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The rise of AI conversational agents has broadened opportunities to enhance human capabilities across various domains. As these agents become more prevalent, it is crucial to investigate the impact of different affective abilities on their performance and user experience. In this study, we surveyed 745 respondents to understand the expectations and preferences regarding affective skills in various applications. Specifically, we assessed preferences concerning AI agents that can perceive, respond to, and simulate emotions across 32 distinct scenarios. Our results indicate a preference for scenarios that involve human interaction, emotional support, and creative tasks, with influences from factors such as emotional reappraisal and personality traits. Overall, the desired affective skills in AI agents depend largely on the application's context and nature, emphasizing the need for adaptability and context-awareness in the design of affective AI conversational agents.

In numerous industrial processes involving fluids, viscosity is a determinant factor for reaction rates, flows, drying, mixing, etc. Its importance is even more determinant for phenomena observed are at the micro- and nano- scales as in nanopores or in micro and nanochannels for instance. However, despite notable progresses of the techniques used in microrheology in recent years, the quantification, mapping and study of viscosity at small scales remains challenging. Fluorescent molecular rotors are molecules whose fluorescence properties are sensitive to local viscosity: they thus allow to obtain viscosity maps by using fluorescence microscopes. While they are well-known as contrast agents in bioimaging, their use for quantitative measurements remains scarce. This paper is devoted to the use of such molecules to perform quantitative, \textit{in situ} and local measurements of viscosity in heterogeneous microfluidic flows. The technique is first validated in the well-controlled situation of a microfluidic co-flow, where two streams mix through transverse diffusion. Then, a more complex situation of mixing in passive micromixers is considered and mixing efficiency is characterized and quantified. The methodology developed in this study thus opens a new path for flow characterization in confined, heterogeneous and complex systems.he methodology developed in this study thus opens a new path for flow characterization in confined, heterogeneous micro- and nano- systems.

The use of cameras and computational algorithms for noninvasive, low-cost and scalable measurement of physiological (e.g., cardiac and pulmonary) vital signs is very attractive. However, diverse data representing a range of environments, body motions, illumination conditions and physiological states is laborious, time consuming and expensive to obtain. Synthetic data have proven a valuable tool in several areas of machine learning, yet are not widely available for camera measurement of physiological states. Synthetic data offer "perfect" labels (e.g., without noise and with precise synchronization), labels that may not be possible to obtain otherwise (e.g., precise pixel level segmentation maps) and provide a high degree of control over variation and diversity in the dataset. We present SCAMPS, a dataset of synthetics containing 2,800 videos (1.68M frames) with aligned cardiac and respiratory signals and facial action intensities. The RGB frames are provided alongside segmentation maps. We provide precise descriptive statistics about the underlying waveforms, including inter-beat interval, heart rate variability, and pulse arrival time. Finally, we present baseline results training on these synthetic data and testing on real-world datasets to illustrate generalizability.

Predicting the impact of treatments from observational data only still represents a majorchallenge despite recent significant advances in time series modeling. Treatment assignments are usually correlated with the predictors of the response, resulting in a lack of data support for counterfactual predictions and therefore in poor quality estimates. Developments in causal inference have lead to methods addressing this confounding by requiring a minimum level of overlap. However,overlap is difficult to assess and usually notsatisfied in practice. In this work, we propose Counterfactual ODE (CF-ODE), a novel method to predict the impact of treatments continuously over time using Neural Ordinary Differential Equations equipped with uncertainty estimates. This allows to specifically assess which treatment outcomes can be reliably predicted. We demonstrate over several longitudinal data sets that CF-ODE provides more accurate predictions and more reliable uncertainty estimates than previously available methods.

Synthetic data is a powerful tool in training data hungry deep learning algorithms. However, to date, camera-based physiological sensing has not taken full advantage of these techniques. In this work, we leverage a high-fidelity synthetics pipeline for generating videos of faces with faithful blood flow and breathing patterns. We present systematic experiments showing how physiologically-grounded synthetic data can be used in training camera-based multi-parameter cardiopulmonary sensing. We provide empirical evidence that heart and breathing rate measurement accuracy increases with the number of synthetic avatars in the training set. Furthermore, training with avatars with darker skin types leads to better overall performance than training with avatars with lighter skin types. Finally, we discuss the opportunities that synthetics present in the domain of camera-based physiological sensing and limitations that need to be overcome.

