Miracle-dz/UniFAR-FTUs huggingface dataset:
Graph Laplacian for Semi-Supervised Learning

Semi-supervised learning is highly useful in common scenarios where labeled data is scarce but unlabeled data is abundant. The graph (or nonlocal) Laplacian is a fundamental smoothing operator for solving various learning tasks. For unsupervised clustering, a spectral embedding is often used, based on graph-Laplacian eigenvectors. For semi-supervised problems, the common approach is to solve a constrained optimization problem, regularized by a Dirichlet energy, based on the graph-Laplacian. However, as supervision decreases, Dirichlet optimization becomes suboptimal. We therefore would like to obtain a smooth transition between unsupervised clustering and low-supervised graph-based classification. In this paper, we propose a new type of graph-Laplacian which is adapted for Semi-Supervised Learning (SSL) problems. It is based on both density and contrastive measures and allows the encoding of the labeled data directly in the operator. Thus, we can perform successfully semi-supervised learning using spectral clustering. The benefits of our approach are illustrated for several SSL problems.
Sublinear Rigidity of Lattices in Semisimple Lie Groups

Let $G$ be a real centre-free semisimple Lie group without compact factors. I prove that irreducible lattices in $G$ are rigid under two types of sublinear distortions. The first result is that the class of lattices in groups that do not admit $\mathbb{R}$-rank $1$ factors is $\textit{SBE complete}$: if $\Lambda$ is an abstract finitely generated group that is Sublinearly BiLipschitz Equivalent (SBE) to a lattice $\Gamma\leq G$, then $\Lambda$ can be homomorphically mapped into $G$ with finite kernel and image a lattice in $G$. For such $G$ this generalizes the well known quasi-isometric completeness of lattices. The second result concerns sublinear distortions within $G$ itself, and holds without any restriction on the rank of the factors: if $\Lambda\leq G$ is a discrete subgroup that $\textit{sublinearly covers}$ a lattice $\Gamma\leq G$, then $\Lambda$ is itself a lattice.
ImMesh: An Immediate LiDAR Localization and Meshing Framework

In this article, we propose a novel light detection and ranging (LiDAR)(-inertial) odometry and mapping framework to achieve the goal of simultaneous localization and meshing in real time. This proposed framework termed immediately meshing (ImMesh) comprises four tightly-coupled modules: receiver, localization, meshing, and broadcaster. The localization module first utilizes the preprocessed sensor data from the receiver, estimates the sensor pose online by registering LiDAR scans to maps, and dynamically grows the map. Then, our meshing module takes the registered LiDAR scan for incrementally reconstructing the triangle mesh on the fly. Finally, the real-time odometry, map, and mesh are published via our broadcaster. The primary contribution of this work is the meshing module, which represents a scene by an efficient voxel structure, performs fast finding of voxels observed by new scans, and incrementally reconstructs triangle facets in each voxel. This voxel-wise meshing operation is delicately designed for the purpose of efficiency; it first performs a dimension reduction by projecting 3-D points to a 2-D local plane contained in the voxel, and then executes the meshing operation with pull, commit, and push steps for incremental reconstruction of triangle facets. To the best of authors' knowledge, this is the first work in the literature that can reconstruct online the triangle mesh of large-scale scenes, just relying on a standard CPU without GPU acceleration.
An overview of open source Deep Learning-based libraries for Neuroscience

