Miracle-dz/UniFAR-FTUs huggingface dataset:
Orientation-sensed optomechanical accelerometers based on exceptional points

We propose an optomechanical design, consisting of a parity-time symmetric multilayer structure tuned at exceptional-point degeneracy (EPD), with an adjustable layer that is coupled to micromechanical springs. The deflections of this layer in response to accelerations ${\alpha}$, lead to square-root resonance detuning ${\Delta}{\omega}\equiv{\omega}-{\omega}_{EPD}\propto \sqrt{\alpha}$ - thus dramatically enhancing the probe of ultra-small accelerations ${\alpha}\ll1$. Our design is scalable and can, in principle, support higher $N$ order EPDs with sensitivity ${\Delta}{\omega}\propto\sqrt[n]{\alpha}$. It also provides a pathway towards a new generation of on-chip hypersensitive accelerometers and vibrometers.
Weak-lensing Peak Statistics in Mocks by the Inverse-Gaussianization Method

Recent studies have shown the great power of peak counts in weak-lensing maps. In this work, we apply the inverse-Gaussianization method proposed in Yu et al. to produce weak-lensing convergence maps quickly and investigate the peak statistics, including the peak height counts and peak steepness counts in these mocks. The distributions of peak height and steepness are in good agreement with the simulation results. The difference is ≲20% for these peak statistics in the maps at source redshift zs = 1. Also, the loss of off-diagonal elements in the peak covariance motivates us to consider the super-sample variance in weak-lensing peak statistics. We propose four correction methods to effectively recover the (anti)correlation among different bins by adding different scatters in the mean value of these mocks. Finally, as an example of the application, we adopt the improved inverse-Gaussianization method to quickly generate 40,000 mocks to calculate precision matrices for the power spectrum and peak-statistics joint analysis.
Virtual KITTI 2

This paper introduces an updated version of the well-known Virtual KITTI dataset which consists of 5 sequence clones from the KITTI tracking benchmark. In addition, the dataset provides different variants of these sequences such as modified weather conditions (e.g. fog, rain) or modified camera configurations (e.g. rotated by 15 degrees). For each sequence, we provide multiple sets of images containing RGB, depth, class segmentation, instance segmentation, flow, and scene flow data. Camera parameters and poses as well as vehicle locations are available as well. In order to showcase some of the dataset's capabilities, we ran multiple relevant experiments using state-of-the-art algorithms from the field of autonomous driving. The dataset is available for download at this https URL.
Stable and Discriminative Topological Graph Analysis

We propose a novel method for topological analysis of unweighted graphs which is based on \textit{persistent homology}. The proposed method maps the input graph to a complete weighted graph where the weighting function maps each edge to a value indicating the degree to which it belongs to a clique. The persistent homology of this weighted graph is subsequently computed to give a topological representation describing the topological features of the input graph plus their significance. A formal and experimental analysis of the proposed and existing methods for topological graph analysis is presented. Through this analysis, we find that the proposed method possesses the properties of being stable and performing accurate discrimination. Therefore this method can make accurate inferences regarding the topological features of a given graph. On the other hand, we find that the existing methods considered do not possess these properties making it difficult from them to make such inferences. These findings are experimentally demonstrated using a number of random and real world graphs.
RPCA-Based High Resolution Through-the-Wall Human Motion Detection and Classification

Radar based assisted living has received great amount of research interest in recent years. By employing the micro-Doppler features of indoor human motions, accurate recognition and classification of different types of movements become possible. Whereas, most of the existing works are focused only on free space detection, the literature on detection and recognition of human motions in through-the-wall scenarios is still in its infancy. As can be anticipated, the wall media and indoor static non-human targets would cause clutters and significantly corrupt the motion information of human subjects behind wall. However, no relevant work is reported to effectively handle this problem. In the present work, we aim to fill the gap and propose to use a low center-frequency ultra-wideband (UWB) radar system to probe the behind wall scene. Then, a Robust Principal Component Analysis (RPCA) based subspace decomposition technique, as its first reported implementation, is employed not only to remove the stationary clutters in raw range slow-time map but also to mitigate the multipath effects in the time-frequency map. Onsite experiments of detecting human motions behind a single layer of concrete wall is carried out to investigate the performance of the technique. Lastly, a two dimensional (2D)-PCA algorithm-based motion classification is provided to further verify the effectiveness of the proposed technique. Classification result shows that an enhanced recognition capability can be achieved using the proposed technique in detection and classification of indoor human motions.
On Newton Screening

