Miracle-dz/UniFAR-FTUs huggingface dataset:
Financing the Web of Data with Delayed-Answer Auctions

The World Wide Web is a massive network of interlinked documents. One of the reasons the World Wide Web is so successful is the fact that most content is available free of any charge. Inspired by the success of the World Wide Web, the Web of Data applies the same strategy of interlinking to data. To this point, most of data in the Web of Data is also free of charge. The fact that the data is freely available raises the question of financing these services, however. As we will discuss in this paper, advertisement and donations cannot easily be applied to this new setting. To create incentives to subsidize data providers, we propose that sponsors should pay the providers to promote sponsored data. In return, sponsored data will be privileged over non-sponsored data. Since it is not possible to enforce a certain ordering on the data the user will receive, we propose to split up the data into different batches and deliver these batches with different delays. In this way, we can privilege sponsored data without withholding any non-sponsored data from the user. In this paper, we introduce a new concept of a delayed-answer auction, where sponsors can pay to prioritize their data. We introduce a new model which captures the particular situation when a user access data in the Web of Data. We show how the weighted Vickrey-Clarke-Groves auction mechanism can be applied to our scenario and we discuss how certain parameters can influence the nature of our auction. With our new concept, we build a first step to a free yet financial sustainable Web of Data.
Molecular and Cellular Characterization of a New α-Thalassemia Mutation (HBA2:c.94A>C) Generating an Alternative Splice Site and a Premature Stop Codon

The identification of α-thalassemia (α-thal) due to point mutations has been increasing significantly with the advancement of molecular diagnostic tools. We describe here the molecular and cellular characteristics of the thalassemia mutation HBA2:c.94A>C, a novel point mutation affecting the α2-globin gene, causing a mild α-thal phenotype in a male patient of undisclosed ethnicity, investigated for unexplained microcytosis. The detected mutation is located at the penultimate nucleotide (nt) of the first exon which we postulated might affect pre mRNA splicing. While an in silico analysis did not predict any aberrant splice variants, experimental analysis using our in vitro model for gene expression studies showed utilization of a cryptic splice site at codon 15 that resulted in an aberrant splice variant. As a result, a frameshift in the reading frame of the mature mRNA was produced, leading to the formation of a premature termination codon (PTC) between codons 48 and 49 in exon 2. This in turn leads to nonsense mediated mRNA decay (NMD) and the phenotype of α-thal.
Particle Competition and Cooperation to Prevent Error Propagation from Mislabeled Data in Semi-supervised Learning

Semi-supervised learning is applied to classification problems where only a small portion of the data items is labeled. In these cases, the reliability of the labels is a crucial factor, because mislabeled items may propagate wrong labels to a large portion or even the entire data set. This paper aims to address this problem by presenting a graph-based (network-based) semi-supervised learning method, specifically designed to handle data sets with mislabeled samples. The method uses teams of walking particles, with competitive and cooperative behavior, for label propagation in the network constructed from the input data set. The proposed model is nature-inspired and it incorporates some features to make it robust to a considerable amount of mislabeled data items. Computer simulations show the performance of the method in the presence of different percentage of mislabeled data, in networks of different sizes and average node degree. Importantly, these simulations reveals the existence of the critical points of the mislabeled subset size, below which the network is free of wrong label contamination, but above which the mislabeled samples start to propagate their labels to the rest of the network. Moreover, numerical comparisons have been made among the proposed method and other representative graph-based semi-supervised learning methods using both artificial and real-world data sets. Interestingly, the proposed method has increasing better performance than the others as the percentage of mislabeled samples is getting larger.
Dynamic spectrum management in green cognitive radio cellular networks

In this paper, we propose a new cellular network operation scheme fulfilling the 5G requirements related to spectrum management and green communications. We focus on cognitive radio cellular networks in which both the primary network (PN) and the secondary network (SN) are maximizing their operational profits. The PN and the SN are required to respect a CO2 emissions threshold by switching off one or more lightly loaded base stations (BSs). In addition, the PN accepts to cooperate with the SN by leasing its spectrum in the cells where the PN is turned off. In return, the corresponding SN BSs host the PN users and impose extra roaming fees to the PN. We propose a low-complexity algorithm that maximizes the profit per CO2 emissions metric while switching on/off the BSs. In the simulations, we show that our proposed algorithm achieves performances close to the exhaustive search method. In addition, we find that the roaming price is a key parameter that affects both PN and SN profits1.
Enabling Contactless Detection of Moving Humans with Dynamic Speeds Using CSI

