Miracle-dz/UniFAR-FTUs huggingface dataset:
Horizontal Federated Learning and Secure Distributed Training for Recommendation System with Intel SGX

With the advent of big data era and the development of artificial intelligence and other technologies, data security and privacy protection have become more important. Recommendation systems have many applications in our society, but the model construction of recommendation systems is often inseparable from users' data. Especially for deep learning-based recommendation systems, due to the complexity of the model and the characteristics of deep learning itself, its training process not only requires long training time and abundant computational resources but also needs to use a large amount of user data, which poses a considerable challenge in terms of data security and privacy protection. How to train a distributed recommendation system while ensuring data security has become an urgent problem to be solved. In this paper, we implement two schemes, Horizontal Federated Learning and Secure Distributed Training, based on Intel SGX(Software Guard Extensions) [1], an implementation of a trusted execution environment [2], and TensorFlow framework [3], to achieve secure, distributed recommendation system-based learning schemes in different scenarios. We experiment on the classical Deep Learning Recommendation Model (DLRM), which is a neural network-based machine learning model designed for personalization and recommendation [4], and the results show that our implementation introduces approximately no loss in model performance. The training speed is within acceptable limits.
A semi-supervised geometric-driven methodology for supervised fishing activity detection on multi-source AIS tracking messages

Automatic Identiﬁcation System (AIS) messages are useful for tracking vessel activity across oceans worldwide using radio links and satellite transceivers. Such data plays a signiﬁcant role in tracking vessel activity and mapping mobility patterns such as those found in ﬁshing. Accordingly, this paper proposes a geometric-driven semi-supervised approach for ﬁshing activity detection from AIS data. Through the proposed methodology we show how to explore the information included in the messages to extract features describing the geometry of the vessel route. To this end, we leverage the unsupervised nature of cluster analysis to label the trajectory geometry highlighting the changes in the vessel’s moving pattern which tends to indicate ﬁshing activity. The labels obtained by the proposed unsupervised approach are used to detect ﬁshing activities, which we approach as a time-series classiﬁcation task. In this context, we propose a solution using recurrent neural networks on AIS data streams with roughly 87 % of the overall F -score on the whole trajectories of 50 different unseen ﬁshing vessels. Such results are accompanied by a broad benchmark study assessing the performance of different Recurrent Neural Network (RNN) architectures. In conclusion, this work contributes by proposing a thorough process that includes data preparation, labeling, data modeling, and model validation. Therefore, we present a novel solution for mobility pattern detection that relies upon unfolding the trajectory in time and observing their inherent geometry.
Long Short-Term Memory to predict 3D Amino acids Positions in GPCR Molecular Dynamics

G-Protein Coupled Receptors (GPCRs) are a big family of eukaryotic cell transmembrane proteins, responsible for numerous biological processes. From a practical viewpoint around 34\% of the drugs approved by the US Food and Drug Administration target these receptors. They can be analyzed from their simulated molecular dynamics, including the prediction of their behavior in the presence of drugs. In this paper, the capability of Long Short-Term Memory Networks (LSTMs) are evaluated to learn and predict the molecular dynamic trajectories of a receptor. Several models were trained with the 3D position of the amino acids of the receptor considering different transformations on the position of the amino acid, such as their centers of mass, the geometric centers and the position of the $\alpha$--carbon for each amino acid. The error of the prediction of the position was evaluated by the mean average error (MAE) and root-mean-square deviation (RMSD). The LSTM models show a robust performance, with results comparable to the state-of-the-art in non-dynamic 3D predictions. The best MAE and RMSD values were found for the mass center of the amino acids with 0.078 {\AA} and 0.156 {\AA} respectively. This work shows the potential of LSTM to predict the molecular dynamics of GPRCs.
Htex

