Miracle-dz/UniFAR-FTUs huggingface dataset:
Electrochemical transport modelling and open-source simulation of pore-scale solid-liquid systems

The modelling of electrokinetic flows is a critical aspect spanning many industrial applications and research fields. This has introduced great demand in flexible numerical solvers to describe these flows. The underlying phenomena are microscopic, non-linear, and often involving multiple domains. Therefore often model assumptions and several numerical approximations are introduced to simplify the solution. In this work we present a multi-domain multi-species electrokinetic flow model including complex interface and bulk reactions. After a dimensional analysis and an overview of some limiting regimes, we present a set of general-purpose finite-volume solvers, based on OpenFOAM® , capable of describing an arbitrary number of electrochemical species over multiple interacting (solid or fluid) domains (Icardi and Barnett in F Municchi spnpFoam, 2021. https://doi.org/10.5281/zenodo.4973896). We provide a verification of the computational approach for several cases involving electrokinetic flows, reactions between species, and complex geometries. We first present three one-dimensional verification test-cases, and then show the capability of the solver to tackle two- and three-dimensional electrically driven flows and ionic transport in random porous structures. The purpose of this work is to lay the foundation of a general-purpose open-source flexible modelling tool for problems in electrochemistry and electrokinetics at different scales.
BLIND CONTRAST ENHANCEMENT ASSESSMENT BY GRADIENT RATIOING AT VISIBLE EDGES

The contrast of outdoor images acquired under adverse weather conditions, especially foggy weather, is altered by the scattering of daylight by atmospheric particles. As a consequence, different methods have been designed to restore the contrast of these images. However, there is a lack of methodology to assess the performances of the methods or to rate them. Unlike image quality assessment or image restoration areas, there is no easy way to have a reference image, which makes the problem not straightforward to solve. In this paper, an approach is proposed which consists in computing the ratio between the gradient of the visible edges between the image before and after contrast restoration. In this way, an indicator of visibility enhancement is provided based on the concept of visibility level, commonly used in lighting engineering. Finally, the methodology is applied to contrast enhancement assessment and to the comparison of tone-mapping operators.
Observations of carbon monoxide in the starburst galaxy M82 with a 690 GHz-wide spectral bandwidth receiver

A 690 GHz wide spectral bandwidth heterodyne receiver was developed to observe the J=6-5 rotational emission line of carbon monoxide (CO) in extragalactic sources. This receiver is based on a niobium superconductor-insulator-superconductor (SIS) mixer with a twin-slot antenna in a superconducting NbTiN ground plane. A 4-8 GHz low-noise amplifier was developed to amplify the intermediate frequency (IF) signal from the mixer with a spectral bandwidth of 1,700 km/s, enough to comfortably observe the entire emission line of the broadest extragalactic submillimeter sources with a single receiver tuning. This amplifier is a quasi-monolithic microwave integrated circuit (QMMIC); three 160 micron gate InP high-electron-mobility transistors (HEMTs) were bump-bonded to a thin-film GaAs substrate containing passive tuning and DC bias circuitry. The measured amplifier gain is 32 dB and the noise is approximately 8 Kelvin from 4 to 8 GHz at a physical temperature of 4 Kelvin. The complete receiver achieves a measured uncorrected double-sideband noise temperature of 180 Kelvin. Prior to this development effort, a versatile microwave simulation package was written to calculate and optimize the signal and noise performance of high-frequency circuits, especially those containing superconductors and superconducting tunnel junctions. Using this package, called SuperMix, C++ programs can be written to simulate and optimize circuits of arbitrary size, complexity, and topology. SuperMix was used to simulate the complete 690 GHz SIS receiver. The receiver was used at the Caltech Submillimeter Observatory (CSO) to map the 12CO J=6-5 emission line in the central kiloparsec of the nearby starburst galaxy M82 at a resolution of 14 arc seconds. Hot spots were found on either side of the dynamical center. A novel deconvolution technique was used to compute a 12CO J=6-5 / 12CO J=2-1 line ratio map based on high-resolution J=2-1 interferometer data. The 12CO J=6-5 map, along with observations of 12CO J=4-3, 12CO J=3-2, 13CO J=3-2, an upper limit for 13CO J=6-5, and five other measured CO lines from the literature, were analyzed in the context of a two-component large velocity gradient (LVG) excitation model. Likelihood curves were calculated for each of the model parameters as well as a variety of related physical quantities for the two hot spots based on the measured line intensities and their associated uncertainties. This approach reveals in an unbiased way how well various quantities can be constrained by the CO observations. The results of this analysis suggest that the warm gas is less dense than the cool gas, and that over half of the total molecular gas mass in these nuclear regions is warmer than 50 K
Population of X-Ray Sources in the Intermediate-age Cluster NGC 3532: a Test Bed for Machine-learning Classification

