Miracle-dz/UniFAR-FTUs huggingface dataset:
Digitally Annealed Solution for the Maximum Clique Problem with Critical Application in Cellular V2X

Cellular V2X, the “Vehicle to Everything” standard, defines a framework for practically feasible information exchange among vehicles and other network entities. This interaction is proved to bring in substantial economic and ecological benefits. LTE V2X uses a portion of uplink frame as a resource pool and in the main mode, relies on a central scheduler for allocating these resources to the users. As an important resource management problem, besides the optimal resource scheduling, finding a proper lower bound for required resources in this mode is NP-hard. Network management entities and service providers require this lower bound to determine the minimum size of the uplink frame slice to be allocated to the V2X resource pool. In this paper, we take advantage of Digital Annealer potentials to target this problem at a new size range with considerably enhanced level of optimality that has not been achievable so far due to computational complexities. The Digital Annealer enables us to find the minimum required size of the V2X slice in the uplink frame with high speed through our maximum clique and minimum cover graph theoretic formulations of this problem. Our approach considerably enhances performance compared to its closest alternatives in the literature.
Contextual Learning and Memory Retention: The use of Near Field Communications, QR Codes, QBIC, and the Spacing Effect in Location Based Learning

An important part of multiplatform or blended learning is designing learning environments that take full advantage of the relative strengths and weakness of the various platforms employed to meet learning objectives. The desktop has strengths that are conducive to immersive learning environments, whereas mobile devices excel in contextual learning and performance support roles. Blended learning then, is not merely porting the same content from one platform to another, but recognizing the need for unique implementations. This chapter will examine two general applications in which mobile learning takes advantage of the flexibility afforded by the platform. In the first case we will explore the possibilities presented by physical hyperlinks through the use of Near Field Communications, QR codes, and image recognition software. In addition to providing contextually relevant information, the mobile platform is ideal for providing enhanced conceptual retention. The Spacing Effect demonstrates that memory decays according to a well-defined logarithmic curve. Once this curve has been optimized for an individual, it is possible to determine the most productive times to review learning objectives. Mobile devices are the perfect platform to review material initially mastered on a desktop or in a classroom, and these scheduled sessions can boost retention times dramatically. Contextual Learning and Enhanced Retention are two applications that cater to the strengths of mobile devices, and augment a holistic multiplatform approach to learning. DOI: 10.4018/978-1-60566-703-4.ch019
EQPR: Enhancing QoS in Named Data Networking using Priority and RTT Driven PIT Replacement Policy

Named Data Networking (NDN) is a newly-emerging future Internet Architecture which focuses on content rather than hosts. It changes the communication model from ‘delivering content to a specific host’ to ‘fetching content by using names’. One of the major components in NDN is Pending Interest Table (PIT). Its unique design brings the following advantages in NDN: anonymity, loop-free forwarding, multipath delivery, and multicast. One of the key challenges in NDN is choosing the appropriate PIT size. Current NFD (NDN Forwarding Daemon) does not limit the PIT size. Over-allocation of the PIT does not help in improving network performance. So, PIT needs to have a limited size. However, under bursty traffic, the PIT may become full. In this paper, we present a novel PIT replacement policy called EQPR. It evicts the existing entry based on two factors: Priority and Estimated Round Trip Time (RTT), aiming towards achieving Quality of Service for the valuable users who pay an additional cost for better service. We evaluate EQPR using ndnSIM simulator and compare the results with basic NDN under moderate to high traffic. Simulation results show that EQPR maintains a similar overall satisfaction rate as Basic NDN and most importantly it provides 99.7% Interest satisfaction rate for valuable users.
Online UAV Scheduling Towards Throughput QoS Guarantee for Dynamic IoVs

Ensuring network QoS for Internet of vehicles (IoVs) is crucial for safe and intelligent transportation system, while the vehicle density variation seems invincible for stationary base station (BS) networks. In this paper, we study IoV's downlink throughput guarantee, in which, in addition to the cellular BS resource, UAVs (equipped with WiFi interfaces) can be dynamically sent out to provide additional wireless connections. To cope with the dynamic IoV density, we propose an Online UAV Scheduling scheme, referred to as OUS, to online schedule and manage UAVs to guarantee seamless connections with reliable throughput performance. In OUS, we first use the complementary cumulative distribution function (CCDF) of IoV throughput to calculate the likelihood of a channel resource shortage. If a shortage condition is imminent and then minimal UAVs will be sent out to their optimal hovering positions. In particular, we revealed the marginal effect for the optimal hovering position acquisition, i.e., the further the UAV is away from the BS, the larger throughput gain can be achieved by the system. We conduct extensive simulations to evaluate the performance of our OUS scheme, and results demonstrate that it can well react to the throughput QoS demand by intelligently sending out minimal UAVs, and its hovering position acquisition method can fully utilize the efficacy of UAVs.
Position-Attitude Prediction Based Beam Tracking for UAV mmWave Communications

