Miracle-dz/UniFAR-FTUs huggingface dataset:
Generalized Grad‐Shafranov Equation for Gravitational Hall‐MHD Equilibria

The consistent theoretical description of gravitational Hall‐MHD (G‐Hall‐MHD) equilibria is of fundamental importance for understanding the phenomenology of accretion disks (AD) around compact objects (black holes, neutron stars, etc.). The very existence of these equilibria is actually suggested by observations, which show evidence of quiescent, and essentially non‐relativistic, AD plasmas close to compact stars, thus indicating that accretion disks may be characterized by slowly varying EM and fluid fields. These (EM) fields, in particular the electric field, may locally be extremely intense, so that AD plasmas are likely to be locally non‐neutral and therefore characterized by the presence of Hall currents. This suggests therefore that such equilibria should be described in the framework of the Hall‐MHD theory. In addition, for the description of equilibria occurring close to compact stars, the effect of space‐time curvature is expected to become significant. Extending previous approaches, holding for .
Optimal Parametrization and Comparative Analysis of Preallocation Methods for Combinatorial Auction-Based Channel Assignment

Algorithms based on combinatorial auction (CA) show signiﬁcant potential regarding their application for channel assignment problems in multiconnective ultra-reliable wireless networks. However the computational eﬀort required by such algorithms grows fast with the number of users and resources. Therefore, preallocation-based CA represents a promising approach for these setups. The aim of preallocation is to constrain the number of bids submitted by participants in the CA process, thus allow the numerical feasibility of the auction problem. Reduction of bid number is achieved via limiting the number of items (in our case channels) considered by auction participants (ten-ants) in their bids. Thus the aim of preallocation is to non-exclusively assign channels to tenants, which assignment serves as a basis for the later bid generation in the CA procedure. In this paper we compare the performance of various preallocation approaches via simulation according to various measures, namely the total utility of the resulting allocation, the number of unassigned channels and the required computational time. In addition to simple iterative random and semi-random algorithms which serve as baseline reference, we consider the many-to-many version of the Gale-Shapley algorithm (M2MGS) and introduce a relaxed version of the combinatorial auction algorithm (RCA) for the preallocation stage, which allows the multiple allocation of single items. Furthermore, we analyze the optimal parametrization of the M2MGS and the RCA preallocation methods, and formulate recom-mendations for optimal performance based on the analysis.
Testing for the Important Components of Posterior Predictive Variance

We give a decomposition of the posterior predictive variance using the law of total variance and conditioning on a ﬁnite dimensional discrete random variable. This random variable summarizes various features of modeling that are used to form the prediction for a future outcome. Then, we test which terms in this decomposition are small enough to ignore. This allows us identify which of the discrete random variables are most important to prediction intervals. The terms in the decomposition admit interpretations based on conditional means and variances and are analogous to the terms in a Cochran’s theorem decomposition of squared error often used in analysis of variance. Thus, the modeling features are treated as factors in completely randomized design. In cases where there are multiple decompositions we suggest choosing the one that that gives the best predictive coverage with the smallest variance.
A Multi-channel Next POI Recommendation Framework with Multi-granularity Check-in Signals

Current study on next point-of-interest (POI) recommendation mainly explores user sequential transitions with the fine-grained individual-user POI check-in trajectories only, which suffers from the severe check-in data sparsity issue. In fact, coarse-grained signals (i.e., region- and global-level check-ins) in such sparse check-ins would also benefit to augment user preference learning. Specifically, our data analysis unveils that user movement exhibits noticeable patterns w.r. t. the regions of visited POIs. Meanwhile, the global all-user check-ins can help reflect sequential regularities shared by the crowd. We are, therefore, inspired to propose the MCMG: a Multi-Channel next POI recommendation framework with Multi-Granularity signals categorized from two orthogonal perspectives, i.e., fine-coarse grained check-ins at either POI/region level or local/global level. The MCMG is equipped with three modules, namely, global user behavior encoder, local multi-channel (i.e., region, category, and POI channels) encoder, and region-aware weighting strategy. Such design enables MCMG to be capable of capturing both fine- and coarse-grained sequential regularities as well as exploring the dynamic impact of multi-channel by differentiating the check-in patterns w.r. t. visited regions. Extensive experiments on four real-world datasets show that our MCMG significantly outperforms state-of-the-art next POI recommendation approaches.
A rapid-prototype MPC tool based on gPROMS platform

