Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimating the Performance of Random Forest versus Multiple Regression for Predicting Prices of the Apartments

The goal of this study is to analyse the predictive performance of the random forest machine learning technique in comparison to commonly used hedonic models based on multiple regression for the prediction of apartment prices. A data set that includes 7407 records of apartment transactions referring to real estate sales from 2008–2013 in the city of Ljubljana, the capital of Slovenia, was used in order to test and compare the predictive performances of both models. Apparent challenges faced during modelling included (1) the non-linear nature of the prediction assignment task; (2) input data being based on transactions occurring over a period of great price changes in Ljubljana whereby a 28% decline was noted in six consecutive testing years; and (3) the complex urban form of the case study area. Available explanatory variables, organised as a Geographic Information Systems (GIS) ready dataset, including the structural and age characteristics of the apartments as well as environmental and neighbourhood information were considered in the modelling procedure. All performance measures (R2 values, sales ratios, mean average percentage error (MAPE), coefficient of dispersion (COD)) revealed significantly better results for predictions obtained by the random forest method, which confirms the prospective of this machine learning technique on apartment price prediction.
Differentially Tolerized Mouse Antigen Presenting Cells Share a Common miRNA Signature Including Enhanced mmu-miR-223-3p Expression Which Is Sufficient to Imprint a Protolerogenic State

Dendritic cells (DCs) are pivotal for the induction and maintenance of antigen-specific tolerance and immunity. miRNAs mediate post-transcriptional gene regulation and control in part the differentiation and stimulation-induced immunogenic function of DCs. However, the relevance of miRNAs for the induction and maintenance of a tolerogenic state of DCs has scarcely been highlighted yet. We differentiated mouse bone marrow cells to conventional/myeloid DCs or to tolerogenic antigen presenting cells (APCs) by using a glucocorticoid (dexamethasone) or interleukin-10, and assessed the miRNA expression patterns of unstimulated and LPS-stimulated cell populations by array analysis and QPCR. Differentially tolerized mouse APCs convergingly down-regulated a set of miRNA species at either state of activation as compared with the corresponding control DC population (mmu-miR-9-5p, mmu-miR-9-3p, mmu-miR-155-5p). These miRNAs were also upregulated in control DCs in response to stimulation. In contrast, miRNAs that were convergingly upregulated in both tolerized APC groups at stimulated state (mmu-miR-223-3p, mmu-miR-1224-5p) were downregulated in control DCs in response to stimulation. Overexpression of mmu-miR-223-3p in DCs was sufficient to prevent stimulation-associated acquisition of potent T cell stimulatory capacity. Overexpression of mmu-miR-223-3p in a DC line resulted in attenuated expression of known (Cflar, Rasa1, Ras) mRNA targets of this miRNA species shown to affect pathways that control DC activation. Taken together, we identified sets of miRNAs convergingly regulated in differentially tolerized APCs, which may contribute to imprint stimulation-resistant tolerogenic function as demonstrated for mmu-miR-223-3p. Knowledge of miRNAs with protolerogenic function enables immunotherapeutic approaches aimed to modulate immune responses by regulating miRNA expression.
Regularizing Sampled Differential Dynamic Programming

Sampled Differential Dynamic Programming (SaDDP) is a recent Monte Carlo version of the widely used Differential Dynamic Programming (DDP). Very much like any algorithm based on DDP, the sampled version also requires regularization in order to work. The method makes heavy use of covariance matrices, for which we need to ensure positive definiteness. We present and compare different ways to regularize the covariance matrices. We also derive bounds for selecting the regularization parameters such that the condition number of each covariance matrix stays below a pre-chosen maximum. Our tests indicate that there is little difference in the convergence properties of the different algorithms. However, the possibility of divergence can be alleviated by two of the regularization techniques presented in this paper. Our tests furthermore show that the closed-loop regularization and rank-one updates in SaDDP are actually detrimental, when the covariance matrices are properly regularized.
Inferencing Human Spatiotemporal Mobility in Greater Maputo via Mobile Phone Big Data Mining

