Miracle-dz/UniFAR-FTUs huggingface dataset:
Parameter Estimation Algorithms for Volterra Digital Systems

Abstract—In this paper, we will propose a novel parameter estimation algorithm which is based on the modified particle swarm optimization (PSO) algorithm for the Volterra digital system. In the modified PSO algorithm, another adjusting factor is added into the velocity updating formula to enhance the algorithm’s search capacity. On the basis of a series of input-output data pairs, we wish that the modified PSO approach can successfully solve for the unknown parameters of the Volterra digital system. The whole design steps based on the modified PSO is presented for parameter estimations. Besides, different sets of algorithm initial conditions are examined to confirm the feasibility and robustness. Finally, simulation results sufficiently reveal that the proposed method can correctly solve for the parameters of the Volterra digital system.
Iterative Identification and Control Using Non-normalized Coprime Factors With Application in Wafer Stage Motion Control

Robustness against model uncertainty is essential in model-based controller design. It is well known that a relatively small uncertainty in lightly damped poles and zeros can result in a large distance measured in the $\nu $ -gap metric, leading to conservative robust stability and performance guarantees. This paper aims to develop an identification and control procedure that results in less conservative robust stability and performance conditions for linear systems with lightly damped poles and zeros. To achieve this, a connection is established between a distance measure based on a nonnormalized coprime factorization of the system and existing identification criteria in closed-loop system identification. A nominal model of the system is determined by minimizing this distance measure by means of a frequency-domain identification algorithm. Then, a controller synthesis method is proposed that addresses both nominal performance as robust stability. Improved robustness by using the proposed approach compared to existing approaches is confirmed in an experimental example for a system with lightly damped poles and zeros.
Pushing the Frontiers: uncovering the earliest galaxies in the Hubble Frontier Fields

Abstract With the combination of deep Hubble Space Telescope imaging and magnification due to gravitational lensing, the Hubble Frontier Fields program offers an unprecedented opportunity to study the faint end of the luminosity function at the highest redshifts. Unfortunately, the region of the field that benefits most from this magnification (the immediate vicinity of the critical line) suffers from a high abundance of bright foreground galaxies and high background due to intracluster light. To overcome these difficulties, some method of modelling and subtracting the foreground light is required. Here, I present results using wavelet decomposition to subtract large-scale structures in the clusters, which significantly increases the effective depth of the images, and crucially opens up the most magnified regions of the clusters.
Minimum time overtaking problem of vehicle handling inverse dynamics based on interval mathematics

To analyze the influence of uncertain factors on minimum time overtaking, interval mathematics is used to describe the uncertainties, and the overtaking safety distance is calculated using interval analysis method. In addition, vehicle handling inverse dynamics is proposed in this paper. In this method, the driver-handling input can be obtained without the modeling of driver. The optimal control problem is first converted into a nonlinear programming problem based on Gauss pseudospectral method. Then sequential quadratic programming is applied to get the solution. The simulation results show that the overtaking behavior will be significantly different, if the drivers’ age groups or vehicles’ braking system parameters are different. Besides, the influence of different drivers’ estimate time is critical. The subjective judgments of the drivers are considered in this paper to realize the user-friendly design. This method may provide a reference for the research of unmanned vehicles and driving assistance system.
Double Acceptance Sampling Plan for Time-Truncated Life Tests Based on Half Normal Distribution

Abstract In this work, we investigate a double acceptance sampling plan (DASP) based on truncated life tests when the lifetime of a product follows the half normal distribution. By fixing the consumer’s confidence level, the minimum sample sizes of the first and second samples needful to assert the specified mean life are calculated. The operating characteristic values and the minimum ratios of the mean life to the specified life are also analyzed. Several important tables are provided and a numerical example is given to illustrate the results.
Parameter-free Network Sparsification and Data Reduction by Minimal Algorithmic Information Loss

