Miracle-dz/UniFAR-FTUs huggingface dataset:
Testing particle acceleration models in BL LAC jets with the Imaging X-ray polarimetry Explorer

To determine if the recently launched Imaging X-ray Polarimetry Explorer (IXPE) can follow the polarization variations induced by different particle acceleration mechanisms in blazars jets we simulate observations of a high synchrotron peak (HSP) blazar variable in polarization degree and angle according to the theoretical predictions of Tavecchio et al., 2020 (shock acceleration model), Bodo et al., 2021 (magnetic reconnection model) and Marscher \&Jorstad, 2021 (Turbulent Extreme Multi-Zone Model). We use the Monte Carlo tool ixpeobssim to create realistic IXPE data products. We create simulated light-curves of polarization degree and angle by time-slicing the simulated data into arbitrary short time bins. We use a $\chi^2$ test to assess the performance of the observations in detecting the time variability of the polarization properties. A series of 10 ks long observations permits IXPE to follow the time variability of the polarization degree in the case of the shock acceleration model. In the case of the magnetic reconnection model, the nominal injected model provides the best fit of the simulated IXPE data for time bins of 5-10 ks, depending on the tested flux level. For the TEMZ model, shorter time slices of 0.5 ks are needed for obtaining a formally good fit of the simulated IXPE data with the injected model. On the other hand, we find that a fit with a constant model provides a $\chi^2$ lower than the fit with the nominal injected model when using time slices of 20 ks, 60/70 ks, and 5 ks for the case of the shock acceleration, magnetic reconnection and TEMZ model, respectively. We conclude that, provided that the statistics of the observation allows slicing of the data in adequately short time bins, IXPE observations of an HSP blazar at a typical flux level can detect the time variability predicted by popular models for particle acceleration in jets.
Uncertainty-Aware Deep Learning Methods for Robust Diabetic Retinopathy Classification

Automatic classification of diabetic retinopathy from retinal images has been increasingly studied using deep neural networks with impressive results. However, there is clinical need for estimating uncertainty in the classifications, a shortcoming of modern neural networks. Recently, approximate Bayesian neural networks (BNNs) have been proposed for this task, but previous studies have only considered the binary referable/non-referable diabetic retinopathy classification applied to benchmark datasets. We present novel results for 9 BNNs by systematically investigating a clinical dataset and 5-class classification scheme, together with benchmark datasets and binary classification scheme. Moreover, we derive a connection between entropy-based uncertainty measure and classifier risk, from which we develop a novel uncertainty measure. We observe that the previously proposed entropy-based uncertainty measure improves performance on the clinical dataset for the binary classification scheme, but not to such an extent as on the benchmark datasets. It improves performance in the clinical 5-class classification scheme for the benchmark datasets, but not for the clinical dataset. Our novel uncertainty measure generalizes to the clinical dataset and to one benchmark dataset. Our findings suggest that BNNs can be utilized for uncertainty estimation in classifying diabetic retinopathy on clinical data, though proper uncertainty measures are needed to optimize the desired performance measure. In addition, methods developed for benchmark datasets might not generalize to clinical datasets.
SpiroMask: Measuring Lung Function Using Consumer-Grade Masks

According to the World Health Organisation (WHO), 235 million people suffer from respiratory illnesses which causes four million deaths annually. Regular lung health monitoring can lead to prognoses about deteriorating lung health conditions. This article presents our system SpiroMask that retrofits a microphone in consumer-grade masks (N95 and cloth masks) for continuous lung health monitoring. We evaluate our approach on 48 participants (including 14 with lung health issues) and find that we can estimate parameters such as lung volume and respiration rate within the approved error range by the American Thoracic Society (ATS). Further, we show that our approach is robust to sensor placement inside the mask.
Training ELF teachers to create a blended learning environment: encouraging CMS adoption and implementation

E-learning has become a crucial component of most tertiary institution’s education initiatives (Park, Lee, & Cheong, 2007) and core to most e-learning strategies is the institution’s Content Management System (CMS). A CMS has the potential to enhance language courses by facilitating engagement with class content, providing students with opportunities to communicate, promoting student confidence during virtual interactions, fostering deeper connections between teachers and peers, and creating more personalized learning activities. However, getting faculty to use a CMS proves to be challenging (Black et al., 2007). As part of a study by the authors to learn how they might encourage teachers in a campus-wide English as a Lingua Franca (ELF) program to adopt a CMS, this paper reports on results from a Technology Acceptance Model (TAM) analysis (Alharbi & Drew, 2014).
Sharp Interface Limit for a Navier-Stokes/Allen-Cahn System with Different Viscosities

