Miracle-dz/UniFAR-FTUs huggingface dataset:
Near-ultraviolet detections of four dwarf nova candidates in the globular cluster 47 Tucanae

We investigate near-ultraviolet variability in the Galactic globular cluster (GC) 47 Tucanae (47 Tuc). This work was undertaken within the GC sub-project of the Transient UV Objects project, a programme which aims to find and study transient and strongly variable UV sources. Globular clusters are ideal targets for transient searches because of their high stellar densities and large populations of variable systems. Using all 75 archival observations of 47 Tuc obtained with the UV/optical telescope (UVOT) aboard theNeil Gehrels Swiftobservatory with theuvm2filter, we searched for UV variability using a specialised pipeline which utilises difference image analysis. We found four clear transients, hereafter SW1–4, with positions consistent with those of known cataclysmic variables (CVs) or CV candidates identified previously usingHubbleSpace Telescope observations. All four sources exhibit significant outbursts, likely brightening by several orders of magnitude. Based on the inferred outburst properties and the association with known CVs, we tentatively identify the UV transients as CV-dwarf novae (DNe). Two DNe have been previously observed in 47 Tuc: V2, which has a position consistent with that of SW4; and AKO 9, which was not in outburst during any of the UVOT observations. We thus increase the known number of DNe in 47 Tuc to 5 and the total number of detected DNe in all Galactic GCs combined from 14 to 17. We discuss our results in the context of the apparent scarcity of DNe in GCs. We suggest that the likely cause is observational biases, such as limited sensitivity due to the high background from unresolved stars in the GC and limited angular resolution of the telescopes used. We additionally detected one strongly variable source in 47 Tuc, which could be identified as the known RR Lyrae star HV 810. We found its period to have significantly increased with respect to that measured from data taken in 1988.
Simulating Lexical Semantic Change from Sense-Annotated Data

We present a novel procedure to simulate lexical semantic change from synchronic sense-annotated data, and demonstrate its usefulness for assessing lexical semantic change detection models. The induced dataset represents a stronger correspondence to empirically observed lexical semantic change than previous synthetic datasets, because it exploits the intimate relationship between synchronic polysemy and diachronic change. We publish the data and provide the first large-scale evaluation gold standard for LSC detection models.
Entropy Regularized Power k-Means Clustering

Despite its well-known shortcomings, $k$-means remains one of the most widely used approaches to data clustering. Current research continues to tackle its flaws while attempting to preserve its simplicity. Recently, the \textit{power $k$-means} algorithm was proposed to avoid trapping in local minima by annealing through a family of smoother surfaces. However, the approach lacks theoretical justification and fails in high dimensions when many features are irrelevant. This paper addresses these issues by introducing \textit{entropy regularization} to learn feature relevance while annealing. We prove consistency of the proposed approach and derive a scalable majorization-minimization algorithm that enjoys closed-form updates and convergence guarantees. In particular, our method retains the same computational complexity of $k$-means and power $k$-means, but yields significant improvements over both. Its merits are thoroughly assessed on a suite of real and synthetic data experiments.
Convolutional-Recurrent Neural Networks on Low-Power Wearable Platforms for Cardiac Arrhythmia Detection

Low-power sensing technologies, such as wearables, have emerged in the healthcare domain since they enable continuous and non-invasive monitoring of physiological signals. In order to endow such devices with clinical value, classical signal processing has encountered numerous challenges. However, data-driven methods, such as machine learning, offer attractive accuracies at the expense of being resource and memory demanding. In this paper, we focus on the inference of neural networks running in microcontrollers and low-power processors which wearable sensors and devices are generally equipped with. In particular, we adapted an existing convolutional-recurrent neural network, designed to detect and classify cardiac arrhythmias from a singlelead electrocardiogram, to the low-power embedded System-onChip nRF52 from Nordic Semiconductor with an ARM’s CortexM4 processing core. We show our implementation in fixedpoint precision, using the CMSIS-NN libraries, yields a drop of F1 score from 0.8 to 0.784, from the original implementation, with a memory footprint of 195.6 KB, and a throughput of 33.98 MOps/s.
In Defense of Grid Features for Visual Question Answering

