Miracle-dz/UniFAR-FTUs huggingface dataset:
Modelling exposure between populations using networks of mobility during COVID-19

The use of mobile phone call detail records and device location data for the calling patterns, movements, and social contacts of individuals, have proven to be valuable for devising models and understanding of their mobility and behaviour patterns. In this study we investigate weighted exposure networks of human daily activities in the capital region of Finland as a proxy for contacts between postal code areas during the pre-pandemic year 2019 and pandemic years 2020, 2021 and early 2022. We investigate the suitability of gravity and radiation type models for reconstructing the exposure networks based on geo-spatial and population mobility information. For this we use a mobile phone dataset of aggregated daily visits from a postal code area to cellphone grid locations, and treat it as a bipartite network to create weighted one mode projections using a weighted co-occurrence function. We fit a classical gravity model and a radiation model to the averaged weekly and yearly projection networks with geo-spatial and socioeconomic variables of the postal code areas and their populations. We also consider an extended gravity type model comprising of additional postal area information such as distance via public transportation and population density. The results show that the co-occurrence of human activities, or exposure, between postal code areas follows both the gravity and radiation type interactions, once fitted to the empirical network. The effects of the pandemic beginning in 2020 can be observed as a decrease of the overall activity as well as of the exposure of the projected networks. These effects can also be observed in the network structure as changes towards lower clustering and higher assortativity. Evaluating the parameters of the fitted models over time shows on average a shift towards a higher exposure of areas in closer proximity as well as a higher exposure towards areas with larger population. In general, the results show that the postal code level networks changed to be more proximity weighted after the pandemic began, following the government imposed non-pharmaceutical interventions, with differences based on the geo-spatial and socioeconomic structure of the areas.
Coffee stain effect on a fibre from axisymmetric droplets

Abstract The so-called coffee stain effect has been intensively studied over the past decades, but most of the studies have focused on sessile droplets. In this paper, we analyse the origin of the difference between the deposition of suspended particles in a sessile drop and in an axisymmetric drop deposited on a fibre. First, we model the shape of a drop on a fibre and its evaporative flux with some approximations to derive analytical calculations. Then, for pinned contact lines, we solve the hydrodynamics equations in the liquid phase under the lubrication approximation to determine the flow velocity toward the contact lines. We comment on these results by comparison to a sessile drop of similar evaporating conditions, and we show that the substrate curvature plays a role on the contact line depinning, the local evaporative flux and the liquid flow field. The competition between the advection and the Brownian motion indicates that the transport of the particles toward the contact line occurs in a volume localised in the close vicinity of the contact lines for a drop on a fibre. Thus, the fibre geometry induces a weaker accumulation of particles at the contact line compared to a sessile drop, leading to the more homogeneous deposit observed experimentally.
Improved Weighted Matching in the Sliding Window Model

We consider the Maximum-weight Matching (MWM) problem in the streaming sliding window model of computation. In this model, the input consists of a sequence of weighted edges on a given vertex set $V$ of size $n$. The objective is to maintain an approximation of a maximum-weight matching in the graph spanned by the $L$ most recent edges, for some integer $L$, using as little space as possible. Prior to our work, the state-of-the-art results were a $(3.5+\varepsilon)$-approximation algorithm for MWM by Biabani et al. [ISAAC'21] and a $(3+\varepsilon)$-approximation for (unweighted) Maximum Matching (MM) by Crouch et al. [ESA'13]. Both algorithms use space $\tilde{O}(n)$. We give the following results: 1. We give a $(2+\varepsilon)$-approximation algorithm for MWM with space $\tilde{O}(\sqrt{nL})$. Under the reasonable assumption that the graphs spanned by the edges in each sliding window are simple, our algorithm uses space $\tilde{O}(n \sqrt{n})$. 2. In the $\tilde{O}(n)$ space regime, we give a $(3+\varepsilon)$-approximation algorithm for MWM, thereby closing the gap between the best-known approximation ratio for MWM and MM. Similar to Biabani et al.'s MWM algorithm, both our algorithms execute multiple instances of the $(2+\varepsilon)$-approximation $\tilde{O}(n)$-space streaming algorithm for MWM by Paz and Schwartzman [SODA'17] on different portions of the stream. Our improvements are obtained by selecting these substreams differently. Furthermore, our $(2+\varepsilon)$-approximation algorithm runs the Paz-Schwartzman algorithm in reverse direction over some parts of the stream, and in forward direction over other parts, which allows for an improved approximation guarantee at the cost of increased space requirements.
SOFIA and ALMA Investigate Magnetic Fields and Gas Structures in Massive Star Formation: The Case of the Masquerading Monster in BYF 73

