Miracle-dz/UniFAR-FTUs huggingface dataset:
The dependence of the hierarchical distribution of star clusters on galactic environment

We use the angular two-point correlation function (TPCF) to investigate the hierarchical distribution of young star clusters in 12 local (3–18 Mpc) star-forming galaxies using star cluster catalogs obtained with the Hubble Space Telescope (HST) as part of the Treasury Program Legacy ExtraGalactic UV Survey. The sample spans a range of different morphological types, allowing us to infer how the physical properties of the galaxy affect the spatial distribution of the clusters. We also prepare a range of physically motivated toy models to compare with and interpret the observed features in the TPCFs. We find that, conforming to earlier studies, young clusters ($T \lesssim 10\, \mathrm{Myr}$) have power-law TPCFs that are characteristic of fractal distributions with a fractal dimension D2, and this scale-free nature extends out to a maximum scale lcorr beyond which the distribution becomes Poissonian. However, lcorr, and D2 vary significantly across the sample, and are correlated with a number of host galaxy physical properties, suggesting that there are physical differences in the underlying star cluster distributions. We also find that hierarchical structuring weakens with age, evidenced by flatter TPCFs for older clusters ($T \gtrsim 10\, \mathrm{Myr}$), that eventually converges to the residual correlation expected from a completely random large-scale radial distribution of clusters in the galaxy in $\sim 100 \, \mathrm{Myr}$. Our study demonstrates that the hierarchical distribution of star clusters evolves with age, and is strongly dependent on the properties of the host galaxy environment.
Correlation Clustering Reconstruction in Semi-Adversarial Models

Correlation Clustering is an important clustering problem with many applications. We study the reconstruction version of this problem in which one is seeking to reconstruct a latent clustering that has been corrupted by random noise and adversarial modiﬁcations. Concerning the latter, we study a standard “post-adversarial” model, in which adversarial modiﬁca-tions come after the noise, and also introduce and analyse a “pre-adversarial” model in which adversarial modiﬁcations come before the noise. Given an input coming from such a semi-adversarial generative model, the goal is to reconstruct almost perfectly and with high probability the latent clustering. We focus on the case where the hidden clusters have equal size and show the following. In the pre-adversarial setting, spectral algorithms are optimal, in the sense that they reconstruct all the way to the information-theoretic threshold beyond which no reconstruction is possible. In contrast, in the post-adversarial setting their ability to restore the hidden clusters stops before the threshold, but the gap is optimally ﬁlled by SDP-based algorithms.
Analytical Modeling of $I-V$ characteristics using 2D Poisson Equations in AlN/$\beta$-Ga$_2$O$_3$ HEMT

In this paper, physics-based analytical models using two-dimensional (2D) Poisson equations for surface potential, channel potential, electric field, and drain current in AlN/βGa2O3 high electron mobility transistor (HEMT) are presented. The analytical expression of different quantities is achieved based on full depletion approximation of the AlN barrier layer and polarization charge induced unified two-dimensional electron gas (2DEG) charge density model. For the validation of the developed model, results are compared with 2D numerical simulation results and a good consistency is found between the two. The drain current model is also validated with experimental results of a similar dimension device. The developed model can be a good reference for different β-Ga2O3-based HEMTs.
Generating Music and Generative Art from Brain activity