We discuss the sharp interface limit of a coupled Navier-Stokes/Allen-Cahn system in a two dimensional, bounded and smooth domain, when a parameter $\varepsilon>0$ that is proportional to the thickness of the diffuse interface tends to zero rigorously. We prove convergence of the solutions of the Navier-Stokes/Allen-Cahn system to solutions of a sharp interface model, where the interface evolution is given by the mean curvature flow with an additional convection term coupled to a two-phase Navier-Stokes system with surface tension. This is done by constructing an approximate solution from the limiting system via matched asymptotic expansions together with a novel Ansatz for the highest order term, and then estimating its difference with the real solution with the aid of a refined spectral estimate of the linearized Allen-Cahn operator near the approximate solution.
Automatic detection of multilevel communities in complex networks with a scalable community fitness function

Community detection in complex networks has been held back by two obstacles: the resolution limit problem, which restrains simultaneous detection for communities of heterogeneous sizes, and the divergent outputs of heuristic algorithm, which are unfavorable for differentiating more relevant and significant results. In this paper, we propose a renewed method for community detection with a multiresolution and rescalable community fitness function. The scalability of the community fitness function on the one hand exempts our method from the resolution limit problem in heterogeneous networks, and on the other hand enables our method to detect multilevel community structures in deep hierarchical networks. Furthermore, we suggest a strict definition of “plateau,” with which we evaluate the stability of the outputs, and remove unstable and irrelevant ones automatically, without any artificial or arbitrary selection. As a result, our method outperforms most previous methods; it reproduces the expected community structures accurately for various classes of synthetic networks, as well as the ground truths for real-world networks.
Long-term Data Sharing under Exclusivity Attacks

The quality of learning generally improves with the scale and diversity of data. Companies and institutions can therefore benefit from building models over shared data. Many cloud and blockchain platforms, as well as government initiatives, are interested in providing this type of service. These cooperative efforts face a challenge, which we call "exclusivity attacks". A firm can share distorted data, so that it learns the best model fit, but is also able to mislead others. We study protocols for long-term interactions and their vulnerability to these attacks, in particular for regression and clustering tasks. We find that the choice of communication protocol is essential for vulnerability: The protocol is much more vulnerable if firms can continuously initiate communication, instead of periodically asked for their inputs. Vulnerability may also depend on the number of Sybil identities a firm can control.
A Generalized Weighted Optimization Method for Computational Learning and Inversion

The generalization capacity of various machine learning models exhibits different phenomena in the under- and over-parameterized regimes. In this paper, we focus on regression models such as feature regression and kernel regression and analyze a generalized weighted least-squares optimization method for computational learning and inversion with noisy data. The highlight of the proposed framework is that we allow weighting in both the parameter space and the data space. The weighting scheme encodes both a priori knowledge on the object to be learned and a strategy to weight the contribution of different data points in the loss function. Here, we characterize the impact of the weighting scheme on the generalization error of the learning method, where we derive explicit generalization errors for the random Fourier feature model in both the under- and over-parameterized regimes. For more general feature maps, error bounds are provided based on the singular values of the feature matrix. We demonstrate that appropriate weighting from prior knowledge can improve the generalization capability of the learned model.
A Pre-Trained Audio-Visual Transformer for Emotion Recognition

In this paper, we introduce a pretrained audio-visual Transformer trained on more than 500k utterances from nearly 4000 celebrities from the VoxCeleb2 dataset for human behavior understanding. The model aims to capture and extract useful information from the interactions between human facial and auditory behaviors, with application in emotion recognition. We evaluate the model performance on two datasets, namely CREMAD-D (emotion classification) and MSP-IMPROV (continuous emotion regression). Experimental results show that fine-tuning the pre-trained model helps improving emotion classification accuracy by 5-7% and Concordance Correlation Coefficients (CCC) in continuous emotion recognition by 0.03-0.09 compared to the same model trained from scratch. We also demonstrate the robustness of finetuning the pre-trained model in a low-resource setting. With only 10% of the original training set provided, finetuning the pre-trained model can lead to at least 10% better emotion recognition accuracy and a CCC score improvement by at least 0.1 for continuous emotion recognition.
CLASSICAL MDS ON METRIC MEASURE SPACES

