Miracle-dz/UniFAR-FTUs huggingface dataset:
Viral Marketing Meets Social Advertising: Ad Allocation with Minimum Regret

Social advertisement is one of the fastest growing sectors in the digital advertisement landscape: ads in the form of promoted posts are shown in the feed of users of a social networking platform, along with normal social posts; if a user clicks on a promoted post, the host (social network owner) is paid a fixed amount from the advertiser. In this context, allocating ads to users is typically performed by maximizing click-through-rate, i.e., the likelihood that the user will click on the ad. However, this simple strategy fails to leverage the fact the ads can propagate virally through the network, from endorsing users to their followers. In this paper, we study the problem of allocating ads to users through the viral-marketing lenses. We show that allocation that takes into account the propensity of ads for viral propagation can achieve significantly better performance. However, uncontrolled virality could be undesirable for the host as it creates room for exploitation by the advertisers: hoping to tap uncontrolled virality, an advertiser might declare a lower budget for its marketing campaign, aiming at the same large outcome with a smaller cost. This creates a challenging trade-off: on the one hand, the host aims at leveraging virality and the network effect to improve advertising efficacy, while on the other hand the host wants to avoid giving away free service due to uncontrolled virality. We formalize this as the problem of ad allocation with minimum regret, which we show is NP-hard and inapproximable w.r. t. any factor. However, we devise an algorithm that provides approximation guarantees w.r. t. the total budget of all advertisers. We develop a scalable version of our approximation algorithm, which we extensively test on four real-world data sets, confirming that our algorithm delivers high quality solutions, is scalable, and significantly outperforms several natural baselines.
Elastic radial curves to model 3D facial deformations

In this paper we study the challenging problem of 3D face recognition in presence of facial expressions. Therefore, the facial surfaces are represented by an indexed collections of radial curves on them, emanating from the nose tips. Facial shapes comparison is done by comparing the shapes of their corresponding curves based on elastic shape analysis of 3D curves. Hence, we obtain algorithm for comapring facial surfaces by computing the length of the geodesic between them. We demonstrate these ideas in the context of face recognition on a difficult subset taken from FRGC v2 database, and present a hierarchical organization of face databases to allow for efficient searches.
Training sample selection for deep learning of distributed data

The success of deep learning — in the form of multi-layer neural networks — depends critically on the volume and variety of training data. Its potential is greatly compromised when training data originate in a geographically distributed manner and are subject to bandwidth constraints. This paper presents a data sampling approach to deep learning, by carefully discriminating locally available training samples based on their relative importance. Towards this end, we propose two metrics for prioritizing candidate training samples as functions of their test trial outcome: correctness and confidence. Bandwidth-constrained simulations show significant performance gain of our proposed training sample selection schemes over convention uniform sampling: up to 15× bandwidth reduction for the MNIST dataset and 25% reduction in learning time for the CIFAR-10 dataset.
Modeling of Nucleation Spectra for Primary Recrystallization and Application to Through-Process Texture Modeling

Schemes to model deformation inhomogeneities and nuclei distributions based on the grain cluster model for deformation texture simulation GIA are presented. The orientation distributions of nuclei in stable orientations, nuclei in grains with orientation gradients and nuclei due to subgrain growth at grain boundaries are predicted. Additionally, nuclei with a random orientation distribution are considered, reflecting nucleation at shear bands or large constituent particles. Furthermore, models for a quantitative assessment of the participating nucleation mechanisms are proposed. The resulting nucleation texture was input to the static recrystallization texture model StaRT. The through-process texture development during a sequence of several hot rolling, cold rolling and annealing steps in industrial production of the aluminum alloy AA5182 is presented.
Recurrent Pseudomonas aeruginosa Infection in Chronic Lung Diseases: Relapse or Reinfection?

Background Pseudomonas aeruginosa infection is particularly associated with progressive and ultimately chronic recurrent respiratory infections in chronic obstructive pulmonary disease, bronchiectasis, chronic destroyed lung disease, and cystic fibrosis. Its treatment is also very complex because of drug resistance and recurrence. Methods Forty eight cultures from 18 patients with recurrent P. aeruginosa pneumonia from 1998 to 2002 were included in this study. Two or more pairs of sputum cultures were performed during 2 or more different periods of recurrences. The comparison of strains was made according to the phenotypic patterns of antibiotic resistance and chromosomal fingerprinting by pulsed field gel electrophoresis (PFGE) using the genomic DNA of P. aeruginosa from the sputum culture. Results Phenotypic patterns of antibiotic resistance of P. aeruginosa were not correlated with their prior antibiotic exposition. Fifteen of 18 patients (83.3%) had recurrent P. aeruginosa pneumonia caused by the strains with same PFGE pattern. Conclusion These data suggest that the most of the recurrent P. aeruginosa infections in chronic lung disease occurred due to the relapse of prior infections. Further investigations should be performed for assessing the molecular mechanisms of the persistent colonization and for determining how to eradicate clonal persistence of P. aeruginosa.
Missing Data in the Field of Otorhinolaryngology and Head & Neck Surgery: Need for Improvement

