From Schoenberg to Pick-Nevanlinna: Toward a complete picture of the variogram class

We show that a large subclass of variograms is closed under products and that some desirable stability properties, such as the product of special compositions, can be obtained within the proposed setting. We introduce new classes of kernels of Schoenberg-L\'{e}vy type and demonstrate some important properties of rotationally invariant variograms.Comment: Published in at http://dx.doi.org/10.3150/10-BEJ277 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm). With Addendu

Towards the prediction of the quality of experience from facial expression and gaze direction

In this paper we investigate on the potentials to implicitly estimate the Quality of Experience (QoE) of a user of video streaming services by acquiring a video of her face and monitoring her facial expression and gaze direction. To this, we conducted a crowdsourcing test in which participants were asked to watch and rate the quality when watching 20 videos subject to different impairments, while their face was recorded with their PC's webcam. The following features were then considered: the Action Units (AU) that represent the facial expression, and the position of the eyes' pupil. These features were then used, together with the respective QoE values provided by the participants, to train three machine learning classifiers, namely, Support Vector Machine with quadratic kernel, RUSBoost trees and bagged trees. We considered two prediction models: only the AU features are considered or together with the position of the eyes' pupils. The RUSBoost trees achieved the best results in terms of accuracy, sensitivity and area under the curve scores. In particular, when all the features were considered, the achieved accuracy is of 44.7%, 59.4% and 75.3% when using the 5-level, 3level and 2-level quality scales, respectively. Whereas these results are not satisfactory yet, these represent a promising basis

Evolution of drop size distribution in natural rain

Both numerical modeling and laboratory experiments document the possibility of a raindrop size distribution (DSD) to evolve to an equilibrium stage (EDSD), where all the principal processes occur at steady rates. The aim of this work is to observe the temporal behavior of the DSD and to directly investigate the conditions favorable to the onset of the EDSD in natural rain. We exploited a large disdrometer dataset collected in the framework of the Ground Validation activities related to the NASA Global Precipitation Measurement mission. More than 200,000 one-minute data of two-dimensional video disdrometer (2DVD) are collected over USA to represent a wide range of precipitation types. The original data are averaged over 2 min and an automatic algorithm is used on a selected subset to identify samples with EDSD. Results show that the EDSD occurs mainly in convective events and lasts for very short time intervals (2 to 4 min). It is more frequent for rain rate between 20 and 40 mm h−1 and it mostly occurs during sharp increase of precipitation rates

Reinstatement of Cortical Outcome Representations during Higher-Order Learning

Naturalistic learning scenarios are characterized by infrequent experience of external feedback to guide behavior. Higher-order learning mechanisms like second-order conditioning (SOC) may allow stimuli that were never experienced together with reinforcement to acquire motivational value. Despite its explanatory potential for real-world learning, surprisingly little is known about the neural mechanism underlying such associative transfer of value in SOC. Here, we used multivariate cross-session, cross-modality searchlight classification on functional magnetic resonance imaging data obtained from humans during SOC. We show that visual first-order conditioned stimuli (CS) reinstate cortical patterns representing previously paired gustatory outcomes in the lateral orbitofrontal cortex (OFC). During SOC, this OFC region showed increased functional covariation with amygdala, where neural pattern similarity between second-order CS and outcomes increased from early to late stages of SOC. Our data suggest a mechanism by which motivational value is conferred to stimuli that were never paired with reinforcement

An iot-based smart building solution for indoor environment management and occupants prediction

Smart buildings use Internet of Things (IoT) sensors for monitoring indoor environmental parameters, such as temperature, humidity, luminosity, and air quality. Due to the huge amount of data generated by these sensors, data analytics and machine learning techniques are needed to extract useful and interesting insights, which provide the input for the building optimization in terms of energy-saving, occupants’ health and comfort. In this paper, we propose an IoT-based smart building (SB) solution for indoor environment management, which aims to provide the following main functionalities: monitoring of the room environmental parameters; detection of the number of occupants in the room; a cloud platform where virtual entities collect the data acquired by the sensors and virtual super entities perform data analysis tasks using machine learning algorithms; a control dashboard for the management and control of the building. With our prototype, we collected data for 10 days, and we built two prediction models: a classification model that predicts the number of occupants based on the monitored environmental parameters (average accuracy of 99.5%), and a regression model that predicts the total volatile organic compound (TVOC) values based on the environmental parameters and the number of occupants (Pearson correlation coefficient of 0.939)

Management of Patients With Hematologic Malignancies During the COVID-19 Pandemic: Practical Considerations and Lessons to Be Learned.