Device-free passive detection is an emerging technology to detect whether there exist any moving entities in the areas of interest without attaching any device to them. It is an essential primitive for a broad range of applications including intrusion detection for safety precautions, patient monitoring in hospitals, child and elder care at home, and so forth. Despite the prevalent signal feature Received Signal Strength (RSS), most robust and reliable solutions resort to a finer-grained channel descriptor at the physical layer, e.g., the Channel State Information (CSI) in the 802.11n standard. Among a large body of emerging techniques, however, few of them have explored the full potential of CSI for human detection. Moreover, space diversity supported by nowadays popular multiantenna systems are not investigated to a comparable extent as frequency diversity. In this article, we propose a novel scheme for device-free PAssive Detection of moving humans with dynamic Speed (PADS). Both full information (amplitude and phase) of CSI and space diversity across multiantennas in MIMO systems are exploited to extract and shape sensitive metrics for accuracy and robust target detection. We prototype PADS on commercial WiFi devices, and experiment results in different scenarios demonstrate that PADS achieves great performance improvement in spite of dynamic human movements.
A quality of life model promotes enablement for children with medical complexity.

Wesley is a physical therapy student, who has been tasked with completing an evaluation on a 3-year-old male with medical complexity. This young child’s impairments include cortical blindness, lack of head and trunk righting and equilibrium reactions, and recurring hospitalization because of pulmonary aspiration. He functions at a 2-month-old developmental level dependent for posture, positioning, mobility, feeding and all other activites of daily living (ADL). His communication is by crying to express distress, but he is also able to smile and giggle. Based on this information and the evaluation, Wesley composes his written assessment and as he begins to develop goals, he asks, “How can I write goals for this child? He cannot do anything. ” For children with medical complexity (CMC), who demonstrate significant medical, physical, and intellectual impairments, this type of response perpetuates the practice of a custodial care model. Such CMC demonstrate multisystem impairments resulting in considerable functional activity limitations and participatory restrictions. Frequent and extensive hospitalizations, primary and specialty physician visits, poly-pharmacy, medical equipment, rehabilitation, and care coordination are typical for these children in order to manage
A Framework for Automatically Extracting Overvoltage Features Based on Sparse Autoencoder

With the development of smart grid, it is of increasing significance to identify and cope with various types of overvoltages, faults and power quality disturbances effectively and automatically. In this paper, a framework for overvoltage identification and classification based on sparse autoencoder is proposed. By using single-layer and stacked sparse autoencoders, dimensionality reduction and automatic feature extraction of ferroresonance overvoltage waveforms in power distribution systems are achieved as an example, which does not require feature engineering to produce a series of features. Classification of different ferroresonance modes is then implemented with a softmax classifier, and favorable classification results are obtained after parameters of feature extraction and classifier models are determined. Application of the proposed framework in smart grids is discussed. The proposed framework provides a brand new idea for establishing a smart identification and classification system for overvoltages, which can be generalized to classification of faults and power quality disturbances.
Proteomic analysis of multidrug‐resistance mechanisms in adriamycin‐resistant variants of DLKP, a squamous lung cancer cell line

Alterations in protein expression associated with adriamycin resistance in a panel of variants derived from the poorly differentiated squamous cell lung carcinoma DLKP were investigated using 2‐D DIGE. Of the 80 proteins identified as being differentially expressed, 32 correlated to adriamycin resistance. Twenty‐four proteins showed positive correlations with drug resistance, 11 correlated directly with increase in the resistance (including NDPK, RPA2, CCT2, HSP70 and Annexin A1) while 13 proteins (including HNRP K and H1, aldehyde dehydrogenase (ALDH), stomatin and CCT3) increased to a similar level in all drug‐resistant variants. Fewer proteins showed an inverse correlation with resistance; two (protein disulphide isomerase (PDI) and HSP70 variant 1) displayed a similar decrease in all variants and six (including prohibitin (PHB) and EIF5A) correlated inversely with resistance. Three proteins (EEF1D, Actin G1 and Annexin 1) correlated with the invasive status of these variants. Some expected targets of adriamycin action showed correlation with resistance including RPA2 (critical for DNA damage repair), while others proteins involved in protection from ROS production (such as GST, peroxiredoxins and thioredoxins) did not. The proteomic analysis revealed a large number of changes in protein expression that may contribute to a more apoptosis‐resistant state. Many of these changes could provide novel targets for overcoming resistance.
Reconstructing Evolving Tree Structures in Time Lapse Sequences by Enforcing Time-Consistency

We propose a novel approach to reconstructing curvilinear tree structures evolving over time, such as road networks in 2D aerial images or neural structures in 3D microscopy stacks acquired in vivo. To enforce temporal consistency, we simultaneously process all images in a sequence, as opposed to reconstructing structures of interest in each image independently. We formulate the problem as a Quadratic Mixed Integer Program and demonstrate the additional robustness that comes from using all available visual clues at once, instead of working frame by frame. Furthermore, when the linear structures undergo local changes over time, our approach automatically detects them.
An Electromagnetic and Thermo-Mechanical Analysis of High Frequency Induction Heating for Steel Plate Bending