Open clusters are thought to be the birthplace of most stars in the galaxy. Thus, they are excellent laboratories for investigating stellar evolution, and X-ray properties of various types of stars (including binary stars, evolved stars, and compact objects). In this work, we investigate the population of X-ray sources in the nearby 300 Myr old open cluster NGC 3532 using Chandra X-ray Observatory and multiwavelength data from several surveys. We apply a random-forest machine-learning pipeline (MUWCLASS) to classify all confidently detected X-ray sources (signal-to-noise ratio, hereafter S/N, > 5) in the field of NGC 3532. We also perform a more detailed investigation of brighter sources, including their X-ray spectra and lightcurves. Most X-ray sources are confirmed as coronally active low-mass stars, many of which are confidently identified by MUWCLASS. Several late B- or early A-type stars are relatively bright in X-rays, most of which are likely binaries. We do not find any compact objects among X-ray sources reliably associated with NGC 3532, down to the limiting X-ray flux of ∼2 × 10−15 erg s−1cm−2, corresponding to L X ∼ 6 × 1028 erg s−1 at the cluster’s distance. We also identify several Galactic sources beyond NGC 3532 that differ from typical coronally active stars, and were classified by MUWCLASS as potential compact objects. Detailed investigation reveals that these sources may indeed belong to rarer classes, and deserve follow-up observations.
Towards Integrated Sensing and Communications in IEEE 802.11bf Wi-Fi Networks

—As Wi-Fi becomes ubiquitous in public and private spaces, it becomes natural to leverage its intrinsic ability to sense the surrounding environment to implement groundbreaking wireless sensing applications such as human presence detection, activity recognition, and object tracking. For this reason, the IEEE 802.11bf Task Group is deﬁning the appropriate modiﬁcations to existing Wi-Fi standards to enhance sensing capabilities through 802.11-compliant devices. However, the new standard is expected to leave the speciﬁc sensing algorithms open to implementation. To ﬁll this gap, this article explores the practical implications of integrating sensing and communications into Wi-Fi networks. We provide an overview of the support that will enable sensing applications, together with an in-depth analysis of the role of different devices in a Wi-Fi sensing system and a description of the open research challenges. Moreover, an experimental evaluation with off-the-shelf devices provides suggestions about the parameters to be considered when designing Wi-Fi sensing systems. To make such an evaluation replicable, we pledge to release all of our dataset and code to the community.
Work Function Engineered Charge Plasma-Germanium Double Gate Tunnel Field Effect Transistor for Low-Power Switching Applications

