Miracle-dz/UniFAR-FTUs huggingface dataset:
Heterogeneous statistical QoS provisioning over 5G wireless full-duplex networks

Recently, both academia and industry are moving their research attention to the fifth-generation (5G) wireless networks - the next new era of wireless networks. The wireless full-duplex transmission, as one of promising candidate techniques for 5G, can significantly boost the spectrum efficiency of the wireless networks, thus providing a powerful thrust to optimize the quality-of-service (QoS) performances for the wireless networks. However, due to the heterogeneity caused by different types of simultaneous traffics over the wireless full-duplex link, supporting QoS guarantees for wireless full-duplex networks imposes the new challenges that we need to provide heterogeneous QoS guarantees for different types of traffics over the same link simultaneously. To overcome the aforementioned problems, in this paper we propose the heterogeneous statistical QoS provisioning framework for bidirectional transmission based wireless full-duplex networks. In particular, we formulate the optimization problems to maximize the system throughput subject to heterogeneous statistical delay-bound QoS requirements. Then, we convert the resulted non-convex optimization problem into an equivalent convex optimization problem, solving which we can derive the optimal QoS-driven power allocation scheme to maximize the system throughput while guaranteeing the heterogeneous statistical delay-bound QoS requirements. The extensive simulation results obtained show that our proposed QoS-driven power allocation scheme for heterogeneous statistical delay-bound QoS requirements can achieve larger aggregate system throughput than the scheme for the homogeneous statistical delay-bound QoS requirement over 5G mobile wireless full-duplex networks.
Optimization of code excited linear prediction speech coder with PSVQ-genetic codebook

The development of high quality speech coders at low bit rate and low delay is a challenging task in the field of speech communication, due to ever increasing users in wireless communication networks. CELP codec gives good quality at 4.8 kbps. By optimizing the excitation vectors in the codebook many changes in the performance can be observed to achieve toll quality speech. Codebook optimization can be achieved by improving the training process in the generation process and by using fast searching algorithms. Gaussian (X-means clustering), Fixed (PSVQ) and PSVQ-Genetic codebooks were generated to be included in CELP 4.8. Variations in performance were evaluated using objective and subjective quality measures. Experiments show that CELP 4.8 provides toll quality and reduced delay with PSVQ-Genetic codebook.
Cooperative spectrum sensing in RF-powered full-duplex cognitive radio networks

In this paper, we consider RF energy harvesting full-duplex (FD) cognitive radio networks (CRNs) which employs cooperative spectrum sensing (CSS) to improve the sensing performance. Equipped with three antennas, secondary users (SUs) in the network can conduct CSS, access to the spectrum opportunistically and harvest RF energy simultaneously. Analytical energy efficiency (EE) and harvesting efficiency (HE) are analyzed for the proposed cooperative RF-powered FD CRNs and a comparison with non-cooperative scenario is provided to measure the performance of our proposed scheme. Both theoretical and simulation results show that there exists a tradeoff between SU's transmit power and its EE, which means an optimal transmit power can be obtained for maximizing the EE. In addition, results indicate that the HE is a monotone increasing function of SU's transmit power.
Risk of fracture in adults on renal replacement therapy: a Danish national cohort study.

BACKGROUND Patients on dialysis treatment or living with a transplanted kidney have several risk factors for bone fracture, especially disturbances in mineral metabolism and immunosuppressive therapy. We describe the incidence of fracture in this retrospective national Danish cohort study and explore the influence of age, gender, comorbidity and prescribed medication. METHODS By individual-level linkage between nationwide administrative registries, the risk of fracture was compared between the group of patients receiving chronic dialysis treatment and patients receiving their first renal transplant in the study period, using the Danish background population as reference group. All three groups were followed up until first fracture, emigration, death or end of study. Cox proportional hazard models with fracture as outcome were fitted to the data. RESULTS The hazard ratio (HR) for any fracture was 3.14 [95% confidence interval (95% CI):2.97-3.31] in the dialysis group and 1.94 (95% CI: 1.72-2.18) in the renal transplanted group. The HR remained increased, but was modified by adjustment for age, gender, comorbidity and prior fracture [dialysis group: 1.85 (95% CI: 1.75-1.95); renal transplanted group: 1.82 (95% CI: 1.62-2.06)]. Prescribed diuretics, lipid-modifying agents and proton pump inhibitors also modulated the fracture risk. CONCLUSIONS Patients on dialysis or living with a transplanted kidney have a significantly higher risk of fracture than the Danish background population. Differences in age, gender, drug use and comorbidity only partly explain this increased risk. Further studies are warranted to explore the reason for this increased fracture risk in patients on renal replacement therapy.
A Comprehensive Trust-Aware Routing Protocol With Multi-Attributes for WSNs