9 CHAPTER 1. PROBLEM DESCRIPTION . 10 1.1. Approach and problem statement . 10 1.2. Justification . 10 1.3. Objectives . 11 1.4. Expected results . 11 1.5. Scope and delimitation . 11 CHAPTER 2. FRAME OF REFERENCE . 12 2.1 Theoretical framework . 12 2.1.1 Brain-Computer Interfaces (BCI). 12 2.1.2 Brain waves classification. 13 2.1.3 Music . 14 2.1.4 Generative Art . 15 2.2 State of the Art . 15 2.2.1 Generative Art . 15 2.2.2 BCI and Music . 18 2.2. 3 Art Installations . 22 CHAPTER 3. BCI DEVICE SELECTION . 23 3.1. Mindwave Mobile . 23 3.2. Emotiv EPOC Model 1.0 . 27 3.3. Ultracortex "Mark IV" EEG Headset . 29 CHAPTER 4. SOFTWARE DEVELOPMENT . 31 4.1. Software Architecture. 32 4.2. Musical component . 33 4.3. Generative Art component . 36 CHAPTER 5. CEREBRUM: INTERACTIVE INSTALLATION DESIGN . 44 5.1. Electronic component . 46 CHAPTER 6. DESIGN AND TESTS EXECUTION . 50 6.1. BCI testing and Signal Processing . 50 6.2. Static testing. 67 6.3. Usability testing . 76 6.4. Results analysis . 76 CHAPTER 7. CONCLUSIONS AND FUTURE WORK. 78 REFERENCES . 80
SnowflakeNet: Point Cloud Completion by Snowflake Point Deconvolution with Skip-Transformer

Point cloud completion aims to predict a complete shape in high accuracy from its partial observation. However, previous methods usually suffered from discrete nature of point cloud and unstructured prediction of points in local regions, which makes it hard to reveal fine local geometric details on the complete shape. To resolve this issue, we propose SnowflakeNet with Snowflake Point Deconvolution (SPD) to generate the complete point clouds. The SnowflakeNet models the generation of complete point clouds as the snowflake-like growth of points in 3D space, where the child points are progressively generated by splitting their parent points after each SPD. Our insight of revealing detailed geometry is to introduce skip-transformer in SPD to learn point splitting patterns which can fit local regions the best. Skip-transformer leverages attention mechanism to summarize the splitting patterns used in the previous SPD layer to produce the splitting in the current SPD layer. The locally compact and structured point cloud generated by SPD is able to precisely capture the structure characteristic of 3D shape in local patches, which enables the network to predict highly detailed geometries, such as smooth regions, sharp edges and corners. Our experimental results outperform the state-of-the-art point cloud completion methods under widely used benchmarks. Code will be available at https://github.com/AllenXiangX/SnowflakeNet.
Visualizing Variable Importance and Variable Interaction Effects in Machine Learning Models

Abstract Variable importance, interaction measures, and partial dependence plots are important summaries in the interpretation of statistical and machine learning models. In this article, we describe new visualization techniques for exploring these model summaries. We construct heatmap and graph-based displays showing variable importance and interaction jointly, which are carefully designed to highlight important aspects of the fit. We describe a new matrix-type layout showing all single and bivariate partial dependence plots, and an alternative layout based on graph Eulerians focusing on key subsets. Our new visualizations are model-agnostic and are applicable to regression and classification supervised learning settings. They enhance interpretation even in situations where the number of variables is large. Our R package vivid (variable importance and variable interaction displays) provides an implementation. Supplementary files for this article are available online.
An empirical investigation into audio pipeline approaches for classifying bird species

This paper is an investigation into aspects of an audio classification pipeline that will be appropriate for the monitoring of bird species on edges devices. These aspects include transfer learning, data augmentation and model optimization. The hope is that the resulting models will be good candidates to deploy on edge devices to monitor bird populations. Two classification approaches will be taken into consideration, one which explores the effectiveness of a traditional Deep Neural Network(DNN) and another that makes use of Convolutional layers. This study aims to contribute empirical evidence of the merits and demerits of each approach.
MotionInput v2.0 supporting DirectX: A modular library of open-source gesture-based machine learning and computer vision methods for interacting and controlling existing software with a webcam