We present Stratospheric Observatory For Infrared Astronomy (SOFIA) + Atacama Large Millimeter/submillimeter Array (ALMA) continuum and spectral-line polarization data on the massive molecular cloud BYF 73, revealing important details about the magnetic field morphology, gas structures, and energetics in this unusual massive star formation laboratory. The 154 μm HAWC+ polarization map finds a highly organized magnetic field in the densest, inner 0.55 × 0.40 pc portion of the cloud, compared to an unremarkable morphology in the cloud’s outer layers. The 3 mm continuum ALMA polarization data reveal several more structures in the inner domain, including a parsec-long, ∼500 M ⊙ “Streamer” around the central massive protostellar object MIR 2, with magnetic fields mostly parallel to the east–west Streamer but oriented north–south across MIR 2. The magnetic field orientation changes from mostly parallel to the column density structures to mostly perpendicular, at thresholds N crit = 6.6 × 1026 m−2, n crit = 2.5 × 1011 m−3, and B crit = 42 ± 7 nT. ALMA also mapped Goldreich–Kylafis polarization in 12CO across the cloud, which traces, in both total intensity and polarized flux, a powerful bipolar outflow from MIR 2 that interacts strongly with the Streamer. The magnetic field is also strongly aligned along the outflow direction; energetically, it may dominate the outflow near MIR 2, comprising rare evidence for a magnetocentrifugal origin to such outflows. A portion of the Streamer may be in Keplerian rotation around MIR 2, implying a gravitating mass 1350 ± 50 M ⊙ for the protostar+disk+envelope; alternatively, these kinematics can be explained by gas in free-fall toward a 950 ± 35 M ⊙ object. The high accretion rate onto MIR 2 apparently occurs through the Streamer/disk, and could account for ∼33% of MIR 2's total luminosity via gravitational energy release.
A 65-nm 738k-Synapse/mm2 Quad-Core Binary-Weight Digital Neuromorphic Processor with Stochastic Spike-Driven Online Learning

Recent trends in the field of artificial neural networks (ANNs) and convolutional neural networks (CNNs) investigate weight binarization for full on-chip weight storage to minimize circuit resources and to avoid the high energy cost of off-chip memory accesses. In parallel, spiking neural network (SNN) architectures are explored to further reduce power when processing sparse event-based data streams, while on-chip spike-based online learning targets applications constrained in power and resources during the training phase. However, leveraging high-density on-chip online learning in binary-weight SNNs is still an open challenge. In this work, we demonstrate MorphIC, a quad-core binary-weight digital neuromorphic processor embedding a stochastic version of the spike-driven synaptic plasticity (S-SDSP) learning rule and a hierarchical routing fabric for large-scale chip interconnection. The MorphIC SNN processor embeds a total of 2k leaky integrate-and-fire (LIF) neurons and more than two million plastic synapses for an active silicon area of 2.86mm2 in 65nm CMOS, achieving a high density of 738k synapses/mm2.
An Analytical Theory for the Growth from Planetesimals to Planets by Polydisperse Pebble Accretion