Due to the impact of an open deployment environment, severe restrictions in power with poor hardware equipment, and a lack of centralized administration in management, wireless sensor networks (WSNs) are extremely vulnerable to malicious attacks aimed at routing and other aspects. To face this problem, we propose a novel trust-aware routing protocol for WSNs which incorporates multiattributes (TRPM) of sensor nodes in terms of communication, data, energy, and recommendation. The proposed trust model relies on an improved sliding time window considering attack frequency to facilitate the discovery of malicious behaviors of attackers. Combined with effective routing detection and maintenance protocol, the performance of our solution is tested through a wide set of simulation experiments. Extensive results reveal that an average packet transfer rate of TRPM is increased by about 19% and time consumption on the routing update is shortened by about 11% in case 20% of all sensor nodes are malicious compared with other existing trust-based routing protocols.
Protein restriction does not affect body temperature pattern in female mice

Daily torpor is a physiological adaptation in mammals and birds characterized by a controlled reduction of metabolic rate and body temperature during the resting phase of circadian rhythms. In laboratory mice, daily torpor is induced by dietary caloric restriction. However, it is not known which nutrients are related to daily torpor expression. To determine whether dietary protein is a key factor in inducing daily torpor in mice, we fed mice a protein-restricted (PR) diet that included only one-quarter of the amount of protein but the same caloric level as a control (C) diet. We assigned six non-pregnant female ICR mice to each group and recorded their body weights and core body temperatures for 4 weeks. Body weights in the C group increased, but those in the PR group remained steady or decreased. Mice in both groups did not show daily torpor, but most mice in a food-restricted group (n=6) supplied with 80% of the calories given to the C group exhibited decreased body weights and frequently displayed daily torpor. This suggests that protein restriction is not a trigger of daily torpor; torpid animals can conserve their internal energy, but torpor may not play a significant role in conserving internal protein. Thus, opportunistic daily torpor in mice may function in energy conservation rather than protein saving.
THE ROLE OF EXCITATION-CONTRACTION COUPLING FAILURE IN MUSCLE FATIGUE AND WEAKNESS OF DYSTROPHIC SKELETAL MUSCLE

