Optimal Order of Decoding for Max-Min Fairness in KK-User Memoryless Interference Channels

A $K$-user memoryless interference channel is considered where each receiver sequentially decodes the data of a subset of transmitters before it decodes the data of the designated transmitter. Therefore, the data rate of each transmitter depends on (i) the subset of receivers which decode the data of that transmitter, (ii) the decoding order, employed at each of these receivers. In this paper, a greedy algorithm is developed to find the users which are decoded at each receiver and the corresponding decoding order such that the minimum rate of the users is maximized. It is proven that the proposed algorithm is optimal.Comment: 11 Pages, Submitted to IEEE International Symposium on Information Theory(ISIT 2007

Tunability of terahertz random lasers with temperature based on superconducting materials

We theoretically demonstrate the tunabiltiy of terahertz random lasers composed of high temperature superconductorYBCO and ruby layers as active medium. The considered system is a one-dimensional disordered medium made of ruby grain and YBCO. Finite-difference time domain method is used to calculate the emission spectrum and spatial distribution of electric field at different temperatures. Our numerical results reveal that the superconductor based random lasers exhibit large temperature tunability in the terahertz domain. The emission spectrum is significantly temperature dependent, the number of lasing modes and their intensities increase with decreasing temperature. Also, we make some discussion to explain the reason for the observed tunability and the effect of temperature variation on the spatial distribution of the electric field in the disordered active medium

Locating Emergency Facilities Using the Weighted k-median Problem: A Graph-metaheuristic Approach

An efficient approach is presented for addressing the problem of finding the optimal facilities location in conjunction with the k-median method. First the region to be investigated is meshed and an incidence graph is constructed to obtain connectivity properties of meshes. Then shortest route trees (SRTs) are rooted from nodes of the generated graph. Subsequently, in order to divide the nodes of graph or the studied region into optimal k subregions, k-median approach is utilized. The weights of the nodes are considered as the risk factors such as population, seismic and topographic conditions for locating facilities in the high-risk zones to better facilitation. For finding the optimal facility locations, a recently developed meta-heuristic algorithm that is called Colliding Bodies Optimization (CBO) is used. The performance of the proposed method is investigated through different alternatives for minimizing the cost of the weighted k-median problem. As a case study, the Mazandaran province in Iran is considered and the above graph-metaheuristic approach is utilized for locating the facilities

COVID-19 in pediatric patients: A case series

The COVID-19 pandemic outbreak has affected the global health system with an urgent need for more sophisticated studies. One of the prominent aspects of COVID-19 is the picture of the disease in pediatric population. Our case series study includes 4 Babyboy patients in a referral children's hospital with different clinical outcomes

PARIS: Part-level Reconstruction and Motion Analysis for Articulated Objects

We address the task of simultaneous part-level reconstruction and motion parameter estimation for articulated objects. Given two sets of multi-view images of an object in two static articulation states, we decouple the movable part from the static part and reconstruct shape and appearance while predicting the motion parameters. To tackle this problem, we present PARIS: a self-supervised, end-to-end architecture that learns part-level implicit shape and appearance models and optimizes motion parameters jointly without any 3D supervision, motion, or semantic annotation. Our experiments show that our method generalizes better across object categories, and outperforms baselines and prior work that are given 3D point clouds as input. Our approach improves reconstruction relative to state-of-the-art baselines with a Chamfer-L1 distance reduction of 3.94 (45.2%) for objects and 26.79 (84.5%) for parts, and achieves 5% error rate for motion estimation across 10 object categories. Video summary at: https://youtu.be/tDSrROPCgUcComment: Presented at ICCV 2023. Project website: https://3dlg-hcvc.github.io/paris

DAHiTrA: Damage Assessment Using a Novel Hierarchical Transformer Architecture

This paper presents DAHiTrA, a novel deep-learning model with hierarchical transformers to classify building damages based on satellite images in the aftermath of hurricanes. An automated building damage assessment provides critical information for decision making and resource allocation for rapid emergency response. Satellite imagery provides real-time, high-coverage information and offers opportunities to inform large-scale post-disaster building damage assessment. In addition, deep-learning methods have shown to be promising in classifying building damage. In this work, a novel transformer-based network is proposed for assessing building damage. This network leverages hierarchical spatial features of multiple resolutions and captures temporal difference in the feature domain after applying a transformer encoder on the spatial features. The proposed network achieves state-of-the-art-performance when tested on a large-scale disaster damage dataset (xBD) for building localization and damage classification, as well as on LEVIR-CD dataset for change detection tasks. In addition, we introduce a new high-resolution satellite imagery dataset, Ida-BD (related to the 2021 Hurricane Ida in Louisiana in 2021, for domain adaptation to further evaluate the capability of the model to be applied to newly damaged areas with scarce data. The domain adaptation results indicate that the proposed model can be adapted to a new event with only limited fine-tuning. Hence, the proposed model advances the current state of the art through better performance and domain adaptation. Also, Ida-BD provides a higher-resolution annotated dataset for future studies in this field

Optimal Design of the Monopole Structures Using the CBO and ECBO Algorithms

Tubular steel monopole structure is widely used for supporting antennas in telecommunication industries. This research presents two recently developed meta-heuristic algorithms, which are called Colliding Bodies Optimization (CBO) and Enhanced Colliding Bodies Optimization (ECBO), for size optimization of monopole steel structures. The design procedure aims to obtain minimum weight of monopole structures subjected to the TIA-EIA222F specification. Two monopole structure examples are examined to verify the suitability of the design procedure and to demonstrate the effectiveness and robustness of the CBO and ECBO in creating optimal design for this problem. The outcomes of the enhanced colliding bodies optimization (ECBO) are also compared to those of the standard colliding bodies optimization (CBO) to illustrate the importance of the enhancement of the CBO algorithm