Miracle-dz/UniFAR-FTUs huggingface dataset:
Radio Planning Considerations in TETRA to LTE Migration for PPDR Systems: A Radioelectric Coverage Case Study

Transitioning a Terrestrial Trunked Radio (TETRA) network to a Long-Term Evolution (LTE) network in public protection and disaster relief (PPDR) systems is a path to providing future services requiring high radio interface throughput and allowing broadband PPDR (BB-PPDR) radio communications. Users of TETRA networks are currently considering how to deploy a BB-PPDR network in the coming years. This study offers several radio planning considerations in TETRA to LTE migration for such networks. The conclusions are obtained from a case study in which both measurements and radioelectric coverage simulations were carried out for the real scenario of the Murcia Region, Spain, for both TETRA and LTE systems. The proposed considerations can help PPDR agencies efficiently estimate the cost of converting a TETRA network to an LTE network. Uniquely in this study, the total area is divided into geographical areas of interest that are defined as administrative divisions (region, municipal areas, etc.). The analysis was carried out using a radio planning tool based on a geographic information system and the measurements have been used to tune the propagation models. According to the real scenario considered, the number of sites needed in the LTE network—for a specific quality of service (90% for the whole region and 85% for municipal areas)—is a factor of 2.4 higher than for TETRA network.
The second boundary value problem for equations of viscoelastic diffusion in polymers

The classical approach to diffusion processes is based on Fick's law that the flux is proportional to the concentration gradient. Various phenomena occurring during propagation of penetrating liquids in polymers show that this type of diffusion exhibits anomalous behavior and contradicts the just mentioned law. However, they can be explained in the framework of non-Fickian diffusion theories based on viscoelasticity of polymers. Initial-boundary value problems for viscoelastic diffusion equations have been studied by several authors. Most of the studies are devoted to the Dirichlet BVP (the concentration is given on the boundary of the domain). In this chapter we study the second BVP, i.e. when the normal component of the concentration flux is prescribed on the boundary, which is more realistic in many physical situations. We establish existence of weak solutions to this problem. We suggest some conditions on the coefficients and boundary data under which all the solutions tend to the homogeneous state as time goes to infinity.
Source Data-Free Cross-Domain Semantic Segmentation: Align, Teach and Propagate

Benefiting from considerable pixel-level annotations collected from a specific situation (source), the trained semantic segmentation model performs quite well but fails in a new situation (target). To mitigate the domain gap, previous cross-domain semantic segmentation methods always assume the co-existence of source data and target data during domain alignment. However, accessing source data in the real scenario may raise privacy concerns and violate intellectual property. To tackle this problem, we focus on an interesting and challenging cross-domain semantic segmentation task where only the trained source model is provided to the target domain. Specifically, we propose a unified framework called \textbf{ATP}, which consists of three schemes, i.e., feature \textbf{A}lignment, bidirectional \textbf{T}eaching, and information \textbf{P}ropagation. First, considering explicit alignment is infeasible due to no source data, we devise a curriculum-style entropy minimization objective to implicitly align the target features with unseen source features via the provided source model. Second, besides positive pseudo labels in vanilla self-training, we introduce negative pseudo labels to this field and develop a bidirectional self-training strategy to enhance the representation learning in the target domain. It is the first work to use negative pseudo labels during self-training for domain adaptation. Finally, the information propagation scheme is employed to further reduce the intra-domain discrepancy within the target domain via pseudo-semi-supervised learning, which is the first step by providing a simple and effective post-process for the domain adaptation field. Furthermore, we also extend the proposed to the more challenging black-box source-model scenario where only the source model's prediction is available.
Antiphase boundaries in III-V semiconductors: Atomic configurations, band structures, and Fermi levels

Here, we comprehensively investigate the atomic structures and electronic properties of different antiphase boundaries in III-V semiconductors with different orientations and stoichiometries, including {110}, {100}, {111}, {112} and {113} ones, based on first-principle calculations. Especially, we demonstrate how the ladder or zigzag chemical bond configuration can lead for the different cases to a gapped semiconducting band structure, to a gapped metallic band structure or to a non-gapped metallic band structure. Besides, we evidence that the ladder APB configurations impact more significantly the Fermi energy levels than the zigzag APB configurations. We finally discuss how these different band structures can have some consequences on the operation of monolithic III-V/Si devices for photonics or energy harvesting.
Limit distributions of two-dimensional quantum walks

One-parameter family of discrete-time quantum-walk models on the square lattice, which includes the Grover-walk model as a special case, is analytically studied. Convergence in the long-time limit t → ∞ of all joint moments of two components of walker’s pseudovelocity, Xt/t and Yt/t, is proved and the probability density of limit distribution is derived. Dependence of the two-dimensional limit density function on the parameter of quantum coin and initial four-component qudit of quantum walker is determined. Symmetry of limit distribution on a plane and localization around the origin are completely controlled. Comparison with numerical results of direct computer-simulations is also shown.
GraphDistNet: A Graph-Based Collision-Distance Estimator for Gradient-Based Trajectory Optimization

