Channel Estimation for Intelligent Reflecting Surface Assisted mmWave Communications

Intelligent reflecting surface (IRS) consists of a large number of low-cost passive reflective elements, which can assist millimeter wave communications to solve the problems of weak penetration and short propagation distance. However, it is challenging to obtain channel state information (CSI) in IRS-aided millimeter wave communication systems. To solve this challenge, this paper proposes a regular alternating least squares (RALS) algorithm based on the canonical/parallel factor (CP) decomposition. Compared with the traditional alternate least squares (ALS) algorithm, the proposed RALS algorithm has better convergence performance, thus solving the problem of divergence or slow convergence of the conventional ALS algorithm. Besides, in order to improve the accuracy of the channel estimation, the convex optimization theory is invoked to devise the regularization parameters, and a regularization parameter selection scheme is developed to ensure that the proposed algorithm obtains the optimal solution. The simulation results verify the theoretical analysis and prove the superiority of the proposed RALS algorithm in terms of estimation error performance.

Gestaltungswille und Algorithm Aversion – Die Auswirkungen der Einflussnahme im Prozess der algorithmischen Entscheidungsfindung auf die Algorithm Aversion

Obwohl Algorithmen in vielen Anwendungsgebieten präzisere Prognosen abgeben als Menschen, weigern sich Entscheidungsträger häufig, auf Algorithmen zurückzugreifen. In einem ökonomischen Experiment untersuchen wir, ob das Ausmaß dieses als „Algorithm Aversion“ bekannten Phänomens reduziert werden kann, indem Entscheidungsträgern eine Einflussmöglichkeit auf die Ausgestaltung des Algorithmus eingeräumt wird (Einflussmöglichkeit auf den algorithmischen Input). Zusätzlich replizieren wir die Studie von Dietvorst, Simmons & Massey (2018). Darin zeigt sich, dass die Algorithm Aversion deutlich zurückgeht, sofern die Subjekte am Ende die Ergebnisse des Algorithmus – und sei es nur um wenige Prozent – verändern können (Einflussmöglichkeit auf den algorithmischen Output). In der vorliegenden Studie bestätigt sich, dass die Algorithm Aversion bei einer Einflussmöglichkeit auf den algorithmischen Output signifikant zurückgeht. Eine Einflussmöglichkeit auf den algorithmischen Input scheint allerdings nur bedingt geeignet, die Algorithm Aversion zu reduzieren. Die begrenzte Möglichkeit zur Modifikation des algorithmischen Outputs reduziert die Algorithm Aversion effektiver als die Möglichkeit, Einfluss auf den algorithmischen Input zu nehmen.

A Recommendation Engine for Predicting Movie Ratings Using a Big Data Approach

In this era of big data, the amount of video content has dramatically increased with an exponential broadening of video streaming services. Hence, it has become very strenuous for end-users to search for their desired videos. Therefore, to attain an accurate and robust clustering of information, a hybrid algorithm was used to introduce a recommender engine with collaborative filtering using Apache Spark and machine learning (ML) libraries. In this study, we implemented a movie recommendation system based on a collaborative filtering approach using the alternating least squared (ALS) model to predict the best-rated movies. Our proposed system uses the last search data of a user regarding movie category and references this to instruct the recommender engine, thereby making a list of predictions for top ratings. The proposed study used a model-based approach of matrix factorization, the ALS algorithm along with a collaborative filtering technique, which solved the cold start, sparse, and scalability problems. In particular, we performed experimental analysis and successfully obtained minimum root mean squared errors (oRMSEs) of 0.8959 to 0.97613, approximately. Moreover, our proposed movie recommendation system showed an accuracy of 97% and predicted the top 1000 ratings for movies.

Segmentation and Positioning of Lecturers in the Department of Computer Science at Pakuan University Based on Student Assessment

Determining the segmentation and positioning of the lecturers in selecting the thesis supervisor is very important to do. It is because, with this information, the supervision process in thesis writing can run well. This study intends to analyze the segmentation and positioning of lecturers related to determine the thesis supervisor using the Clusterwise Bilinear Spatial Multidimensional Scaling Model (CBSMSM) method. The data used is survey data for fifth-semester bachelor students of the 2019/2020 academic year of the Department of Computer Science, Pakuan University. One hundred sixty-one student observations provide an assessment of 10 attributes regarding the characteristics of 32 lecturers of the department. Furthermore, the estimation of the segment coordinate parameters, lecturer coordinates, dimensions, and attributes simultaneously uses the alternating least square (ALS) algorithm. The number of segments and dimensions are selected based on the smallest sum square error (SSE) value for combining segments and other dimensions. As a result, we get four segments and four dimensions with an SSE value of 4864.003. Furthermore, the department can use this result to illustrate student assessments of their lecturers' characteristics regarding thesis supervision.

MCR-ALS with Sample Insertion Constraint to Enhance the Sensitivity of Surface-Enhanced Raman Scattering Detection

The multivariate curve resolution-alternative least square (MCR-ALS) algorithm was modified with sample insertion constraint to deconvolute the overlapping peaks in SERS spectra. The developed method was evaluated by the spectral...

