Miracle-dz/UniFAR-FTUs huggingface dataset:
Dendritic Integration Based Quadratic Neural Networks Outperform Traditional Aritificial Ones

Incorporating biological neuronal properties into Artificial Neural Networks (ANNs) to enhance computational capabilities poses a formidable challenge in the field of machine learning. Inspired by recent findings indicating that dendrites adhere to quadratic integration rules for synaptic inputs, we propose a novel ANN model, Dendritic Integration-Based Quadratic Neural Network (DIQNN). This model shows superior performance over traditional ANNs in a variety of classification tasks. To reduce the computational cost of DIQNN, we introduce the Low-Rank DIQNN, while we find it can retain the performance of the original DIQNN. We further propose a margin to characterize the generalization error and theoretically prove this margin will increase monotonically during training. And we show the consistency between generalization and our margin using numerical experiments. Finally, by integrating this margin into the loss function, the change of test accuracy is indeed accelerated. Our work contributes a novel, brain-inspired ANN model that surpasses traditional ANNs and provides a theoretical framework to analyze the generalization error in classification tasks.
Multilayer overlaps and correlations in the bank-firm credit network of Spain

We investigate the structural dependencies in the bank-firm credit market of Spain in the year 2007 under a multilayer network perspective. In particular, we decompose the original bipartite network into different layers representing different industrial sectors. We then study the correlations between layers based on normalized measures of overlaps of links and weights of banks between layers. To assess the statistical significance of such correlations, we compare the observed values with the expected ones obtained from random graph models and so-called configuration models. While the former impose only global constraints, i.e. the total degree or the total strength in single layers, the latter preserve the intrinsic heterogeneity of the data in the form of its observed degree sequence and/or strength sequence in single layers. We find that, first, the raw dependencies between layers of the observed network are highly heterogeneous. Second, when evaluated against the null models, the rescaled correlations after filtering out the effects of the global constraints typically display no significant difference to the observed correlations. Similarly, in the binary version, almost all correlations are still present after subtracting the effects of the observed degree sequences in all layers while the observed correlations are only partially explained by the local constraints maintained in the weighted configuration models. Under all null models, we find that the multilayer credit network has a significant, non-random structure of correlations that cannot be explained by more primitive network properties alone. Disentangling the underlying contributions to the non-random elements we find that in 2007 the loan portfolios of different categories of Spanish banks (commercial and savings banks) have been very homogeneous, generating also strong overlaps in lending structure between many sectors of the economy.
Gaussian Graph with Prototypical Contrastive Learning in E-Commerce Bundle Recommendation

Bundle recommendation aims to provide a bundle of items to satisfy the user preference on e-commerce platform. Existing successful solutions are based on the contrastive graph learning paradigm where graph neural networks (GNNs) are employed to learn representations from user-level and bundle-level graph views with a contrastive learning module to enhance the cooperative association between different views. Nevertheless, they ignore the uncertainty issue which has a significant impact in real bundle recommendation scenarios due to the lack of discriminative information caused by highly sparsity or diversity. We further suggest that their instancewise contrastive learning fails to distinguish the semantically similar negatives (i.e., sampling bias issue), resulting in performance degradation. In this paper, we propose a novel Gaussian Graph with Prototypical Contrastive Learning (GPCL) framework to overcome these challenges. In particular, GPCL embeds each user/bundle/item as a Gaussian distribution rather than a fixed vector. We further design a prototypical contrastive learning module to capture the contextual information and mitigate the sampling bias issue. Extensive experiments demonstrate that benefiting from the proposed components, we achieve new state-of-the-art performance compared to previous methods on several public datasets. Moreover, GPCL has been deployed on real-world e-commerce platform and achieved substantial improvements.
Compactness criterion for families of quantum operations in the strong convergence topology and its applications

A revised version of the compactness criterion for families of quantum operations in the strong convergence topology (obtained previously) is presented, along with a more detailed proof and the examples showing the necessity of this revision. Several criteria for the existence of a limit point of a sequence of quantum operations w.r. t. the strong convergence are obtained and discussed. Applications in different areas of quantum information theory are described.
The effectiveness of using diversity to select multiple classifier systems with varying classification thresholds