The mobility patterns and trip behavior of people are usually extracted from data collected by traditional survey methods. However, these methods are generally costly and difficult to implement, especially in developing cities with limited resources. The massive amounts of call detail record (CDR) data passively generated by ubiquitous mobile phone usage provide researchers with the opportunity to innovate alternative methods that are inexpensive and easier and faster to implement than traditional methods. This paper proposes a method based on proven techniques to extract the origin–destination (OD) trips from the raw CDR data of mobile phone users and process the data to capture the mobility of those users. The proposed method was applied to 3.4 million mobile phone users over a 12-day period in Mozambique, and the data processed to capture the mobility of people living in the Greater Maputo metropolitan area in different time frames (weekdays and weekends). Subsequently, trip generation maps, attraction maps, and the OD matrix of the study area, which are all practically usable for urban and transportation planning, were generated. Furthermore, spatiotemporal interpolation was applied to all OD trips to reconstruct the population distribution in the study area on an average weekday and weekend. Comparison of the results obtained with actual survey results from the Japan International Cooperation Agency (JICA) indicate that the proposed method achieves acceptable accuracy. The proposed method and study demonstrate the efficacy of mining big data sources, particularly mobile phone CDR data, to infer the spatiotemporal human mobility of people in a city and understand their flow pattern, which is valuable information for city planning.
Classifying Patents by Tracing the Chronology of Patent Citation Increments

This paper proposes a new patent classification method that accounts for both time inflation and application year. We compare the annual citation increment of patents and classify the patents into four types: dominant, precocious, admirable, and late-recognized. Substantially different citation curves were obtained for different patent types. Additionally, early citation history was highly related to the probability of a patent being classified as important.
Old data, new scheme: An exploration of metadata migration using expert-guided computational techniques

This paper explores the evaluation of metadata quality within the context of migration to new conceptual models. While metadata quality and interoperability are commonly studied areas, few studies have explored the issues introduced when migrating metadata to models that require a re-definition of metadata record to resource relationships. In order to explore this issue, this study asks the question “How do human and computational techniques compare with regards to the creation of FRBR work-sets from existing bibliographic metadata? ” The study compared cataloger assessment of FRBR relationships in a selection of 848 MARC records with automatically generated worksets. The Levenhstein edit-distance algorithm was used to identify work-sets in the automated approach. The comparison found that an improved version of work-set keys from the OCLC FRBR algorithm provided the highest match rate with 62% of keys matching perfectly and 87% of correct work-sets showing an 80% or higher similarity rate. The study methodology and evaluative measure overall indicate that a hybrid expert/automated approach and an improved key generation algorithm are effective alternatives to manual or automated approaches alone.
Dual-rate adaptive decoupling controller and its application to a dual-tank water system

For a class of nonlinear multivariable dual-rate systems with strong couplings and uncertainties, a dual-rate adaptive decoupling controller with nonlinear compensation is proposed. First, a dual-rate model is derived by using the polynomial transformation technique, where the unknown parameter matrices are identified by using the available dual-rate data, and the unknown nonlinear term is compensated by using the identification error. Based on the dual-rate model, the adaptive decoupling controller is then designed. The stability at sampling time of the closed-loop system is presented. Finally, simulations are conducted on a dual-tank water flow-level cascade control system. The results illustrate the effectiveness of the proposed method.
MPEG-DASH MPD for Tile-based Hybrid Stereoscopic 360-degree Video Streaming

In this paper, we propose tile-based hybrid stereoscopic 360° video streaming service, in which low-quality 2D, high-quality 2D and 3D services can be provided by a single scalable HEVC (SHVC) en/decoder. In addition to this, its associated MPEG-DASH media presentation description (MPD) signaling is also presented as an example.
Cerebrovascular Reactivity and Central Arterial Stiffness in Habitually Exercising Healthy Adults