Objective: Clinical studies are often facing missing data. Data can be missing for various reasons, for example, patients moved, certain measurements are only administered in high-risk groups, and patients are unable to attend clinic because of their health status. There are various ways to handle these missing data (e.g., complete cases analyses, mean substitution). Each of these techniques potentially influences both the analyses and the results of a study. The first aim of this structured review was to analyze how often researchers in the field of otorhinolaryngology/head & neck surgery report missing data. The second aim was to systematically describe how researchers handle missing data in their analyses. The third aim was to provide a solution on how to deal with missing data by means of the multiple imputation technique. With this review, we aim to contribute to a higher quality of reporting in otorhinolaryngology research. Design: Clinical studies among the 398 most recently published research articles in three major journals in the field of otorhinolaryngology/head & neck surgery were analyzed based on how researchers reported and handled missing data. Results: Of the 316 clinical studies, 85 studies reported some form of missing data. Of those 85, only a small number (12 studies, 3.8%) actively handled the missingness in their data. The majority of researchers exclude incomplete cases, which results in biased outcomes and a drop in statistical power. Conclusions: Within otorhinolaryngology research, missing data are largely ignored and underreported, and consequently, handled inadequately. This has major impact on the results and conclusions drawn from this research. Based on the outcomes of this review, we provide solutions on how to deal with missing data. To illustrate, we clarify the use of multiple imputation techniques, which recently became widely available in standard statistical programs.
Tradeoff Study of Heat Sink and Output Filter Volume in a GaN HEMT Based Single-Phase Inverter

This paper presents the tradeoff study of heat sink and output filter volume of a GaN HEMT based single-phase inverter. The selected topology is three-level active neutral point clamped (ANPC) inverter, and the main aim is to explore the benefits of the GaN HEMTs at 600 V blocking class on the system level efficiency, and power density under the wide range of operating conditions. The paper starts by introducing the inverter topology, selected pulse width modulation scheme and followed by the device features, static, and dynamic characterization and continues with presenting and discussing the results of extensive experimental and analytical characterization. After this, the impact of GaN HEMTs on inverter volume is discussed in terms of heat sink and output filter volume analysis under different switching frequency and heat sink temperature conditions. The calculation of heat sink volume and single stage LC output filter volume are presented with respect to experimental results of the single phase prototype. The findings from static, dynamic characterization, and single phase prototype results clearly show that GaN HEMT has excellent switching performance under wide load current and heat sink temperature conditions. The high performance of the inverter leads to reduction of the combined total volume, including output filter and heat sink volume.
Classification of Chronic Obstructive Pulmonary Disease (COPD) according to the new Global Initiative for Chronic Obstructive Lung Disease (GOLD) 2017: Comparison with GOLD 2011

ABSTRACT Chronic Obstructive Pulmonary Disease (COPD) is a major cause of morbidity and mortality worldwide. The Global Initiative for Chronic Obstructive Lung Disease (GOLD) project has been working to improve awareness, prevention and management of this disease. The aim of this study is to evaluate how COPD patients are reclassified by the 2017 GOLD system (versus GOLD 2011), to calculate the level of agreement between these two classifications in allocation to categories and to compare the performance of each classification to predict future exacerbations. Two-hundred COPD patients (>40 years, post bronchodilator forced expiratory volume in one second/forced vital capacity<0.7) followed in pulmonology consultation were recruited into this prospective multicentric study. Approximately half of the patients classified as GOLD D [2011] changed to GOLD B [2017]. The extent of agreement between GOLD 2011 and GOLD 2017 was moderate (Cohen's Kappa = 0.511; p < 0.001) and the ability to predict exacerbations was similar (69.7% and 67.6%, respectively). GOLD B [2017] exacerbated 17% more than GOLD B [2011] and had a lower percent predicted post bronchodilator forced expiratory volume in one second (FEV1). GOLD B [2017] turned to be the predominant category, more heterogeneous and with a higher risk of exacerbation versus GOLD B [2011]. Physicians should be cautious in assessing the GOLD B [2017] patients. The assessment of patients should always be personalized. More studies are needed to evaluate the impact of the 2017 reclassification in predicting outcomes such as future exacerbations and mortality.
Battery management and application for energy-efficient buildings