In classification applications, the goal of fusion techniques is to exploit complementary approaches and merge the information provided by these methods to provide a solution superior than any single method. Associated with choosing a methodology to fuse pattern recognition algorithms is the choice of algorithm or algorithms to fuse. Historically, classifier ensemble accuracy has been used to select which pattern recognition algorithms are included in a multiple classifier system. More recently, research has focused on creating and evaluating diversity metrics to more effectively select ensemble members. Using a wide range of classification data sets, methodologies, and fusion techniques, current diversity research is extended by expanding classifier domains before employing fusion methodologies. The expansion is made possible with a unique classification score algorithm developed for this purpose. Correlation and linear regression techniques reveal that the relationship between diversity metrics and accuracy is tenuous and optimal ensemble selection should be based on ensemble accuracy. The strengths and weaknesses of popular diversity metrics are examined in the context of the information they provide with respect to changing classification thresholds and accuracies.
Unmasking Anomalies in Road-Scene Segmentation

Anomaly segmentation is a critical task for driving applications, and it is approached traditionally as a per-pixel classification problem. However, reasoning individually about each pixel without considering their contextual semantics results in high uncertainty around the objects’ boundaries and numerous false positives. We propose a paradigm change by shifting from a per-pixel classification to a mask classification. Our mask-based method, Mask2Anomaly, demonstrates the feasibility of integrating an anomaly detection method in a mask-classification architecture. Mask2Anomaly includes several technical novelties that are designed to improve the detection of anomalies in masks: i) a global masked attention module to focus individually on the foreground and background regions; ii) a mask contrastive learning that maximizes the margin between an anomaly and known classes; and iii) a mask refinement solution to reduce false positives. Mask2Anomaly achieves new state-of-the-art results across a range of benchmarks, both in the per-pixel and component-level evaluations. In particular, Mask2Anomaly reduces the average false positives rate by 60% w.r. t. the previous state-of-the-art.
Covering triangular grids with multiplicity

Motivated by classical work of Alon and F\"uredi, we introduce and address the following problem: determine the minimum number of affine hyperplanes in $\mathbb{R}^d$ needed to cover every point of the triangular grid $T_d(n) := \{(x_1,\dots,x_d)\in\mathbb{Z}_{\ge 0}^d\mid x_1+\dots+x_d\le n-1\}$ at least $k$ times. For $d = 2$, we solve the problem exactly for $k \leq 4$, and obtain a partial solution for $k>4$. We also obtain an asymptotic formula (in $n$) for all $d \geq k - 2$. The proofs rely on combinatorial arguments and linear programming.
A Novel Framework for Optimizing Gurney Flaps using RBF Neural Network and Cuckoo Search Algorithm

Enhancing aerodynamic efficiency is vital for optimizing aircraft performance and operational effectiveness. It enables greater speeds and reduced fuel consumption, leading to lower operating costs. Hence, the implementation of Gurney flaps represents a promising avenue for improving airfoil aerodynamics. The optimization of Gurney flaps holds considerable ramifications for improving the lift and stall characteristics of airfoils in aircraft and wind turbine blade designs. The efficacy of implementing Gurney flaps hinges significantly on its design parameters, namely, flap height and mounting angle. This study attempts to optimize these parameters using a design optimization framework, which incorporates training a Radial Basis Function surrogate model based on CFD data from two-dimensional (2D) Reynolds-Averaged Navier-Stokes (RANS) simulations. The Cuckoo Search algorithm is then employed to obtain the optimal design parameters and compared with other competing optimization algorithms. The optimized Gurney flap configuration shows a notable improvement of 10.28% in Cl/Cd, with a flap height of 1.9%c and a flap mounting angle of -58 degrees. The study highlights the effectiveness of the proposed design optimization framework and furnishes valuable insights into optimizing Gurney flap parameters. The comparison of metaheuristic algorithms serves to enhance the study's contribution to Gurney flap design optimization.
Quantum GravitoElectromagnetic Dynamics

We propose a renormalisable quantum theory of gravity (QGED) based on the standard BRST quantisation used to quantise the Yang-Mills theory. The BRST-invariant Lagrangian of the gravitationally interacting $U(1)$-gauge theory, including gauge fixing and ghost parts, is provided. From this Lagrangian, we extract a set of Feynman rules in the local inertial frame where gravity vanishes locally. Utilising Feynman rules of the QGED prepared here, we construct all renormalisation constants and show that the theory is perturbatively renormalisable in one-loop order. We replace infinite-valued bare objects in the bare Lagrangian with experimentally measured ones. In addition to standard QED parameters, we show that the gravitational coupling constant is measurable experimentally. We also discuss a running effect of the gravitational coupling constant and the perturbative estimation of the Hawking radiation as examples of the perturbative QGED.
Job-specific workers’ health surveillance for construction workers