—This paper presents a rapid-prototype Model Pre- dictive Control (MPC) tool based on the gPROMS platform, with the support for the whole MPC design ﬂow. Our gPROMS- MPC tool can not only directly interact with a ﬁrst-principle-based gPROMS model for closed-loop simulations but also utilizes its mathematical information to derive simpliﬁed MPC models, basically via linearization techniques. It can inherit the interpretability of the ﬁrst-principle-based gPROMS model, unlike previous tools (like the PAROC framework [1]), in which simpliﬁed MPC models are obtained from black-box system identiﬁcation based on gPROMS simulation data. Our gPROMS-MPC tool allows users to choose when to linearize, such as linearization at each sampling time (successive lin- earization) or at some speciﬁc points to obtain one or multiple good linear models. Our gPROMS-MPC tool implements our previous construction-free CDAL and the online parametric active-set qpOASES algorithm to solve sparse or condensed MPC problem formulations for possible successive linearization or high state-dimension cases. And our CDAL algorithm is also matrix-free and library-free, thus having the support for embedded C-code generation. A ﬂash-separation numerical ex- ample is used to validate the correctness of our gPROMS-MPC tool and investigate performance of different MPC schemes.
Isotropic plasma-thermal atomic layer etching of aluminum nitride using SF6 plasma and Al(CH3)3

We report the isotropic plasma atomic layer etching (ALE) of aluminum nitride using sequential exposures of SF6 plasma and trimethylaluminum [Al(CH3)3]. ALE was observed at temperatures greater than 200 °C, with a maximum etch rate of 1.9 Å/cycle observed at 300 °C as measured using ex situ ellipsometry. After ALE, the etched surface was found to contain a lower concentration of oxygen compared to the original surface and exhibited a ∼35% decrease in surface roughness. These findings have relevance for applications of AlN in nonlinear photonics and wide bandgap semiconductor devices.
Dress code for infrared safe scattering in QED

We study the S-matrix and inclusive cross-section for general dressed states in quantum electrodynamics. We obtain an infrared factorization formula of the S-matrix elements for general dressed states. It enables us to study what dressed states lead to infrared-safe S-matrix elements. The condition for dressed states can be interpreted as the memory effect which is nothing but the conservation law of the asymptotic symmetry. We derive the generalized soft photon theorem for general dressed states. We also compute inclusive cross-section using general dressed states. It is necessary to use appropriate initial and final dressed states to evaluate interference effects, which cannot be computed correctly by using Fock states due to the infrared divergence.
Unsupervised EHR‐based phenotyping via matrix and tensor decompositions

Computational phenotyping allows for unsupervised discovery of subgroups of patients as well as corresponding co‐occurring medical conditions from electronic health records (EHR). Typically, EHR data contains demographic information, diagnoses and laboratory results. Discovering (novel) phenotypes has the potential to be of prognostic and therapeutic value. Providing medical practitioners with transparent and interpretable results is an important requirement and an essential part for advancing precision medicine. Low‐rank data approximation methods such as matrix (e.g., nonnegative matrix factorization) and tensor decompositions (e.g., CANDECOMP/PARAFAC) have demonstrated that they can provide such transparent and interpretable insights. Recent developments have adapted low‐rank data approximation methods by incorporating different constraints and regularizations that facilitate interpretability further. In addition, they offer solutions for common challenges within EHR data such as high dimensionality, data sparsity and incompleteness. Especially extracting temporal phenotypes from longitudinal EHR has received much attention in recent years. In this paper, we provide a comprehensive review of low‐rank approximation‐based approaches for computational phenotyping. The existing literature is categorized into temporal versus static phenotyping approaches based on matrix versus tensor decompositions. Furthermore, we outline different approaches for the validation of phenotypes, that is, the assessment of clinical significance.
A Lower Bound on the Mass of Compact Objects from Dissipative Dark Matter

