Miracle-dz/UniFAR-FTUs huggingface dataset:
Smart Crawling: A New Approach toward Focus Crawling from Twitter

Twitter is a social network that offers a rich and interesting source of information challenging to retrieve and analyze. Twitter data can be accessed using a REST API. The available operations allow retrieving tweets on the basis of a set of keywords but with limitations such as the number of calls per minute and the size of results. Besides, there is no control on retrieved results and finding tweets which are relevant to a specific topic is a big issue. Given these limitations, it is important that the query keywords cover unambiguously the topic of interest in order to both reach the relevant answers and decrease the number of API calls. In this paper, we introduce a new crawling algorithm called"SmartTwitter Crawling"(STiC) that retrieves a set of tweets related to a target topic. In this algorithm, we take an initial keyword query and enrich it using a set of additional keywords that come from different data sources. STiC algorithm relies on a DFS search in Twittergraph where each reached tweet is considered if it is relevant with the query keywords using a scoring, updated throughout the whole crawling process. This scoring takes into account the tweet text, hashtags and the users who have posted the tweet, replied to the tweet, been mentioned in the tweet or retweeted the tweet. Given this score, STiC is able to select relevant tweets in each iteration and continue by adding the related valuable tweets. Several experiments have been achieved for different kinds of queries, the results showedthat the precision increases compared to a simple BFS search.
Anatomy of OntoGUM—Adapting GUM to the OntoNotes Scheme to Evaluate Robustness of SOTA Coreference Algorithms

SOTA coreference resolution produces increasingly impressive scores on the OntoNotes benchmark. However lack of comparable data following the same scheme for more genres makes it difficult to evaluate generalizability to open domain data. Zhu et al. (2021) introduced the creation of the OntoGUM corpus for evaluating geralizability of the latest neural LM-based end-to-end systems. This paper covers details of the mapping process which is a set of deterministic rules applied to the rich syntactic and discourse annotations manually annotated in the GUM corpus. Out-of-domain evaluation across 12 genres shows nearly 15-20% degradation for both deterministic and deep learning systems, indicating a lack of generalizability or covert overfitting in existing coreference resolution models.
Searching for Dark Matter with circularly polarised photons from the Galactic Centre

Conventional indirect dark matter (DM) searches look for an excess in the electromagnetic emission from the sky that cannot be attributed to known astrophysical sources, but its polarisation has not been explored to date. In this proceeding, we argue that photon polarisation is an important feature to understand new physics interactions. In particular, circular polarisation can be generated from Beyond the Standard Model interactions if they violate parity and there is an asymmetry in the number density of the initial state particles which participate in the interaction. We consider a simplified model for fermionic (Majorana) DM and study the circularly polarised gamma rays below 10 GeV from DM cosmic ray electron interactions. We study the differential flux of positive and negative polarised photons from the Galactic Centre and show that the degree of circular polarization can reach up to 90%. Finally, we discuss the detection prospects of this signal in future experiments.
Deepfilternet: A Low Complexity Speech Enhancement Framework for Full-Band Audio Based On Deep Filtering

Complex-valued processing has brought deep learning-based speech enhancement and signal extraction to a new level. Typically, the process is based on a time-frequency (TF) mask which is applied to a noisy spectrogram, while complex masks (CM) are usually preferred over real-valued masks due to their ability to modify the phase. Recent work proposed to use a complex filter instead of a point-wise multiplication with a mask. This allows to incorporate information from previous and future time steps exploiting local correlations within each frequency band.In this work, we propose DeepFilterNet, a two stage speech enhancement framework utilizing deep filtering. First, we enhance the spectral envelope using ERB-scaled gains modeling the human frequency perception. The second stage employs deep filtering to enhance the periodic components of speech. Additionally to taking advantage of perceptual properties of speech, we enforce network sparsity via separable convolutions and extensive grouping in linear and recurrent layers to design a low complexity architecture.We further show that our two stage deep filtering approach outperforms complex masks over a variety of frequency resolutions and latencies and demonstrate convincing performance compared to other state-of-the-art models.
System for multi-robotic exploration of underground environments CTU-CRAS-NORLAB in the DARPA Subterranean Challenge