Screening and working set techniques are important approaches to reducing the size of an optimization problem. They have been widely used in accelerating first-order methods for solving large-scale sparse learning problems. In this paper, we develop a new screening method called Newton screening (NS) which is a generalized Newton method with a built-in screening mechanism. We derive an equivalent KKT system for the Lasso and utilize a generalized Newton method to solve the KKT equations. Based on this KKT system, a built-in working set with a relatively small size is first determined using the sum of primal and dual variables generated from the previous iteration, then the primal variable is updated by solving a least-squares problem on the working set and the dual variable updated based on a closed-form expression. Moreover, we consider a sequential version of Newton screening (SNS) with a warm-start strategy. We show that NS possesses an optimal convergence property in the sense that it achieves one-step local convergence. Under certain regularity conditions on the feature matrix, we show that SNS hits a solution with the same signs as the underlying true target and achieves a sharp estimation error bound with high probability. Simulation studies and real data analysis support our theoretical results and demonstrate that SNS is faster and more accurate than several state-of-the-art methods in our comparative studies.
From galactic nuclei to the halo outskirts: tracing supermassive black holes across cosmic history and environments

We study the mass assembly and spin evolution of supermassive black holes (BHs) across cosmic time as well as the impact of gravitational recoil on the population of nuclear and wandering black holes (wBHs) by using the semi-analytical model LGalaxies run on top of Millennium merger trees. We track spin changes that BHs experience during both coalescence events and gas accretion phases. For the latter, we assume that spin changes are coupled with the bulge assembly. This assumption leads to predictions for the median spin values of z=0 BHs that depend on whether they are hosted by pseudobulges, classical bulges or ellipticals, being a ~ 0.9, 0.7 and 0.4, respectively. The outcomes of the model display a good consistency with z 10^{13} M_{\odot} and M_{stellar}>10^{10} M_{\odot}, being orphan wBHs the dominant type. Besides, independently of redshift and halo mass, \textit{ejected} wBHs inhabit the central regions ( 0.3R_{200}). Finally, we find that gravitational recoils cause a progressive depletion of nuclear BHs with decreasing redshift and stellar mass. Moreover, ejection events lead to changes in the predicted local black hole-bulge relation, in particular for BHs in pseudobulges, for which the relation is flattened at M_{bulge}>10^{10.2} M{\odot} and the scatter increase up to ~3dex.
IoT Behavioral Monitoring via Network Traffic Analysis

Smart homes, enterprises, and cities are increasingly being equipped with a plethora of Internet of Things (IoT), ranging from smart-lights to security cameras. While IoT networks have the potential to benefit our lives, they create privacy and security challenges not seen with traditional IT networks. Due to the lack of visibility, operators of such smart environments are not often aware of their IoT assets, let alone whether each IoT device is functioning properly safe from cyber-attacks. This thesis is the culmination of our efforts to develop techniques to profile the network behavioral pattern of IoTs, automate IoT classification, deduce their operating context, and detect anomalous behavior indicative of cyber-attacks. We begin this thesis by surveying IoT ecosystem, while reviewing current approaches to vulnerability assessments, intrusion detection, and behavioral monitoring. For our first contribution, we collect traffic traces and characterize the network behavior of IoT devices via attributes from traffic patterns. We develop a robust machine learning-based inference engine trained with these attributes and demonstrate real-time classification of 28 IoT devices with over 99% accuracy. Our second contribution enhances the classification by reducing the cost of attribute extraction while also identifying IoT device states. Prototype implementation and evaluation demonstrate the ability of our supervised machine learning method to detect behavioral changes for five IoT devices. Our third and final contribution develops a modularized unsupervised inference engine that dynamically accommodates the addition of new IoT devices and/or updates to existing ones, without requiring system-wide retraining of the model. We demonstrate via experiments that our model can automatically detect attacks and firmware changes in ten IoT devices with over 94% accuracy.
Convergence Guarantees for Gaussian Process Approximations Under Several Observation Models

Gaussian processes are ubiquitous in statistical analysis, machine learning and applied mathematics. They provide a flexible modelling framework for approximating functions, whilst simultaneously quantifying our uncertainty about this task in a computationally tractable manner. An important question is whether these approximations will be accurate, and if so how accurate, given our various modelling choices and the difficulty of the problem. This is of practical relevance, since the answer informs our choice of model and sampling distribution for a given application. Our paper provides novel approximation guarantees for Gaussian process models based on covariance functions with finite smoothness, such as the Mat\'ern and Wendland covariance functions. They are derived from a sampling inequality which facilitates a systematic approach to obtaining upper bounds on Sobolev norms in terms of properties of the design used to collect data. This approach allows us to refine some existing results which apply in the misspecified smoothness setting and which allow for adaptive selection of hyperparameters. However, the main novelty in this paper is that our results cover a wide range of observation models including interpolation, approximation with deterministic corruption and regression with Gaussian noise.
Treatment effect estimation with disentangled latent factors