Pebble accretion is recognized as a significant accelerator of planet formation. Yet only formulae for single-sized (monodisperse) distribution have been derived in the literature. These can lead to significant underestimates for Bondi accretion, for which the best accreted pebble size may not be the one that dominates the mass distribution. We derive in this paper the polydisperse theory of pebble accretion. We consider a power-law distribution in pebble radius, and we find the resulting surface and volume number density distribution functions. We derive also the exact monodisperse analytical pebble accretion rate for which 3D accretion and 2D accretion are limits. In addition, we find analytical solutions to the polydisperse 2D Hill and 3D Bondi limits. We integrate the polydisperse pebble accretion numerically for the MRN distribution, finding a slight decrease (by an exact factor 3/7) in the Hill regime compared to the monodisperse case. In contrast, in the Bondi regime, we find accretion rates 1–2 orders of magnitude higher compared to monodisperse, also extending the onset of pebble accretion to 1–2 orders of magnitude lower in mass. We find megayear timescales, within the disk lifetime, for Bondi accretion on top of planetary seeds of masses 10−6 to 10−4 M ⊕, over a significant range of the parameter space. This mass range overlaps with the high-mass end of the planetesimal initial mass function, and thus pebble accretion is possible directly following formation by streaming instability. This alleviates the need for mutual planetesimal collisions as a major contribution to planetary growth.
DiffTalk: Crafting Diffusion Models for Generalized Talking Head Synthesis

Talking head synthesis is a promising approach for the video production industry. Recently, a lot of effort has been devoted in this research area to improve the generation quality or enhance the model generalization. However, there are few works able to address both issues simultaneously, which is essential for practical applications. To this end, in this paper, we turn attention to the emerging powerful Latent Diffusion Models, and model the Talking head generation as an audio-driven temporally coherent denoising process (DiffTalk). More specifically, instead of employing audio signals as the single driving factor, we investigate the control mechanism of the talking face, and incorporate reference face images and landmarks as conditions for personality-aware generalized synthesis. In this way, the proposed DiffTalk is capable of producing high-quality talking head videos in synchronization with the source audio, and more importantly, it can be naturally generalized across different identities without any further finetuning. Additionally, our DiffTalk can be gracefully tailored for higher-resolution synthesis with negligible extra computational cost. Extensive experiments show that the proposed DiffTalk efficiently synthesizes high-fidelity audiodriven talking head videos for generalized novel identities. For more video results, please refer to this demonstration https://cloud.tsinghua. edu.cn/f/ e13f5aad2f4c4f898ae7/.
On adversarial robustness and the use of Wasserstein ascent-descent dynamics to enforce it

We propose iterative algorithms to solve adversarial problems in a variety of supervised learning settings of interest. Our algorithms, which can be interpreted as suitable ascent-descent dynamics in Wasserstein spaces, take the form of a system of interacting particles. These interacting particle dynamics are shown to converge toward appropriate mean-field limit equations in certain large number of particles regimes. In turn, we prove that, under certain regularity assumptions, these mean-field equations converge, in the large time limit, toward approximate Nash equilibria of the original adversarial learning problems. We present results for nonconvex-nonconcave settings, as well as for nonconvex-concave ones. Numerical experiments illustrate our results.
Benchmarking Robustness in Neural Radiance Fields

Neural Radiance Field (NeRF) has demonstrated excellent quality in novel view synthesis, thanks to its ability to model 3D object geometries in a concise formulation. However, current approaches to NeRF-based models rely on clean images with accurate camera calibration, which can be difficult to obtain in the real world, where data is often subject to corruption and distortion. In this work, we provide the first comprehensive analysis of the robustness of NeRF-based novel view synthesis algorithms in the presence of different types of corruptions. We find that NeRF-based models are significantly degraded in the presence of corruption, and are more sensitive to a different set of corruptions than image recognition models. Furthermore, we analyze the robustness of the feature encoder in generalizable methods, which synthesize images using neural features extracted via convolutional neural networks or transformers, and find that it only contributes marginally to robustness. Finally, we reveal that standard data augmentation techniques, which can significantly improve the robustness of recognition models, do not help the robustness of NeRF-based models. We hope that our findings will attract more researchers to study the robustness of NeRF-based approaches and help to improve their performance in the real world.
Adaptive Quarklet Tree Approximation