Workers’ health surveillance (WHS) aims at the assessment of workers’ health and work ability by detecting any clinical or preclinical abnormalities. In that way, it can be verified whether the occupational exposures have any detrimental effect on the health of workers and whether the worker is fit for the job. Each WHS should be designed based on evidence and in such a way that it is relevant to the nature of the demands and health effects of the occupation of interest. Due to the demanding nature of the sector, construction workers face high rates of occupational injuries and illnesses. Hence, a job-specific health surveillance programme for construction workers seems a necessary complementary approach in guarding the health of the workers. The aim of this thesis is to develop and evaluate a job-specific WHS for construction workers. This thesis focuses on two construction occupations as representatives for a more physically or more mentally demanding occupation: bricklayers and construction supervisors, respectively.
On the detection of dark matter: likelihoods and limits on spin-independent and spin-dependent WIMP couplings and the implementation of radiative muon decays in dark matter analyses

This thesis presents advancements in two of the three avenues for the detection of particle dark matter: direct and indirect searches. The first four chapters provide an introductory overview of particle dark matter and its distribution, evidence and constraints as well as statistical tools used in direct and indirect analyses. The second main segment of the thesis presents LUXCalc, a new utility for calculating likelihoods and deriving WIMP-nucleon coupling limits from the recent results of the LUX direct search dark matter experiment. After a comprehensive review of WIMP-nucleon scattering, we derive LUX limits on the spin-dependent WIMP-nucleon couplings over a broad range of WIMP masses, under standard assumptions on the relevant astrophysical parameters. We find that, under these and other common assumptions, LUX excludes the entire spin-dependent parameter space consistent with a dark matter interpretation of DAMA’s anomalous signal, the first time a single experiment has been able to do so. We also revisit the case of spin-independent couplings, and demonstrate good agreement between our results and the published LUX results. Finally, we derive constraints on the parameters of an effective dark matter theory in which a spin-1 mediator interacts with a fermionic WIMP and Standard Model fermions via axial-vector couplings. A detailed section describes the use of LUXCalc with standard codes to place constraints on generic dark matter theories. The final segment looks at the fact that photons can be produced from final state muons, and the consequences of considering such a process in indirect searches. Modern Monte Carlo generators and DM codes include the effects of final state radiation from muons produced in the dark matter annihilation process itself, but neglect the O(1%) radiative correction that arises from the subsequent muon decay. After implementing this correction we demonstrate the effect that it can have on dark matter phenomenology by considering the case of dark matter annihilation to four muons via scalar mediator production. We first show that the AMS-02 positron excess can no longer easily be made consistent with this final state once the Fermi-LAT dwarf limits are calculated with the inclusion of radiative muon decays, and we next show that the Fermi-LAT galactic centre gamma excess can be improved
Sugarcane growth prediction based on meteorological parameters using extreme learning machine and artificial neural network

ABSTRACT Management strategies for sustainable sugarcane production need to deal with the increasing complexity and variability of the whole sugar system. Moreover, they need to accommodate the multiple goals of different industry sectors and the wider community. Traditional disciplinary approaches are unable to provide integrated management solutions, and an approach based on whole systems analysis is essential to bring about beneficial change to industry and the community. The application of this approach to water management, environmental management and cane supply management is outlined, where the literature indicates that the application of extreme learning machine (ELM) has never been explored in this realm. Consequently, the leading objective of the current research was set to filling this gap by applying ELM to launch swift and accurate model for crop production data-driven. The key learning has been the need for innovation both in the technical aspects of system function underpinned by modelling of sugarcane growth. Therefore, the current study is an attempt to establish an integrate model using ELM to predict the concluding growth amount of sugarcane. Prediction results were evaluated and further compared with artificial neural network (ANN) and genetic programming models. Accuracy of the ELM model is calculated using the statistics indicators of Root Means Square Error (RMSE), Pearson Coefficient (r), and Coefficient of Determination (R2) with promising results of 0.8, 0.47, and 0.89, respectively. The results also show better generalization ability in addition to faster learning curve. Thus, proficiency of the ELM for supplementary work on advancement of prediction model for sugarcane growth was approved with promising results.
Lie group analysis of retarded delay differential equations in human postural balance model

This paper discusses the application of second-order delay differential equation in human postural balance model (HPB) of inverted pendulum in ankle joint. The model described by delay differential equation (DDE) presents a challenge for computational neuroscientists, since it is the key to control the process of human walking as well as this model is difficult to solve. In this paper we consider the case when the model is represented by retarded DDE (RDDE), for which there is no direct way to solve. The postural balance is examined using Lie group analysis which allows us to classify HPB model to solvable Lie algebra. The classification provides a theoretical basis for constructing accurate transformation solutions that lead to solve RDDE. Along with these results, more complex models for computational neuroscientists aimed at explaining additional effects can be studied using this technique. Mathematics Subject Classification: 22E99, 34K06, 17B3
Hilbert-Schmidt Independence Criterion in sufficient dimension reduction and feature screening