As the building stock consumes 40% of the U.S. primary energy consumption, it is critically important to improve building energy efficiency. This involves reducing the total energy consumption of buildings, reducing the peak energy demand, and leveraging renewable energy sources, etc. To achieve such goals, hybrid energy supply has becoming popular, where multiple energy sources such as grid electricity, on-site fuel cell generators, solar, wind, and battery storage are scheduled together to improve energy efficiency. In this work, we focus on the application and management of battery storage for energy-efficient buildings. We will first introduce a system-level approach to co-schedule the usage of battery storage (in addition to grid electricity) with the control of building HVAC (heating, ventilation, and air conditioning) system, to reduce the total building energy cost, including the electricity consumption charge, the peak demand charge, and the battery cost. Then, in a separate formulation, we will introduce another system-level study to reduce the energy cost of EV charging and other fixed building energy load through the usage of battery storage and solar PV. Finally, we will present an ARM processor based programmable embedded battery management system (BMS), which monitors battery status, controls charging and discharging at the circuit level, and provides battery protection. The system also works with off-the-shelf battery management IC (Texas Instrument BMS sensor IC) from industry. Comparing to conventional BMS, this software module based BMS is a more suitable solution for energy efficient buildings due to its high flexibility, scalability, and reusability. We will introduce an industrial building testbed with battery storage and solar PV at the University of California, Riverside, and present initial field tests and simulation results for above approaches.
Speaker identification using vector quantization and I-vector with reference to Assamese language

This paper describes the implementation of a speaker identification system with reference to Assamese language. The database consists of speech samples that were collected from 15 (fifteen) speakers for ten Assamese words representing the Assamese digits from 0 (shounyo) to 9 (no). Mel Frequency Cepstral Coefficients (MFCC) are used as features for this study. Two independent speaker identification systems have been built in this paper using Vector Quantization (VQ) and I-vector technique. The system built using the I-vector technique obtains comparatively better identification accuracy for speakers when compared with the system developed using VQ technique. Three different systems have been built for both the techniques based on variable feature size. A maximum accuracy of 92.38% is achieved using I-vector technique with 39 MFCC features.
Removal of Organic Pollutants and Decolorization of Bleaching Effluents from Pulp and Paper Mill by Adsorption using Chemically Treated Oil Palm Empty Fruit Bunch Fibers

Treatment of bleaching effluents from pulp and paper mills using oil palm empty fruit bunch (EFB) fibers as an adsorbent was conducted to remove color and organic pollutants. Empty fruit bunch fibers were chemically modified with polyethylenimine to enhance the adsorption capacity toward anionic species in the effluents. Effluents from the primary clarifier and aerated treatment pond were treated, and the performance of the adsorbent was investigated in terms of decolorization, total organic carbon, and oxygen demand level. Increasing adsorbent dosage and lower pH resulted in greater adsorption performance. The highest decolorization and reduction of total organic carbon of the effluents were 95.0% and 58.2%, respectively. The adsorption equilibrium can be achieved after 4 h of the adsorption process.
Outlook on Developments in Power Devices and Integration: Recent Investigations and Future Requirements

The switching cell at the heart of power electronic circuits is the central key for improvements in terms of performance, size, cost, and efficiency, and it will remain this way for the foreseeable future. Nowadays, developments in switching cells are driven by different component manufacturers, e.g., for semiconductors, capacitors, drivers, and packages. Although there has been tremendous progress, especially on power semiconductors, a paradigm change is necessary for further advancement on optimization. Higher switching speeds and lower switching losses make the handling of discrete components more and more difficult for the average user. Furthermore, the interfaces between the components are evolving to be major bottlenecks in terms of electromagnetic performance.
Cognitive Target Tracking via Angle-Range-Doppler Estimation With Transmit Subaperturing FDA Radar

Cognitive radar is an intelligent active sensing technique, which can learn the interactions between radar and its surrounding environment and adaptively adjust the transmit waveforms or parameters for improved performance. In this paper, we propose a cognitive target tracking scheme via angle-range-Doppler estimation with transmit subaperturing frequency diverse array (TS-FDA) radar. FDA is an emerging array technique that employs a small frequency increment across its array elements to produce a range-angle-dependent beampattern, which provides promising applications for joint angle-range-Doppler estimation of targets. In order to jointly enjoy the advantages of FDA localization in angle-range dimension and phased-array in coherent gain, we divide the FDA elements into multiple subarrays and propose two optimization criteria, respectively, based on signal-to-noise ratio and Cramér–Rao bound, to adaptively design the transmit weight matrix according to the prior knowledge extracted from the cognitive observation data at each transmission updating for improved tracking performance. All proposed approaches are verified by numerical results.
Automatic creation of photo books from stories in social media