Trajectory optimization (TO) aims to find a sequence of valid states while minimizing costs. However, its fine validation process is often costly due to computationally expensive collision searches, otherwise coarse searches lower the safety of the system losing a precise solution. To resolve the issues, we introduce a new collision-distance estimator, GraphDistNet, that can precisely encode the structural information between two geometries by leveraging edge feature-based convolutional operations, and also efficiently predict a batch of collision distances and gradients through 25,000 random environments with a maximum of 20 unforeseen objects. Further, we show the adoption of attention mechanism enables our method to be easily generalized in unforeseen complex geometries toward TO. Our evaluation show GraphDistNet outperforms state-of-the-art baseline methods in both simulated and real world tasks.
Positive Unlabeled Contrastive Learning

Self-supervised pretraining on unlabeled data followed by supervised fine-tuning on labeled data is a popular paradigm for learning from limited labeled examples. We extend this paradigm to the classical positive unlabeled (PU) setting, where the task is to learn a binary classifier given only a few labeled positive samples, and (often) a large amount of unlabeled samples (which could be positive or negative). We first propose a simple extension of standard infoNCE family of contrastive losses, to the PU setting; and show that this learns superior representations, as compared to existing unsupervised and supervised approaches. We then develop a simple methodology to pseudo-label the unlabeled samples using a new PU-specific clustering scheme; these pseudo-labels can then be used to train the final (positive vs. negative) classifier. Our method handily outperforms state-of-the-art PU methods over several standard PU benchmark datasets, while not requiring a-priori knowledge of any class prior (which is a common assumption in other PU methods). We also provide a simple theoretical analysis that motivates our methods.
PARTIAL CLASSIFICATION RESULTS FOR POSITIVE QUATERNION KÄHLER MANIFOLDS

Positive Quaternion Kahler Manifolds are Riemannian manifolds with holonomy contained in Sp(n)Sp(1) and with positive scalar curvature. Conjecturally, they are symmetric spaces. We prove this conjecture in dimension 20 under additional assumptions and we provide recognition theorems for the real Grassmannian in almost all dimensions.
Understanding Deep Contrastive Learning via Coordinate-wise Optimization

We show that Contrastive Learning (CL) under a broad family of loss functions (including InfoNCE) has a unified formulation of coordinate-wise optimization on the network parameter $\boldsymbol{\theta}$ and pairwise importance $\alpha$, where the \emph{max player} $\boldsymbol{\theta}$ learns representation for contrastiveness, and the \emph{min player} $\alpha$ puts more weights on pairs of distinct samples that share similar representations. The resulting formulation, called $\alpha$-CL, unifies not only various existing contrastive losses, which differ by how sample-pair importance $\alpha$ is constructed, but also is able to extrapolate to give novel contrastive losses beyond popular ones, opening a new avenue of contrastive loss design. These novel losses yield comparable (or better) performance on CIFAR10, STL-10 and CIFAR-100 than classic InfoNCE. Furthermore, we also analyze the max player in detail: we prove that with fixed $\alpha$, max player is equivalent to Principal Component Analysis (PCA) for deep linear network, and almost all local minima are global and rank-1, recovering optimal PCA solutions. Finally, we extend our analysis on max player to 2-layer ReLU networks, showing that its fixed points can have higher ranks.
Ice friction at the nanoscale

Significance Sliding on ice is a familiar experience, but we still do not fully understand why is ice slippery. Faraday hypothesized that ice forms spontaneously a thin quasi-liquid layer on its surface, while Reynolds suggested that this layer could act as a lubricant. However, experimentally probing the structure of ice below a slider is extremely challenging. Here, we perform computer simulations of a slider on ice and obtain atomic-scale insight into ice friction. We find that, depending on their hydrophobicity, sliders can either stick or slip atop a nanometer-thick self-healing, self-lubricating quasi-liquid layer with properties similar to bulk undercooled water. This allows us to explain the low friction of ice using macroscopic laws borrowed from continuum hydrodynamics and lubrication theory.
Latent Topology Induction for Understanding Contextualized Representations