The COVID-19 pandemic has created unprecedented hurdles to the delivery of care to patients with cancer. Patients with hematologic malignancies appear to have a greater risk of SARS-CoV-2 infection and severe disease due to myelosuppression and lymphopenia. The first challenge, therefore, is how to continue to deliver effective, curative therapy to vulnerable patients and at the same time avoid exposing them, and their health care teams (HCT), to SARS-CoV-2. An additional challenge is the timely completion of the diagnostic and staging studies required to formulate appropriate treatment plans. Deferred procedures and avoidance of multiple trips to the surgical, diagnostic, and laboratory suites require same day consolidation of all procedures. With laboratory medicine absorbed by the need to deploy large scale COVID-testing, the availability of routine molecular tests is affected. Finally, we are increasingly faced with the challenge of making complex treatment decisions in SARS-CoV-2 positive patients with aggressive but potentially curable blood cancers. When to treat, how to treat, when to wait, how long to wait, how to predict and manage toxicities, and how to avoid compromising cure rates remains unknown. We present an outline of the scientific, medical, and operational challenges posed by the COVID-19 pandemic at selected American and European institutions and offer our current view of the key elements of a response. While the peak of the pandemic may be past us, in the absence of a vaccine risks remain, and our alertness and response to future challenges need to be refined and consolidated

Analysis of elastic nonlinearity for impact damage detection in composite laminates

This paper concerns the experimental analysis of nonlinear response features of a composite laminate plate for impact damage detection. The measurement procedure is based on the Scaling Subtraction Method (SSM) and consists in exciting the damaged specimen with two sinusoidal signals at different amplitude. The linearly rescaled response signal at low amplitude excitation is subtracted from the response at large amplitude excitation to extract the nonlinear signatures. The latter are analysed in the time domain to infer the presence of damage. Results are compared with frequency domain analyses using the nonlinear vibro-acoustic modulation technique (NWMS). Changes in amplitude and phase as well as modulation effects of the acquired responses are also monitored. Surface-bonded, low profile piezoceramic transducers are used for excitation and sensing. Both measurements techniques are applied to detect barely visible impact damage in laminate composite plate. Non-destructive penetrant-enhanced X-ray inspections are carried out to characterize the extent of internal damage. The behavior of the nonlinear features and the sensitivity of each technique are also investigated in the paper

Commentary : SARS-CoV-2 Transmission in Patients With Cancer at a Tertiary Care Hospital in Wuhan, China

COVID-19; Atenció al càncer; Orientació del pacientCOVID-19; Cuidado del cáncer; Orientación al pacienteCOVID-19; Cancer care; Patient guidanceA Commentary on: SARS-CoV-2 Transmission in Patients With Cancer at a Tertiary Care Hospital in Wuhan, China. By Yu, J., Ouyang, W., Chua, M. L. K., and Xie, C. (2020). JAMA Oncol. doi: 10.1001/jamaoncol.2020.0980:ca_E

How Integrated Marketing Communications (IMC) works? A theoretical review and an analysis of its main drivers and effects

Since the mid-1990’s, the concept of Integrated Marketing Communications (IMC) has come to the fore and attracted considerable attention in the literature. The main purpose of this paper is twofold: (1) to explore IMC by delimitating its conceptual boundaries and identifying its main dimensions and (2) to develop a whole theoretical model of its antecedents and consequences. To achieve this goal, this study analyzes the conceptual framework around IMC and its dimensions and it presents an extensive literature review. Moreover, a new definition is presented and the main dimensions of the concept are highlighted. A systematic literature review is carried out to identify IMC antecedents and consequences and the research hypotheses are formulated. Following the obtained results, a conceptual model has been developed and the most relevant findings are discussed. Finally, the main academic and managerial implications are described. This model will help academics and marketers to better understand the role that this new marketing paradigm plays within modern management, in identifying the key variables that promote or hinder IMC and the benefits derived from its implementation