Miracle-dz/UniFAR-FTUs huggingface dataset:
Plasmon dispersion in semimetallic armchair graphene nanoribbons

The dispersion relations for plasmons in intrinsic and extrinsic semimetallic armchair graphene nanoribbons (acGNR) are calculated in the random phase approximation using the orthogonal ${p}_{z}$-orbital tight-binding method. Our model predicts new plasmons for acGNR of odd atomic widths, $N=5,11,17,. $ Our model further predicts plasmons in acGNR of even atomic widths, $N=2,8,14,. $, related to those found using a Dirac continuum model but with different quantitative dispersion characteristics. We find that the dispersion of all plasmons in semimetallic acGNR depends strongly on the localization of the ${p}_{z}$ electronic wavefunctions. We also find that overlap integrals for acGNR behave in a more complex way than predicted by the Dirac continuum model, suggesting that these plasmons will experience a small damping for all $q\ensuremath{\ne}0$. Plasmons in extrinsic semimetallic acGNR with the chemical potential in the lowest (highest) conduction (valence) band are found to have dispersion characteristics nearly identical to their intrinsic counterparts, with negligible differences in dispersion arising from the slight differences in overlap integrals for the interband and intraband transitions.
Multiple Packing: Lower Bounds via Error Exponents

We derive lower bounds on the maximal rates for multiple packings in high-dimensional Euclidean spaces. For any <inline-formula> <tex-math notation="LaTeX">$ N > 0 $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$ L\in \mathbb {Z}_{\ge 2} $ </tex-math></inline-formula>, a multiple packing is a set <inline-formula> <tex-math notation="LaTeX">$\mathcal {C}$ </tex-math></inline-formula> of points in <inline-formula> <tex-math notation="LaTeX">$ \mathbb {R}^{n} $ </tex-math></inline-formula> such that any point in <inline-formula> <tex-math notation="LaTeX">$ \mathbb {R}^{n} $ </tex-math></inline-formula> lies in the intersection of at most <inline-formula> <tex-math notation="LaTeX">$ L-1 $ </tex-math></inline-formula> balls of radius <inline-formula> <tex-math notation="LaTeX">$ \sqrt {nN} $ </tex-math></inline-formula> around points in <inline-formula> <tex-math notation="LaTeX">$ \mathcal {C} $ </tex-math></inline-formula>. This is a natural generalization of the sphere packing problem. We study the multiple packing problem for both bounded point sets whose points have norm at most <inline-formula> <tex-math notation="LaTeX">$\sqrt {nP}$ </tex-math></inline-formula> for some constant <inline-formula> <tex-math notation="LaTeX">$P > 0$ </tex-math></inline-formula>, and unbounded point sets whose points are allowed to be anywhere in <inline-formula> <tex-math notation="LaTeX">$ \mathbb {R}^{n} $ </tex-math></inline-formula>. Given a well-known connection with coding theory, multiple packings can be viewed as the Euclidean analog of list-decodable codes, which are well-studied over finite fields. We derive the best known lower bounds on the optimal multiple packing density. This is accomplished by establishing an inequality which relates the list-decoding error exponent for additive white Gaussian noise channels, a quantity of average-case nature, to the list-decoding radius, a quantity of worst-case nature. We also derive novel bounds on the list-decoding error exponent for infinite constellations and closed-form expressions for the list-decoding error exponents for the power-constrained AWGN channel, which may be of independent interest beyond multiple packing.
Reaching 10 ms single photon lifetimes for superconducting aluminum cavities

Three-dimensional microwave cavities have recently been combined with superconducting qubits in the circuit quantum electrodynamics architecture. These cavities should have less sensitivity to dielectric and conductor losses at surfaces and interfaces, which currently limit the performance of planar resonators. We expect that significantly (>103) higher quality factors and longer lifetimes should be achievable for 3D structures. Motivated by this principle, we have reached internal quality factors greater than 0.5 × 109 and intrinsic lifetimes of 0.01 s for multiple aluminum superconducting cavity resonators at single photon energies and millikelvin temperatures. These improvements could enable long lived quantum memories with submicrosecond access times when strongly coupled to superconducting qubits.
An extended Zel'dovich model for the halo mass function

A new way to construct a fitting formula for the halo mass function is presented. Our formula is expressed as a solution to the modified Jedamzik matrix equation that automatically satisfies the normalization constraint. The characteristic parameters expressed in terms of the linear shear eigenvalues are empirically determined by fitting the analytic formula to the numerical results from the high-resolution N-body simulation and found to be independent of scale, redshift and background cosmology. Our fitting formula with the best-fit parameters is shown to work excellently in the wide mass-range at various redshifts: The ratio of the analytic formula to the N-body results departs from unity by up to 10% and 5% over 1011 ≤ M/(h−1M⊙) ≤ 5 × 1015 at z = 0,0.5 and 1 for the FoF-halo and SO-halo cases, respectively.
Generalizing Performance Bounds for the Greedy Algorithm: Approximate Submodularity and Localization

