Miracle-dz/UniFAR-FTUs huggingface dataset:
A Transfer Learning Pipeline for Educational Resource Discovery with Application in Survey Generation

Effective human learning depends on a wide selection of educational materials that align with the learner’s current understanding of the topic. While the Internet has revolutionized human learning or education, a substantial resource accessibility barrier still exists. Namely, the excess of online information can make it challenging to navigate and discover high-quality learning materials in a given subject area. In this paper, we propose an automatic pipeline for building an educational resource discovery system for new domains. The pipeline consists of three main steps: resource searching, feature extraction, and resource classification. We first collect frequent queries from a set of seed documents, and search the web with these queries to obtain candidate resources such as lecture slides and introductory blog posts. Then, we process these resources for BERT-based features and meta-features. Next, we train a tree-based classifier to decide whether they are suitable learning materials. The pipeline achieves F1 scores of 0.94 and 0.82 when evaluated on two similar but novel domains. Finally, we demonstrate how this pipeline can benefit two applications: prerequisite chain learning and leading paragraph generation for surveys. We also release a corpus of 39,728 manually labeled web resources and 659 queries from NLP, Computer Vision (CV), and Statistics (STATS).
Annotating Discursive Roles of Sentences in Patent Descriptions

Patent descriptions are a crucial component of patent applications, as they are key to understanding the invention and play a significant role in securing patent grants. While discursive analyses have been undertaken for scientific articles, they have not been as thoroughly explored for patent descriptions, despite the increasing importance of Intellectual Property and the constant rise of the number of patent applications. In this study, we propose an annotation scheme containing 16 classes that allows categorizing each sentence in patent descriptions according to their discursive roles. We publish an experimental human-annotated corpus of 16 patent descriptions and analyze challenges that may be encountered in such work. This work can be base for an automated annotation and thus contribute to enriching linguistic resources in the patent domain.
An ANI gap within bacterial species that advances the definitions of intra-species units

ABSTRACT Large-scale surveys of prokaryotic communities (metagenomes), as well as isolate genomes, have revealed that their diversity is predominantly organized in sequence-discrete units that may be equated to species. Specifically, genomes of the same species commonly show genome-aggregate average nucleotide identity (ANI) >95% among themselves and ANI <90% to members of other species, while genomes showing ANI 90%–95% are comparatively rare. However, it remains unclear if such “discontinuities” or gaps in ANI values can be observed within species and thus used to advance and standardize intra-species units. By analyzing 18,123 complete isolate genomes from 330 bacterial species with at least 10 genome representatives each and available long-read metagenomes, we show that another discontinuity exists between 99.2% and 99.8% (midpoint 99.5%) ANI in most of these species. The 99.5% ANI threshold is largely consistent with how sequence types have been defined in previous epidemiological studies but provides clusters with ~20% higher accuracy in terms of evolutionary and gene-content relatedness of the grouped genomes, while strains should be consequently defined at higher ANI values (>99.99% proposed). Collectively, our results should facilitate future micro-diversity studies across clinical or environmental settings because they provide a more natural definition of intra-species units of diversity. IMPORTANCE Bacterial strains and clonal complexes are two cornerstone concepts for microbiology that remain loosely defined, which confuses communication and research. Here we identify a natural gap in genome sequence comparisons among isolate genomes of all well-sequenced species that has gone unnoticed so far and could be used to more accurately and precisely define these and related concepts compared to current methods. These findings advance the molecular toolbox for accurately delineating and following the important units of diversity within prokaryotic species and thus should greatly facilitate future epidemiological and micro-diversity studies across clinical and environmental settings. Bacterial strains and clonal complexes are two cornerstone concepts for microbiology that remain loosely defined, which confuses communication and research. Here we identify a natural gap in genome sequence comparisons among isolate genomes of all well-sequenced species that has gone unnoticed so far and could be used to more accurately and precisely define these and related concepts compared to current methods. These findings advance the molecular toolbox for accurately delineating and following the important units of diversity within prokaryotic species and thus should greatly facilitate future epidemiological and micro-diversity studies across clinical and environmental settings.
The longer the better: longer acrosomes contain more proteins involved in sperm-egg interactions

