Miracle-dz/UniFAR-FTUs huggingface dataset:
Ultralow-Loss Substrate-Integrated Waveguides in Glass-Based Substrates for Millimeter-Wave Applications

This letter, for the first time, presents low-loss substrate-integrated waveguides (SIWs) in fused silica and borosilicate glass and a comparison of their performance with various organic-based low-loss substrates for millimeter-wave applications. Utilizing ring resonators designed for frequencies in the 5G New Radio (NR) n257 band (26.5–29.5 GHz), this letter begins with the determination of the dielectric constant of fused silica to model SIWs. This letter also introduces the designs of SIWs that are fed by conductor-backed coplanar waveguides and discusses the fabrication and measurement results with deembedding analysis. In addition to the excellent correlation between simulations and measurements, the characterization results show more than $2\times $ reduction in the insertion loss compared to those of low-loss organic-based SIWs at the 28-GHz frequency band.
Survey on Customer Centric Sales Analysis and Prediction

Business management is a field which is highly dependent on sales prediction. In order to predict these sales many Data Mining techniques and algorithms can be used. But to predict these sales there is a requirement of customer data that gives us an insight on how the customer perceives the products or services being offered. In this survey we are trying to compare various Data Mining techniques and algorithms used in the past to predict these sales, and then we will try to propose better ways to solve the same problem. This paper also compares the efficiency of ANN techniques in comparison with data mining technique. Until now, there have been a number of enhancements and achievements done in this field by using Market Basket Analysis. There have been algorithms employed such as Apriori, FP-Growth, AIS, clustering, etc. for getting suitable mining suitable patterns. All the advantages and drawbacks have been highlighted in this paper of various algorithms that have been employed as of now.
Assessing Losses for Point Set Registration

This letter introduces a framework for evaluation of the losses used in point set registration. In order for a loss to be useful with a local optimizer, such as e.g. Levenberg-Marquardt, or expectation maximization (EM), it must be monotonic with respect to the sought transformation. This motivates us to introduce monotonicity violation probability (MVP) curves, and use these to assess monotonicity empirically for many different local distances, such as point-to-point, point-to-plane, and plane-to-plane. We also introduce a local shape-to-shape distance, based on the Wasserstein distance of the local normal distributions. Evaluation is done on a comprehensive benchmark of terrestrial lidar scans from two publicly available datasets. It demonstrates that matching robustness can be improved significantly, by using kernel versions of local distances together with inverse density based sample weighting.
Stability Analysis of Nash Equilibrium in Loss-Aversion-Based Noncooperative Dynamical Systems

In this paper, we consider the stability problem of Nash equilibrium for a two-agent noncooperative dynamical system with hybrid myopic pseudo-gradient dynamics based on loss-aversion phenomena. In the considered noncooperative dynamical system, each agent adopts different constant sensitivity parameters for the case of losing utilities and gaining utilities. To characterize the stability property, some general characteristics of the active modes and rotational directions are discussed. Based on the integral of normalized radial growth rate, sufficient conditions under which agents’ state converges towards the Nash equilibrium are derived. We present a numerical example to illustrate the efficacy of our results.
A semidefinite programming approach for stochastic switched optimal control problems

We consider solving stochastic switched optimal control problems (SSOCPs) with polynomial data using sum-of-squares (S.O.S) and semidefinite programming (SDP), where both the number of switches and the switching signal are not known in advance. The problem is transformed into an embedded form using a new control variable that is responsible for controlling the switching policy. Then, through a hierarchy of polynomial S.O. S problems, we find an approximation of the value function corresponding to the original optimal control problem and use it to design feedback control. Finally, one simulation experiment is included to demonstrate the performance of our new approach.
An algebraic solution for tracking Bernoulli’s lemniscate flight trajectory in airborne wind energy systems

In this paper we present a method to greatly enhance the computing performance of kinematic controllers responsible of defining the direction a power kite of an airborne wind energy system should fly to in order to track a Bernoulli’s lemniscate trajectory. The method employs the orthogonal trajectories of the Cassini ovals, allowing for the algebraic computation of the kite velocity reference. The advantage of the proposed approach when compared to numerical methods found in the literature is the reduction in the computational load by two orders of magnitude. Also, the proposed solution could be promptly used in virtually any other application where Bernoulli’s lemniscate should be tracked by a moving object. The effectiveness of the proposed method is demonstrated by computer simulation results.
Distributed Event-Triggered Communication Scheme for Economic Dispatch Problem in Power System With Uncoordinated Step-Sizes