The greedy algorithm is perhaps the simplest heuristic in combinatorial optimization. When maximizing a nonnegative, increasing, submodular function, the greedy algorithm has a worst-case performance bound proportional to its optimal objective value at a given cardinality. We introduce new metrics that quantify a function's proximity to being submodular. Using our proposed criteria, we derive performance bounds for the greedy algorithm applied to non-submodular functions. Insights from our initial derivations allow us to generalize existing bounds, satisfy the criteria, and improve upon the numerical bounds. We examine multiple bounds using a generalization of the facility location problem that does not have a submodular objective function generally. In numerical examples, our new bounds are competitive with, and often an improvement on, those of Das and Kempe (2011), Horel and Singer (2016), and Zhou and Spanos (2016). We observe this collection of bounds provides complementary information about the optimized function.
FedGrad: Optimisation in Decentralised Machine Learning

—Federated Learning is a machine learning paradigm where we aim to train machine learning models in a distributed fashion. Large number of clients/edge devices collaborate with each other to train a single model on the central. Clients do not share their own dataset with each other, decoupling computation and data on the same device. In this paper, we propose yet another adaptive federated optimization method, and some other ideas in the field of federated learning. We also perform experiments using these methods and showcase the improvement in overall performance of federated learning.
An hp Weak Galerkin FEM for singularly perturbed problems

We present the analysis for an $hp$ weak Galerkin-FEM for singularly perturbed reaction-convection-diffusion problems in one-dimension. Under the analyticity of the data assumption, we establish robust exponential convergence, when the error is measured in the energy norm, as the degree $p$ of the approximating polynomials is increased. The Spectral Boundary Layer mesh is used, which is the minimal (layer adapted) mesh for such problems. Numerical examples illustrating the theory are also presented.
Dynamics of (mis)information flow and engaging power of narratives

The debate around misinformation and its potentially detrimental effects on public opinion is complex and multifaceted, to the extent that even the relevant academic research has not found unanimity on the prevalence and consumption of misinformation compared with mainstream content. The methodological framework presented here emphasises the importance of considering data representative of the complexity of the phenomenon and metrics that control for possible scale effects. By combining statistical, econometric and machine learning models, we shed light on the real impact of misinformation about a subject of general interest and social relevance, such as vaccines, on both the information available to citizens and their news diet. Our results show the prominent role achieved by misinformation sources in the news ecosystem, but also and above all the inability of mainstream media to drive the public debate over time on issues that are particularly sensitive and emotional. Taking properly account for the temporal dynamics of public debate seems crucial to prevent the latter from moving into uncontrolled spaces where false narratives
VLT/FLAMES-ARGUS observations of stellar wind–ISM cloud interactions in NGC 6357

We present optical/near-IR integral field unit observations of a gas pillar in the Galactic H ii region NGC 6357 containing the young open star cluster Pismis 24. These observations have allowed us to examine in detail the gas conditions of the strong wind–clump interactions taking place on its surface. By accurately decomposing the Hα line profile, we identify the presence of a narrow (∼20 km s−1) and broad (50–150 km s−1) component, both of which we can associate with the pillar and its surroundings. Furthermore, the broadest broad component widths are found in a region that follows the shape of the eastern pillar edge. These connections have allowed us to firmly associate the broad component with emission from ionized gas within turbulent mixing layers on the pillar's surface set up by the shear flows of the winds from the O stars in the cluster. We discuss the implications of our findings in terms of the broad emission-line component that is increasingly found in extragalactic starburst environments. Although the broad linewidths found here are narrower, we conclude that the mechanisms producing both must be the same. The difference in linewidths may result from the lower total mechanical wind energy produced by the O stars in Pismis 24 compared to that from a typical young massive star cluster found in a starburst galaxy. The pillar's edge is also clearly defined by dense (≲5000 cm−3), hot (≳20 000 K) and excited (via the [N ii]/Hα and [S ii]/Hα ratios) gas conditions, implying the presence of a D-type ionization front propagating into the pillar surface. Although there must be both photoevaporation outflows produced by the ionization front and mass loss through mechanical ablation, we see no evidence for any significant bulk gas motions on or around the pillar. We postulate that the evaporated/ablated gas must be rapidly heated before being entrained.
The Hubble Space Telescope UV Legacy Survey of galactic globular clusters - II. The seven stellar populations of NGC 7089 (M2)