We present a field report of the CTU-CRAS-NORLAB team from the Subterranean Challenge (SubT) organized by the Defense Advanced Research Projects Agency (DARPA). The contest seeks to advance technologies that would improve the safety and efficiency of search-andrescue operations in GPS-denied environments. During the contest rounds, teams of mobile robots have to find specific objects while operating in environments with limited radio communication, e.g., mining tunnels, underground stations or natural caverns. We present a heterogeneous exploration robotic system of the CTU-CRAS-NORLAB team, which achieved the third rank at the SubT Tunnel and Urban Circuit rounds and surpassed the performance of all other non-DARPA-funded teams. The field report describes the team’s hardware, sensors, algorithms and strategies, and discusses the lessons learned by participating at the DARPA SubT contest.
Hotel Preference Rank based on Online Customer Review

Top line hotels are now shifting into the digital way in how they understand their customers to maintain and ensuring satisfaction. Rather than the conventional way which uses written review or interviews, the hotel is now heavily investing in Artificial Intelligence particularly Machine Learning solutions. Analysis of online customer reviews changes the way companies make decisions in a more effective way than using conventional analysis. The purpose of this research is to measure hotel service quality. The proposed approach emphasizes on service quality dimensions reviews of the top-5 luxury hotel in Indonesia that appear on online travel site TripAdvisor based on section “Best of 2018”. In this research, we use a model based on a simple Bayesian classifier to classify each of customer review into one of service quality dimensions. Our model was able to separate each classification properly by accuracy, kappa, recall, precision, and F-measure measurements. To uncover latent topics in the customer opinion we use Topic Modelling. We found that the common issues occur is about responsiveness as it got the lowest percentage compared to others. Our research provides a faster outlook of hotel rank based on service quality to end customer based on a summary of the previous online review. Keywords— Text Analytics, Topic Modelling, Service Quality
TSK Fuzzy System Towards Few Labeled Incomplete Multi-View Data Classification

Data collected by multiple methods or from multiple sources is called multi-view data. To make full use of the multi-view data, multi-view learning plays an increasingly important role. Traditional multi-view learning methods rely on a large number of labeled and completed multi-view data. However, it is expensive and time-consuming to obtain a large number of labeled multi-view data in real-world applications. Moreover, multi-view data is often incomplete because of data collection failures, self-deficiency, or other reasons. Therefore, we may have to face the problem of fewer labeled and incomplete multi-view data in real application scenarios. In this paper, a transductive semi-supervised incomplete multi-view TSK fuzzy system modeling method (SSIMV_TSK) is proposed to address these challenges. First, in order to alleviate the dependency on labeled data and keep the model interpretable, the proposed method integrates missing view imputation, pseudo label learning of unlabeled data, and fuzzy system modeling into a single process to yield a model with interpretable fuzzy rules. Then, two new mechanisms, i.e. the bidirectional structural preservation of instance and label, as well as the adaptive multiple alignment collaborative learning, are proposed to improve the robustness of the model. The proposed method has the following distinctive characteristics: 1) it can deal with the incomplete and few labeled multi-view data simultaneously; 2) it integrates the missing view imputation and model learning as a single process, which is more efficient than the traditional twostep strategy; 3) attributed to the interpretable fuzzy inference rules, this method is more interpretable. Experimental results on real datasets show that the proposed method significantly outperforms the state-of-the-art methods. Index Terms – incomplete multi-view data, few labeled data, fuzzy systems, pseudo label learning, missing view imputation.
What Should Future Wireless Network Architectures Be?