Touchless computer interaction has become an important consideration during the COVID-19 pandemic period. Despite progress in machine learning and computer vision that allows for advanced gesture recognition, an integrated collection of such open-source methods and a user-customisable approach to utilising them in a low-cost solution for touchless interaction in existing software is still missing. In this paper, we introduce the MotionInput v2.0 application. This application utilises published open-source libraries and additional gesture definitions developed to take the video stream from a standard RGB webcam as input. It then maps human motion gestures to input operations for existing applications and games. The user can choose their own preferred way of interacting from a series of motion types, including single and bi-modal hand gesturing, full-body repetitive or extremities-based exercises, head and facial movements, eye tracking, and combinations of the above. We also introduce a series of bespoke gesture recognition classifications as DirectInput triggers, including gestures for idle states, auto calibration, depth capture from a 2D RGB webcam stream and tracking of facial motions such as mouth motions, winking, and head direction with rotation. Three use case areas assisted the development of the modules: creativity software, office and clinical software, and gaming software. A collection of open-source libraries has been integrated and provide a layer of modular gesture mapping on top of existing mouse and keyboard controls in Windows via DirectX. With ease of access to webcams integrated into most laptops and desktop computers, touchless computing becomes more available with MotionInput v2.0, in a federated and locally processed method.
WALLABY pre-pilot survey: H i content of the Eridanus supergroup

We present observations of the Eridanus supergroup obtained with the Australian Square Kilometre Array Pathfinder (ASKAP) as part of the pre-pilot survey for the Widefield ASKAP L-band Legacy All-sky Blind surveY (WALLABY). The total number of detected H i sources is 55, of which 12 are background galaxies not associated with the Eridanus supergroup. Two massive H i clouds are identified and large H i debris fields are seen in the NGC 1359 interacting galaxy pair, and the face-on spiral galaxy NGC 1385. We describe the data products from the source finding algorithm and present the basic parameters. The presence of distorted H i morphology in all detected galaxies suggests ongoing tidal interactions within the subgroups. The Eridanus group has a large fraction of H i-deficient galaxies as compared to previously studied galaxy groups. These H i-deficient galaxies are not found at the centre of the group. We find that galaxies in the Eridanus supergroup do not follow the general trend of the atomic gas fraction versus stellar mass scaling relation, which indicates that the scaling relation changes with environmental density. In general, the majority of these galaxies are actively forming stars.
Characterization of groundwater composition in Punjab state with special emphasis on uranium content, speciation and mobility

Abstract Groundwaters (borewell and handpump) were sampled from two districts (Bathinda and Mansa) of Punjab state and analyzed for their major ionic concentrations and uranium isotope compositions in order to assess the possible origins of the waters and water–rock interactions that occurred in the deep aquifer system. The major ionic concentrations of waters were plotted on a Piper diagram and grouped into four dominant hydrochemical facies as (Na+K)-SO4+Cl type (69% – 73%), (Ca+Mg)-SO4+Cl type (6% – 21%), (Ca+Mg)–HCO3 type (4% – 6%) and (Na+K)-HCO3 type (2% – 19%). It was observed that mobility of uranium in groundwater was very much influenced by TDS (total dissolved solids). To investigate the various mechanisms for deriving the elevated uranium concentrations in groundwater, 234U/238U activity ratios (ARs) were calculated using the determined activity levels of 234U and 238U. The mean ARs was found to be near unity (i.e. secular equilibrium) in the study regions confirmed that uranium in groundwaters was mainly resulted from its host/parent rocks through weathering processes. The concentration of HCO3− in ground water showed one order of magnitude higher than the total dissolved SiO2 indicates that carbonate weathering was the dominant process due to major water–rock interaction. The uranium speciation in groundwaters was investigated by an equilibrium model calculation using MEDUSA (make equilibrium diagrams using sophisticated algorithms) under the influence of redox conditions and complexant concentration. At the observed range of pH values, the predominant redox speciation of uranium was observed as hydroxo-carbonato complexes of (UO2)2(CO3)(OH)3− and hydroxyl complexes of UO2(OH)3− which might be caused for increasing the solubility of uranium. Due to very low concentration of phosphate in groundwater, its effects on U(VI)-aqueous speciation was negligible.
Several Weighted Hardy, Hardy-Sobolev-Maz'ya and Related Inequalities

