Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-feature Classification of Hyperspectral Image via Probabilistic SVM and Guided Filter

To deal with “curse of dimensionality” caused by high-dimensional data and the underutilization of spatial information in the classification of hyperspectral imagery (HSI), a new spectral-spatial classification method based on probabilistic support vector machine (SVM) and guided filter is proposed. The proposed method utilizes spatial information in two aspects. Firstly, the extended morphological profiles (EMP), differential morphological profiles (DMP) and Gabor textural features of hyperspectral image are extracted, which can preserve the texture, outline and shape of the original image. The spectral and extracted spatial features are joint classified by probabilistic SVM to obtain the initial classification probabilistic maps. Secondly, the guided filter is used to regularize the initial classification probabilistic maps, which can enhance the smoothness of local neighbourhood and preserve the edge information. The proposed algorithm is examined by the Indian Pines and University of Pavia hyperspectral datasets. On the same experimental conditions, the proposed method achieves the highest classification accuracy and the most reasonable classification map, demonstrating the proposed algorithm has obvious advantages in hyperspectral remote image classification.
Not the Typical Pneumonia: An Unusual Case of Rasburicase-induced Methomoglobinemia

We present a rare case of rasburicase-induced methemoglobinemia and hemolytic anemia in the setting of presumed glucose-6-phosphate dehydrogenase (G6PD) deficiency. A 78-year-old male with a known history of chronic lymphocytic leukemia presented to the clinic with fever of unknown origin. Laboratory results were significant for hyperuricemia. He was empirically started on levofloxacin and rasburicase. He then presented to the emergency department with shortness of breath and syncope. Physical examination was remarkable for a fever of 102.8 °F, conjunctival pallor, and scleral icterus. An infiltrate was observed on his computed tomography (CT) angiogram of the chest. Arterial blood gas on 50% fraction of inspired oxygen was significant for an arterial oxygen level of 222 millimeters mercury and oxyhemoglobin of 85.9%. Co-oximetry was then obtained and methemoglobin level was 13.4%. Laboratory results were noteworthy for a drop-in hemoglobin, indirect hyperbilirubinemia, low haptoglobin and elevated lactate dehydrogenase; depicting hemolytic anemia. The patient received two units of packed red blood cells, intravenous broad-spectrum antibiotics and he clinically improved.
AKT inhibition-mediated dephosphorylation of TFE3 promotes overactive autophagy independent of MTORC1 in cadmium-exposed bone mesenchymal stem cells

ABSTRACT Cadmium (Cd) is a toxic metal that is widely found in numerous environmental matrices and induces serious adverse effects in various organs and tissues. Bone tissue seems to be a crucial target of Cd contamination. Macroautophagy/autophagy has been proposed to play a pivotal role in Cd-mediated bone toxicity. However, the mechanisms that underlie Cd-induced autophagy are not yet completely understood. We demonstrated that Cd treatment increased autophagic flux and inhibition of the autophagic process using Atg7 gene silencing blocked the Cd-induced mesenchymal stem cell death. Mechanistically, Cd activated nuclear translocation of TFE3 but not that of TFEB or MITF, which contributed to the expression of autophagy-related genes and lysosomal biogenesis. Specifically, Cd decreased expression of phospho-AKT (Ser473). The reduction in AKT activity led to dephosphorylation of cytosolic TFE3 at Ser565 and promoted TFE3 nuclear translocation independently of MTORC1. Notably, Cd treatment increased the activity of PPP3/calcineurin, and pharmacological inhibition of PPP3/calcineurin with FK506 suppressed AKT dephosphorylation and TFE3 activity. These results suggest that PPP3/calcineurin negatively regulates AKT phosphorylation and is involved in Cd-induced TFE3-dependent autophagy. Modulation of the PPP3/calcineurin-AKT-TFE3 autophagic-lysosomal machinery may offer novel therapeutic approaches for the treatment of Cd-induced bone damage. Abbreviations: ACTB: actin: beta; AKT: thymoma viral proto-oncogene; AMPK: AMP-activated protein kinase; ATG: autophagy related; Baf A1: bafilomycin A1; Cd: cadmium; FOXO3: forkhead box O3; MAP1LC3/LC3: microtubule-associated protein 1 light chain 3; MITF: melanogenesis associated transcription factor; MSC: mesenchymal stem sell; MTORC1: mechanistic target of rapamycin kinase complex 1; RPS6KB1: ribosomal protein S6 kinase: polypeptide 1; SGK1: serum/glucocorticoid regulated kinase 1; SQSTM1/p62: sequestosome 1;TFE3: transcription factor E3; TFEB: transcription factor EB; TFEC: transcription factor EC
Towards Development of Complete and Conflict-Free Requirements