The accelerated convergence of digital and real-world lifestyles has imposed unprecedented demands on today's wireless network architectures, as it is highly desirable for such architectures to support wireless devices everywhere with high capacity and minimal signaling overhead. Conventional architectures, such as cellular architectures, are not able to satisfy these requirements simultaneously, and are thus no longer suitable for the future era. In this paper, we propose a capacity-centric (C$^2$) architecture for future wireless communication networks. It is designed based on the principles of maximizing the number of non-overlapping clusters with the average cluster capacity guaranteed to be higher than a certain threshold, and thus provides a flexible way to balance the capacity requirement against the signaling overhead. Our analytical results reveal that C$^2$ has superior generality, wherein both the cellular and the fully coordinated architectures can be viewed as its extreme cases. Simulation results show that the average capacity of C$^2$ is at least three times higher compared to that of the cellular architecture. More importantly, different from the widely adopted conventional wisdom that base-station distributions dominate architecture designs, we find that the C$^2$ architecture is independent of base-station distributions, and instead user-side information should be the focus in future wireless network architecture designs.
Deep kernel machines and fast solvers for deep kernel machines

Deep neural networks (DNNs) with the ﬂexi-bility to learn good top-layer representations have eclipsed shallow kernel methods without that ﬂexibility. Here, we take inspiration from DNNs to develop the ﬁrst non-Bayesian deep kernel method, the deep kernel machine. In addition, we develop a solver for the intermediate layer kernels in deep kernel machines that converges in around 10 steps, exploiting matrix solvers initially developed in the control theory literature. These are many times faster the usual gradient descent approach and generalise to arbitrary architectures. While deep kernel machines currently scale poorly in the number of datapoints, we believe that this can be rectiﬁed in future work, allowing deep kernel machines to form the basis of a new class of much more eﬃcient deep nonlinear function approximators.
pH-Responsive Polymers for Biosurfaces: An Investigation of Solution Properties and Surface Modifying Performance

pH-RESPONSIVE POLYMERS FOR BIOSURFACES: AN INVESTIGATION OF SOLUTION PROPERTIES AND SURFACE MODIFYING PERFORMANCE by Yuhong Wei May 2012 Gantrez® copolymers are used widely in the personal care industry, but their mechanisms of interaction with dental surfaces are poorly understood. In this study, we determined a number of the solution and polymer properties that control deposition, adsorption and adhesion in Gantrez® polymer aqueous solutions to model dental surfaces. QCM studies specifically showed that polymer adsorption to model hydroxyapatite surfaces occurred only in solutions with polymer concentration above c*. Adsorption and adhesion was greater in solutions of higher pH, where greater charge density along the polymer backbone was expected. For films prepared via spin-coating onto mica surfaces, addition of surfactants to the solution resulted in film surfaces with more regular morphology and surface coverage. Flow-through studies in QCM, however, showed that there was no polymer deposition or adhesion to the silicon surface from solution in the presence or absence of surfactants. The results from these studies provide an expanded database to allow researchers to design formulations to meet specific requirements of deposition and adhesion for personal care applications.
Sonic Hedgehog Induces Mesenchymal Stromal Cell Senescence-Associated Secretory Phenotype and Chondrocyte Apoptosis in Human Osteoarthritic Cartilage

Hedgehog (HH) signaling plays a critical role in osteoarthritis (OA) pathogenesis, but the molecular mechanism remains to be elucidated. We show here that Sonic Hedgehog (SHH) gene expression is initiated in human normal cartilage stromal cells (NCSC) and increased in OA cartilage mesenchymal stromal cells (OA-MSCs) during aging. Manifesting a reciprocal cellular distribution pattern, the SHH receptors PTCH1 and SMO and transcription factors GLI2 and GLI3 are expressed by chondrocytes (OAC) in OA cartilage. SHH autocrine treatment of osteoarthritis MSC stimulates proliferation, chondrogenesis, hypertrophy, and replicative senescence with elevated SASP gene expression including IL1B, IL6, CXCL1, and CXCL8. SHH paracrine treatment of OAC suppresses COL2A1, stimulates MMP13, and induces chondrocyte apoptosis. The OA-MSC conditioned medium recapitulates the stimulatory effects of SHH on OAC catabolism and apoptosis. SHH knock-down in OA-MSC not only inhibits catabolic and senescence marker expression in OA-MSC, but also abolishes the effect of the OA-MSC conditioned medium on OAC catabolism and apoptosis. We propose that SHH is a key mediator between OA-MSC and OA chondrocytes interaction in human OA cartilage via two mechanisms: (1) SHH mediates MSC growth and aging by activating not only its proliferation and chondrogenesis, but also low-grade inflammation and replicative senescence, and (2) SHH mediates OA-MSC-induced OAC catabolism and apoptosis by creating a pro-inflammatory microenvironment favoring tissue degeneration during OA pathogenesis.
Supraspinatus-to-Glenoid Contact Occurs During Standardized Overhead Reaching Motion