Inspired by the distributed economic dispatch problem (EDP) in power system, this paper considers a problem of optimizing a sum of $m$ local convex cost functions on an undirected network of $m$ agents. Each agent in the network privately knows its own local convex objective function and is subjected to both coupling linear constraint and individual box constraints. To be able to reduce the requirements for information exchange among agents, we propose a novel fully event-triggered based distributed primal-dual algorithm for the convex optimization problem. Our algorithm allows the use of uncoordinated step-sizes and assumes that each agent communicates with its neighboring agents (the corresponding variables are updated) only at some independent event-triggered sampling time instants. Under some relatively standard assumptions (strong convexity and smoothness) on the objective functions, our theoretical analysis proves that the proposed algorithm can linearly seek the exact optimal solution when the upper bound of the uncoordinated step-sizes is smaller than a certain constant. We also conduct a clear estimate of the rate. Finally, a simple numerical example on distributed economic dispatch problem in power system is provided to illustrate the effectiveness of our event-triggered based optimization algorithm and validate the correctness of the analysis process.
Less is More! Preliminary Evaluation of Multi-Functional Document-Based Online Learning Environment

This work-in-progress paper in innovative practice category presents and evaluates a multi-functional document-based learning management system, TIM (The Interactive Material). This system is developed with the goal of integrating a rich set of features seamlessly into teachers’ every-day pedagogical and disciplinary needs. The aim is that a single system (“Less”) would provide all technological solutions necessary for online teaching and learning (“More”), hence the punchline “Less is More! ” We illustrate the system and evaluate it based on feedback from teachers. This preliminary evaluation focuses on how teachers reacted to the multi-functional system and is discussed in the context of Technology Acceptance Model (TAM).
Facial Age Synthesis With Label Distribution-Guided Generative Adversarial Network

The existing research work on facial age synthesis has been mostly focused on long-term aging (e.g., over an age span of 10 years or more). In this paper, we employ generative adversarial networks (GANs) as a tool to investigate age synthesis over different age spans. Compared with long-term aging, short-term age synthesis suffers from the reduced amount of available training data, which can severely hinder the model training. We conduct a series of experiments to validate this. To facilitate short-term age synthesis, we further propose label distribution-guided generative adversarial network (ldGAN), where each sample is associated with an age label distribution (ALD) rather than a single age group. Accordingly, each sample can contribute not only to the learning of its own age group but also to neighbouring groups’ learning. This is useful when addressing short-term aging to cope with the reduced amount of training data. In addition, unlike one-hot encoding which treats age groups as independent from one another, ldGAN can well capture the correlation among different age groups, so that smooth aging sequences can be achieved. The ALD model is integrated into GAN with a two-step process. Firstly, instead of the traditional one-hot encoding, ALD is applied as the condition of the generator. Secondly, we add a sequence of label distribution learners on top of several multi-scale discriminators, with the aim of minimizing the label distribution learning loss when optimizing both the generator and discriminators. Both qualitative and quantitative evaluations are conducted to assess ldGAN’s ability in dealing with two core issues of face aging, i.e., aging effect generation and identity preservation. The obtained experimental results demonstrate the effectiveness of ldGAN in both learning short-term aging patterns and coping with the lack of training data.
Automated Brain Image Classification Based on VGG-16 and Transfer Learning

The last few decades have witnessed active research in the domain of pathological brain image classification starting from classical to the deep learning approaches like convolutional neural networks(CNN). The classical machine learning methods need hand-crafted features to perform classification. CNN's, on the other hand, performs classification by extracting image features directly from raw images. The features extracted by CNN strongly depends on the training data set size. If the size is small, CNN tends to overfit. So, deep CNN's(DCNN) with transfer learning has evolved. The prime aim of the present paper is to explore the capability of a pre-trained DCNN VGG-16 model with transfer learning for pathological brain image categorization. Only, the last few layers of the VGG-16 model were replaced to accommodate new image categories in the present application. The pre-trained model with transfer learning has been validated on the dataset taken from the Harvard Medical School repository, comprising of normal as well as abnormal MR images with different neurological diseases. The data set was then partitioned using a 10-fold cross-validation mechanism. The validation on the test set using sensitivity (Se), specificity (Sp), and, accuracy (Acc), reveal that the pre-trained VGG-16 model with transfer learning exhibited the best performance in contrast to the other existing state-of-the-art works. Moreover, the approach provides categorization with an end-to-end structure on raw images without any hand-crafted attribute extraction.
A Simple Computational Model of Light-sensitive Cardiac tissue for Simulation of the optogenetic Defibrillation

