Miracle-dz/UniFAR-FTUs huggingface dataset:
Complexity Analysis of Global Temperature Time Series

Climate has complex dynamics due to the plethora of phenomena underlying its evolution. These characteristics pose challenges to conducting solid quantitative analysis and reaching assertive conclusions. In this paper, the global temperature time series (TTS) is viewed as a manifestation of the climate evolution, and its complexity is calculated by means of four different indices, namely the Lempel–Ziv complexity, sample entropy, signal harmonics power ratio, and fractal dimension. In the first phase, the monthly mean TTS is pre-processed by means of empirical mode decomposition, and the TTS trend is calculated. In the second phase, the complexity of the detrended signals is estimated. The four indices capture distinct features of the TTS dynamics in a 4-dim space. Hierarchical clustering is adopted for dimensional reduction and visualization in the 2-dim space. The results show that TTS complexity exhibits space-time variability, suggesting the presence of distinct climate forcing processes in both dimensions. Numerical examples with real-world data demonstrate the effectiveness of the approach.
Leveraging Audio Signals for Early Recognition of Inattentive Driving with Smartphones

Real-time driving behavior monitoring is a corner stone to improve driving safety. Most of the existing studies on driving behavior monitoring using smartphones only provide detection results after an abnormal driving behavior is finished, not sufficient for driver alerting and avoiding car accidents. In this paper, we leverage built-in audio devices on smartphones to realize early recognition of inattentive driving events including <italic>Fetching Forward </italic>, <italic>Picking up Drops</italic>, <italic>Turning Back</italic>, and <italic>Eating or Drinking</italic>. Through empirical studies of driving traces collected in real driving environments, we find that each type of inattentive driving event exhibits unique patterns on Doppler profiles of audio signals. This enables us to develop an <underline><italic>E</italic></underline>arly <underline><italic>R</italic></underline>ecognition system, <italic>ER </italic>, which can recognize inattentive driving events at an early stage and alert drivers timely. ER employs machine learning methods to first generate binary classifiers for every pair of inattentive driving events, and then develops a <italic>modified vote mechanism</italic> to form a multi-classifier for all four types of inattentive driving events, for atypical inattentive driving events along with other driving behaviors. It next turns the multi-classifier into a <italic>gradient model forest</italic> to achieve early recognition of inattentive driving. Through extensive experiments with eight volunteers driving for about two months, ER can achieve an average total accuracy of 94.80 percent for inattentive driving recognition and recognize over 80 percent inattentive driving events before the event is 50 percent finished.
Run Procrustes, Run! On the convergence of accelerated Procrustes Flow

In this work, we present theoretical results on the convergence of non-convex accelerated gradient descent in matrix factorization models. The technique is applied to matrix sensing problems with squared loss, for the estimation of a rank $r$ optimal solution $X^\star \in \mathbb{R}^{n \times n}$. We show that the acceleration leads to linear convergence rate, even under non-convex settings where the variable $X$ is represented as $U U^\top$ for $U \in \mathbb{R}^{n \times r}$. Our result has the same dependence on the condition number of the objective --and the optimal solution-- as that of the recent results on non-accelerated algorithms. However, acceleration is observed in practice, both in synthetic examples and in two real applications: neuronal multi-unit activities recovery from single electrode recordings, and quantum state tomography on quantum computing simulators.
Relationship between Emotional Abuse and Depression among Community-Dwelling Older Adults in Korea