Millimeter wave offers large bandwidth for high data-rate unmanned aerial vehicle (UAV)-to-UAV communications. Because of high mobility and attitude variations, it is challenging to maintain the communication link among the navigating UAVs with narrow beam in the mmWave band. To the best of our knowledge, this is the first paper to establish a transmission-oriented UAV attitude prediction model for the UAV-to-UAV mmWave communication link. In particular, a position-attitude prediction based beam tracking algorithm is proposed. First, a Guassian Process (GP) based learning algorithm is presented for the transmitting UAV to predict the position and attitude of the receiving UAV by using the previous position-attitude data and exploiting the relationship between the position and attitude. Then, the analog beamforming vectors are derived by using the predicted spatial angles. Simulation results demonstrate that the proposed learning algorithm can achieve high accurate position-attitude prediction, and the beam tracking algorithm considering UAV attitude variations significantly outperforms the existing algorithms with only position information.
Design of a Purely Mechanical Sensor-Controller Integrated System for Walking Assistance on an Ankle-Foot Exoskeleton

Propulsion during push-off (PO) is a key factor to realize human locomotion. Through the detection of real-time gait stage, assistance could be provided to the human body at the proper time. In most cases, ankle-foot exoskeletons consist of electronic sensors, microprocessors, and actuators. Although these three essential elements contribute to fulfilling the function of the detection, control, and energy injection, they result in a huge system that reduces the wearing comfort. To simplify the sensor-controller system and reduce the mass of the exoskeleton, we designed a smart clutch in this paper, which is a sensor-controller integrated system that comprises a sensing part and an executing part. With a spring functioning as an actuator, the whole exoskeleton system is completely made up of mechanical parts and has no external power source. By controlling the engagement of the actuator based on the signal acquired from the sensing part, the proposed clutch enables the ankle-foot exoskeleton (AFE) to provide additional ankle torque during PO, and allows free rotation of the ankle joint during swing phase, thus reducing the metabolic cost of the human body. There are two striking advantages of the designed clutch. On the one hand, the clutch is lightweight and reliable—it resists the possible shock during walking since there is no circuit connection or power in the system. On the other hand, the detection of gait relies on the contact states between human feet and the ground, so the clutch is universal and does not need to be customized for individuals.
Adverse drug event and medication extraction in electronic health records via a cascading architecture with different sequence labeling models and word embeddings

OBJECTIVE An adverse drug event (ADE) refers to an injury resulting from medical intervention related to a drug including harm caused by drugs or from the usage of drugs. Extracting ADEs from clinical records can help physicians associate adverse events to targeted drugs. MATERIALS AND METHODS We proposed a cascading architecture to recognize medical concepts including ADEs, drug names, and entities related to drugs. The architecture includes a preprocessing method and an ensemble of conditional random fields (CRFs) and neural network-based models to respectively address the challenges of surrogate string and overlapping annotation boundaries observed in the employed ADEs and medication extraction (ADME) corpus. The effectiveness of applying different pretrained and postprocessed word embeddings for the ADME task was also studied. RESULTS The empirical results showed that both CRFs and neural network-based models provide promising solution for the ADME task. The neural network-based models particularly outperformed CRFs in concept types involving narrative descriptions. Our best run achieved an overall micro F-score of 0.919 on the employed corpus. Our results also suggested that the Global Vectors for word representation embedding in general domain provides a very strong baseline, which can be further improved by applying the principal component analysis to generate more isotropic vectors. CONCLUSIONS We have demonstrated that the proposed cascading architecture can handle the problem of overlapped annotations and further improve the overall recall and F-scores because the architecture enables the developed models to exploit more context information and forms an ensemble for creating a stronger recognizer.
A multivariate neuroimaging biomarker of individual outcome to transcranial magnetic stimulation in depression

