Miracle-dz/UniFAR-FTUs huggingface dataset:
Numerical Modeling of Cell Trajectory Inside a Dielectrophoresis Microdevice Designed for Breast Cancer Cell Screening

This paper deals with mathematical modeling of the particle trajectory inside a microfluidic device based on the application of dc dielectrophoresis (DEP). The proposed device is designed to separate the breast cancer cell lines (e.g., MDA-MB-231 and MCF-7) from normal blood cells in the specific outlets. A complementary mechanism is employed in the channel that involves the drag and DEP forces acting cooperatively on the cells to deflect the cells into corresponding outlets. Closed-form equations are yielded for predicting the particle position along the x- and y-axis versus time. An effect of both DEP and drag forces is considered and Newton’s second law of motion is exploited for formulations. The initial values required by the particle trajectory equation, including the primary location and velocity, and the physical features of different cells, are determined. The proposed microchannel is numerically simulated using the finite-element simulation software, and the results are compared to the data derived from modeling equations. Our findings confirm the capability of the proposed model to accurately track the location of all types of under-study cells inside the proposed microchannel. In this paper, a user-friendly software package is designed and implemented in the MATLAB environment to predict the cell trajectory of different cells with various diameters. Results for different cells with various diameters reveal that this software is capable of predicting the cell trajectory with an error less than 7%.
A Comparative Study of Supervised Machine Learning Algorithms for Stock Market Trend Prediction

Impact of many factors on the stock prices makes the stock prediction a difficult and highly complicated task. In this paper, machine learning techniques have been applied for the stock price prediction in order to overcome such difficulties. In the implemented work, five models have been developed and their performances are compared in predicting the stock market trends. These models are based on five supervised learning techniques i.e., Support Vector Machine (SVM), Random Forest, K-Nearest Neighbor (KNN), Naive Bayes, and Softmax. The experimental results show that Random Forest algorithm performs the best for large datasets and Naive Bayesian Classifier is the best for small datasets. The results also reveal that reduction in the number of technical indicators reduces the accuracies of each algorithm.
No Panic! Verification of Rust Programs by Symbolic Execution

The Rust language stands out with a type system and underlying memory model targeting memory safety. The accompanying rustc compiler is typically able to statically prove the safety conditions and accept or reject input programs accordingly. However, e.g., cases of raw array indexing are in general out of reach for static analysis and promoted to runtime verification rendering executables with partial correctness guarantees (aborting with a panic on safety violations). For safety-critical applications, this requires proper panic handling, which is by itself a hard problem. Moreover, runtime verification may be undesirable for performance reasons and hence calling for stronger methods to static program analysis. In this paper, we take the approach of symbolic execution and propose a generic contract based verification process for programs written in Rust. For the verified properties (assertions), the program is both safe and panic free. Besides correctness, this implies further performance improvements as code for runtime verification can be safely dropped. We demonstrate the feasibility of the approach by adopting the KLEE symbolic execution engine to analyze LLVM bitcode extracted from Rust programs and libraries under verification and discuss its implications and limitations.
Stratospheric circulation in the WACCM chemistry-climate model: mean age of air against observations and CTM

