Theoretical investigation of one-dimensional cavities in two-dimensional photonic crystals

We study numerically the features of the resonant peak of one-dimensional (1-D) dielectric cavities in a two-dimensional (2-D) hexagonal lattice. We use both the transfer matrix method and the finite difference time-domain (FDTD) method to calculate the transmission coefficient. We compare the two methods and discuss their results for the transmission and quality factor Q of the resonant peak. We also examine the dependence of Q on absorption and losses, the thickness of the sample and the lateral width of the cavity. The Q- factor dependence on the width of the source in the FDTD calculations is also given.Comment: 25 pages, 8 figure

Electromagnetic wave propagation in two-dimensional photonic crystals

In this dissertation, we have undertaken the challenge to understand the unusual propagation properties of the photonic crystal (PC)---a medium with periodically modulated dielectric function. PCs have frequency regions were propagation is prohibited (gaps) and regions where propagation is allowed (bands). We first study a two-dimensional (2D) photonic crystal system in the gap region. A line defect introduces allowed states in an otherwise prohibited frequency spectrum. The dependence of the defect resonance state on the system\u27s parameters, such as the lateral width of the structure and the profile of the source, is investigated in detail. Subsequently, we examine the band properties of periodic 2D PCs, and study their unusual refractive behavior. In some cases, of anomalous refraction in PCs, the beam refracts on the wrong side of the surface normal, a phenomenon known as negative refraction. This phenomenon occurs in materials with the wave vector, the electric field, and the magnetic field forming a left-handed set of vectors---called left-handed materials (LHM) or negative index materials (NIM). We examine carefully the conditions to obtain left-handed behavior in PCs. We find with a wedge type simulation experiment, that in the PC system negative refraction is neither a prerequisite nor guarantees left-handed behavior. We identify a frequency region where the PC shows left-handed behavior and acts in some respects like a homogeneous medium with a negative refractive index. Using this realistic PC system we show how negative refraction occurs. Our findings indicate that the formation of the negatively refracted beam is not instantaneous and involves a transient time. This way, we address previous controversial issues about negative refraction concerning causality and the speed of light limit. Lastly, we systematically study anomalous refractive phenomena in PCs. We classify these different effects according to their Bragg order and type of propagation (left-handed or not). Moreover, we discuss the validity of our findings in the low index modulation PCs

Breaking transmission symmetry without breaking reciprocity in linear all-dielectric polarization-preserving metagratings

Transmission asymmetry in reciprocal systems offers an appealing alternative to bulkier non-reciprocal implementations for certain applications. Common reciprocal routes to transmission asymmetry of linearly polarized light involve a rotation of its polarization. Here, we explore a different route with a linear all-dielectric metagrating that preserves polarization, while lacking inversion symmetry along the surface-normal direction. Our all-angle transmission calculations reveal an abrupt transition from a symmetric to an asymmetric transmission response that traces the Bragg critical wavelength of higher-order beam emergence as a function of the incident angle. By adopting an analogy between scattering from a multi-port network and the metagrating paradigm we establish why the only necessary condition for transmission symmetry breaking in this class of systems, is the emergence of any higher-order Bragg diffracted beam. We further show how such a transmission symmetry breaking is consistent with reciprocity and also demonstrate the underpinning symmetry-breaking mechanism with a first-principle numerical experiment. Finally, we elucidate on some previous misconceptions regarding transmission symmetry breaking related to the role of the substrate or need for change of diffraction order number at each interface. Our proposed metagrating can exhibit a strong transmission asymmetry, with contrast that can be as high as ~75%, thus underlining its potential as a blueprint for passive asymmetric or non-linear self-biasing non-reciprocal metasurfaces relevant to integrated and active photonics.Comment: 10 pages, 6 figure

Machine Learning Approaches for Fine-Grained Symptom Estimation in Schizophrenia: A Comprehensive Review