In this work, we study the representation space of contextualized embeddings and gain insight into the hidden topology of large language models. We show there exists a network of latent states that summarize linguistic properties of contextualized representations. Instead of seeking alignments to existing well-defined annotations, we infer this latent network in a fully unsupervised way using a structured variational autoencoder. The induced states not only serve as anchors that mark the topology (neighbors and connectivity) of the representation manifold but also reveal the internal mechanism of encoding sentences. With the induced network, we: (1). decompose the representation space into a spectrum of latent states which encode fine-grained word meanings with lexical, morphological, syntactic and semantic information; (2). show state-state transitions encode rich phrase constructions and serve as the backbones of the latent space. Putting the two together, we show that sentences are represented as a traversal over the latent network where state-state transition chains encode syntactic templates and state-word emissions fill in the content. We demonstrate these insights with extensive experiments and visualizations.
Exploring Transformers for Behavioural Biometrics: A Case Study in Gait Recognition

Biometrics on mobile devices has attracted a lot of attention in recent years as it is considered a user-friendly authentication method. This interest has also been motivated by the success of Deep Learning (DL). Architectures based on Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have been established to be convenient for the task, improving the performance and robustness in comparison to traditional machine learning techniques. However, some aspects must still be revisited and improved. To the best of our knowledge, this is the first article that intends to explore and propose novel gait biometric recognition systems based on Transformers, which currently obtain state-of-the-art performance in many applications. Several state-of-the-art architectures (Vanilla, Informer, Autoformer, Block-Recurrent Transformer, and THAT) are considered in the experimental framework. In addition, new configurations of the Transformers are proposed to further increase the performance. Experiments are carried out using the two popular public databases whuGAIT and OU-ISIR. The results achieved prove the high ability of the proposed Transformer, outperforming state-of-the-art CNN and RNN architectures.
Wildebeest Herds on Rolling Hills: Flocking on Arbitrary Curved Surfaces

The collective behavior of active agents, whether herds of wildebeest or microscopic actin filaments propelled by molecular motors, is an exciting frontier in biological and soft matter physics. Almost three decades ago, Toner and Tu developed a hydrodynamic theory of the collective action of flocks, or herds, that helped launch the modern field of active matter. One challenge faced when applying continuum active matter theories to living phenomena is the complex geometric structure of biological environments. Both macroscopic and microscopic herds move on asymmetric curved surfaces, like undulating grass plains or the surface layers of cells or embryos, which can render problems analytically intractable. In this work, we present a formulation of the Toner-Tu flocking theory that uses the finite element method to solve the governing equations on arbitrary curved surfaces. First, we test the developed formalism and its numerical implementation in channel flow with scattering obstacles and on cylindrical and spherical surfaces, comparing our results to analytical solutions. We then progress to surfaces with arbitrary curvature, moving beyond previously accessible problems to explore herding behavior on a variety of landscapes. Our approach allows the investigation of transients and dynamic solutions not revealed by analytic methods. It also enables versatile incorporation of new geometries and boundary conditions and efficient sweeps of parameter space. Looking forward, the work presented here lays the groundwork for a dialogue between Toner-Tu theory and data on collective motion in biologically-relevant geometries, from drone footage of migrating animal herds to movies of microscopic cytoskeletal flows within cells.
Global Self-Attention as a Replacement for Graph Convolution

We propose an extension to the transformer neural network architecture for general-purpose graph learning by adding a dedicated pathway for pairwise structural information, called edge channels. The resultant framework - which we call Edge-augmented Graph Transformer (EGT) - can directly accept, process and output structural information of arbitrary form, which is important for effective learning on graph-structured data. Our model exclusively uses global self-attention as an aggregation mechanism rather than static localized convolutional aggregation. This allows for unconstrained long-range dynamic interactions between nodes. Moreover, the edge channels allow the structural information to evolve from layer to layer, and prediction tasks on edges/links can be performed directly from the output embeddings of these channels. We verify the performance of EGT in a wide range of graph-learning experiments on benchmark datasets, in which it outperforms Convolutional/Message-Passing Graph Neural Networks. EGT sets a new state-of-the-art for the quantum-chemical regression task on the OGB-LSC PCQM4Mv2 dataset containing 3.8 million molecular graphs. Our findings indicate that global self-attention based aggregation can serve as a flexible, adaptive and effective replacement of graph convolution for general-purpose graph learning. Therefore, convolutional local neighborhood aggregation is not an essential inductive bias.
Bounds on the mass and mixing of $Z^\prime$ and $W^\prime$ bosons decaying into different pairings of $W$, $Z$, or Higgs bosons using CMS data at the LHC