The mean age of air is a classical diagnostic of the transit time from the troposphere to the various region of the stratosphere, providing insights on the strength and structure of the Brewer-Dobson Circulation (BDC), the polar vortex and irreversible mixing in the mid-latitudes. We apply this diagnostic to the WACCM chemistry-cliamate model for the 1985-2014 period. A comparison is presented between unconstrained simulations (FR-WACCM) with and without the representation of the Quasi-Biennial Oscillation (QBO), simulations nudged to the MERRA-2 reanalysis (SD-WACCM), in-situ measurements and a chemistry-transport model (CTM). The results are different depending on the simulations, with SD-WACCM resulting closer to the observation w.r. t. FR-WACCM. In the FR-WACCM the QBO plays a role leading to older age of air. The time evolution is slightly decreasing, while observations show a positive (not significant) trend. Given those results, further studies have to be carried out investigating the multi-decadal trends of mean age of air and involving actual tracers. This study is part of the ACCROSS project, in collaboration with the University of Liege. over the considered period Introduction The mean age of air (aoa) is defined as the interval between the time when the mixing ratio of a monotonically increasing tracer reaches a certain value at some location in the stratosphere and the (earlier) time when the same mixing ratio was reached at the reference location: where and are the spatial coordinates of the tracer (Waugh and Hall, 2002). It is widely used as a proxy for the Brewer-dobson Circulation (BDC): the aoa absolute value gives the strength of the BDC, i.e. small values are related to strong BDC, while its trends are used to evaluate its changes (speed up ro slow down). Chemistry-Climate models (CCMs) predict negative trends for simulations of the 20th century (increasing GHGs); observational studies, on the other hand, find positive (not significant) trends. X T=t(X,Y)-t(X,Y ), Y Y 0
Alignment-Supervised Bidimensional Attention-Based Recursive Autoencoders for Bilingual Phrase Representation

Exploiting semantic interactions between the source and target linguistic items at different levels of granularity is crucial for generating compact vector representations for bilingual phrases. To achieve this, we propose alignment-supervised bidimensional attention-based recursive autoencoders (ABattRAE) in this paper. ABattRAE first individually employs two recursive autoencoders to recover hierarchical tree structures of bilingual phrase, and treats the subphrase covered by each node on the tree as a linguistic item. Unlike previous methods, ABattRAE introduces a bidimensional attention network to measure the semantic matching degree between linguistic items of different languages, which enables our model to integrate information from all nodes by dynamically assigning varying weights to their corresponding embeddings. To ensure the accuracy of the generated attention weights in the attention network, ABattRAE incorporates word alignments as supervision signals to guide the learning procedure. Using the general stochastic gradient descent algorithm, we train our model in an end-to-end fashion, where the semantic similarity of translation equivalents is maximized while the semantic similarity of nontranslation pairs is minimized. Finally, we incorporate a semantic feature based on the learned bilingual phrase representations into a machine translation system for better translation selection. Experimental results on NIST Chinese–English and WMT English–German test sets show that our model achieves substantial improvements of up to 2.86 and 1.09 BLEU points over the baseline, respectively. Extensive in-depth analyses demonstrate the superiority of our model in learning bilingual phrase embeddings.
Metformin Increases Cardiac Rupture After Myocardial Infarction via the AMPK-MTOR/PGC-1α Signaling Pathway in Rats with Acute Myocardial Infarction

Background Cardiac rupture often occurs after acute myocardial infarction due to complex and unclear pathogenesis. This study investigated whether metformin increases the incidence of cardiac rupture after myocardial infarction through the AMPK-MTOR/PGC-1α signaling pathway. Material/Methods An acute myocardial infarction (MI) mouse model was established. A series of experiments involving RT-qPCR, Western blot, TUNEL staining, and Masson staining were performed in this study. Results Myocardial infarction occurred, resulting in the cardiac rupture, and the expression level of PGC-1α increased in the cardiac myocardium. Meanwhile, the proportion of myocardial NT-PGC-1α/PGC-1α decreased. The expression level of myocardial PGC-1α in MI mice with cardiac rupture after MI was significantly higher than that in the mice without cardiac rupture, and the ratio of myocardial NT-PGC-1α/PGC-1α was low. In addition, increasing the dose of metformin significantly increased the incidence of cardiac rupture after myocardial infarction in MI mice. High-dose metformin caused cardiac rupture in MI mice. Moreover, high-dose metformin (Met 2.0 nM) reduces the proportion of NT-PGC-1α/PGC-1α in primary cardiomyocytes of SD mice (SD-NRVCs [Neonatal rat ventricular cardiomyocytes]), and its effect was inhibited by Compound C (AMPK inhibitor). Further, after 3 days of treatment with high-dose metformin in MI mice, myocardial fibrin synthesis decreased and fibrosis was significantly inhibited. Meanwhile, cardiomyocyte apoptosis increased significantly. With the increase in metformin concentration, the expression level of myocardial LC3b gradually increased in MI mice, suggesting that metformin enhances the autophagy of cardiomyocytes. Conclusions These results suggest that metformin increases cardiac rupture after myocardial infarction through the AMPK-MTOR/PGC-1α signaling pathway.
Refining Manually-Designed Symbol Grounding and High-Level Planning by Policy Gradients