Popularized as `bottom-up' attention, bounding box (or region) based visual features have recently surpassed vanilla grid-based convolutional features as the de facto standard for vision and language tasks like visual question answering (VQA). However, it is not clear whether the advantages of regions (e.g. better localization) are the key reasons for the success of bottom-up attention. In this paper, we revisit grid features for VQA, and find they can work surprisingly well -- running more than an order of magnitude faster with the same accuracy (e.g. if pre-trained in a similar fashion). Through extensive experiments, we verify that this observation holds true across different VQA models (reporting a state-of-the-art accuracy on VQA 2.0 test-std, 72.71), datasets, and generalizes well to other tasks like image captioning. As grid features make the model design and training process much simpler, this enables us to train them end-to-end and also use a more flexible network design. We learn VQA models end-to-end, from pixels directly to answers, and show that strong performance is achievable without using any region annotations in pre-training. We hope our findings help further improve the scientific understanding and the practical application of VQA. Code and features will be made available.
Campfire: Compressible, Regularization-Free, Structured Sparse Training for Hardware Accelerators

This paper studies structured sparse training of CNNs with a gradual pruning technique that leads to fixed, sparse weight matrices after a set number of epochs. We simplify the structure of the enforced sparsity so that it reduces overhead caused by regularization. The proposed training methodology Campfire explores pruning at granularities within a convolutional kernel and filter. We study various tradeoffs with respect to pruning duration, level of sparsity, and learning rate configuration. We show that our method creates a sparse version of ResNet-50 and ResNet-50 v1.5 on full ImageNet while remaining within a negligible <1% margin of accuracy loss. To ensure that this type of sparse training does not harm the robustness of the network, we also demonstrate how the network behaves in the presence of adversarial attacks. Our results show that with 70% target sparsity, over 75% top-1 accuracy is achievable.
The formation times and building blocks of Milky Way-mass galaxies in the FIRE simulations

Surveys of the Milky Way (MW) and M31 enable detailed studies of stellar populations across ages and metallicities, with the goal of reconstructing formation histories across cosmic time. These surveys motivate key questions for galactic archaeology in a cosmological context: When did the main progenitor of an MW/M31-mass galaxy form, and what were the galactic building blocks that formed it? We investigate the formation times and progenitor galaxies of MW/M31-mass galaxies using the Feedback In Realistic Environments-2 cosmological simulations, including six isolated MW/M31-mass galaxies and six galaxies in Local Group (LG)-like pairs at z = 0. We examine main progenitor ‘formation’ based on two metrics: (1) transition from primarily ex-situ to in-situ stellar mass growth and (2) mass dominance compared to other progenitors. We find that the main progenitor of an MW/M31-mass galaxy emerged typically at z ∼ 3–4 ($11.6\! \! -\! \! 12.2\, \rm {Gyr}$ ago), while stars in the bulge region (inner 2 kpc) at z = 0 formed primarily in a single main progenitor at z ≲ 5 (${\lesssim} \! 12.6\, \rm {Gyr}$ ago). Compared with isolated hosts, the main progenitors of LG-like paired hosts emerged significantly earlier (Δz ∼ 2, $\Delta t\! \sim \! 1.6\, \rm {Gyr}$), with ∼4× higher stellar mass at all z ≳ 4 (${\gtrsim} \! 12.2\, \rm {Gyr}$ ago). This highlights the importance of environment in MW/M31-mass galaxy formation, especially at early times. On average, about 100 galaxies with $\rm {\it{ M}}_\rm {star}\! \gtrsim \! 10^5\, \rm {M}_\odot$ went into building a typical MW/M31-mass system. Thus, surviving satellites represent a highly incomplete census (by ∼5×) of the progenitor population.
Dynamic Interaction between Shared Autonomous Vehicles and Public Transit: A Competitive Perspective