We introduce per-halfedge texturing (Htex) a GPU-friendly method for texturing arbitrary polygon-meshes without an explicit parameterization. Htex builds upon the insight that halfedges encode an intrinsic triangulation for polygon meshes, where each halfedge spans a unique triangle with direct adjacency information. Rather than storing a separate texture per face of the input mesh as is done by previous parameterization-free texturing methods, Htex stores a square texture for each halfedge and its twin. We show that this simple change from face to halfedge induces two important properties for high performance parameterization-free texturing. First, Htex natively supports arbitrary polygons without requiring dedicated code for, e.g, non-quad faces. Second, Htex leads to a straightforward and efficient GPU implementation that uses only three texture-fetches per halfedge to produce continuous texturing across the entire mesh. We demonstrate the effectiveness of Htex by rendering production assets in real time.
High‐order leader‐follower tracking control under limited information availability

Limited information availability represents a fundamental challenge for control of multi‐agent systems, since an agent often lacks sensing capabilities to measure certain states of its own and can exchange data only with its neighbors. The challenge becomes even greater when agents are governed by high‐order dynamics. The present work is motivated to conduct control design for linear and nonlinear high‐order leader‐follower multi‐agent systems in a context where only the first state of an agent is measured. To address this open challenge, we develop novel distributed observers to enable followers to reconstruct unmeasured or unknown quantities about themselves and the leader and on such a basis, build observer‐based tracking control approaches. We analyze the convergence properties of the proposed approaches and validate their performance through simulation.
Reward Maximisation through Discrete Active Inference

Active inference is a probabilistic framework for modelling the behaviour of biological and artificial agents, which derives from the principle of minimising free energy. In recent years, this framework has successfully been applied to a variety of situations where the goal was to maximise reward, offering comparable and sometimes superior performance to alternative approaches. In this paper, we clarify the connection between reward maximisation and active inference by demonstrating how and when active inference agents perform actions that are optimal for maximising reward. Precisely, we show the conditions under which active inference produces the optimal solution to the Bellman equation--a formulation that underlies several approaches to model-based reinforcement learning and control. On partially observed Markov decision processes, the standard active inference scheme can produce Bellman optimal actions for planning horizons of 1, but not beyond. In contrast, a recently developed recursive active inference scheme (sophisticated inference) can produce Bellman optimal actions on any finite temporal horizon. We append the analysis with a discussion of the broader relationship between active inference and reinforcement learning.
Trans-anal irrigation in patients with multiple sclerosis: Efficacy in treating disease-related bowel dysfunctions and impact on the gut microbiota: A monocentric prospective study

Background Constipation and faecal incontinence are not so uncommon in patients with multiple sclerosis, impairing quality of life. The gut microbiota is altered in multiple sclerosis patients and likely contributes to disease pathogenesis. Trans-anal irrigation has been proven to allow treatment of neurogenic bowel dysfunction and may affect gut microbiota. Objectives The primary outcome was trans-anal irrigation effectiveness on constipation and faecal incontinence. The secondary outcome was gut microbiota profiling compared to healthy subjects and during trans-anal irrigation adoption. Methods We conducted a prospective cohort study on multiple sclerosis patients, screened with Patient Assessment of Constipation Quality of Life questionnaire before undergoing constipation and faecal incontinence scoring, abdomen X-ray for intestinal transit time, compilation of food and evacuation diaries and faecal sample collection for gut microbiota analysis before and after 4 weeks of trans-anal irrigation. Results and Conclusions Eighty patients were screened of which nearly half had intestinal symptoms. The included population (n = 37) was predominantly composed of women with significantly longer disease duration, higher mean age and disability than the excluded one (p < 0.05). Twelve patients completed the trans-anal irrigation phase, which led to significant improvement of bowel dysfunction symptom-related quality of life, increase in gut microbiota diversity and reduction of the proportions of pro-inflammatory taxa (p < 0.05). Trans-anal irrigation was safe, satisfactory and could help counteract multiple sclerosis-related dysbiosis.
WheaCha: A Method for Explaining the Predictions of Models of Code