In recent years, deep learning has revolutionized machine learning and its applications, producing results comparable to human experts in several domains, including neuroscience. Each year, hundreds of scientific publications present applications of deep neural networks for biomedical data analysis. Due to the fast growth of the domain, it could be a complicated and extremely time-consuming task for worldwide researchers to have a clear perspective of the most recent and advanced software libraries. This work contributes to clarifying the current situation in the domain, outlining the most useful libraries that implement and facilitate deep learning applications for neuroscience, allowing scientists to identify the most suitable options for their research or clinical projects. This paper summarizes the main developments in deep learning and their relevance to neuroscience; it then reviews neuroinformatic toolboxes and libraries collected from the literature and from specific hubs of software projects oriented to neuroscience research. The selected tools are presented in tables detailing key features grouped by the domain of application (e.g., data type, neuroscience area, task), model engineering (e.g., programming language, model customization), and technological aspect (e.g., interface, code source). The results show that, among a high number of available software tools, several libraries stand out in terms of functionalities for neuroscience applications. The aggregation and discussion of this information can help the neuroscience community to develop their research projects more efficiently and quickly, both by means of readily available tools and by knowing which modules may be improved, connected, or added.
ViTs for SITS: Vision Transformers for Satellite Image Time Series

In this paper we introduce the Temporo-Spatial Vision Transformer (TSViT), a fully-attentional model for general Satellite Image Time Series (SITS) processing based on the Vision Transformer (ViT). TSViT splits a SITS record into non-overlapping patches in space and time which are tokenized and subsequently processed by a factorized temporo-spatial encoder. We argue, that in contrast to natural images, a temporal-then-spatial factorization is more intuitive for SITS processing and present experimental evidence for this claim. Additionally, we enhance the model's discriminative power by introducing two novel mechanisms for acquisition-time-specific temporal positional encodings and multiple learnable class tokens. The effect of all novel design choices is evaluated through an extensive ablation study. Our proposed architecture achieves state-of-the-art performance, surpassing previous approaches by a significant margin in three publicly available SITS semantic segmentation and classification datasets. All model, training and evaluation codes can be found at https://github.com/michaeltrs/DeepSatModels.
Neural Systematic Binder

The key to high-level cognition is believed to be the ability to systematically manipulate and compose knowledge pieces. While token-like structured knowledge representations are naturally provided in text, it is elusive how to obtain them for unstructured modalities such as scene images. In this paper, we propose a neural mechanism called Neural Systematic Binder or SysBinder for constructing a novel structured representation called Block-Slot Representation. In Block-Slot Representation, object-centric representations known as slots are constructed by composing a set of independent factor representations called blocks, to facilitate systematic generalization. SysBinder obtains this structure in an unsupervised way by alternatingly applying two different binding principles: spatial binding for spatial modularity across the full scene and factor binding for factor modularity within an object. SysBinder is a simple, deterministic, and general-purpose layer that can be applied as a drop-in module in any arbitrary neural network and on any modality. In experiments, we find that SysBinder provides significantly better factor disentanglement within the slots than the conventional object-centric methods, including, for the first time, in visually complex scene images such as CLEVR-Tex. Furthermore, we demonstrate factor-level systematicity in controlled scene generation by decoding unseen factor combinations.
High-resolution in-die metrology using beam profile reflectometry and ellipsometry

A new application for ultra-fast and repeatable in-die determination of CD structures at the ~1 &mgr;m length scale using BPR®/BPE® (Beam Profile Reflectometry/Ellipsometry) technologies on an Opti-Probe OP9000 series system, is presented and summarized. Two structures were measured and analyzed, including a poly-silicon CD standard and an advanced poly-silicon recessed structure relevant to advanced memory devices. A focused beam spot (~1 &mgr;m) and "fast BPR" data acquisition capability (~17 ms) were utilized to perform high-resolution scans across wafer and within single die regions. Rotating Compensator Spectroscopic Ellipsometry (RCSE®) signals were also used to independently determine and compare to BPR results from data collected over larger areas (~15 &mgr;m). The BPR/BPE and SE results for line CD were found to have high correlation. Further, model regression for SE data coupled with an artificial neural network model and fast BPR were utilized to measure and calculate 10,000 points across a 1 mm2 area in a matter of minutes. Overall, the results were found to be repeatable and correlated well to CD-SEM analysis.
Machine Learning for the Posture Evaluation of Women Snatch Barbell Trajectory

