Miracle-dz/UniFAR-FTUs huggingface dataset:
Explainable Adaptive Tree-based Model Selection for Time-Series Forecasting

Tree-based models have been successfully applied to a wide variety of tasks, including time series forecasting. They are increasingly in demand and widely accepted because of their comparatively high level of interpretability. However, many of them suffer from the overfitting problem, which limits their application in real-world decision-making. This problem becomes even more severe in online-forecasting settings where time series observations are incrementally acquired, and the distributions from which they are drawn may keep changing over time. In this context, we propose a novel method for the online selection of tree-based models using the TreeSHAP explainability method in the task of time series forecasting. We start with an arbitrary set of different tree-based models. Then, we outline a performance-based ranking with a coherent design to make TreeSHAP able to specialize the tree-based forecasters across different regions in the input time series. In this framework, adequate model selection is performed online, adaptively following drift detection in the time series. In addition, explainability is supported on three levels, namely online input importance, model selection, and model output explanation. An extensive empirical study on various real-world datasets demonstrates that our method achieves excellent or on-par results in comparison to the state-of-the-art approaches as well as several baselines.
Unique Ergodicity of Stochastic Theta Method for Monotone SDEs driven by Nondegenerate Multiplicative Noise

We establish the unique ergodicity of the Markov chain generated by the stochastic theta method (STM) with $\theta \in [1/2, 1]$ for monotone SODEs, without growth restriction on the coefficients, driven by nondegenerate multiplicative noise. The main ingredient of the arguments lies in the construction of new Lyapunov functions, involving the coefficients, the stepsize, and $\theta$, and the irreducibility and the strong Feller property for the STM. We also generalize the arguments to the STM and its Galerkin-based full discretizations for a class of monotone SPDEs driven by infinite-dimensional nondegenerate multiplicative trace-class noise. Applying these results to the stochastic Allen--Cahn equation indicates that its drift-implicit Euler scheme is uniquely ergodic for any interface thickness, which gives an affirmative answer to a question proposed in (J. Cui, J. Hong, and L. Sun, Stochastic Process. Appl. (2021): 55--93). Numerical experiments verify our theoretical results.
Tensor Networks for Explainable Machine Learning in Cybersecurity

In this paper we show how tensor networks help in developing explainability of machine learning algorithms. Specifically, we develop an unsupervised clustering algorithm based on Matrix Product States (MPS) and apply it in the context of a real use-case of adversary-generated threat intelligence. Our investigation proves that MPS rival traditional deep learning models such as autoencoders and GANs in terms of performance, while providing much richer model interpretability. Our approach naturally facilitates the extraction of feature-wise probabilities, Von Neumann Entropy, and mutual information, offering a compelling narrative for classification of anomalies and fostering an unprecedented level of transparency and interpretability, something fundamental to understand the rationale behind artificial intelligence decisions.
Stress Effects on Flow Partitioning in Fractured Reservoirs: Equivalent Porous Media Versus Poro-Elasticity Coupled Modeling

In this paper the effects of overburden stress on fracture-matrix flow partitioning were numerically analyzed using two different numerical approaches; the analysis was validated using a fractured Clashach core flood laboratory experimental data. In the first numerical approach, the fracture aperture variation under different overburden stresses, measured using a back calculation method based on the treatment of the fracture as an equivalent porous medium, was adopted in a coupled Darcy law, Brinkman flow and Navier-Stokes fluid flow formulation. In the second numerical approach, poro-elasticity was applied in order to accurately account for fracture aperture change under overburden stress loading. The resulting displacements were coupled to the same fluid flow equations used in the first approach through a moving mesh technique. This was further coupled with stress dependent permeability within the matrix. Flow partitioning from the two numerical approaches were compared to the experimental data. This comparison highlighted the inefficiency of treating fractures as equivalent porous medium. Moreover the cross-flow between the fracture and the matrix was monitored in both modeling approaches and a critical stress beyond which the matrix can no longer feed the fracture was identified. This critical stress can be very important in designing production scenarios for highly-stressed fractured reservoirs.
The induction of SHP-1 degradation by TAOK3 ensures the responsiveness of T cells to TCR stimulation