This dissertation undertakes the theory and methods of sufficient dimension reduction in the content of Hilbert-Schmidt Independence Criterion (HSIC). The proposed estimation methods enjoy model free property and require no link function to be smoothed or estimated. Two tests: Permutation test and Bootstrap test, are investigated to examine the true underlying dimension of data considered. Sampling distribution of our estimator is established in single-index regressions. Root-n consistency of our estimator is proved for multiple-index models. Finite sample performances are examined through simulation studies and compared with well-established methods, for example, SIR (Li, 1991), SAVE (Cook and Weisberg, 1991), rMAVE (Xia et al., 2002), and EFM (Cui et al., 2011). Two real data sets are analyzed to demonstrate the efficacy of our proposed approaches. We also explored the use of HSIC for feature screening purpose. Our procedures do not require model specification for responses and predictors, for example, linear model or generalized linear model, discrete or non-normal predictors, which makes our methods robust against model misspecification. In addition, differing from usual screening approaches in which only marginal relations are used, our procedure provides a sufficient selection path by using joint relations among the response variable and predictors. Index words: Bootstrap, Central Subspace, Dimension Reduction Subspace, Hilbert-Schmidt Independence Criterion, Marginal Feature Screening, Multiple-index Models, Permutation Test, Single-index Models, Sufficient Dimension Reduction, Sufficient Variable Selection. Hilbert-Schmidt Independence Criterion in Sufficient Dimension Reduction and Feature Screening
A Nonergodic Ground‐Motion Model for California with Spatially Varying Coefficients

Traditional probabilistic seismic‐hazard analysis as well as the estimation of ground‐motion models (GMMs) is based on the ergodic assumption, which means that the distribution of ground motions over time at a given site is the same as their spatial distribution over all sites for the same magnitude, distance, and site condition. With a large increase in the number of recorded ground‐motion data, there are now repeated observations at given sites and from multiple earthquakes in small regions, so that assumption can be relaxed. We use a novel approach to develop a nonergodic GMM, which is cast as a varying‐coefficient model (VCM). In this model, the coefficients are allowed to vary by geographical location, which makes it possible to incorporate effects of spatially varying source, path, and site conditions. Hence, a separate set of coefficients is estimated for each source and site coordinate in the data set. The coefficients are constrained to be similar for spatially nearby locations. This is achieved by placing a Gaussian process prior on the coefficients. The amount of correlation is determined by the data. The spatial correlation structure of the model allows one to extrapolate the varying coefficients to a new location and trace the corresponding uncertainties. The approach is illustrated with the Next Generation Attenuation‐West2 data set, using only Californian records. The VCM outperforms a traditionally estimated GMM in terms of generalization error and leads to a reduction in the aleatory standard deviation by ∼40%, which has important implications for seismic‐hazard calculations. The scaling of the model with respect to its predictor variables such as magnitude and distance is physically plausible. The epistemic uncertainty associated with the predicted ground motions is small in places where events or stations are close and large where data are sparse. Online Material: Maps showing the spatially varying coefficients across California and tables of correlation functions.
A fast channel state information feedback scheme based on compressive sensing for underwater acoustic MIMO systems

In this paper, a compressive feedback scheme based on compressed sensing is investigated to reduce feedback load and enhance the sum throughput at low feedback rates for underwater acoustic MIMO systems. Meanwhile, to avoid the out-of-date channel state information at the transmitter under the high dynamic underwater channel, we present a modified SL0 (M-SL0) algorithm to recover the CSI at the transmitter, which adopts more steep approximate hyperbolic tangent function to approximate the l 0 norm in the recovery. The minimization problem of the l 0 norm can be transformed into a convex optimization problem for the smoothed function. Then, in order to solve the problem of slow convergence and inaccuracy estimation caused by notched effect of the gradient method, hybrid optimization algorithm that combines the advantages of the gradient method and the revised Newton method is introduced to improve the accuracy of sparse recovery. Numerical simulations show that the recovery accuracy and speed are improved by the proposed M-SL0 algorithm comparing with SL0, OMP algorithm. Meanwhile, CSI feedback based compressive sensing performs better than the feedback based on a vector quantitation codebook.