Barbell trajectory provides much kinematic information which can indicate the lifter's performance. However, kinematic parameters are not only gathering difficult but also hard to understand. This paper proposes a barbell trajectory evaluation inference that indicates the lifter's snatch performance from the barbell trajectory. We gathered four competitions and obtained the barbell trajectories from each lifter's attempt. Furthermore, five weightlifting experts recruit to indicate the performance categories, which are goodlift-good posture, goodlift-normal posture, nolift-good posture, and nolift-normal posture, as our data label. VGG16 convolution neural network utilize in our trajectory evaluation inference. The accuracy of the proposed inference is approximate 71.11%. From these results, our proposed barbell trajectory inference can provide a high accuracy performance evaluator for athlete self-training and competition performance analysis.
Proxy-based Metric Learning for Emotion Recognition

Emotion Recognition (ER) is an essential research area of natural language processing that can be applied to various fields. Texts in the fields of health care, marketing, and psychological counseling take various forms, and it is very important from a business point of view to find the emotions inherent in these texts. Recently, ER using text embeddings generated through a pre-trained language model with a large corpus was performed. However, since the embeddings are generalized to various domains, there is a limitation to directly using them for ER. In this study, to overcome the limitation, we propose a method that modifies generalized embeddings to emotional embeddings by performing proxy-based metric learning. In the proposed method, we fine-tuned the pre-trained language model by using proxy-anchor loss so that embeddings represent emotion appropriately. Previous studies only added linear classifiers. But, it is possible to capture emotional relationships between data by using proxy-based metric learning. In this study, we conducted ER experiments with benchmark datasets. The experimental result shows that the proposed method achieves better performance than the baseline and creates emotion-specific embeddings.
Measuring Airport Service Quality Using Machine Learning Algorithms

The airport industry is a highly competitive market that has expanded quickly during the last two decades. Airport management usually measures the level of passenger satisfaction by applying the traditional methods, such as user surveys and expert opinions, which require time and effort to analyse. Recently, there has been considerable attention on employing machine learning techniques and sentiment analysis for measuring the level of passenger satisfaction. Sentiment analysis can be implemented using a range of different methods. However, it is still uncertain which techniques are better suited for recognising the sentiment for a particular subject domain or dataset. In this paper, we analyse the sentiment of air travellers using five different algorithms, namely Logistic Regression, XGBoost, Support Vector Machine, Random Forest and Naïve Bayes. We obtain our data set through the SKYTRAX website which is a collection of reviews of around 600 airports. We apply some pre-processing steps, such as converting the textual reviews into numerical form, by using the term frequency-inverse document frequency. We also remove stopwords from the text using the NLTK list of stopwords. We evaluate our results using the accuracy, precision, recall and F1_score performance metrics. Our analysis shows that XGBoost provides the most accurate results when compared with other algorithms.
Low-Rank Gradient Descent for Memory-Efficient Training of Deep In-Memory Arrays

The movement of large quantities of data during the training of a deep neural network presents immense challenges for machine learning workloads, especially those based on future functional memories deployed to store network models. As the size of network models begins to vastly outstrip traditional silicon computing resources, functional memories based on flash, resistive switches, magnetic tunnel junctions, and other technologies can store these new ultra-large models. However, new approaches are then needed to minimize hardware overhead, especially on the movement and calculation of gradient information that cannot be efficiently contained in these new memory resources. To do this, we introduce streaming batch principal component analysis (SBPCA) as an update algorithm. Streaming batch principal component analysis uses stochastic power iterations to generate a stochastic rank-k approximation of the network gradient. We demonstrate that the low-rank updates produced by streaming batch principal component analysis can effectively train convolutional neural networks on a variety of common datasets, with performance comparable to standard mini-batch gradient descent. Our approximation is made in an expanded vector form that can efficiently be applied to the rows and columns of crossbars for array-level updates. These results promise improvements in the design of application-specific integrated circuits based around large vector-matrix multiplier memories.
Relationship between histopathological lesions and oxidative stress in mice infected with Angiostrongylus costaricensis