The neurobiology of major depressive disorder (MDD) remains incompletely understood, and many individuals fail to respond to standard treatments. Repetitive transcranial magnetic stimulation (rTMS) of the dorsolateral prefrontal cortex (DLPFC) has emerged as a promising antidepressant therapy. However, the heterogeneity of response underscores a pressing need for biomarkers of treatment outcome. We acquired resting state functional magnetic resonance imaging (rsfMRI) data in 47 MDD individuals prior to 5–8 weeks of rTMS treatment targeted using the F3 beam approach and in 29 healthy comparison subjects. The caudate, prefrontal cortex, and thalamus showed significantly lower blood oxygenation level‐dependent (BOLD) signal power in MDD individuals at baseline. Critically, individuals who responded best to treatment were associated with lower pre‐treatment BOLD power in these regions. Additionally, functional connectivity (FC) in the default mode and affective networks was associated with treatment response. We leveraged these findings to train support vector machines (SVMs) to predict individual treatment responses, based on learned patterns of baseline FC, BOLD signal power and clinical features. Treatment response (responder vs. nonresponder) was predicted with 85–95% accuracy. Reduction in symptoms was predicted to within a mean error of ±16% (r = .68, p < .001). These preliminary findings suggest that therapeutic outcome to DLPFC‐rTMS could be predicted at a clinically meaningful level using only a small number of core neurobiological features of MDD, warranting prospective testing to ascertain generalizability. This provides a novel, transparent and physiologically plausible multivariate approach for classification of individual response to what has become the most commonly employed rTMS treatment worldwide. This study utilizes data from a larger clinical study (Australian New Zealand Clinical Trials Registry: Investigating Predictors of Response to Transcranial Magnetic Stimulation for the Treatment of Depression; ACTRN12610001071011; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx? id=336262).
Structural Basis of Resistant Starch (RS) in Bread: Natural and Commercial Alternatives

Bread is categorized as having a high amount of rapidly digested starch that may result in a rapid increase in postprandial blood glucose and, therefore, poor health outcomes. This is mostly the result of the complete gelatinization that starch undergoes during baking. The inclusion of resistant starch (RS) ingredients in bread formulas is gaining prominence, especially with the current positive health outcomes attributed to RS and the apparition of novel RS ingredients in the market. However, many RS ingredients contain RS structures that do not resist baking and, therefore, are not suitable to result in a meaningful RS increase in the final product. In this review, the structural factors for the resistance to digestion and hydrothermal processing of RS ingredients are reviewed, and the definition of each RS subtype is expanded to account for novel non-digestible structures recently reported. Moreover, the current in vitro digestion methods used to measure RS content are critically discussed with a view of highlighting the importance of having a harmonized method to determine the optimum RS type and inclusion levels for bread-making.
Eosinophil extracellular traps activate type 2 innate lymphoid cells through stimulating airway epithelium in severe asthma

Activated eosinophils release extracellular traps (EETs), which contribute to airway inflammation in severe asthma (SA). However, the role of EETs in innate immunity has not yet been completely determined. The present study aimed to demonstrate the mechanism of airway inflammation in SA mediated by EETs.
Object Grouping using Learning methods in a Simulated Environment

Since the beginning of the information age, humans live fascinated with the idea of creating intelligent systems capable of understanding and interacting with what is around them and sometimes adapt to new scenarios. Machine learning allows the exploration of this adaption, enabling learning capabilities in real or virtual environments in order to achieve better results for the tasks such systems were created for. With this idea, this work intends to use machine learning techniques to create a simulation that would be capable of classify and group objects given by a user and understand the logic behind such organization. At a later stage the simulation will be tested with real users to understand how it performed.
From Healthy Aging to Frailty: In Search of the Underlying Mechanisms.

Population aging is accelerating rapidly worldwide, from 461 million people older than 65 years in 2004 to an estimated 2 billion people by 2050, leading to critical implications for the planning and delivery of health and social care. The most problematic expression of population aging is the clinical condition of frailty, which is a state of increased vulnerability that develops as a consequence of the accumulation of microscopic damages in many physiological systems that lead to a striking and disproportionate change in health state, even after an apparently small insult. Since little is known about the biology of frailty, an important perspective to understand this phenomenon is to establish how the alterations that physiologically occurs during a condition of healthy aging may instead promote cumulative decline with subsequent depletion of homoeostatic reserve and increase the vulnerability also after minor stressor events. In this context, the present review aims to provide a description of the molecular mechanisms that, by having a critical impact on behavior and neuronal function in aging, might be relevant for the development of frailty. Moreover, since these biological systems are also involved in the coping strategies set in motion to respond to environmental challenges, we propose a role for lifestyle stress as an important player to drive frailty in aging.
Contributions of Indoor and Outdoor Sources to Ozone in Residential Buildings in Nanjing