Hierarchical planners that produce interpretable and appropriate plans are desired, especially in its application to supporting human decision making. In the typical development of the hierarchical planners, higher-level planners and symbol grounding functions are manually created, and this manual creation requires much human effort. In this paper, we propose a framework that can automatically refine symbol grounding functions and a high-level planner to reduce human effort for designing these modules. In our framework, symbol grounding and high-level planning, which are based on manually-designed knowledge bases, are modeled with semi-Markov decision processes. A policy gradient method is then applied to refine the modules, in which two terms for updating the modules are considered. The first term, called a reinforcement term, contributes to updating the modules to improve the overall performance of a hierarchical planner to produce appropriate plans. The second term, called a penalty term, contributes to keeping refined modules consistent with the manually-designed original modules. Namely, it keeps the planner, which uses the refined modules, producing interpretable plans. We perform preliminary experiments to solve the Mountain car problem, and its results show that a manually-designed high-level planner and symbol grounding function were successfully refined by our framework.
Interpreting user inaction in recommender systems

Temporally, users browse and interact with items in recommender systems. However, for most systems, the majority of the displayed items do not elicit any action from users. In other words, the user-system interaction process includes three aspects: browsing, action, and inaction. Prior recommender systems literature has focused more on actions than on browsing or inaction. In this work, we deployed a field survey in a live movie recommender system to interpret what inaction means from both the user's and the system's perspective, guided by psychological theories of human decision making. We further systematically study factors to infer the reasons of user inaction and demonstrate with offline data sets that this descriptive and predictive inaction model can provide benefits for recommender systems in terms of both action prediction and recommendation timing.
Perfect Match: A Simple Method for Learning Representations For Counterfactual Inference With Neural Networks

Learning representations for counterfactual inference from observational data is of high practical relevance for many domains, such as healthcare, public policy and economics. Counterfactual inference enables one to answer "What if. " questions, such as "What would be the outcome if we gave this patient treatment $t_1$? ". However, current methods for training neural networks for counterfactual inference on observational data are either overly complex, limited to settings with only two available treatments, or both. Here, we present Perfect Match (PM), a method for training neural networks for counterfactual inference that is easy to implement, compatible with any architecture, does not add computational complexity or hyperparameters, and extends to any number of treatments. PM is based on the idea of augmenting samples within a minibatch with their propensity-matched nearest neighbours. Our experiments demonstrate that PM outperforms a number of more complex state-of-the-art methods in inferring counterfactual outcomes across several benchmarks, particularly in settings with many treatments.
Bidirectional effects of Internet-specific parenting practices and compulsive social media and Internet game use