Here, we propose a Charge Plasma (CP)-based Germanium Double Gate Tunnel Field-Effect Transistor (Ge-DGTFET) device structure, where a CP is induced in the heavily doped source region using the work function engineering of source electrode. The CP enables creation of electrical metallurgical junction and converts n-p-n to p-n-p-n structure of TFET and enhances the drain current, reliability, eliminate additional pocket ion-implantation. The proposed CP-Ge-DGTFET device structure revealed excellent electrical DC performance as compared to the conventional Ge-DGTFET device structure such as high ON current (I ON ), excellent I ON /I OFF ratio, and low sub-threshold swing of ~4.7×10 -4 A/µm, ~1.8×10 9 , and ~5.23 mV/dec, respectively. Furthermore, analog/RF analyses revealed high transconductance, upright cut-off frequency, low overall capacitance, transit time, and power delay product. Therefore, the proposed CP-Ge-DGTFET device structure with alternate channel material Ge, High-κ Al 2 O 3 , and work function engineered CP in source region furnishes high performance and cost-effective solution for next-generation energy-efficient switching applications.
FPT algoritms providing constant ratio approximation of hypertree width parameters for hypergraphs of bounded rank

We propose an algorithm whose input are parameters k and r and a hypergraph H of rank at most r . The algorithm either returns a tree decomposition of H of generalized hypertree width at most 4 k or ’NO’. In the latter case, it is guaranteed that the hypertree width of H is greater than k . Most importantly, the runtime of the algorithm is FPT in k and r . The approach extends to fractional hypertree width with a slightly worse approximation (4 k + 1 instead of 4 k ). We hope that the results of this paper will give rise to a new research direction whose aim is design of FPT algorithms for computation and approximation of hypertree width parameters for restricted classes of hypergraphs.
2022 Molecular scale ion separation driven by surface roughness and ion size asymmetry: new analytical solutions for diﬀerential capacitance of EDL

Electrode surface roughness signiﬁcantly impacts the structure of electric double layer on a molecular scale. We derive analytical solutions for diﬀerential capacitance (DC) of electric double layer near rough electrode surface, comparing them with a range of experimental and numerical studies. Two causes of ions separation are considered: ion size asymmetry and electrode surface roughness. The model has three scale parameters determining DC properties: the Debye length, diﬀerence of ion penetration depths, and surface roughness parameter. For the ﬁrst time, DC proﬁle with more than two peaks was obtained analytically due to account for ions reorientation eﬀect. The model predicts DC curve transform from bell to camel and inverse induced by electrode surface roughness. The behavior of DC-potential relation with temperature and ion size ratio diverges depending on the type of ions separation. Additionally, we provide analytical solutions for zero charge potential and demonstrate roughness eﬀect on its value. Based on the results, we give recommendations for properties required to design eﬀective electric double layer capacitors.
Extrinsic Bayesian Optimizations on Manifolds

We propose an extrinsic Bayesian optimization (eBO) framework for general optimization problems on manifolds. Bayesian optimization algorithms build a surrogate of the objective function by employing Gaussian processes and quantify the uncertainty in that surrogate by deriving an acquisition function. This acquisition function represents the probability of improvement based on the kernel of the Gaussian process, which guides the search in the optimization process. The critical challenge for designing Bayesian optimization algorithms on manifolds lies in the difficulty of constructing valid covariance kernels for Gaussian processes on general manifolds. Our approach is to employ extrinsic Gaussian processes by first embedding the manifold onto some higher dimensional Euclidean space via equivariant embeddings and then constructing a valid covariance kernel on the image manifold after the embedding. This leads to efficient and scalable algorithms for optimization over complex manifolds. Simulation study and real data analysis are carried out to demonstrate the utilities of our eBO framework by applying the eBO to various optimization problems over manifolds such as the sphere, the Grassmannian, and the manifold of positive definite matrices.
Triviality of quantum trajectories close to a directed percolation transition

We study quantum circuits consisting of unitary gates, projective measurements, and control operations that steer the system towards a pure absorbing state. Two types of phase transition occur as the rate of these control operations is increased: a measurement-induced entanglement transition, and a directed percolation transition into the absorbing state (taken here to be a product state). In this work we show analytically that these transitions are generically distinct, with the quantum trajectories becoming disentangled before the absorbing state transition is reached, and we analyze their critical properties. We introduce a simple class of models where the measurements in each quantum trajectory define an Effective Tensor Network (ETN) -- a subgraph of the initial spacetime graph where nontrivial time evolution takes place. By analyzing the entanglement properties of the ETN, we show that the entanglement and absorbing-state transitions coincide only in the limit of infinite local Hilbert-space dimension. Focusing on a Clifford model which allows numerical simulations for large system sizes, we verify our predictions and study the finite-size crossover between the two transitions at large local Hilbert space dimension. We give evidence that the entanglement transition is governed by the same fixed point as in hybrid circuits without feedback.
Periocular Biometrics: A Modality for Unconstrained Scenarios