Despite recent advances, there is little research on factors associated with emotional abuse among Korean older adults. The present study investigated the relationships between depression and emotional abuse among community-dwelling Korean older adults. We analyzed the dataset from the Survey of Living Conditions and Welfare Needs of Korean Older Persons conducted by the Korea Institute for Health and Social Affairs in 2011. Older adults (aged >65 years, n=10674) were randomly selected. Multivariate logistic regression was used to investigate factors associated with emotional abuse in terms of sociodemographic, health-related, and psychological variables, including depression using the short version of the Geriatric Depression Scale. In our study, 9.4% of the older adults reported experiencing emotional abuse in the previous year. In addition, 44.0% of those experiencing emotional abuse suffered from depression. Multiple logistic regression modeling showed that depression in older adults was independently associated with emotional abuse [odds ratio (OR)=1.788, p<0.001]. Along with depression, poor social support (OR for no social support=1.712, p<0.001) and having chronic illnesses (OR for more than 3=1.481, p<0.005) were associated an increased risk of emotional abuse. Our results showed that emotional abuse among older adults in Korea is prevalent. Our findings suggest that depression, poor social support, and having chronic illnesses are significant factors associated with emotional abuse among the Korean older adult population. Addressing these factors with preventive interventions could have significant public health implications. Further studies are warranted to gain a better understanding of emotional abuse in older Korean adults.
Prognostic impact of miR-196a/b expression in adult acute myeloid leukaemia: a single-centre, retrospective cohort study

Objective The prognostic effect of miR-196a/b expression in adult patients with leukaemia remains unclear. This study aimed to determine whether miR-196a/b expression can serve as a prognostic factor for patients with acute myeloid leukaemia. Methods We enrolled 124 patients with acute myeloid leukaemia. We measured miR-196a/b expression by real-time reverse transcription-polymerase chain reaction. We classified patients into high and low expression groups based on the median expression value. Cox regression analyses were carried out to assess the prognostic significance of miR-196a/b expression in the context of well-established predictors. Results Patients with high miR-196a/b expression were older in age, and had higher white blood cell and platelet counts than did patients with low miR-196a/b expression. Patients with high miR-196a/b expression were associated with the French–American–British classification M5 subtype and with the presence of nucleophosmin and FLT3 internal tandem duplication mutations, but were not associated with the favourable karyotype risk subgroup. Moreover, patients with high miR-196a/b expression had a shorter event-free survival rate compared with those with low miR-196a/b expression in univariate and multivariate analyses. Conclusion High miR-196a/b expression is associated with poor event-free survival.
TGF-b Increases Retinal Endothelial Cell Permeability by Increasing MMP-9 : Possible Role of Glial Cells in Endothelial Barrier Function

PURPOSE. To determine transforming growth factor (TGF) b effects on matrix metalloproteinases (MMPs) as a potential cause of the blood–retinal barrier breakdown at the onset of angiogenesis. Previously, glial cells were shown to play a role in the angiogenesis process and to express the angiogenic regulating factor TGF-b, which becomes active under hypoxia conditions. Here, the authors demonstrate that retinal endothelial cells express MMP-9 when treated with TGF-b or cocultured with glial cells and that both TGF-b and MMP-9 increase endothelial cell permeability.
Methylthioadenosine Suppresses Salmonella Virulence

In order to deploy virulence factors at appropriate times and locations, microbes must rapidly sense and respond to various metabolite signals. Previously, we showed a transient elevation of the methionine-derived metabolite methylthioadenosine (MTA) concentration in serum during systemic Salmonella enterica serovar Typhimurium infection. ABSTRACT In order to deploy virulence factors at appropriate times and locations, microbes must rapidly sense and respond to various metabolite signals. Previously, we showed a transient elevation of the methionine-derived metabolite methylthioadenosine (MTA) concentration in serum during systemic Salmonella enterica serovar Typhimurium infection. Here we explored the functional consequences of increased MTA concentrations on S. Typhimurium virulence. We found that MTA, but not other related metabolites involved in polyamine synthesis and methionine salvage, reduced motility, host cell pyroptosis, and cellular invasion. Further, we developed a genetic model of increased bacterial endogenous MTA production by knocking out the master repressor of the methionine regulon, metJ. Like MTA-treated S. Typhimurium, the ΔmetJ mutant displayed reduced motility, host cell pyroptosis, and invasion. These phenotypic effects of MTA correlated with suppression of flagellar and Salmonella pathogenicity island 1 (SPI-1) networks. S. Typhimurium ΔmetJ had reduced virulence in oral and intraperitoneal infection of C57BL/6J mice independently of the effects of MTA on SPI-1. Finally, ΔmetJ bacteria induced a less severe inflammatory cytokine response in a mouse sepsis model. Together, these data indicate that exposure of S. Typhimurium to MTA or disruption of the bacterial methionine metabolism pathway suppresses S. Typhimurium virulence.
Transformation Optics: Large Multiphysics Simulation of Nonlinear Optomechanical Coupling in Microstructured Resonant Cavities