Facial action unit recognition has many applications from market research to psychotherapy and from image captioning to entertainment. Despite its recent progress, deployment of these models has been impeded due to their limited generalization to unseen people and demographics. This work conducts an in-depth analysis of performance across several dimensions: individuals(40 subjects), genders (male and female), skin types (darker and lighter), and databases (BP4D and DISFA). To help suppress the variance in data, we use the notion of self-supervised denoising autoencoders to design a method for deep face normalization(DeepFN) that transfers facial expressions of different people onto a common facial template which is then used to train and evaluate facial action recognition models. We show that person-independent models yield significantly lower performance (55% average F1 and accuracy across 40 subjects) than person-dependent models (60.3%), leading to a generalization gap of 5.3%. However, normalizing the data with the newly introduced DeepFN significantly increased the performance of person-independent models (59.6%), effectively reducing the gap. Similarly, we observed generalization gaps when considering gender (2.4%), skin type (5.3%), and dataset (9.4%), which were significantly reduced with the use of DeepFN. These findings represent an important step towards the creation of more generalizable facial action unit recognition systems.

The ability to monitor audience reactions is critical when delivering presentations. However, current videoconferencing platforms offer limited solutions to support this. This work leverages recent advances in affect sensing to capture and facilitate communication of relevant audience signals. Using an exploratory survey (N = 175), we assessed the most relevant audience responses such as confusion, engagement, and head-nods. We then implemented AffectiveSpotlight, a Microsoft Teams bot that analyzes facial responses and head gestures of audience members and dynamically spotlights the most expressive ones. In a within-subjects study with 14 groups (N = 117), we observed that the system made presenters significantly more aware of their audience, speak for a longer period of time, and self-assess the quality of their talk more similarly to the audience members, compared to two control conditions (randomly-selected spotlight and default platform UI). We provide design recommendations for future affective interfaces for online presentations based on feedback from the study.

Non-contact physiological measurement has the potential to provide low-cost, non-invasive health monitoring. However, machine vision approaches are often limited by the availability and diversity of annotated video datasets resulting in poor generalization to complex real-life conditions. To address these challenges, this work proposes the use of synthetic avatars that display facial blood flow changes and allow for systematic generation of samples under a wide variety of conditions. Our results show that training on both simulated and real video data can lead to performance gains under challenging conditions. We show state-of-the-art performance on three large benchmark datasets and improved robustness to skin type and motion.

With the recent COVID-19 breakup, it became necessary to implement remote classes in schools and universities to safeguard health and life. However, many students (teachers and parents, also) face great difficulties accessing and staying in class due to technology limitations, affecting their education. Using several nationally representative datasets in Colombia, this article documents how the academic performance of students in their final high school year is affected due to technologies, aggregated by municipalities. We conclude that internet access strongly affects these results, and little improvement on the internet/computer access will reflect better academic performance. Under these conditions, belonging to an ethnic group or high rurality (non-geographic centralized municipalities) has a negative impact. Policy implications are discussed.

Noise modeling in power line communications has recently drawn the attention of researchers. However, when characterizing the noise process in narrowband communications, previous works have only focused on small-scale phenomena involving fine-grained details. Nevertheless, the communication link's reliability is also affected by long-term noise phenomena that might affect transfer rates at higher layers as well. This paper addresses the problem of long-term noise characterization for narrowband power line communications and provides a statistical analysis of the long-term trends affecting the noise levels. We present a statistical description of the noise process in the time and frequency domains based on real field measurements in the FCC band (10 kHz - 490 kHz). The collected data comprises more than 1.8 billion samples taken from three different locations over a time period of approximately 10 days. The noise samples have been statistically analyzed by considering stationarity, autocorrelation, and independence. Although our results -- being unprecedented -- are interesting per se, they improve the noise pattern knowledge, thus paving the way for the design and implementation of more robust PLC protocols.

In this manuscript we anticipate axion detection in order to theorize on a novel telecommunications system based on the coherent axion-two-photon vertex Primakoff effect conversion and reversion of microwave photons transmitting a modulated signal. We suggest a possible set-up for an experiment or industrial application using state-of-the-art technology and we estimate the output power and photon rate expected to be received as a function of the axion-photon coupling constant $g_{a\gamma\gamma}$. We find that, although challenging,this system has no physical restriction to render it unfeasible. Finally, we summarize the advantages and disadvantages of a hypothetical axion-based telecommunications system compared to traditional telecommunication systems. We then extend the discussion, noting the more important conclusions., Comment: Requieres for further analysis on geometric factor and signal propagation

