globular clusters in the 4000-6400 Å region. All galaxies were observed at Lick Observatory between 1972 and 1984 with the Cassegrain Image Dissector Scanner spectrograph, which makes this study one of the largest homogeneous collections of galaxy spectral line data to date. We also present a catalog of nuclear velocity dispersions used to correct the absorption-line strengths onto the stellar Lick/IDS system. Extensive discussion of both random and systematic errors of the Lick/IDS system is provided. Indices are seen to fall into three families: α-element-like indices (including CN, Mg, Na D, and TiO2) that correlate positively with velocity dispersion; Fe-like indices (including Ca, the G band, TiO1, and all Fe indices) that correlate only weakly with velocity dispersion and the α indices; and Hβ that anticorrelates with both velocity dispersion and the α indices. C24668 seems to be intermediate between the α and Fe groups. These groupings probably represent different element abundance families with different nucleosynthesis histories.
M ULTIPLE C HANGE -P OINT D ETECTION U SING A G ENETIC A LGORITHM AND A B AYESIAN M INIMUM D ESCRIPTION L ENGTH FOR T IME E XCEEDANCES

The change-point detection problem has been widely studied in time series and signal processing literature. The current methods can be resumed in the search for the appropiate partitions of a whole time series such that the problem can be approached as one of optimization; nevertheless, an exact optimization approach could result computationally expensive and approximate ones discard potential scenarios for change-points conﬁgurations in a non-rigorous manner. Thus, a framework it is presented to detect change-points in a univariate time series using a decision criterion based on the Minimum Description Length ( MDL ), modiﬁed such that a Bayesian analysis is included. To search for the points of change, the times where mean value deviations occur (exceedances) are analyzed and then it is evaluated which of these could constitute a change-point through a genetic algorithm using as a ﬁtness function the previously described MDL. The effectiveness of the method it is assessed through a simulation study and on the other hand, it is analyzed its practical validity in a real dataset for the presence of Particulate Matter of less than 2.5 microns ( µm ) PM 2 . 5 in Bogotá, Colombia for the 2018-2020 period under different settings to understand the algorithm convergence. It is found that this deﬁnition for the objective function tends to ﬁnd better results for both the number of change-points and their location in the series for most of cases reducing the error in comparison to other available methods in the literature.
Coresets for Vertical Federated Learning: Regularized Linear Regression and K-Means Clustering

Vertical federated learning (VFL), where data features are stored in multiple parties distributively, is an important area in machine learning. However, the communication complexity for VFL is typically very high. In this paper, we propose a unified framework by constructing coresets in a distributed fashion for communication-efficient VFL. We study two important learning tasks in the VFL setting: regularized linear regression and $k$-means clustering, and apply our coreset framework to both problems. We theoretically show that using coresets can drastically alleviate the communication complexity, while nearly maintain the solution quality. Numerical experiments are conducted to corroborate our theoretical findings.
Revealed Preferences of One-Sided Matching

Consider the object allocation (one-sided matching) model of Shapley and Scarf (1974). When final allocations are observed but agents' preferences are unknown, when might the allocation be in the core? This is a one-sided analogue of the model in Echenique, Lee, Shum, and Yenmez (2013). I build a model in which the strict core is testable -- an allocation is"rationalizable"if there is a preference profile putting it in the core. In this manner, I develop a theory of the revealed preferences of one-sided matching. I study rationalizability in both non-transferrable and transferrable utility settings. In the non-transferrable utility setting, an allocation is rationalizable if and only if: whenever agents with the same preferences are in the same potential trading cycle, they receive the same allocation. In the transferrable utility setting, an allocation is rationalizable if and only if: there exists a price vector supporting the allocation as a competitive equilibrium; or equivalently, it satisfies a cyclic monotonicity condition. The proofs leverage simple graph theory and combinatorial optimization and tie together classic theories of consumer demand revealed preferences and competitive equilibrium.