Writing requirements is no easy task. Common problems include ambiguity in statements, specifications at the wrong level of abstraction, statements with inconsistent references to types, conflicting requirements, and incomplete requirements. These pitfalls lead to errors being introduced early in the design process. The longer the gap between error introduction and error discovery, the higher the cost associated with the error. To address the growing cost of system development, we introduce a tool called ASSERT" (Analysis of Semantic Specifications and Efficient generation of Requirements-based Tests) for capturing requirements, backed by a formal requirements analysis engine. ASSERT" also automatically generates a complete set of requirements-based test cases. Capturing requirements in an unambiguous way and then formally analyzing them with an automated theorem prover eliminates errors as soon as requirements are written. It also addresses the historical problem that analysis engines are hard to use for someone without formal methods expertise and analysis results are often difficult for the end-user to understand and make actionable. ASSERT"'s major contribution is to bring powerful requirements capture and analysis capability to the domain of the end-user. We provide explainable and automated formal analysis, something we found important for a tool's adoptability in industry.
Spike Counts Based Low Complexity Learning with Binary Synapse

Two main difficulties encountered when implementing the neuromorphic system that supports pre- and post-synaptic textbf spike timing based real-time learning, are 1) huge memory size to store synaptic weights and 2) large amount of computations to update the synaptic weights. In this paper, we present a textbf spike counts based learning method that can significantly relieve the hardware burden of the real time unsupervised learning process. The novel learning approach uses both pre- and post-synaptic textbf spike counts as decision metrics in the following two ways: First, the synaptic weights are updated following the proposed simplified mean-based weight update rules, where binary feature images are produced based on pre-synaptic spike counts. Using the feature image, 1 bit synaptic weights are updated only once for each input image. When updating the weights, the post-synaptic spiking counts are also used to select the most active excitatory neuron. The actual weight updates are performed only for the weights connected to the dominant excitatory neuron, leading to further reduction of the weight updates without sacrificing accuracy. The simulation results show that using only 1 bit synaptic weights with 400 output neurons, the proposed learning approach achieves 82 % recognition accuracy with MNIST test set. In addition, the number of weight updates is reduced by 15.3 times compared to the state-of-the-art learning method.
Principled Schedulability Analysis for Distributed Storage Systems Using Thread Architecture Models

In this article, we present an approach to systematically examine the schedulability of distributed storage systems, identify their scheduling problems, and enable effective scheduling in these systems. We use Thread Architecture Models (TAMs) to describe the behavior and interactions of different threads in a system, and show both how to construct TAMs for existing systems and utilize TAMs to identify critical scheduling problems. We specify three schedulability conditions that a schedulable TAM should satisfy: completeness, local enforceability, and independence; meeting these conditions enables a system to easily support different scheduling policies. We identify five common problems that prevent a system from satisfying the schedulability conditions, and show that these problems arise in existing systems such as HBase, Cassandra, MongoDB, and Riak, making it difficult or impossible to realize various scheduling disciplines. We demonstrate how to address these schedulability problems using both direct and indirect solutions, with different trade-offs. To show how to apply our approach to enable scheduling in realistic systems, we develop Tamed-HBase and Muzzled-HBase, sets of modifications to HBase that can realize the desired scheduling disciplines, including fairness and priority scheduling, even when presented with challenging workloads.
Social determinants associated with the quality of life of baccalaureate nursing students: A cross‐sectional study

BACKGROUND There is a paucity of studies that have explored the quality of life (QoL) of nursing students in the Philippines. OBJECTIVES The study assessed the QoL and identified the social determinants associated with the four domains of QoL among nursing students. METHODS This cross-sectional study was conducted among 444 randomly selected baccalaureate nursing students in a university in the Philippines. The QoL was determined using an adapted WHOQOL-BREF questionnaire (∝ = 0.89). Multiple linear regression determined the association between the variables of the study. RESULTS The students exhibited better scores in social relationships and environment than psychological and physical health domains of QoL. Physical health was significantly correlated to sex, hours of sleep, and medical condition, whereas sex, year level, academic load, hours of sleep, and medical condition were correlated to psychological domain. However, no significant association was noted between the factors and social relationships, but monthly allowance was positively correlated with the environment domain. CONCLUSIONS Various social determinants influence the QoL; thus, it is essential to be considered as a critical element in planning the curriculum to improve the QoL among baccalaureate students.
An Investigation of Child and Family Factors Predicting Parental Response to Children's Conduct Problems

iii LIST OF TABLES . v
Reliability and validity of a new accelerometer-based device for detecting physical activities and energy expenditure