APM and HJ acknowledge support by the Australian Research Council through Discovery Project grant DP120100475. MZ acknowledges support by Proyecto Fondecyt Regular 1110393, by the BASAL Center for Astrophysics and Associated Technologies PFB-06, and by Project IC120009 ‘Millennium Institute of Astrophysics (MAS)’ of Iniciativa Cient´ifica Milenio by the Chilean Ministry of Economy, Development and Tourism.
Improving Graph Neural Networks at Scale: Combining Approximate PageRank and CoreRank

Graph Neural Networks (GNNs) have achieved great successes in many learning tasks performed on graph structures. Nonetheless, to propagate information GNNs rely on a message passing scheme which can become prohibitively expensive when working with industrial-scale graphs. Inspired by the PPRGo model, we propose the CorePPR model, a scalable solution that utilises a learnable convex combination of the approximate personalised PageRank and the CoreRank to diffuse multi-hop neighbourhood information in GNNs. Additionally, we incorporate a dynamic mechanism to select the most influential neighbours for a particular node which reduces training time while preserving the performance of the model. Overall, we demonstrate that CorePPR outperforms PPRGo, particularly on large graphs where selecting the most influential nodes is particularly relevant for scalability. Our code is publicly available at: https://github.com/arielramos97/CorePPR.
Eat-Radar: Continuous Fine-Grained Intake Gesture Detection Using FMCW Radar and 3D Temporal Convolutional Network With Attention

Unhealthy dietary habits are considered as the primary cause of various chronic diseases, including obesity and diabetes. The automatic food intake monitoring system has the potential to improve the quality of life (QoL) of people with diet-related diseases through dietary assessment. In this work, we propose a novel contactless radar-based approach for food intake monitoring. Specifically, a Frequency Modulated Continuous Wave (FMCW) radar sensor is employed to recognize fine-grained eating and drinking gestures. The fine-grained eating/drinking gesture contains a series of movements from raising the hand to the mouth until putting away the hand from the mouth. A 3D temporal convolutional network with self-attention (3D-TCN-Att) is developed to detect and segment eating and drinking gestures in meal sessions by processing the Range-Doppler Cube (RD Cube). Unlike previous radar-based research, this work collects data in continuous meal sessions (more realistic scenarios). We create a public dataset comprising 70 meal sessions (4,132 eating gestures and 893 drinking gestures) from 70 participants with a total duration of 1,155 minutes. Four eating styles (fork & knife, chopsticks, spoon, hand) are included in this dataset. To validate the performance of the proposed approach, seven-fold cross-validation method is applied. The 3D-TCN-Att model achieves a segmental F1-score of 0.896 and 0.868 for eating and drinking gestures, respectively. The results of the proposed approach indicate the feasibility of using radar for fine-grained eating and drinking gesture detection and segmentation in meal sessions.
Automatic Change-Point Detection in Time Series via Deep Learning

Detecting change-points in data is challenging because of the range of possible types of change and types of behaviour of data when there is no change. Statistically efficient methods for detecting a change will depend on both of these features, and it can be difficult for a practitioner to develop an appropriate detection method for their application of interest. We show how to automatically generate new offline detection methods based on training a neural network. Our approach is motivated by many existing tests for the presence of a change-point being representable by a simple neural network, and thus a neural network trained with sufficient data should have performance at least as good as these methods. We present theory that quantifies the error rate for such an approach, and how it depends on the amount of training data. Empirical results show that, even with limited training data, its performance is competitive with the standard CUSUM-based classifier for detecting a change in mean when the noise is independent and Gaussian, and can substantially outperform it in the presence of auto-correlated or heavy-tailed noise. Our method also shows strong results in detecting and localising changes in activity based on accelerometer data.
An HDG Method for Time-dependent Drift-Diffusion Model of Semiconductor Devices

We propose a hybridizable discontinuous Galerkin (HDG) finite element method to approximate the solution of the time dependent drift-diffusion problem. This system involves a nonlinear convection diffusion equation for the electron concentration $u$ coupled to a linear Poisson problem for the the electric potential $\phi$. The non-linearity in this system is the product of the $\nabla \phi$ with $u$. An improper choice of a numerical scheme can reduce the convergence rate. To obtain optimal HDG error estimates for $\phi$, $u$ and their gradients, we utilize two different HDG schemes to discretize the nonlinear convection diffusion equation and the Poisson equation. We prove optimal order error estimates for the semidiscrete problem. We also present numerical experiments to support our theoretical results.
Surrogate light curve models for kilonovae with comprehensive wind ejecta outflows and parameter estimation for AT2017gfo