The interfacing between optical cavities and mechanical systems has given rise to the rapid development of cavity optomechanics, which aims to confine light into small volumes by means of high-Q resonant recirculation [1], [2]. The purpose of an optomechanical system is to investigate the interaction of light with a mechanical oscillator. Its highly interdisciplinary nature leads to several potential applications in various fields of research, especially in quantum processing.
Poor outcome after a surgically treated chondral injury on the medial femoral condyle: early evaluation with dGEMRIC and 17-year radiographic and clinical follow-up in 16 knees

Background and purpose — The optimal treatment for traumatic cartilage injuries remains unknown. Contrast-enhanced MRI of cartilage (dGEMRIC) evaluates cartilage quality and a low dGEMRIC index may predict radiographic osteoarthritis (OA). The purpose of this study was (a) to explore the results 17 years after surgical treatment of an isolated cartilage knee injury and (b) to evaluate the predictive value of dGEMRIC. Patients and methods — 16 knees with an isolated traumatic cartilage injury of the medial femoral condyle had cartilage repair surgery either by microfracture or autologous cartilage implantation. dGEMRIC of the injured knee was performed 2 years after surgery and radiographic examinations were performed 17 years after the operation. Results — Radiographic OA was present in 12 of 16 knees. Irrespective of surgical method, the dGEMRIC index was lower in repair tissue compared with adjacent cartilage in the medial compartment, 237 ms vs. 312 ms (p < 0.001), which in turn had lower value than in the non-injured lateral cartilage, 312 ms vs. 354 ms (p < 0.008). The dGEMRIC index in the cartilage adjacent to the repair tissue correlated negatively with radiographic osteophyte score, r = –0.75 (p = 0.03). Interpretation — A traumatic cartilage injury is associated with a high prevalence of OA after 17 years. The low dGEMRIC index in the repair tissue 2 years postoperatively indicates fibrocartilage of low quality. The negative correlation between the dGEMRIC index in the adjacent cartilage and future OA suggests that the quality of the surrounding cartilage influences outcome after cartilage repair surgery.
Structure-Aware Multimodal Feature Fusion for RGB-D Scene Classification and Beyond

While convolutional neural networks (CNNs) have been excellent for object recognition, the greater spatial variability in scene images typically means that the standard full-image CNN features are suboptimal for scene classification. In this article, we investigate a framework allowing greater spatial flexibility, in which the Fisher vector (FV)-encoded distribution of local CNN features, obtained from a multitude of region proposals per image, is considered instead. The CNN features are computed from an augmented pixel-wise representation consisting of multiple modalities of RGB, HHA, and surface normals, as extracted from RGB-D data. More significantly, we make two postulates: (1) component sparsity—that only a small variety of region proposals and their corresponding FV GMM components contribute to scene discriminability, and (2) modal nonsparsity—that features from all modalities are encouraged to coexist. In our proposed feature fusion framework, these are implemented through regularization terms that apply group lasso to GMM components and exclusive group lasso across modalities. By learning and combining regressors for both proposal-based FV features and global CNN features, we are able to achieve state-of-the-art scene classification performance on the SUNRGBD Dataset and NYU Depth Dataset V2. Moreover, we further apply our feature fusion framework on an action recognition task to demonstrate that our framework can be generalized for other multimodal well-structured features. In particular, for action recognition, we enforce interpart sparsity to choose more discriminative body parts, and intermodal nonsparsity to make informative features from both appearance and motion modalities coexist. Experimental results on the JHMDB and MPII Cooking Datasets show that our feature fusion is also very effective for action recognition, achieving very competitive performance compared with the state of the art.
System Level Challenges and Resolutions in Organ Donation and Transplantation Program in Qatar