Abstract This study describes changes in oxidative stress (OS) parameters in mice experimentally infected with Angiostrongylus costaricensis, which causes abdominal angiostrongyliasis. For this, 28 Swiss mice were used, divided into two groups (G1 and G2), with 14 animals each. Of these, eight were infected with ten infective larvae each, by gavage, and six were used as a control group. Mice from G1 and G2 were euthanized at 14 days and 24 days post-infection, respectively. Tissue samples were used for histopathological analysis and blood (serum) samples were taken to assess the levels of proteins, non-protein thiols (NPTs) and nitric oxide (NO), from centrifugation and subsequent collection of aliquots of the supernatant. Among OS parameters, infected mice in both groups had higher NO levels than the control group, due to the presence of: eosinophil infiltrate in the liver and intestine; pancreatitis; and intestinal granuloma. However, the infected mice of both groups showed a reduction in the levels of NPTs, in relation to the control group, due to the presence of: eosinophilic infiltrate in the liver and intestine; and intestinal granuloma. Our results suggest that A. costaricensis infection has important effects on the intestine, liver and pancreas, and the analyses were performed from the tissue of these organs. The mechanisms for these changes are related to the decrease in the body's main antioxidant defences, as demonstrated by the reduction of NPTs, thus contributing to the development of more severe tissue damage. Thus, the objective of the present study was to evaluate the relationship between histopathological lesions and markers for OS.
Perfluorooctane sulfonate promotes hepatic lipid accumulation and steatosis in high‐fat diet mice through AMP‐activated protein kinase/acetyl‐CoA carboxylase (AMPK/ACC) pathway

Perfluorooctane sulfonate (PFOS) is a hepatotoxic environmental organic pollutant that can cause aberrant lipid accumulation in the liver. However, the molecular mechanism underlying PFOS‐induced hepatic steatosis remains unclear. Our research showed that subchronic PFOS exposure inhibited AMP‐activated protein kinase (AMPK) phosphorylation, leading to increased acetyl‐CoA carboxylase (ACC) activity, attenuated fatty acid β‐oxidation, and consequent liver lipid accumulation. We found that 1 mg/kg/day PFOS exposure significantly aggravated steatosis in high‐fat diet (HFD)‐fed mice, along with reduced AMPK activity. Oil Red O results showed that PFOS exposure caused fat accumulation in HepG2 cells. As predicted, PFOS treatment reduced the level of phosphorylated AMPK in a concentration‐dependent manner, leading to subsequent increase in ACC activity and lipid droplet accumulation in HepG2 cells. Treatment with 200‐μM AMPK agonist AICAR alleviated PFOS‐induced ACC activation and lipid accumulation. In summary, our data highlight a crucial role of AMPK/ACC pathway in PFOS‐mediated liver lipid metabolic disorders.
Diabetic Foot Assessment using Skin Impedance in a Custom Made Sensor-sock

Abstract Diabetic peripheral neuropathy (DPN) may lead to several changes in the skin, and some of these may influence the skin impedance spectrum. In the present study we have developed a prototype solution for skin impedance spectroscopy at selected skin sites (big toe pulp, heel and toe ball) that was tested in a pilot study on five patients with DPN and five healthy controls. At the big toe, most of the controls had markedly lower impedance than the DPN group, especially in the range of 1-100 kHz. The separation between the groups seems to be weaker at the heel and weakest at the toeball. The results may indicate that monitoring of the skin impedance spectrum may be a method for detection of skin changes associated with DPN, encouraging further studies with the big toe sensor in particular.
Intrinsic diffraction losses in photonic crystal waveguides with line defects

Intrinsic diffraction losses of linear defect modes in photonic crystal slabs are calculated for membrane-type waveguides with strong refractive index contrast. In the frequency region of high group velocity of the defect mode, the radiative losses increase with the air fraction of the lattice and decrease on increasing the channel width or the slab thickness. Close to a mini-gap in the mode dispersion, a complex frequency dependence of the losses is found. The calculated losses agree well with those measured in a Si slab [M. Loncar et al., Appl. Phys. Lett. 80, 1689 (2002)].
Associative white matter tracts selectively predict sensorimotor learning