The electromagnetic emission resulting from neutron star mergers have been shown to encode properties of the ejected material in their light curves. The ejecta properties inferred from the kilonova emission has been in tension with those calculated based on the gravitational wave signal and numerical relativity models. Motivated by this tension, we construct a broad set of surrogate light curve models derived for kilonova ejecta. The four-parameter family of two-dimensional anisotropic simulations and its associated surrogate explore different assumptions about the wind outflow morphology and outflow composition, keeping the dynamical ejecta component consistent. We present the capabilities of these surrogate models in interpolating kilonova light curves across various ejecta parameters and perform parameter estimation for AT2017gfo both without any assumptions on the outflow and under the assumption that the outflow must be representative of solar r-process abundance patterns. Our parameter estimation for AT2017gfo shows these surrogate models help alleviate the ejecta property discrepancy while also illustrating the impact of systematic modeling uncertainties on these properties, urging further investigation.
Second-order amplitudes in loop quantum gravity

We explore some second-order amplitudes in loop quantum gravity. In particular, we compute some second-order contributions to diagonal components of the graviton propagator in the large distance limit, using the old version of the Barrett–Crane vertex amplitude. We illustrate the geometry associated with these terms. We find some peculiar phenomena in the large distance behavior of these amplitudes, related to the geometry of the generalized triangulations dual to the Feynman graphs of the corresponding group field theory. In particular, we point out a possible further difficulty with the old Barrett–Crane vertex: it appears to lead to flatness instead of Ricci flatness, at least in some situations. The observation raises the question whether this difficulty remains with the new version of the vertex.
Robust Manifold Nonnegative Tucker Factorization for Tensor Data Representation

Nonnegative Tucker Factorization (NTF) minimizes the euclidean distance or Kullback-Leibler divergence between the original data and its low-rank approximation which often suffers from grossly corruptions or outliers and the neglect of manifold structures of data. In particular, NTF suffers from rotational ambiguity, whose solutions with and without rotation transformations are equally in the sense of yielding the maximum likelihood. In this paper, we propose three Robust Manifold NTF algorithms to handle outliers by incorporating structural knowledge about the outliers. They first applies a half-quadratic optimization algorithm to transform the problem into a general weighted NTF where the weights are influenced by the outliers. Then, we introduce the correntropy induced metric, Huber function and Cauchy function for weights respectively, to handle the outliers. Finally, we introduce a manifold regularization to overcome the rotational ambiguity of NTF. We have compared the proposed method with a number of representative references covering major branches of NTF on a variety of real-world image databases. Experimental results illustrate the effectiveness of the proposed method under two evaluation metrics (accuracy and nmi).
Strong electron-phonon coupling and enhanced phonon Gr\"uneisen parameters in valence-fluctuating metal EuPd$_2$Si$_2$

We study the valence crossover and strong electron-phonon coupling of EuPd$_2$Si$_2$ by polarization-resolved Raman spectroscopy. The fully-symmetric phonon mode shows strongly asymmetric lineshape at low temperature, indicating Fano-type interaction between this mode and a continuum of electron-hole excitations. Moreover, the frequency and linewidth of the phonon modes exhibit anomalies across the valence-crossover temperature, suggesting the coupling between valence fluctuations and lattice vibration. In particular, two phonon modes show significantly enhanced Gr\"uneisen parameter, suggesting proximity to a critical elasticity regime. The relative contribution of the structural change and valence change to the phonon anomalies is evaluated by density functional theory calculations.
Complexity of High-Dimensional Identity Testing with Coordinate Conditional Sampling

We study the identity testing problem for high-dimensional distributions. Given as input an explicit distribution $\mu$, an $\varepsilon>0$, and access to sampling oracle(s) for a hidden distribution $\pi$, the goal in identity testing is to distinguish whether the two distributions $\mu$ and $\pi$ are identical or are at least $\varepsilon$-far apart. When there is only access to full samples from the hidden distribution $\pi$, it is known that exponentially many samples (in the dimension) may be needed for identity testing, and hence previous works have studied identity testing with additional access to various"conditional"sampling oracles. We consider a significantly weaker conditional sampling oracle, which we call the $\mathsf{Coordinate\ Oracle}$, and provide a computational and statistical characterization of the identity testing problem in this new model. We prove that if an analytic property known as approximate tensorization of entropy holds for an $n$-dimensional visible distribution $\mu$, then there is an efficient identity testing algorithm for any hidden distribution $\pi$ using $\tilde{O}(n/\varepsilon)$ queries to the $\mathsf{Coordinate\ Oracle}$. Approximate tensorization of entropy is a pertinent condition as recent works have established it for a large class of high-dimensional distributions. We also prove a computational phase transition: for a well-studied class of $n$-dimensional distributions, specifically sparse antiferromagnetic Ising models over $\{+1,-1\}^n$, we show that in the regime where approximate tensorization of entropy fails, there is no efficient identity testing algorithm unless $\mathsf{RP}=\mathsf{NP}$. We complement our results with a matching $\Omega(n/\varepsilon)$ statistical lower bound for the sample complexity of identity testing in the $\mathsf{Coordinate\ Oracle}$ model.