Miracle-dz/UniFAR-FTUs huggingface dataset:
Graph Auto-Encoders for Financial Clustering

Deep learning has shown remarkable results on Euclidean data (e.g. audio, images, text) however this type of data is limited in the amount of relational information it can hold. In mathematics we can model more general relational data in a graph structure while retaining Euclidean data as associated node or edge features. Due to the ubiquity of graph data, and its ability to hold multiple dimensions of information, graph deep learning has become a fast emerging field. We look at applying and optimising graph deep learning on a finance graph to produce more informed clusters of companies. Having clusters produced from multiple streams of data can be highly useful in quantitative finance; not only does it allow clusters to be tailored to the specific task but the culmination of multiple streams allows for cross source pattern recognition that would have otherwise gone unnoticed. This can provide financial institutions with an edge over competitors which is crucial in the heavily optimised world of trading. In this paper we use news co-occurrence and stock price for our data combination. We optimise our model to achieve an average testing precision of 78% and find a clear improvement in clustering capabilities when dual data sources are used; cluster purity rises from 32% for just vertex data and 42% for just edge data to 64% when both are used in comparisons to ground-truth Bloomberg clusters. The framework we provide utilises unsupervised learning which we view as key for future work due to the volume of unlabelled data in financial markets.
Nonrelativistic energy of tritium-containing hydrogen molecule isotopologues

The nonrelativistic energy of low lying rovibrational levels of HT, DT, and T is determined to an absolute accuracy of using the variational method with the four-body nonadiabatic James-Coolidge functions. The new results increase the accuracy of the nonrelativistic component of the energy levels by several orders of magnitude. As a consequence, the total transition energies are improved by at least an order of magnitude. GRAPHICAL ABSTRACT
Few-shot Deep Representation Learning based on Information Bottleneck Principle

In a standard anomaly detection problem, a detection model is trained in an unsupervised setting, under an assumption that the samples were generated from a single source of normal data. In practice, however, normal data often consist of multiple classes. In such settings, learning to diﬀerentiate between normal instances and anomalies among discrepancies between normal classes without large-scale labeled data presents a signiﬁcant challenge. In this work, we attempt to overcome this challenge by preparing few examples from each normal class, which is not excessively costly. The above setting can also be described as a few-shot learning for multiple, normal classes, with the goal of learning a useful representation for anomaly detection. In order to utilize the limited labeled examples in training, we integrate the inter-class distances among the labeled examples in the deep feature space into the MAP loss. We derive their relations from an information-theoretic principle. Our empirical study shows that the proposed model improves the segmentation of normal classes in the deep feature space which contributes to identifying the anomaly class examples.
Job Recommender Systems: A Review

This paper provides a review of the job recommender system (JRS) literature published in the past decade (2011-2021). Compared to previous literature reviews, we put more emphasis on contributions that incorporate the temporal and reciprocal nature of job recommendations. Previous studies on JRS suggest that taking such views into account in the design of the JRS can lead to improved model performance. Also, it may lead to a more uniform distribution of candidates over a set of similar jobs. We also consider the literature from the perspective of algorithm fairness. Here we find that this is rarely discussed in the literature, and if it is discussed, many authors wrongly assume that removing the discriminatory feature would be sufficient. With respect to the type of models used in JRS, authors frequently label their method as `hybrid'. Unfortunately, they thereby obscure what these methods entail. Using existing recommender taxonomies, we split this large class of hybrids into subcategories that are easier to analyse. We further find that data availability, and in particular the availability of click data, has a large impact on the choice of method and validation. Last, although the generalizability of JRS across different datasets is infrequently considered, results suggest that error scores may vary across these datasets.
Diagnosis of sickle cell anemia using AutoML on UV-Vis absorbance spectroscopy data

Sickle cell anemia is a genetic disorder that is widespread in many regions of the world. Early diagnosis through screening and preventive treatments are known to reduce mortality in the case of sickle cell disease (SCD). In addition, the screening of individuals with the largely asymptomatic condition of sickle cell trait (SCT) is necessary to curtail the genetic propagation of the disease. However, the cost and complexity of conventional diagnostic methods limit the feasibility of early diagnosis of SCD and SCT in resource-limited areas worldwide. Recently, our group developed a low-cost UVVis absorbance spectroscopy based diagnostic test for SCD and SCT. Here, we propose an AutoML based approach to classify the raw spectra data obtained from the developed UVVis spectroscopy technique with high accuracy. The proposed approach can detect the presence of sickle hemoglobin with 100% sensitivity and 93.84% specificity. This study demonstrates the potential utility of the machine learning-based absorbance spectroscopy test for deployment in mass screening programs in resource-limited settings.
Confounder Identification-free Causal Visual Feature Learning