Speciation and sperm competition have been shown to be the major driving forces for sperm morphology variation, swimming velocity and metabolism. We used light microscopy to measure sperm traits and nLC-MS/MS to detect proteomic variation in three species of rodents – promiscuous Apodemus flavicollis, less promiscuous Microtus arvalis, and the least promiscuous Mus musculus musculus. We show that the length of sperm apical hook containing the acrosome is the most variable trait and that this variation is reflected by proteomes on interspecific and intraspecific levels. Thus, we provide potential markers of selection such as Ldhc (in Mus and Apodemus) for long acrosomes which is a gene coding L-lactate dehydrogenase that is involved in sperm motility and Spaca1, which is important in sperm-oocyte fusion), and e.g. Mup17 for short acrosomes – a gene coding a Major urinary protein that likely chelates lipophilic compounds after spermiogenesis. In short, longer acrosomes are characteristic of proteins involved in fertilisation and gluconeogenesis, while shorter acrosomes contain more cytoskeletal proteins important for spermiogenesis. For the first time, we demonstrate that there is an innate and evolvable variability in sperm morphology and corresponding proteomes within species that can be driven by sperm competition to species-specific reproductive optima. Summary statement We show that interspecific and intraspecific variation in sperm morphology traits are detectable on proteomic level thus providing markers of selection due to sperm competition.
Molecular Dynamics Simulations on Quercetin-3-(6-Malonylglucoside) From Morus Alba Shows Optimal Inhibition of Bcl-2 with Favorable Anti-Tumor Activities

The target of most cancer chemotherapeutic agents is to drive cancer cells toward death. A fine balance between anti-apoptotic and pro-apoptotic proteins is needed to maintain cellular homeostasis. Any shift favoring the pro-apoptotic ones is needed to drive cellular death in cancer chemotherapy. However, anti-apoptotic proteins such as Bcl-2 and Bcl-xL bind with pro-apoptotic proteins to hinder apoptosis mechanisms. Overexpression of these anti-apoptotic proteins lead to several cancers by preventing apoptosis. In this study, molecular docking, ADMET predictions, and molecular dynamics simulations were performed for the identification of potent inhibitors of anti-apoptotic Bcl-2 with compounds of Morus alba. Our study discovered that Quercetin-3-(6-Malonylglucoside) and Epigallocatechin gallate recorded excellent binding affinity with Bcl-2. Therefore, we conclude that compounds of Morus alba should be subjected to further experimental studies (in vitro and in vivo) in order to confirm the findings that they could be used as better options in cancer chemotherapy.
Basal MET Phosphorylation is an Indicator of Hepatocyte Dysregulation in Liver Disease

Chronic liver diseases are worldwide on the rise. Due to the rapidly increasing incidence, in particular in Western countries, Non-alcoholic fatty liver disease (NAFLD) is gaining importance as the disease can develop into hepatocellular carcinoma. Lipid accumulation in hepatocytes has been identified as the characteristic structural change in NAFLD development, but molecular mechanisms responsible for disease progression remained unresolved. Here, we uncover in primary hepatocytes from a preclinical model fed with a Western diet (WD) a strong downregulation of the PI3K-AKT pathway and an upregulation of the MAPK pathway. Dynamic pathway modeling of hepatocyte growth factor (HGF) signal transduction combined with global proteomics identifies that an elevated basal MET phosphorylation rate is the main driver of altered signaling leading to increased proliferation of WD-hepatocytes. Model-adaptation to patient-derived hepatocytes reveal patient-specific variability in basal MET phosphorylation, which correlates with patient outcome after liver surgery. Thus, dysregulated basal MET phosphorylation could be an indicator for the health status of the liver and thereby inform on the risk of a patient to suffer from liver failure after surgery.
Labeling the nodes in the intrinsic order graph with their weights