Eddy currents of electromagnetic field leads not only to the local heating of plate but also to the thermal-elasto-plastic deformation in the induction heating. It is necessary to have a simulation model to attract the possibility of induction heating equipment and to study the deformation behavior. The goal of present paper is to investigate the possibility of induction heating equipment for steel plate bending. The residual stress distribution of induction heating is investigated by an electromagnetic analysis in conjunction with thermal-mechanical analysis. A computational model based on FEA is used to study the electromagnetic field and thermalmechanical process. Comparison with the residual strain fields and deformation of both heating shows that the induction heating has good similarity with the gas heating.
Artificial neural network based hybrid spectrum sensing scheme for cognitive radio

Spectrum sensing is a key aspect of Cognitive Radio (CR). The main requirement in CR systems is the ability to sense the primary signal accurately and rapidly. In this paper, a novel hybrid spectrum sensing scheme in CR is proposed which considers the hypothesis problem as a binary classification problem. The proposed scheme is a combination of classical energy detection, Likelihood Ratio Test statistic (LRS-G2) and Artificial Neural Network (ANN). The scheme utilises energy from energy detection and Zhang test statistic from LRS-G2 as features to train the ANN while ANN provides the adaptive learning and stable performance to the scheme. The performance of proposed sensing scheme is evaluated on several real-world primary signals of various radio technologies and it has been found out that for all those radio technologies the proposed scheme outperforms the classical energy detection and the improved energy detection.
Stabilizing multiple sliding surface control of quad-rotor rotorcraft

The interest into the unmanned aerial vehicles (UAVs) has largely increased recently. With the advances in technologies it has become possible to test efficiently and cost-effectively different autonomous flight control concepts using small-scale aircrafts. In this paper the stabilizing control problem of quad-rotor rotorcraft using multiple sliding surface (MSS) controllers has been investigated. The ability of the MSS control approach to stabilize under-actuated systems and to deal with existing nonlinear mismatched uncertainties in the dynamic model makes it a suitable choice for controlling roll and pitch angles of the rotorcraft. The proposed method is based on the definition of multiple switching surfaces that define certain relationships between variables to be maintained after the system passes into a sliding mode. Its effectiveness on the stabilizing control of the rotorcraft is demonstrated by the results obtained from flight simulations with an accurate dynamic model of the Draganflyer V Ti four-rotor miniature helicopter.
TUCAN: Twitter User Centric ANalyzer

Twitter has attracted millions of users that generate a humongous flow of information at constant pace. The research community has thus started proposing tools to extract meaningful information from tweets. In this paper, we take a different angle from the mainstream of previous works: we explicitly target the analysis of the timeline of tweets from “single users”. We define a framework - named TUCAN - to compare information offered by the target users over time, and to pinpoint recurrent topics or topics of interest. First, tweets belonging to the same time window are aggregated into “bird songs”. Several filtering procedures can be selected to remove stop-words and reduce noise. Then, each pair of bird songs is compared using a similarity score to automatically highlight the most common terms, thus highlighting recurrent or persistent topics. TUCAN can be naturally applied to compare bird song pairs generated from timelines of different users. By showing actual results for both public profiles and anonymous users, we show how TUCAN is useful to highlight meaningful information from a target user's Twitter timeline.
Making Behavior Change Interventions Available to Young African American Women: Development and Feasibility of an eHealth Lifestyle Program

Background:Less is known about young African American (AA) women, largely because the young are hard to reach. Traditional approaches to behavior changes interventions impose several challenges, especially for AA women at risk for developing hypertension. Purpose:This feasibility study describes the process of transforming a face-to-face lifestyle change intervention into a Web-based platform (eHealth) accessible by iPads, iPhones, smartphones, and personal computers. Methods:Four sequential phases were conducted using elements of formative evaluation and quantitative analysis. A convenience sample of AA women, aged 18 to 45 years, with self-reported prehypertension and regular access to the Internet were eligible to participate. Results:Eleven women involved in phase 1 expressed that they (1) currently use the Internet to retrieve health-related information, (2) prefer to use the Internet rather than face-to-face contact for nonserious conditions, (3) need convenience and easily accessible health-related interventions, and (4) are amenable to the idea of an eHealth lifestyle modification program. During phase 2, learning modules derived from printed manuals were adapted and compressed for a Web audience. The modules were designed to present evidence-based content but allowed for tailoring and individualization according to the needs of the target population. During phase 3, 8 women provided formative information concerning appeal and usability of the eHealth program in relation to delivery, visual quality, interactivity, and engagement. Phase 4 involved 8 women beta testing the 12-week program, with a 63% completion rate. Most of the women agreed that the program and screens opened with ease, the functions on the screens did what they were supposed to do, and the discussion board was easy to access. Program completion was greater for physical activity compared with dietary content. Conclusion:This study outlines a step-by-step process for transforming face-to-face content into a Web-based platform, which, importantly, can serve as a template for promoting other health behaviors.
Seasonality of acute kidney injury incidence and mortality among hospitalized patients