The study of large and complex datasets, or big data, organized as networks has emerged as one of the central challenges in most areas of science and technology. Cellular and molecular networks in biology is one of the prime examples. Henceforth, a number of techniques for data dimensionality reduction, especially in the context of networks, have been developed. Yet, current techniques require a predefined metric upon which to minimize the data size. Here we introduce a family of parameter-free algorithms based on (algorithmic) information theory that are designed to minimize the loss of any (enumerable computable) property contributing to the object's algorithmic content and thus important to preserve in a process of data dimension reduction when forcing the algorithm to delete first the least important features. Being independent of any particular criterion, they are universal in a fundamental mathematical sense. Using suboptimal approximations of efficient (polynomial) estimations we demonstrate how to preserve network properties outperforming other (leading) algorithms for network dimension reduction. Our method preserves all graph-theoretic indices measured, ranging from degree distribution, clustering-coefficient, edge betweenness, and degree and eigenvector centralities. We conclude and demonstrate numerically that our parameter-free, Minimal Information Loss Sparsification (MILS) method is robust, has the potential to maximize the preservation of all recursively enumerable features in data and networks, and achieves equal to significantly better results than other data reduction and network sparsification methods.
Linear Least-squares Dyna-style Planning

World model is very important for model-based reinforcement learning. For example, a model is frequently used in Dyna: in learning steps to select actions and in planning steps to project sampled states or features. In this paper we propose least-squares Dyna (LS-Dyna) algorithm to improve the accuracy of the world model and provide better planning. LS-Dyna is a special Dyna architecture in that it estimates the world model by a least-squares method. LS-Dyna is more data eﬃcient, yet it has the same complexity with existing linear Dyna that is based on gradient descent estimation of the world model. Furthermore, the least-squres modeling is computed in an online recursive fashion and does not have to record historical experience or tune a step-size. Experimental results on a 98-state Boyan chain example and a Mountain-car problem show that LS-Dyna performs signiﬁcantly better than TD/Q-learning and the gradient-descent linear Dyna algorithm.
Analytical guarantees for reduced precision fixed-point margin hyperplane classifiers

Margin hyperplane classifiers such as support vector machines are strong predictive models having gained considerable success in various classification tasks. Their conceptual simplicity makes them suitable candidates for the design of embedded machine learning systems. Their accuracy and resource utilization can effectively be traded off each other through precision. We analytically capture this trade-off by means of bounds on the precision requirements of general margin hyperplane classifiers. In addition, we propose a principled precision reduction scheme based on the trade-off between input and weight precisions. Our analysis is supported by simulation results illustrating the gains of our approach in terms of reducing resource utilization. For instance, we show that a linear margin classifier with precision assignment dictated by our approach and applied to the ‘two vs. four’ task of the MNIST dataset is ∼ 2× more accurate than a standard 8 bit lowprecision implementation in spite of using ∼ 2 × 10 fewer 1 bit full adders and ∼ 2× 10 fewer bits for data and weight representation.
Knock-down of a regulatory barcode shifts macrophage polarization destination from M1 to M2 and increases pathogen burden upon S. aureus infection

Macrophages are driven to form distinct functional phenotypes in response to different immunological stimuli, in a process widely referred to as macrophage polarization. Transcriptional regulators that guide macrophage polarization in response to a given trigger remain largely unknown. In this study, we interrogate the programmable landscape in macrophages to find regulatory panels that determine the precise polarization state that a macrophage is driven to. Towards this, we configure an integrative network analysis pipeline that utilizes macrophage transcriptomes in response to 28 distinct stimuli and reconstructs contextualized human gene regulatory networks, and identifies epicentres of perturbations in each case. We find that these contextualized regulatory networks form a spectrum of thirteen distinct clusters with M1 and M2 at the two ends. Using our computational pipeline, we identify combinatorial panels of epicentric regulatory factors (RFs) for each polarization state. We demonstrate that a set of three RFs i.e., CEBPB, NFE2L2 and BCL3, is sufficient to change the polarization destination from M1 to M2. siRNA knockdown of the 3-RF set in THP1 derived M0 cells, despite exposure to an M1 stimulant, significantly attenuated the shift to M1 phenotype, and instead increased the expression of M2 markers. Single knockdown of each RF also showed a similar trend. The siRNA-mediated knockdown of the 3-RF set rendered the macrophages hyper-susceptible to Staphylococcus aureus infection, demonstrating the importance of these factors in modulating immune responses. Overall, our results provide insights into the transcriptional mechanisms underlying macrophage polarization and identify key regulatory factors that may be targeted to modulate immune responses.
Immune experience at time of circulation and time since virus circulation are predictors of HAI titre