Short-term immediate post-shock pacing in adult advanced life support – a feasibility study of modified ALS algorithm

Introduction It is well known that the majority of patients remains transiently pulseless after successfull defibrillation shock during routine advanced cardiovascular life support (ACLS). The post-shock asystole is longer than 120 seconds in as many as 25% of patients. Not only asystole, but also profound postshock bradycardia and high degree AV blocks are described as common. The need for post-shock pacing (PSP) in adult ACLS is unknown, but there is extensive use of that feature in implantable cardioverters – defibrillators (ICD). PSP feature is widely used in these devices. The wide utilisation of PSP in ICD patients warrants the research towards any possible benefit of it during ACLS measures. Material and methods We performed high-fidelity simulation study including 60 paramedic at 20 scenarios. The participants were asked to perform routine resuscitation scenarios according to the AHA ACLS algorithm. In the control group paramedics had to resume compressions after each shock. In experimental group simultaneously with the compressions, transcutaneous pacing with 200 mA output and rate of 80 ppm was delivered. Several parameters were monitored: chest compression fraction, compressions depth and rate, percent of recoiled compression, compressions on correct depth, and other. Results There were no statistically significant differences between both groups in respect of compressions depth and rate, time needed to achieve advanced airway, initiate and achieve intravenous line, administer medications (Table 1). The detailed results are presented in Table 1. Discussion According to the best of the authors knowledge, this is the first study describing the feasibility of PSP in resuscitation. The quality of resuscitation depends on adherence to the protocol. Any additional element in the protocol can negatively affect the outcome. In the presented study we present that major ACLS steps are delivered without significant delay when PSP is utilised. Conclusions The implementation of PSP had no negative impact on adherence to ACLS protocol. The authors see the need for further intensive research in this area. Figure 1. Design of the study. Funding Acknowledgement Type of funding source: None

OCTAVVS: A Graphical Toolbox for High-Throughput Preprocessing and Analysis of Vibrational Spectroscopy Imaging Data

Modern vibrational spectroscopy techniques enable the rapid collection of thousands of spectra in a single hyperspectral image, allowing researchers to study spatially heterogeneous samples at micrometer resolution. A number of algorithms have been developed to correct for effects such as atmospheric absorption, light scattering by cellular structures and varying baseline levels. After preprocessing, spectra are commonly decomposed and clustered to reveal informative patterns and subtle spectral changes. Several of these steps are slow, labor-intensive and require programming skills to make use of published algorithms and code. We here present a free and platform-independent graphical toolbox that allows rapid preprocessing of large sets of spectroscopic images, including atmospheric correction and a new algorithm for resonant Mie scattering with improved speed. The software also includes modules for decomposition into constituent spectra using the popular Multivariate Curve Resolution–Alternating Least Squares (MCR-ALS) algorithm, augmented by region-of-interest selection, as well as clustering and cluster annotation.

OCTAVVS: A graphical toolbox for high-throughput preprocessing and analysis of vibrational spectroscopy imaging data

Modern vibrational spectroscopy techniques enable rapid collection of thousands of spectra in a single hyperspectral image, allowing researchers to resolve spatially heterogeneous samples down to a resolution of a few μm. A number of algorithms have been developed to correct for effects such as atmospheric absorption, light scattering by cellular structures and varying baseline levels. Following such preprocessing, spectra are commonly decomposed and clustered to reveal informative patterns and subtle spectral changes. Several of these steps are slow, labor-intensive and require programming skills to make use of published algorithms and code. We here present a free and platform-independent graphical toolbox that allows rapid processing of large sets of spectroscopic images, including atmospheric correction and an algorithm for resonant Mie scattering with improved speed and stability. The software includes modules for decomposition into constituent spectra using the popular MCR-ALS algorithm, augmented by region-of-interest selection, as well as clustering and cluster annotation.

A self-adaptive regularized alternating least squares method for tensor decomposition problems

Though the alternating least squares algorithm (ALS), as a classic and easily implemented algorithm, has been widely applied to tensor decomposition and approximation problems, it has some drawbacks: the convergence of ALS is not guaranteed, and the swamp phenomenon appears in some cases, causing the convergence rate to slow down dramatically. To overcome these shortcomings, the regularized-ALS algorithm (RALS) was proposed in the literature. By employing the optimal step-size selection rule, we develop a self-adaptive regularized alternating least squares method (SA-RALS) to accelerate RALS in this paper. Theoretically, we show that the step-size is always larger than unity, and can be larger than [Formula: see text], which is quite different from several optimization algorithms. Furthermore, under mild assumptions, we prove that the whole sequence generated by SA-RALS converges to a stationary point of the objective function. Numerical results verify that the SA-RALS performs better than RALS in terms of the number of iterations and the CPU time.

CHALLENGES IN IDENTIFYING PERSONS WITH NEURODEGENERATIVE DISEASES USING HEALTH ADMINISTRATIVE DATABASES

Health administrative databases can be used to quantify prevalence and incidence of neurodegenerative diseases and their impact on health service utilization outcomes at the population level. Algorithms based on diagnosis codes and health service patterns can be used to identify persons suspected to have a neurodegenerative disease. Previous studies have developed and validated algorithms to identify persons with Alzheimer’s and related dementias using primary care medical records as the reference standard, however, little previous work has focused on developing algorithms for rare neurodegenerative diseases including amyotrophic lateral sclerosis (ALS). This session will discuss challenges in developing algorithms to identify persons with neurodegenerative diseases accurately and opportunities to improve existing definitions using novel data sources including electronic medical record databases. Preliminary findings regarding the development of an ALS algorithm will be presented.