Title of Document: THE ROLE OF EXCITATION-CONTRACTION COUPLING FAILURE IN MUSCLE FATIGUE AND WEAKNESS OF DYSTROPHIC SKELETAL MUSCLE Davi Augusto Garcia Mázala, Master’s Thesis Defense, 2011 Directed By: Dr. Eva Chin, PhD, Department of Kinesiology, University of Maryland Abstract Alterations in intracellular calcium (Ca ) are thought to play an important role in skeletal muscle weakness associated with muscular dystrophy due to the act ivation of Caregulated proteases (calpains). It was hypothesized that imp irments in Ca 2+ regulation are exacerbated in dystrophic muscle and that calpain inhibition c ould attenuate the muscle weakness induced by fatiguing contractions. Single muscl e fibres from control and dystrophic mice lacking dystrophin ( mdx) and utrophin plus dystrophin (Utr /mdx) were used. Fibres from Utr /mdx mice had similar peak tetanic Ca 2+ compared to control and mdx mice, however Utr /mdx mice took longer to clear the released Ca . All fibres showed similar time to fatigue but fewer mdx and Utr/mdx fibres remained excitable 1hr after fatiguing contractions. Exposure to a calpain inhibitor impr oved Ca levels in dystrophic fibres ( mdx; trend only in Utr /mdx) after fatigue. Together, these data indicate that calpains play a role in prolonged muscle weakness after fatiguing contractions.Alterations in intracellular calcium (Ca ) are thought to play an important role in skeletal muscle weakness associated with muscular dystrophy due to the act ivation of Caregulated proteases (calpains). It was hypothesized that imp irments in Ca 2+ regulation are exacerbated in dystrophic muscle and that calpain inhibition c ould attenuate the muscle weakness induced by fatiguing contractions. Single muscl e fibres from control and dystrophic mice lacking dystrophin ( mdx) and utrophin plus dystrophin (Utr /mdx) were used. Fibres from Utr /mdx mice had similar peak tetanic Ca 2+ compared to control and mdx mice, however Utr /mdx mice took longer to clear the released Ca . All fibres showed similar time to fatigue but fewer mdx and Utr/mdx fibres remained excitable 1hr after fatiguing contractions. Exposure to a calpain inhibitor impr oved Ca levels in dystrophic fibres ( mdx; trend only in Utr /mdx) after fatigue. Together, these data indicate that calpains play a role in prolonged muscle weakness after fatiguing contractions. THE ROLE OF EXCITATION-CONTRACTION COUPLING FAILURE IN MUSCLE FATIGUE AND WEAKNESS OF DYSTROPHIC SKELETAL MUSCLE
Dynamic vs. static social networks in models of parasite transmission: predicting Cryptosporidium spread in wild lemurs

Social networks provide an established tool to implement heterogeneous contact structures in epidemiological models. Dynamic temporal changes in contact structure and ranging behaviour of wildlife may impact disease dynamics. A consensus has yet to emerge, however, concerning the conditions in which network dynamics impact model outcomes, as compared to static approximations that average contact rates over longer time periods. Furthermore, as many pathogens can be transmitted both environmentally and via close contact, it is important to investigate the relative influence of both transmission routes in real-world populations. Here, we use empirically derived networks from a population of wild primates, Verreaux's sifakas (Propithecus verreauxi), and simulated networks to investigate pathogen spread in dynamic vs. static social networks. First, we constructed a susceptible-exposed-infected-recovered model of Cryptosporidium spread in wild Verreaux's sifakas. We incorporated social and environmental transmission routes and parameterized the model for two different climatic seasons. Second, we used simulated networks and greater variation in epidemiological parameters to investigate the conditions in which dynamic networks produce larger outbreak sizes than static networks. We found that average outbreak size of Cryptosporidium infections in sifakas was larger when the disease was introduced in the dry season than in the wet season, driven by an increase in home range overlap towards the end of the dry season. Regardless of season, dynamic networks always produced larger average outbreak sizes than static networks. Larger outbreaks in dynamic models based on simulated networks occurred especially when the probability of transmission and recovery were low. Variation in tie strength in the dynamic networks also had a major impact on outbreak size, while network modularity had a weaker influence than epidemiological parameters that determine transmission and recovery. Our study adds to emerging evidence that dynamic networks can change predictions of disease dynamics, especially if the disease shows low transmissibility and a long infectious period, and when environmental conditions lead to enhanced between-group contact after an infectious agent has been introduced.
Multi-object tracking by virtual nodes added min-cost network flow

In this paper, we propose a Virtual Missing-detection and Occlusion Model (VMOM) for the cost flow network approaches in multiple object tracking (MOT) systems. The VMOM generates virtual nodes which represent occlusions and missing detections. Regular nodes which indicate tracklets together with virtual nodes are used to establish a cost flow network. A Counter Embedded Iterative Shortest Paths (CEISP) algorithm is also proposed to solve the new network flow optimization efficiently. Finally, the tracker with our scheme is tested on the open Multiple Object Tracking Benchmark. Experimental results show the advantages of our approach in main performance indicators comparing with many excellent trackers.
Multi-view deep metric learning for image classification