We study the fragmentation scale of dark gas formed in dissipative dark-matter halos and show that the simple atomic-dark-matter model consistent with all current observations can create low-mass fragments that can evolve into compact objects forbidden by stellar astrophysics. We model the collapse of the dark halo’s dense core by tracing the thermochemical evolution of a uniform-density volume element under two extreme assumptions for density evolution: hydrostatic equilibrium and pressure-free collapse. We then compute the opacity-limited minimum fragment mass from the minimum temperature achieved in these calculations. The results indicate that much of the parameter space is highly unstable to small-scale fragmentation.
Gait Recognition in the Wild with Multi-hop Temporal Switch

Existing studies for gait recognition are dominated by in-the-lab scenarios. Since people live in real-world senses, gait recognition in the wild is a more practical problem that has recently attracted the attention of the community of multimedia and computer vision. Current methods that obtain state-of-the-art performance on in-the-lab benchmarks achieve much worse accuracy on the recently proposed in-the-wild datasets because these methods can hardly model the varied temporal dynamics of gait sequences in unconstrained scenes. Therefore, this paper presents a novel multi-hop temporal switch method to achieve effective temporal modeling of gait patterns in real-world scenes. Concretely, we design a novel gait recognition network, named Multi-hop Temporal Switch Network (MTSGait), to learn spatial features and multi-scale temporal features simultaneously. Different from existing methods that use 3D convolutions for temporal modeling, our MTSGait models the temporal dynamics of gait sequences by 2D convolutions. By this means, it achieves high efficiency with fewer model parameters and reduces the difficulty in optimization compared with 3D convolution-based models. Based on the specific design of the 2D convolution kernels, our method can eliminate the misalignment of features among adjacent frames. In addition, a new sampling strategy, i.e., non-cyclic continuous sampling, is proposed to make the model learn more robust temporal features. Finally, the proposed method achieves superior performance on two public gait in-the-wild datasets, i.e., GREW and Gait3D, compared with state-of-the-art methods.
Variable selection for varying multi-index coefficients models with applications to synergistic GxE interactions

Epidemiological evidence suggests that simultaneous exposures to multiple environmental risk factors (Es) can increase disease risk larger than the additive effect of individual exposure acting alone. The interaction between a gene and multiple Es on a disease risk is termed as synergistic gene-environment interactions (synG$\times$E). Varying multi-index coefficients models (VMICM) have been a promising tool to model synergistic G$\times$E effect and to understand how multiple Es jointly influence genetic risks on a disease outcome. In this work, we proposed a 3-step variable selection approach for VMICM to estimate different effects of gene variables: varying, non-zero constant and zero effects which respectively correspond to nonlinear synG$\times$E, no synG$\times$E and no genetic effect. For multiple environmental exposure variables, we also estimated and selected important environmental variables that contribute to the synergistic interaction effect. We theoretically evaluated the oracle property of the proposed variable selection approach. Extensive simulation studies were conducted to evaluate the finite sample performance of the method, considering both continuous and discrete gene variables. Application to a real dataset further demonstrated the utility of the method. Our method has broad applications in areas where the purpose is to identify synergistic interaction effect.
The JWST Early-release Science Program for Direct Observations of Exoplanetary Systems II: A 1 to 20 μm Spectrum of the Planetary-mass Companion VHS 1256–1257 b