Nearly everyone is infected with seasonal influenza viruses multiple times over the course of their lives due to the antigenic evolution of the virus to escape immunity induced by prior infections and vaccinations. Because antibody escape is usually partial, the antibody response to new infections depends on prior responses which wane and are boosted throughout life. We used serum antibody binding measurements of 864 individuals against a range of historical A/H3N2 viruses collected in previous studies in combination with statistical models to investigate how metrics of age and strain heterogeneities affect haemagglutination inhibition titres. We refine prior modelling of antigenic seniority by characterising immune experience as years of life since a (sub)type’s emergence at time of strain circulation, rather than age at time of circulation. Using Bayesian statistical modeling, we show that this variable, combined with time since circulation of the variant and an individual’s age at circulation, yields the best model fit. Based on the best-fit model, we devised a novel parametric model, and use it to demonstrate and estimate how effects of antigenic seniority, individual age, strain age and strain effects act in concert to shape an individual’s HAI titre.
Artificial selection optimizes pollutant-degrading bacterial communities

Artificial selection is a promising way to improve microbial community functions, but previous experiments have only shown moderate success. Here, we experimentally evaluate a new method that was inspired by genetic algorithms to artificially select small bacterial communities of known species composition based on their degradation of an industrial pollutant. Starting from 29 randomly generated four-species communities, we repeatedly grew communities for four days, selected the 10 best-degrading communities, and rearranged them into 29 new communities with species compositions that resembled those of the most successful ones. The best community after 18 such rounds of selection degraded the pollutant better than the best community in the first round. It featured member species that degrade well, species that degrade badly alone but improve community degradation, and free-rider species that did not contribute to community degradation. Most species in the evolved communities did not differ significantly from their ancestors, suggesting that genetic evolution plays a small role at this time scale. These experiments show that artificial selection on microbial communities can work in principle, and inform on how to improve future experiments.
Overcoming the nutritional immunity by engineering iron scavenging bacteria for cancer therapy

Certain bacteria demonstrate the ability to target and colonize the tumor microenvironment, a characteristic that positions them as innovative carriers for delivering various therapeutic agents in cancer therapy. Nevertheless, our understanding of how bacteria adapt their physiological condition to the tumor microenvironment remains elusive. In this work, we employed liquid chromatography-tandem mass spectrometry to examine the proteome of E. coli colonized in murine tumors. Comparing to E. coli cultivated in the rich medium, we found that E. coli colonized in tumors notably upregulated the processes related to ferric ions, including the enterobactin biosynthesis and iron homeostasis. This finding indicated that the tumor is an iron-deficient environment to E. coli. We also found that the colonization of E. coli in the tumor led to an increased expression of lipocalin 2 (LCN2), a host’s protein that can sequester the enterobactin. We therefore engineered E. coli in order to evade the nutritional immunity provided by LCN2. By introducing the IroA cluster, the E. coli synthesizes the glycosylated enterobactin, which creates steric hindrance to avoid the LCN2 sequestration. The IroA-E. coli showed enhanced resistance to LCN2 and significantly improved the anti-tumor activity in mice. Moreover, the mice cured by the IroA-E. coli treatment became resistant to the tumor re-challenge, indicating the establishment of immunological memory. Overall, our study underscores the crucial role of bacteria’s ability to acquire ferric ions within the tumor microenvironment for effective cancer therapy.
Evolution End Classification of tfd Gene Clusters Mediating Bacterial Degradation of 2,4-Dichlorophenoxyacetic Acid (2,4-D)