Introduction This two-wave prospective study investigated the bidirectional relation between Internet-specific parenting (reactive restrictions, Internet-specific rules, and frequency and quality of communication about Internet) and adolescents’ symptoms of social media disorder (SMD) and Internet gaming disorder (IGD). In addition, we investigated whether this relation was different for boys and girls. Methods A sample of 352 adolescents (48.9% boys, Mage = 13.9, SDage = 0.74, range: 11–15) completed questionnaires at two waves. Zero-inflated cross-lagged analyses in Mplus were performed to predict the level of IGD and SMD symptoms by Internet-specific parenting practices and vice versa, while controlling for age, level of education, and outcome at T1. Results More frequent parent–adolescent communication about Internet predicted more IGD (β = 0.26, p = .03) and SMD symptoms among boys, and more restrictive rules predicted fewer SMD symptoms among girls (β = −0.23, p = .08). More IGD symptoms predicted more reactive rules (β = 0.20, p = .08) among boys and girls and a higher frequency (β = 0.16, p = .02) and lower quality of communication (β = −0.24, p < .001) among boys and girls, respectively. Conclusions This study demonstrates bidirectional relations between Internet-specific parenting and IGD symptoms, but not SMD symptoms. Displaying IGD symptoms seems to elicit ineffective parental responses, which may further exacerbate problematic involvement in gaming. With respect to problematic social use media among girls, this study suggests that parents should set strict rules regarding Internet use, prior to problematic use of social media. Longitudinal studies on the role of parenting in development of Internet-related disorders would be promising in enhancing our understanding of how parents can effectively prevent problematic involvement in online behaviors among their children.
Performance Analysis of POX and Ryu with Different SDN Topologies

This paper deals with the performance comparison of two python-based Software Defined Network (SDN) controllers i.e. POX and Ryu under different network topologies such as Single, Linear, Tree, Dumbbell, Data Center Networks (DCN) and Software-Defined naval networks which use satellite communication systems (SATCOM) i.e. SDN-SAT [1]. Experimental results, validated through Mininet has clearly indicated that Ryu has superior performance i.e. A TCP throughput increase of 25.56%, 282.54%, 44.85%, 19.88%, 45.45% and latency decrease of 93.48%, 99.96%, 99.90%, 97.08%, 99.33% in single, linear, tree, dumbbell and DCN topologies respectively. Similarly, in SDN-SAT topology Ryu has 0.21% increase in TCP throughput and 34.62% decrease in latency as compared to POX controller.
Nrf2-mediated anti-oxidant effects contribute to suppression of non-alcoholic steatohepatitis-associated hepatocellular carcinoma in murine model

The exact mechanisms of hepatocellular carcinoma development in non-alcoholic steatohepatitis remain unclear. In this study, we used a new class of high-fat diet, which could induce hepatocellular carcinoma development without the use of general chemical carcinogens or knockout mice. We investigated the correlation between hepatocellular carcinoma and oxidative stress/anti-oxidant effects after depletion of the gut microbiota by treatment with antibiotics. Mice fed with the steatohepatitis-inducing high-fat diet (STHD-01) for 41 weeks developed hepatocellular carcinoma. Antibiotic-treatment in mice fed with STHD-01 significantly depleted the gut microbiota and significantly ameliorated liver injury/histology. The tumor numbers of hepatocellular carcinoma were dramatically decreased by the antibiotics-treatment. We analyzed the factors involved in oxidative stress and anti-oxidant effects. Oxidative stress was elevated in mice fed with STHD-01, whereas some anti-oxidant factors were significantly elevated after antibiotics treatment. These results suggest that the gut microbiota is a key factor in improving oxidative stress induced by STHD-01 feeding.
An Assistive Strategy for Compliantly Actuated Exoskeletons Using Non-Linear Model Predictive Control Method

Assistive exoskeletons are a category of exoskeleton robots designed to provide extra powers and energies for elderlies or healthy subjects. The control algorithms of these robots, termed as assistive strategies, should control the robot in way that a portion of the user's required torques is provided by the robot actuators. This paper uses non-linear model predictive control (NMPC) method to propose a new assistive strategy for compliantly actuated exoskeletons. The user's required torques are estimated by using an identified model of the human-exoskeleton system. The NMPC method is then used to control the series elastic actuators of the robot to provide a user-selected portion of the estimated torques. The successful performance of the proposed assistive strategy is verified through multiple simulations. The force tracking performance of the NMPC method is also compared with a PID controller. The results clearly show the outperformance of the NMPC method in providing lower tracking errors and less control efforts.
A C-RAN Based 5G Platform with a Fully Virtualized, SDN Controlled Optical/Wireless Fronthaul