Photos are a special way to tell stories of our best memories. The representation of those photos in appealing physical photo books is highly appreciated by many people. Today, many photos are shared via social networking sites, where people upload their photos and share their stories with their social network. The members of the social network often comment on the photos, add tags or descriptions and upload photos for the same events in their albums. While the media of different personal events are now available on the social network, there is no easy way to collect and bundle them into some story and print the story into a photo book. We propose an approach to automatically detect media elements that match a query (where, when, what, who) in the user's social network and intelligently arrange and compose them into a printed photo book. We combine content analysis of text and images to automatically and semi-automatically select photos of a specific story. Photos that are highly probable to belong to the same event are clustered using an Expectation-Maximization algorithm to be able to easily retrieve them using online queries. People tags that are added to the photos are employed to select important photos by automatically calculating the individual social importance of friends. The selected content and derived semantics are employed to create an appealing layout for automatically created photo books.
Detection of Impact Damage in Composite Plates Using Surface Contact Method

It is a well known fact that the fundamental causes of most failures in composite structures are in the forms of incipient damages such as delaminations and cracks which usually remain undetected until they grow to levels large enough to cause failure. In this study, unidirectional carbon/epoxy composite plates with known defects are investigated. The known defects are generated by impacting the composite specimens simulating external collision. A pair of transmitter and receiver was used for generation of Lamb waves and reception of signals. The received signals were monitored by scanning the receiver toward internal defect or by scanning both transmitter and receiver with confined distance over the surface of the composite plates which have known defects lie beneath them. The proper selection of incident angle and frequency are also considered. The characteristics of received signals such as amplitude, energy and wave form are analyzed. The acquired information is used to locate and to measure the size of the impact damage. The suggested method is very effective if the internal damage is presented closed to surface of the plate where the conventional pulse-echo method has problems. The proposed technique can be used widely for the real time and online monitoring of composite structures.
Fully automated highly accurate 3D reconstruction from multiple views

The reconstruction of real world objects becomes even more important in the view creating highly realistic scenes for Virtual Reality applications. In this paper, we present a fully automated algorithmic pipeline for high-quality 3D reconstruction of real world objects. The proposed method refines an initial 3D model by exploiting the results of additional pairwise stereo depth estimation. An automatic camera selection approach provides different point clouds, which are fused into a common coherent and highly detailed 3D model. The quality of the reconstruction results is discussed in comparison to several state-of-the-art tools, also in the context of automation and performance.
Issues, solutions and radio network optimization for the next generation heterogeneous cellular network—A review

Journey of cellular mobile network could be considered from rags to riches. The base of cellular network and its operation lies in its appropriate planning, addressing the issues and optimization of the network parameters. This paper has been focused on the issues associated with the next generation heterogeneous wireless network (LTE-A) and optimization of the Network comprising of macro cells, micro cells, pico Cells and femto cells. Various modern optimization techniques like Genetic Algorithm, Particle Swarm Optimization and Simulated Annealing are considered in the paper. Few radio network parameters like transmitted power, antenna tilt and its azimuth angle variation are considered for optimization using Simulated Annealing algorithm. The main challenge is the bursty nature of network users, interference co-ordination from adjacent cells and the change in the coverage area. Addressing of various issues, discussed in the paper, is required for seamlessly integrating all the cells within a single umbrella and thereby attaining fruitful working of a heterogeneous network. Considering the motive, optimization of the network parameters has to be done in such a way that even after decades; the serving radio network could be adaptive. Hence with meager modifications, it should be seamlessly integrated into the heart of next generation mobile network.
Big Data in Psychology: A Framework for Research Advancement

The potential for big data to provide value for psychology is significant. However, the pursuit of big data remains an uncertain and risky undertaking for the average psychological researcher. In this article, we address some of this uncertainty by discussing the potential impact of big data on the type of data available for psychological research, addressing the benefits and most significant challenges that emerge from these data, and organizing a variety of research opportunities for psychology. Our article yields two central insights. First, we highlight that big data research efforts are more readily accessible than many researchers realize, particularly with the emergence of open-source research tools, digital platforms, and instrumentation. Second, we argue that opportunities for big data research are diverse and differ both in their fit for varying research goals, as well as in the challenges they bring about. Ultimately, our outlook for researchers in psychology using and benefiting from big data is cautiously optimistic. Although not all big data efforts are suited for all researchers or all areas within psychology, big data research prospects are diverse, expanding, and promising for psychology and related disciplines.