This paper deals with the study of some new properties of the intrinsic order graph. The intrinsic order graph is the natural graphical representation of a complex stochastic Boolean system (CSBS). A CSBS is a system depending on an arbitrarily large number n of mutually independent random Boolean variables. The intrinsic order graph displays its 2 vertices (associated to the CSBS) from top to bottom, in decreasing order of their occurrence probabilities. New relations between the intrinsic ordering and the Hamming weight (i.e., the number of 1-bits in a binary n-tuple) are derived. Further, the distribution of the weights of the 2 nodes in the intrinsic order graph is analyzed.
Single-cell landscape reveals NAMPT mediated macrophage polarization that regulate smooth muscle cell phenotypic switch in pulmonary arterial hypertension

Rationale Pulmonary arterial hypertension (PAH) is a progressive and lethal disease that leads to elevated pulmonary vascular resistance and right ventricular failure. The phenotypic switching of pulmonary arterial smooth muscle cells (SMCs) plays a crucial role in the pathological progression of PAH. However, the underlying mechanism of SMC phenotypic modulation remains unclear. Objectives We aim to provide a comprehensive understanding of SMC phenotypes and regulatory networks by analyzing hypertensive and non-diseased pulmonary arteries. Methods We performed single-cell RNA sequencing (scRNA-seq) on pulmonary arteries obtained from patients with PAH and healthy donors. This was followed by bioinformatics analyses, mouse models, and in vitro studies to construct a normal pulmonary artery atlas, characterize SMC phenotypes, investigate intercellular communication, and explore the molecular mechanisms underlying SMC phenotypic switching. Measurements and Main Results Our scRNA-seq analysis identified specific activation of vascular cells, including myofibrocytes, macrophage M2 polarization, endothelial-mesenchymal transition, and chondroid-like SMCs in healthy pulmonary arteries. In PAH pathology, there was an enhanced phenotypic switch of SMCs from contractile to fibroblast-like. Intercellular communication revealed increased M1 macrophage-SMC crosstalk in PAH, which was facilitated by NAMPT. Using a cellular co-culture system, we found that NAMPT-mediated M1 macrophage polarization induced fibroblast-like phenotypic switching in SMCs via the CCR2/CCR5 axis. Conclusions Our findings provide a comprehensive cell atlas of healthy human pulmonary arteries and demonstrate that NAMPT-driven M1 macrophage polarization plays a critical role in the fibroblast-like phenotypic switching of SMCs through CCR2/CCR5 cellular crosstalk in PAH.
Cell contacts and pericellular matrix in the Xenopus gastrula chordamesoderm

Convergent extension of the chordamesoderm is the best-examined gastrulation movement in Xenopus. Here we study general features of cell-cell contacts in this tissue by combining depletion of adhesion factors C-cadherin, Syndecan-4, fibronectin, and hyaluronic acid, the analysis of respective contact width spectra and contact angles, and La3+ staining of the pericellular matrix. We provide evidence that like in other gastrula tissues, cell-cell adhesion in the chordamesoderm is largely mediated by different types of pericellular matrix. Specific glycocalyx structures previously identified in Xenopus gastrula tissues are absent in chordamesoderm but other contact types like 10-20 nm wide La3+ stained structures are present instead. Knockdown of any of the adhesion factors reduces the abundance of cell contacts but not the average relative adhesiveness of the remaining ones: a decrease of adhesiveness at low contact widths is compensated by an increase of contact widths and an increase of adhesiveness proportional to width. From the adhesiveness-width relationship, we derive a model of chordamesoderm cell adhesion that involves the interdigitation of distinct pericellular matrix units. Quantitative description of pericellular matrix deployment suggests that reduced contact abundance upon adhesion factor depletion is due to some contact types becoming non-adhesive and others being lost.
Advanced Homomorphic Encryption for Cloud Data Security

This paper aims to provide security of data in the Cloud using Multiplicative Homomorphic Approach. Encryption process is done with RSA algorithm. In this RSA algorithm, Shor’s algorithm is used for generating Public key Component, which enhances the security. Shor’s algorithm plays as important role in generating public key. Plain Text Message is encrypted with Public Key to generate Cipher Text and for decryption Chinese Remainder Theorem (CRT) is used to speed up the computations. By doing so, it shows how the CRT representation of numbers in Zn can be used to perform modular exponentiation about much more efficiently using three extra values pre-computed from the prime factors of n. Hence, security is enhanced in the cloud provider.
AI-UPV at EXIST 2023 - Sexism Characterization Using Large Language Models Under The Learning with Disagreement Regime

