Topology Control for Maintaining Network Connectivity and Maximizing Network Capacity Under the Physical Model

In this paper we study the issue of topology control under the physical Signal-to-Interference-Noise-Ratio (SINR) model, with the objective of maximizing network capacity. We show that existing graph-model-based topology control captures interference inadequately under the physical SINR model, and as a result, the interference in the topology thus induced is high and the network capacity attained is low. Towards bridging this gap, we propose a centralized approach, called Spatial Reuse Maximizer (MaxSR), that combines a power control algorithm T4P with a topology control algorithm P4T. T4P optimizes the assignment of transmit power given a fixed topology, where by optimality we mean that the transmit power is so assigned that it minimizes the average interference degree (defined as the number of interferencing nodes that may interfere with the on-going transmission on a link) in the topology. P4T, on the other hand, constructs, based on the power assignment made in T4P, a new topology by deriving a spanning tree that gives the minimal interference degree. By alternately invoking the two algorithms, the power assignment quickly converges to an operational point that maximizes the network capacity. We formally prove the convergence of MaxSR. We also show via simulation that the topology induced by MaxSR outperforms that derived from existing topology control algorithms by 50%-110% in terms of maximizing the network capacity

Retrieval of interatomic separations of molecules from laser-induced high-order harmonic spectra

We illustrate an iterative method for retrieving the internuclear separations of N$_2$, O$_2$ and CO$_2$ molecules using the high-order harmonics generated from these molecules by intense infrared laser pulses. We show that accurate results can be retrieved with a small set of harmonics and with one or few alignment angles of the molecules. For linear molecules the internuclear separations can also be retrieved from harmonics generated using isotropically distributed molecules. By extracting the transition dipole moment from the high-order harmonic spectra, we further demonstrated that it is preferable to retrieve the interatomic separation iteratively by fitting the extracted dipole moment. Our results show that time-resolved chemical imaging of molecules using infrared laser pulses with femtosecond temporal resolutions is possible.Comment: 14 pages, 9 figure

Multi-level caching with delayed-multicast for video-on-demand

Delayed-Multicast is a novel transmission technique to support Video-on-Demand. It introduces buffers within the network to bridge the temporal delays between similar requests thus minimizing the aggregate bandwidth and server load. This paper introduces an improved online algorithm for resource allocation with Delayed-Multicast by utilizing prior knowledge of each clip's popularity. The algorithm is intended to be simple so as to allow for deployment at multiple levels in a distribution network. The result is greater backbone traffic savings and a corresponding reduction in the server load

Inspecting Explainability of Transformer Models with Additional Statistical Information

Transformer becomes more popular in the vision domain in recent years so there is a need for finding an effective way to interpret the Transformer model by visualizing it. In recent work, Chefer et al. can visualize the Transformer on vision and multi-modal tasks effectively by combining attention layers to show the importance of each image patch. However, when applying to other variants of Transformer such as the Swin Transformer, this method can not focus on the predicted object. Our method, by considering the statistics of tokens in layer normalization layers, shows a great ability to interpret the explainability of Swin Transformer and ViT

EnSolver: Uncertainty-Aware CAPTCHA Solver Using Deep Ensembles

The popularity of text-based CAPTCHA as a security mechanism to protect websites from automated bots has prompted researches in CAPTCHA solvers, with the aim of understanding its failure cases and subsequently making CAPTCHAs more secure. Recently proposed solvers, built on advances in deep learning, are able to crack even the very challenging CAPTCHAs with high accuracy. However, these solvers often perform poorly on out-of-distribution samples that contain visual features different from those in the training set. Furthermore, they lack the ability to detect and avoid such samples, making them susceptible to being locked out by defense systems after a certain number of failed attempts. In this paper, we propose EnSolver, a novel CAPTCHA solver that utilizes deep ensemble uncertainty estimation to detect and skip out-of-distribution CAPTCHAs, making it harder to be detected. We demonstrate the use of our solver with object detection models and show empirically that it performs well on both in-distribution and out-of-distribution data, achieving up to 98.1% accuracy when detecting out-of-distribution data and up to 93% success rate when solving in-distribution CAPTCHAs.Comment: Epistemic Uncertainty - E-pi UAI 2023 Worksho

Stability of twin circular tunnels in cohesive-frictional soil using the node-based smoothed finite element method (NS-FEM)

This paper presents an upper bound limit analysis procedure using the node-based smoothed finite element method (NS-FEM) and second order cone programming (SOCP) to evaluate the stability of twin circular tunnels in cohesive-frictional soils subjected to surcharge loading. At first stage, kinematically admissible displacement fields of the tunnel problems are approximated by NS-FEM using triangular elements (NS-FEM-T3). Next, commercial software Mosek is employed to deal with the optimization problems, which are formulated as second order cone. Collapse loads as well as failure mechanisms of plane strain tunnels are obtained directly by solving the optimization problems. For twin circular tunnels, the distance between centers of two parallel tunnels is the major parameter used to determine the stability. In this study, the effects of mechanical soil properties and the ratio of tunnel diameter and the depth to the tunnel stability are investigated. Numerical results are verified with those available to demonstrate the accuracy of the proposed method

Nano strain-amplifier: making ultra-sensitive piezoresistance in nanowires possible without the need of quantum and surface charge effects

This paper presents an innovative nano strain-amplifier employed to significantly enhance the sensitivity of piezoresistive strain sensors. Inspired from the dogbone structure, the nano strain-amplifier consists of a nano thin frame released from the substrate, where nanowires were formed at the centre of the frame. Analytical and numerical results indicated that a nano strain-amplifier significantly increases the strain induced into a free standing nanowire, resulting in a large change in their electrical conductance. The proposed structure was demonstrated in p-type cubic silicon carbide nanowires fabricated using a top down process. The experimental data showed that the nano strain-amplifier can enhance the sensitivity of SiC strain sensors at least 5.4 times larger than that of the conventional structures. This result indicates the potential of the proposed strain-amplifier for ultra-sensitive mechanical sensing applications.Comment: 4 pages, 5 figure

Network-Aided Intelligent Traffic Steering in 6G O-RAN: A Multi-Layer Optimization Framework

To enable an intelligent, programmable and multi-vendor radio access network (RAN) for 6G networks, considerable efforts have been made in standardization and development of open RAN (O-RAN). So far, however, the applicability of O-RAN in controlling and optimizing RAN functions has not been widely investigated. In this paper, we jointly optimize the flow-split distribution, congestion control and scheduling (JFCS) to enable an intelligent traffic steering application in O-RAN. Combining tools from network utility maximization and stochastic optimization, we introduce a multi-layer optimization framework that provides fast convergence, long-term utility-optimality and significant delay reduction compared to the state-of-the-art and baseline RAN approaches. Our main contributions are three-fold: i) we propose the novel JFCS framework to efficiently and adaptively direct traffic to appropriate radio units; ii) we develop low-complexity algorithms based on the reinforcement learning, inner approximation and bisection search methods to effectively solve the JFCS problem in different time scales; and iii) the rigorous theoretical performance results are analyzed to show that there exists a scaling factor to improve the tradeoff between delay and utility-optimization. Collectively, the insights in this work will open the door towards fully automated networks with enhanced control and flexibility. Numerical results are provided to demonstrate the effectiveness of the proposed algorithms in terms of the convergence rate, long-term utility-optimality and delay reduction.Comment: 15 pages, 10 figures. A short version will be submitted to IEEE GLOBECOM 202