In this paper, we propose a new deep metric learning approach, called MVDML, for multi-view image classification. Multi-view features can provide more information than single view, however, it is a challenge to exploit and fuse the complementary information from multiple views. Multiple deep neural networks are constructed, each corresponds to a view, to extract nonlinear information from images. The nonlinear transformation is an improvement on linear transformation of metric learning. All the original images will be transformed into a lower-dimensional space. In each new space, the difference between intra-class distance and inter-class distance is maximized. To extract information from different views as much as possible, the difference between different views of the same image is minimized. The numerical experiments verify that our model can obtain competitive performance in image classification and runs faster than the baseline methods.
Shadow-Based Vehicle Detection in Urban Traffic

Vehicle detection is a fundamental task in Forward Collision Avoiding Systems (FACS). Generally, vision-based vehicle detection methods consist of two stages: hypotheses generation and hypotheses verification. In this paper, we focus on the former, presenting a feature-based method for on-road vehicle detection in urban traffic. Hypotheses for vehicle candidates are generated according to the shadow under the vehicles by comparing pixel properties across the vertical intensity gradients caused by shadows on the road, and followed by intensity thresholding and morphological discrimination. Unlike methods that identify the shadow under a vehicle as a road region with intensity smaller than a coarse lower bound of the intensity for road, the thresholding strategy we propose determines a coarse upper bound of the intensity for shadow which reduces false positives rates. The experimental results are promising in terms of detection performance and robustness in day time under different weather conditions and cluttered scenarios to enable validation for the first stage of a complete FACS.
Fc receptor-mediated phagocytosis occurs in macrophages at exceedingly low cytosolic Ca2+ levels

Cytosolic free Ca2+ ([Ca2+]i) homeostasis was investigated in mouse peritoneal macrophages and in the macrophage-like cell line J774. [Ca2+]i measurements were performed in both cells in suspension and cells in monolayers loaded with either quin2 or fura-2. Resting [Ca2+]i was 110-140 and 85-120 nM for cell suspensions and monolayers, respectively. There were no significant differences in [Ca2+]i between the two macrophage populations whether quin2 or fura-2 were used as Ca2+ indicators. Addition of heat-aggregated IgG, IgG-coated erythrocyte ghosts, or a rat monoclonal antibody (2.4G2) directed against mouse Fc receptor II induced a rise in [Ca2+]i. This [Ca2+]i increase was consistently observed in J774 and peritoneal macrophage suspensions and in J774 macrophage monolayers; in contrast it was observed inconsistently in peritoneal macrophages in monolayer cultures. The increase in [Ca2+]i induced by ligation of Fc receptors was inhibited totally in macrophages in suspension and by 80% in macrophages in monolayers by a short preincubation of macrophages with PMA; however, phagocytosis itself was unaffected. The effect of reducing cytosolic Ca2+ to very low concentrations on Fc receptor- mediated phagocytosis was also investigated. By incubating macrophages with high concentrations of quin2/AM in the absence of extracellular Ca2+, or by loading EGTA into the cytoplasm, the [Ca2+]i was buffered and clamped to 1-10 nM. Despite this, the phagocytosis of IgG-coated erythrocytes proceeded normally. These observations confirm the report of Young et al. (Young, J. D., S. S. Ko, and Z. A. Cohn. 1984. Proc. Natl. Acad. Sci. USA. 81:5430-5434) that ligation of Fc receptors causes Ca2+ mobilization in macrophages. However, these results confirm and extend the findings of McNeil et al. (McNeil, P. L., J. A. Swanson, S. D. Wright, S. C. Silverstein, and D. L. Taylor. 1986. J. Cell Biol. 102:1586-1592) that a rise in [Ca2+]i is not required for Fc receptor- mediated phagocytosis; and they provide direct evidence that Fc receptor-mediated phagocytosis occurs normally even at exceedingly low [Ca2+]i.
A novel SRC based method for face recognition with low quality images