Much research has been devoted to the problem of estimating treatment effects from observational data; however, most methods assume that the observed variables only contain confounders, i.e., variables that affect both the treatment and the outcome. Unfortunately, this assumption is frequently violated in real-world applications, since some variables only affect the treatment but not the outcome, and vice versa. Moreover, in many cases only the proxy variables of the underlying confounding factors can be observed. In this work, we first show the importance of differentiating confounding factors from instrumental and risk factors for both average and conditional average treatment effect estimation, and then we propose a variational inference approach to simultaneously infer latent factors from the observed variables, disentangle the factors into three disjoint sets corresponding to the instrumental, confounding, and risk factors, and use the disentangled factors for treatment effect estimation. Experimental results demonstrate the effectiveness of the proposed method on a wide range of synthetic, benchmark, and real-world datasets.
Spiking Inception Module for Multi-layer Unsupervised Spiking Neural Networks

Spiking Neural Network (SNN), as a brain-inspired approach, is attracting attention due to its potential to produce ultra-high-energy-efficient hardware. Competitive learning based on Spike-Timing-Dependent Plasticity (STDP) is a popular method to train an unsupervised SNN. However, previous unsupervised SNNs trained through this method are limited to a shallow network with only one learnable layer and cannot achieve satisfactory results when compared with multi-layer SNNs. In this paper, we eased this limitation by: 1)We proposed a Spiking Inception (Sp-Inception) module, inspired by the Inception module in the Artificial Neural Network (ANN) literature. This module is trained through STDP-based competitive learning and outperforms the baseline modules on learning capability, learning efficiency, and robustness. 2)We proposed a Pooling-Reshape-Activate (PRA) layer to make the Sp-Inception module stackable. 3)We stacked multiple Sp-Inception modules to construct multilayer SNNs. Our algorithm outperforms the baseline algorithms on the hand-written digit classification task, and reaches state-of-the-art results on the MNIST dataset among the existing unsupervised SNNs.
Offline Recommender Learning Meets Unsupervised Domain Adaptation.

It is critical to eliminate selection bias of the rating feedback to construct a well-performing recommender offline. Currently, a promising solution to the challenge is the propensity weighting approach that models the missing mechanism of rating feedback. However, the performance of existing propensity-based algorithms can be significantly affected by the propensity estimation bias. To alleviate the problem, we formulate the missing-not-at-random recommendation as the unsupervised domain adaptation problem and drive the propensity-independent generalization error bound. We further propose a corresponding algorithm that minimizes the bound via adversarial learning. Our proposed theoretical framework and algorithm do not depend on the propensity score and can obtain a well-performing rating predictor without the true propensity information. Empirical evaluation using benchmark real-world datasets demonstrates the effectiveness and real-world applicability of the proposed approach.
Towards Spatially Explicit Quantification of Pre- and Postfire Fuels and Fuel Consumption from Traditional and Point Cloud Measurements

Methods to accurately estimate spatially explicit fuel consumption are needed because consumption relates directly to fire behavior, effects, and smoke emissions. Our objective was to quantify sparkleberry (Vaccinium arboretum Marshall) shrub fuels before and after six experimental prescribed fires at Fort Jackson in South Carolina. We used a novel approach to characterize shrubs non-destructively from three-dimensional (3D) point cloud data collected with a terrestrial laser scanner. The point cloud data were reduced to 0.001 m–3 voxels that were either occupied to indicate fuel presence or empty to indicate fuel absence. The density of occupied voxels was related significantly by a logarithmic function to 3D fuel bulk density samples that were destructively harvested (adjusted R2 = .32, P < .0001). Based on our findings, a survey-grade Global Navigation Satellite System may be necessary to accurately associate 3D point cloud data to 3D fuel bulk density measurements destructively collected in small (submeter) shrub plots. A recommendation for future research is to accurately geolocate and quantify the occupied volume of entire shrubs as 3D objects that can be used to train models to map shrub fuel bulk density from point cloud data binned to occupied 3D voxels.
Recommandations méthodologiques préalables à l'utilisation du cortisol salivaire comme marqueur biologique de stress.