This paper is concerned with near-optimal approximation of a given function $f \in L_2([0,1])$ with elements of a polynomially enriched wavelet frame, a so-called quarklet frame. Inspired by $hp$-approximation techniques of Binev, we use the underlying tree structure of the frame elements to derive an adaptive algorithm that, under standard assumptions concerning the local errors, can be used to create approximations with an error close to the best tree approximation error for a given cardinality. We support our findings by numerical experiments demonstrating that this approach can be used to achieve inverse-exponential convergence rates.
RR Lyrae Mid-infrared Period–Luminosity–Metallicity and Period–Wesenheit–Metallicity Relations Based on Gaia DR3 Parallaxes

We present new empirical infrared period–luminosity–metallicity (PLZ) and period–Wesenheit–metallicity (PWZ) relations for RR Lyae based on the latest Gaia Early Data Release 3 (EDR3) parallaxes. The relations are provided in the Wide-field Infrared Survey Explorer (WISE) W1 and W2 bands, as well as in the W(W1, V − W1) and W(W2, V − W2) Wesenheit magnitudes. The relations are calibrated using a very large sample of Galactic halo field RR Lyrae stars with homogeneous spectroscopic [Fe/H] abundances (over 1000 stars in the W1 band), covering a broad range of metallicities (−2.5 ≲ [Fe/H] ≲ 0.0). We test the performance of our PLZ and PWZ relations by determining the distance moduli of both galactic and extragalactic stellar associations: the Sculptor dwarf spheroidal galaxy in the Local Group (finding μ¯0=19.47±0.06 ), the Galactic globular clusters M4 ( μ¯0=11.16±0.05 ), and the Reticulum globular cluster in the Large Magellanic Cloud ( μ¯0=18.23±0.06 ). The distance moduli determined through all our relations are internally self-consistent (within ≲0.05 mag) but are systematically smaller (by ∼2–3σ) than previous literature measurements taken from a variety of methods/anchors. However, a comparison with similar recent RR Lyrae empirical relations anchored with EDR3 likewise shows, to varying extents, a systematically smaller distance modulus for PLZ/PWZ RR Lyrae relations.
Perforated Ordered K0-Groups

Abstract A simple ${{C}^{*}}$ -algebra is constructed for which the Murray-von Neumann equivalence classes of projections, with the usual addition—induced by addition of orthogonal projections—form the additive semigroup $$\left\{ 0,\,2,\,3,\ldots \right\}. $$ (This is a particularly simple instance of the phenomenon of perforation of the ordered ${{K}_{0}}$ -group, which has long been known in the commutative case—for instance, in the case of the four-sphere—and was recently observed by the second author in the case of a simple ${{C}^{*}}$ -algebra.)
SatNetOps: Toward Multi-Layer Networking for Satellite Network Operations

Recent advancements in low-Earth-orbit (LEO) satellites aim to bring resilience, ubiquitous, and high-quality service to future Internet infrastructure. However, the soaring number of space assets, increasing dynamics of LEO satellites and expanding dimensions of network threats call for an enhanced approach to efficient satellite operations. To address these pressing challenges, we propose an approach for satellite network operations based on multi-layer satellite networking (MLSN), called"SatNetOps". Two SatNetOps schemes are proposed, referred to as LEO-LEO MLSN (LLM) and GEO-LEO MLSN (GLM). The performance of the proposed schemes is evaluated in 24-hr satellite scenarios with typical payload setups in simulations, where the key metrics such as latency and reliability are discussed with the consideration of the Consultative Committee for Space Data Systems (CCSDS) standard-compliant telemetry and telecommand missions. Although the SatNetOps approach is promising, we analyze the factors affecting the performance of the LLM and GLM schemes. The discussions on the results and conclusive remarks are made in the end.
Geometry of the space of monodromy data

In [32], with Yousuke Ohyama, we defined and studied a “space of monodromy data” underlying the well known derivation of q-Painlevé VI equation from “q-isomonodromy” conditions by Jimbo and Sakai [13]. In [16], Nalini Joshi and Pieter Roffelsen pursued our work. However, both [32] and [16] are ambiguous on some foundational algebro-geometric matters. We proceed here to provide sound bases. Résumé Dans [32], avec Yousuke Ohyama, nous avons défini et étudié un “espace des données de monodromie” sous-jacent à la célèbre déduction de l’équation de q-Painlevé VI à partir de conditions de “q-isomonodromie” par Jimbo et Sakai [13]. Dans [16], Nalini Joshi and Pieter Roffelsen ont prolongé notre travail. Cependant, aussi bien [32] que [16] présentent des ambiguı̈tés sur certains aspects de leurs fondements algébro-géométriques. Nous en proposons ici des bases rigoureuses.
Some ordering properties of highest and lowest order statistics with exponentiated Gumble type-II distributed components