The emerging autonomous vehicles (AV) can either supplement the public transportation (PT) system or be a competitor with it. This paper focuses on this competition in a hypothetical scenario--"if both AV and PT operators are profit-oriented," and uses an ABM to quantitatively evaluate the system performance in this competition from the perspectives of four stakeholders--AV operator, PT operator, passengers, and public authority. In our model, AV operator updates its supply by changing fleet sizes while PT by adjusting headways, and both use heuristic approaches to update supply in order to increase profits. We implement the model in the first-mile scenario in Tampines. In four regulation scenarios--two by two combinations regarding whether AV and PT are allowed to change supplies--we find that since AV can release the bus operator from low-demand routes, the competition can lead to higher profits of both, and higher system efficiency, simultaneously, rather than a one-sided loss-gain result. For PT, after supply updates, spatially the services are concentrated to short feeder routes directly to the subway station, and temporally concentrated to peak hours. For passengers, the competition reduces their travel time but increases their travel costs. Nonetheless, the generalized travel cost is still reduced when counting the value of time. For system efficiency and sustainability, bus supply adjustment can increase the bus average load and reduce total PCE, while the AV supply adjustment shows the opposite effect. For policy implications, the paper suggests that PT should be allowed to optimize its supply strategies under specific operation goal constraints, and AV operation should be regulated to relieve its externality on the system, including limiting the number of licenses, operation time, and service areas, which makes AV operate like a complementary mode to PT.
Density Fluctuations in the Solar Wind Based on Type III Radio Bursts Observed by Parker Solar Probe

Radio waves are strongly scattered in the solar wind, so that their apparent sources seem to be considerably larger and shifted than the actual ones. Since the scattering depends on the spectrum of density turbulence, a better understanding of the radio wave propagation provides indirect information on the relative density fluctuations, , at the effective turbulence scale length. Here, we analyzed 30 type III bursts detected by Parker Solar Probe (PSP). For the first time, we retrieved type III burst decay times, , between 1 and 10 MHz thanks to an unparalleled temporal resolution of PSP. We observed a significant deviation in a power-law slope for frequencies above 1 MHz when compared to previous measurements below 1 MHz by the twin-spacecraft Solar TErrestrial RElations Observatory (STEREO) mission. We note that altitudes of radio bursts generated at 1 MHz roughly coincide with an expected location of the Alfvén point, where the solar wind becomes super-Alfvénic. By comparing PSP observations and Monte Carlo simulations, we predict relative density fluctuations, ϵ, at the effective turbulence scale length at radial distances between 2.5 and 14 to range from 0.22 to 0.09. Finally, we calculated relative density fluctuations, ϵ, measured in situ by PSP at a radial distance from the Sun of 35.7 during perihelion #1, and perihelion #2 to be 0.07 and 0.06, respectively. It is in a very good agreement with previous STEREO predictions ( ) obtained by remote measurements of radio sources generated at this radial distance.
Cordial elements and dimensions of affine Deligne–Lusztig varieties

Abstract The affine Deligne–Lusztig variety $X_w(b)$ in the affine flag variety of a reductive group ${\mathbf G}$ depends on two parameters: the $\sigma $ -conjugacy class $[b]$ and the element w in the Iwahori–Weyl group $\tilde {W}$ of ${\mathbf G}$ . In this paper, for any given $\sigma $ -conjugacy class $[b]$ , we determine the nonemptiness pattern and the dimension formula of $X_w(b)$ for most $w \in \tilde {W}$ .
Detectability of embedded protoplanets from hydrodynamical simulations