Background Understanding disease seasonality is important for improving clinical practice, hospital resource utilization and community-based preventive care. However, no studies have investigated the seasonality of acute kidney injury (AKI). Methods In the Tokushukai Medical Database, which includes 38 Japanese community hospitals, we identified hospitalized patients with AKI based on the Kidney Disease: Improving Global Outcomes serum creatinine criteria from January 2012 to December 2014. We plotted the number and proportion of patients with AKI among hospitalized patients by month of hospital admission. Subgroup analyses were conducted by the admission diagnosis category, timing of AKI diagnosis and age. We also examined the association between month of hospital admission and AKI, adjusting for patient characteristics and AKI risk factors. Finally, we assessed seasonal variations in disease severity and 30-day mortality of patients with AKI. Results We identified 81 279 (14.6%) patients with AKI among 555 940 hospitalized patients. The proportion of patients with AKI was highest in January (16.7%) and lowest in June (13.4%). Subgroup analyses suggested that the seasonality of AKI incidence was driven by community-acquired AKI associated with the admission diagnosis of cardiovascular and pulmonary diseases among older patients. The adjusted odds ratio for AKI (January versus June) was 1.24 (95% confidence interval, 1.17-1.31). Patients with AKI showed a larger number of failing organs in winter, and their 30-day mortality was 16.4% in spring, 14.5% in summer, 15.6% in autumn and 18.4% in winter. Conclusion AKI is more common among hospitalized patients and patients with AKI are more severely ill in winter.
Nonlinear optics in novel periodic media.

NONLINEAR OPTICS IN NOVEL PERIODIC MEDIA by Ravi Sadananda Hegde Chair: Herbert G. Winful The major focus of the work in this thesis is on the solution of the electromagnetic wave equations in novel structures that exhibit nonlinearity in their response to the incident field. These structures are further characterized by the presence of a spatial periodicity in their dielectric permittivity. The presence of spatial periodicity results in a number of coherent scattering phenomena when the light wavelength is comparable to the period of this spatial perturbation. Here we use a combination of analytical and numerical methods to gain insight into these phenomena. Two different problems each involving a different nonlinear phenomenon have been considered. First, we take the classical problem of hysteretic switching response exhibited by a nonlinear periodic structure. This problem is reexamined in the context of the presence of a negative refractive index (left-handed electromagnetic structure). We theoretically predict an exotic behavior that involves an omnidirectional response quite distinct from the well known behavior. We examine the field distribution in detail and propose the existence of a spatial soliton called the zero-n gap soliton. Next, we investigate the practical problem of output power scaling in fiber lasers. Self
Early Huntington's Disease: Impulse Control Deficits but Correct Judgment Regarding Risky Situations.

BACKGROUND Huntington's disease (HD) patients show alterations in decision making tasks. However, it is still uncertain if these deficits are due to poor judgment regarding risky situations, or to impulse control deficits. OBJECTIVE To elucidate whether decision-making in patients is related to genuine risk behavior or to impulse control deficits. METHODS To test between these two alternative possibilities, we evaluated the performance of 19 prodromal HD patients and 19 matched healthy controls in the Cambridge Gambling Task (CGT). This task assesses decision-making while dissociating between genuine risk-taking behaviors (ascending condition) from impulsive behavior (descending condition). RESULTS The results showed that patients and controls had the same performance during all trials in the ascending condition, reflecting a correct judgment regarding risky situations; however, during the descending condition, patients responded before the controls in all trials, making a significantly larger number of higher bets. Unlike the control group, they did not wait for more optimal subsequent options. CONCLUSION These results suggest impulse control deficits in HD gene carriers, but unimpaired risk-taking judgment.
Experimental and Numerical Study of Moisture Effects on the Bond Fracture Energy of FRP/Concrete Joints

This study experimentally investigates the mechanism of deterioration for the bond between CFRP plate and concrete due to moisture attack. The debonding failure in the concrete structures with externally bonded FRP has two major modes: cohesive failure in the concrete surface layer and adhesion failure at the adhesive/concrete interface. A modified double cantilever beam (MDCB) test was used to measure the interfacial fracture energy for the CFRP plate debonding from concrete substrate under Mode I loading. A simple method was developed to measure the value of interface region relative humidity (IRRH). The relation between IRRH and the bond fracture energy was then obtained. By quantitatively measuring the residual thickness of concrete (RTC) on the detached plate, it was found that RTC was directly related to the value of IRRH. Based on the experimental data, a constitutive relation between RTC and IRRH was obtained. Using this relation and virtual crack closure technique (VCCT), the bond fracture energy.