Reduced cerebrovascular reactivity to a vasoactive stimulus is associated with age-related diseases such as stroke and cognitive decline. Habitual exercise is protective against cognitive decline and is associated with reduced stiffness of the large central arteries that perfuse the brain. In this context, we evaluated the age-related differences in cerebrovascular reactivity in healthy adults who habitually exercise. In addition, we sought to determine the association between central arterial stiffness and cerebrovascular reactivity. We recruited 22 young (YA: age = 27 ± 5 years, range 18–35 years) and 21 older (OA: age = 60 ± 4 years, range 56–68 years) habitual exercisers who partake in at least 150 min of structured aerobic exercise each week. Middle cerebral artery velocity (MCAv) was recorded using transcranial Doppler ultrasound. In order to assess cerebrovascular reactivity, MCAv, end-tidal carbon dioxide (ETCO2), and mean arterial pressure (MAP) were continuously recorded at rest and during stepwise elevations of 2, 4, and 6% inhaled CO2. Cerebrovascular conductance index (CVCi) was calculated as MCAv/MAP. Central arterial stiffness was assessed using carotid–femoral pulse wave velocity (PWV). Older adults had higher PWV (YA: 6.2 ± 1.2 m/s; OA: 7.5 ± 1.3 m/s; p < 0.05) compared with young adults. MCAv and CVCi reactivity to hypercapnia were not different between young and older adults (MCAv reactivity, YA: 2.0 ± 0.2 cm/s/mmHg; OA: 2.0 ± 0.2 cm/s/mmHg; p = 0.77, CVCi reactivity, YA: 0.018 ± 0.002 cm/s/mmHg2; OA: 0.015 ± 0.001 cm/s/mmHg2; p = 0.27); however, older adults demonstrated higher MAP reactivity to hypercapnia (YA: 0.4 ± 0.1 mmHg/mmHg; OA: 0.7 ± 0.1 mmHg/mmHg; p < 0.05). There were no associations between PWV and cerebrovascular reactivity (range: r = 0.00–0.39; p = 0.07–0.99). Our results demonstrate that cerebrovascular reactivity was not different between young and older adults who habitually exercise; however, MAP reactivity was augmented in older adults. This suggests an age-associated difference in the reliance on MAP to increase cerebral blood flow during hypercapnia.
Virtualized Infrastructure Managers for Edge Computing: OpenVIM and OpenStack Comparison

The evolution of the centralized cloud computing architecture towards the edge of the network, driven by virtualization technologies such as Software-Defined Networking (SDN), Network Functions Virtualization (NFV) and Multi-access Edge Computing (MEC), brings new opportunities to the multimedia industry. Offloading computing power to the edge, local caching, minimized latency and flexibility in the deployment of services are only some of the benefits that the multimedia can gain from utilizing the edge computing capabilities. To achieve such progress the potential elements of an edge computing framework have to be studied and evaluated. In this work, we focus on the component managing the edge resources which is defined as the Virtual Infrastructure Manager (VIM) in the MEC reference architecture. We analyze the time overhead which virtual machines (VMs) provisioning brings into the system. This is done by a comparison of two popular open-source VIM solutions, OpenStack and Open VIM. We discuss and compare the logical architectures of the two VIMs and present the results of their performance evaluation. We extended the open-source cloud benchmarking framework CloudBench by adding a new cloud adapter for Open VIM and used it to gather the cloud management metrics.
Executive attention moderates the effect of trait anxiety on hyperarousal symptoms

ABSTRACT The majority of individuals exposed to trauma do not go on to develop posttraumatic stress disorder (PTSD); thus, researchers have sought to identify individual difference variables that make one particularly susceptible to posttraumatic stress symptoms. Trait anxiety is one individual difference variable implicated in the pathogenesis of posttraumatic stress symptoms. Following from cognitive theories of anxiety and extant data, the purpose of the present study was to examine executive attention as a moderator of the relation between trait anxiety and posttraumatic stress symptoms, particularly hyperarousal symptoms, among undergraduate women reporting trauma exposure (N = 88). As predicted, executive attention moderated the association between trait anxiety and hyperarousal symptoms, such that there was a significantly weaker relation as executive attention increased. Study results further support the potential buffering effect of executive attention in relation to posttraumatic stress symptoms, as well as the possible importance of targeting executive attention following trauma exposure.
Kinematics, Dynamics, and Control of a Cable-Driven Hyper-Redundant Manipulator