Periocular refers to the externally visible region of the face that surrounds the eye socket. This feature-rich area can provide accurate identification in unconstrained or uncooperative scenarios, where the iris or face modalities may not offer sufficient biometric cues due to factors such as partial occlusion or high subject-to-camera distance. The COVID-19 pandemic has further highlighted its importance, as the ocular region remained the only visible facial area even in controlled settings due to the widespread use of masks. This paper discusses the state of the art in periocular biometrics, presenting an overall framework encompassing its most significant research aspects, which include: (a) ocular definition, acquisition, and detection; (b) identity recognition, including combination with other modalities and use of various spectra; and (c) ocular soft-biometric analysis. Finally, we conclude by addressing current challenges and proposing future directions.
VegaProf: Profiling Vega Visualizations

Domain-specific languages (DSLs) for visualization aim to facilitate visualization creation by providing abstractions that offload implementation and execution details from users to the system layer. Therefore, DSLs often execute user-defined specifications by transforming them into intermediate representations (IRs) in successive lowering operations. However, DSL-specified visualizations can be difficult to profile and, hence, optimize due to the layered abstractions. To better understand visualization profiling workflows and challenges, we conduct formative interviews with visualization engineers who use Vega in production. Vega is a popular visualization DSL that transforms specifications into dataflow graphs, which are then executed to render visualization primitives. Our formative interviews reveal that current developer tools are ill-suited for visualization profiling since they are disconnected from the semantics of Vega’s specification and its IRs at runtime. To address this gap, we introduce VegaProf, the first performance profiler for Vega visualizations. VegaProf instruments the Vega library by associating a declarative specification with its compilation and execution. Integrated into a Vega code playground, VegaProf coordinates visual performance inspection at three abstraction levels: function, dataflow graph, and visualization specification. We evaluate VegaProf through use cases and feedback from visualization engineers as well as original developers of the Vega library. Our results suggest that VegaProf makes visualization profiling more tractable and actionable by enabling users to interactively probe time performance across layered abstractions of Vega. Furthermore, we distill recommendations from our findings and advocate for co-designing visualization DSLs together with their introspection tools.
On a diffuse interface model for incompressible viscoelastic two-phase flows

Abstract. This paper concerns a diffuse interface model for the flow of two incompressible viscoelastic fluids in a bounded domain. More specifically, the fluids are assumed to be macroscopically immiscible, but with a small transition region, where the two components are partially mixed. Considering the elasticity of both components, one ends up with a coupled Oldroyd-B/Cahn–Hilliard type system, which describes the behavior of two-phase viscoelastic fluids. We prove the existence of weak solutions to the system in two dimensions for general (unmatched) mass densities, variable viscosities, different shear moduli, and a class of physically relevant and singular free energy densities that guarantee that the order parameter stays in the physically reasonable interval. The proof relies on a combination of a novel regularization of the original system and a new hybrid implicit time discretization for the regularized system together with the analysis of an Oldroyd-B type equation.
Cortical Xi-Alpha model for resting state electric neuronal activity