We present the highest fidelity spectrum to date of a planetary-mass object. VHS 1256 b is a <20 M Jup widely separated (∼8″, a = 150 au), young, planetary-mass companion that shares photometric colors and spectroscopic features with the directly imaged exoplanets HR 8799c, d, and e. As an L-to-T transition object, VHS 1256 b exists along the region of the color–magnitude diagram where substellar atmospheres transition from cloudy to clear. We observed VHS 1256 b with JWST's NIRSpec IFU and MIRI MRS modes for coverage from 1 to 20 μm at resolutions of ∼1000–3700. Water, methane, carbon monoxide, carbon dioxide, sodium, and potassium are observed in several portions of the JWST spectrum based on comparisons from template brown dwarf spectra, molecular opacities, and atmospheric models. The spectral shape of VHS 1256 b is influenced by disequilibrium chemistry and clouds. We directly detect silicate clouds, the first such detection reported for a planetary-mass companion.
Quantum information, Bell inequalities and the no-signalling principle

This PhD thesis contains a general introduction and three main chapters. Chapter 2 investigates Bell inequalities that generalize the CHSH and Braunstein-Caves inequalities. Chapter 3 shows a derivation of an upper bound on the success probability of a class of quantum teleportation protocols, denoted as port-based teleportation, from the no-cloning theorem and the no-signalling principle. Chapter 4 introduces the principle of quantum information causality. Chapter 2 considers the predictions of quantum theory and local hidden variable theories (LHVT) for the correlations obtained by measuring a pair of qubits by projections defined by randomly chosen axes separated by a given angle θ. The predictions of LHVT correspond to binary colourings of the Bloch sphere with antipodal points oppositely coloured. We show a Bell inequality for all θ, which generalizes the CHSH and the Braunstein-Caves inequalities in the sense that the measurement choices are not restricted to be in a finite set, but are constrained only by the angle θ. We motivate and explore the hypothesis that for a continuous range of θ > 0, the maximum correlation (anticorrelation) is obtained by assigning to one qubit the colouring with one hemisphere black and the other white, and assigning the same (reverse) colouring to the other qubit. We describe numerical tests that are consistent with this hypothesis and bound the range of θ. Chapter 3 shows a derivation of an upper bound on the success probability of port-based teleportation from the no-cloning theorem and the no-signalling principle. Chapter 4 introduces the principle of quantum information causality, a quantum version of the information causality principle. The quantum information causality principle states the maximum amount of quantum information that a transmitted quantum system can communicate as a function of its dimension, independently of any quantum physical resources previously shared by the communicating parties. These principles reduce to the no-signalling principle if no systems are transmitted. We present a new quantum information task, the quantum information causality game, whose success probability is upper bounded by the new principle, and show that an optimal strategy to perform it combines the quantum teleportation and superdense coding protocols with a task that has classical inputs.
River Surface Patch-wise Detector Using Mixture Augmentation for Scum-cover-index

—Urban rivers provide a water environment that influ- ences residential living. River surface monitoring has become crucial for making decisions about where to prioritize cleaning and when to automatically start the cleaning treatment. We focus on the organic mud, or “scum”, that accumulates on the river’s surface and contributes to the river’s odor and has external economic effects on the landscape. Because of its feature of a sparsely distributed and unstable pattern of organic shape, automating the monitoring process has proved difficult. We propose a patch-wise classification pipeline to detect scum features on the river surface using mixture image augmentation to increase the diversity between the scum floating on the river and the entangled background on the river surface reflected by nearby structures like buildings, bridges, poles, and barriers. Furthermore, we propose a scum-index cover on rivers to help monitor worse grade online, collect floating scum, and decide on chemical treatment policies. Finally, we demonstrate the application of our method on a time series dataset with frames every ten minutes recording river scum events over several days. We discuss the significance of our pipeline and its experimental findings.
Radio Detection