Human learning is a complex phenomenon that varies greatly among individuals and is related to the microstructure of major white matter tracts in several learning domains, yet the impact of the existing myelination of white matter tracts on future learning outcomes remains unclear. We employed a machine-learning model selection framework to evaluate whether existing microstructure might predict individual differences in the potential for learning a sensorimotor task, and further, if the mapping between the microstructure of major white matter tracts and learning was selective for learning outcomes. We used diffusion tractography to measure the mean fractional anisotropy (FA) of white matter tracts in 60 adult participants who then underwent training and subsequent testing to evaluate learning. During training, participants practiced drawing a set of 40 novel symbols repeatedly using a digital writing tablet. We measured drawing learning as the slope of draw duration over the practice session and visual recognition learning as the performance accuracy in an old/new 2-AFC recognition task. Results demonstrated that the microstructure of major white matter tracts selectively predicted learning outcomes, with left hemisphere pArc and SLF 3 tracts predicting drawing learning and the left hemisphere MDLFspl predicting visual recognition learning. These results were replicated in a repeat, held-out data set and supported with complementary analyses. Overall, results suggest that individual differences in the microstructure of human white matter tracts may be selectively related to future learning outcomes and open avenues of inquiry concerning the impact of existing tract myelination in the potential for learning. Significance statement A selective mapping between tract microstructure and future learning has been demonstrated in the murine model and, to our knowledge, has not yet been demonstrated in humans. We employed a data-driven approach that identified only two tracts, the two most posterior segments of the arcuate fasciculus in the left hemisphere, to predict learning a sensorimotor task (drawing symbols) and this prediction model did not transfer to other learning outcomes (visual symbol recognition). Results suggest that individual differences in learning may be selectively related to the tissue properties of major white matter tracts in the human brain.
Long Non-Coding RNA Generated from CDKN1A Gene by Alternative Polyadenylation Regulates p21 Expression during DNA Damage Response

Alternative Polyadenylation (APA) is an emerging mechanism for dynamic changes in gene expression. Previously, we described widespread APA occurrence in introns during the DNA damage response (DDR). Here, we show that a DNA damage activated APA event occurs in the first intron of CDKN1A, inducing an alternate last exon (ALE)-containing lncRNA. We named this lncRNA SPUD (Selective Polyadenylation Upon Damage). SPUD localizes to polysomes in the cytoplasm and is detectable as multiple isoforms in available high throughput studies. SPUD has low abundance compared to the CDKN1A full-length isoform and is induced in cancer and normal cells under a variety of DNA damaging conditions in part through p53 transcriptional activation. RNA binding protein (RBP) HuR and the transcriptional repressor CTCF regulate SPUD levels. SPUD induction increases p21 protein, but not CDKN1A full-length levels, affecting p21 functions in cell-cycle, CDK2 expression, and cell viability. Like CDKN1A full-length isoform, SPUD can bind two competitive p21 translational regulators, the inhibitor calreticulin and the activator CUGBP1; SPUD can change their association with CDKN1A full-length in a DDR-dependent manner. Together, these results show a new regulatory mechanism by which a lncRNA controls p21 expression post-transcriptionally, highlighting lncRNA relevance in DDR progression and cellcycle.
Concerning the Erection of Ichnogenera and Ichnospecies in Vertebrate Ichnotaxonomy

In accord with currently accepted principles of ichnological nomenclature, ichnotaxa must be named based on morphology. An ichnotaxobase is a set of characters of a footprint or trackway that must satisfy some conditions such as coherence clarity. Quantitative measurements must be utilized in descriptive statistical studies and potentiality of typical parameters. When formulating a diagnosis the ichnologist is not obliged to consider all the characters of an ichnite but only the most informative. The Information Theory confirms this method showing, by means of mathematics, that a description of a phenomenon does not require a statement on all details.