Attribution methods have emerged as a popular approach to interpreting model predictions based on the relevance of input features. Although the feature importance ranking can provide insights of how models arrive at a prediction from a raw input, they do not give a clear-cut definition of the key features models use for the prediction. In this paper, we present a new method, called WheaCha, for explaining the predictions of code models. Although WheaCha employs the same mechanism of tracing model predictions back to the input features, it differs from all existing attribution methods in crucial ways. Specifically, WheaCha divides an input program into"wheat"(i.e., the defining features that are the reason for which models predict the label that they predict) and the rest"chaff"for any prediction of a learned code model. We realize WheaCha in a tool, HuoYan, and use it to explain four prominent code models: code2vec, seq-GNN, GGNN, and CodeBERT. Results show (1) HuoYan is efficient - taking on average under twenty seconds to compute the wheat for an input program in an end-to-end fashion (i.e., including model prediction time); (2) the wheat that all models use to predict input programs is made of simple syntactic or even lexical properties (i.e., identifier names); (3) Based on wheat, we present a novel approach to explaining the predictions of code models through the lens of training data.
EVALUATING A HIGH DYNAMIC RANGE ALGORITHM FOR REAL-TIME MICROSCOPIC INSPECTION SYSTEMS.

High Dynamic Range (HDR) Imaging/Video is an important part of the microscopic analysis. In this paper, common methods for obtaining a well-exposed image/video are brieﬂy reviewed, then an alternative method to generate high-quality HDR video in real-time is proposed and compared using general objective metrics for its correct interpretation. The primary application of this implementation is for real-time of non-high speed microscopic inspection systems. Several highly reﬂective samples are evaluated and compared between this state-of-the-art algorithm and other common methods. Results have shown the success of the described method. This publication can serve as a baseline for further evaluation and inspection in the ﬁeld of HDR Microscopy.
Adult Height in Patients with Congenital Adrenal Hyperplasia: A Systematic Review and Metaanalysis

Context: Treatment for patients with congenital adrenal hyperplasia (CAH) may affect the final height of these patients. Objective: Our objective was to determine the distribution of achieved height in patients with classic CAH diagnosed at infancy or early childhood and treated with glucocorticoids. Data study characteristics and outcomes and determined each study’s risk of bias. Data Synthesis: The SD score (SDS) for final height and corrected height (defined as final height SDS (cid:1) midparental height SDS) were estimated from each study and pooled using random-effects metaanalysis. The I 2 statistic was used to assess inconsistency in results across studies. Results: We found 35 eligible studies, most of which were retrospective single-cohort studies. The final height SDS achieved by CAH patients was (cid:1) 1.38 ( (cid:1) 1.56 to (cid:1) 1.20; I 2 (cid:2) 90.2%), and the corrected height SDS was (cid:1) 1.03 ( (cid:1) 1.20 to (cid:1) 0.86; I 2 (cid:2) 63.1%). This was not significantly associated with age at diagnosis, gender, type and dose of steroid, and age of onset of puberty. Mineralocorticoid users had a better height outcome in comparison with the nonusers ( P (cid:2) 0.02). Conclusion: Evidence derived from observational studies suggests that the final height of CAH patientstreatedwithglucocorticoidsislowerthanthepopulationnormandislowerthanexpected given parental height. ( J Clin Endocrinol Metab 95: 4161–4172, 2010) adherence conducted weighted meta-regression using the final SDS as the dependent variable and the as the independent variable to determine whether secular trends in height over time have affected study conclusions. We explored in sensitivity analyses how the results of the metaanalyses would change when borderline eligible studies were excluded.
Chitosan-Coated Selenium Nanoparticles Attenuate PRRSV Replication and ROS/JNK-Mediated Apoptosis in vitro