Background Objective assessments of sedentary behavior and physical activity (PA) by using accelerometer-based wearable devices are ever expanding, given their importance in the global context of health maintenance. This study aimed to determine the reliability and validity of a new accelerometer-based analyzer (Fibion) for detecting different PAs and estimating energy expenditure (EE) during a simulated free-living day. Methods The study consisted of two parts: a reliability (n = 18) and a validity (n = 19) test. Reliability was assessed by a 45 min protocol of repeated sitting, standing, and walking (i.e., 3 × 15 min, repeated twice), using both Fibion and ActiGraph. Validity was assessed by a 12 h continuous sequence tasks of different types (sitting, standing, walking, and cycling) and intensities (light [LPA], moderate [MPA], and vigorous [VPA]) of PA. Two Fibion devices were worn on the thigh (FT) and in the pocket (FP), respectively and were compared with criteria measures, such as direct observation (criterion 1) and oxygen consumption by a portable gas analyzer, K4b2 (criterion 2). Results FT (intra-class correlation coefficients (ICCs): 0.687–0.806) provided similar reliability as the Actigraph (ICCs: 0.661–0.806) for EE estimation. However, the measurement error (ME) of FT compared to the actual time records indicated an underestimation of duration by 5.1 ± 1.2%, 3.8 ± 0.3% and 14.9 ± 2.6% during sitting, walking, and standing, respectively. During the validity test, FT but not FP showed a moderate agreement but lager variance with the criteria (1 and 2) in assessing duration of sitting, long sitting, LPA, MPA, and VPA (p > 0.05, ICCs: 0.071–0.537), as well as for EE estimation of standing, LPA, MPA, and VPA (p > 0.05, ICCs: 0.673–0.894). Conclusions FT provided similar reliability to that of the Actigraph. However, low correlations between subsequent measurements of both devices indicated large random MEs, which were somewhat diminished during the simulated 12 h real-life test. Furthermore, FT may accurately determine the types, intensities of PA and EE during prolonged periods with substantial changes in postures, indicating that the location of the accelerometer is essential. Further study with a large cohort is needed to confirm the usability of Fibion, especially for detecting the low-intensity PAs.
A temporal-based deep learning method for multiple objects detection in autonomous driving

This paper proposes a novel vision-based object detection method in autonomous driving, which introduces the temporal information into the deep learning-based detection method for moving object detection. Vision-based object detection is a critical technology for autonomous driving. The objects in the real world such as driving cars, don’t have great changes in their positions and velocities. So the position change of objects between two consecutive frames is not large. This is usually ignored by traditional works, which usually use object detection methods on still-images to detect moving objects. Considering the relationship among consecutive frames (temporal information), we present a robust and real-time tracking method following image detection to refine the object detection results. Based on the three key attributes (distances, sizes and positions), the tracking method aims to build the association between the detected objects on the current frame and those in previous frames. The proposed object detection with temporal information dramatically improves the performance of existing object detection algorithms based on stillimage. With the proposed method, we won the champion in the preceding vehicle detection task in 2017 intelligent vehicle future challenge(2017 IVFC)1.1http://mp.weixin. qq.com/s/IDrTDlJqb2Qx360nhgCXDw
Utilizing Product Usage Data for Requirements Evaluation

Requirements engineering in software systems has two main aspects: (1) functional system requirements, and (2) non-functional requirements of the network/community. Despite the difference in their complexities, the two aspects are strongly coupled. Requirements engineering can be done by performing scientific analysis which requires the collection, processing, and modelling of large sets of data. This data usually originates from different data sources. The challenge is identifying the right sources and finding relationships between them to make the analysis useful. To explore the relationship between product usage and performance, as well as changes in requirements, we have utilized data related to secure health communications to gain insight into possible links. We will explore the collection and processing of different types of data to facilitate requirements engineering in this setting. We will use data from multiple sources to get the volume of requirements and bugs, volume of users and user types, as well as system utilization and service usage. The analysis allows us to investigate high-level connections between user requirements and system utilization. Moreover, it provides a method for the collection, processing, and analysis of large data during requirements engineering.
Joint Representation of Primitive and Non-primitive Objects for 3D Vision

The use of structural information in 3D scanning is becoming more and more popular. However, most approaches exploit this structural information either in the form of geometric primitives (mostly planes) or known rigid bodies, but not both. We overcome this limitation and propose an object representation that combines primitive and non-primitive objects using one unified formulation that is based on signed distance fields. Object pose manifolds are introduced to represent the rigid movement of primitives and non-primitives in a natural way. We show that different components of volumetric scanning, such as global trajectory optimization or geometry completion and denoising, benefit from our formulation.
Effect of Visual Information on Active Touch During Mirror Visual Feedback