With the increasing influence of social media platforms, it has become crucial to develop automated systems capable of detecting instances of sexism and other disrespectful and hateful behaviors to promote a more inclusive and respectful online environment. Nevertheless, these tasks are considerably challenging considering different hate categories and the author's intentions, especially under the learning with disagreements regime. This paper describes AI-UPV team's participation in the EXIST (sEXism Identification in Social neTworks) Lab at CLEF 2023. The proposed approach aims at addressing the task of sexism identification and characterization under the learning with disagreements paradigm by training directly from the data with disagreements, without using any aggregated label. Yet, performances considering both soft and hard evaluations are reported. The proposed system uses large language models (i.e., mBERT and XLM-RoBERTa) and ensemble strategies for sexism identification and classification in English and Spanish. In particular, our system is articulated in three different pipelines. The ensemble approach outperformed the individual large language models obtaining the best performances both adopting a soft and a hard label evaluation. This work describes the participation in all the three EXIST tasks, considering a soft evaluation, it obtained fourth place in Task 2 at EXIST and first place in Task 3, with the highest ICM-Soft of -2.32 and a normalized ICM-Soft of 0.79. The source code of our approaches is publicly available at https://github.com/AngelFelipeMP/Sexism-LLM-Learning-With-Disagreement.
Easily in the Middle

The aim of this paper is to elucidate the nature of adverbial modification in Germanic middles. I present new arguments against semantic/pragmatic accounts and in favour of a ‘structural’ approach: the adverb is required in languages that lack a syntactically represented Agent in order for the latter to be recoverable via identification with the adverb’s Experiencer/Benefactor. This enables us to make sense of the fact that French and Greek middles do not require adverbial modification: these are languages, whose middles have a syntactically active Agent, and hence do not require adverbial modification as a means of recovering it.
A Subspace Multi-Relation Model for the Expression of System Complexity

Modeling expression of complexity is one of the frontier research fields in system science. Classical graphical models characterize two-relations but lack of the ability of depicting multi-relations among objects in a system. In this paper, a multi-relation expression model is proposed based the subspaces of the attribute space of objects in a system. Properties of the multi-relation model are analyzed and an algorithm is provided to find the multi-relation subspaces in a data set. The relationship between the multi-relation model and a hyper graphical model is also discussed.
Evaluating the robustness of machine learning algorithm on Human Activity Recognition

Physical inactivity can cause several diseases, like diabetes, obesity, hypertension, cardiovascular diseases, depression, osteoporosis, and many other physical problems. To diagnose physical inactivity and avoid this kind of problems, it is needed to monitor the movements performed by someone and classify them as one of the Activities of Daily Living, like walking, laying down, standing, and other activities. In this context, signals from smartphone sensors can be used as input to machine learning models to perform such classiﬁcation. The present work has the objective to bring new results to the classiﬁcation phase using Multiple Classiﬁer Systems. It is used two public data sets with data collected from smartphone triaxial sensors: gyroscope and accelerometer. Thus, this work uses these two data sets to compare monolithic classiﬁers with Multiple Classiﬁer Systems using static and dynamic selection, using metrics like accuracy, precision, recall, and F1-score. For one of the data sets the best classiﬁer is K-Nearest-Oracle-Union (KNORA-U) achieving an accuracy of 95.62% and for the other data set is Random Forest with an accuracy of 88.04%. It was not possible to determine which is the best classiﬁer to use on Human Activity Recognition, because the monolithic classiﬁers and Multi-Classiﬁer System has similar results.
Intravoxel incoherent motion (IVIM)-DWI of the head and neck: comparisons of IVIM parameters between 1.5-T and 3-T scanners

Poster: "ECR 2016 / C-0576 / Intravoxel incoherent motion (IVIM)-DWI of the head and neck: comparisons of IVIM parameters between 1.5-T and 3-T scanners" by: "K. Nagatomo1, H. Yabuuchi1, S. Sonoda1, K. Kobayashi1, M. Kimura1, R. Mikayama2, H. Honda1; 1Fukuoka/JP, 2Fukuoka /JP"
Rapid broad area search and detection of Chinese surface-to-air missile sites using deep convolutional neural networks