Detection techniques at radio wavelengths play an important role in the future of astrophysics experiments. The radio detection of cosmic rays, neutrinos, and photons has emerged as the technology of choice at the highest energies. Cosmological surveys require the detection of radiation at mm wavelengths at thresholds down to the fundamental noise limit. High energy astroparticle and neutrino detectors use large volumes of a naturally occurring suitable dielectric: the Earth's atmosphere and large volumes of cold ice as available in polar regions. The detection technology for radio detection of cosmic particles has matured in the past decade and is ready to move beyond prototyping or midscale applications. Instrumentation for radio detection has reached a maturity for science scale detectors. Radio detection provides competitive results in terms of the measurement of energy and direction and in particle identification when to compared to currently applied technologies for high-energy neutrinos when deployed in ice and for ultra-high-energy cosmic rays, neutrinos, and photons when deployed in the atmosphere. It has significant advantages in terms of cost per detection station and ease of deployment.
Dipole low-order g-mode instability of metal-poor low-mass main-sequence stars due to the mechanism

We analysed the vibrational stability of metal-poor low-mass main-sequence stars due to the ɛ mechanism. Since outer convection zones of metal-poor stars are limited only to the very outer layers, uncertainty in the treatment of convection does not affect the result significantly. We found that the dipole g1 and g2 modes definitely become unstable due to the ɛ mechanism for . Besides that, we found that as the metallicity decreases the mass range of ɛ-mechanism instability extends toward higher mass.
Optimal bispectrum estimator and simulations of the CMB lensing-integrated Sachs Wolfe non-Gaussian signal

We present the tools to optimally extract the lensing-integrated Sachs Wolfe (L-ISW) bispectrum signal from future cosmic microwave background (CMB) data. We implemented two different methods to simulate the non-Gaussian CMB maps with the L-ISW signal: a non-perturbative method based on the FLINTS lensing code and the separable mode-expansion method. We implemented the Komatsu, Spergel, and Wandelt (KSW) optimal estimator analysis for the L-ISW bispectrum and tested it on the non-Gaussian simulations for realistic CMB experimental settings with an inhomogeneous sky coverage. We show that the estimator approaches the Cramer-Rao bound and that Wiener filtering the L-ISW simulations slightly improves the estimate of f L-ISW NL by ≤10%. For a realistic CMB experimental setting that accounts for anisotropic noise and masked sky, we show that the linear term of the estimator is highly correlated to the cubic term and it is necessary to recover the signal and the optimal error bars. We also show that the L-ISW bispectrum, if not correctly accounted for, yields an underestimation of the f local NL error bars of 4%. A joint analysis of the non-Gaussian shapes and/or L-ISW template subtraction is needed to recover unbiased results of the primordial non-Gaussian signal from ongoing and future CMB experiments.
New implementation of elliptic systems method for time dependent difiusion tomography with novel boundary treatment for back re∞ected and transmitted data

It is common in applied work in engineering such as the medical imaging for diagnosis of possible breast tumors, to have only limited boundary measurement information using either back re∞ected or transmitted data. Here we formulate the problem as one of coe‐cient recovery from incomplete boundary data in inverse problems. We have completed a new implementation of the Elliptic Systems Method (ESM) in time dependent difiusion tomography. The basic formulation of the ESM involves solving a system of (typically 4) coupled 4th-order partial difierential equations, with the time variable integrated out using Legendre polynomials. Here, unlike the previous implementation that creates a larger (typically of size 8) mixed system of 2nd-order problems with quadratic elements over triangles, we use C 1 Bogner-Fox-Schmit bi-cubic elements over rectangles, with a new treatment of boundary conditions in the common case of incomplete boundary data. This new method is 4th-order accurate for su‐ciently smooth functions. The new boundary condition approach allows the use of homogeneous natural boundary conditions on parts of the boundary where no measured data is available. This combined efiort is being reported elsewhere, but without extensive comparisons of di‐cult applications against the literature. Here we will focus on three previously published examples using back re∞ected or transmitted data with one or two inclusions. The new implementation in comparison gives markedly improved results for inclusion recovery, all of which are achieved without use of additional aids such as weight functions which have previously been thought to be essential. In addition the new implementation is shown to be surprisingly robust with respect to noise. We conclude with two examples illustrating the efiect of increasing levels of noise.