Several studies have demonstrated that observation of a dummy or mirror-reflected hand being stroked or moving at the same time as the hidden hand evokes a feeling that the dummy hand is one’s own, such as the rubber hand illusion (RHI) and mirror visual feedback (MVF). Under these conditions, participants also report sensing the tactile stimulation applied to the fake hands, suggesting that tactile perception is modulated by visual information during the RHI and MVF. Previous studies have utilized passive stimulation conditions; however, active touch is more common in real-world settings. Therefore, we investigated whether active touch is also modulated by visual information during an MVF scenario. Twenty-three participants (13 men and 10 women; mean age ± SD: 21.6 ± 2.0 years) were required to touch a polyurethane pad with both hands synchronously, and estimate the hardness of the pad while observing the mirror reflection. When participants observed the mirror reflection of the other hand pushing a softer or harder pad, perceived hardness estimates were significantly biased toward softer or harder, respectively, even though the physical hardness of the pad remained constant. Furthermore, perceived hardness exhibited a strong correlation with finger displacement of the mirrored, but not hidden, hand. The modulatory effects on perceived hardness diminished when participants touched the pad with both hands asynchronously or with their eyes closed. Moreover, participants experienced ownership of the mirrored hand when they touched the pad with both hands synchronously but not asynchronously. These results indicate that hardness estimates were modulated by observation of the mirrored hand during synchronous touch conditions. The present study demonstrates that, similar to passive touch, active touch is also modulated by visual input.
Two-stage Unsupervised Multiple Kernel Extreme Learning Machine

As a powerful learning tool, Extreme Learning Machine (ELM) shows its merits in classification, regression and clustering by offering both high prediction accuracy and high learning speed. Among numerous ELM varieties, multiple kernel ELM draws intensive attention from researchers because it can leverage information from multiple heterogeneous sources, which is a common scenario in big data era. Despite remarkable efforts for supervised multiple kernel ELM, few publications have addressed the unsupervised case, which is more critical yet challenging for tackling realistic problem. In this paper, we address this problem by proposing a two-stage unsupervised multiple kernel extreme learning machine, which is suitable for fast multiple-view clustering. This approach learns the cluster and kernel combination weights alternatively. At the first stage, it generates cluster label based on a given combined kernel. Then, at the second stage, the kernel combination weights are learned by distance label based extreme learning machine based on the label generated at the previous stage. Experimental results on both synthetic and real data sets demonstrate its outstanding performance in term of both accuracy and learning speed.
Topology Design of Transparent Optical Networks Resilient to Multiple Node Failures

Consider the resilience of a network defined by the average 2-terminal reliability (A2TR) against a set of critical node failures. Consider an existing transparent optical network with a total fibre length L. The first goal of this paper is to assess the resiliency gap between the existing topology and a new network topology designed to maximize its resilience with the same fibre budget L. The resiliency gap gives us a measure of how good the resilience of existing network topologies are. Consider now that an existing network is upgraded with new links aiming to maximize its resiliency improvement with a fibre budget L′. The second goal of this paper is to assess how much the resiliency gap can be reduced between a good upgraded solution and a network topology designed to maximize its resiliency with the same fibre budget L + L′. The gap reduction gives us a measure of how close to the best resilience the upgraded solutions can get for different values of L′. To reach these goals, we first describe how the Critical Node Detection problem is defined and solved in the context of transparent optical networks. Then, we propose a multi-start greedy randomized method to generate network topologies, with a given fibre length budget, that are resilient to critical node failures. This method is also adapted to the upgrade of an existing network topology. At the end, we run the proposed methods on network topologies with public available information. The computational results show that the resiliency gap of existing topologies is significantly large but network upgrades with L′ = 10%L can significantly reduce the resiliency gaps provided that such upgrades are aimed at maximizing the network resilience to multiple node failures.
Extreme Graph Kernels for Online Learning on a Memory Budget

Learning with limited resources (processing power and memory) on a stream of data is a challenging problem. When dealing with structured data, in particular with graphs, state-of- the-art graph kernels coupled with budget-aware online learning algorithms provide an efficient and effective solution. They map the input graph in a feature space that can be represented explicitly in sparse format. In this paper, we propose a method to enhance existing graph kernels in a streaming scenario with strict memory constraints. Specifically, we combine state-of-the-art online kernel methods with the power and flexibility of the feature representation of Extreme Learning Machines (ELM). Although being in principle a simple idea, our proposal, applied to several real-world datasets, outperformed state-of-the-art algorithms on streams of graphs with respect to predictive performance.
Message-Passing Algorithm for Sum-rate Maximization in Multi-antenna Underlay D2D Communications

In this paper, we propose a resource allocation of device-to-device (D2D) communications underlaying multiantenna cellular systems by employing message-passing algorithm. It is possible to construct the factor graph of the D2D pairs by considering the interference power at the base station and among the D2D pairs. The goal is to avoid interfering the cellular transmission while increasing the sum data rate of D2D pairs. We show that the proposed resource allocation based on message passing algorithm allow us to almost reach the performance of the exhaustive search and outperforms the random allocation. Since the associated factor graph is not fully connected, the complexity of the algorithm is significantly reduced compared to the exhaustive search.