Thousand-and-one–amino acid kinase 3 (TAOK3) is a serine and threonine kinase that belongs to the STE-20 family of kinases. Its absence reduces T cell receptor (TCR) signaling and increases the interaction of the tyrosine phosphatase SHP-1, a major negative regulator of proximal TCR signaling, with the kinase LCK, a component of the core TCR signaling complex. Here, we used mouse models and human cell lines to investigate the mechanism by which TAOK3 limits the interaction of SHP-1 with LCK. The loss of TAOK3 decreased the survival of naïve CD4+ T cells by dampening the transmission of tonic and ligand-dependent TCR signaling. In mouse T cells, Taok3 promoted the secretion of interleukin-2 (IL-2) in response to TCR activation in a manner that depended on Taok3 gene dosage and on Taok3 kinase activity. TCR desensitization in Taok3−/− T cells was caused by an increased abundance of Shp-1, and pharmacological inhibition of Shp-1 rescued the activation potential of these T cells. TAOK3 phosphorylated threonine-394 in the phosphatase domain of SHP-1, which promoted its ubiquitylation and proteasomal degradation. The loss of TAOK3 had no effect on the abundance of SHP-2, which lacks a residue corresponding to SHP-1 threonine-394. Modulation of SHP-1 abundance by TAOK3 thus serves as a rheostat for TCR signaling and determines the activation threshold of T lymphocytes. TAOK3 enhances T cell receptor signaling by promoting the degradation of SHP-1. Editor’s summary The phosphatase SHP-1 is a critical negative regulator of T cell receptor (TCR) signaling because it dephosphorylates several components downstream of the TCR. Poirier et al. found that the kinase TAOK3 promoted TCR signaling by phosphorylating a threonine residue in the phosphatase domain of SHP-1, leading to SHP-1 ubiquitylation and degradation. The loss of TAOK3 reduced both tonic and ligand-dependent TCR signaling in mouse CD4+ T cells and in a human T cell line. Thus, TAOK3 sets the threshold for T cell activation by controlling SHP-1 abundance. —Annalisa M. VanHook
Seasonal acoustic presence of marine mammals at the South Orkney Islands, Scotia Sea

Increased knowledge about marine mammal seasonal distribution and species assemblage from the South Orkney Islands waters is needed for the development of management regulations of the commercial fishery for Antarctic krill (Euphausia superba) in this region. Passive acoustic monitoring (PAM) data were collected during the autumn and winter seasons in two consecutive years (2016, 2017), which represented highly contrasting environmental conditions due to the 2016 El Niño event. We explored differences in seasonal patterns in marine mammal acoustic presence between the two years in context of environmental cues and climate variability. Acoustic signals from five baleen whale species, two pinniped species and odontocete species were detected and separated into guilds. Although species diversity remained stable over time, the ice-avoiding and ice-affiliated species dominated before and after the onset of winter, respectively, and thus demonstrating a shift in guild composition related to season. Herein, we provide novel information about local marine mammal species diversity, community structure and residency times in a krill hotspot. Our study also demonstrates the utility of PAM data and its usefulness in providing new insights into the marine mammal habitat use and responses to environmental conditions, which are essential knowledge for the future development of a sustainable fishery management in a changing ecosystem.
Automatic weld joint type recognition in intelligent welding using image features and machine learning algorithms