Introduction Porcine reproductive and respiratory syndrome virus (PRRSV) is a highly prevalent and endemic swine pathogen that causes significant economic losses to the global swine industry. Selenium nanoparticles (SeNPs) have attracted increasing attention in the biomedical field, given their antiviral effects. This study aimed to investigate the inhibitory effect of chitosan-coated SeNPs (CS-SeNPs) on PRRSV replication. Methods In this study, CS-SeNPs were synthesized by chemical reduction and characterized by assessing the morphology, size distribution, zeta potential, and element composition. Marc-145 cells were infected with r-PRRSV-EGFP (0.1 MOI) and inoculated with CS-SeNPs (10 μM). Subsequently, the concentrations of hydrogen peroxide (H2O2) and glutathione (GSH), and glutathione peroxidase (GSH-Px) activity were measured using specific commercial assay kits. ORF5 RNA expression, viral titer, and nucleocapsid (N) protein expression were assessed using qRT-PCR, TCID50, and Western blot. ROS generation, apoptosis rates, and JNK /caspase-3/PARP protein expression were evaluated using dihydroethidium staining, flow cytometry, and Western blot. Results The results showed that CS-SeNPs treatment significantly suppressed oxidative stress induced by r-PRRSV-EGFP infection by increasing GSH-Px activity, promoting GSH production, and inhibiting H2O2 synthesis. CS-SeNPs treatment significantly inhibited ORF5 gene expression, viral titers, and N protein of r-PRRSV-EGFP at 24 and 48 hours post-infection (hpi) in Marc-145 cells. The increase in apoptosis rates induced by r-PRRSV-EGFP infection was significantly decreased by CS-SeNPs inoculation through inhibiting ROS generation, JNK phosphorylation levels, and cleavage of caspase-3 and PARP mainly at 48 hpi. Conclusion These results demonstrated that CS-SeNPs suppress PRRSV-induced apoptosis in Marc-145 cells via the ROS/JNK signaling pathway, thereby inhibiting PRRSV replication, which suggested the potential antiviral activity of CS-SeNPs that deserves further investigation for clinical applications.
eRoDes: a Web-based Framework for the Development of Semantic-Enhanced Learning Objects

—Learning objects are often created reusing multimedia resources available in the Web. The metadata of these new objects are usually annotated using semantic technologies. Nevertheless, some relevant challenges related to the creation of semantic-based metadata and their use in learning experiences are still open. The combination of solutions of service-oriented computing, Linked Data Cloud and semantic technologies allows to tackle these challenges. The result is eRoDes , a web-based and service-oriented framework able to semantically annotate learning objects in an automatic way and that provides services for the development of learning activities using these objects. eRoDes functionality has been tested in a subject of Computer Engineering’s Degree at the University of Zaragoza during 2014-15. The results are reported and discussed.
Applying Prim’s Algorithm to Identify Isolated Areas for Natural Disaster Prevention and Protection

Based on the principle of “pre-disaster prevention outweighs rescue during disasters”, this study targets areas threatened by natural disasters, and develops an automatic algorithm based on the Prim algorithm to serve as an automatic identification system. In the face of natural disasters that disable key facilities in the region and prevent settlements from contacting the outside world or outsiders from sending rescuers to the settlements, the proposed system helps to identify whether these regions will become isolated areas and conduct disaster mitigation and relief resource allocation before any natural disaster in order to reduce potential disaster losses. An automatic identification system, based on the threshold of channel blocking due to broken roads and bridges, determines through the decision tree model and relevant patterns whether such regions will become isolated areas by identifying areas based on the results of model analysis. The proposed system’s identification results are verified by actual case histories and comparisons; the results can be used to correctly identify isolated areas. Finally, Microsoft Visual Studio C # and Google Map are employed to apply the results and to produce an information mode for the determination and decision support of isolated areas affected by natural disasters.
The Progress of Social Isolation in Patients with Enterostomy during Discharge Transition Period