Background: Rotator cuff tears may result from repeated mechanical deformation of the cuff tendons, and internal impingement of the supraspinatus tendon against the glenoid is one such proposed mechanism of deformation. Purpose: To (1) describe the changing proximity of the supraspinatus tendon to the glenoid during a simulated overhead reaching task and (2) determine the relationship between scapular morphology and this proximity. Additionally, the patterns of supraspinatus-to-glenoid proximity were compared with previously described patterns of supraspinatus-to-coracoacromial arch proximity. Study Design: Descriptive laboratory study. Methods: Shoulder models were created from magnetic resonance images of 20 participants. Standardized kinematics were imposed on the models to simulate functional reaching, and the minimum distances between the supraspinatus tendon and the glenoid and the supraspinatus footprint and the glenoid were calculated every 5° between 0° and 150° of humerothoracic elevation. The angle at which contact between the supraspinatus and the glenoid occurred was documented. Additionally, the relationship between glenoid morphology (version and inclination) and the contact angle was evaluated. Descriptive statistics were calculated for the minimum distances, and glenoid morphology was assessed using Pearson correlation coefficients and simple linear regressions. Results: The minimum distances between the tendon and the glenoid and between the footprint and the glenoid decreased as elevation increased. Contact between the tendon and the glenoid occurred in all participant models at a mean elevation of 123° ± 10°. Contact between the footprint and the glenoid occurred in 13 of 20 models at a mean of 139° ± 10°. Less glenoid retroversion was associated with lower tendon-to-glenoid contact angles (r = –0.76; R 2 = 0.58; P < .01). Conclusion: This study found that the supraspinatus tendon progressively approximated the glenoid during simulated overhead reaching. Additionally, all participant models eventually made contact with the glenoid by 150° of humerothoracic elevation, although anatomic factors influenced the precise angle at which contact occurred. Clinical Relevance: Contact between the supraspinatus and the glenoid may occur frequently within the range of elevation required for overhead activities. Therefore, internal impingement may be a prevalent mechanism for rotator cuff deformation that could contribute to cuff pathology.
p53-Induced Autophagy Regulates Chemotherapy and Radiotherapy Resistance in Multidrug Resistance Cancer Cells

Background Multidrug resistance (MDR), a major problem in oncology therapy, limits the effectiveness of anticancer drugs. Although p53 functions as a tumor suppressor, the associations between p53 status, autophagy, and MDR are complicated and conditional. Method In this report, p53-null human ovarian cancer cell line SKOV3 and its MDR phenotype SKVCR and human leukemia cell line CEM and its MDR phenotype CEM-VLB) (p53 mutant cell line) were used. Results Compared to parental SKOV3, the mRNA and protein levels of MAPLC3-II and Beclin1 were higher in SKVCR cells. The inhibition of autophagy by 3-MA significantly sensitized SKVCR to VCR. Conversely, in drug-resistant leukemic cells CEM-VLB, the expressions of Beclin1 and MAPLC3-II were lower than CEM. CEM and CEM-VLB cells were treated with VLB . 01 or 0.5 μg/mL, respectively, and the expression of p53 and autophagy up-regulated after VLB (.01 μg/mL) treatment in CEM cells. The percentage of S-phase and G2/M phase cells up-regulated significantly by . 01 μg/mL VLB in CEM, which may relate to the status of p53 of CEM cells. A combination of radiation with 3-MA significantly increased apoptosis in CEM-VLB cells. Conclusion Our discovery found that p53 is an important regulator controlling the balance between autophagy and MDR, as a potential drug target for ovarian cancer and leukemia.
Classification of Alzheimer's Disease Using Gaussian-Based Bayesian Parameter Optimization for Deep Convolutional LSTM Network