This paper describes in detail the design of a 5G platform based on the C-RAN architecture, with a fully virtualized Radio Access Network (RAN) and an optical/wireless Fronthaul. The wireless and optical domains are controlled by a hierarchy of Software Defined Networking (SDN) controllers, which are responsible for end-to-end optimization of the platform, including the Fronthaul and 5G air interfaces. The proposed architecture adopts a modular eNodeB (eNB) design, where virtualized BBU and RRH entities are implemented with Commercial Off-Τhe-Shelf (COTS) components. Moreover, a mmWave RAU is connected to the BBU via a feeder optical fiber, interconnecting one or more RRH units with the BBU over mmWave interfaces. COTS components and Ethernet interfaces are employed for C-RAN prototyping of our platform, facilitating flexibility, cost reduction and increased scalability.
Foxp3 Molecular Dynamics in Treg in Juvenile Idiopathic Arthritis

Since the identification of the regulatory T-cell (Treg)-associated transcription factor Foxp3, there have been intensive research efforts to understand its biology and roles in maintaining immune homeostasis. It is well established that thymic selection of a repertoire of self-reactive Foxp3+ T-cells provides an essential mechanism to minimize reactions to self-antigens in the periphery, and thus aid in the prevention of autoimmunity. It is clear from both genetic and immunological analyses of juvenile idiopathic arthritis (JIA) patients that T-cells have a strong role to play in both the initiation and propagation of disease. The current paradigm is to view autoimmunity as a consequence of an imbalance between inflammatory and immunoregulatory mechanisms. This view has led to the assigning of cells and inflammatory mediators to different classes based on their assumed pro- or anti-inflammatory roles. This is typically reported as ratios of effector T-cells to Treg cells. Problematically, many analyses are based on static “snapshots-in-time,” even though both mouse models and human patient studies have highlighted the dynamic nature of Foxp3+ T-cells in vivo, which can exhibit plasticity and time-dependent functional states. In this review, we discuss the role of Foxp3 dynamics in the control of T-cell responses in childhood arthritis, by reviewing evidence in humans and relevant mouse models of inflammatory disease. Whilst the cellular dynamics of Treg have been well evaluated—leading to standard data outputs such as frequency, quantity and quality (often assessed by in vitro suppressive capacity)—we discuss how recent insights into the molecular dynamics of Foxp3 transcription and its post-translational control may open up tantalizing new avenues for immunotherapies to treat autoimmune arthritis.
Boolean-based Multi Secret Sharing Scheme using Meaningful Shares

Conventional visual secret sharing (VSS) scheme encodes a secret image into noise-like images, called as meaningless shares. Sharing capacity of VSS schemes is increased by sharing multiple secret images using multi secret sharing (MSS) schemes. Diversity in MSS schemes is utilization of a universal share which is a common share amongst all participants. A recent development in MSS schemes is generating meaningful shares to decrease the risk of suspicion while sharing the secret images. However, the poor visual quality of meaningful shares makes such system ineffective for MSS. In this paper, we propose a Boolean-based MSS scheme with improved visual quality of meaningful shares. The proposed method encrypts n grayscale secret images using n+ 1 cover images into a meaningful universal share and n meaningful shares. A Boolean XOR operation is utilized to recover n secret images. Experimental results show efficacy of the proposed method.
Enabling Collaborative Edge Computing for Software Defined Vehicular Networks

Edge computing has great potential to address the challenges in mobile vehicular networks by transferring partial storage and computing functions to network edges. However, it is still a challenge to efficiently utilize heterogeneous edge computing architectures and deploy large-scale IoV systems. In this article, we focus on the collaborations among different edge computing anchors and propose a novel collaborative vehicular edge computing framework, called CVEC. Specifically, CVEC can support more scalable vehicular services and applications by both horizontal and vertical collaborations. Furthermore, we discuss the architecture, principle, mechanisms, special cases, and potential technical enablers to support the CVEC. Finally, we present some research challenges as well as future research directions.