Abstract Welding is the most basic and widely used manufacturing process. Intelligent robotic welding is an area that has received much consideration owing to the widespread use of robots in welding operations. With the dawn of Industry 4.0, machine learning is substantially developing to alleviate issues around applying robotic welding intelligently. Identifying the correct weld joint type is essential for intelligent robotic welding. It affects the quality of the weldment and impacts the per-unit cost. The robot controller must change different welding parameters per joint type to attain the desired weld quality. This article presents an approach that uses image features like edges, corners, and blobs to identify different weld joint types using machine learning algorithms. Feature extractors perform the task of feature extraction. The feature extractor choice is crucial for accurate weld joint identification. The present study compares the performance of five feature extractors, namely (1) Histogram of gradients, (2) Local binary pattern, (3) ReLU3 layer, (4) ReLU4 layer, and (5) Pooling layer of ResNet18 Neural network applied to classifiers like Support Vector machines, K-Nearest Neighbor and Decision trees. We trained and tested the proposed model using the Kaggle Weld joint dataset (for Butt and Fillet Joints) and our in-house dataset (for Vee, lap, and corner joints). The experimental findings show that out of the 15 models, the pre-trained ResNet18 feature extractor with an Support Vector Machines classifier has excellent performance with a threefold recognition accuracy of 98.74% for the mentioned dataset with a computation time of 31 ms per image.
Evaluation of the antiparasitic and antifibrotic effects of gallic acid on experimental hepatic schistosomiasis mansoni

Abstract Schistosomiasis afflicts approximately 120 million individuals globally. The hepatic pathology that occurs due to egg-induced granuloma and fibrosis is commonly attributed to this condition. However, there is currently no efficacious treatment available for either of these conditions. Our study aimed to investigate the potential antifibrotic and antiparasitic properties of different doses of gallic acid (GA) in experimental schistosomiasis mansoni. In addition, we investigated the outcomes of co-administering it with the standard anti-schistosomiasis treatment, praziquantel (PZQ). In experiment I, Schistosoma mansoni-infected mice were administered GA at doses of 10, 20, or 40 mg/kg. Their effectiveness was evaluated through parasitological (worm and egg loads, granuloma number and diameter), pathological (fibrosis percentage and H-score of hepatic stellate cells (HSCs)), and functional (liver enzymes) tests. In experiment II, we investigated the optimal dosage that yielded the best outcomes. This dosage was administered in conjunction with PZQ and was evaluated regarding the parasitological, pathological, functional, and immunological (fibrosis-regulating cytokines) activities. Our findings indicate that the administration of 40 mg/kg GA exhibited the highest level of effectiveness in experiment I. In experiment II, it exhibited lower antiparasitic efficacy in comparison to PZQ. However, it surpassed PZQ in other tests. It showed enhanced outcomes when combined with PZQ. In conclusion, our findings reveal that GA only slightly increased the antischistosomal activity of PZQ. However, it was linked to decreased fibrosis, particularly when administrated with PZQ. Our pilot study identifies GA as a natural antifibrotic agent, which could be administered with PZQ to mitigate the development of fibrosis.
Dynamic Availability and Definite Finality in IOTA 2.0

IOTA 2.0 addresses the dual challenge of dynamic availability and definite finality in distributed ledgers. By combining voting-based and proof-based consensus models, it allows users to strike a balance between these objectives. This innovative approach represents a significant advancement in the field, offering both dynamic availability and definite finality, all within the evolving landscape of distributed ledger technologies.
Tartare: Automatic Generation of C Pointer Statements and Feedback

This paper addresses the difficulties students face when learning and practicing pointers (i.e., variables storing the memory address of another variable as its value) in a computer programming class. To improve their understanding and practice, we have developed Tartare, an automatic C pointer statement and feedback generator. By creating statements with automatic feedback, students are given the opportunity to practice at will, each time on a different instance. In addition, if the statement must be done remotely and accounts in the final grade, Tartare discourages academic dishonesty since each student faces their own statement to solve. This paper describes the techniques implemented in Tartare, relying on a pattern template-based approach. The statement variety of Tartare is evaluated. Finally, current limitations and further improvements are discussed. We believe our approach for Tartare can be transposed for automatic exercises generation in various other fields.
AncestryGrapher toolkit: Python command-line pipelines to visualize global– and local– ancestry inferences from the RFMix2 software