We study a generalization of the classical Multidimensional Scaling procedure (cMDS) to the setting of general metric measure spaces. We identify certain crucial spectral properties of the generalized cMDS operator thus providing a natural and rigorous mathematical formulation of cMDS. Furthermore, we characterize the cMDS output of several continuous exemplar metric measures spaces such as high dimensional spheres and tori (both with their geodesic distance)). In particular, the case of spheres (with geodesic distance) requires that we establish that its cMDS operator is trace class, a condition which is natural in the context when the cMDS operator has inﬁnite rank. Finally, we establish the stability of the generalized cMDS method with respect to the Gromov-Wasserstein distance.
Constraining Galaxy Overdensities around Three z ∼ 6.5 Quasars with ALMA and MUSE

We quantify galaxy overdensities around three high-redshift quasars with known [C ii]158 μm companions: PJ231–20 (z = 6.59), PJ308–21 (z = 6.24), and J0305–3150 (z = 6.61). Recent SCUBA2 imaging revealed the presence of 17 submillimeter galaxies (SMGs) with sky separations 0. ′7 < θ < 2. ′4 from these three quasars. We present ALMA Band 6 follow-up observations of these SCUBA2-selected SMGs to confirm their nature and redshift. We also search for continuum-undetected [C ii]158 μm emitters in the ALMA pointings and make use of archival MUSE observations to search for Lyα emitters (LAEs) associated with the quasars. While most of the SCUBA2-selected sources are detected with ALMA in the continuum, no [C ii]158 μm line emission could be detected, indicating that they are not at the quasar redshifts. Based on the serendipitous detection of CO 7–6 and [C i]809 μm emission lines, we find that four SMGs in the field of PJ231–20 are at z ∼ 2.4, which is coincident with the redshift of an Mg ii absorber in the quasar rest-frame UV spectrum. We report the discovery of two LAEs within <0.6 cMpc of PJ231–20 at the same redshift, indicating an LAE overdensity around this quasar. Taken together, these observations provide new constraints on the large-scale excess of Lyα- and [C ii]158 μm-emitting galaxies around z > 6 quasars and suggest that only wide-field observations, such as MUSE, ALMA, or JWST mosaics, can reveal a comprehensive picture of large-scale structure around quasars in the first billion years of the universe.
Massive quiescent galaxies at z ∼ 3: A comparison of selection, stellar population, and structural properties with simulation predictions

We study stellar population and structural properties of massive log (M⋆/M⊙) > 11 galaxies at z ≈ 2.7 in the Magneticum and IllustrisTNG hydrodynamical simulations and GAEA semi-analytic model. We find stellar mass functions broadly consistent with observations, with no scarcity of massive, quiescent galaxies at z ≈ 2.7, but with a higher quiescent galaxy fraction at high masses in IllustrisTNG. Average ages of simulated quiescent galaxies are between ≈0.8 and ${1.0\, \textrm {Gyr}}$, older by a factor ≈2 than observed in spectroscopically confirmed quiescent galaxies at similar redshift. Besides being potentially indicative of limitations of simulations in reproducing observed star formation histories, this discrepancy may also reflect limitations in the estimation of observed ages. We investigate the purity of simulated UVJ rest-frame colour-selected massive quiescent samples with photometric uncertainties typical of deep surveys (e.g. COSMOS). We find evidence for significant contamination (up to ${60\, \rm {per\, cent}}$) by dusty star-forming galaxies in the UVJ region that is typically populated by older quiescent sources. Furthermore, the completeness of UVJ-selected quiescent samples at this redshift may be reduced by $\approx {30\, \rm {per\, cent}}$ due to a high fraction of young quiescent galaxies not entering the UVJ quiescent region. Massive, quiescent galaxies in simulations have on average lower angular momenta and higher projected axis ratios and concentrations than star-forming counterparts. Average sizes of simulated quiescent galaxies are broadly consistent with observations within the uncertainties. The average size ratio of quiescent and star-forming galaxies in the probed mass range is formally consistent with observations, although this result is partly affected by poor statistics.
Learning to Minimize the Remainder in Supervised Learning

The learning process of deep learning methods usually updates the model’s parameters in multiple iterations. Each iteration can be viewed as the first-order approximation of Taylor’s series expansion. The remainder, which consists of higher-order terms, is usually ignored in the learning process for simplicity. This learning scheme empowers various multimedia-based applications, such as image retrieval, recommendation system, and video search. Generally, multimedia data (e.g. images) are semantics-rich and high-dimensional, hence the remainders of approximations are possibly non-zero. In this work, we consider that the remainder is informative and study how it affects the learning process. To this end, we propose a new learning approach, namely gradient adjustment learning (GAL), to leverage the knowledge learned from the past training iterations to adjust vanilla gradients, such that the remainders are minimized and the approximations are improved. The proposed GAL is model- and optimizer-agnostic, and is easy to adapt to the standard learning framework. It is evaluated on three tasks, i.e. image classification, object detection, and regression, with state-of-the-art models and optimizers. The experiments show that the proposed GAL consistently enhances the evaluated models, whereas the ablation studies validate various aspects of the proposed GAL. The code is available at https://github.com/luoyan407/gradient_adjustment. git.
An integrated recurrent neural network and regression model with spatial and climatic couplings for vector-borne disease dynamics