A cable-driven hyper-redundant manipulator has superior dexterity for confined space applications. However, the modeling and control considering the cables are very complex. In this paper, we established the kinematics and dynamics models and proposed a dynamics control strategy. The multilevel mapping between the motors, cables, joints, and end-effector was first analyzed. The corresponding kinematics equations were derived and solved by combining analytical and numerical methods. Especially, the cable coupling relationship was established and a decoupling method was addressed to compensate the coupled motion between cables. Furthermore, we derived the dynamics equations including the cable forces and the joint variables. Considering practical control requirements, the cables’ forces were distributed by simplifying the dynamics equations and obtaining the minimal solutions. Then, we presented a dynamics control strategy, which uses the forward and inverse kinematics of multilevel mapping for motion resolution and compensation, and computes the feedforward torques for the motors using recursive dynamics and “cable force–motor torque” relationship. Finally, a prototype and a truss inspection experiment system were developed to verify the corresponding models and methods. Experiment results show that the derived kinematic and the dynamic equations, and the proposed dynamic control strategy are effective.
Predicting Taxi Destination by Regularized RNN with SDZ

SUMMARY The traditional Markov prediction methods of the taxi destination rely only on the previous 2 to 3 GPS points. They negelect long-term dependencies within a taxi trajectory. We adopt a Recurrent Neural Network (RNN) to explore the long-term dependencies to predict the taxi destination as the multiple hidden layers of RNN can store these dependencies. However, the hidden layers of RNN are very sensitive to small perturbations to reduce the prediction accuracy when the amount of taxi trajectories is increasing. In order to improve the prediction accuracy of taxi destination and reduce the training time, we embed suprisal-driven zoneout (SDZ) to RNN, hence a taxi destination prediction method by regularized RNN with SDZ (TDPRS). SDZ can not only improve the robustness of TDPRS, but also reduce the training time by adopting partial update of parameters instead of a full update. Experiments with a Porto taxi trajectory data show that TDPRS improves the prediction accuracy by 12% compared to RNN prediction method in literature[4]. At the same time, the prediction time is reduced by 7%.
3DQoE-Oriented and Energy-Efficient 2D plus Depth Based 3D Video Streaming Over Centrally Controlled Networks

IP networks have become the dominant platform for video delivery. However, bandwidth-hungry video is pushing networks to their limits: costs are rising for the operators and the viewing experience is not always satisfactory for the users. When considering 3D video delivery, the previous problems are exacerbated because of the higher volume of data that must be communicated, and the difficulty in characterizing the viewing experience of the end user. Consequently, network operators may be reluctant to deliver 3D video due to costs and unclear quality improvements to their users. In this setting, the true immersive experience of 3D video remains elusive. In this paper, we focus on the efficient delivery of 3D video in terms of quality and energy cost over centrally controlled networks. As a representative example of a centrally controlled network, a software-defined network (SDN) is assumed. Our approach is based on a comprehensive network-dependent 3D quality of experience (3DQoE) model and an energy cost model for 3D video streaming. By using the developed models, we formulate the problem of energy-efficient and 3DQoE-optimized 3D video flow path routing. The particular characteristic of video/depth rate allocation presented in 3D video is embedded seamlessly into the selection of the optimal routing paths for multiple 3D video streams. The formulated problem is NP-hard and is solved with a heuristic algorithm based on the branch-and-bound method after significant reduction of the solution search space. Extensive 3D video streaming experiments are conducted over an OpenFlow-based SDN with subjective and objective evaluations and they highlight the significant benefits of the proposed approach.
Accuracy Estimation of the Sounding Rocket Navigation System