Introduction: Organ donation and transplantation programs are affected by factors impacting donation decisions of individuals. These factors have been widely researched world over. However, few researches have studied the organ donation system level challenges that jeopardize the system even when prospective donors are available. It is important to understand these challenges and find appropriate resolutions for them. This qualitative study was conducted to understand the challenges and resolutions in the organ donation and transplant system of Qatar. Methodology: Eight focus group discussions were carried out with homogenous groups of health care workers working in the field of organ donation or organ transplantation in Qatar. After consenting, they were asked open ended questions by an unbiased moderator. Each session lasted for 60-90 min and ended with session summary and participant feedback. The question workbooks were prepared after extensive review of literature and discussion with non-participating stake holders of the organ donation and transplant program in Qatar. Discussions were audio recorded, transcribed and later coded and analyzed on Nvivo software. COREQ checklist was used while reporting the results of this study. Results: Underutilization of potential donors, organ donation campaign, brain dead donors, family consenting, living donation, infrastructure, staff training, allocation and recruitment were main system level challenges to resolve to ensure maximum utilization of potential donors and donated organs. Conclusion: Inclusion of multidisciplinary teams during organ transplant, increased representation from departments doing transplants in transplant committee, minimizing wastage of available organs, streamlining the process of brain dead identification, declaration and maintenance of deceased organ, centralized facility for living donor work up and improved infrastructural facility were advocated to improve utilization of donated organs to resolve system level challenges. Improvement in after care and compliance in transplanted patients, registering higher number of donors and increasing family consent by ensuring communication of donation decision to family are the need of the hour.
Double Coupled Canonical Polyadic Decomposition for Joint Blind Source Separation

Joint blind source separation (J-BSS) is an emerging data-driven technique for multi-set data-fusion. In this paper, J-BSS is addressed from a tensorial perspective. We show how, by using second-order multi-set statistics in J-BSS, a specific double coupled canonical polyadic decomposition (DC-CPD) problem can be formulated. We propose an algebraic DC-CPD algorithm based on a coupled rank-1 detection mapping. This algorithm converts a possibly underdetermined DC-CPD to a set of overdetermined CPDs. The latter can be solved algebraically via a generalized eigenvalue decomposition based scheme. Therefore, this algorithm is deterministic and returns the exact solution in the noiseless case. In the noisy case, it can be used to effectively initialize optimization based DC-CPD algorithms. In addition, we obtain the deterministic and generic uniqueness conditions for DC-CPD, which are shown to be more relaxed than their CPD counterpart. We also introduce optimization based DC-CPD methods, including alternating least squares, and structured data fusion based methods. Experiment results are given to illustrate the superiority of DC-CPD over standard CPD based BSS methods and several existing J-BSS methods, with regards to uniqueness and accuracy.
Inhibitory effect of α-mangostin on Acinetobacter baumannii biofilms – an in vitro study

Abstract The present study was designed to investigate the anti-biofilm potential of alpha-mangostin (α-MG) against Acinetobacter baumannii (AB). The biofilm inhibitory concentration (BIC) of α-MG against AB was found to be 2 μg ml−1. α-MG (0.5, 1 and 2 μg ml−1) exhibited non-bactericidal concentration-dependent anti-biofilm activities against AB. However, α-MG failed to disintegrate the mature biofilms of AB even at a 10-fold increased concentration from its BIC. Results from qRT-PCR and in vitro bioassays further demonstrated that α-MG downregulated the expression of bfmR, pgaA, pgaC, csuA/B, ompA, bap, katE, and sodB genes, which correspondingly affects biofilm formation and its associated virulence traits. The present study suggests that α-MG exerts its anti-biofilm property by interrupting initial biofilm formation and the cell-to-cell signaling mechanism of AB. Additional studies are required to understand the mode of action responsible for the anti-biofilm property.
New wrapper method based on normalized mutual information for dimension reduction and classification of hyperspectral images