Summary Admixture is a fundamental process that has shaped patterns of genetic variation and the risk for disease in human populations. Here, we introduce the AncestryGrapher toolkit for visualizing inferred global– and local– ancestry by the RFMix v.2 software. Currently, there is no straightforward method to summarize population ancestry results from RFMix analysis. Results To demonstrate the utility of our method, we applied the AncestryGrapher toolkit to the output files of RFMix v.2 to visualize the global and local ancestry of individuals in the Mozabite Berber population from North Africa. Our results showed that the Mozabite Berbers derived their ancestry from the Middle East, Europe, and sub-Saharan Africa (global ancestry). Furthermore, we found that the population origin of ancestry varied considerably along chromosomes. More specifically, we observed variance in ancestry along chromosome 2 (local ancestry), in the genomic region containing the common genetic polymorphisms associated with lactase persistence, a trait known to be under strong positive selection. This finding indicates that the demographic process of admixture has influenced patterns of allelic variation in addition to natural selection. Overall, the AncestryGrapher toolkit facilitates the exploration, interpretation, and reporting of ancestry patterns in human populations. Availability and implementation The AncestryGrapher toolkit is free and open source on https://github.com/alisi1989/RFmix2-Pipeline-to-plot.
Cryo-EM images of phase separated lipid bilayer vesicles analyzed with a machine learning approach

Lateral lipid heterogeneity (i.e., raft formation) in biomembranes plays a functional role in living cells. Three-component mixtures of low- and high-melting lipids plus cholesterol offer a simplified experimental model for raft domains in which a liquid-disordered (Ld) phase coexists with a liquid-ordered (Lo) phase. Using such models, we recently showed that cryogenic electron microscopy (cryo-EM) can detect phase separation in lipid vesicles based on differences in bilayer thickness. However, the considerable noise within cryo-EM data poses a significant challenge for accurately determining the membrane phase state at high spatial resolution. To this end, we have developed an image processing pipeline that utilizes machine learning (ML) to predict the bilayer phase in projection images of lipid vesicles. Importantly, the ML method exploits differences in both the thickness and molecular density of Lo compared to Ld, which leads to improved phase identification. To assess accuracy, we used artificial images of phase-separated lipid vesicles generated from all-atom molecular dynamics simulations of Lo and Ld phases. Synthetic ground truth datasets mimicking a series of compositions along a tieline of Ld+Lo coexistence were created and then analyzed with various ML models. For all tieline compositions, we find that the ML approach can correctly identify the bilayer phase at 5 nm lateral resolution with > 90% accuracy, thus providing a means to isolate the intensity profiles of coexisting Ld and Lo phases, as well as accurately determine domain size distributions, number of domains, and phase area fractions. The method described here provides a framework for characterizing nanoscopic lateral heterogeneities in membranes and paves the way for a more detailed understanding of raft properties in biological contexts. Significance Lipid rafts are important for cell function, but in most cases cannot be detected with conventional optical microscopy because of their extremely small size. Cryogenic electron microscopy (cryo-EM), because of its much greater spatial resolution, is capable of imaging domains as small as 5-10 nm. In this report, we show how machine learning techniques can be used to automatically and accurately identify raft-like domains in simulated cryo-EM images, a powerful approach that could ultimately lead to a better understanding of raft properties.
Maternal gut Bifidobacterium breve modifies fetal brain metabolism in mice