Atrial and ventricular fibrillations are two types of severe heart arrhythmias. As have claimed in Karathanos et al. (2014 and 2016), and Boyle et al. (2015) optogenetic treatments are considered as a convincing substitute for cardiac therapies. However, investigating these techniques under different constraints and the condition of in vivo testing is not always straight-forward. Computational modeling can be used as the first step before any experimental study. The objective of this study is to modify and simplify an already developed cardiac cell model of Ten Tusscher in order to simulating an optogenetic cardiac tissue in silico. In the original model Action Potential (AP) generation in each cardiac cell is described by a dynamic model of order 17. We estimated the parameters of a second-order Aliev-Panfilov model such that it can mimic the behavior of both light-sensitive and normal cardiac cells. Then we showed that the electrophysiological behavior of the new model of the tissue (consisted of 128*128 cells) is similar to the original complex model. The criteria for comparing the behavior of the two models is their ability to generate the spiral waves representing cardiac tissue fibrillation and also the characteristics of these spiral waves. Afterwards, the model is used to predict the propagation of the spiral waves on the tissue, and to describe the function of the optogenetic cells used to defibrillate a stable-induced spiral wave.
Hollow Silica Photonic Crystal Fiber Guiding 101 Orbital Angular Momentum Modes Without Phase Distortion in C+L Band

We propose a new hollow ring core silica photonic crystal fiber that can support 101 orbital angular momentum (OAM) modes, maintaining a high mode quality without phase distortion, a low confinement loss, and a large effective index difference between the adjacent modes, which could open a new avenue of OAM mode multiplexing applications. The fiber consists of three layers: the circular central air hole, silica ring core, and circumferencing silica-air hole porous inner cladding. Using the full-vectorial finite element method (FEM), the modal characteristics of individual OAM modes in the proposed fiber were thoroughly analyzed by varying the number of air-holes in the circularly symmetric cladding. We found a general selection rule for the number of air-holes in the first layer and the topological charge to cause the phase distortion of OAM modes, for the first time. The phase distribution of the guided OAM modes was thoroughly investigated to select usable modes for mode division multiplexing. Parametric analyses of the proposed PCF are reported to optimize optical properties of the OAM modes.
Updated safety and efficacy results from a phase I/II study of the oral BRAF inhibitor dabrafenib (GSK2118436) combined with the oral MEK 1/2 inhibitor trametinib (GSK1120212) in patients with BRAFi-naive metastatic melanoma.

8510 Background: In preclinical models, the BRAFi/MEKi combination has demonstrated enhanced activity against BRAF-mutant cancer cells compared with either drug alone, delayed emergence of BRAFi resistance, and prevented BRAFi-related proliferative skin lesions. A 3-part study investigating the dabrafenib/trametinib combination was conducted in patients (pts) with V600 BRAF mutant solid tumors. Interim data from the study were previously reported (Infante, ASCO 2011); updated safety and efficacy data are presented. Methods: In Part 2, 125 pts with V600 BRAF mutant solid tumors enrolled, including 77 melanoma pts with no prior BRAFi, and measurable disease according to RECIST 1.1. Pts were treated on 4 escalating dose levels of dabrafenib/trametinib (mg BID/mg QD): 75/1, 150/1, 150/1.5, 150/2. Demographic and efficacy data for the 77 melanoma pts with no prior BRAFi and safety data for all 125 Part 2 pts are reported. Results: Among 77 melanoma pts, median age was 52 years, 61% male, 57% ECOG PS of 0, 91% .
Majorization–Minimization-Based Target Localization Problem From Range Measurements

In this letter, received signal strength (RSS) based target localization is studied. First we start with the case of known emitted power of target. The least squares (LS) approach is adopted, and the object function is shown to be decomposed as a convex quadratic plus a concave term. Consequently, a Majorization-Minimization (MM) based approach from range measurements termed R-MM is proposed, which iteratively finds the local optimum through simple operations. To further improve the performance, unconstrained squared-range (USR)-based LS estimation is used as an initial point for R-MM, named R-MM-USR. Then R-MM-USR is extended to solve the target localization under unknown emitted power through alternating minimization. For both cases Cramèr Rao bound (CRB) is derived. In addition, the computation complexity of R-MM-USR is lowest among the high accuracy approaches. Besides, numerical simulations are conducted to demonstrate the near optimal performance, compared with CRB and other state-of-art methods.
Finite-Time Distributed Flow Balancing