Abstract. In this paper we establish several Hardy and Hardy-Sobolev type inequalities with homogeneous weights on the first orthant R ∗ := {(x1, . . . , xn) : x1 > 0, . , xn > 0}. We then use some of them to produce Hardy type inequalities with remainder terms. Furthermore, we obtain some interpolation inequalities and Maz’ya type inequalities with remainder terms with the help of Maz’ya inequality and Sobolev inequality of Cabré and Ros-Orton on the upper half space Rn+ := {x = (x1, . . . , xn)|xn > 0}.
Hierarchical Latent Relation Modeling for Collaborative Metric Learning

Collaborative Metric Learning (CML) recently emerged as a powerful paradigm for recommendation based on implicit feedback collaborative filtering. However, standard CML methods learn fixed user and item representations, which fails to capture the complex interests of users. Existing extensions of CML also either ignore the heterogeneity of user-item relations, i.e. that a user can simultaneously like very different items, or the latent item-item relations, i.e. that a user’s preference for an item depends, not only on its intrinsic characteristics, but also on items they previously interacted with. In this paper, we present a hierarchical CML model that jointly captures latent user-item and item-item relations from implicit data. Our approach is inspired by translation mechanisms from knowledge graph embedding and leverages memory-based attention networks. We empirically show the relevance of this joint relational modeling, by outperforming existing CML models on recommendation tasks on several real-world datasets. Our experiments also emphasize the limits of current CML relational models on very sparse datasets.
Recurrent neural network-based Internal Model Control of unknown nonlinear stable systems

Owing to their superior modeling capabilities, gated Recurrent Neural Networks (RNNs), such as Gated Recurrent Units (GRUs) and Long Short-Term Memory networks (LSTMs), have become popular tools for learning dynamical systems. This paper aims to discuss how these networks can be adopted for the synthesis of Internal Model Control (IMC) architectures. To this end, a ﬁrst gated RNN is used to learn a model of the unknown input-output stable plant. Then, another gated RNN approximating the model inverse is trained. The proposed scheme is able to cope with the saturation of the control variables, and it can be deployed on low-power embedded controllers since it does not require any online computation. The approach is then tested on the Quadruple Tank benchmark system, resulting in satisfactory closed-loop performances.
Known operator learning and hybrid machine learning in medical imaging—a review of the past, the present, and the future

In this article, we perform a review of the state-of-the-art of hybrid machine learning in medical imaging. We start with a short summary of the general developments of the past in machine learning and how general and specialized approaches have been in competition in the past decades. A particular focus will be the theoretical and experimental evidence pro and contra hybrid modelling. Next, we inspect several new developments regarding hybrid machine learning with a particular focus on so-called known operator learning and how hybrid approaches gain more and more momentum across essentially all applications in medical imaging and medical image analysis. As we will point out by numerous examples, hybrid models are taking over in image reconstruction and analysis. Even domains such as physical simulation and scanner and acquisition design are being addressed using machine learning grey box modelling approaches. Towards the end of the article, we will investigate a few future directions and point out relevant areas in which hybrid modelling, meta learning, and other domains will likely be able to drive the state-of-the-art ahead.
Localized Graph Collaborative Filtering

User-item interactions in recommendations can be naturally de-noted as a user-item bipartite graph. Given the success of graph neural networks (GNNs) in graph representation learning, GNN-based C methods have been proposed to advance recommender systems. These methods often make recommendations based on the learned user and item embeddings. However, we found that they do not perform well wit sparse user-item graphs which are quite common in real-world recommendations. Therefore, in this work, we introduce a novel perspective to build GNN-based CF methods for recommendations which leads to the proposed framework Localized Graph Collaborative Filtering (LGCF). One key advantage of LGCF is that it does not need to learn embeddings for each user and item, which is challenging in sparse scenarios. Alternatively, LGCF aims at encoding useful CF information into a localized graph and making recommendations based on such graph. Extensive experiments on various datasets validate the effectiveness of LGCF especially in sparse scenarios. Furthermore, empirical results demonstrate that LGCF provides complementary information to the embedding-based CF model which can be utilized to boost recommendation performance.
BioCode: A Data-Driven Approach for Learning How Biological Networks Grow