Sparse representation-based classification (SRC) shows a good performance for face recognition in recent years, but SRC can not be suitable for low quality data with disguise or noise, which are often presented in the practical applications. To solve the problem, in this paper, we propose a novel SRC based method for face recognition with low quality images named sparse low-rank component based representation (SLCR). In SLCR, we utilize the low-rank component from training dataset to construct dictionary. The dictionary composed of low-rank component and non-low-rank component is able to describe the face feature better, especially for low quality training samples. Our recognition rule is based on the minimum class-wise reconstruction residual which leads to a substantial improvement on the proposed SLCR's performance. Extensive experiments on benchmark face databases demonstrate that the proposed method consistently outperforms the other sparse representation based approaches for disguised and corrupted face recognition.
An assistive interface for people with dementia

This paper describes a human machine interface approach focused on use by people with dementia. The core concept is that the interface is not visible, so the user believes they are performing the task undertaken without assistance, but the system will correct any non-completed or missed steps of the task. It also identifies abnormal operational steps which may indicate that the person is having difficulties so their carer should investigate. It is designed this way to address specific behavioral traits of persons with dementia. The interface has been tested by simulation and demonstrated ability to meet these requirements. The significance of this interface approach is that it can facilitates the introduction of automated devices into the person's home to help them undertake tasks but without creating new problems for themselves or others.
ArtGAN: Artwork synthesis with conditional categorical GANs

This paper proposes an extension to the Generative Adversarial Networks (GANs), namely as ArtGAN to synthetically generate more challenging and complex images such as artwork that have abstract characteristics. This is in contrast to most of the current solutions that focused on generating natural images such as room interiors, birds, flowers and faces. The key innovation of our work is to allow back-propagation of the loss function w.r. t. the labels (randomly assigned to each generated images) to the generator from the discriminator. With the feedback from the label information, the generator is able to learn faster and achieve better generated image quality. Empirically, we show that the proposed ArtGAN is capable to create realistic artwork, as well as generate compelling real world images that globally look natural with clear shape on CIFAR-10.
A critical analysis of the methods of evaluating MRI brain segmentation algorithms

Many papers are published every year containing new methodologies for brain tissue segmentation in magnetic resonance images. The evaluation of these methods is fundamental to understand their behavior and to observe their weak and strong points. Even though improvements have been proposed, the analysis of the segmentation results can still lead to incorrect conclusions. This paper contains an investigation of the state-of-the-art in brain tissue segmentation evaluation, which includes tissue classification or segmentation, handling partial volume effect, and evaluation metrics. It uncovers previously unnoticed pitfalls and proposes standard procedures to avoid them. Experiments show that the proposed evaluation strategy gives a better insight about the method's strong and weak points.
Behavior of Fiber-Reinforced Concrete Slabs Under Low Velocity Projectile Impact

An investigation was carried out to study the effects of steel fibers in improving the performance of concrete under impact loading. Both conventionally reinforced and fiber reinforced concrete slabs were considered. 50 mm (.195 in) thick slabs of size 1000 mm square (1.549 in square) clamped on all four edges were tested on a span of 900 mm (35 in). Hooked-end steel fibers having an aspect ratio of 60 with volume fractions of 1% and 2% were studied. The instrumented drop-weight impact setup developed is capable of dropping a 40 kg (88 lb) mass from heights of up to 4.5 m (152 in). The transient impact load on the instrumented striking head, accelerations of the slab, steel strains, and concrete strains, were all recorded using a high-speed data acquisition system. The cracking pattern and the damage due to impact were examined. The study confirmed the superior impact resistance of steel fiber reinforced concrete.
Face anti-spoofing via deep local binary patterns

Convolutional neural networks (CNNs) have achieved excellent performance in the field of pattern recognition when huge amount of training data is available. However, training a CNN model is less obvious when only a limited amount of data is given such as in the case of face anti-spoofing problem. It is indeed not easy to collect very large sets of fake faces. Especially for the fully-connected layers, tens of thousands of parameters need to be learned. To tackle this problem of lack of training data in face anti-spoofing, we propose to explore the incorporation of hand-crafted features in the CNN framework. In our proposed approach, the color local binary patterns (LBP) features are extracted from the convolutional feature maps, which are fine tuned based on the VGG-face model. These features are then fed into support vector machine (SVM) classifier. Extensive experiments are conducted on two benchmark and publicly available databases showing very interesting performance compared to state-of-the-art methods.