Confounders in deep learning are in general detrimental to model's generalization where they infiltrate feature representations. Therefore, learning causal features that are free of interference from confounders is important. Most previous causal learning based approaches employ back-door criterion to mitigate the adverse effect of certain specific confounder, which require the explicit identification of confounder. However, in real scenarios, confounders are typically diverse and difficult to be identified. In this paper, we propose a novel Confounder Identification-free Causal Visual Feature Learning (CICF) method, which obviates the need for identifying confounders. CICF models the interventions among different samples based on front-door criterion, and then approximates the global-scope intervening effect upon the instance-level interventions from the perspective of optimization. In this way, we aim to find a reliable optimization direction, which avoids the intervening effects of confounders, to learn causal features. Furthermore, we uncover the relation between CICF and the popular meta-learning strategy MAML, and provide an interpretation of why MAML works from the theoretical perspective of causal learning for the first time. Thanks to the effective learning of causal features, our CICF enables models to have superior generalization capability. Extensive experiments on domain generalization benchmark datasets demonstrate the effectiveness of our CICF, which achieves the state-of-the-art performance.
Leading-Edge Vortex Structure over Multiple Revolutions of a Rotating Wing

T HE flow produced by a bio-inspired flapping wing is unsteady, three-dimensional, and dominated by separated flow and strong vortices. To better understand which structures in this complicated flow are most responsible for the production of lift and drag, several canonical problems have been designed to model portions of a natural wing stroke. These include both twoand threedimensional transient, reciprocating, and quasi-steady variations of pitching, plunging, translating, and rotatingwings. The rotatingwing model is designed to represent the translational phase of an insect wing stroke. In this model, the wing rotates about its root in a propeller-likemotion at a fixed angle of attack. The spanwisevelocity and pressure gradients that exist on an insect wing due to forward/aft sweep about the wing root are preserved, along with the threedimensionality induced by the root and tip vortices. The result is a relatively simple flowfield that preserves the most important characteristics of the translational phase of an entomological wing stroke. Some of the earliest rotating-wing experiments were performed by Usherwood and Ellington on hawkmoth wings at Reynolds numbers O 10 [1,2]. In these experiments, the wing’s lift coefficient was found to decrease as Reynolds number increased from 10,000 to 50,000, and it was postulated that this change in lift production was due to the formation of aweaker leading-edge vortex (LEV) at higher Reynolds numbers. Later, Ozen and Rockwell used particle image velocimetry (PIV) to characterize the steady-state flow structure on a low-aspect-ratio rotating plate at fixed angles of attack between 30 and 75 deg. They observed a stable LEV for a range of Reynolds numbers between 3600 and 14,500 [3]. However, other experiments focusing on the start of a rotating wing accelerating to Reynolds numbers between 10,000 and 60,000 revealed an LEV that formed and shed early in the wing stroke, resulting in a high-lift transient, after which lift dropped to about half of themaximum value [4–6]. At lower Reynolds numbersO 1000 , flow visualizations and PIV have demonstrated spanwise flow on a rotatingwing, and an attached LEV during wing acceleration that later burst over the outboard half of the wing during deceleration [7–9]. The objective of the work presented here is to identify the formation, structure, and possible separation of the leading-edge vortex at the beginning of thewing stroke (i.e., within the first 90 deg of wing rotation) as well as after long convective times (i.e., for wing strokes greater than 90 degrees including multiple revolutions). To this end, flow visualization is performed for threewing revolutions to analyze the vortex structure and the location of the burst point. In addition, unsteady lift and drag measurements are acquired for two revolutions to relate the flow structure to the aerodynamic forces produced by the wing.
QMagFace: Simple and Accurate Quality-Aware Face Recognition

In this work, we propose QMagFace, a simple and effective face recognition solution (QMagFace) that combines a quality-aware comparison score with a recognition model based on a magnitude-aware angular margin loss. The proposed approach includes model-specific face image qualities in the comparison process to enhance the recognition performance under unconstrained circumstances. Exploiting the linearity between the qualities and their comparison scores induced by the utilized loss, our quality-aware comparison function is simple and highly generalizable. The experiments conducted on several face recognition databases and benchmarks demonstrate that the introduced quality-awareness leads to consistent improvements in the recognition performance. Moreover, the proposed QMagFace approach performs especially well under challenging circumstances, such as cross-pose, cross-age, or cross-quality. Consequently, it leads to state-of-the-art performances on several face recognition benchmarks, such as 98.50% on AgeDB, 83.95% on XQLFQ, and 98.74% on CFP-FP. The code for QMagFace is publicly available1.
Quantification of network structural dissimilarities based on graph embedding