Network growth models, such as duplication-based models, preferential attachment, Kronecker model have been used for tasks such as null model representation, anomaly detection, testing algorithm, and developing and understanding of various mechanistic biological growth processes. Nevertheless, new models must be frequently developed as new networks are studied. Probabilistic models of biological network growth have been extensively studied as idealized representations of network evolution. Models, such as the Kronecker model, duplication-based models, and preferential attachment models have been used for tasks such as representing null models, detecting anomalies, algorithm testing, and developing an understanding of various mechanistic biological growth processes. However, developing a new growth model to fit the observed properties of a network is a difficult task, and as new networks are studied, new models must constantly be developed. Here, we present a framework, called BioCode, for the automatic discovery of novel biological growth models that match user-specified topological features in undirected graphs. BioCode introduces a set of basic commands that are general enough to encode several previously developed models. Coupling this formal representation with an optimization approach, we show that BioCode is able to discover models for protein interaction networks, gene regulatory networks, and biological collaboration networks that closely match properties such as the degree distribution, the clustering coefficient, and assortativity that are observed in real biological networks of these classes. Additional tests on simulated networks show that the models learned by BioCode generate distributions of biological graphs with similar variance as existing models for these classes.
A Parallel Algorithm for Minimum Cost Submodular Cover

In a minimum cost submodular cover problem (MinSMC), given a monotone non-decreasing submodular function f : 2 V → Z + , a cost function c : V → R + , an integer k ≤ f ( V ), the goal is to ﬁnd a subset A ⊆ V with the minimum cost such that f ( A ) ≥ k . MinSMC has a lot of applications in machine learning and data mining. In this paper, we design a parallel algorithm for MinSMC which obtains a solution with approximation ratio at most H (min { ∆ ,k } ) 1 − 5 ε with probability 1 − 3 ε in O ( log m log n log 2 mn ε 4 ) rounds, where ∆ = max v ∈ V f ( v ), H ( · ) is the Hamornic number, n = f ( V ), m = | V | and ε is a constant in (0 , 15 ). This is the ﬁrst paper obtaining a parallel algorithm for the weighted version of the MinSMC problem with an approximation ratio arbitrarily close to H (min { ∆ , k } ).
A Generalizable Model-and-Data Driven Approach for Open-Set RFF Authentication

Radio-frequency fingerprints (RFFs) are promising solutions for realizing low-cost physical layer authentication. Machine learning-based methods have been proposed for RFF extraction and discrimination. However, most existing methods are designed for the closed-set scenario where the set of devices is remains unchanged. These methods cannot be generalized to the RFF discrimination of unknown devices. To enable the discrimination of RFF from both known and unknown devices, we propose a new end-to-end deep learning framework for extracting RFFs from raw received signals. The proposed framework comprises a novel preprocessing module, called neural synchronization (NS), which incorporates the data-driven learning with signal processing priors as an inductive bias from communication-model based processing. Compared to traditional carrier synchronization techniques, which are static, this module estimates offsets by two learnable deep neural networks jointly trained by the RFF extractor. Additionally, a hypersphere representation is proposed to further improve the discrimination of RFF. Theoretical analysis shows that such a data-and-model framework can better optimize the mutual information between device identity and the RFF, which naturally leads to better performance. Experimental results verify that the proposed RFF significantly outperforms purely data-driven DNN-design and existing handcrafted RFF methods in terms of both discrimination and network generalizability.