Abstract. We evaluated how deep convolutional neural networks (DCNN) could assist in the labor-intensive process of human visual searches for objects of interest in high-resolution imagery over large areas of the Earth’s surface. Various DCNN were trained and tested using fewer than 100 positive training examples (China only) from a worldwide surface-to-air-missile (SAM) site dataset. A ResNet-101 DCNN achieved a 98.2% average accuracy for the China SAM site data. The ResNet-101 DCNN was used to process ∼19.6 M image chips over a large study area in southeastern China. DCNN chip detections (∼9300) were postprocessed with a spatial clustering algorithm to produce a ranked list of ∼2100 candidate SAM site locations. The combination of DCNN processing and spatial clustering effectively reduced the search area by ∼660X (0.15% of the DCNN-processed land area). An efficient web interface was used to facilitate a rapid serial human review of the candidate SAM sites in the China study area. Four novice imagery analysts with no prior imagery analysis experience were able to complete a DCNN-assisted SAM site search in an average time of ∼42 min. This search was ∼81X faster than a traditional visual search over an equivalent land area of ∼88,640 km2 while achieving nearly identical statistical accuracy (∼90% F1).
A kind of graded sub-pixel motion estimation algorithm combining time-domain characteristics with frequency-domain phase correlation

The mode of navigation based on scene match can assist UAV to achieve autonomous navigation and other missions. However, aerial multi-frame images of the UAV in the complex flight environment easily be affected by the jitter, noise and exposure, which will lead to image blur, deformation and other issues, and result in the decline of detection rate of the interested regional target. Aiming at this problem, we proposed a kind of Graded sub-pixel motion estimation algorithm combining time-domain characteristics with frequency-domain phase correlation. Experimental results prove the validity and accuracy of the proposed algorithm.
Mindfulness and Negative Emotions: Chain Mediation Effect of Perceived Social Support and Interpersonal Sensitivity

Previous research has confirmed that mindfulness may decrease negative emotions in college students. However, there has been less focus on the mechanism of mindfulness’s effect on negative emotions. This study explores the relationship between mindfulness and negative emotions and examines the mediating role of perceived social support and interpersonal sensitivity among college students in China. A total of 386 college students (65.8% female, 34.2% male) completed measures of The Five Facet Mindfulness Questionnaire (FFMQ), The 21-item Depression Anxiety Stress Scale (DASS-21), The Multidimensional Scale of Perceived Social Support (MSPSS), and The Interpersonal Sensitivity Measure (IPSM). Using model 6 in SPSS PROCESS macro 3.3 software, we found： (1) mindfulness was negatively related to negative emotions; (2) interpersonal sensitivity mediated the relationship between mindfulness and negative emotions; (3) mindfulness was negatively related to negative emotions through the chain mediating of perceived social support and interpersonal sensitivity. Mindfulness, perceived social support, interpersonal sensitivity, and negative emotions in college students exerted significant associations with each other. Students can try mindfulness training to increase the perception of social support, reduce interpersonal sensitivity, and reduce negative emotions. The findings of this study may shed new light on the prevention and treatment of negative emotions among college students.
Practically and Theoretically Efficient Garbage Collection for Multiversioning

Multiversioning is widely used in databases, transactional memory, and concurrent data structures. It can be used to support read-only transactions that appear atomic in the presence of concurrent update operations. Any system that maintains multiple versions of each object needs a way of efficiently reclaiming them. We experimentally compare various existing reclamation techniques by applying them to a multiversion tree and a multiversion hash table. Using insights from these experiments, we develop two new multiversion garbage collection (MVGC) techniques. These techniques use two novel concurrent version list data structures. Our experimental evaluation shows that our fastest technique is competitive with the fastest existing MVGC techniques, while using significantly less space on some workloads. Our new techniques provide strong theoretical bounds, especially on space usage. These bounds ensure that the schemes have consistent performance, avoiding the very high worst-case space usage of other techniques.