We developed an integrated recurrent neural network and nonlinear regression spatio-temporal model for vector-borne disease evolution. We take into account climate data and seasonality as external factors that correlate with disease transmitting insects (e.g. flies), also spill-over infections from neighboring regions surrounding a region of interest. The climate data is encoded to the model through a quadratic embedding scheme motivated by recommendation systems. The neighboring regions' influence is modeled by a long short-term memory neural network. The integrated model is trained by stochastic gradient descent and tested on leish-maniasis data in Sri Lanka from 2013-2018 where infection outbreaks occurred. Our model outperformed ARIMA models across a number of regions with high infections, and an associated ablation study renders support to our modeling hypothesis and ideas.
Energy absorption characteristics and a meta-model of miniature frusta under axial impact

ABSTRACT In this paper, crushing characteristics of small-sized conical tubes called miniature frusta under axial loading have been studied. Finite-element model is developed using non-linear explicit code LS_DYNA to investigate the non-symmetrical fold patterns as well as material and geometrical non-linearity of frusta. Numerical simulation is first validated by confirming the results using experimental test data. Effects of shell thickness, semi-epical angle of cone and frusta's length on energy absorption characteristics are then studied by carrying out a parametric study. Based on the crushing parameters of numerous examples, a simplified analytical model as a meta-model for the mean crushing force of miniature frusta is presented using a Genetic Algorithm optimization for finding the meta-model coefficients. Miniature frusta show promising behaviour in lightweight design and crash analysis as their response results in low peak force and efficient-specific energy absorption. Obtained results from the developed meta-model showed good concurrence with finite elements method (FEM) model.
Cache Allocation in Multi-Tenant Edge Computing via online Reinforcement Learning

We consider in this work Edge Computing (EC) in a multi-tenant environment: the resource owner, i.e., the Network Operator (NO), virtualizes the resources and lets third party Service Providers (SPs - tenants) run their services, which can be diverse and with heterogeneous requirements. Due to confidentiality guarantees, the NO cannot observe the nature of the traffic of SPs, which is encrypted. This makes resource allocation decisions challenging, since they must be taken based solely on observed monitoring information.We focus on one specific resource, i.e., cache space, deployed in some edge node, e.g., a base station. We study the decision of the NO about how to partition cache among several SPs in order to minimize the upstream traffic. Our goal is to optimize cache allocation using purely data-driven, model-free Reinforcement Learning (RL). Differently from most applications of RL, in which the decision policy is learned offline on a simulator, we assume no previous knowledge is available to build such a simulator. We thus apply RL in an online fashion, i.e., the policy is learned by directly perturbing the actual system and monitoring how its performance changes. Since perturbations generate spurious traffic, we also limit them. We show in simulation that our method rapidly converges toward the theoretical optimum, we study its fairness, its sensitivity to several scenario characteristics and compare it with a method from the state-of-the-art. Our code to reproduce the results is available as open source.1
Towards Sustainable Deep Learning for Wireless Fingerprinting Localization

Location based services, already popular with end users, are now inevitably becoming part of new wireless infrastructures and emerging business processes. The increasingly popular Deep Learning (DL) artificial intelligence methods perform very well in wireless fingerprinting localization based on extensive indoor radio measurement data. However, with the increasing complexity these methods become computationally very intensive and energy hungry, both for their training and subsequent operation. Considering only mobile users, estimated to exceed 7.4 billion by the end of 2025, and assuming that the networks serving these users will need to perform only one localization per user per hour on average, the machine learning models used for the calculation would need to perform 65×1012 predictions per year. Add to this equation tens of billions of other connected devices and applications that rely heavily on more frequent location updates, and it becomes apparent that localization will contribute significantly to carbon emissions unless more energy-efficient models are developed and used. This motivated our work on a new DL-based architecture for indoor localization that is more energy efficient compared to related state-of-the-art approaches while showing only marginal performance degradation. A detailed performance evaluation shows that the proposed model produces only 58% of the carbon footprint while maintaining 98.7% of the overall performance compared to state of the art model external to our group. Additionally, we elaborate on a methodology to calculate the complexity of the DL model and thus the CO2 footprint during its training and operation.