Un marqueur fidele et facile a determiner La mesure du cortisol dans la salive fournit aux scientifiques et aux cliniciens un outil fiable pour l'investigation de l'axe hypothalamo-hypophysosurrenalien. Tout comme la cortisolemie, le taux de cortisol dans la salive est un marqueur fidele des etats de stress. L'avantage majeur du cortisol salivaire repose sur une technique de prelevement non invasive pouvant s'effectuer en dehors de tout etat de stress et s'exporter aisement hors laboratoire. Indications L'utilisation du cortisol salivaire ouvre donc des perspectives de recherche interessantes autant en sciences pour mener des recherches en situations hors laboratoire qu'en clinique ou les prelevements peuvent etre effectues par le patient lui-meme a son domicile. En pratique Neanmoins, une utilisation fiable du cortisol salivaire presuppose quelques prerequis methodologiques concernant autant la technique de prelevements (recueil et conservation) que le controle des facteurs endogenes (facteurs individuels, variations circadiennes, âge, sexe, statut hormonal, poids) ou comportementaux (tabac, repas, posture) impliques dans la regulation de sa secretion. Le respect de ces prerequis est indispensable pour echapper aux influences artefactuelles potentielles lors de l'elaboration d'un protocole de recherche ou de diagnostic sur le terrain.
High variability within pet foods prevents the identification of native species in pet cats’ diets using isotopic evaluation

Domestic cats preying on wildlife is a frequent conservation concern but typical approaches for assessing impacts rely on owner reports of prey returned home, which can be biased by inaccurate reporting or by cats consuming prey instead of bringing it home. Isotopes offer an alternative way to quantify broad differences in animal diets. By obtaining samples of pet food from cat owners we predicted that we would have high power to identify cats feeding on wild birds or mammals, given that pet food is thought to have higher C isotope values, due to the pervasive use of corn and/or corn by-products as food ingredients, than native prey. We worked with citizen scientists to quantify the isotopes of 202 cat hair samples and 239 pet food samples from the US and UK. We also characterized the isotopes of 11 likely native prey species from the southeastern US and used mixing models to assess the diet of 47 cats from the same region. Variation in C and N isotope values for cat food was very high, even within the same brand/flavor, suggesting that pet food manufacturers use a wide range of ingredients, and that these may change over time. Cat food and cat hair from the UK had lower C values than the US, presumably reflecting differences in the amount of corn used in the food chains of the two countries. This high variation in pet food reduced our ability to classify cats as hunters of native prey, such that only 43% of the animals could be confidently assigned. If feral or free ranging cats were considered, this uncertainty would be even higher as pet food types would be unknown. Our results question the general assumption that anthropogenic foods always have high C isotope values, because of the high variability we documented within one product type (cat food) and between countries (US vs. UK), and emphasize the need to test a variety of standards before making conclusions from isotope ecology studies.
The effects of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) on the transcriptome of aryl hydrocarbon receptor (AhR) knock-down porcine granulosa cells

Background 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) is a toxic man-made chemical, adversely affecting reproductive processes. The well-characterized canonical mechanism of TCDD action involves the activation of aryl hydrocarbon receptor (AhR) pathway, but AhR-independent mechanisms were also suggested. By applying RNA interference technology and Next Generation Sequencing (NGS) we aimed to identify genes involved in the mechanism of TCDD action in AhR knock-down porcine granulosa cells. Methods Porcine granulosa cells were transfected with small interfering RNAs targeting mRNA of AhR. After transfection, medium was exchanged and the AhR knock-down cells were treated with TCDD (100 nM) for 3, 12 or 24 h, total cellular RNA was isolated and designated for NGS. Following sequencing, differentially expressed genes (DEGs) were identified. To analyze functions and establish possible interactions of DEGs, the Gene Ontology (GO) database and the Search Tool for the Retrieval of Interacting Genes (STRING) database were used, respectively. Results The AhR gene expression level and protein abundance were significantly decreased after AhR-targeted siRNAs transfection of the cells. In TCDD-treated AhR knock-down cells we identified 360 differentially expressed genes (DEGs; P-adjusted < 0.05 and log2 fold change [log2FC] ≥ 1.0). The functional enrichment analysis of DEGs revealed that TCDD influenced the expression of genes involved, among other, in the metabolism of vitamin A, follicular development and oocyte maturation, proliferation and differentiation as well as inflammation, stress response, apoptosis and oncogenesis. The three-time point study demonstrated that TCDD-induced changes in the transcriptome of AhR knock-down porcine granulosa cells were especially pronounced during the early stages of the treatment (3 h). Conclusions TCDD affected the transcriptome of AhR knock-down porcine granulosa cells. The molecules involved in the AhR-independent action of TCDD were indicated in the study. The obtained data contribute to better understanding of molecular processes induced by xenobiotics in the ovary.