1. Abstract The power spectra of awake resting state EEG recordings in humans typically have an Alpha peak at around 10 Hz riding a decreasing background “ Xi process ” . Normal and pathological variations occur in the form of more than one peak or none at all. The single channel Xi-Alpha model (Pascual-Marqui et al 1988, https://doi.org/10.3109/00207458808985730) separated these two processes and provided an adequate low-dimensional parametric description of EEG spectra. Currently lacking is a generative whole cortex model for activity spectra and intracortical functional connectivity for each process . Here we introduce the “cort ical Xi- Alpha model” for such a purpose. Th e cross-spectral density matrices are modeled as additive components, where each one consists of a scalar spectrum that multiplies a frequency invariant Hermitian covariance matrix for the cortical functional connectivity structure. This model has very low dimension, and despite its simple “separation of variables” form, it offers a very rich repertoire of diverse spatio-spectral properties, as well as diverse whole cortex functional connectivity patterns. The scalp EEG model conserves the same separation of variables form,
Climate Change and Potential Demise of the Indian Deserts

In contrast to the “wet gets wetter and dry gets drier” paradigm, here, using observations and climate model simulations, we show that the mean rainfall over the semi‐arid northwest parts of India and Pakistan has increased by 10%–50% during 1901–2015 and is expected to increase by 50%–200% under moderate greenhouse gas (GHG) scenarios. The GHG forcing primarily drives the westward expansion of the Indian summer monsoon rainfall (ISMR) and is facilitated by a westward expansion of the Indian Ocean (IO) warm pool. Mechanistically, the westward expansion of ISMR is a consequence of the episodic genesis over IO and the northward propagation of an expanded Inter‐Tropical Convergence Zone on a sub‐seasonal time scale. While an adaptation strategy to increased hydrological disasters is a must, harnessing the augmented rainfall would lead to a substantial boost in food productivity, bringing transformative changes in the socio‐economic condition of people in the region.
Simple Yet Surprisingly Effective Training Strategies for LSTMs in Sensor-Based Human Activity Recognition

—Human Activity Recognition (HAR) is one of the core research areas in mobile and wearable computing. With the application of deep learning (DL) techniques such as CNN, recognizing periodic/static activities (e.g, walking, lying, cycling, etc.) has become a well-studied problem. What remains a major challenge though is the sporadic activity recognition (SAR) problem, where activities of interest tend to be non-periodic, and occur less frequently when compared with the often large amount of irrelevant “background” activities. Recent works suggested that sequential DL models (such as LSTMs) have great potential for modeling non-periodic behaviours, and in this paper we studied some LSTM training strategies for SAR. Speciﬁcally, we proposed two simple yet effective LSTM variants, namely delay model and inverse model, for two SAR scenarios (with and without time-critical requirement). For time-critical SAR, the delay model can effectively exploit pre-deﬁned delay intervals (within tolerance) in form of contextual information for improved performance. For regular SAR task, the second proposed, inverse model can learn patterns from the time-series in an inverse manner, which can be complementary to the forward model (i.e., LSTM), and combining both can boost the performance. These two LSTM variants are very practical, and they can be deemed as training strategies without alteration of the LSTM fundamentals. We also studied some additional LSTM training strategies, which can further improve the accuracy. We evaluated our models on two SAR and one non-SAR datasets, and the promising results demonstrated the effectiveness of our approaches in HAR applications.
Persistence-based operators in machine learning

Artiﬁcial neural networks can learn complex, salient data features to achieve a given task. On the opposite end of the spectrum, mathematically grounded methods such as topological data analysis allow users to design analysis pipelines fully aware of data constraints and symmetries. We introduce a class of persistence-based neural network layers. Persistence-based layers allow the users to easily inject knowledge about symmetries (equivariance) respected by the data, are equipped with learnable weights, and can be composed with state-of-the-art neural architectures.
The Bel-Robinson tensor and the classical pseudotensors

Describing the gravitational energy and momentum, the Bel-Robinson tensor is the best tensor. However, the classical pseudotensors can also manage the the same job. As Deser mentioned in 1999, a certain linear combination between Einstein pseudotensor and Landau-Lifshitz pseudoetnsor give a pure Bel-Robinson tensor. Here we used the same idea but adapted the harmonic gauge, we found that all the classical pseudotensor alone cannot give a multiple of the Bel-Robinson tensor. But under a modiﬁcation, all of them contribute the same energy-momentum and stress as the Bel-Robinson tensor does.