Ozone has become one of the most serious air pollutants in China in recent years. Since people spend most of their time indoors, the ozone in the indoor environment could be a major factor affecting the occupants’ health. The indoor ozone in residential buildings mainly comes from two sources: outdoor atmosphere and indoor ozone produced by electrical devices. In this study, a typical residence in Nanjing was taken as an example to calculate and compare the contributions of indoor and outdoor sources to ozone in the building. A questionnaire survey about the type, the placement, and the frequency of use of the ozone emission devices was performed to provide the basis for the settings of indoor ozone sources. The multi-zone software CONTAM was used hourly to simulate the ozone concentration in summer and in winter with inner doors either closed or open, and it was noted whether there were ozone emission devices indoors or not. Source contribution was quantified and compared by three methods in this paper: (1) the average indoor/outdoor (I/O) ratio, (2) the I/O ratio frequency, and (3) the ratio of indoor ozone concentration without ozone sources to that with ozone sources. The results showed that the contribution of outdoor sources was much greater than that of indoor sources in summer, but in winter, the frequency of I/O > 1 could reach 55.8% of the total seasonal time, and the ratio of indoor ozone concentration without sources to that with sources could reach as high as 74.3%. This meant that the indoor concentration had the potential to exceed the outdoor. Furthermore, human respiratory exposure in different ages and genders was calculated. It was found that teenagers aged 10–18 years old and female adults had a higher respiratory exposure level.
Addressing NGS Data Challenges: Efficient High Throughput Processing and Sequencing Error Detection

Next generation sequencing (NGS) technologies have facilitated the identification of disease causing mutations, which has significantly improved patient’s diagnosis and treatment. Since its emergence, NGS has been used in many applications like genome sequencing, DNA resequencing, transcriptome sequencing and epigenomics, to unfold the various layers of genome biology. Because of this broad spectrum of applications and recent decrement in cost, usage of NGS has become a routine approach to address many research as well as medical questions. It is producing huge amounts of data, which necessitate highly efficient and accurate computational analysis as well as data management. This thesis addresses some of the challenges of NGS data analysis, mainly for targeted DNA sequencing data. It describes the various steps required for data analysis including their significance and potential negative effects on consecutive downstream analysis and so on the final variant lists. In order to make the analysis more accurate and efficient, an extensive testing of different bioinformatics tools and algorithms was preformed and a fully automated data analysis workflow was developed. This workflow is implemented and optimized on high performance computing (HPC) systems. I describe different design principles and parallelization strategies that enable proper exploitation of HPC resources to achieve high throughput of data analysis. Besides correcting for known sequencing errors by using existing tools, this work is also aimed at the detection of a new class of systematic sequencing errors called recurrent systematic sequencing errors. I present an approach for the exploration of this class of errors and describe the probable causes and patterns behind them. This includes some known and novel patterns observed during this work. Furthermore, I provide a tool to filter the false variants due to these errors from any variant list. Overall, the work performed during this thesis has been already used (and will be used in future as well), to provide accurate and efficient data analysis, which enables exploration of the genetic background of various diseases.
The Effect of the Color Filter Array Layout Choice on State-of-the-Art Demosaicing

Interpolation from a Color Filter Array (CFA) is the most common method for obtaining full color image data. Its success relies on the smart combination of a CFA and a demosaicing algorithm. Demosaicing on the one hand has been extensively studied. Algorithmic development in the past 20 years ranges from simple linear interpolation to modern neural-network-based (NN) approaches that encode the prior knowledge of millions of training images to fill in missing data in an inconspicious way. CFA design, on the other hand, is less well studied, although still recognized to strongly impact demosaicing performance. This is because demosaicing algorithms are typically limited to one particular CFA pattern, impeding straightforward CFA comparison. This is starting to change with newer classes of demosaicing that may be considered generic or CFA-agnostic. In this study, by comparing performance of two state-of-the-art generic algorithms, we evaluate the potential of modern CFA-demosaicing. We test the hypothesis that, with the increasing power of NN-based demosaicing, the influence of optimal CFA design on system performance decreases. This hypothesis is supported with the experimental results. Such a finding would herald the possibility of relaxing CFA requirements, providing more freedom in the CFA design choice and producing high-quality cameras.
Non-Cytotoxic Agarose/Hydroxyapatite Composite Scaffolds for Drug Release

Healing of large bone defects requires implants or scaffolds that provide structural guidance for cell growth, differentiation, and vascularization. In the present work, an agarose-hydroxyapatite composite scaffold was developed that acts not only as a 3D matrix, but also as a release system. Hydroxyapatite (HA) was incorporated into the agarose gels in situ in various ratios by a simple procedure consisting of precipitation, cooling, washing, and drying. The resulting gels were characterized regarding composition, porosity, mechanical properties, and biocompatibility. A pure phase of carbonated HA was identified in the scaffolds, which had pore sizes of up to several hundred micrometers. Mechanical testing revealed elastic moduli of up to 2.8 MPa for lyophilized composites. MTT testing on Lw35human mesenchymal stem cells (hMSCs) and osteosarcoma MG-63 cells proved the biocompatibility of the scaffolds. Furthermore, scaffolds were loaded with model drug compounds for guided hMSC differentiation. Different release kinetic models were evaluated for adenosine 5′-triphosphate (ATP) and suramin, and data showed a sustained release behavior over four days.