Feature selection is one of the most important problems in hyperspectral images classification. It consists to choose the most informative bands from the entire set of input datasets and discard the noisy, redundant and irrelevant ones. In this context, we propose a new wrapper method based on normalized mutual information (NMI) and error probability (PE) using support vector machine (SVM) to reduce the dimensionality of the used hyperspectral images and increase the classification efficiency. The experiments have been performed on two challenging hyperspectral benchmarks datasets captured by the NASA's Airborne Visible/Infrared Imaging Spectrometer Sensor (AVIRIS). Several metrics had been calculated to evaluate the performance of the proposed algorithm. The obtained results prove that our method can increase the classification performance and provide an accurate thematic map in comparison with other reproduced algorithms. This method may be improved for more classification efficiency.
Robust-control using model-error-feedback CDOB and DOB under variable-time-delay

This paper presents the robustness improvement of the Communication-Disturbance-OBserver (CDOB) based on Lyapunov-Krasovskii functional(LKF), and proposes the Network-based-Control-System (NCS) using the LKF based CDOB and a local-side DOB. The CDOB is a compensation method for a output signal includes a communication delay. By using LKF, a steady-state output-estimation-error of the CDOB under variable time-delay and parameter-perturbations is suppressed. However, the latest studies have not shown the robustness to the disturbance-input. In this paper, the frequency- weight of CDOB to the disturbance is introduced to the design of the LKF based CDOB, to suppress the error. From a calculated error-characteristic and a numerical simulation, the account of the weight is validated. Additionally, implementing a disturbance observer in the local (controlled object) side, the robustness of the NCS is improved much in the numerical simulation.
Blockchain-Enabled Security in Electric Vehicles Cloud and Edge Computing

EVCE computing is an attractive network paradigm involving seamless connections among heterogeneous vehicular contexts. It will be a trend along with EVs becoming popular in V2X. The EVs act as potential resource infrastructures referring to both information and energy interactions, and there are serious security challenges for such hybrid cloud and edge computing. Context-aware vehicular applications are identified according to the perspectives of information and energy interactions. Blockchain-inspired data coins and energy coins are proposed based on distributed consensus, in which data contribution frequency and energy contribution amount are applied to achieve the proof of work. Security solutions are presented for securing vehicular interactions in EVCE computing.
CaL: Extending Data Locality to Consider Concurrency for Performance Optimization

Big data applications demand a better memory performance. Data Locality has been the focus of reducing data access delay. Data access concurrency, however, has become prevalent in modern memory systems in recent years. How to extend existing locality-based performance optimization to consider data concurrency becomes a timely issue facing the researchers and practitioners in the field of computing, especially in the field of big data computing. In this study, we introduce the concept and definition of Concurrency-aware data access Locality (CaL), which, as its name states, extends the concept of locality by considering concurrency. Compared to the conventional concept of locality, CaL accurately reflects the combined impact of data access locality and concurrency in modern memory systems and is very effective for data intensive applications. The value of CaL can be quantitatively measured directly by performance counters in mainstream commercial processors and is practically feasible. Two theoretical results are presented to reveal the relationships between CaL and existing memory system performance metrics of memory accesses per cycle (APC), average memory access time (AMAT), and memory bandwidth (B). In this way, we provide a methodology to use existing locality-based optimization methods directly or in combination with data concurrency optimizations, to improve the value of CaL and to improve the performance of a memory system. To demonstrate the practical value of CaL, we conduct four case studies to illustrate the power of concurrency-aware locality optimization. Compared with the conventional locality based optimization, the CaL-aware design has achieved significant performance improvement. It achieved a 3.12-fold speedup on K-means, which is a widely-used data analytic kernel from the big data benchmarks.