We predict magnitudes for young planets embedded in transition discs, still affected by extinction due to material in the disc. We focus on Jupiter-sized planets at a late stage of their formation, when the planet has carved a deep gap in the gas and dust distributions and the disc starts to being transparent to the planet flux in the infrared (IR). Column densities are estimated by means of three-dimensional hydrodynamical models, performed for several planet masses. Expected magnitudes are obtained by using typical extinction properties of the disc material and evolutionary models of giant planets. For the simulated cases located at 5.2 au in a disc with a local unperturbed surface density of 127 $\mathrm{g} \, \mathrm{cm}^{-2}$, a 1MJ planet is highly extinct in the J, H, and Kbands, with predicted absolute magnitudes ≥ 50 mag. In the L and Mbands, extinction decreases, with planet magnitudes between 25 and 35 mag. In the Nband, due to the silicate feature on the dust opacities, the expected magnitude increases to ∼40 mag. For a 2MJ planet, the magnitudes in the J, H, and Kbands are above 22 mag, while for the L, M, and Nbands, the planet magnitudes are between 15 and 20 mag. For the 5MJ planet, extinction does not play a role in any IR band, due to its ability to open deep gaps. Contrast curves are derived for the transition discs in CQ Tau, PDS 70, HL Tau, TW Hya, and HD 163296. Planet mass upper limits are estimated for the known gaps in the last two systems.
In vitro vascularized tumor platform for modeling tumor‐vasculature interactions of inflammatory breast cancer

Inflammatory breast cancer (IBC), a rare form of breast cancer associated with increased angiogenesis and metastasis, is largely driven by tumor‐stromal interactions with the vasculature and the extracellular matrix (ECM). However, there is currently a lack of understanding of the role these interactions play in initiation and progression of the disease. In this study, we developed the first three‐dimensional, in vitro, vascularized, microfluidic IBC platform to quantify the spatial and temporal dynamics of tumor‐vasculature and tumor‐ECM interactions specific to IBC. Platforms consisting of collagen type 1 ECM with an endothelialized blood vessel were cultured with IBC cells, MDA‐IBC3 (HER2+) or SUM149 (triple negative), and for comparison to non‐IBC cells, MDA‐MB‐231 (triple negative). Acellular collagen platforms with endothelialized blood vessels served as controls. SUM149 and MDA‐MB‐231 platforms exhibited a significantly (p < .05) higher vessel permeability and decreased endothelial coverage of the vessel lumen compared to the control. Both IBC platforms, MDA‐IBC3 and SUM149, expressed higher levels of vascular endothelial growth factor (p < .05) and increased collagen ECM porosity compared to non‐IBCMDA‐MB‐231 (p < .05) and control (p < .01) platforms. Additionally, unique to the MDA‐IBC3 platform, we observed progressive sprouting of the endothelium over time resulting in viable vessels with lumen. The newly sprouted vessels encircled clusters of MDA‐IBC3 cells replicating a key feature of in vivo IBC. The IBC in vitro vascularized platforms introduced in this study model well‐described in vivo and clinical IBC phenotypes and provide an adaptable, high throughput tool for systematically and quantitatively investigating tumor‐stromal mechanisms and dynamics of tumor progression.
Explaining the Explainer: A First Theoretical Analysis of LIME

Machine learning is used more and more often for sensitive applications, sometimes replacing humans in critical decision-making processes. As such, interpretability of these algorithms is a pressing need. One popular algorithm to provide interpretability is LIME (Local Interpretable Model-Agnostic Explanation). In this paper, we provide the first theoretical analysis of LIME. We derive closed-form expressions for the coefficients of the interpretable model when the function to explain is linear. The good news is that these coefficients are proportional to the gradient of the function to explain: LIME indeed discovers meaningful features. However, our analysis also reveals that poor choices of parameters can lead LIME to miss important features.
Adaptive Anomaly Detection for IoT Data in Hierarchical Edge Computing

Advances in deep neural networks (DNN) greatly bolster real-time detection of anomalous IoT data. However, IoT devices can barely afford complex DNN models due to limited computational power and energy supply. While one can offload anomaly detection tasks to the cloud, it incurs long delay and requires large bandwidth when thousands of IoT devices stream data to the cloud concurrently. In this paper, we propose an adaptive anomaly detection approach for hierarchical edge computing (HEC) systems to solve this problem. Specifically, we first construct three anomaly detection DNN models of increasing complexity, and associate them with the three layers of HEC from bottom to top, i.e., IoT devices, edge servers, and cloud. Then, we design an adaptive scheme to select one of the models based on the contextual information extracted from input data, to perform anomaly detection. The selection is formulated as a contextual bandit problem and is characterized by a single-step Markov decision process, with an objective of achieving high detection accuracy and low detection delay simultaneously. We evaluate our proposed approach using a real IoT dataset, and demonstrate that it reduces detection delay by 84% while maintaining almost the same accuracy as compared to offloading detection tasks to the cloud. In addition, our evaluation also shows that it outperforms other baseline schemes.
Parameter learning and fractional differential operators: application in image regularization and decomposition