In this paper, we have studied the stochastic comparisons of the highest and lowest order statistics of exponentiated Gumble type-II distribution with three parameters. We have compared both the statistics by using three different stochastic ordering. First, we consider a system with different scale and outer shape parameters and then we study the usual stochastic ordering of the lowest and highest order statistics in the sense of multivariate chain majorization. In addition, we construct two examples to support our results. Second, by using the vector majorization technique, we study the usual stochastic ordering, the reversed failure rate ordering and the likelihood ratio ordering with respect to different outer shape parameters, next, by varying the inner shape parameter, we discuss the usual stochastic order of the lowest order statistics and we have shown that the highest order statistics are not comparable in the usual stochastic ordering by an example.
Protein Co-Enrichment Analysis of Extracellular Vesicles

Extracellular Vesicles (EVs) carry cell-derived proteins that confer functionality and selective cell uptake. However, whether proteins are packaged stochastically or co-enriched within individual EVs, and whether co-enrichment fluctuates under homeostasis and disease, has not been measured. EV abundance and protein global relative expression have been qualified by bulk analysis. Meanwhile, co-enrichment is not directly accessible via bulk measurement and has not been reported for single EV analysis. Here, we introduce the normalized index of co-enrichment (NICE) to measure protein co-enrichment. NICE was derived by (i) capturing EVs based on the expression of a membrane-bound protein, (ii) probing for the co-expression of a second protein at the population level - EV integrity underwrites the detection of single EV co-expression without the need to resolve single EVs - and (iii) normalizing measured values using two universal normalization probes. Axiomatically, NICE = 1 for stochastic inclusion or no overall co-enrichment, while for positive and negative co-enrichment NICE>1 or<1, respectively. We quantified the NICE of tetraspanins, growth factor receptors and integrins in EVs of eight breast cancer cell lines of varying metastatic potential and organotropism, combinatorially mapping up to 104 protein pairs. Our analysis revealed protein enrichment and co-expression patterns consistent with previous findings. For the organotropic cell lines, most protein pairs were co-enriched on EVs, with the majority of NICE values between 0.2 to 11.5, and extending from 0.037 to 80.4. Median NICE were either negative, neutral or positive depending on the cells. NICE analysis is easily multiplexed and is compatible with microarrays, bead-based and single EV assays. Additional studies are needed to deepen our understanding of the potential and significance of NICE for research and clinical uses.
Non-contact Respiratory Anomaly Detection using Infrared Light Wave Sensing

Human respiratory rate and its pattern convey essential information about the physical and psychological states of the subject. Abnormal breathing can indicate fatal health issues leading to further diagnosis and treatment. Wireless light-wave sensing (LWS) using incoherent infrared light shows promise in safe, discreet, efficient, and non-invasive human breathing monitoring without raising privacy concerns. The respiration monitoring system needs to be trained on different types of breathing patterns to identify breathing anomalies.The system must also validate the collected data as a breathing waveform, discarding any faulty data caused by external interruption, user movement, or system malfunction. To address these needs, this study simulated normal and different types of abnormal respiration using a robot that mimics human breathing patterns. Then, time-series respiration data were collected using infrared light-wave sensing technology. Three machine learning algorithms, decision tree, random forest and XGBoost, were applied to detect breathing anomalies and faulty data. Model performances were evaluated through cross-validation, assessing classification accuracy, precision and recall scores. The random forest model achieved the highest classification accuracy of 96.75% with data collected at a 0.5m distance. In general, ensemble models like random forest and XGBoost performed better than a single model in classifying the data collected at multiple distances from the light-wave sensing setup.