Contemporary achievements in the inertial systems technology puts the question how precise consumer grade sensors are and are they sufficient to use in the navigation. The hypothetical subsonic sounding rocket will be examined, since these objects need accurate and not expensive sensors. Following types of the inertial navigation systems will be examined: based on optical gyroscope, high grade and low-grade MEMS systems. Additionally, the individual constituents of the navigation error will be examined.
Typhoon track prediction using satellite images in a Generative Adversarial Network

Tracks of typhoons are predicted using satellite images as input for a Generative Adversarial Network (GAN). The satellite images have time gaps of 6 hours and are marked with a red square at the location of the typhoon center. The GAN uses images from the past to generate an image one time step ahead. The generated image shows the future location of the typhoon center, as well as the future cloud structures. The errors between predicted and real typhoon centers are measured quantitatively in kilometers. 42.4% of all typhoon center predictions have absolute errors of less than 80 km, 32.1% lie within a range of 80 - 120 km and the remaining 25.5% have accuracies above 120 km. The relative error sets the above mentioned absolute error in relation to the distance that has been traveled by a typhoon over the past 6 hours. High relative errors are found in three types of situations, when a typhoon moves on the open sea far away from land, when a typhoon changes its course suddenly and when a typhoon is about to hit the mainland. The cloud structure prediction is evaluated qualitatively. It is shown that the GAN is able to predict trends in cloud motion. In order to improve both, the typhoon center and cloud motion prediction, the present study suggests to add information about the sea surface temperature, surface pressure and velocity fields to the input data.
Computing Fault-Containment Times of Self-Stabilizing Algorithms Using Lumped Markov Chains

The analysis of self-stabilizing algorithms is often limited to the worst case stabilization time starting from an arbitrary state, i.e., a state resulting from a sequence of faults. Considering the fact that these algorithms are intended to provide fault tolerance in the long run, this is not the most relevant metric. A common situation is that a running system is an a legitimate state when hit by a single fault. This event has a much higher probability than multiple concurrent faults. Therefore, the worst case time to recover from a single fault is more relevant than the recovery time from a large number of faults. This paper presents techniques to derive upper bounds for the mean time to recover from a single fault for self-stabilizing algorithms based on Markov chains in combination with lumping. To illustrate the applicability of the techniques they are applied to a new self-stabilizing coloring algorithm.
An Active Voltage-Doubler Rectifier Based Hybrid Resonant DC/DC Converter for Wide-Input-Range Thermoelectric Power Generation

To develop thermoelectric power conditioning systems that are highly efficient over a wide range of input voltages and support medium-rated power, this paper introduces a hybrid resonant dc/dc converter using an active voltage-doubler rectifier. When the input voltage is higher than the preset nominal input voltage, the converter operates in the phase-shift full-bridge series-resonant converter mode, in which it achieves high efficiency by softly switching the primary-side switches and reducing the conduction loss. When the input voltage is lower than the nominal one, it operates in the resonant boost mode that boosts the resonant inductor current. By using the active voltage-doubler rectifier on the secondary side of the converter, we can build the converter with less number of active components. As a result, the proposed converter achieves, at low cost, high boost-ratio and high efficiency over a wide range of input voltages. A detailed analysis of the converter operation is presented along with the design procedure. At 35–55 V, a 600-W/360-V prototype of the proposed converter has been built and tested to demonstrate its effectiveness.
A New Lattice Representation and Sub-Optimal Decoding for ATSC 3.0 MIMO

This paper presents a new lattice representation for ATSC 3.0 MIMO which transforms 2×2 complex channel model into 4×4 real-valued linear system. It provides a better understanding of the ATSC 3.0 MIMO precoding and allows the design of simple decoding algorithms. Based on the new lattice representation, a new maximum-likelihood (ML) decoding algorithm is derived. Also, a sub-optimal decoding algorithm based on successive interference cancellation is proposed. The simulation results show that the new ML decoding achieves similar performance to the conventional ML decoder. Furthermore, the sub-optimal decoding algorithm can significantly reduce the decoding complexity.