Identifying and quantifying structural dissimilarities between complex networks is a fundamental and challenging problem in network science. Previous network comparison methods are based on the structural features, such as the length of shortest path, degree and graphlet, which may only contain part of the topological information. Therefore, we propose an efficient network comparison method based on network embedding, i.e., \textit{DeepWalk}, which considers the global structural information. In detail, we calculate the distance between nodes through the vector extracted by \textit{DeepWalk} and quantify the network dissimilarity by spectral entropy based Jensen-Shannon divergences of the distribution of the node distances. Experiments on both synthetic and empirical data show that our method outperforms the baseline methods and can distinguish networks perfectly by only using the global embedding based distance distribution. In addition, we show that our method can capture network properties, e.g., average shortest path length and link density. Moreover, the experiments of modularity further implies the functionality of our method.
Active morphogenesis of patterned epithelial shells

Shape transformations of epithelial tissues in three dimensions, which are crucial for embryonic development or in vitro organoid growth, can result from active forces generated within the cytoskeleton of the epithelial cells. How the interplay of local differential tensions with tissue geometry and with external forces results in tissue-scale morphogenesis remains an open question. Here, we describe epithelial sheets as active viscoelastic surfaces and study their deformation under patterned internal tensions and bending moments. In addition to isotropic effects, we take into account nematic alignment in the plane of the tissue, which gives rise to shape-dependent, anisotropic active tensions and bending moments. We present phase diagrams of the mechanical equilibrium shapes of pre-patterned closed shells and explore their dynamical deformations. Our results show that a combination of nematic alignment and gradients in internal tensions and bending moments is sufficient to reproduce basic building blocks of epithelial morphogenesis, including fold formation, budding, neck formation, flattening, and tubulation.
System Dynamics Simulation and Adjustment on Coal Resource Utilization Based on Circular Economy

Coal resource utilization system, based on circular economy, is an opening, complex and time-varying system. It composes of subsystems such as industry, population, economy and environment etc. Taking the industry subsystem as prime object, this paper builds its system dynamics model adopting the system dynamics method based on full life cycle. The adjustment parameters such as utilization rate of by-product of mining, the proportion of coal use in each industry and waste recycling rate etc. were confirmed. As a case of coal utilization system being designed, thirteen development projects belong to two types of scenarios were run on the model. The efficacy coefficient method was applied to analyze the simulation results and determine the comparatively best project of coal resource utilization system for the first time. The results indicate that the C4 among them are the best project comparatively, its waste emission is the least, and the benefits of economy, environment and society are the maximum. Research shows that extending industrial chain, increasing production proportion of high added-value product and raising waste recycling rate are beneficial to decrease coal-mining quantity for unit output value, protect coal resource and achieve sustainable development, namely that adopting circular economy development pattern is undoubtedly worthy of advocating for sustainable development of economy, environment and society.
VaxNeRF: Revisiting the Classic for Voxel-Accelerated Neural Radiance Field

Neural Radiance Field (NeRF) is a popular method in data-driven 3D reconstruction. Given its simplicity and high quality rendering, many NeRF applications are being developed. However, NeRF's big limitation is its slow speed. Many attempts are made to speeding up NeRF training and inference, including intricate code-level optimization and caching, use of sophisticated data structures, and amortization through multi-task and meta learning. In this work, we revisit the basic building blocks of NeRF through the lens of classic techniques before NeRF. We propose Voxel-Accelearated NeRF (VaxNeRF), integrating NeRF with visual hull, a classic 3D reconstruction technique only requiring binary foreground-background pixel labels per image. Visual hull, which can be optimized in about 10 seconds, can provide coarse in-out field separation to omit substantial amounts of network evaluations in NeRF. We provide a clean fully-pythonic, JAX-based implementation on the popular JaxNeRF codebase, consisting of only about 30 lines of code changes and a modular visual hull subroutine, and achieve about 2-8x faster learning on top of the highly-performative JaxNeRF baseline with zero degradation in rendering quality. With sufficient compute, this effectively brings down full NeRF training from hours to 30 minutes. We hope VaxNeRF -- a careful combination of a classic technique with a deep method (that arguably replaced it) -- can empower and accelerate new NeRF extensions and applications, with its simplicity, portability, and reliable performance gains. Codes are available at https://github.com/naruya/VaxNeRF .
IMBENS: Ensemble Class-imbalanced Learning in Python