The tfd (tfdI and tfdII) are gene clusters originally discovered in plasmid pJP4 which is involved in the bacterial degradation of 2,4-dichlorophenoxyacetic acid (2,4-D) via the ortho-cleavage pathway of chlorinated catechols. They share this activity, with respect to substituted catechols, with clusters tcb and clc. Although great efforts have been devoted over nearly forty years to exploring the structural diversity of these clusters, their evolution has been poorly resolved to date, and their classification is clearly obsolete. Employing comparative genomic and phylogenetic approaches revealed that all tfd clusters can be classified as one of four different types. The following four-type classification and new nomenclature are proposed: tfdI, tfdII, tfdIII and tfdIV(A,B,C). Horizontal gene transfer between Burkholderiales and Sphingomonadales provided phenomenal linkage between tfdI, tfdII, tfdIII and tfdIV type clusters and their mosaic nature. It is hypothesized that the evolution of tfd gene clusters proceeded within first (tcb, clc and tfdI), second (tfdII and tfdIII) and third (tfdIV(A,B,C)) evolutionary lineages in each of which the genes were clustered in specific combinations. Their clusterization has been discussed through the prism of hot spots and driving forces of various models, theories, and hypotheses of cluster and operon formation. Two hypotheses about series of gene deletions and displacements have also been proposed to explain the structural variations across members of clusters tfdII and tfdIII, respectively. Taking everything into account, these findings reconstructing the phylogeny of tfd clusters, have delineated their evolutionary trajectories, and allowed the contribution of various evolutionary processes to be assessed.
Somatic DIS3 mutations in Multiple Myeloma arise early in clonal evolution, but are later counterselected due to toxicity

DIS3 encodes an essential ribonucleic subunit of the nuclear exosome complex, responsible for degrading RNA in the nucleus. Somatic DIS3 mutations drive translocations in B cells, leading to multiple myeloma (MM). Clinical data analysis reveals that 42% of DIS3 mutations occur at three recurrent residues (D479, D488, and R780). These mutations, deactivating DIS3 exonucleolytic activity, are never homozygous, often appearing as minor subclones in advanced MM. Surprisingly, mutant DIS3 alleles undergo loss-of-heterozygosity, correlating with frequent del(13q) encompassing DIS3. Overexpression of wild-type DIS3 enhances growth and viability of DIS3-mutated MM cells, while CRISPR-mediated knock-out of the mutant variant, followed by longitudinal co-culture, replicates its elimination through counterselection, observed in the natural course of the disease. In mice, the heterozygous DIS3 D479 mutation is embryolethal, confirming its dominant toxic effects. Transcriptome analysis of patients and cell lines reveals specific transcriptional signatures of DIS3 mutations with accumulation of non-coding unstable RNA species and including secondary indications of decreased proliferation. All these signatures are reversible upon mutant DIS3 loss-of-heterozygosity. DIS3 is an intriguing hit-and-run oncogene that drives MM, but is subsequently eliminated during clonal evolution.
Local Disordered Region Sampling (LDRS) for Ensemble Modeling of Proteins with Experimentally Undetermined or Low Confidence Prediction Segments