The full CMS Run 2 datasets with time-integrated luminosity of 137 fb$^{-1}$ in the diboson channels are used to probe benchmark models with extended gauge sectors such as $E_6$, left-right symmetric (LR) and the sequential standard model (extended gauge model, EGM), that predict the existence of neutral $Z'$- and charged $W'$-bosons decaying to a pair of bosons $WW$, $ZH$, $WZ$ and $WH$ in the semileptonic final state. These benchmark models are used to interpret the results. Exclusion limits at the 95% C.L. on the $Z'$ and $W'$ resonance production cross section times branching ratio to electroweak gauge boson pairs in the resonance mass range between 1.0 and 4.5 TeV are here converted to constraints on $Z$-$Z'$ and $W$-$W'$ mixing parameters and masses. We present exclusion regions on the parameter spaces of the $Z'$ and $W'$ and show that the obtained exclusion regions are significantly extended compared to those derived from the previous analysis performed with Tevatron data as well as with the CMS data collected at 7 and 8 TeV in Run 1. The reported limits are the most restrictive to date.
Cosmological Information in the Marked Power Spectrum of the Galaxy Field

Marked power spectra are two-point statistics of a marked field obtained by weighting each location with a function that depends on the local density around that point. We consider marked power spectra of the galaxy field in redshift space that up-weight low-density regions, and we perform a Fisher matrix analysis to assess the information content of this type of statistics using the Molino mock catalogs built on the Quijote simulations. We identify four different ways to up-weight the galaxy field, and we compare the Fisher information contained in their marked power spectra to that of the standard galaxy power spectrum, when considering the monopole and quadrupole of each statistic. Our results show that each of the four marked power spectra can tighten the standard power spectrum constraints on the cosmological parameters Ω m , Ω b , h, n s , and M ν by 15%–25% and on σ 8 by a factor of 2. The same analysis performed by combining the standard and four marked power spectra shows a substantial improvement compared to the power spectrum constraints that is equal to a factor of 6 for σ 8 and a factor of 2.5–3 for the other parameters. Our constraints may be conservative, since the galaxy number density in the Molino catalogs is much lower than the ones in future galaxy surveys, which will allow them to probe lower-density regions of the large-scale structure.
Quijote-PNG: Quasi-maximum Likelihood Estimation of Primordial Non-Gaussianity in the Nonlinear Dark Matter Density Field

Future large-scale structure surveys are expected to improve current bounds on primordial non-Gaussianity (PNG), with a significant impact on our understanding of early universe physics. The level of such improvements will however strongly depend on the extent to which late-time nonlinearities erase the PNG signal on small scales. In this work, we show how much primordial information remains in the bispectrum of the nonlinear dark matter density field by implementing a new, simulation-based methodology for joint estimation of PNG amplitudes (f NL) and standard Lambda cold dark matter parameters. The estimator is based on optimally compressed statistics, which, for a given input density field, combine power spectrum and modal bispectrum measurements, and numerically evaluate their covariance and their response to changes in cosmological parameters. In this first analysis, we focus on the matter density field, and we train and validate the estimator using a large suite of N-body simulations (Quijote-png), including different types of PNG (local, equilateral, orthogonal). We explicitly test the estimator’s unbiasedness, optimality, and stability with respect to changes in the total number of input realizations. While the dark matter power spectrum itself contains negligible PNG information, as expected, including it as an ancillary statistic increases the PNG information content extracted from the bispectrum by a factor of order 2. As a result, we prove the capability of our approach to optimally extract PNG information on nonlinear scales beyond the perturbative regime, up to kmax=0.5hMpc−1 . At the same time, we discuss the significant information on cosmological parameters contained on these scales.
Algorithmic Stability of Heavy-Tailed Stochastic Gradient Descent on Least Squares

Recent studies have shown that heavy tails can emerge in stochastic optimization and that the heaviness of the tails have links to the generalization error. While these studies have shed light on interesting aspects of the generalization behavior in modern settings, they relied on strong topological and statistical regularity assumptions, which are hard to verify in practice. Furthermore, it has been empirically illustrated that the relation between heavy tails and generalization might not always be monotonic in practice, contrary to the conclusions of existing theory. In this study, we establish novel links between the tail behavior and generalization properties of stochastic gradient descent (SGD), through the lens of algorithmic stability. We consider a quadratic optimization problem and use a heavy-tailed stochastic differential equation (and its Euler discretization) as a proxy for modeling the heavy-tailed behavior emerging in SGD. We then prove uniform stability bounds, which reveal the following outcomes: (i) Without making any exotic assumptions, we show that SGD will not be stable if the stability is measured with the squared-loss $x\mapsto x^2$, whereas it in turn becomes stable if the stability is instead measured with a surrogate loss $x\mapsto |x|^p$ with some $p<2$. (ii) Depending on the variance of the data, there exists a \emph{`threshold of heavy-tailedness'} such that the generalization error decreases as the tails become heavier, as long as the tails are lighter than this threshold. This suggests that the relation between heavy tails and generalization is not globally monotonic. (iii) We prove matching lower-bounds on uniform stability, implying that our bounds are tight in terms of the heaviness of the tails. We support our theory with synthetic and real neural network experiments.