Schizophrenia is a severe yet treatable mental disorder, it is diagnosed using a multitude of primary and secondary symptoms. Diagnosis and treatment for each individual depends on the severity of the symptoms, therefore there is a need for accurate, personalised assessments. However, the process can be both time-consuming and subjective; hence, there is a motivation to explore automated methods that can offer consistent diagnosis and precise symptom assessments, thereby complementing the work of healthcare practitioners. Machine Learning has demonstrated impressive capabilities across numerous domains, including medicine; the use of Machine Learning in patient assessment holds great promise for healthcare professionals and patients alike, as it can lead to more consistent and accurate symptom estimation.This survey aims to review methodologies that utilise Machine Learning for diagnosis and assessment of schizophrenia. Contrary to previous reviews that primarily focused on binary classification, this work recognises the complexity of the condition and instead, offers an overview of Machine Learning methods designed for fine-grained symptom estimation. We cover multiple modalities, namely Medical Imaging, Electroencephalograms and Audio-Visual, as the illness symptoms can manifest themselves both in a patient's pathology and behaviour. Finally, we analyse the datasets and methodologies used in the studies and identify trends, gaps as well as opportunities for future research.Comment: 19 pages, 5 figure

EmoCLIP: A Vision-Language Method for Zero-Shot Video Facial Expression Recognition

Facial Expression Recognition (FER) is a crucial task in affective computing, but its conventional focus on the seven basic emotions limits its applicability to the complex and expanding emotional spectrum. To address the issue of new and unseen emotions present in dynamic in-the-wild FER, we propose a novel vision-language model that utilises sample-level text descriptions (i.e. captions of the context, expressions or emotional cues) as natural language supervision, aiming to enhance the learning of rich latent representations, for zero-shot classification. To test this, we evaluate using zero-shot classification of the model trained on sample-level descriptions on four popular dynamic FER datasets. Our findings show that this approach yields significant improvements when compared to baseline methods. Specifically, for zero-shot video FER, we outperform CLIP by over 10\% in terms of Weighted Average Recall and 5\% in terms of Unweighted Average Recall on several datasets. Furthermore, we evaluate the representations obtained from the network trained using sample-level descriptions on the downstream task of mental health symptom estimation, achieving performance comparable or superior to state-of-the-art methods and strong agreement with human experts. Namely, we achieve a Pearson's Correlation Coefficient of up to 0.85 on schizophrenia symptom severity estimation, which is comparable to human experts' agreement. The code is publicly available at: https://github.com/NickyFot/EmoCLIP.Comment: 10 pages, 3 figure

Estimating continuous affect with label uncertainty

Continuous affect estimation is a problem where there is an inherent uncertainty and subjectivity in the labels that accompany data samples -- typically, datasets use the average of multiple annotations or self-reporting to obtain ground truth labels. In this work, we propose a method for uncertainty-aware continuous affect estimation, that models explicitly the uncertainty of the ground truth label as a uni-variate Gaussian with mean equal to the ground truth label, and unknown variance. For each sample, the proposed neural network estimates not only the value of the target label (valence and arousal in our case), but also the variance. The network is trained with a loss that is defined as the KL-divergence between the estimation (valence/arousal) and the Gaussian around the ground truth. We show that, in two affect recognition problems with real data, the estimated variances are correlated with measures of uncertainty/error in the labels that are extracted by considering multiple annotations of the data

Monte Carlo simulations of densely-packed athermal polymers in the bulk and under confinement

We review the main results from extensive Monte Carlo (MC) simulations on athermal polymer packings in the bulk and under confinement. By employing the simplest possible model of excluded volume, macromolecules are represented as freely-jointed chains of hard spheres of uniform size. Simulations are carried out in a wide concentration range: from very dilute up to very high volume fractions, reaching the maximally random jammed (MRJ) state. We study how factors like chain length, volume fraction and flexibility of bond lengths affect the structure, shape and size of polymers, their packing efficiency and their phase behaviour (disorder–order transition). In addition, we observe how these properties are affected by confinement realized by flat, impenetrable walls in one dimension. Finally, by mapping the parent polymer chains to primitive paths through direct geometrical algorithms, we analyse the characteristics of the entanglement network as a function of packing density