In terms of social and psychological health of enterostomy patients during hospitalization and discharge transition period, the degree of social isolation in patients during discharge transition period is higher than that during hospitalization period, which is usually manifested by poor self-perception of body image changes. Self-esteem (shame) frustration, severe negative emotions, low psychosocial adjustment, and other factors are closely related to postoperative complications, coping self-efficacy, social support level, family living conditions, and other influencing factors. This is an important reason why patients are difficult to adapt to ostomy life and low quality of life. At present, it is believed that the social isolation related evaluation scale can be used to understand the status quo of patients with social isolation and provide nursing guidance, so as to better cope with the adverse medical outcomes caused by social isolation.
RBR-type E3 ligases and the Ub-conjugating enzyme UBC26 regulate ABA receptor levels and signaling

34 The turnover of abscisic acid (ABA) signaling core components modulates the 35 plant’s response to ABA and is regulated by ubiquitination. We show that 36 Arabidopsis (Arabidopsis thaliana) RING finger ABA-related 1 (RFA1) and 37 RFA4 E3 ubiquitin ligases, members of the RING between RING fingers (RBR)38 type RSL1/RFA family, are key regulators of ABA receptor stability in root and 39 leaf tissues, targeting ABA receptors for degradation in different subcellular 40 locations. RFA1 is localized both in the nucleus and cytosol, whereas RFA4 41 shows specific nuclear localization and promotes nuclear degradation of ABA 42 receptors. Therefore, members of the RSL1/RFA family interact with ABA 43 receptors at plasma membrane, cytosol and nucleus, targeting them for 44 degradation via the endosomal/vacuolar RSL1-dependent pathway or 26S 45 proteasome. Additionally, we provide insight into the physiological function of 46 the relatively unexplored plant RBR-type E3 ligases, and through mutagenesis 47 and biochemical assays we identified Cys361 in RFA4 as the putative active 48 site cysteine, which is a distinctive feature of RBR-type E3 ligases. Endogenous 49 levels of PYR1 and PYL4 ABA receptors were higher in the rfa1 rfa4 double 50 mutant than in wild-type plants. UBC26 was identified as the cognate nuclear 51 E2 enzyme that interacts with the RFA4 E3 ligase and forms UBC26-RFA452 Receptor complexes in nuclear speckles. Loss-of-function ubc26 alleles and the 53 rfa1 rfa4 double mutant showed enhanced sensitivity to ABA and accumulation 54 of ABA receptors compared to the wild type. Together our results reveal a 55 sophisticated mechanism by which ABA receptors are targeted by ubiquitin (Ub) 56 at different subcellular locations, in which the complexity of the ABA receptor 57 family is mirrored in the partner RBR-type E3 ligases. 58
CCID_A_327825 1675..1683

Dermatology Hospital of Jiangxi Province, Nanchang, 330001, Jiangxi, People’s Republic of China Background: Gallic acid (GA) has an anti-inflammatory effect by regulating inflammatory molecules. This study aimed to investigate the effect of GA on atopic dermatitis (AD)-like skin inflammation. Methods: 4-dinitrochlorobenzene (DNCB) was used to induce an AD-like skin inflammation model. The effect of GA on DNCB-induced inflammation was assessed by measuring the thickness and histopathological examination of the ear. Serum IgE and TNF-α levels were detected. The effect of GA on lymph nodes was determined by measuring the weights and mRNA/protein expression levels of TNF-α, IL-4, IFN-γ and IL-17. Ratio of Treg cells and Th17 cells was also analyzed. Results: It was found that the thickness and pathology of the ear were significantly improved by GA in the DNCB-induced mice. Serum IgE and TNF-α levels were significantly reduced in GA-treated model mice compared to the model group. GA treatment lowered the weight of lymph node and the expression of mRNAs of TNF-α, IL-4, IFN-γ, and IL-17 of lymph node. In the ear, inflammatory factors (IL-4, IL-5, IL-17, or IL-23) showed a significant decrease in GA-treated model mice versus model mice, while the expression levels of IL-10 and TGF-β showed a great increase in GA-treated model mice. ROR-γt showed a decrease in GA-treated model group, along with an increase expression of SOCS3. Conclusion: GA could ameliorate AD-like skin inflammation possibly through Th17 mediated immune regulation in a DNCB-induced mouse model.