In this article we present the Dark-photons$\&$Axion-Like particles Interferometer (DALI), a novel experiment designed for the detection of photon-mixing cold dark matter in the microwave band between 6 and 60 GHz. DALI is a haloscope for the simultaneous search for axions, ALPs and dark photons, with a number of novelties that make it unique. First, it is a dark matter telescope, with a capacity for pointing, tracking and rastering objects and areas in the sky. This potentially allows one to detect relativistic dark matter particles, substructures and flows, without compromising the simultaneous scanning for dark matter relic particles present in the laboratory. Second, it has been designed using commercial technology. This will allow feasible manufacture at a reasonable cost, thereby mitigating the need for R\&D and facilitating maintenance. Finally, it benefits from a high sensitivity over a broad band of frequencies with only minimal reconfiguration., Comment: Published in JCAP. Open access version here https://iopscience.iop.org/article/10.1088/1475-7516/2021/04/075/pdf

We propose the first correct special-purpose quantum circuits for preparation of Bell-diagonal states (BDS), and implement them on the IBM Quantum computer, characterizing and testing complex aspects of their quantum correlations in the full parameter space. Among the circuits proposed, one involves only two quantum bits but requires adapted quantum tomography routines handling classical bits in parallel. The entire class of Bell-diagonal states is generated, and a number of characteristic indicators, namely entanglement of formation, CHSH non-locality, steering and discord, are experimentally evaluated over the full parameter space and compared with theory. As a by-product of this work we also find a remarkable general inequality between "quantum discord" and "asymmetric relative entropy of discord": the former never exceeds the latter. We also prove that for all BDS the two coincide., Comment: 20 pages, 23 figures

The QUIJOTE Experiment was developed to study the polarization in the Cosmic Microwave Background (CMB) over the frequency range of 10-50 GHz. Its first instrument, the Multi Frequency Instrument (MFI), measures in the range 10-20 GHz which coincides with one of the naturally transparent windows in the atmosphere. The Tenerife Microwave Spectrometer (TMS) has been designed to investigate the spectrum between 10-20 GHz in more detail. The MFI bands are 2 GHz wide whereas the TMS bands will be 250 MHz wide covering the complete 10-20 GHz range with one receiver chain and Fourier spectral filter bank. It is expected that the relative calibration between frequency bands will be better known than the MFI channels and that the higher resolution will provide essential information on narrow band interference and features such as ozone. The TMS will study the atmospheric spectra as well as provide key information on the viability of ground-based absolute spectral measurements. Here the novel Fourier transform spectrometer design is described showing its suitability to wide band measurement and $\sqrt{N}$ advantage over the usual scanning techniques., Comment: 8 pages, 10 figures. Accepted for publication in TIM

The literature on horn antennas dedicated to radio astronomy and satellite communications applications is very extensive and at times disjointed, relevant contributions being distributed as far back as from the 60's until the present today. This work combines a compact but complete review of the different theories, methodologies and techniques used to describe corrugations and metamaterials in their application to feedhorns used in radio astronomy and satellite communications along with some new work to help explain the theory in a more practical way. Starting with the hybrid-mode condition firstly corrugated horns are explained describing soft and hard boundaries and also the theory from a plasmonic optics point of view. Following this the use of metamaterials in order to design horn antennas with quasi-null cross-polarization and low E-Plane sidelobes level over an ultra-wideband is described. The objective of this work is to help to ease the learning curve of the post graduate students and young professionals dedicated to these tasks, and try to inspire the work of the senior professionals toward a new direction and approach., Comment: 51 pages, 29 figures. accepted for publciation in IOPscience JINST journal

The electromagnetic metamaterials at microwaves frequencies are well established in industrial applications nowadays. Recent research has shown that a specific kind of metallic metamaterial can contribute to improve the performance of the microwave feedhorns used in radioastronomy and satellite telecommunications. In this article, we theoretically justify this argument finding a new type of meta-ring with a record bandwidth in terms of cross-polarization, and we explore the manufacturability of these particular metamaterials, successfully fabricating a meta-ring and applying it to a novel and very compact prototype microwave antenna which covers a 2:1 bandwidth., Comment: 21 pages, 16 figures. Accepted for publication in Elsevier MECMAT journal

The fundamentals of the whispering gallery mode (WGM) resonators are well established in the literature, with several successful proof-of-concept experiments. One remarkable benefit of this technology is the room-temperature operation. This characteristic could be used to build a new generation of radiometers that do not need to be cooled down to cryogenic temperatures to reach high sensitivities. In this article, a study of the viability of technological transfer is undertaken, beginning with a brief review of the theoretical background that will be applied and leading to a proposal for a novel spectro-polarimeter design. Simulations for a radiometer based on WGM resonance are analyzed and compared with state-of-the-art coherent receivers. The results are then discussed, finding that although promising, WGM technology needs R\&D in several directions in order to be competitive, whose are suggested by the authors with the idea of inspiring the work of the researchers in the field towards a new direction or approach., Comment: 24 pages, 5 figs