We consider a flow network that is described by a digraph (physical topology), each edge of which can admit a flow within a certain interval, with nonnegative end points that correspond to lower and upper flow limits. The paper proposes and analyzes a distributed iterative algorithm for computing, in finite time, admissible and balanced flows, i.e., flows that are within the given intervals at each edge and balance the total in-flow with the total out-flow at each node. The algorithm assumes a communication topology that allows bidirectional exchanges between pairs of nodes that are physically connected (i.e., nodes that share a directed edge in the physical topology). If the given initial flows and flow limits are commensurable (i.e., integer multiples of a given constant), then the proposed distributed algorithm operates exclusively with flows that are commensurable and is shown to complete in a finite number of steps (assuming a solution set of admissible and balanced flows exists). When no upper limits are imposed on the flows, a variation of the proposed algorithm is shown to complete in finite time even when initial flows and lower limits are arbitrary nonnegative real values (not necessarily commensurable).
Onboard Spectral Analysis for Low-Complexity IoT Devices

The lack of coordinated spectrum access for IoT wireless technologies in unlicensed bands creates inefficient spectrum usage and poses growing concerns in several IoT applications. Spectrum awareness becomes then crucial, especially in the presence of strict quality-of-service (QoS) requirements and mission-critical communication. In this work, we propose a lightweight spectral analysis framework designed for strongly resource-constrained devices, which are the norm in IoT deployments. The proposed solution enables model-based reconstruction of the spectrum of single radio-bursts entirely onboard without DFT processing. The spectrum sampling exploits pattern-based frequency sweeping, which enables the spectral analysis of short radio-bursts while minimizing the sampling error induced by non-ideal sensing hardware. We carry out an analysis of the properties of such sweeping patterns, derive useful theoretical error bounds, and explain how to design optimal patterns for radio front-ends with different characteristics. The experimental campaign shows that the proposed solution enables the estimation of central frequency, bandwidth, and spectral shape of signals at runtime by using a strongly hardware-limited radio platform. Finally, we test the potential of the proposed solution in combination with a proactive blacklisting scheme, allowing a substantial improvement in real-time QoS of a radio link under interference.
An RST Control Design Based on Interval Technique for Piezomicropositoning Systems with Rate-Dependent Hysteresis Nonlinearities

We propose a feedforward-feedback control of piezomicropositioning systems for precise positioning over different operating conditions. Such systems exhibit rate-dependent hysteresis nonlinearities and badly damped oscillations characteristics. First, we introduce a rate-dependent Prandtl-Ishlinskii inverse model for feedforward compensation of hysteresis. This yields to compensation that can be described with an uncertain linear model with disturbances. To model the uncertainties, we use intervals. Then we propose a new interval design for a RST structured feedback controller. The proposed method permits to satisfy prescribed performances. Simulation and experiments on a piezoelectric actuator are carried out and show the eﬃciency of the proposed controller.
Linear Controllability and Observability of n-Link Underactuated Planar Revolute Robot Moving in Constantly Rotating Frame in Horizontal Plane

For an n-link underactuated planar revolute robot connected to a fixed base in a horizontal plane, in this paper, we study an open problem: Under what actuator configuration is the robot moving in constantly rotating frame with all its links stretching straight out linearly controllable and observable? Different from the existing result for the robot with one unactuated joint, we prove that such linear controllability and observability are guaranteed for all its possible physical parameters, if and only if its first joint is active (actuated). Furthermore, when its first joint is passive (unactuated), we show that for any of its physical parameters, the robot is linearly uncontrollable and unobservable regardless of whether any of its rest joint(s) is active or not. We validate the above results via a physical 3-link underactuated planar revolute robot and a 4-link planar model of a gymnast on the high bar in existing references. This paper presents new insight of the linear controllability and observability for an n-link underactuated planar revolute robot moving in constantly rotating frame in a horizontal plane.
Adaptive Resource Allocation in Future Wireless Networks With Blockchain and Mobile Edge Computing

In this paper, we present a blockchain-based mobile edge computing (B-MEC) framework for adaptive resource allocation and computation offloading in future wireless networks, where the blockchain works as an overlaid system to provide management and control functions. In this framework, how to reach a consensus between the nodes while simultaneously guaranteeing the performance of both MEC and blockchain systems is a major challenge. Meanwhile, resource allocation, block size, and the number of consecutive blocks produced by each producer are critical to the performance of B-MEC. Therefore, an adaptive resource allocation and block generation scheme is proposed. To improve the throughput of the overlaid blockchain system and the quality of services (QoS) of the users in the underlaid MEC system, spectrum allocation, size of the blocks, and number of producing blocks for each producer are formulated as a joint optimization problem, where the time-varying wireless links and computation capacity of the MEC servers are considered. Since this problem is intractable using traditional methods, we resort to the deep reinforcement learning approach. Simulation results show the effectiveness of the proposed approach by comparing with other baseline methods.