In this paper, we focus on learning optimal parameters for PDE-based image regularization and decomposition. First we learn the regularization parameter and the differential operator for gray-scale image denoising using the fractional Laplacian in combination with a bilevel optimization problem. In our setting the fractional Laplacian allows the use of Fourier transform, which enables the optimization of the denoising operator. We prove stable and explainable results as an advantage in comparison to other machine learning approaches. The numerical experiments correlate with our theoretical model setting and show a reduction of computing time in contrast to the ROF model. Second we introduce a new image decomposition model with the fractional Laplacian and the Riesz potential. We provide an explicit formula for the unique solution and the numerical experiments illustrate the efficiency.
A Converse Bound Characterization on Zero-Delay Indirect RDF for Vector Gauss-Markov Sources.

We consider a zero-delay remote source coding problem where a hidden source modeled as a time-invariant vector-valued Gauss-Markov process is partially observed to an encoder whereas the performance criterion is the long-term mean squared-error (MSE) distortion between the hidden process and the reconstructed process. For this setup, we characterize a converse bound on the minimum long-term average length of all causal prefix-free codes. This characterization is then used to derive a novel closed form expression for stationary scalar-valued Gaussian processes. We justify the generality of our solution by revealing well-known special cases from it like the widely used analytical expression of sequential or nonanticipative RDF derived for "fully observable" stationary scalar-valued Gauss-Markov processes with MSE distortion constraint in [1], from which we also compute analytically the rate-loss (RL) gap from our solution.
Limitation of simple np-n tunnel junction based LEDs grown by metal-organic vapor phase epitaxy

We show evidence that tunnel junctions (TJs) in GaN grown by metal-organic vapor phase epitaxy are dominated by defect level-assisted tunneling. This is in contrast with the common belief that highly doped layers (>1020 cm−3) are required to narrow the TJ space charge region and promote the band-to-band tunneling. Our conclusion stems from the study and the review of the major doping limitations of carefully optimized p++ and n++ layers. The secondary ions mass spectroscopy profiles of GaN based TJ LEDs show a strong oxygen concentration located close to the p++/n++ interface, typical for three dimensional growth. In addition, considering the doping limitation asymmetry and Mg carry-over, our simulations indicate a depletion region of more than 10 nm which is buried in a rough and defective n++ layer. However, decent electrical characteristics of the studied TJ based LEDs are obtained, with a low penalty voltage of 1.1 V and a specific differential resistance of about 10–2 Ω. cm2 at 20 mA. This indicates that a common TJ could be greatly optimized by using a moderate doping (∼1019 cm−3) while intentionally introducing local defects within the TJ.
The Effectiveness of Supervised Machine Learning Algorithms in Predicting Software Refactoring

Refactoring is the process of changing the internal structure of software to improve its quality without modifying its external behavior. Empirical studies have repeatedly shown that refactoring has a positive impact on the understandability and maintainability of software systems. However, before carrying out refactoring activities, developers need to identify refactoring opportunities. Currently, refactoring opportunity identification heavily relies on developers’ expertise and intuition. In this paper, we investigate the effectiveness of machine learning algorithms in predicting software refactorings. More specifically, we train six different machine learning algorithms (i.e., Logistic Regression, Naive Bayes, Support Vector Machine, Decision Trees, Random Forest, and Neural Network) with a dataset comprising over two million refactorings from 11,149 real-world projects from the Apache, F-Droid, and GitHub ecosystems. The resulting models predict 20 different refactorings at class, method, and variable-levels with an accuracy often higher than 90 percent. Our results show that (i) Random Forests are the best models for predicting software refactoring, (ii) process and ownership metrics seem to play a crucial role in the creation of better models, and (iii) models generalize well in different contexts.