imbalanced-ensemble, abbreviated as imbens, is an open-source Python toolbox for leveraging the power of ensemble learning to address the class imbalance problem. It provides standard implementations of popular ensemble imbalanced learning (EIL) methods with extended features and utility functions. These ensemble methods include resampling-based, e.g., under/over-sampling, and reweighting-based, e.g., cost-sensitive learning. Beyond the implementation, we empower EIL algorithms with new functionalities like customizable resampling scheduler and verbose logging, thus enabling more flexible training and evaluating strategies. The package was developed under a simple, well-documented API design that follows scikit-learn for increased ease of use. imbens is released under the MIT open-source license and can be installed from Python Package Index (PyPI) or https://github.com/ZhiningLiu1998/imbalanced-ensemble.
Machine learning the 2D percolation model

We use deep-learning strategies to study the 2D percolation model on a square lattice. We employ standard image recognition tools with a multi-layered convolutional neural network. We test how well these strategies can characterise densities and correlation lengths of percolation states and whether the essential role of the percolating cluster is recognised.
On a Stochastic Fundamental Lemma and Its Use for Data-Driven MPC

—Data-driven Model Predictive Control (MPC) based on the fundamental lemma by Willems et al. has shown promising results for deterministic LTI systems subject to measurement noise. However, besides measurement noise, stochastic disturbances might also directly affect the dynamics. In this paper, we extend deterministic data-driven MPC towards stochastic systems. Leveraging Polynomial Chaos Expansions (PCE), we propose a novel variant of the fundamental lemma for stochastic LTI systems. This extension allows to predict future distributions of the behaviors for stochastic LTI systems based on the knowledge of previously recorded behavior realizations and based on the knowledge of the noise distribution. Finally, we propose a framework for data-driven stochastic MPC which is applicable to a large class of noise distributions with ﬁnite variance. Numerical examples illustrate the efﬁcacy of the proposed scheme.
Optimal Design of Experiments for Simulation-Based Inference of Mechanistic Acyclic Biological Networks

Biological signaling pathways based upon proteins binding to one another to relay a signal for genetic expression, such as the Bone Morphogenetic Protein (BMP) signaling pathway, can be modeled by mass action kinetics and conservation laws that result in non-closed form polynomial equations. Accurately determining parameters of biological pathways that represent physically relevant features, such as binding affinity of proteins and their associated uncertainty, presents a challenge for biological models lacking an explicit likelihood function. Additionally, parameterizing non-closed form biological models requires copious amounts of data from expensive perturbation-response experiments to fit model parameters. We present an algorithm (SBIDOEMAN) for determining optimal experiments and parameters of systems biology models with implicit likelihoods. We evaluate our algorithm using simulations of held-out true parameter values and demonstrate an improvement in the rate of accurate parameter inference over random and equidistant experimental designs when evaluated on two simple models of the BMP signaling pathway with an implicit likelihood function.
Stochastic Optimization under Distributional Drift

We consider the problem of minimizing a convex function that is evolving according to unknown and possibly stochastic dynamics, which may depend jointly on time and on the decision variable itself. Such problems abound in the machine learning and signal processing literature, under the names of concept drift, stochastic tracking, and performative prediction. We provide novel non-asymptotic convergence guarantees for stochastic algorithms with iterate averaging, focusing on bounds valid both in expectation and with high probability. The efficiency estimates we obtain clearly decouple the contributions of optimization error, gradient noise, and time drift. Notably, we identify a low drift-to-noise regime in which the tracking efficiency of the proximal stochastic gradient method benefits significantly from a step decay schedule. Numerical experiments illustrate our results.
Mending Neural Implicit Modeling for 3D Vehicle Reconstruction in the Wild

Reconstructing high-quality 3D objects from sparse, partial observations from a single view is of crucial importance for various applications in computer vision, robotics, and graphics. While recent neural implicit modeling methods show promising results on synthetic or dense data, they perform poorly on sparse and noisy real-world data. We discover that the limitations of a popular neural implicit model are due to lack of robust shape priors and lack of proper regularization. In this work, we demonstrate high-quality in-the-wild shape reconstruction using: (i) a deep encoder as a robust-initializer of the shape latent-code; (ii) regularized test-time optimization of the latent-code; (iii) a deep discriminator as a learned high-dimensional shape prior; (iv) a novel curriculum learning strategy that allows the model to learn shape priors on synthetic data and smoothly transfer them to sparse real world data. Our approach better captures the global structure, performs well on occluded and sparse observations, and registers well with the ground-truth shape. We demonstrate superior performance over state-of-the-art 3D object reconstruction methods on two real-world datasets.