SUMMARY The Local Disordered Region Sampling (LDRS, pronounced loaders) tool, developed for the IDPConformerGenerator platform (Teixeira et al. 2022), provides a method for generating all-atom conformations of intrinsically disordered regions (IDRs) at N- and C-termini of and in loops or linkers between folded regions of an existing protein structure. These disordered elements often lead to missing coordinates in experimental structures or low confidence in predicted structures. Requiring only a pre-existing PDB structure of the protein with missing coordinates or with predicted confidence scores and its full-length primary sequence, LDRS will automatically generate physically meaningful conformational ensembles of the missing flexible regions to complete the full-length protein. The capabilities of the LDRS tool of IDPConformerGenerator include modeling phosphorylation sites using enhanced Monte Carlo Side Chain Entropy (MC-SCE) (Bhowmick and Head-Gordon 2015), transmembrane proteins within an all-atom bilayer, and multi-chain complexes. The modeling capacity of LDRS capitalizes on the modularity, ability to be used as a library and via command-line, and computational speed of the IDPConformerGenerator platform. AVAILABILITY AND IMPLEMENTATION The LDRS module is part of the IDPConformerGenerator modeling suite, which can be downloaded from GitHub at https://github.com/julie-forman-kay-lab/IDPConformerGenerator. IDPConformerGenerator is written in Python and works on Linux, Microsoft Windows, and Mac OS versions that support DSSP. Users can utilize LDRS’s Python API for scripting the same way they can use any part of IDPConformerGenerator’s API, by importing functions from the ‘idpconfgen. ldrs_helper’ library. Otherwise, LDRS can be used as a command line interface application within IDPConformerGenerator. Full documentation is available within the command-line interface (CLI) as well as on IDPConformerGenerator’s official documentation pages (https://idpconformergenerator.readthedocs.io/en/latest/). CONTACT For support with LDRS please contact Zi Hao (Nemo) Liu via nemo.liu@sickkids.ca or submit an issue in the IDPConformerGenerator repository on GitHub (https://github.com/julie-forman-kay-lab/IDPConformerGenerator/issues). SUPPLEMENTARY INFORMATION The supplementary information document contains, or links to, all the conformer ensembles generated for this publication, the generalized Python scripts using the LDRS Python API, figures of detailed methods, fractional secondary structure information, torsion angle sampling, and the time required to generate the different protein cases.
Application of a generalized finite difference method to mould filling process

This paper proposes the use of a generalized finite difference method for the numerical simulation of free surface single phase flows during mould filling process which are common in some industrial processes particularly in the area of metal casting. A novel and efficient idea for the computation of the normal vectors for free surface flows is introduced and presented for the first time. The incompressible Navier–Stokes equations are numerically solved by the well-known Chorin's projection method. After we showed the main ideas behind the meshless approach, some numerical results in two and three dimensions are presented corresponding to mould filling process simulation.
Modified Dynamic Time Warping Based on Direction Similarity for Fast Gesture Recognition

We propose a modified dynamic time warping (DTW) algorithm that compares gesture-position sequences based on the direction of the gestural movement. Standard DTW does not specifically consider the two-dimensional characteristic of the user’s movement. Therefore, in gesture recognition, the sequence comparison by standard DTW needs to be improved. The proposed gesture-recognition system compares the sequences of the input gesture’s position with gesture positions saved in the database and selects the most similar gesture by filtering out unrelated gestures. The suggested algorithm uses the cosine similarity of the movement direction at each moment to calculate the difference and reflects the characteristics of the gesture movement by using the ratio of the Euclidean distance and the proportional distance to the calculated difference. Selective spline interpolation assists in solving the issue of recognition-decline at instances of gestures. Through experiments with public databases (MSRC-12 and G3D), the suggested algorithm revealed an improved performance on both databases compared to other methods.
A robust clustering procedure with unknown number of clusters

A new methodology for robust clustering without specifying in advancethe underlying number of Gaussian clusters is proposed. The procedure is based oniteratively trimming, assessing the goodness of fit, and reweighting. The forwardversion of our procedure is initialized with a high trimming level and K = 1 populations. The procedure is then iterated throughout a fixed sequence of decreasingtrimming levels. New observations are added at each step and, whenever a goodnessof fit rule is not satisfied, the number of components K is increased. A stoppingrule prevents our procedure from using outlying observations. Additional use of abackward criterion is discussed.