Multimodal representation and learning

Recent years have seen an explosion in multimodal data on the web. It is therefore important to perform multimodal learning to understand the web. However, it is challenging to join various modalities because each modality has a different representation and correlational structure. In addition, various modalities generally carry different kinds of information that may provide enrich understanding; for example, the visual signal of a flower may provide happiness; however, its scent might not be pleasant. Multimodal information may be useful to make an informed decision. Therefore, we focus on improving representations from individual modalities to enhance multimodal representation and learning. In this doctoral thesis, we presented techniques to enhance representations from individual and multiple modalities for multimodal applications including classification, cross-modal retrieval, matching and verification on various benchmark datasets

Direct dark mode excitation by symmetry matching of a single-particle based metasurface

This paper makes evidence for direct dark mode excitation mechanism in a metasurface structure. The dark mode excitation mechanism is entirely determined by structures' symmetry and does not depend on near-field coupling between elements. In our examples, we consider single element based metasurface composed of two V antennas connected in an anti-symmetric arrangement. Both experimental and modeling results show an efficient excitation of magnetic dipolar mode in such structures. The direct dark mode excitation mechanism provides a design that is more robust with respect to technology imperfections. The considered approach opens promising perspectives for new type of nanostructure designs and greatly relaxes fabrication constraints for the optical domain.Comment: 21 pages, 5 figure

Multimodal representation and learning

Recent years have seen an explosion in multimodal data on the web. It is therefore important to perform multimodal learning to understand the web. However, it is challenging to join various modalities because each modality has a different representation and correlational structure. In addition, various modalities generally carry different kinds of information that may provide enrich understanding; for example, the visual signal of a flower may provide happiness; however, its scent might not be pleasant. Multimodal information may be useful to make an informed decision. Therefore, we focus on improving representations from individual modalities to enhance multimodal representation and learning. In this doctoral thesis, we presented techniques to enhance representations from individual and multiple modalities for multimodal applications including classification, cross-modal retrieval, matching and verification on various benchmark datasets

Ultra directive antenna via transformation optics

Spatial coordinate transformation is used as a reliable tool to control electromagnetic fields. In this paper, we derive the permeability and permittivity tensors of a metamaterial able to transform an isotropically radiating source into a compact ultradirective antenna in the microwave domain. We show that the directivity of this antenna is competitive with regard to conventional directive antennas horn and reflector antennas, besides its dimensions are smaller. Numerical simulations using finite element method are performed to illustrate these properties. A reduction in the electromagnetic material parameters is also proposed for an easy fabrication of this antenna from existing materials. Following that, the design of the proposed antenna using a layered metamaterial is presented. The different layers are all composed of homogeneous and uniaxial anisotropic metamaterials, which can be obtained from simple metal-dielectric structures. When the radiating source is embedded in the layered metamaterial, a highly directive beam is radiated from the antenn

Comparison Of Dynamic Hip Screw (Dhs) And Proximal Femoral Nail (Pfn) Fixation For Unstable Intertrochanteric Femoral Fractures On Basis Of Collapse

Introduction:Proximal femoral nail (PFN) is an intramedullary device for fixation of intertrochanteric femoral fracture and has shown promise in unstable intertrochanteric fractures, which have been treated with dynamic hip screw (DHS) till recently.Introduction:Proximal femoral nail (PFN) is an intramedullary device for fixation of intertrochanteric femoral fracture and has shown promise in unstable intertrochanteric fractures, which have been treated with dynamic hip screw (DHS) till recently.Objectives: To compare the frequency of collapse in the early postoperative period between fixation with DHS versus PFN in unstable intertrochanteric femoral fractures. Patients and methods: This randomized controlled trial was conducted at CMH Rawalpindi in 2017. A total of 310 male and female adult patients between 18 to 75 years in age, with unstable intertrochanteric fractures were included. Patients having pathological fractures, renal disease and open fracture were excluded. The patients were assigned randomly to one of the two groups. Group A was treated with DHS and Group B was treated with PFN. The collapse was measured initially on standard x-rays taken on the zero postoperative day. Patients were allowed to bear partial weight at 4 weeks and second measurement for collapse was done after 6 weeks postoperatively.Results: The mean age of patients in group A was 51.27 ± 11.54 years and in group B was 53.75 ± 12.28 years. Collapse was seen in 26 (16.77%) patients in group A (DHS) and 07 (4.52%) patients in group B (PFN) with p-value of 0.0001. Age has a bearing on the rate of collapse while BMI does not. Conclusion: The frequency of collapse in early postoperative period is less after treatment with proximal femoral nail as compared to dynamic hip screw in unstable intertrochanteric fractures.Keywords: Intertrochanteric femoral fractures; Unstable intertrochanteric fracture; proximal femoral nail; dynamic hip screw; sliding hip screw; collapse