Alzheimer's disease (AD) is one of the most important causes of mortality in elderly people, and it is often challenging to use traditional manual procedures when diagnosing a disease in the early stages. The successful implementation of machine learning (ML) techniques has also shown their effectiveness and its reliability as one of the better options for an early diagnosis of AD. But the heterogeneous dimensions and composition of the disease data have undoubtedly made diagnostics more difficult, needing a sufficient model choice to overcome the difficulty. Therefore, in this paper, four different 2D and 3D convolutional neural network (CNN) frameworks based on Bayesian search optimization are proposed to develop an optimized deep learning model to predict the early onset of AD binary and ternary classification on magnetic resonance imaging (MRI) scans. Moreover, certain hyperparameters such as learning rate, optimizers, and hidden units are to be set and adjusted for the performance boosting of the deep learning model. Bayesian optimization enables to leverage advantage throughout the experiments: A persistent hyperparameter space testing provides not only the output but also about the nearest conclusions. In this way, the series of experiments needed to explore space can be substantially reduced. Finally, alongside the use of Bayesian approaches, long short-term memory (LSTM) through the process of augmentation has resulted in finding the better settings of the model that too in less iterations with an relative improvement (RI) of 7.03%, 12.19%, 10.80%, and 11.99% over the four systems optimized with manual hyperparameters tuning such that hyperparameters that look more appealing from past data as well as the conventional techniques of manual selection.
The Impact of PTPRK and ROS1 Polymorphisms on the Preeclampsia Risk in Han Chinese Women

Objective Preeclampsia (PE) is a severe complication in pregnancy and a leading cause of maternal and infant mortality. However, the exact underlying etiology of PE remains unknown. Emerging evidence indicates that the cause of PE is associated with genetic factors. Therefore, the aim of this study is to identify susceptibility genes to PE. Materials and Methods Human Exome BeadChip assays were conducted using 370 cases and 482 controls and 21 loci were discovered. A further independent set of 958 cases and 1007 controls were recruited for genotyping to determine whether the genes of interest ROS1 and PTPRK are associated with PE. Immunohistochemistry was used for localization. Both qPCR and Western blotting were utilized to investigate the levels of PTPRK in placentas of 20 PE and 20 normal pregnancies. Results The allele frequency of PTPRK rs3190930 differed significantly between PE and controls and was particularly significant in severe PE subgroup and early-onset PE subgroup. PTPRK is primarily localized in placental trophoblast cells. The mRNA and protein levels of PTPRK in PE were significantly higher than those in controls. Conclusion These results suggest that PTPRK appears to be a previously unrecognized susceptibility gene for PE in Han Chinese women, and its expression is also associated with PE, while ROS1 rs9489124 has no apparent correlation with PE risk.
Interactions between Endoplasmic Reticulum Stress and Autophagy: Implications for Apoptosis and Neuroplasticity-Related Proteins in Palmitic Acid-Treated Prefrontal Cells

Lipotoxicity of palmitic acid (PA) or high-fat diets has been reported to increase endoplasmic reticulum (ER) stress and autophagy in peripheral tissue as well as apoptotic cell death. It also can lead to an AD-like pathological pattern. However, it has been unknown that PA-induced ER stress and autophagy are involved in the regulation of neuroplastic abnormalities. Here, we investigated the roles of ER stress and autophagy in apoptosis and neuroplasticity-related protein expression in PA-treated prefrontal cells. Prefrontal cells dissected from newborn Sprague-Dawley rats were treated with PA compound with ER stress inhibitor 4-phenylbutyric acid (4-PBA) and autophagy inhibitor 3-methyladenine (3-MA) or PA alone. PA promoted ER stress and autophagy and also cause apoptosis as well as a decline in the expression of neuroplasticity-related proteins. Inhibition of ER stress decreased the expressions of neuroplasticity-related proteins and reduced autophagy activation and apoptosis in PA-treated prefrontal cells. Inhibition of autophagy exacerbated apoptosis and enhanced ER stress in PA-treated prefrontal cells. The present study illustrated that both ER stress and autophagy could be involved in apoptosis and decreased neuroplasticity-related proteins, and the interaction between ER stress and autophagy may play a critical role in apoptosis in PA-treated prefrontal cells. Our results provide new insights into the molecular mechanisms in vitro of lipotoxicity in obesity-related cognitive dysfunction.