In recent years, our understanding of the gut microbiome’s impact on host physiology and metabolism has grown exponentially. Yet, the specific role of certain microorganisms in regulating gestational health and fetal development remains largely unexplored. During pregnancy, Bifidobacterium represents a key beneficial microbiota genus that provides multiple benefits, including changes in placental development and fetal glycaemia. In this study, using germ-free mice colonized with or without Bifidobacterium breve UCC2003 during pregnancy, we demonstrated that this bacterium is important for controlling fetal brain metabolism. In particular, presence of maternal Bifidobacterium led to reduced levels of ten metabolites (including citrate, 3-hydroxyisobutyrate, and carnitine) in the fetal brain, with concurrent elevated abundance of transporters involved in glucose and branched-chain amino acid uptake. B. breve supplementation was also associated with increased expression of critical metabolic and cellular pathways, including the PI3K-AKT, AMPK, STAT5 and Wnt-β-catenin (including its receptor Frizzled-7) in the fetal brain. Furthermore, maternal-associated Bifidobacterium resulted in HIF-2 protein stabilization and altered a number of genes and proteins involved in cellular growth, axogenesis, and mitochondrial function. These findings highlight that Bifidobacterium breve colonisation of the maternal gut is important for the metabolism and growth of the fetal brain.
Soils-on-a-chip reveal unforeseen motility parameters of microconfined Bradyrhizobium diazoefficiens

Soil bacteria of the order of the Rhizobiales associate symbiotically with legume plants. Particulary, Bradyrhizobium diazoefficiens is a nitrogen-fixing symbiont of soybean, that helps to improve grain quality among other benefits. This bacterium possess two flagellar systems, which enable it to swim in water-saturated pores. However, the motility of B. diazoefficiens, which may be crucial for its competitiveness in root nodulation, has not been well understood. To address this knowledge gap, we designed and fabricated microfluidic soil-on-a-chip (SOC) devices that offer sustainable agriculture an original tool for directly visualizing bacterial behavior in confined-environments. Using these microdevices, we measured the population velocities and changes of direction along their paths for two strains of B. diazoefficiens, namely the wild-type and a mutant with only one flagellar system. Our detailed statistical analysis revealed that both strains exhibited reduced speeds and increased changes of direction of 180°, in channels of decreasing microscopic cross sectional area, down to a few microns. Interestingly, while the wild-type strain displayed faster swimming speeds in unconfined spaces, this advantage was negated in the SOCs that mimicked porous soils. Moreover, we employed the measured motility parameters to model and simulate B. diazoefficiens motion in SOC devices for extended periods and larger scales, enabling further predictions of diffusion in real soils. Thanks to miniaturization, microfabrication, and multidisciplinary knowledge, this study represents a significant breakthrough in soil bacteria field and methods, useful both for farmers and environment. Furthermore, the potential applications of this work extend to multiple beneficial bacteria widely used as biofertilizers.
Transcription promotes discrete long-range chromatin loops besides organizing cohesin-mediated DNA folding

The multi-layered arrangement of eukaryotic genomes and chromosome spatial organization dynamics are of functional importance for gene expression, DNA replication and segregation. SMC complexes are essential instruments of chromosome folding by carrying out long range intra-chromatid DNA looping. Cohesin, in addition to tether sister chromatids, also ensures dynamic regulation of gene expression in mammals by promoting interaction between distal regulatory elements and promoters whereas transcription affects genome folding in numerous organisms and in multiple ways. Here, we comprehensively dissect the relative contributions of transcription and cohesin complexes, as well as their interplay, on the yeast S. cerevisiae genome organization through DNA borders and loops. Transcription activation specifically induces appearance of DNA borders and loops, independently of SMC complexes, while also directly interfering in addition with cohesin-mediated loop expansion.
Large-Scale Classification of Structured Objects using a CRF with Deep Class Embedding

This paper presents a novel deep learning architecture to classify structured objects in datasets with a large number of visually similar categories. We model sequences of images as linear-chain CRFs, and jointly learn the parameters from both local-visual features and neighboring classes. The visual features are computed by convolutional layers, and the class embeddings are learned by factorizing the CRF pairwise potential matrix. This forms a highly nonlinear objective function which is trained by optimizing a local likelihood approximation with batch-normalization. This model overcomes the difficulties of existing CRF methods to learn the contextual relationships thoroughly when there is a large number of classes and the data is sparse. The performance of the proposed method is illustrated on a huge dataset that contains images of retail-store product displays, taken in varying settings and viewpoints, and shows significantly improved results compared to linear CRF modeling and unnormalized likelihood optimization.